Journal of Managed Care Nursing, Volume 6, Number 2

Vol. 6, No. 2, June 2019

2019 FALL MANAGED CARE FORUM October 10-11, 2019 Bellagio Las Vegas, NV

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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 Jackie Beilhart 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 Jackie Beilhart jbeilhart@aamcn.org GRAPHIC DESIGN Jackie Beilhart jbeilhart@aamcn.org

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.

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

Vol. 6, No. 2, June 2019

TABLE OF CONTENTS Articles Improving Care Management for Patients with Chronic Disease in a Transitional Care Setting Using Digital Health and Artificial Intelligence Patrik Bachtiger, Patrick Reidy, Elaine Goodman, Trishan Panch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Preoperative Bathing and Surgical Site Infections Emily Peters, Annapoorna Mary . . . . . . . . . . . . . . . . . . . . . . 12 Association of Medicare Spending With Subspecialty Consultation for Elderly Hospitalized Adults Kira L. Ryskina, Yihao Yuan, Rachel M. Werner . . . . . . . . . . 21 Comparison of a Potential Hospital Quality Metric With Existing Metrics for Surgical Quality–Associated Readmission Laura A. Graham, Hillary J. Mull, Todd H. Wagner, Melanie S. Morris, Amy K. Rosen, Joshua S. Richman, Jeffery Whittle, Edith Burns, Laurel A. Copeland, Kamal M. F. Itani, Mary T. Hawn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Managed Care Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Newly Certified Managed Care Nurses (CMCNs) . . . . . . . 46 New AAMCN Members . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Author Submission Guidelines . . . . . . . . . . . . . . . . . . . . . 49

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Improving Care Management for Patients with Chronic Disease in a Transitional Care Setting Using Digital Health and Artificial Intelligence Patrik Bachtiger(1), Patrick Reidy(2), Elaine Goodman(2), Trishan Panch(2) 1. Masters Candidate, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 2. Wellframe Inc., Boston, Massachusetts Summary Digital health technologies can enhance the provision of care management. We describe the implementation of a digital health solution (a smartphone app, clinician dashboard, and adaptive care programs) for transitional care management in a commercially insured population across one state. The population who used the digital health solution had a similar demographic, chronic disease, and cost profile to the general care management population.

Messages sent on the app between patients and care managers offered timely insights into prevalent care management priorities. Furthermore, the data generated can be leveraged using AI technologies to create new and clinically useful information.

Key Points • Digital health platforms are a valuable tool in care management, even for complex patients in the transitional care setting • The patients who use these technologies are representative of the general care management population • Previously unattainable improvements in efficiency, clinical outcomes, and patient satisfaction are made possible by digital health • Digital health enables the collection of the large, rich datasets required to take advantage of artificial intelligence INTRODUCTION The Agency for Healthcare Research and Quality (AHRQ) sets benchmarks for care managers when dealing with patients discharged from a hospital or health care facility. Care managers have a broad spectrum of responsibilities, including patient engagement, patient education, and care coordination. Care managers report that it is becoming increasingly difficult to reach their patients through traditional means of communication i.e. telephone call, which is of particular concern for the transitional care population given their high risk of complications and readmission. Digital health therefore represents an opportunity for care managers to reach more patients in the postdischarge setting, while facilitating good value care—namely by 4

reducing costs and improving outcomes.1 And, in fact, the US Food and Drug Administration has recognized the addition of digital health to the repertoire of effective health care interventions and has encouraged the development and integration of these technologies.2 While there is sometimes a perception that only young, healthy patients will engage with digital health, several published articles already exist that use cases in complex, high risk populations. Community mental health support programs are able to leverage digital tools to assess a patient’s transition to community living frequently, efficiently, and at low cost.1 Mental health in general has seen an explosion of commercially available digital health applications covering the spectrum of disease type and severity.2 Participation in

