Journal of Managed Care Nursing, Volume 6, Number 1

Vol. 6, No. 1, January 2019

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JMCN JOURNAL OF MANAGED CARE NURSING 4435 Waterfront Drive, Suite 101 Glen Allen, VA 23060 EDITOR-IN-CHIEF Jacqueline Cole, RN, MS, CNOR, CPHQ, CMCN, CHC, CHPC, FNAHQ, FAHM, FHIAS PUBLISHER Jeremy Williams VICE PRESIDENT OF COMMUNICATIONS April Snyder, BACom JOURNAL MANAGEMENT American Association of Managed Care Nurses 4435 Waterfront Drive, Suite 101 Glen Allen, VA 23060 phone (804) 747-9698 fax (804) 747-5316 MANAGING EDITOR April Snyder, BACom asnyder@aamcn.org GRAPHIC DESIGN April Snyder, BACom asnyder@aamcn.org

Journal of Managed Care Nursing The Official Journal of the AMERICAN ASSOCIATION OF MANAGED CARE NURSES A Peer-Reviewed Publication

Vol. 6, No. 1, January 2019

TABLE OF CONTENTS Articles Association Between Health Insurance Literacy and Avoidance of Health Care Services Owing to Cost Renuka Tipirneni, MD, MSc, Mary C. Politi, PhD, and Jeffrey T. Kullgren, MD, MS, MPH. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Analysis of a Commercial Insurance Policy to Deny Coverage for Emergency Department Visits With Nonemergent Diagnoses Shih-Chuan Chou, MD, MPH, Suhas Gondi, BA, and Olesya Baker, PhD . . . 13 Emerging Evidence for Cannabis’ Role in Opioid Use Disorder Beth Wiese and Adrianne R. Wilson-Poe . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Association of a Care Coordination Model with Health Care Costs and Utilization - The Johns Hopkins Community Health Partnership (J-CHiP) Scott A. Berkowitz, MD, MBA, Shriram Parashuram, PhD, MPH, and Kathy Rowan, PhD, MPH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Editorials Medicaid Expansion and In-Hospital Cardiovascular Mortality Failure or Unrealistic Expectations?

Rishi K. Wadhera, MD, MPhil and Karen E. Joynt Maddox, MD, MPH . . . . 41 Payment and Care Delivery Improvements Could Benefit All Patients Laura L. Sessums, JD, MD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

ISSN: 2374-359X. The Journal of Managed Care Nursing is published by AAMCN. Corporate and Circulation offices: 4435 Waterfront Drive, Suite 101, Glen Allen, VA 23060; Tel (804) 747-9698; Fax (804) 747-5316.

Advertising Offices: April Snyder, 4435 Waterfront Drive, Suite 101, Glen Allen, VA 23060 asnyder@aamcn.org; Tel (804) 7479698. All rights reserved. Copyright 2019.

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage or retrieval system, without written consent from the publisher. The publisher does not guarantee, either expressly or by implication, the factual accuracy of the articles and descriptions herein, nor does the publisher guarantee the accuracy of any views or opinions offered by the authors of said articles or descriptions.

Managed Care Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Newly Certified Managed Care Nurses (CMCNs) . . . . . . . . . . . . . . . . . . . 46 New AAMCN Members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Author Submission Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

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Association Between Health Insurance Literacy and Avoidance of Health Care Services Owing to Cost Renuka Tipirneni, MD, MSc, Mary C. Politi, PhD, and Jeffrey T. Kullgren, MD, MS, MPH

Summary In this US national survey study of 506 insured adults, 29.6% reported having delayed or foregone care because of cost. Higher health insurance literacy was associated with a lower likelihood of delayed or foregone care owing to cost for both preventive and nonpreventive care. These findings suggest that to improve appropriate use of recommended health care services, including preventive health services, clinicians and policymakers may need to adopt communication strategies that make health insurance concepts accessible to individuals regardless of health insurance literacy and improve consumers’ understanding of services exempt from outof-pocket costs.

Key Points •

•

Navigating health insurance and health care choices requires considerable health insurance literacy. Although recommended preventive services are exempt from out-of-pocket costs under the Affordable Care Act, many people may remain unaware of this provision and its effect on their required payment. Little is known about the association between individuals’ health insurance literacy and their use of preventive or nonpreventive health care services.

This study’s findings suggest that lower health insurance literacy may be associated with greater avoidance of both preventive and nonpreventive services. It appears that to improve appropriate use of recommended health care services, including preventive health services, clinicians, health plans, and policymakers may need to communicate health insurance concepts in accessible ways regardless of individuals’ health insurance literacy. Plain language communication may be able to improve patients’ understanding of services exempt from out-of-pocket costs.

INTRODUCTION Few Americans have a complete understanding of health insurance terms and details. In 2 recent national studies, only 4% to 14% of individuals were able to respond correctly to a set of questions assessing basic understanding of health insurance.1,2 This lack of understanding was most pronounced among low socioeconomic status, racial/ethnic minority, older, or previously uninsured populations who often have high health needs necessitating use of health care services.1-5 Knowledge and application of health insurance concepts (ie, health insurance literacy) have become increasingly important for all Americans as a greater number of health plans have complex cost-sharing features that can change annually, and many previously uninsured individuals may be newly accessing health care after coverage expansion through the Affordable Care Act (ACA).6,7 Despite this need, there is an overall dearth of studies examining the association between individuals’ health insurance literacy and their health and health care use.8 Although there is a broad amount of literature on the outcome of general health literacy (defined as individuals’ ability to understand health information needed to make health care decisions), studies of health insurance literacy 4

are limited.9,10 Most prior studies on this topic have focused on consumers’ ability to select a health insurance plan.11-18 Less is known about how patients navigate and use health insurance after obtaining insurance. Understanding insurance cost-sharing features, including deductibles, copayments, and coinsurance, may facilitate appropriate care-seeking and may help to prevent individuals from delaying or avoiding needed care owing to costs.

In 1 study of Medicare beneficiaries, those who were less familiar with the details of their Medicare coverage were more likely to delay care because of cost, have multiple emergency department visits, and rate their overall health as poorer.19 Other studies have found that aspects of low health insurance literacy—including lack of knowledge of drug coverage and difficulty estimating copayments—were associated with medication nonadherence20 and delays or avoidance of outpatient care.21 Understanding health insurance coverage may be particularly important for patients’ decisions about whether to seek preventive services. Preventive care may be perceived as discretionary because it does not address acute care needs or distressing symptoms. Under the ACA, services such as cancer screenings and vaccinations that are recommended by the US Preventive Services Task Force, Advisory Committee on Immunization Practices, Health

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Resources and Services Administration, or National Academy of Medicine are considered exempt from out-of-pocket payments by consumers.22 Yet many individuals may remain unaware of this cost-sharing exemption, worry about possible out-of-pocket costs, and consequently delay preventive care.23,24 This study sought to determine the association between health insurance literacy and avoidance of health care services owing to perceived out-of-pocket costs. Specifically, we aimed to assess delay or avoidance of preventive and nonpreventive health services.

METHODS Study Design and Participants We recruited a national, geographically diverse, nonprobability sample to participate in an online survey using Amazon’s Mechanical Turk (MTurk), an online source of participants used frequently in social science research.25,26 MTurk allows researchers to post online studies that individuals may complete for a small monetary payment. MTurk participants are more representative of the US population than in-person convenience samples,27 and the method has been demonstrated to provide similar results for psychological and political outcomes as benchmark national samples.28 The study was deemed exempt by the University of Michigan Institutional Review Board with waiver of informed consent. The participants were compensated $1.50 after completing the survey. This study followed the American Association for Public Opinion Research (AAPOR) reporting guideline.

To be included in the study, participants had to be US residents aged 18 years or older with health insurance at the time of the survey. After recruitment and electronic consent, participants completed an online Qualtrics survey, available through MTurk, between February 22 and 23, 2016; the data were accessed for analysis from the site on February 23, 2016.

Measures Delayed or Foregone Care Our primary dependent variable was delayed or foregone care owing to perceived costs. Participants were asked, “During the past 12 months, have you delayed seeking medical care because of worry about the cost?” and “During the past 12 months, was there any time when you needed medical care but did not get it because you could not afford it?”29 Those who responded yes to either item were asked whether they delayed or went without care for several examples of preventive services (ie, annual routine physical examination; blood test for cholesterol level check; influenza vaccination; colon cancer screening test, such as a stool test, colonoscopy, or sigmoidoscopy; routine mammogram; Papanicolaou testing); response options included yes, no, or not applicable. These are services that would be exempt from cost sharing under the ACA. Participants were also asked whether they delayed or went without care for several examples of nonpreventive services (visit to the physician for cough, radiograph for broken bone, magnetic resonance imaging for muscle or joint pain). These services were selected as representative types of urgent or nonroutine care that participants may have experienced. Participants could respond yes, no, or that the service was not applicable to them. We dichotomized these items as avoidance vs no avoidance of each

service and analyzed aggregate measures for delaying or foregoing any preventive care and any nonpreventive care. Participant eligibility for health care services was defined as cholesterol level check (men aged ≥35 years and women aged ≥45 years; n = 133), colon cancer screening test (participants aged 50-75 years; n = 57), mammogram (women aged 21-65 years; n = 35), Papanicolaou test (women aged 21-65 years; n = 224), and all other health care services (all participants; N = 506).

Use of Preventive and Nonpreventive Services Our secondary dependent variable was participants’ self-reported use of selected common preventive (influenza vaccination in the past 12 months, cholesterol level check in the past 5 years)30 and nonpreventive (emergency department visit, hospital admission) services.29 As above, we considered these items individually and in aggregate for any preventive or any nonpreventive health services use.

Health Insurance Literacy, Health Literacy, and Numeracy Our key independent variable was health insurance literacy, as assessed by the validated 21-item Health Insurance Literacy Measure (HILM), which is, to our knowledge, the only currently validated measure of health insurance literacy for the general population.8 Similar to other subjective measures that have been demonstrated to be important in medical decision making (eg, subjective numeracy and health literacy),31,32 an advantage of the HILM is that, although it is correlated with objective measures of health insurance–related knowledge and skills, it measures motivation to engage in health insurance–related behaviors (eg, information seeking, document literacy), in addition to selfreported ability and knowledge.8 We assigned a score to each of the 21 items based on confidence in health insurance understanding and navigation (score of 1 was assigned to a response of not at all confident; 2, slightly confident; 3, moderately confident; and 4, very confident) and summed scores for individual items to create a scaled score with a range of 0 to 84. Higher HILM scores indicate greater levels of health insurance literacy. To facilitate interpretation of the regression results, we categorized the scaled score into 12-point intervals, as this approximated the SD and allowed us to ensure a meaningful difference in categories by classifying the full 84-point score range into 7 categories (0-12, 13-24, 25-36, 37-48, 49-60, 61-72, and 73-84).

We also report some descriptive statistics based on individuals categorized as lower HILM score vs higher HILM score. We were most interested in how participants with the lowest HILM scores compared with all others, because these would represent individuals for whom a future intervention to improve health insurance literacy would be designed. As a result, participants with HILM scores in the bottom third of the sample were designated as lower HILM (HILM scores, 0-60) and all other participants (HILM scores, 61-84) were designated as higher HILM for these basic descriptive analyses. General health literacy32 and numeracy33 were also assessed using previously validated measures. For general health literacy, a 3-question screening assessment was used. Responses were scored on a Likert scale, as previously validated, and the mean was estimated across the 3 items (score range, 1-5). Higher scores indicate greater perceived ability to understand health information. For numeracy, the 4-item ability subscale of the Subjective Numeracy Scale was used. Responses

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were scored on a Likert scale and the mean was estimated across the 4 items (score range, 1-6). Higher scores indicate greater perceived ability and interest in using numbers.

Demographic and Health Characteristics We assessed demographics (age, sex, race/ethnicity, educational level, income, geographic location),34 health status,30 chronic conditions,35 insurance status,34 and enrollment in a high-deductible health plan29 (private health insurance plan with a deductible greater than $1,300 for an individual or $2,600 for a family), using standard items from established surveys. For income, we used the midpoint of each income category and household size to estimate participants’ income as a percentage of the federal poverty level.

Statistical Analysis We used descriptive statistics to report responses to individual survey items and bivariate and multivariable logistic regression to explore associations between health insurance literacy and our dependent variables (avoidance and use of preventive and nonpreventive health services). In multivariable analyses, we adjusted for age, sex, race/ethnicity, income, educational level, high-deductible health plan, health literacy, numeracy, and chronic health conditions. Adjusted odds ratios (aORs) and 95% CIs were calculated based on these multivariable regression results. Predicted probabilities were also assessed for selected examples by calculating marginal estimates based on multivariable regression results.

We also conducted analyses that were stratified by whether the survey participant did or did not have a high-deductible health insurance plan, as those individuals may be responsible for more out-of-pocket costs before insurance starts sharing the costs of care. Stata, version 13 (StataCorp), was used for all analyses, and a 2-sided Pâ&#x20AC;&#x2030;value <.05 was considered statistically significant.

RESULTS Participant Characteristics The survey was completed by 506 of the 511 participants who began it (participation rate, 99.0%).36 Participants had a mean (SD) age of 34 (10.4) years, with 339 (67.0%) younger than 35 years. A total of 228 participants (45.1%) were women, 406 were white (80.6%), 41 were Hispanic (8.1%), and 245 were college educated (48.4%), with diverse representation of income groups and geographic locations (Table 1). Almost half (228 [45.1%]) had at least 1 chronic health condition. A total of 361 of 500 participants (72.2%) who responded to the survey item had significant experience with health care (â&#x2030;Ľ1 outpatient visit in the past 12 months) and 446 of the 501 participants (89.0%) who responded to the survey item had their current health insurance plan for at least 12 months.

Delayed or Foregone Care There were 150 participants (29.6%) who reported having delayed or foregone care owing to cost in the past 12 months. For preventive care, 80 participants (15.8%) reported avoidance of preventive services, including 53 (10.5%), physical examination; 26 (5.1%), cholesterol level check; 27 6

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(aOR, 0.61; 95% CI, 0.48-0.78) and also for nonpreventive care (aOR, 0.71; 95% CI, 0.55-0.91) (Table 3). For example, an individual with an HILM score of 50 (approximately 1 SD below the mean) would have a 22.8% predicted probability of delaying or foregoing preventive services compared with an individual with an HILM score of 75 (approximately 1 SD above the mean), who would have a 9.9% predicted probability of delaying or foregoing preventive services owing to cost. For nonpreventive services, the lower HILM individual would also have a higher predicted probability of delaying or foregoing care (19.5%) compared with the higher HILM individual (11.4%).

(5.3%), influenza vaccination; 17 (3.4%), colon cancer screening test; 20 (4.0%), a mammogram; and 30 (5.9%), a Papanicolaou test (Table 2). For nonpreventive care, 76 participants (15.0%) reported avoidance of nonpreventive services, including 50 (9.9%), an urgent care visit; 13 (2.6%), a roentgenogram; and 40 (7.9%), magnetic resonance imaging. We also observed greater avoidance of both preventive and nonpreventive care for those in high-deductible health plans.

Health Insurance Literacy The mean (SD) HILM score was 63.5 (12.3) of a maximum possible score of 84 across the 21 items assessed. The HILM score was significantly positively correlated with numeracy and income.

The overall sample had a mean general health literacy score of 2.54 (0.57) and mean numeracy of 4.56 (1.23).

Association of Health Insurance Literacy With Delayed or Foregone Preventive vs Nonpreventive Care Participants with lower HILM scores had higher rates of avoiding preventive services (43 of 181 [23.8%] lower HILM vs 37 of 325 [11.4%] higher HILM groups) and nonpreventive services (35 of 181 lower HILM [19.3%] vs 41 of 325 [12.6%] higher HILM groups) (Table 2). Because of the low numbers of participants who received each service, we did not conduct statistical analyses for individual services but considered these services in aggregate for any preventive or nonpreventive care in regression analyses.

In multivariable logistic regression analyses, each 12-point increase in HILM score was associated with a lower likelihood of delayed or foregone care owing to cost for preventive care

Association of Health Insurance Literacy With Use of Preventive vs Nonpreventive Services We also examined participantsâ&#x20AC;&#x2122; reported use of selected preventive (influenza vaccination, cholesterol level check) and nonpreventive (emergency department visit, hospital admission) services in the full sample of participants. Each 12-point increase in HILM score was associated with a higher likelihood of preventive services use (aOR, 1.57; 95% CI, 1.28-1.92), but no significant change in nonpreventive services use (aOR, 1.23; 95% CI, 0.93-1.63) (Table 4). When we compared an individual with an HILM score of 50 with an individual with an HILM score of 75, the participant with the lower HILM score had a 53.1% predicted probability of any preventive services use compared with 74.8% for the participant with the higher HILM score. For nonpreventive services use, the predicted probability was more similar between individuals with a lower (12.1%) and higher (17.0%) HILM score. In sensitivity analyses for all regressions, we additionally examined the results continuously and the results did not change.