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cardiac rehabilitation has been improved through use of a digital tool, and digital tools have also been successful in engaging seniors, despite common concerns being raised about the acceptability of digital health in this population.3 The advantages have also been highlighted in combating highly prevalent and costly diseases such as diabetes and obesity.4 While it is sometimes appropriate and unavoidable to resort to rehospitalization, conservative estimates suggest that up to 20% of readmissions experienced by chronically ill adults are theoretically preventable.5 Preventing readmissions requires both clinical input from care managers and sustained patient action to adhere to a recommended care plan. Digital health solutions can amplify the work of care managers during the post discharge period by not only presenting a care plan to a patient in smaller, more easily digestible components to promote adherence but also by extending a therapeutic relationship into a patient’s home.6 We describe the characteristics of a commercially insured, midwestern population receiving transitional care management that integrates a digital health solution from a statewide health insurer. A similar population receiving telephonic (phone call) transitional care management is used for comparison. In total, 2255 patients completed transitional care programs over two years. The solution consisted of a mobile-enabled care management platform based around an app that includes a customized, interactive, daily health checklist displayed to patients on their smartphone or tablet.

Responses and interactions from users are collected as actionable day-to-day health information. The app serves to provide a sustained and supportive portal of communication between patients and their care teams.

The app offers a concierge service to help patients get their benefits questions answered, navigate the health care system, and pursue their health goals—all through a single, direct channel of communication.

The rules of the app can be configured to generate insights from communications received from patients, which are actionable into impactful decisions for clinical care. For example, a survey response confirming active smoking status is an insight that triggers a decision support tool to offer a smoking cessation discussion. Furthermore, we discuss how the variety of data obtained through this platform allows the use of artificial intelligence to generate clinically useful information.

DESCRIPTION AND FINDINGS FROM A TRANSITIONAL CARE POPULATION IMPACTED BY DIGITAL HEALTH The distribution of age and gender of the postdischarge population receiving the digital health intervention as part of their transitional care management is described in figure 1. Levels of education and household income (figures 2-3) in the population receiving the digital health application are comparable to that of the postdischarge population receiving telephonic care transitional management. Common baseline comorbidities e.g. essential hypertension, met-

Figure 1. Distribution of age (years) and gender (male/ female) of members. M:F 42:58, median age 54 years.

Figure 2. Comparison of average household incomes between members enrolled into digital transitional care management (‘postdischarge mobile’) versus patients who received telephonic care management (‘postdischarge telephonic’).

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Figure 3. Comparison of educational attainment between members enrolled into digital transitional care management (‘postdischarge’) versus patients who received telephonic care management (‘postdischarge telephonic’).

abolic disorders, osteoarthritis were similar to disease profiles one might expect in the general population.

Over the two years, 33% of patients engaged daily with the app, accruing a total of 25 295 messages sent by care managers and 14 207 messages sent by users. Care managers would send out a median of 10 messages per patient. Patients sent a median of six messages to care managers. In comparison, traditional telephonic care management saw patients answer calls between one to five times over a 12 month period and engage with care managers an average of two times in the first month of discharge.7 As a result of the increased efficiency of the digital care management solution, care managers were able to increase their panel size by 600% during the intervention period.

Figure 4. Difference over 3 months between spend-permonth-per-member (SPMPM) between care management group receiving digital health tool versus propensity-score matched care management control group.

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COST SAVING FROM DIGITAL HEALTH INTERVENTION Over three months of analysis, the difference in mean cost between the digital health group and a matched control group was $1500 per member per month (figure 4). The control group was matched with a propensity score calculated from age, gender, risk score, and baseline inpatient cost.8 INSIGHTS FROM APP USE Patients receiving transitional care management that integrated the digital health tool participated in validated patient-reported outcome measures and other validated health surveys through the mobile app, serving to provide insight into opportunities for improving care. Responses triggered 1457 such “insights” for these patients. For example, as described in table 1, a survey response confirming status as a current smoker would be considered an insight that should trigger an intervention, in this case a discussion of smoking cessation. A variety of patient feedback creates diverse insights that can trigger responses including direct prompts to care managers that help customize each user’s care to their needs. Across the population, the most frequent terms used in messages sent by patients can be extracted (figure 5). This elucidates the most common types of queries and concerns e.g. “pain” and “work” were commonly used terms, and know-

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Alert text: [Patient] has indicated that they currently smoke or use tobacco products and have recently been in the hospital. Send [Patient] a message to encourage them to consider quitting.

Suggested message: Hi [Patient], I see that you currently use tobacco products. Letting go of cigarettes or other tobacco products is the single most important thing you can do for your health, especially after your recent hospital stay. Quitting tobacco can be really hard, and I’m here to help.

Are you ready to think about quitting?

Question: How often do you need to have someone help you when you read instructions, pamphlets, or other written material from your doctor or pharmacy?

Alert text: [Patient] Often needs someone to help them when reading instructions, pamphlets, or other written material from their doctor or pharmacy. Please reach out to patient to offer any help they may need with these tasks and determine what specific barriers they face when reading these kinds of materials.

Question: Did your doctor order home health care services?

Alert text: [Patient] has indicated that home health services have been ordered, but that no one has contacted them to start services. Please message the member to determine if they need assistance with home health services.

Question: Are you having any trouble picking up your medications regularly (either for financial reasons or otherwise)?

Alert text: [Patient] has indicated that they are having trouble picking up their prescriptions (either for financial reasons or otherwise). Please message the member to assess needs and offer support/ resources as needed.

Table 1. Examples of automated message prompts triggered by ‘insights’ obtained from patient responses to surveys.

ing this information allows care managers to respond by having support around these issues built into the app. Feedback from patients highlighted that they find the provision of educational materials useful, and that they are empowered with a high self-reported sense of control over their health (table 2).

APPLICATION OF ARTIFICIAL INTELLIGENCE It is possible to analyze the content of messages being sent to care managers by patients using natural language processing techniques—a subset of artificial intelligence. These techniques can determine the content of a message as it is sent and then tie this content to the relevant decision support for a clinician, thereby

helping to improve both the quality and the timeliness of care. In figure 6, the 10 most common words used in patient messages sent to care managers are listed. In figure 6, all the topics that are covered in care manager and patient interactions are mapped out—where each topic consists of groups of keywords, and the closer topics are to each other the more similar they are.9 For example, topic 1 features the words “pain,” “medication,” and “pill” together and is very different from other topics (based on the observable distance from other topics); topic 2 features “doctor,” “appointment,” “follow-up,” and “office” together; and topic 3 features “incision,” “operation,” “surgery,” “drainage,” and “discomfort” together. Depending on the topic(s) identified in the message the appropriate team member can be identified to deal with the underlying concern.

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COMMENTARY

Figure 5. Top 10 most commonly used terms in messages from app users.

For example, a topic identified as a surgical complication would require a different response to one posing a query about insurance coverage. These techniques can be used to tie real-time decision support for care managers to inbound messages from patients and therefore ensure that patients get the right intervention at the right time, allowing the clinical team to practice at the top of their license.

In addition, AI can also be used to effectively allocate transitional care resources to those most likely to benefit. AI can identify the features of those patients who derive the most impact e.g. cost savings from a digital health intervention. This data can then be used to build an AI algorithm that can predict the impact of the intervention on subsequent patients.10 This AI tool can then identify which patients are most impactable by digital care management and therefore how to spend patient recruitment and clinical management resources most effectively. Such targeting maximizes benefits across a population and is a novel approach for improving cost-effectiveness.

How successful do you think you’ve been in using the information you’ve read about in the app to improve your recovery?

I am in control of my health

Median rating scale, out of 10 8.0

8.5

Table 2. Patients’ self-reported rating of the impact of digital health tool on their agency.

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This study population illustrates not only the utility of digital health in transitional care management but confirms that benefits can extend to those within the postdischarge population. These solutions can do more with less; impacts on efficiency are particularly attractive, given the unyielding trend towards increased health expenditures. Cost impacts are not only directly measurable by comparing billing to populations without the mobile technology but can be further impacted by predictive modelling that uses AI and by acting on redundancies highlighted in data.