DISCUSSION In this national study of an insured sample, 150 people (29.6%) reported delaying or foregoing health care owing to perceptions of costs. Although participants were overall equally likely to avoid preventive and nonpreventive care owing to cost concerns, those with lower health insurance literacy reported significantly greater avoidance of both preventive and nonpreventive services.

Likewise, participants with lower health insurance literacy were less likely to report use of preventive services. These findings suggest that health insurance literacy is important for patients, not only while selecting a health plan, but also in health care navigation and uptake of recommended health services.

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efforts.

Regardless of patients’ health insurance literacy, these messages should aim to increase their understanding that recommended preventive services are exempt from out-of-pocket costs. Simple advertising may be used by clinicians’ practices and pharmacies to draw attention to such services. In addition, health care professionals could address costs in discussions of health care recommendations.39,40 There are several possible explanations for the study’s findings. First, it is likely that individuals with lower health insurance literacy may not understand many cost-sharing and costreduction features of their health plan, despite the ACA mandate that preventive services be covered without out-of-pocket costs to consumers. Thus, they may be less likely to use care they perceive as optional, particularly preventive care. Second, patients may perceive other costs besides monetary copayments, such as taking time off from work, arranging for child care, and waiting during long medical appointments. Such costs would not be addressed by the copayment and deductible exemption. Because these nonmonetary costs may be felt more by individuals of lower socioeconomic status, we adjusted for income and educational level and found no change in the study results. Third, it is possible that those with greater health insurance literacy generally were more knowledgeable about the value of preventive services.

However, our findings were consistent even after adjustment for general health literacy and numeracy, which could potentially be associated with knowledge about the value of preventive services.

Our findings echo those of earlier research examining the importance of general health literacy to health care access. Levy and Janke37 found that individuals with lower general health literacy were more likely to delay care or have difficulty accessing health care than individuals with adequate health literacy. Our study suggests a similar association between patients’ health insurance literacy and their ability to navigate and receive health care.

This study may have implications for policy and program changes that aim to improve communication about health insurance policies and patients’ health care decision making. Furtado and colleagues38 noted that uninsured individuals’ most trusted sources of health insurance information were health care professionals and their social networks. Thus, developing simple messages that can be delivered by health plans, health care professionals, community health workers, or health navigators may help to make health insurance concepts accessible and trusted by patients. Plainlanguage communication by trusted sources has the potential to significantly improve patients’ health insurance literacy and confidence making health care decisions. Community-based education may be particularly important for the newly insured and could be linked to local and national outreach and enrollment 8

Limitations This study should be interpreted in the context of its limitations. First, although the sample was a national group of participants with economic and geographic diversity, it was not a nationally representative sample. The participants were younger and had higher educational attainment than the general population, which is a known limitation of MTurk samples.41 Younger age and higher educational level could be associated with greater health insurance literacy, yet many of these individuals avoided preventive health care because of perceived costs. In addition, older adults may face even greater challenges navigating health care services owing to lower health insurance literacy and greater health care needs.3 Second, the HILM measures confidence in understanding and using health insurance, but it does not directly measure knowledge of specific health insurance concepts. We chose to use HILM for our independent variable as, to our knowledge, it is currently the only validated measure of health insurance literacy available. Third, we cannot definitively determine whether individuals who reported delayed or foregone care would have had a medical need for the health care service. Our analyses considering the full sample in the eligible denominator may underestimate small effects of health insurance literacy on delays of specific types of health care services.

CONCLUSIONS To our knowledge, this study presents some of the first evidence on the importance of considering health insurance literacy in relation to health care navigation and use. We found that patients with lower health insurance literacy had greater avoidance of both nonpreventive and preventive services, despite the ACA cost-sharing exemption for recommended preventive services. Future work should examine potential drivers of foregone care among those with lower health insurance literacy as potential targets for intervention. To improve appropriate use of recommended health care services, including preventive health services, clinicians, health plans, and policymakers should adopt communication strategies that make health insurance concepts accessible to individuals regardless of health insurance literacy and improve consumers’ understanding of services exempt from out-of-pocket costs.

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School of Medicine in St Louis, Missouri.

Jeffrey T. Kullgren, MD, MS, MPH is affiliated with the Institute for Healthcare Policy & Innovation, University of Michigan, Ann Arbor and the Division of General Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, the Veterans Affairs Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, and the Center for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor.

Conflict of Interest Disclosures: Dr Politi reported grants from Merck & Co outside the submitted work. Dr Kullgren reported receiving personal and consulting fees from SeeChange Health, personal and consulting fees from HealthMine, and personal fees and a speaking honorarium from AbilTo, Inc outside the submitted work. Dr Tipirneni is additionally supported by a K08 Clinical Scientist Development Award from the National Institute on Aging.

Support was also provided to Dr Kullgren by the Department of Veterans Affairs (VA), Veterans Health Administration, Health Services Research and Development Service. Dr Kullgren is a VA Health Services Research and Development Service Career Development awardee at the VA Center for Clinical Management Research, VA Ann Arbor Healthcare System. No other disclosures were reported.

Funding/Support: The study was supported by the Department of Internal Medicine, Division of General Medicine, University of Michigan.

Role of the Funder/Sponsor: The Department of Internal Medicine, Division of General Medicine, University of Michigan had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government.

Renuka Tipirneni, MD, MSc is affiliated with the Institute for Healthcare Policy & Innovation, University of Michigan, Ann Arbor and the Division of General Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor.

Mary C. Politi, PhD is affiliated with the Division of Public Health Sciences, Department of Surgery, Washington University

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Reed ME, Graetz I, Fung V, Newhouse JP, Hsu J. In consumer-directed health plans, a majority of patients were unaware of free or low-cost preventive care. Health Aff (Millwood). 2012;31(12):2641-2648. doi:10.1377/ hlthaff.2012.0059 Mortensen K, Hughes TL. Comparing Amazon’s Mechanical Turk platform to conventional data collection methods in the health and medical research literature. J Gen Intern Med. 2018;33(4):533-538. doi:10.1007/s11606-017-4246-0 Amazon Mechanical Turk. Human intelligence through an API. http://www.mturk.com. Accessed February 19, 2016.

Berinsky AJ, Huber GA, Lenz GS. Evaluating online labor markets for experimental research: Amazon. com’s Mechanical Turk. Polit Anal. 2012;20(3):351-368. doi:10.1093/pan/ mpr057 Clifford S, Jewell RM, Waggoner PD. Are samples drawn from Mechanical Turk valid for research on political ideology?

Research & Politics. 2015;2(4):2053168015622072. doi:10.1177/2053168015622072 Centers for Disease Control and Prevention. National Health Interview Survey. https://www.cdc.gov/nchs/nhis/index.htm.

Updated September 26, 2018. Accessed September 5, 2018.

Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. https://www.cdc.gov/brfss/index. html. Updated September 4, 2018. Accessed September 5, 2018.

Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: development of the Subjective Numeracy Scale.

Med Decis Making. 2007;27(5):672-680. doi:10.1177/0272989X07304449 Chew LD, Griffin JM, Partin MR, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561566. doi:10.1007/s11606-008-0520-5 McNaughton CD, Cavanaugh KL, Kripalani S, Rothman RL, Wallston KA. Validation of a short, 3-item version of the Subjective Numeracy Scale. Med Decis Making.

2015;35(8):932-936. doi:10.1177/0272989X15581800 US Census Bureau. American Community Survey. https:// www.census.gov/programs-surveys/acs. Accessed September 5, 2018.

Epstein AM, Sommers BD, Kuznetsov Y, Blendon RJ. Low-income residents in three states view Medicaid as equal to or better than private coverage, support expansion.

Health Aff (Millwood). 2014;33(11):2041-2047. doi:10.1377/ hlthaff.2014.0747 American Association for Public Opinion Research. Standard definitions: final dispositions of case codes and outcome rates for surveys. https://www.aapor.org/AAPOR_Main/media/ publications/Standard-Definitions20169theditionfinal.pdf.

Accessed September 5, 2018.

Levy H, Janke A. Health literacy and access to care. J Health

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Commun. 2016;21(suppl 1):43-50.

38. Furtado KS, Kaphingst KA, Perkins H, Politi MC. Health insurance information-seeking behaviors among the uninsured. J Health Commun. 2016;21(2):148-158. doi:10.1 080/10810730.2015.1039678 39. Ubel PA, Abernethy AP, Zafar SY. Full disclosure—out-ofpocket costs as side effects. N Engl J Med. 2013;369(16):14841486. doi:10.1056/NEJMp1306826 40. Hunter WG, Zhang CZ, Hesson A, et al. What strategies

do physicians and patients discuss to reduce out-of-pocket costs? analysis of cost-saving strategies in 1,755 outpatient clinic visits. Med Decis Making. 2016;36(7):900-910. doi:10.1177/0272989X15626384 41. Mortensen K, Alcalá MG, French MT, Hu T. Selfreported health status differs for Amazon’s Mechanical Turk respondents compared with nationally representative surveys.

Med Care. 2018;56(3):211-215.

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Analysis of a Commercial Insurance Policy to Deny Coverage for Emergency Department Visits with Nonemergent Diagnoses Shih-Chuan Chou, MD, MPH, Suhas Gondi, BA, and Olesya Baker, PhD

Summary Insurers have increasingly adopted policies to reduce emergency department (ED) visits that they consider unnecessary. One common approach is to retrospectively deny coverage if the ED discharge diagnosis is determined by the insurer to be nonemergent. A cross-sectional analysis of probability-sampled ED visits from the nationally representative National Hospital Ambulatory Medical Care Survey ED subsample occurring from January 1, 2011, to December 31, 2015, was conducted. Visits by commercially insured patients aged 15 to 64 years were examined. Those with ED discharge diagnoses defined by Anthem’s policy as nonemergent and therefore subject to possible denial of coverage were classified as denial diagnosis visits. The primary presenting symptoms among denial diagnosis visits were identified, and all visits by commercially insured adults presenting with these primary symptoms were classified as denial symptom visits.

Key Points • •

Anthem’s nonemergent ED discharge diagnoses were not associated with identification of unnecessary ED visits when assessed from the patient’s perspective.

This cost-reduction policy could place many patients who reasonably seek ED care at risk of coverage denial.

INTRODUCTION Emergency department (ED) visits and payment for ED visits by insurers and patients have grown substantially in the United States over the past several decades.1-3 This growth has compelled public and commercial payers to pursue strategies to reduce ED care use. One tactic is to apply financial disincentives, such as coverage denial, to ED visits that could presumably be cared for in alternative settings, such as a physician’s office, urgent care center, or retail clinic. These ED visits are often labeled as inappropriate or nonemergent using definitions based on ED discharge diagnoses,4-6 which are readily available in insurance billing claims. This diagnosis-based approach has not been well studied, but an analysis by Raven et al7 using the Billings algorithm demonstrated that nearly 90% of US ED visits had the same presenting symptoms as the ED visits with diagnoses considered primary care–treatable. Their findings indicated that there is no clear link between many presenting symptoms and discharge diagnoses considered primary care–treatable. Because patients make care-seeking decisions based on their symptoms, using a diagnosis-based approach to retrospectively identify inappropriate visits as means of determining coverage may be problematic.

Recently, Anthem, Inc, a large national health insurance company that provides coverage for 1 in 8 Americans,8 instituted a policy that will deny coverage and payments for ED visits that it deems unnecessary.9 Under this policy, if the final ED diagnosis is among a prespecified list of nonemergent conditions, the insurer will review the ED visit and may deny patient coverage.

In 2017, Anthem implemented this policy in Georgia, Missouri, and Kentucky, expanding in 2018 to New Hampshire, Indiana, and Ohio, with expansion to more states under way.9 The news media have reported individual cases of patients with Anthem insurance presenting to the ED with concerning symptoms such as abdominal pain or severe headache only to have coverage denied after ED evaluation ruled out emergent conditions.10,11 As Anthem remains one of the largest health insurers in the nation, it is important to examine the population that may be affected if other insurers across the nation adopt similar policies of diagnosisbased retroactive coverage denial for ED visits.

Using a national sample of ED visits, we characterized US ED patient visits that may be denied coverage if Anthem’s policy were implemented by all commercial insurers nationally. Our first objective was to examine the ability of this policy to identify inappropriate ED visits by describing ED visits that would be

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Figure

classified as nonemergent under this policy and the proportion of these that received ED-level care. Because patients make careseeking decisions based on symptoms, our second objective was to describe ED visits with the same presenting symptoms as the nonemergent visits to examine the link between Anthem’s list of nonemergent diagnoses and patient symptoms upon presentation to the ED. METHODS Study Design and Data Set We performed a cross-sectional analysis of data from January 1, 2011, to December 31, 2015, that were part of the National Hospital Ambulatory Medical Care Survey ED subsample (NHAMCS-ED), a multistage probability sample of US ED visits administered by the National Center for Health Statistics. The NHAMCS-ED uses a 4-stage sampling design: (1) county-level geographic region as primary sampling units, (2) hospitals, (3) emergency service areas, and (4) 100 to 150 patient records from a randomly assigned 4-week period of the survey year. The National Center for Health Statistics excluded federal, military, and Veterans Affairs hospitals. Final samples included from 267 to 408 responding EDs per year. Probability weights and survey design variables were assigned to every visit to allow the calculation of nationally representative estimates and standard errors. Full details of NHAMCS methodology are available online.12 This study was exempt from review by the Brigham Heath Institutional Review Board. This study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional research.13 Participants and Cohort Definitions To study the potentially eligible commercially insured population, we restricted our analysis to NHAMCS-ED visits by commercially insured adults, defined as patients aged 15 to 64 years with the NHAMCS-ED variable “primary expected source 14

of payment” listed as “private insurance.”

We classified visits into 2 non–mutually exclusive cohorts: (1) denial diagnosis visits and (2) denial symptom visits. The Figure shows the schema used to construct the study cohorts.

Denial Diagnosis Visits To examine the accuracy of Anthem’s policy in identifying unnecessary ED visits, we defined “denial diagnosis visits” as ED visits with at least 1 nonemergent diagnosis specified by Anthem’s denial policy after applying their exclusion criteria. There are 2 publicly published lists of nonemergent diagnoses: the first from Missouri in 2017 and the second from Indiana in 2018.9 We identified visits with nonemergent diagnoses from both lists but based our subsequent analyses on the Indiana diagnosis list, as it is the most recent version of the policy. We then applied the exclusion criteria for Anthem’s coverage denial policy, excluding patients who were younger than 15 years, arrived at the ED during the weekend (defined by Anthem as time between 8 am on a Saturday and 8 am on Monday), arrived via ambulance, received a computed tomographic or magnetic resonance imaging scan, received intravenous fluids, or were admitted to observation or an inpatient ward.14 The NHAMCS does not allow identification of several exclusion criteria, including administration of intravenous medications or a patient who was traveling or was located farther than 15 miles from an urgent care center.

Denial Symptom Visits The assumption underlying Anthem’s policy is that, prior to ED evaluation, patients should be able to determine that their symptoms were of a nonemergent condition for which ED care is not appropriate. To examine the validity of this assumption, we identified denial symptom visits, defined as all ED visits by commercially insured adults that share the same primary symptoms or reasons for visit with the denial diagnosis visits.

We identified primary symptoms that were commonly associated with diagnoses among the denial diagnosis visits. We

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first grouped similar diagnoses among the denial diagnosis visits, which are likely to present with similar symptoms, into clinically meaningful categories using the Agency of Healthcare Quality and Research clinical classification software (CCS).15 We then removed CCS categories associated with fewer than 5 denial diagnosis visits. For each included CCS category, we identified all unique primary symptoms (using the variable of primary reason for visit) among the denial diagnosis visits with the corresponding CCS category and included any primary symptoms associated with 2 or more denial diagnosis visits.

For example, there were a total of 30 denial diagnosis visits with an ED discharge diagnosis of tonsillitis (CCS 124). Among these tonsillitis visits, there were 8 unique primary symptoms, of which throat soreness was associated with 23 visits, and all other symptoms (such as fever, headache, and earache) were associated with only 1 visit each. Therefore, throat soreness was included as a common primary symptom, but the other symptoms were not.

The other presenting symptoms could still be included if they were associated with 2 or more visits for another CCS category.

We performed 2 sensitivity analyses to assess the stability of our findings among denial symptom visits. First, we constructed a denial symptom cohort with a more restrictive definition, including only primary symptoms associated with 5 or more visits within each CCS category. Second, we constructed a denial symptom visit cohort using only 2014 and 2015 NHAMCS-ED data to assess whether there were notable changes after the implementation of the US Affordable Care Act, which included emergency care as 1 of the 10 essential health benefits.

Statistical Analysis We report descriptive statistics for each visit cohort. We calculated the proportions of visits in each cohort by patient characteristics (age, sex, race, insurance status, and region), visit characteristics (triage severity and visit timing), ED care received (diagnostic testing and intravenous fluid use), and disposition.

For visit timing, we defined evenings as arrival to ED after 5 pm Monday through Friday and before 8 am the next day, weekends as arrival after 8 am on Saturday and before 8 am on Monday (consistent with Anthem’s exclusion criteria), and office hours as the remaining times of the week. We examined the total number of diagnostic services used in each visit, including blood, urine, electrocardiogram, and imaging tests. For disposition, we defined admission as patients admitted to observation or inpatient units.

We also calculated the weighted proportion of visits in each cohort that received “ED-level care,” defined as visits with any of these characteristics: (1) triaged as urgent or emergent, (2) received 2 or more diagnostic tests, or (3) admitted to the hospital or transferred to other facilities.

Secondary Analyses To further examine the policy from a patient’s perspective, we performed several secondary analyses. First, we identified the most common primary reasons for visits among denial diagnosis visits and calculated the weighted proportion of visits with each presenting symptom that received ED-level care. Second, we identified the most common reasons for visits among denial symptom visits that were admitted or transferred. Third, we examined the most common reasons for visits among all denial symptom visits and, for each reason for visits, calculated the

weighted proportions of visits that were denial diagnosis visits (visits that may be denied coverage under Anthem’s policy) and the weighted proportions that were admitted to the hospital or transferred.

For all analyses, we used SVY procedures in STATA/MP 15 (StataCorp) to incorporate probability weights and survey design variables in generating national estimates for weighted proportions and 95% confidence intervals.

RESULTS From 2011 to 2015, NHAMCS-ED included a total of 130,219 ED visits, of which 28,304 were visits by patients aged 15 to 64 years with commercial insurance. These visits represented 21.9% (95% CI, 20.9%-22.9%) of all US ED visits over the study period, accounting for 29.6 million (95% CI, 25.7 million to 33.5 million) ED visits annually. Of these ED visits, 46.6% (95% CI, 45.5%47.8%) had a nonemergent diagnosis according to the Missouri policy, and 35.5% (95% CI, 32.4%-38.7%) had a nonemergent diagnosis according to the Indiana policy.

When we applied Anthem’s exclusion criteria to the cohort with the Indiana policy’s nonemergent diagnoses, 15.7% (95% CI 15.0%-16.4%) of adult commercially insured ED visits may be denied coverage (denial diagnosis visits) (4,440 of 28,304; mean [SD] patient age, 36.6 [14.0] years; 2,592 [58.7%] female and 2,962 [63.5%] white), representing 4.6 million (95% CI, 4.1 million to 5.2 million) ED visits annually (Table 1). Of the denial diagnosis visits, 24.5% (95% CI, 21.7%-27.4%) were triaged as urgent or emergent and 26.0% (95% CI, 23.8%-28.3%) required 2 or more diagnostic tests. Overall, 39.7% (95% CI, 37.2%-42.3%) of denial diagnosis visits had at least 1 of the characteristics of receiving ED-level care. No patients in the denial diagnosis visit cohort were admitted because admission was an exclusion from Anthem’s coverage denial policy.

There were 329 unique primary symptoms among the denial diagnosis visits, of which 192 were identified as common primary symptoms. Among adult commercially insured ED visits, 87.9% (95% CI, 87.3%-88.4%) were denial symptom visits (24,882 of 28,304; mean [SD] patient age, 38.5 [14.1] years; 14,362 [57.9%] female and 17,483 [68.7%] white) (Table 1), of which 43.2% (95% CI, 40.2%-46.4%) were triaged as urgent or emergent, 51.9% (95% CI, 50.0%-53.9%) received 2 or more diagnostic tests, and 9.7% (95% CI, 8.8%-10.6%) were admitted or transferred; 2.0% (95% CI, 1.7%-2.4%) required critical care or immediate intervention in the operating room or cardiac catheterization laboratory. Overall, 65.1% (95% CI, 63.4%-66.9%) of denial symptom visits had at least 1 of these characteristics of receiving ED-level care.

Sensitivity Analyses When we restricted the definition of the denial symptom visits to include only primary symptoms with 5 or more visits (94 of the 329 primary symptoms among the denial diagnosis visits), 74.7% (95% CI, 73.8%-75.5%) of all commercially insured adult ED visits were included. In this more restrictive visit cohort, the patient characteristics, ED care provided, and final dispositions were not materially different from the primary definition of denial symptom visits.

When limiting our analysis to 2014 and 2015 NHAMCS-ED visits, the years after implementation of the Affordable Care Act,

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80.0% (95% CI, 78.7%-81.2%) of all included ED visits were denial symptom visits. There were no material changes to patient characteristics, ED care provided, and disposition of these visits from the analysis performed using 2011 to 2015 NHAMCS-ED data.

Secondary Analysis Table 2 shows that, for the most common presenting symptoms among visits that would otherwise be considered nonemergent by the Anthem policy (denial diagnosis visits), a substantial proportion still received ED-level care. Of these presenting symptoms, abdominal pain, chest pain, and headache were also among the most common presenting symptoms of denial symptom visits that led to hospital admission or transfer (Table 3).

Among visits for the most common presenting symptoms in denial symptom visits, many presenting symptoms with high rates of admission or transfer also had a meaningful proportion of ED visits that could potentially be denied coverage (denial diagnosis visits) (Table 4). For example, among denial symptom visits with abdominal pain, 15.9% (95% CI, 13.6%-18.6%) required admission or hospital transfer but 4.3% (95% CI, 3.3%-5.6%) may be considered nonemergent and potentially denied coverage.

DISCUSSION Amidst rising health care costs, public and commercial insurers are adopting policies to limit their payments for emergency care.

One approach recently implemented by a major commercial

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insurer, Anthem, is to disincentivize unnecessary ED visits by denying coverage and payments for visits with nonemergent ED discharge diagnoses. Our results demonstrate the inaccuracy of such a policy in identifying unnecessary ED visits. Furthermore, patients cannot reliably avoid coverage denial as most presenting symptoms could potentially lead to a nonemergent diagnosis.

The main limitation of retrospectively judging the necessity of ED care is that the determination is based on information not available to patients prior to the medical evaluation. When patients become acutely ill, they must decide whether to seek care (and, if so, when and where) based not on a diagnosis but on the symptoms they are experiencing. The link between presenting symptoms, ED care required, and final diagnoses is often uncertain. While clinicians possess the appropriate training to elucidate the care patients need, as our results show, a diagnosis-based algorithm cannot capture the complexity of these decisions.

We found that if Anthem’s policy was adopted by all other commercial insurers, nearly 1 in 6 ED visits by commercially insured adults would have a nonemergent ED discharge diagnosis and could be denied coverage. Moreover, in up to 9 of 10 adult commercially insured ED visits, patients presented with the same primary symptoms as the visits that resulted in nonemergent diagnoses that could be denied coverage. Even among patients with potentially life-threatening symptoms such as chest pain, who would likely be instructed to seek emergency care if they consulted outpatient clinicians, up to 4% may be denied coverage and possibly receive an uncovered medical bill. This result is consistent with the study by Raven et al,7 who found the presenting symptoms from ED visits with “primary care-treatable” diagnoses, as defined by the Billings algorithm, were present in nearly 9 of 10 ED visits. These findings illustrate that the link between presenting symptoms and final ED diagnoses are often not straightforward.

18

From the patients’ perspective, if similar retrospective coverage denial policies were implemented widely, up to 90% of patients have symptoms that may lead to nonemergent ED diagnoses.

These patients would, therefore, need to consider the possibility of coverage denial before seeking ED care.

Furthermore, nearly 40% of visits that had nonemergent diagnoses and could be denied coverage received substantial ED care, including being triaged as urgent or emergent and receiving multiple diagnostic tests. Even triage nurses, the most experienced ED nurses, considered nearly one-quarter of visits with nonemergent ED discharge diagnoses urgent or emergent prior to a full clinical evaluation. These results demonstrate the substantial discrepancy between what may be considered an unnecessary ED visit based on ED discharge diagnoses compared with a prospective assessment based on patients’ presentation.

The absence of reliable information on patient presentation, such as presenting symptoms or reasons for visit, in administrative billing data remains a key barrier in examining the appropriateness of ED care. This is highlighted by a new National Quality Forum project that aims to develop systems for chief complaint–based quality of emergency care.16 Therefore, insurers implementing similar policies of retrospective denial based on billing data would need to devote substantial resources to audit and review the medical records of up to 1 in 6 adult ED visits. A recent Senate investigation of Anthem’s policy found that less than half of the visits that were initially flagged and reviewed were ultimately denied coverage, confirming the inaccuracy and inefficiency of this approach.17 How a retrospective coverage denial policy, such as that implemented by Anthem, would influence ED utilization and patient outcomes remains to be studied. Existing evidence evaluating the effects of ED cost sharing shows that even modest

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copayments of $25 to $35 would lead to decreases in both lowseverity and high-severity ED visits.18,19 Cost sharing in the form of high-deductible health plans have also been associated with decreased low-severity ED visits.20 But among populations of lower socioeconomic status, high deductibles also reduce highseverity ED visits.20 In contrast to copayments or high deductibles, diagnosis-based coverage denial poses a financial disincentive that further adds an element of uncertainty. As a result, patients with acute illnesses are put in a difficult position of weighing the risk of delayed treatment for severe disease vs an uncovered medical bill.

Three decades ago, managed care organizations and insurers deterred patients from emergency care through similar strategies of coverage denial and preauthorization requirements.21 They used triage algorithms, which were unable to exclude the presence of severe disease with adequate sensitivity, leading to adverse events and even deaths.21,22 In response, states and the federal government passed laws requiring insurers to cover emergency care based on the “prudent layperson standard.” The standard defines “emergency medical conditions” as “acute symptoms of sufficient severity (including severe pain) such that a prudent layperson, who possesses an average knowledge of health and medicine, could reasonably expect the absence of immediate medical attention to result in [serious deterioration of health].”23 In 1992, Maryland passed the first prudent layperson law. Other states soon followed, and in 1997 it became federal law in the Balanced Budget Act. In 2010, the Affordable Care Act included this standard for emergency care coverage as 1 of the 10 essential health benefits.

Anthem’s policy of retroactive coverage denial based on discharge diagnosis has led to a congressional investigation by Sen Claire McCaskill (D, Missouri) for potential violation of the prudent layperson standard.17 This investigation found that, while 10% to 20% of ED visits were reviewed by Anthem for denial, only 4% to 7% of ED visits were ultimately denied.17 While the proportion denied is small, it is not clear if this will expand over time to additional states or be adopted by other insurers. Both the American College of Emergency Physicians and the American Medical Association have expressed similar concerns about this policy,24,25 particularly given Anthem’s prominence as one of the nation’s largest health insurers. If retrospective denial policies are widely adopted, they would place undue financial stress on patients with acute illness and could increase barriers to timely emergency care, particularly to those least able to pay.

Limitations Our study is bound by the limitations intrinsic to a crosssectional national survey, including potential misclassification of presenting symptoms, ED care received, or discharge diagnoses.26 Two additional limitations warrant mention. First, 2 of the exclusion criteria published by Anthem could not be operationalized in NHAMCS-ED. We could not exclude visits based on intravenous medication use as NHAMCS-ED does not contain information on the route of medication administration. Based on prior studies on use of peripheral intravenous catheter in the ED, we anticipate that accounting for patients receiving intravenous medications would have reduced the proportion of adult commercially insured patients at risk of coverage denial by approximately 1%, as there is substantial overlap with patients receiving intravenous fluids and other exclusion criteria.27,28 Also, we could not determine whether visits occurred while the patient was traveling or was

outside of a 15-mile range from an urgent care center. These visits likely account for a small proportion of ED visits, particularly as the number of urgent care centers has increased dramatically, with 6,707 centers across the United States by 2015.29 Second, the primary symptoms from denial diagnosis visits that we used to identify denial symptom visits may be overly inclusive, resulting in the inclusion of uncommon symptoms among denial symptom visits. In our primary analysis we included only primary symptoms that occurred in 2 or more visits of the same diagnosis category.

We further performed a sensitivity analysis restricting to primary symptoms present in 5 or more visits of the same diagnosis category.

We found no substantial changes in the patient characteristics, ED care delivered, and final dispositions of denial symptom visits between the primary and sensitivity analysis, suggesting that our approach is not overly inclusive.

CONCLUSIONS One in 6 ED visits by commercially insured adults could be denied coverage if a cost-reduction policy recently implemented by a large commercial insurer is widely adopted. However, the policy cannot accurately identify unnecessary visits, as up to 40% of the visits that were considered nonemergent were likely appropriate ED visits. Furthermore, these visits presented with the same spectrum of symptoms as nearly 9 of 10 ED visits. This costreduction policy could place many patients who reasonably seek ED care at risk of coverage denial.

Shih-Chuan Chou, MD, MPH and Olesya Baker, PhD are affiliated with the Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts.

Suhas Gondi, BA is affiliated with Harvard Medical School, Boston, Massachusetts.

This article first appeared in Health Policy, JAMA Network and is republished here under a Creative Commons license. https:// jamanetwork.com/journals/jamanetworkopen/fullarticle/2707430 ?widget=personalizedcontent&previousarticle=0 REFERENCES 1. Tang N, Stein J, Hsia RY, Maselli JH, Gonzales R. Trends and characteristics of US emergency department visits, 1997-2007.

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O’Grady KF, Manning WG, Newhouse JP, Brook RH. The impact of cost sharing on emergency department use. N Engl J Med. 1985;313(8):484-490. doi:10.1056/ NEJM198508223130806 Selby JV, Fireman BH, Swain BE. Effect of a copayment on use of the emergency department in a health maintenance organization.

N Engl J Med. 1996;334(10):635-641. doi:10.1056/NEJM199603073341006 Wharam JF, Zhang F, Landon BE, Soumerai SB, RossDegnan D. Low-socioeconomic-status enrollees in highdeductible plans reduced high-severity emergency care.

Health Aff (Millwood). 2013;32(8):1398-1406. doi:10.1377/ hlthaff.2012.1426 Young GP, Lowe RA. Adverse outcomes of managed care gatekeeping. Acad Emerg Med. 1997;4(12):1129-1136. doi:10.1111/j.1553-2712.1997.tb03695.x Lowe RA, Bindman AB, Ulrich SK, et al. Refusing care to emergency department of patients: evaluation of published triage guidelines. Ann Emerg Med. 1994;23(2):286-293. doi:10.1016/S0196-0644(94)70042-7Pu Cornell Law School. 29 CFR 2590.715-2719A—Patient protections. Ithaca, NY: Legal Information Institute; 2015. https://www.law.cornell.edu/cfr/text/29/2590.715-2719A. Accessed April 15, 2018.

Robeznieks A. Physicians protest harmful Anthem emergency care coverage policy. Chicago, IL: American Medical Association; 2017. https://wire.ama-assn.org/practicemanagement/physicians-protest-harmful-anthem-emergencycare-coverage-policy. Accessed March 8, 2018.

American College of Emergency Physicians. Emergency physicians: Anthem Blue Cross Blue Shield policy violates federal law. http://newsroom.acep.org/2017-05-16Emergency-Physicians-Anthem-Blue-Cross-Blue-ShieldPolicy-Violates-Federal-Law. Accessed March 8, 2018.

McCaig LF, Burt CW, Schappert SM, et al. NHAMCS: does it hold up to scrutiny? Ann Emerg Med. 2013;62(5):549551. doi:10.1016/j.annemergmed.2013.04.028 Limm EI, Fang X, Dendle C, Stuart RL, Egerton Warburton D. Half of all peripheral intravenous lines in an Australian tertiary emergency department are unused: pain with no gain? Ann Emerg Med. 2013;62(5):521-525. doi:10.1016/j. annemergmed.2013.02.022 Gentges J, Arthur A, Stamile T, Figureido M. Peripheral intravenous line placement and utilization in an academic emergency department. J Emerg Med. 2016;50(2):235-238. doi:10.1016/j.jemermed.2015.08.006 Urgent Care Association of America. Benchmark report summary: 2016. http://c.ymcdn.com/sites/www.ucaoa.org/ resource/resmgr/benchmarking/2016BenchmarkReport.pdf.