A key design imperative in order to achieve impact with digital health technology is maintaining the holistic relationship between patient and provider—facilitated by a direct channel of communication. Mobile health applications that take a self-management approach without a connection to a health professional have extremely low retention rates.6 Secondly, scalability and impact requires buy-in from both patients and providers; change management is therefore crucial for successful implementation and needs to be rooted in collaboration. An important observation is that patient adoption follows clinician adoption; therefore, navigation support needs to be available for both to maximize utility. Lastly, these technologies must remain patient-centered and ensure that patients see value in the technology, rather than focusing primarily on how to obtain valuable health data from patients to capitalize on with AI. By remaining mindful of these principles, we can fulfil digital health’s promise to improve the efficiency and effectiveness of transitional care management and better support patients during times of need.

REFERENCES 1. Mueller, N. E. et al. Using Smartphone Apps to Promote Psychiatric Rehabilitation in a Peer-Led Community Support Program: Pilot Study. JMIR Ment. Health 5, e10092 (2018).

2. Torous, J. et al. Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry 18, 97–98 (2019).

3. Forman, D. E. et al. Utility and Efficacy of a Smartphone Application to Enhance the Learning

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Figure 6. Intertopic distance map (termite graph) for visualization of textual topic modelling; 30 topics (numbers 1-30 in circle) derived from messages sent between app users and care managers. The artificial intelligence algorithm looks through every message and identifies which words are commonly used together. These combinations can denote a topic e.g. concern about surgical wound. Termite graph shows the map of topics and how they are related to each other, where topics that are closer in the two dimensional space are more related. For example, topics 1 and 2 are far apart and more distinct than topics 6 and 13, which have significant overlap. www.aamcn.org | Vol. 6, No. 2 | Journal of Managed Care Nursing

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4.

5.

6.

7.

and Behavior Goals of Traditional Cardiac Rehabilitation: A FEASIBILITY STUDY. J. Cardiopulm. Rehabil. Prev. 34, 327–334 (2014).

Castelnuovo, G., Mauri, G. & Waki, K. mHealth and eHealth for Obesity and Types 2 and 1 Diabetes. J. Diabetes Res. 2016, 1–1 (2016).

Hirshman, K., Shaid, E., McCauley, K., Pauly, M. & Mary, N. Continuity of Care: The Transitional Care Model. Online J. Issues Nurs. 20, (2015).

Panch, T. & Goodman, E. Technology, Seniors, and Sense Making. Am. J. Manag. Care 22, (2016).

Domenge, N. Driving Patient Engagement

Through Mobile Care Management. Healthc. Inf. Manag. Syst. Soc. Conf. Session #97, (2017).

8. Elze, M. C. et al. Comparison of Propensity Score Methods and Covariate Adjustment. J. Am. Coll.

Cardiol. 69, 345–357 (2017).

9. Chuang, J., Manning, C. & Heer, J. Termite: Visualization Techniques for Assessing Textual Topic Models. Stanf. Univ. Comput. Sci. Dep. (2012).

10. Mattie, H., Reidy, P., Bachtiger, P. & Jouni, M. Using Prediction of Healthcare Cost from Sociodemographic and Claims Data to Enable a Machine Learning Solution for Impactability. Press (2019).

www.AAMCN.org Fall Managed Care Forum 2019 • Visit the AAMCN website at www.aamcn.org to register for the Fall Managed Care Forum being held October 10-11 at the Bellagio in Las Vegas, NV. • Nurses’ Pre-Conference: October 9th • Sit the CMCN exam the morning of October 9th Social Media • Members of AAMCN can join our new Facebook discussion group at www.facebook.com/ groups/AAMCN

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Preoperative Bathing and Surgical Site Infections Emily Peters, BSN, RN, MSN (Student) and Annapoorna Mary, MSc(N), PhD, RN, CNE

Summary Preoperative bathing with an antiseptic agent has been used in practice to prevent surgical site infections. However, the efficacy of this practice has been called into question within the last decade. The aim of this paper was to determine if preoperative bathing with an antiseptic agent was more efficacious in reducing surgical site infections than a placebo, standard soap and water, or standard preoperative preparation by the surgical team in adult surgical patients. The method was a literature review. Although the results were moderately inconsistent regarding efficacy, the consensus was that this practice was unlikely to cause harm.

Keywords Surgical site infections, preoperative bathing, antiseptic, infection prevention.