Accessed March 20, 2018.

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Emerging Evidence for Cannabis’ Role in Opioid Use Disorder Beth Wiese and Adrianne R. Wilson-Poe

Summary The opioid epidemic has become an immense problem in North America, and despite decades of research on the most effective means to treat opioid use disorder (OUD), overdose deaths are at an all-time high, and relapse remains pervasive. Although there are a number of FDA-approved opioid replacement therapies and maintenance medications to help ease the severity of opioid withdrawal symptoms and aid in relapse prevention, these medications are not risk free nor are they successful for all patients. Furthermore, there are legal and logistical bottlenecks to obtaining traditional opioid replacement therapies such as methadone or buprenorphine, and the demand for these services far outweighs the supply and access. To fill the gap between efficacious OUD treatments and the widespread prevalence of misuse, relapse, and overdose, the development of novel, alternative, or adjunct OUD treatment therapies is highly warranted.

Key Points •

•

In this article, we review emerging evidence that suggests that cannabis may play a role in ameliorating the impact of OUD. Herein, we highlight knowledge gaps and discuss cannabis’ potential to prevent opioid misuse (as an analgesic alternative), alleviate opioid withdrawal symptoms, and decrease the likelihood of relapse.

The compelling nature of these data and the relative safety profile of cannabis warrant further exploration of cannabis as an adjunct or alternative treatment for OUD.

INTRODUCTION The opioid epidemic has become an increasingly pressing problem with an estimated 26–36 million people abusing opioids around the world.1 At the time of this publication, the Centers for Disease Control reports that 115 people die every day of an opioid related cause in the United States, and more than 33,000 people lost their lives to an accidental opioid overdose in the United States in 2015 alone.1–4 The United States consumes 80% of the world’s supply of prescription opioid analgesics (POAs), and opioid prescriptions have climbed by 300% since 1991.5 The rise in opioid prescriptions has also widened the demographic of individuals dying from opioid overdose; historically, overdose was most prevalent in urban, minority adolescent males; however, today these lethal effects are similar across race, gender, socioeconomic status, and geography.7–11 The spike in prescriptions has also directly contributed to an increase in the number of first-time consumers of illicit opioids (heroin, which is commonly laced with fentanyl or its analogs), which has continued to climb since the mid 1990’s.6 Patients who become physically dependent upon POAs frequently switch to illicit opioids because POAs are more costly and/or difficult to obtain.3,8,12,13 However, ease of access 22

is a dangerous tradeoff for the lethal risk that is associated with synthetic opioids. Fentanyl, for instance, is 100 times more potent than morphine, which partially explains why there was a 250% increase in synthetic opioid mortality between 2012 and 2015.14,15 This unprecedented public health crisis warrants the investigation of novel sustainable interventions which would directly address the current opioid misuse crisis, complement current treatment strategies, and prevent future misuse through alternative first line analgesics.

Mechanistic Interactions between Cannabis and Opioids The endocannabinoid and opioidergic systems are known to interact in many different ways, from the distribution of their receptors to cross-sensitization of their behavioral pharmacology.

Cannabinoid-1 (CB1) receptors and mu opioid receptors (MORs) are distributed in many of the same areas in the brain, including but not limited to the periaqueductal gray,16,17 locus coeruleus,18,19 ventral tegmental area (VTA), nucleus accumbens, prefrontal cortex (PFC),20 central amygdala (CeA), bed nucleus of stria terminalis (BNST),21 caudate putamen (CP), substantia nigra, dorsal hippocampus, raphe nuclei, and medial basal hypothalamus.22 The extent of this overlapping expression and frequent colocalization

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of the CB1 and MOR provide clear morphological underpinnings for interactions between the opioid and cannabinoid systems in reward and withdrawal.19,23 There is a bidirectional relationship between MORs and CB1 receptors in the rewarding properties of drugs of misuse.20,24–28 That is, modulation of the CB1 receptor has profound effects on the rewarding properties of opioids, and vice versa. For example, MOR and CB1 receptors are reciprocally involved in the development of drug-induced conditioned place preference (CPP).

Coadministration of a cannabinoid antagonist and morphine attenuates the development of morphine CPP,26 and coadministration of an opioid antagonist blocks tetrahydrocannabinol (THC)induced CPP.25 Interestingly, microinjections of CB1 agonists into the medial PFC creates an aversion to doses of morphine that are normally rewarding (CPP), while CB1 antagonism in this brain region creates a rewarding effect of subthreshold morphine doses.24 In addition, administration of cannabinoids to MOR knockout (KO) mice produces a weaker CPP compared to wild-type animals,22 reviewed in Wills and Parker.27 This mutual involvement in reward is at least partially mediated by presynaptic cannabinoid and opioid disinhibition of dopamine neurons in the VTA, a well-characterized mechanism in the rewarding properties of drugs of misuse.20 Although these mechanisms have not been well studied in humans, one study has found CB1 upregulation in the reward pathway of individuals who use opioids, which supports a role for the endocannabinoid system in the development of opioid misuse.29 There is abundant support for the role of CB1 receptors in the rewarding effects of opioids and the amelioration of tolerance.

However, the effects of endogenous and exogenous cannabinoids in opioid withdrawal are somewhat paradoxical: endogenous cannabinoids seem to have no role in somatic withdrawal,27,30–32 yet exogenous CB1 agonists readily alleviate somatic symptoms such as escape jumps, diarrhea, weight loss, and paw tremors.28,33,34 Endogenous cannabinoid tone within the amygdala is also involved in the affective component of opioid withdrawal, as blockade of CB1 receptors in the CeA or BNST ameliorates opioid withdrawal.21 The kappa opioid receptor (KOR) system may also play a role in cannabis’ impact on the affective opioid withdrawal, given its pivotal contributions to dysphoria and negative effect.35 However, both KOR agonism (with U50, 488H30) and KOR antagonism (naloxone31,32) have both been shown to attenuate conditioned place aversion in CB1 KO mice.30 These contradicting data highlight the need for additional mechanistic insights into the involvement of the CB1 receptor in opioid reward and withdrawal.

Cannabis as a First Line Analgesic The primary use for both prescription opioids and cannabis is for analgesia. Currently, up to 90% of patients in state-level medical cannabis registries list chronic pain as their qualifying condition for the medical program.36 In an exhaustive review, the National Academies of Science and Medicine recently confirmed the efficacy of cannabis for chronic pain in adults.36 Interestingly, when given access to cannabis, individuals currently using opioids for chronic pain decrease their use of opioids by 40–60% and report that they prefer cannabis to opioids.37–42 Patients in these studies reported fewer side effects with cannabis than with their opioid medications (including a paradoxical improvement in cognitive function) and a better quality of life with cannabis use,

compared to opioids. Despite the vast array of cannabis products and administration routes used by patients in states with medical cannabis laws, cannabis has been consistently shown to reduce the opioid dose needed to achieve desirable pain relief.41,43 One of the mechanisms that may explain the opioid sparing effects of cannabis is its ability to produce synergistic analgesia.44–46 In humans, subanalgesic doses of THC and morphine are equally unsuccessful at reducing the sensory or affective components of pain; however, when the same doses of THC and morphine are coadministered, they produce a significant reduction in the affective component of pain.47 These synergistic effects are also observed when patients using opioids for pain vaporize wholeplant cannabis, as opposed to experimentally administered isolated THC.48 Adjunct whole plant cannabis has no effect on the pharmacokinetics of opioids, which further supports a synergistic mechanism behind the opioid sparing and enhanced analgesia produced by cannabis.48 Furthermore, in pre-clinical models, coadministration of opioids and cannabinoids attenuates the development of opioid tolerance.49,50 Combined, these clinical and pre-clinical data suggest that analgesic synergy produced by coadministered cannabis and opioids could be harnessed to achieve clinically relevant pain relief at doses that would normally be subanalgesic. This strategy could have significant impacts on the opioid epidemic, given that it could entirely prevent two of the hallmarks of opioid misuse: dose escalation and physical dependence.

Because patients report substituting cannabis for several types of pharmaceutical drugs, including opioids, benzodiazepines, and antidepressants,51 analgesic synergy may not entirely explain the opioid-sparing effects of cannabis in pain patients. Economic and lifestyle considerations may also play a pivotal role in opioid sparing and substitution. Patients report that their reasons for substituting cannabis for other medications include less severe side effects, less withdrawal potential, ease of access, and better symptom management for their conditions.52 Although there is insufficient clinical literature to support the use of cannabis as a treatment for acute pain, there is a long-standing body of pre-clinical evidence that demonstrates the antinociceptive effects of cannabinoids in pain-free, drug-naive animals.17,49,53–57 The mechanisms of cannabinoid antinociception are remarkably similar to those of opioid analgesics. Both the CB1 and MOR are G-protein coupled receptors, and agonist-initiated disinhibition of GABA release in the descending pain pathway is just one example of overlapping antinociceptive mechanisms between these drugs.17,23,58–62 Evidence supporting the role of cannabis in acute, nonsevere pain management could lead to a substantial reduction in opioid prescription rates, thereby eliminating patient exposure to the risks of opioid dose escalation and physical dependence.

This critical gap in the clinical literature and potential clinical impacts of this therapy warrants further exploration of the efficacy of cannabis for acute pain relief.

Current Opioid Use Disorder Therapies and Their Shortcomings The most prominent and pervasive problem in opioid use disorder (OUD) treatment is the prevention of drug relapse, which is extremely common during acute withdrawal (detoxification), as well as during protracted recovery after physical withdrawal symptoms have subsided.63–66 Abstinence-based protocols are

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particularly ineffective, as 85% of individuals relapse within 12 months of the initiation of treatment.65 In-patient residential treatment facilities do not appear to improve abstinence-based therapy, as relapse rates in this paradigm are as high as 80%, when measured 2 years after treatment initiation.67 Compared to abstinence, opioid replacement and medication-assisted therapies, which began in the 1960s, are more efficacious for relapse prevention; however, there are currently only four FDAapproved medications for the treatment of OUD.68–71 Off-label prescription medications such as benzodiazepines and antiemetics are also common, but these therapies are largely directed at symptom management during acute detoxification rather than relapse prevention.72 In this review, we focus on the most widely used OUD therapies, their shortcomings, and the bottlenecks to accessing them.11 Methadone, a full MOR agonist, was approved by the FDA in 1974 to aid in opioid cessation.9,73 Individuals enrolled in consistent dose methadone maintenance programs are more likely to stop using nonprescribed opioids than individuals not enrolled in the maintenance program.74 Although methadone has an encouraging safety profile,75 it carries some risk for misuse and mortality when the dose exceeds the patient’s level of tolerance.76,77 Withdrawal symptoms from methadone mimic those of other opioids when stopped abruptly or tapered too quickly, and these symptoms last up to 3 weeks longer than withdrawal from other opioids.9,78,79 There are only 1590 methadone distributers in the United States, which are highly regulated clinics that are concentrated in urban areas, creating geographical disparities in OUD treatment.10,11,79 In addition to geographical barriers, these clinics frequently have stringent and stigmatizing compliance requirements, such as daily visits and frequent urine screenings for illicit drugs.11,80 Although these barriers to treatment could potentially be addressed through concerted efforts to expand access, 40% of patients still relapse within 1 year of initiating methadone therapy.67 Buprenorphine (Subutex) is a partial MOR agonist and KOR antagonist that can reduce withdrawal symptoms, cravings, and additional opioid use.76,81,82 The inclusion of naloxone in some buprenorphine formulations (Suboxone, Zubsolv) is intended to reduce misuse by precipitating withdrawal when it is used intravenously,82,83 and despite the presence of naloxone, there is still some risk for misuse and overdose.77,83,84 The inclusion of naloxone can also induce withdrawal when administered too soon after the most recent dose of other opioids.63,67,85 Unlike methadone, Suboxone offers a primary care approach to medication-assisted therapy, as it can be dispensed by a pharmacy rather than a specialized clinic.86,87 However, only 3% of physicians possess the additional Drug Enforcement Agency credentials required to prescribe buprenorphine,76,88 and there are strict limits on the number of patients they are permitted to serve.89 Buprenorphine-licensed physicians also tend to be concentrated in larger cities, leaving 46.8% of counties in the United States, especially rural areas and the Midwest, with a shortage in convenient access to these treatment options.88,90,91 While longterm treatment retention with buprenorphine or Suboxone is not as well characterized as methadone, a Swedish study has shown retention rates of up to 75% following a year of buprenorphine/ Suboxone treatment.92 However, a 24-week clinical trial in the United States reveals that buprenorphine retention is only 46%.93 Evidence suggests that the most effective tool for relapse 24

prevention is medication-assisted pharmacotherapy, combined with social support.76,78,94,95 Because of the overwhelming evidence that supports this concurrent treatment model, there is little rationale to deviate from this approach. However, expanded access to these therapies is highly warranted, as are novel and alternative therapies which improve efficacy, diminish geographical disparities, and eliminate the need for specialty physicians.96 Cannabis During Acute Opioid Withdrawal The first barrier to overcoming OUD is getting patients through the acute withdrawal period, or detoxification. Although pharmacotherapies such as methadone and buprenorphine are largely successful and widely utilized for this purpose, there are shortcomings to this approach, which are highlighted abo ve.9,76,78,80,82,83,87,89–91,97 In May of 2018, the FDA approved the use of lofexidine, an alpha-2 adrenergic receptor agonist for acute (14 day) opioid withdrawal. Lofexidine provides substantially more symptom relief than placebo; however, the comparative efficacy of lofexidine in combination with long-acting opioid agonists or opioid antagonists is still being characterized.71,98–100 There is also nascent evidence that suggests that cannabis may be an efficacious tool during the acute opioid withdrawal period.

Numerous pre-clinical studies have shown that cannabis and cannabinoids decrease opioid withdrawal symptoms.6,33,34,97,101–103 Although this evidence supports the use of cannabinoids as a possible treatment in OUD treatment,28 conflicting evidence demonstrates that CB1 agonism increases the rewarding properties of opioids22,102 and may actually increase the severity of opioid withdrawal symptoms.18,104 These conflicting data highlight the need for a mechanistic characterization of CB1 agonism as a therapeutic target for opioid withdrawal, a need that is further substantiated by the pharmacology of CB1 antagonism. For instance, some studies show that acute administration of SR-141716A, a CB1 antagonist, can reduce opioid withdrawal; however, this effect is profoundly affected by the experimental conditions.22,105 Because this effect can be recapitulated in CB1 KO mice, CB1 antagonism only partially mediates these effects.102 To complicate the story further, the administration of cannabidiol (CBD), a very promiscuous phytocannabinoid with at least a dozen mechanisms of action, also alleviates naloxone-precipitated withdrawal in morphine tolerant rats.106–112 Although the mechanisms by which cannabinoids alleviate opioid withdrawal are complex and unclear, some reports suggest that cannabis may alleviate opioid withdrawal in humans.18,113 For instance, patients engaging in medication-assisted detoxification from opioids reported using cannabis when opioid maintenance doses were not high enough to prevent withdrawal and cravings.114 However, some individuals reported that cannabis was often ineffective and sometimes worsened overall severity of the withdrawal symptoms. Because the phytochemical makeup and cannabinoid content of cannabis have a significant effect on subjective human experiences,115 it is plausible that these variable experiences are the result of variable phytochemistry in cannabis products that are self-selected by study participants. Unfortunately, blinded, placebo-controlled clinical trials evaluating the efficacy of cannabis, either alone or as an adjunct therapy for acute opioid withdrawal, are lacking. This is not entirely surprising, given cannabis’ status as a Schedule I substance in the United States, which precludes federal funding to investigate cannabis as a

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medication-assisted therapy.

Unlike whole-plant cannabis, dronabinol, an FDA-approved analog of THC, has been evaluated for opioid withdrawal relief in a placebo-controlled study in patients receiving the opioid antagonist naltrexone. Low-dose adjunct dronabinol improved the tolerability of symptoms such as insomnia, reduced appetite, and reduced energy levels during opioid detoxification, whereas adverse events such as tachycardia were reported at higher dronabinol doses.113,116,117 In many studies, cannabinoids were safe and tolerable when coadministered with an opioid or opioid replacement medication.47,113,118–120 However, the comparative efficacy of dronabinol or other cannabinoids versus traditional replacement therapies such as methadone or buprenorphine remains to be elucidated. Given the efficacy and tolerability of Sativex (a whole-plant cannabis derivative) for pain and spasticity, investigation of adjunct Sativex for opioid withdrawal is warranted.121–123 Like opioids, chronic cannabis exposure induces the development of tolerance, physical dependence, and withdrawal symptoms during abstinence. Patients commonly report that cannabis withdrawal symptoms, most commonly anger, aggression, irritability, anxiety, decreased appetite, weight loss, restlessness, and sleeping difficulties,124–129 are similar to those produced by nicotine withdrawal.129 Comparatively, the magnitude and severity of cannabis withdrawal are significantly and substantially more benign than opioid withdrawal.20,126 In addition, and unlike opioids, cannabinoid withdrawal and subsequent relapse are nonlethal after periods of abstinence. The reduced intensity of cannabinoid withdrawal symptoms compared to opioids could at least partially be explained by the prolonged period of metabolization of cannabinoids in the body,102 contributing to the mounting support for cannabis as a harm-reduction tool to combat OUD. Cannabis as a Harm Reduction Tool in OUD Pre-clinical evidence suggests that the CB1 receptor plays a critical role in opioid reward. Cannabinoid antagonism reduces the rewarding properties of opioids and prevents reinstatement of drug seeking.105,130,131 However, these effects were not reproducible in human clinical trials.132–134 Unlike CB1 antagonism, CB1 agonism may play a role in OUD treatment. Several studies have shown that adjunct cannabis decreases opioid consumption or prevents opioid dose escalation.37–42,121,135 Although these findings are promising, several other studies have shown that cannabis use either has no impact on opioid consumption or may increase nonmedical opioid use.136–138 The mechanisms underlying cannabis alteration of opioid consumption are yet to be determined; however, there is significant pre-clinical evidence which suggests that CBD, one of the most prevalent cannabinoid molecules in cannabis, plays a critical role. CBD does not have reinforcing effects in rodents, which supports its low potential for misuse.16,139 CBD has been shown to reduce the rewarding aspects of multiple drugs of abuse, such as cocaine, amphetamine,16 and nicotine.140 Administration of CBD also attenuates morphine CPP and cue-induced reinstatement of heroin self-administration in rats, without creating any aversive or rewarding effects on its own.106,141–143 These findings provide promising rationale for the use of CBD in opioid relapse prevention in humans. In fact, pilot clinical studies have shown that in individuals recently abstinent from

heroin, CBD reduces heroin craving.142 This effect occurs as soon as 1 h after administration and lasts for up to 7 days. Adjunct CBD appears to be safe and tolerable, as 400 and 800 mg oral CBD administration does not intensify the effects of intravenous fentanyl or create any adverse effects.118 Because CBD is neither intoxicating nor rewarding and has an extremely large therapeutic window and impressive safety profile, the use of CBD to inhibit opioid craving has great therapeutic potential.

Adjunct cannabis use alongside current treatment strategies could help to improve the number of individuals engaging in OUD treatment, as well as increase treatment retention rates.

Both dronabinol and intermittent whole-plant cannabis appear to increase the length of time patients remain in treatment for OUD.6,113 However, chronic cannabis consumption during naltrexone treatment was ineffective at improving treatment retention, highlighting the need for further research into the dose and frequency of cannabis use in OUD treatment retention and relapse prevention.144 Although the ubiquitous and ever-growing regulated cannabis markets across North America could potentially address the aforementioned shortcomings in OUD treatment accessibility and retention, there are currently very few addiction and recovery centers that have embraced concurrent social support and cannabis-assisted OUD treatment.51 This is unsurprising given the lack of empirical evidence to support this approach, and the lack of federal research funding that would support this work.

In addition to the clinical and experimental observations outlined above, epidemiological investigations in U.S. states with legal cannabis have provided insight into the promising role for cannabis in the opioid crisis. The implementation of both medical and adult-use cannabis laws appears to have a significant impact on opioid consumption and overdose. These states experience a 23% reduction in nonfatal opioid overdoses, as measured at hospital emergency departments.145 By analyzing death certificates, Bachhuber et al. found a 24% reduction in the annual rate of fatal opioid overdoses in the first year following medical cannabis legalization,146 an effect that gets larger the longer a state has had legal cannabis (33% in California, which has had medical use since 1996 and the lowest rate of opioid overdose fatalities in the country).146,147 This finding was also seen in data from the FARS, which demonstrates a similar drop in mortalities of opioid positive automobile accidents in states with implemented cannabis legalization for individuals aged 21–40.148 The mechanisms underlying cannabis’ ability to reduce opioid hospitalization and mortality are unclear; however, analysis of the Medicare Part D prescription drug program has unveiled the possibility that cannabis may be serving as an analgesic alternative to opioids for individuals living in these states.149 The number of filled POAs is substantially lower in states with the most liberal cannabis laws, where there are 3.742 million fewer daily doses than in states with the most prohibitive laws.150 These epidemiological impacts are not exclusive to opioid prescriptions, hospitalizations, and mortality; the U.S. economy could also benefit from expanded cannabis legalization. Opioids cost patients and insurance companies upwards of 2.6 billion dollars in healthcare costs annually.151 While cannabis is still federally illegal, and in most cases dispensary purchases are not eligible to be covered under any healthcare insurance plan, states with legalized cannabis have seen significant decreases in Medicare Part D prescription drug spending, including, but not

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limited to, prescription opioids.149,152–155 Reductions in spending from Medicare Part D were over $165 million dollars.149 If cannabis were removed from Schedule I of the Controlled Substance Act and more patients had access to cannabis, savings from pharmaceutical costs incurred by the Medicare Part D prescription plan are projected to continue to climb.149

could be an effective tool against OUD, while also minimizing the risk for CUD. Because cannabis does not carry the risk of fatal overdose, the use of cannabis as a harm-reduction treatment in the opioid epidemic warrants further investigation.

Shortcomings of Cannabis in Medication-Assisted Therapy Although the literature thoroughly supports the safety and tolerability of cannabis,38,118,142,156 there is conflicting evidence for its efficacy as a treatment for opioid misuse. Throughout the history of methadone administration, patients have reported that cannabis provides relief from opioid withdrawal symptoms, as well as breakthrough pain and anxiety.119 However, other evidence demonstrates that cannabis does not relieve withdrawal symptoms for individuals undergoing methadone tapering, and some participants even reported increased severity of their withdrawal symptoms.104 All the participants in the latter study procured their own cannabis and reported smoking as the route of administration.

Because the dose of cannabinoids and phytochemical makeup of whole-plant cannabis have significant impacts on physiological responses (such as tachycardia) and subjective experiences (such as anxiety), additional research is needed to characterize maximally efficacious treatment protocols.116,157 When used to treat opioid withdrawal symptoms, undesirable side effects also occur in a dose-dependent manner for the FDA-approved cannabinoid dronabinol.113 The homogenous and consistent formulation of this pharmaceutical combined with the logistical ease of prescribing the drug may make it more feasible than whole-plant cannabis for clinical trials on cannabinoid alleviation of opioid withdrawal symptoms and relapse prevention.

Despite the promising results of reducing or maintaining a consistent opioid dose, it is plausible that the substitution of one rewarding substance (opioids) for another (THC) could be problematic, leading to cannabis use disorder (CUD). In 2016, ~1.4–2.9% of adults over the age of 18 in the United States met criteria for CUD.79 With revisions to the criteria of substance use disorders in 2013, ~19% of individuals who use cannabis throughout their lifetime would eventually meet the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM5) criteria for CUD.153 The interpersonal or employment hardships experienced by these individuals that resulted in the meeting of DSM criteria may have simply been due to the legality of cannabis use; that is, a false CUD diagnosis is less likely to occur in the postprohibition era, when patients are no longer breaking the law.

Risks of CUD seem to be correlated with higher THC concentrations,153 which is a valid concern in legal markets where average THC potency is upward of 20%.158 Recreational users of cannabis have historically consumed cultivars higher in THC and lower in CBD, due to the desired intoxicating effects of THC.38 Medical users, however, have turned to cultivars higher in CBD and lower in THC in an attempt to optimize the medicinal benefits of cannabis.38,153 Although misuse potential is a valid concern, it is notable that the misuse liability of cannabis is very low.159 One possible approach to alleviate the concern of misuse is the concurrent administration of opioid antagonists. This approach seems to reduce the rewarding properties, but not the hyperphagia or withdrawal-relieving properties of THC.160–164 These data suggest that combined cannabis and opioid-antagonist therapy

The opioid overdose epidemic is arguably the worst public health crisis in U.S. history. At the time of this publication, more people are dying than at the peak of the AIDS epidemic, and for the first time, drug overdoses outnumber automobile and handgun deaths.165 A continental crisis of this magnitude warrants the immediate implementation of novel strategies that prevent opioid misuse, overdose, and death.

Growing pre-clinical and clinical evidence appears to support the use of cannabis for these purposes. The evidence summarized in this article demonstrates the potential cannabis has to ease opioid withdrawal symptoms, reduce opioid consumption, ameliorate opioid cravings, prevent opioid relapse, improve OUD treatment retention, and reduce overdose deaths. Cannabis’ greatest potential to positively impact the opioid epidemic may be due to its promising role as a first line analgesic in lieu of or in addition to opioids. The comparative efficacy of cannabis alone or in conjunction with current medication-assisted OUD therapies is not well characterized. However, no other intervention, policy, pharmacotherapy, or treatment paradigm has been as impactful as cannabis legislation has been on the rates of opioid consumption, overdose, and death.

Many of the barriers that prevent people from accessing traditional OUD treatment do not apply to cannabis therapy, and access to cannabis medicine is rapidly growing as more U.S. states roll back prohibition. However, a major barrier in universal patient access and improvement in the opioid epidemic is cannabis’ status as a Schedule I controlled substance.166 Undoubtedly, more high-quality clinical evidence is needed to further support the use of cannabis to combat OUD; however, federal grant funding that would support these types of clinical trials is currently outside the scope of interest of the National Institutes of Health (because of Schedule I, cannabis is federally considered to have no medical benefit). Patients, healthcare providers, and regulating bodies would all greatly benefit from additional evidence that fills in massive gaps in the knowledge base about the utility of cannabis for OUD treatment: dosing, cannabinoid content and ratios, bioavailability, contraindications, misuse liability, route of administration, and many other questions remain. Even the clinical work that has been conducted thus far may have little validity in the modern landscape of legalized cannabis; all federally-funded cannabis research in the United States is conducted using a single source of cannabis (NIDA drug supply), which is notoriously low in potency and quality, and does not resemble the staggering phytochemical variability in wholeplant cannabis products in regulated state markets.36 These barriers to research funding and access to “real world” cannabis for clinical research directly contribute to our inability to address the opioid epidemic with what appears to be a safe and efficacious tool.

In light of the evidence presented in this article, and despite a lack of FDA approval, some U.S. states and private treatment centers have already begun to include cannabis as a part of OUD treatment protocols. The state of New Jersey recently added OUD

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SUMMARY AND FUTURE DIRECTIONS

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to their list of qualifying conditions for participation in the state’s medical cannabis program.167,168 Some private treatment centers are also citing the benefits of harm reduction, which greatly outweigh the risks of cannabis use during the first 28 days of recovery, a critical time period for patient survival.76 Many clinicians remain skeptical of cannabis as a viable treatment option, either due to the stigma surrounding cannabis use or the belief that there is not enough clinical evidence for them to feel confident providing patients with cannabis recommendations.169 This is unsurprising, given that 85% of recent medical school graduates still receive no education whatsoever about cannabis throughout their training, residencies, or fellowships.170 As the evidence in this field accumulates, it will be critically important to widen opportunities for clinicians to participate in Continuing Medical Education programs, which include the harm reduction and medical benefits that cannabis could provide. Evidence-based opioid prescription and cannabis recommendation practices are a critical component of continuing education, so that clinicians can continue to uphold their Hippocratic oaths to “do no harm.”

11.

12.

13.

14.

Beth Wiese and Adrianne R. Wilson-Poe are affiliated with the Department of Anesthesiology, Pain Center, Washington University School of Medicine, St. Louis, Missouri.

15.

This article first appeared in Cannabis and Cannabinoid Research, Vol. 3, No. 1 and is republished here under a Creative Commons license. https://www.liebertpub.com/doi/10.1089/can.2018.0022

16.

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153. Hasin DS. US Epidemiology of cannabis use and associated problems. Neuropsychopharmacology. 2018;43:195–212.

154. Wen H, Hockenberry JM. Association of medical and adultuse marijuana laws with opioid prescribing for Medicaid enrollees. JAMA Intern Med. 2018;178:673–679.

155. Liang D, Bao Y, Wallace M, et al. Medical cannabis legalization and opioid prescriptions: evidence on US Medicaid enrollees during 1993–2014. Addiction. 2018. [Epub ahead of print]; DOI: 10.1111/add.14382.

156. Robson P. Human studies of cannabinoids and medicinal cannabis. Handb Exp Pharmacol. 2005:719–756.

157. Cuttler C, Spradlin A, McLaughlin RJ. A naturalistic examination of the perceived effects of cannabis on negative affect. J Affect Disord. 2018;235:198–205.

158. Smart R, Caulkins JP, Kilmer B, et al. Variation in cannabis potency and prices in a newly-legal market: evidence from 30 million cannabis sales in Washington State. Addiction.

2017;112:2167–2177.

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159. Anthony JC, Warner LA, Kessler RC. Comparative epidemiology of dependence on tobacco, alcohol, controlled substances, and inhalants: basic findings from the National Comorbidity Survey. Exp Clin Psychopharmacol. 1994;2:244– 268.

160. Yamamoto T, Takada K. Current perspective—role of cannabinoid receptor in the brain as it relates to drug reward.

Jpn J Pharmacol. 2001;84:229–236.

161. Haney M, Ramesh D, Glass A, et al. Naltrexone maintenance decreases cannabis self-administration and subjective effects in daily cannabis smokers. Neuropsychopharmacology.

2015;40:2489–2498.

162. Curran HV, Freeman TP, Mokrysz C, et al. Keep off the grass? Cannabis, cognition and addiction. Nat Rev Neurosci.

2016;17:293–306.

163. Ranganathan M, Carbuto M, Braley G, et al. Naltrexone does not attenuate the effects of intravenous Δ9-tetrahydrocannabinol in healthy humans. Int J Neuropsychopharmacol. 2012;15:1251– 1264.

164. Clasen MM, Flax SM, Hempel BJ, et al. Antagonism of

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the kappa opioid receptor attenuates THC-induced place aversions in adult male Sprague-Dawley rats. Pharmacol Biochem Behav. 2017;163:30–35.

165. Clark AK, Wilder CM, Winstanley EL. A systematic review of community opioid overdose prevention and naloxone distribution programs. J Addict Med. 2014;8:153–163.

166. Maher DP, Carr DB, Hill K, et al. Cannabis for the treatment of chronic pain in the era of an opioid epidemic: a symposiumbased review of sociomedical science. Pain Med. 2017 [Epub ahead of print]; doi: 10.1093/pm/pnx143.

167. Department of Health. Medical marijuana program.

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169. Fitzcharles MA, Eisenberg E. Medical cannabis: a forward vision for the clinician. Eur J Pain. 2018;22:485–491.

170. Evanoff AB, Quan T, Dufault C, et al. Physicians-in-training are not prepared to prescribe medical marijuana. Drug Alcohol Depend. 2017;180:151–155.

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Association of a Care Coordination Model With Health Care Costs and Utilization The Johns Hopkins Community Health Partnership (J-CHiP) Scott A. Berkowitz, MD, MBA, Shriram Parashuram, PhD, MPH, and Kathy Rowan, PhD, MPH

Summary The Johns Hopkins Community Health Partnership was created to improve care coordination across the continuum in East Baltimore, Maryland. Nonrandomized acute care intervention (ACI) and community intervention (CI) Medicare and Medicaid participants were analyzed in a quality improvement study using difference-in-differences designs with propensity score–weighted and matched comparison groups. The study spanned 2012 to 2016 and took place in acute care hospitals, primary care clinics, skilled nursing facilities, and community-based organizations. The ACI analysis compared outcomes of participants in Medicare and Medicaid during their 90-day postacute episode with those of a propensity score–weighted preintervention group at Johns Hopkins Community Health Partnership hospitals and a concurrent comparison group drawn from similar Maryland hospitals. The CI analysis compared changes in outcomes of Medicare and Medicaid participants with those of a propensity score–matched comparison group of local residents.