Preoperative Bathing and Surgical Site Infections Surgical site infections (SSIs) have been considered a preventable, disparaging complication of surgical procedures and a worldwide contributor to poor patient outcomes, prolonged hospital stays, and rising health care costs. Preoperative bathing with an antiseptic agent has been widely used in practice to prevent SSIs.1 In the last decade, a growing body of literature has demonstrated conflicting results regarding the efficacy of this practice, spawning a continual debate within the field of research. A literature review will be conducted to determine if preoperative bathing with an antiseptic agent is representative of best practice.

Based on the findings, recommendations for future research and practice will be identified.

Background Surgical site infections are the most frequent surgical complication with some of the most harmful effects.2 Treatment can be extended for months and additional surgical procedures may be required to eliminate the infection.2,3 Surgical site infection incidence increases tenfold when surgical revisions are required.3 Even minor infections can cause permanent disfigurement 12

and delayed healing.4 Extended treatment, repeat surgeries, and consequential deformities can significantly impact one’s quality of life. Patients may begin to lose confidence in the quality care delivered by health care providers and the health care system, respectively.

Surgical site infections are the most common type of health care associated infection (HAI) in low-income countries and rank second in high-income countries.5 In 2011, SSI prevalence was 157 000 according to Magill et al.’s multistate study.5 Between 2006 and 2008, the SSI rate was 1.9% according to Mu, Edwards, Horan, Berrios-Torres, and Fridkin.5 Health care costs, length of stay, and patient morbidity are increased by SSIs.6 In the United States, SSIs increased hospital length of stay by 406 730 days, and readmissions from SSIs added an additional 521 933 days.5 Annually, SSIs cost between $3.2—10 billion, designating SSIs as the costliest of all HAIs.5 It is known that preoperative bathing with an antiseptic decreases bacterial colonization on the skin, but research lacks clarity as to whether this practice decreases SSI incidence.1,4,5,7,8 The literature lacks knowledge on standardization of the practice regarding bath number, bath duration, and antiseptic solution

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concentration.3 Further gaps exist in evidence-based guidance, as SSI prevention strategies are not internationally standardized and neglect to include more modern approaches.5 A common concern in the literature was a lack of institution-wide standard protocol for preoperative skin cleansing for all surgeries.2,6,9,10 This gap can be ethically problematic if a protocol implementation pertains to certain types of surgical procedures and excludes others. Theoretically, any surgical incision creates an SSI risk by compromising the protective mechanism of intact skin. Ethical soundness should be considered when withholding preoperative bathing with an antiseptic for any incisional procedure. The most recent clinical guidelines recommended preoperative bathing with standard soap or antiseptic soap.5 If health care workers are not aware of these guidelines, patients may not be educated on preoperative bathing of any kind. If a form of preoperative skin cleansing is neglected in practice, patients can be at increased risk for SSIs. Population, Intervention, Comparison Group, and Outcome Question The Population, Intervention, Comparison Group, and Outcome (PICO) question format was utilized in this paper to form a research question relevant to an area of interest or clinical practice. Will preoperative bathing with an antiseptic agent be more effective in reducing SSIs than a placebo, standard soap and water, or standard preoperative preparation by the surgical team in adult surgical patients? The aim of this paper was to answer the PICO question by reviewing the highest quality evidence available to guide future research and practice.

The population of interest was adult surgical patients over the age of 18 who had undergone any type of invasive surgical procedure. The intervention was preoperative bathing with an antiseptic agent. The comparison group was adult surgical patients over the age of 18 who preoperatively bathed with a placebo, standard soap and water, or received standard perioperative skin preparation by the surgical team. The outcome to be measured was a decreased incidence of SSIs. Method Description of Literature Search

Four databases were searched to discover literature pertinent to the PICO question components. The databases included CINAHL Complete, EBSCO Discovery Service, PubMed, and Science Direct. The initial search terms were “preoperative bathing” and “surgical site infection”, which yielded five CINAHL Complete articles, 5058 EBSCO Discovery Service articles, 78 PubMed articles, and 1118 Science Direct articles. The search was narrowed by adding the terms “antiseptic” and “prevent” for all databases, which resulted in one CINAHL Complete article, 1701 EBSCO Discovery Service articles, 18 PubMed articles, and 301 Science Direct articles. The search was then restricted to publications within the last 10 years, which resulted in one CINAHL Complete article, 1271 EBSCO Discovery Service articles, 11 PubMed articles, and 189 Science Direct articles. The EBSCO Discovery Service and Science Direct databases were narrowed further due to the large number of remaining results. The EBSCO Discovery Service database search was restricted to academic journals, which achieved 363 results. The search was restricted further to include surgical wound infections as the subject, which achieved 35 results. The Science Direct database search was restricted to research articles, which achieved 71 results. A flow diagram of the literature search method is depicted in Figure 1.