Key Points • •

The ACI bundle aimed to improve transition planning following discharge. The CI included enhanced care coordination and integrated behavioral support from local primary care sites in collaboration with community-based organizations A care coordination model consisting of complementary bundled interventions in an urban academic environment was associated with lower spending and improved health outcomes.

INTRODUCTION The nearly 200,000 residents of East Baltimore, where life expectancy can be 20 years shorter than in more affluent communities nearby, face multiple challenges to their health and well-being.1 Many of these residents receive care at Johns Hopkins facilities, and there are challenges in coordinating care for patients, especially during transitions, related to both medical complexity and underlying social factors. We also know that effective care coordination can lead to improved health outcomes, especially for those of greatest need with chronic conditions.2-6 The Johns Hopkins Community Health Partnership (J-CHiP) is a care coordination initiative spanning the care continuum. It has 2 principal program components: (1) a bundle of interventions deployed in 2 acute care East Baltimore hospitals (Johns Hopkins Hospital [JHH] and Johns Hopkins Bayview Medical Center [JHBMC]), with additional focus on those discharged to local skilled nursing facilities (SNFs) and (2) a care management model embedded in ambulatory primary care sites located in the community. The Johns Hopkins Community Health Partnership was catalyzed by a Health

Care Innovation Award (HCIA) from the Center for Medicare & Medicaid Innovation, a component of the Centers for Medicare & Medicaid Services, an agency of the US Department of Health & Human Services. Launched in July 2012, external funding for the acute care component (acute care intervention [ACI]) ended in June 2015, while funding for the community component (community intervention [CI]) ended in June 2016.7 It is estimated that more than 80,000 participants received services through the award. Previous articles, including a case study, have described the interventions.7,8 This article is a follow-up to a report on outcomes as assessed by NORC (formerly the National Opinion Research Center) at the University of Chicago, the independent evaluator for the J-CHiP HCIA award.9,10 The ACI, an evidence-based bundle of services, focused on improving the coordination of care for all patients and included (1) early screen for discharge planning to predict service needs following discharge; (2) daily multidisciplinary unit-based rounds to review goals and priorities of care; (3) innovative patient education using tablet-based modules; (4) enhanced medication management, including the option of medications in hand at the

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time of discharge; (5) telephone follow-up after discharge by nurses staffing a patient access line; and (6) skilled home care, remote patient monitoring, and/or a skilled nurse transition guide for high-risk patients.11 Most of these services were deployed across 35 adult inpatient units at either JHH or JHBMC. An additional intervention comprising discharge and disease-based protocols was offered to a subset of patients discharged to 5 local SNFs. The CI, a care coordination model with integrated behavioral health care, was embedded at 8 ambulatory sites. The intervention used risk prediction models to identify and target Medicare and Medicaid (M/M) patients at greatest risk for future hospitalization.

In the original rollout of the intervention, called J-CHiP Classic, multidisciplinary teams providing care coordination and community-based health workers (CHWs) were deployed to the neighboring community, working in close partnership with patients’ primary care practitioners. These CHWs focused on addressing patients’ barriers to care, often meeting patients at appointments and in their homes. Health behavioral specialists embedded in the care teams intervened around substance use or other psychiatric diagnoses.12 About 1 year into the program, the Tumaini (Swahili for hope) for health component was added to the intervention. This component was implemented by 2 communitybased organizations, Sisters Together and Reaching13 and the Men and Families Center.14 Both organizations became key partners in delivering a complementary component of the CI. Sisters Together and Reaching hired and trained an additional nurse case manager and a team of CHWs to supplement those already trained by Johns Hopkins HealthCare, the managed care arm of Johns Hopkins Medicine. The Men and Families Center trained and deployed neighborhood-based support navigators, akin to block captains, in a single census tract to provide outreach and support services to all residents regardless of their insurance status.

For recipients of the ACI or CI, we hypothesized that improved care coordination with team-based deployment would reduce avoidable health care utilization following discharge and in other circumstances, including hospital admissions, readmissions, and emergency department (ED) visits, and would subsequently lead to a reduction in total cost of care from the perspective of the Centers for Medicare & Medicaid Services.

METHODS Source of Data and Study Population Difference-in-differences (DID) study designs were used to study the association between the intervention and health care utilization in the ACI and CI groups. For the ACI, the preintervention period was for claims from January 1, 2011, to June 30, 2012, and the ramp-up period when the intervention was beginning to be implemented was from July 1, 2012, through March 31, 2013; the postintervention period began on April 1, 2013, and M/M claims data through June 30, 2015, were used for the analysis. For the CI, the preintervention period was the 2-year span (8 quarters) before the beneficiary enrolled in the intervention. Medicare claims data for the CI group were available through June 30, 2016, and we used a 90-day claims runoff date of March 31, 2016, for the analysis. Medicaid claims data for the CI group were available through March 31, 2016, with a 90-day claims runoff date of December 31, 2015. Evaluation efforts with respect to the J-CHiP 34

effort were approved by the NORC institutional review board as well as by the Johns Hopkins University institutional review board. A waiver of consent was also granted by the Johns Hopkins institutional review board. This study adhered to the Standards for Quality Improvement Reporting Excellence (SQUIRE) 2.0 reporting guideline.

Acute Care Intervention The acute care analysis compared the associated outcomes of M/M program participants during their 90-day postacute episode with those of a propensity score–weighted preintervention group at J-CHiP hospitals and with a comparison group drawn from similar hospitals in Maryland (Table 1). The Johns Hopkins Community Health Partnership provided an enrollment file of the acute care participants at JHH and JHBMC who were hospitalized in particular inpatient units, which were linked to fee-for-service M/M enrollment and claims data to obtain their demographic and health characteristics and analyze their utilization and cost outcomes. A pool of episodes from these same hospitals during the preintervention period served as the pretreatment group. For the comparison group, a pool of M/M fee-for-service episodes discharged from 3 similar hospitals in geographic proximity to JHH and JHBMC during the preimplementation and postimplementation periods were used. Comparison hospitals were chosen based on case mix and patient demographic characteristics and were all located in Maryland to account for the unique all-payer hospital payment model. We expected that the global budgeting would have similar effects on all Maryland hospitals, but note that being an HCIA awardee, there may be unobserved hospital- and practitioner-level selection effects that are also correlated with outcomes.

Community Intervention The analysis of the CI compares the changes in outcomes of M/M program participants before and after their enrollment in the intervention with those of a propensity score–matched comparison group (Table 2). We linked an intervention enrollment file of the CI participants to M/M enrollment and claims data in the preintervention and postintervention periods to obtain their demographic and health characteristics and to analyze their utilization and cost outcomes. As described previously,7 the J-CHiP team identified eligible Medicare and Medicaid patients aged 18 years or older with at least 1 chronic condition who received care at a designated primary care site. From this population, high-risk patients were identified based on their risk of future hospitalization.

For the Medicare population, we screened 27,000 Medicare feefor-service patients and calculated hospitalization risk using the ACG (adjusted clinical groups) score. For the Medicaid population, we screened approximately 53,000 Priority Partners Managed Care Organization Medicaid patients using a multivariable logistic regression model to augment the ACG hospitalization score with supplemented data (claims, health plan enrollment, health risk assessment, and lab data). For the comparison group, we analyzed claims to identify a pool of Medicare fee-for-service beneficiaries who lived in or near geographic proximity (defined by the 7 zip codes in East Baltimore where the participating ambulatory sites were located) and who had at least 1 evaluation and management visit to a practitioner within the time period of the community intervention to determine a pseudo–enrollment start date. This allowed us to control for contextual factors that could affect both

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the treatment and comparison groups. From this pool of potential comparison beneficiaries, selected beneficiaries had at least 1 evaluation and management visit to a practitioner during the year prior to the J-CHiP intervention start date.

Outcome Measures We used claims-based measures for all outcomes, as electronic medical record data were not available for the comparison groups.

Additionally, because both the ACI and CI did not target participants with particular clinical profiles (eg, those with diabetes or heart disease) but rather were designed to improve care coordination and care management for participants with broader medical and social needs, we used claims-based measures capturing utilization, quality, and cost of care that reflect these goals. Outcome measures were constructed for each quarter of the study period. Utilization outcomes were coded as binary events, indicating whether or not the beneficiary had an event during the quarter. Total cost of care (to the payer) was constructed for each 90-day beneficiaryepisode (ACI), or for each beneficiary quarter (CI). Utilization measures included all-cause hospital admissions, ED visits, and 30-day readmissions. In addition, we expected the ACI to affect timely receipt of practitioner follow-up (7 and 30 days) after hospital discharge. We assessed physician, nurse practitioner, and physician assistant follow-up visits following hospital discharge

as a quality of care measure, as evidence has suggested that 7and 30-day follow-up visits with practitioners may lead to higherquality outcomes. This follow-up visit measure does not take into consideration phone calls made to patients via the patient access line after discharge. We expected the CI to have an impact on how participants received care coordination through medical and social supports, and therefore we assessed ambulatory careâ&#x20AC;&#x201C;sensitive conditions visits and potentially avoidable hospitalizations as quality measures. We computed the former for Medicare and the latter for Medicaid participants because the ambulatory careâ&#x20AC;&#x201C; sensitive conditions hospitalization measure requires the Present on Admission indicator, which was present on Medicare claims but absent on Medicaid claims. All cost estimates were based solely on data from M/M claims and did not include the cost of the intervention.

Analytic Design For the ACI, participants were selected based on having an inpatient stay in a preintervention or postintervention period; the unit of analysis was therefore a beneficiary-episode. For the CI group, participants were selected based on being in the community (and enrolled in the program for the treatment group); therefore, the unit of analysis was the beneficiary. Race/ethnicity were determined and cataloged by claims history for both the ACI

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and CI. A DID approach was used to estimate the associations between the interventions and utilization and cost outcome measures in each quarter and over the entire preintervention and postintervention period. As a DID model is valid when there are no differences in the preintervention period trend, we examined unadjusted data to assess parallel trends and included prior-year outcomes in the propensity score models. In this study, we report aggregate cost measures to show the total savings accrued to M/M over the intervention period. The aggregate measures are the net difference, which is calculated by summing the quarterly impacts weighted by the number of episodes or beneficiaries each quarter.

We report quarterly utilization and quality outcomes (average number of events per quarter) per 1,000 episodes (ACI) or per 1,000 beneficiaries (CI). Quarterly utilization and quality estimates are a weighted average for all quarters in the intervention.

Statistical Analysis Propensity Score Weighting and Matching For both the ACI and CI, we used propensity score models to minimize differences between the treatment and comparison groups. In the analysis of the ACI, the propensity score was estimated as the probability of a patient being enrolled in the intervention, conditional on the patient’s covariates. Discharges from both groups during the pretreatment and posttreatment

comparison groups were assigned a weight based on the likelihood of being in the posttreatment intervention group. Table 1 shows the descriptive characteristics of the ACI populations after propensity score weighting. In the analysis of the CI, propensity score matching was used to find comparison group beneficiaries comparable to treatment beneficiaries. As with the ACI, the model predicted the likelihood of enrolling in the program. The common support (overlap in the distribution of scores) was then examined, and differences in demographic and health measures in the weighted or matched treatment and comparison groups were assessed to ensure sufficient comparability. Table 2 shows the descriptive characteristics of the CI populations after propensity score matching.

Statistical Tests Used The primary statistical tests for the research question on impact were generalized linear regression models with a gamma distribution or 2-part models with the best-fitting distribution for cost measures, and logit link for binary outcomes, with a prespecified confidence interval of 90% (in accordance with Center for Medicare & Medicaid Innovation guidance). P values were noted at P < .10 (P < .10, P < .05, P < .01) and were 2-sided.

For some cost categories in the acute care Medicare analyses, 2-part models with the best-fitting distributional form were used. Regression adjustment variables included age category, race/ethnicity, sex, prior-year hospitalizations and cost, dualeligibility indicator, health risk scores, an indicator of end-stage renal disease, and disability. In the ACI, we also included prioryear hospitalizations and cost and discharge disposition.

Using the regression coefficients, we estimated predicted probabilities; all analyses were conducted in Stata statistical software version 14.0 (StataCorp).

RESULTS

Abbreviations: J-CHiP, John Hopkins Community Health Partnership; NA, not applicable. a

Adjusted Clinical Group Resource Utilization Band score is used for Medicaid beneficiaries and Hierarchical

Condition Category score is used for Medicare beneficiaries.

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Acute Care Intervention For the ACI, there were 26,144 beneficiary-episodes for Medicare (13,726 [52.5%] female patients; mean patient age, 68.4 years) and 13,921 beneficiary-episodes for Medicaid (7,392 [53.1%] female patients; mean patient age, 52.2 years). For Medicare, the ACI was associated with a statistically significant reduction in aggregate cost of care of $29.2 million ($1,115 per beneficiaryepisode) with increases in 90day hospitalization and 30-day readmission of 11 and 14 per 1,000 beneficiary-episodes, respectively,

c

P < .10.

and reduction in practitioner follow-up visits of 41 and 29 per 1,000 beneficiary-episodes for 7-day and 30-day visits, respectively. For Medicaid, the statistically significant reduction in aggregate cost of care was $59.8 million ($4,295 per beneficiary-episode), and 90 day ED visit rates were reduced by 133 per 1,000 beneficiaryepisodes while the hospitalization rate was increased by 49 per 1,000 beneficiary-episodes and the practitioner follow-up visits were reduced by 70 and 182 per 1,000 beneficiary-episodes for 7-day and 30-day visits, respectively (Table 3). Savings for the Medicaid population were associated with relative reductions in outpatient care and acute care inpatient costs. For the Medicare population, reductions in total cost of care were largely associated with relative reductions in SNF expenses, although there were other contributors as well.

Community Intervention A total of 2,154 Medicare beneficiaries (1,320 [61.3%] female; mean age, 69.3 years) and 2,532 Medicaid beneficiaries (1483 [67.3%] female; mean age, 55.1 years) received the CI. For Medicaid, statistically significant aggregate total cost-ofcare reduction associated with the CI was $24.4 million (average savings of $1,643 per beneficiary per quarter), and reductions of hospitalizations, ED visits, and 30-day readmissions were 33, 51, and 36 per 1,000 beneficiaries, respectively, and there was a reduction of avoidable hospitalizations by 7 per 1,000 beneficiaries.

There were no statistically significant findings for Medicare (Table 4). In total, the CI and ACI were associated with $113.3 million in cost savings.

DISCUSSION The J-CHiP program, consisting of multiple complementary and intersecting bundles of strategies deployed within an urban academic environment, was associated with desired outcomes in many utilization, quality, and costs measures for high-risk M/M beneficiaries in East Baltimore by improving the coordination of services across the health care continuum. This was particularly notable with respect to cost savings, and the results showed a statistically significant total cost of care reduction in the ACI for both M/M populations and in the CI for the Medicaid population.

Greater aggregate cost savings and lower utilization were found among the Medicaid population than the Medicare population for both the ACI and CI (of note, results of utilization were examined as counts rather than binary events and conclusions were consistent with the binary models).

With respect to the ACI and the Medicaid population, there was a statistically significant increase in hospitalizations, a reduction in ED visits, and an overall statistically significant reduction in total aggregate costs related to reduced acute inpatient and outpatient costs. This suggests the reduced ED visit rate, as well as possibly less intensive acute care resource use during a hospitalization and/or postacute costs, may have contributed to the overall decreased costs. For example, if a hospitalization was required, it was less expensive, likely because of the increased emphasis on early discharge planning, in particular for those with greatest coordination needs. With respect to the ACI and the Medicare population, although hospital use and readmissions

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increased, there was an overall statistically significant reduction in total aggregate costs, which was likely driven at least in part by a reduction in SNF costs.

Other measures not used in this evaluation (eg, measures of procedures, tests, physician and other staff costs, and community care) may be useful to consider for future impact assessment.

However, because of the nature of the evaluation, which required assessment of 107 awardees in total, evaluators used a focused and consistent set of measures; more specific tailoring of outcomes was not within the study’s scope. Finally, it is notable that even with the focus on services following discharge, the results show reductions in practitioner follow-up visits. Because transitional and followup care provided by transition guides or care coordinators after discharge was an important intervention component and would not be considered a follow-up practitioner visit, those patients who did not then attend their scheduled follow-up appointment may have done so either because they could not arrange travel or thought it was not needed. The follow-up visits measure reflects completed, and not scheduled, visits.

As mentioned, the cost analyses show that the greatest cost reduction for the Medicare population receiving the ACI was associated with a reduction in SNF utilization. Although related, this was distinct from our additional DID analyses presented in our report that focused on the patients discharged from JHH and JHBMC to 5 partner SNFs near JHH and JHBMC in comparison with those discharged to other SNFs in the same market area.9 While we did not find relative savings or lower utilization in this subgroup analysis,9 we do observe herein significantly lower SNF costs for those receiving the overall ACI for the Medicare population.

The CI showed a statistically significant reduction in costs, admissions, and ED visits for the Medicaid population, but not for the Medicare population. While there was a trend toward cost reduction for the Medicare population in the final year, this changed in the final quarter, netting a nonsignificant increase in overall costs.

The disparate outcomes between M/M patients may reflect the CI focus on patients’ social determinants of health—such as whether the patient can obtain medications, food, or housing—described previously7 as a greater need among the Medicaid population. In this context, it is worth considering previously reported findings of subgroup DID analyses of the CI that showed similar outcomes for both the J-CHiP Classic and Tumaini models as well as for patients who differed in their frequency of receiving care coordination

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services (receiving program staff contact each enrollment quarter vs otherwise).9 In addition to our analyses of claims-based outcomes, our evaluation also assessed quality of care using survey data for both clinicians and CHWs.9 The Johns Hopkins Community Health Partnership fielded a modified Consumer Assessment of Healthcare Providers and Systems survey for J-CHiP participants in the CI (but who may also have been in the ACI). While these data on patient experience were not collected from the comparison beneficiaries and cannot be attributed solely to J-CHiP, the data provide insight on the process of care delivery. The high survey scores indicate that beneficiaries experienced high levels of quality of care. Respondents reported that their health care practitioner explained things clearly (99%), listened carefully (95%), showed respect (99%), provided easy-to-understand instructions (98%), knew their medical history (95%), and spent enough time with them (98%). Likewise, respondents reported good communication with CHWs, who explained things in a way that was easy to understand (95%), listened carefully (91%), and showed respect for what patients had to say (94%). About 78% of respondents noted that in the previous 12 months they had a discussion with someone in the practitioner’s office about specific goals for their health. Overall, J-CHiP community respondents were extremely satisfied with their practitioners. On a scale of 0 to 10, with 10 being the best, J-CHiP patients rated their health care practitioner an average of 8.9 and rated their trust of their CHW an average of 9.1. About 92% of respondents said they would recommend the CHW to their family and friends.9 Limitations There were several limitations to this study. Although the ACI was designed to be all-payer, the evaluation focused only on the M/M populations due to data availability. For the CI, enrollment for the comparison group was based on the patient having an evaluation and management visit on the claim; as a result, while both groups have similar baseline utilization and costs, the comparison group, by definition, was as likely, if not more likely, to get care at the time of enrollment as J-CHiP’s participants. It is unclear how selection of the comparison group based on realized ambulatory care may bias the results. In addition, Maryland hospitals where beneficiaries received care were participating in health care delivery reforms such as the Maryland All-Payer Model, which began in January 2014 (midway through the HCIA

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award period) and involved hospital global budgeting efforts.15 The purposeful selection by NORC of Maryland hospitals for the comparison group should minimize the impact of these state reform activities; however, it is possible that the response of each hospital was different, which could bias the results in ways we cannot measure. We acknowledge that unobserved differences in socioeconomic characteristics between Medicare participants in the CI group and the comparison group may bias results toward the null. Even though comparison beneficiaries typically reside in the same general zip code vicinity as those participating in the CI, they are likely to differ on unobserved socioeconomic characteristics. In contrast, our Medicaid CI findings are unbiased, as both comparison and intervention beneficiaries have similar socioeconomic characteristics by virtue of their eligibility for Medicaid. Also, we do not include the cost of the intervention, as this would require a cost-benefit analysis that is beyond the scope of the evaluation and would have necessitated a better understanding of the full scope of associated benefits as well as of costs associated with in-kind contributions. For example, a costbenefit analysis would entail a collaborative agreement between the J-CHiP implementation team and Center for Medicare & Medicaid Innovation to monetize the return on investment with respect to both direct benefits (beneficiary health, staff training) and indirect benefits (quality of life, improved employment due to better functioning).

In addition to results reported here, there are additional J-CHiP evaluation efforts.16 With respect to the ACI, the analysis herein included a focus on intervention units, while additional, currently unpublished analyses look more broadly at the entire target population. The NORC analysis included comparison discharges from selected hospitals in Maryland, while the additional analysis focuses on intrahospital comparisons with yet-to-be-deployed units at the same hospitals. With respect to the CI, this analysis focused on community participants who were touched by a care manager across the entire program duration, while a recently published analysis studied a broader population, including those who may have only received a CHW intervention.

The J-CHiP award was for $19.9 million and included additional institutional investment in these interventions.

Statistically significant cost savings achieved by the Centers for Medicare & Medicaid Services were approximately $113.3 million. The initially projected cost savings estimate for the award from the application in 2011 was $52.6 million.17 Overall, this suggests a very favorable outcome in terms of cost savings. Nearly all aspects of the J-CHiP award have subsequently been sustained through either state-based initiatives related to the Maryland All-Payer Model18 or other initiatives such as the Johns Hopkins HealthCare Medicaid managed care plan or the JHM accountable care organization.19 CONCLUSIONS In summary, the evaluation of the J-CHiP program suggests that a care coordination model that includes separate but complementary bundles of intervention strategies in an urban academic environment can be associated with dramatic improvements in many key utilization and cost indices. However, it is worth noting that the size of the effect is likely not wholly generalizable, as other efforts to implement such care delivery transformations will

reflect investments made by the organizations, baseline health and utilization of patients served, and the communities in which they reside. State and federal efforts should continue to focus on best practices, such as those used by J-CHiP, to achieve improvements in health outcomes.

Scott A. Berkowitz, MD, MBA is affiliated with the Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Shriram Parashuram, PhD, MPH and Kathy Rowan, PhD, MPH are affiliated with NORC at the University of Chicago, Bethesda, Maryland.

This article first appeared in Health Policy, JAMA Network and is republished here under a Creative Commons license. https:// jamanetwork.com/journals/jamanetworkopen/fullarticle/2712183 ?widget=personalizedcontent&previousarticle=0 REFERENCES 1. Baltimore City Health Department. Supplementary maps: neighborhood health profiles 2008. http://health.baltimorecity. gov/sites/default/files/Supplementary%20maps3.pdf.

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Case study: Johns Hopkins Community Health Partnership: a model for transformation. Healthc (Amst). 2016;4(4):264270. doi:10.1016/j.hjdsi.2016.09.001 8. Hsiao YL, Bass EB, Wu AW, et al; on behalf of the J-CHiP program. Implementation of a comprehensive program to improve coordination of care in an urban academic health care system. J Health Organ Manag. 2018;32(5):638-657. doi:10.1108/JHOM-09-2017-0228 9. NORC. Third Annual Report of the Health Care Innovation Award for the J-CHiP Program. https://downloads.cms.gov/ files/cmmi/hcia-chspt-thirdannualrpt.pdf. Accessed October 1, 2018.

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Published March 12, 2014. Accessed October 1, 2018.

Murphy SME, Hough DE, Sylvia ML, et al. Going beyond clinical care to reduce health care spending: findings from the J-CHiP community-based population health management program evaluation.

Med Care. 2018;56(7):603-609. doi:10.1097/MLR.0000000000000934 Center for Medicare & Medicaid Services. Health Care Innovation Awards round one project profiles. https:// innovation.cms.gov/files/x/hcia-project-profiles.pdf. Updated December 2013. Accessed October 1, 2018.

Rajkumar R, Patel A, Murphy K, et al. Marylandâ&#x20AC;&#x2122;s allpayer approach to delivery-system reform. N Engl J Med. 2014;370(6):493-495. doi:10.1056/NEJMp1314868 Berkowitz SA, Ishii L, Schulz J, Poffenroth M. Academic medical centers forming accountable care organizations and partnering with community providers: the experience of the Johns Hopkins Medicine Alliance for Patients. Acad Med. 2016;91(3):328-332. doi:10.1097/ACM.0000000000000976

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Medicaid Expansion and In-Hospital Cardiovascular Mortality Failure or Unrealistic Expectations?

Rishi K. Wadhera, MD, MPhil and Karen E. Joynt Maddox, MD, MPH

A CENTRAL OBJECTIVE OF THE AFFORDABLE CARE Act was to expand insurance coverage for low-income, uninsured Americans. Prior to the Affordable Care Act, eligibility for Medicaid varied greatly among states. Texas, for example, did not offer Medicaid insurance to poor adults without children, but the same adults living in Maine were eligible for Medicaid if they earned less than 100% of the federal poverty level in income. To ensure that low-income persons could access insurance regardless of their state of residence, the Affordable Care Act required states to expand Medicaid eligibility to any individual with an income at or below 138% of the federal poverty level. A subsequent US Supreme Court ruling in 2012, however, effectively made Medicaid expansion optional rather than mandatory. To date, 33 states (including the District of Columbia) have expanded Medicaid, while 18 states have elected to not implement expansion. This distinction has created an opportunity for researchers and policy makers to evaluate the relationship between Medicaid expansion and care delivery and outcomes.

Akhabue and colleagues1 do just that, exploring whether uninsured hospitalizations for major cardiovascular events (acute myocardial infarction, heart failure, and stroke) and in-hospital mortality changed after states implemented Medicaid expansion, compared with states that elected to not expand Medicaid. To do so, they evaluated rates of uninsured and Medicaid hospitalizations among all non-Medicare hospitalizations from 17 expansion states and 13 nonexpansion states in the years preceding expansion (2009-2013) and the year after expansion (2014).

Overall, the authors found that among expansion states, the proportion of uninsured hospitalizations declined by 5.0% (95% CI, −6.2% to −3.8%) and Medicaid hospitalizations increased by 10.2% (95% CI, 8.8% to 11.6%). In contrast, among nonexpansion states, the proportions of uninsured and Medicaid hospitalizations were unchanged. A multivariable adjusted difference-in-differences analysis demonstrated that expansion states experienced a significant reduction in uninsured hospitalizations after expansion relative to nonexpansion states (adjusted difference-in-differences estimate, −5.8%; 95% CI, −7.5% to −4.2%; P < .001), as well as a significant increase in Medicaid hospitalizations (adjusted difference-in-differences estimate, 8.4%; 95% CI, 6.5% to 10.2%; P < .001).

There are a number of reasons one might think insurance coverage would translate into better in-hospital outcomes. First, uninsured patients lack longitudinal, reliable access to outpatient care services required to identify and treat comorbid conditions.

Insurance could plausibly improve outpatient management and

reduce severity at presentation, reducing in-hospital mortality. In addition, lack of insurance is associated with delays in seeking emergency care in part out of concern for financial liability; insurance could remove this important barrier. Finally, prior studies have shown that uninsured patients hospitalized for acute cardiovascular conditions are less likely to receive guidelinedirected medical therapy, aggressive care, and invasive cardiac procedures, which may explain their worse outcomes compared with insured patients.2,3 Insurance could influence the care delivered during hospitalization by removing any financial barriers to optimal care delivery.

However, Akhabue and colleagues observed no significant change in in-hospital mortality in expansion vs nonexpansion states during the study period. Does this mean that Medicaid expansion was a failure? Have we put billions of dollars into health reform for no good reason?

To the contrary, a growing body of evidence suggests that Medicaid expansion has had a number of benefits. Medicaid expansion is associated with greater access to primary and preventive care in the outpatient setting, as well as better detection and treatment of some chronic conditions such as depression.4,5 This perhaps explains why expansion has also been associated with improved self-reported health.4,6 For cardiovascular care in particular, the identification and treatment of risk factors, such as high cholesterol level, hypertension, and diabetes, have improved since expansion,7,8 as has the use of prescription cardiovascular drugs.4,9 It is possible that the single postexpansion year examined

by Akhabue and colleagues was too short to appreciate the incremental, cumulative health benefits of access to preventive care, medications, and treatment of chronic illnesses; it is also possible that better care of chronic illness before an acute exacerbation might not be associated with better outcomes for that event. Or, perhaps in the immediate aftermath of insurance expansion, we are seeing pent-up demand, and longer follow-up will be required before these patterns settle out. Future research should evaluate more years after expansion, all states, and outcomes in the period following discharge to provide a comprehensive picture of expansion and outcomes in the context of acute hospitalization.

Finally, the most important aspect of Medicaid expansion may not be a direct health effect at all. Insurance protects against unbearable financial risk associated with health care costs, particularly among low-income individuals who are most susceptible, which explains why Medicaid expansion has been associated with reduced catastrophic expenditures, out-of-pocket spending, and bankruptcies.5 In this context, the decline in

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uninsured cardiovascular hospitalizations observed by Akhabue and colleagues in expansion states is extremely important regardless of whether it changed inpatient outcomes, as prior to Medicaid expansion, an estimated three-quarters of uninsured persons hospitalized for acute myocardial infarction or stroke in the United States experienced catastrophic health expenditures.10 Protection against bankruptcy brought on by cardiovascular disease may have many positive downstream effects, both for individuals and for society more broadly.

Akhabue and colleagues’ investigation comes at a time when Medicaid expansion is particularly contentious. Recently, residents of Maine decisively voted to expand Medicaid even further, although the state’s governor is now being sued for refusing to do so. Other states, such as Idaho, are considering moving forward with efforts to expand. As such, Akhabue and colleagues make an important contribution to our understanding of Medicaid expansion and acute hospitalizations for cardiovascular conditions at a time when it is vital that evidence inform the ongoing policy debate.

Rishi K. Wadhera, MD, MPhil is affiliated with the Brigham and Women’s Hospital Heart & Vascular Center, Harvard Medical School, Boston, Massachusetts and Richard and Susan Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical and Harvard Medical School, Boston, Massachusetts.

Karen E. Joynt Maddox, MD, MPH is affiliated with the Cardiovascular Division, Department of Medicine, Washington University School of Medicine in St Louis, Missouri.

This article first appeared in Health Policy, JAMA Network and is republished here under a Creative Commons license. https:// jamanetwork.com/journals/jamanetworkopen/fullarticle/2698075 REFERENCES 1. Akhabue E, Pool LR, Yancy CW, Greenland P, LloydJones D. Association of state Medicaid expansion with rate of uninsured hospitalizations for major cardiovascular events, 2009-2014. JAMA Netw Open. 2018;1(4):e181296.

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doi:10.1001/jamanetworkopen.2018.1296 2. Canto JG, Rogers WJ, French WJ, Gore JM, Chandra NC, Barron HV. Payer status and the utilization of hospital resources in acute myocardial infarction: a report from the National Registry of Myocardial Infarction 2. Arch Intern Med. 2000;160(6):817-823. doi:10.1001/archinte.160.6.817 3. Shen JJ, Washington EL. Disparities in outcomes among patients with stroke associated with insurance status.

Stroke. 2007;38(3):1010-1016. doi:10.1161/01.

STR.0000257312.12989.af 4. Sommers BD, Blendon RJ, Orav EJ, Epstein AM. Changes in utilization and health among low-income adults after Medicaid expansion or expanded private insurance.

JAMA Intern Med. 2016;176(10):1501-1509. doi:10.1001/ jamainternmed.2016.4419 5. Baicker K, Taubman SL, Allen HL, et al; Oregon Health Study Group. The Oregon experiment—effects of Medicaid on clinical outcomes. N Engl J Med. 2013;368(18):17131722. doi:10.1056/NEJMsa1212321 6. Sommers BD, Gawande AA, Baicker K. Health insurance coverage and health—what the recent evidence tells us. N Engl J Med. 2017;377(6):586-593. doi:10.1056/NEJMsb1706645 7. Wherry LR, Miller S. Early coverage, access, utilization, and health effects associated with the Affordable Care Act Medicaid expansions: a quasi-experimental study. Ann Intern Med. 2016;164(12):795-803. doi:10.7326/M15-2234 8. Cole MB, Galárraga O, Wilson IB, Wright B, Trivedi AN. At federally funded health centers, Medicaid expansion was associated with improved quality of care. Health Aff (Millwood). 2017;36(1):40-48. doi:10.1377/hlthaff.2016.0804 9. Ghosh A, Simon K, Sommers BD. The Effect of State Medicaid Expansions on Prescription Drug Use: Evidence From the Affordable Care Act. Cambridge, MA: National Bureau of Economic Research; 2017. doi:10.3386/w23044 10. Khera R, Hong JC, Saxena A, et al. Burden of catastrophic health expenditures for acute myocardial infarction and stroke among uninsured in the United States.