Inclusion and exclusion criteria were incorporated to determine which of the 118 articles should be selected for further review. Inclusion criteria included quantitative studies with population characteristics of adults over the age of 18 of any race, gender, or geographic region having any type of surgical procedure, the use of an antiseptic agent as the intervention, and SSI occurrence as the outcome. Exclusion criteria included the pediatric population, qualitative studies, expert opinion pieces, and reviews of qualitative studies.

Review of Results from Research The literature search yielded ten articles that met the inclusion criteria. Different quantitative methods were utilized for an array of surgical procedures including general surgery, plastic surgery, and orthopedic surgery. This aided in the discovery of evidence-based results that can be useful for diverse practice settings and various surgical procedures.

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Searched databases with terms “preoperative bathing” and “surgical site infection”

CIANAHL Complete

EBSCO Discovery Service

PubMed

Science Direct

5 results

5,058 results

78 results

1,118 results

Added search terms “antiseptic” and “prevent”

1 result

1,701 results

18 results

301 results

Restricted results to last 10 years

1 result

1,271 results

Restricted results to academic journals

Restricted subject to surgical wound

11 results

Restricted results to academic journals

363 results

35 results Figure 1. Flow diagram depicting the literature search method 14

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189 results

71 results

Veiga et al. (2009) conducted a randomized controlled trial (RCT) to assess the effect of chlorhexidine gluconate (CHG) showers on preoperative bacterial colonization and postoperative SSIs in truncal plastic surgical procedures. Veiga et al. (2009) defined SSIs as infections that occur postoperatively in the surgical site, and bacterial colonization was defined as “colony- forming units per plate” (p. 78). Perioperative bacterial colonization within the surgical site was identified as the only risk factor and cause.4 Signs, symptoms, and clinical presentations were not well defined, as elaboration was provided for superficial SSIs alone. Superficial SSIs presented as infections limited to the skin or subcutaneous tissue.4 Assessment of SSIs were conducted weekly by a surgeon over 30 days, and assessment of bacterial culture growth was evaluated by a microbiologist after 48 hours of incubation.4 Diagnosis methods were standard microbial methodology for bacterial colonization and physician observation for SSIs. The results were two superficial SSIs, with one in the placebo group and one in the intervention group.4 Microbial colonies that grew were enterobacteria, yeast, and staphylococcus aureus, and none of these participants experienced SSIs. Treatment for the SSIs was not specified and none of the patients experienced an adverse reaction.4 The relationship between SSIs and quality care was implied as affording patients the highest degree of infection control attainable.

Johnson et al. (2010) conducted a prospective cohort study on the effectiveness of preoperative at-home CHG cloth use protocol to prevent deep SSIs following hip arthroplasties. Deep SSIs were defined as those involving “the deep fascial layers or the joint space itself” (Johnson et al., 2010, p. 99).9 Infection risk factors were identified utilizing the National Nosocomial Infections Surveillance System (NNIS), which incorporated wound class, the American Society of Anesthesiologists Score, and surgery length.9 The researchers suggested that the cause of SSIs was inadequate preoperative preparation procedures.

Signs and symptoms and clinical presentations were presumably guided by the definition of deep SSIs. A tracking database determined the number of patients who underwent hip arthroplasties, and compliance with the protocol was assessed by the patients bringing adhesive stickers from the CHG cloth package to the

hospital on the day of surgery.9 The SSI incidence was compared in compliant and noncompliant participants and infection rates were further stratified by NNIS risk category and surgeon.9 There were no SSIs in the compliant or partially compliant group, while 14 SSIs were experienced in the noncompliant group.9 Treatment and complications of SSIs were not specified.