Circulation. 2018;137(4):408-410. doi:10.1161/ CIRCULATIONAHA.117.030128

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Payment and Care Delivery Improvements Could Benefit All Patients Laura L. Sessums, JD, MD

THE TIMES THEY ARE A-CHANGIN’.”1 THE MANTRA of the health care system, moving “from volume to value,”2 now is heard in boardrooms and medical practices from policy makers, payers, and health care practitioners. Yet the question Fraze et al3 have asked in their study is whether all patients or just the most advantaged are benefiting from the changes.

Fraze and colleagues analyzed medical practices that voluntarily joined the Centers for Medicare & Medicaid Services (CMS) Comprehensive Primary Care Plus (CPC+) model— America’s largest initiative to transform primary care payment and care delivery. They found that participating practices within the 14 geographical regions that started CPC+ in 2017 were in more advantaged areas compared with nonparticipating practices and were more likely to include nonphysician health care practitioners and have lower inpatient utilization.

As the insurer of one-third of the US population,4 CMS has a substantial impact on the payment for and delivery of health care in the United States. That effect is magnified when CMS partners with other payers, as CMS has done in CPC+. What can CMS and other payers do to ensure that models developed through its Innovation Center to test improvements in the delivery of and payment for care, such as CPC+, are available to and benefit all Americans, including those who are most vulnerable and at highest need?

Models developed by the CMS Innovation Center “succeed” and may expand more broadly only if they either reduce the cost of care without reducing the quality of care, or improve the quality of care without increasing its cost.5 It is often a race against time for participants to join a model, learn its requirements, adjust to payment changes, implement care delivery changes while providing ongoing patient care, refine their implementation when time-lagged outcome data become available, and obtain improvements in the cost and quality of care before the model ends in 4 to 5 years. Because it is critical to the model’s success to have participants able to quickly do the hard work of the model, CPC+ targeted medical practices with some prior experience with payment and care delivery transformation. This might have resulted in the exclusion of practices in less well-resourced areas caring for less advantaged populations.

Regardless of any past experience with transformation, primary care practices that are Federally Qualified Health Centers (FQHCs) (and, depending on their billing processes, Rural Health Clinics [RHCs]) were not eligible for CPC+. The Medicare payment structure for FQHCs and RHCs is both complex and quite different from traditional fee for service (FFS), making the payment transformation aspects of CPC+ (and some other Innovation Center models) not only inapplicable but also potentially detrimental to

them financially.

The FQHCs and many RHCs are paid amounts in excess of usual Medicare FFS based on the proposition that their unique circumstances and patient populations are costlier. This preexisting additional payment creates quite a conundrum for Innovation Center models that are designed to save money and are focused on the individual practice. However, the finding by Fraze and colleagues that the majority of areas without a CPC+ practice had an FQHC, as well as significantly higher inpatient utilization and potentially avoidable discharges, provides a noteworthy foundation for cost savings and quality improvement for FQHCs in future, similar models. However, to include FQHCs and RHCs in these Innovation Center models will require substantial effort across CMS to understand and address the barriers to, and opportunities for, payment redesign in these unique settings.

Medical practice eligibility for CPC+ also hinged on the percentage of the practice population covered by CMS and other payers—including Medicaid—who partnered with CMS in the model. Practices needed approximately half of their practice population to be covered by CMS and payer partners to be eligible for CPC+. Medicaid’s partnership with CMS in CPC+ was likely important for participation by practices that care for less advantaged populations. Practices with a larger percentage of Medicaid-enrolled patients had difficulty meeting the eligibility criteria either if state Medicaid agencies in Medicaid FFS states chose not to partner with CMS in CPC+ or if commercial partners chose not to include (or were not authorized to include) their Medicaid managed care lines of business in the model. State Medicaid agencies partnered with CMS in approximately half of the 14 regions, and at least 1 Medicaid Managed Care Organization partnered in an additional 4 regions. As a result, a few regions have no Medicaid involvement and other regions have only limited involvement.

Even for the most motivated of Medicaid agencies, determining and obtaining the necessary authority to align their payment, quality measurement, and other requirements with the model as well as navigating budgetary constraints and state-level political changes affecting their funding can be daunting. Some Managed Care Organizations are limited in their ability to join such an initiative by their state Medicaid agency. Providing Medicaid with flexibility and encouragement to align with Innovation Center models quickly and with relative ease would reduce their administrative burden and would facilitate the payer alignment that allows medical practices to focus their limited transformation time and resources on the goals of the model. Such encouragement, however, would necessitate CMS commitment, and the flexibility would likely require congressional action.

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While the CPC+ practices were, as a whole, located in more advantaged areas compared with non-CPC+ practices, some individual CPC+ practices are serving highly vulnerable and less advantaged patient populations. Effects on the quality of care across various subpopulations within such practices and across practices in the model as a whole could be determined from the practices’ reporting on quality measures. The CPC+ practices report electronic clinical quality measures that include all patients in their practice population who meet the measure specifications, regardless of payer. If and when those measures are reported using the 2015 Edition Health Information Technology (Health IT) Certification Criteria (c)(4) filter,6 it will be possible to stratify the results by race, ethnicity, and other criteria, allowing for critical examination of the effects of CPC+ practices’ quality of care overall compared with subpopulations. With the information from this evaluation, it could be possible to know whether CPC+ practices need to adjust their care delivery changes to ensure that individual subpopulations of patients benefit from overall improvements in care.

The times are indeed changing. As we transform primary care payment and care delivery, policy makers, payers, practices, and patients must be vigilant to ensure that improvements in care benefit all patients and not just those who are already advantaged.

Laura L. Sessums, JD, MD is affiliated with the Centers for Medicare and Medicaid Services, Baltimore, Maryland.

This article first appeared in Health Policy, JAMA Network and

is republished here under a Creative Commons license. https:// jamanetwork.com/journals/jamanetworkopen/fullarticle/2703942 REFERENCES 1. Dylan B. The times they are a-changin’. On: The Times They Are a-Changin’ [album]. New York, NY: Columbia; 1964.

2. Miller HD. From volume to value: better ways to pay for health care. Health Aff (Millwood). 2009;28(5):1418-1428. doi:10.1377/hlthaff.28.5.1418 3. Fraze TK, Fisher ES, Tomaino MR, Peck KA, Meara E. Comparison of populations served in hospital service areas with and without comprehensive primary care plus medical homes. JAMA Netw Open. 2018;1(5):e182169. doi:10.1001/ jamanetworkopen.2018.2169 4. Kaiser Family Foundation. Health insurance coverage of the total population. https://www.kff.org/other/state-indicator/ total-population/?currentTimeframe=0&sortModel=%7B% 22colId%22:%22Location%22,%22sort%22:%22asc%22% 7D. Accessed August 10, 2018.

5. Centers for Medicare & Medicaid Services. About the CMS Innovation Center. https://innovation.cms.gov/About/index. html. Accessed August 10, 2018.

6. Office of the National Coordinator for Health Information Technology. 2015 Edition certification companion guide. https://www.healthit.gov/sites/default/files/2015Ed_CCG_ c4-CQM-filter.pdf. Accessed August 10, 2018.

AAMCN Would Like to Recognize Our Corporate Partners Avedro CareNational® CareSource CEAgent Chamberlain University DexComTM, Inc. Eisai, Inc. Exact Sciences Foundation Medicine, Inc. Gilead Sciences, Inc. Guardian Life Insurance Home Instead Senior Care® Humana, Inc. 44

Mallinckrodt Pharmaceuticals Medicaid Matters Talk Show Morgan Consulting Resources, Inc. Novocure Paratek Pharma Purdue Pharma L.P. SeniorBridge® TCS Healthcare University of Rochester,

School of Nursing Uroplasty, Inc. Woundtech

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Managed Care UPDATES

Feds Fine Hospital That Didn’t Cut Data Access to Former Employee Pagosa Springs Medical Center in Colorado, a critical access hospital, will pay $111,400 to the HHS Office for Civil Rights. The small provider is being hit after neglecting to terminate access to protected heath information after an employee left the practice. A complaint sent to OCR alleged the former employee continued to have access to a web-based scheduling calendar, which resulted in the hospital the protected health information of 557 individuals being disclosed to the former employee. Read more at goo.gl/b2mErZ

Insurers, Businesses and Consumer Advocates Unite Against Surprise Medical Bills Nine organizations representing consumers, businesses and health insurance providers revealed they are banding together to protect patients from surprise, unexpected medical bills. Surprise medical bills may occur when a patient receives care at an out-ofnetwork hospital or from an out-of-network provider at an in-network facility. Read more at goo.gl/Y3gV42

Momentum is Building to Take a Fresh Look at HIPAA Professional associations and other stakeholders believe the time is right to revisit the HIPAA law. The electronic transaction standards and operating rules that were created under the HIPAA law to reduce administrative burdens and move the healthcare industry out of the paper-based era have broken down and need a significant tune-up. That’s the position of the Medical Group Management Association, which represents more than 50,000 medical group practice leaders. Read more at goo.gl/F1nxHm

10 Top Analytics and Business Intelligence Trends for 2019 New data quality management practices, data discovery techniques and predictive and prescriptive analytics tools will be among the top 10 trends that will affect analytics and business intelligence initiatives for healthcare organizations in 2019. Read more at goo. gl/qzRF53

Medicare Deregulation Bills Introduced by House Republicans Republicans on the powerful House Ways and Means Committee Tuesday introduced seven bills to roll back Medicare regulations on hospitals. The bills touch on prior authorizations and quality measures, among other issues. The proposals represented the next stage of their “Red Tape Relief” effort even though Republicans will not steer the committee’s legislative priorities next year. Read more at goo.gl/quNDrj

December Saw the Most New Healthcare Hires in Decades December brought the U.S. healthcare industry’s largest monthly spike in the number of new hires since at least February 1990.

Healthcare added 50,200 jobs last month. Hiring in the industry jumped 56% from November, according to the U.S. Bureau of Labor Statistics’ December jobs report released Friday. The last time healthcare hiring surpassed 40,000 in a single month was in October 2015, the data show. Read more at goo.gl/wfefz4 www.aamcn.org | Vol. 6, No. 1 | Journal of Managed Care Nursing

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Congratulations to the Newly Certified Managed Care Nurses (CMCNs)!

Amy M. Adams, RN, CMCN Kara E. Allen, RN, CMCN Christy Allman, RN, CMCN Bailey H. Bartholomew, LMSW, CMCP Tammy Berg, RN, CMCN Michele L. Bodenschatz, RN, CMCN Michael J. Brakel, RN-BC, CMCN Homel Breneville, RN, BSN, CMCN Juliandra L. Bryan, LVN, CMCN Brittany M. Collins, LPN, CMCN April D. Combs, RN, CMCN Sherri D. Cooner, BSN, CMCN Angie R. Cope-Johnson, LPN, CMCN Paula M. Daigle, LPN, CMCN Beverly Daley, RN, CMCN Bridget L. Dauterive, LPN, CMCN Maria A. Davalos, LPN, CMCN Cynthia K. Deerman, RN, CMCN Sarah Dotson, LVN, CMCN Sandy L. Eckhoff, LPN, CMCN Caroline N. Egbo, BSN, RN, PHN, CMCN Kristi G. Eldridge, RN, CMCN Haley Ensminger, BS-SW, CMCP Dana Fernandez, LPN, CMCN Jerrica L. Freeman, RN, CMCN Deborah G. Fuller, LPN, CMCN Michael J. Furlow, BSN, RN, CMCN Sonya C. Furlow, RN, CMCN Susy George, RN, CMCN Sheryl A. Gratton, RN, CMCN Kristen M. Gregory, RN, CMCN Tonya A. Hagler, LPN, CMCN Whitney T. Hazlett, RN, BSN, CMCN Melissa H. Hernandez, RN, CMCN Shelia R. Hodrick, RN, BS, CMCN Germaine Jack, RN, CMCN

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Bonnie L. Janjanin, LVN, CMCN Tracey Jones, LPN, CMCN Charity A. Jones, RN, CMCN Erin L. Jurgens, RN, CMCN Natalie Karber, RN, CMCN Eduardo Lopez, LVN, CMCN Tina D. Martinez, LPN, CMCN Regina C. McCord, LPN, CMCN Michelle L. Molina, LVN, CMCN Margo S. Moore, LPN, CMCN Natalie N. Parks, RN, CMCN Kim Pierce, RN, BSN, CMCN Vivian Pirics, RN, CMCN Cynthia A. Potter, RN, CMCN Vi M. Probert, BSN, RN, CMCN Courtney H. Ray, LPN, CMCN Donna Raye-Sullivan, BSN, CCM, MBA, CMCN Laura J Richards, MS, RN, CMCN Jeannie Richardson, RN, CMCN Angela M. Rolle, RN, CMCN Whitney Sapp, RN, CMCN Lisa Smith, RN, CMCN Lori Spero, RN, CMCN Sophia GO Tagsip, BSN, RN, CMCN Paulette M. Tenison, LVN, CMCN Julie R. Truesdell, LVN, CMCN Tashia R. Turner, LVN, CMCN Eva L. Ussher, LVN, CMCN Cassandra Van Zalinge, RN, ADN, CMCN Dannette S. Vidrine, LPN, CMCN Georgie L. Vinet, LPN, CMCN Vickie Lynn Wales, RN, CMCN Cindy J. Wyatt, MSN, RN, CMCN Anita M. Zacheis, RN, CMCN Debra Zona, RN, CMCN

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Welcome New AAMCN Members!

Emelita N. Acebedo, RN, BSN Yvanessa Acloque Teresa Acosta Maureen Alba-Cobarrubias, RN Sandra Allen Lutesia Anderson Khris Baker Leah Bechtel-Oganda Theresa Bercel Doronna Bolser, RN Felicia Brown, RN, BSN Malinda Buratti Cara Burgos Alexandra Burnett, LVN Arlene Cabading-Cruz, RN, BSN Jamelia Caddell Julie Cantrell, RN Stephanie Cassis, LPN Stella Cazarian Patience Celender, RN Janet Cleland Kelva Clemmons, RN Kelley Coker Judith Conway, RN Corrinne Cooper Sierralyn Corona, RN Dominique Crews, RN Cheryll Culver, RN Paula Daigle Ann Davies, RN Cassie Dollard, LPN Katie Doyle Ginger Dunn, RN Rena Durkin Carolina Esparza, RN Arlene Espino, RN Lisa K. Fallert, RN Christy Featherstone Laura Fennimore, RN Lidia Fernandez, RN Heidi Fuernstahl, RN

Brianna Gaddy, LPN Adesuwa Tess Giwa-Osagie Jessica Green, RN Katrina Griffin, RN Cathleen Gustafson, RN Kimberly Harper Susan Hicks, RN Darci Hiemenz Beth Hite Heather Holman, RN Teresa Irby Tiffany Jackson Melissa Jenna, RN Jennifer King, RN Darlene Koritsky Stephanie Kritzberger Jacqueline Laldee, RN Emily Linden Thalia Long, MSN, RN, ACM Sue Mai Eileen Manuel, RN Rachel Marcantel Ashpley Matthews GIovanni Maya Linda McDaniel Carol McEvoy, RN Michelle Mendoza-Bradbury, RN Gregg Miller, RN Danielle Moore, LPN Claudia Moreira Craig Morey Justine Morton Carol Murphy, RN Joan Novak Stacian Nwaukwa, RN Ogeikpe Nwogu Maureen Oâ&#x20AC;&#x2122;Hagan Ginny Olson Joan Marie Operario, MSN Tanya Ortego Molly Paganie, RN

Dinorah Placer Jennifer Purcell Faith Ragsdale Pamela Ramsey Courtney Ray Amber Rejouis, RN Laura Richards, RN, MSN Teresa Riley Cheyenne RIofrio, RN Angelic Robb, RN Kristina Robinson, RN Earlie Rockette, RN, NP, MSN, AP Patricia Ross, RN, CCM Jeremy Schneider, LPN Candace Shelton, RN, CCM Ruth Shenberger, RN Janelle Shepard Brenda Shinaul, LPN Katherine Shipman Maria Singson, RN Dominique Smith, NP Lori Spero, RN, CMCN Jeannette Spina Kelly Stickno Angela Suarez, RN Gayle Taylor Lynette Townsend Joyace G. Ussin, MSN, RN, BSBM, CCM, CPAN Wendy Vaughn, RN Maura Vo, LPN Celeen Walsh Ashley Walz Elizabeth Wheeler, RN Donna Wilder Gabrielle Wilson Sandra Winn, RN, BSN Heather Wulf Aimee Yoches Stephanie Ziaja, RN

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