The results showed that CHG cloths have the potential to be a cost-effective way to decrease SSIs, implying that this intervention can improve patient outcomes and quality care.

Zywiel et al. (2010) conducted a prospective cohort study on deep SSI incidence in those who used an at-home whole body CHG protocol preoperatively compared to those who underwent standard in-hospital perioperative preparation in knee arthroplasties.

Zywiel et al. (2010) defined deep surgical infections as “either a deep incisional or joint space infection occurring within one year of the surgical procedure and with the infection appearing to be related to the surgical procedure (defined as an absence of a focus of infection or precipitating event unrelated to the index arthroplasty)” (Zywiel et al., 2010, p. 1003).2 The cause of SSIs was dermal bacterial colonization of native skin flora.2 Infection risk was identified using the NNIS.2 Signs and symptoms and clinical presentations were guided by the definition of deep SSIs. Assessment of participant SSI occurrence was monitored for one year postoperatively.2 Zywiel et al. (2010) noted that serologic markers diagnose SSIs, and treatment consists of surgical revisions in combination with local and systemic antibiotic treatment. The intervention group had no SSIs, while the control group had 21 SSIs.2 The results indicated that patient outcomes can be improved with whole body antiseptic bathing versus cleansing the surgical site alone.

Graling and Vasaly (2013) conducted a prospective cohort study to determine if SSIs decreased with preoperative CHG cloth bathing. They defined SSIs as incisional or deep, with the former occurring above the fascia and the latter occurring below the fascia. Signs and symptoms as well as clinical presentations of SSIs included purulent drainage, abscess, or spontaneous dehiscence if the temperature is greater than 100.4 degrees Fahrenheit.6 The cause of SSIs was microbial contamination, and risk factors were those undergoing cardiac, orthopedic, colorectal, or transplant surgical

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procedures.6 Assessment took place 30 days postoperatively and SSI diagnosis was guided by the definition of SSIs and associated symptoms. Treatment for SSIs was not specified. Complications were increased morbidity and length of hospitalization.6 Participants who preoperatively bathed with 2% CHG had a statistically significant reduction in SSIs (p = 0.01). A cost analysis was conducted based on actual CHG cloth cost and nurse time costs such as educating the patient and aiding with bathing, which totaled $7 per patient.6 The researchers concluded that 2% CHG cloths can be a cost-effective means to reduce SSIs, as SSIs negatively impact patient care and fuel economic burden.

Kapadia et al. (2013) conducted a prospective cohort study to determine if preadmission CHG cloth use decreased SSIs when compared to standard perioperative preparation by the surgical team in total hip arthroplasties. Kapadia et al. (2013) defined SSIs as superficial (infected cutaneous or subcutaneous areas within 30 days of surgery) or deep (infected joint space or fascia). Risk factors were comorbidities, specifically “hypertension, hyperlipidemia, coronary artery disease, diabetes mellitus, hypothyroidism, renal disease, chronic obstructive pulmonary disease, sickle cell trait, hepatitis C, human immunodeficiency virus, and smoking” (Kapadia et al., 2013, p. 401).10 The cause of SSIs was bacterial contamination from existent skin flora or microbes in the air intraoperatively.10 Signs and symptoms and clinical presentations of SSIs were “sinus tract communication with the prosthesis; increased percentage of synovial polymorphonucleocytes; elevated erythrocyte sedimentation rate, C- reactive protein, or synovial leukocyte count; pathogen growth from fluid or tissue culture; purulence; or frozen tissue over five poly-morpho-nucleocytes per high-powered field” (Kapadia et al., 2013, p. 491).10 Kapadia et al. (2013) assessed SSI occurrence for one year postoperatively, and the diagnosis method was based on the SSI definition, clinical presentation, and signs and symptoms. Kapadia et al. (2013) found that participants who were compliant with preoperative 2% CHG cloth usage had a statistically significant decrease in SSIs compared to those who were noncompliant (p = 0.0428). Treatment for SSIs and complications were not specified. A CHG cloth protocol can decrease financial burden, morbidity, and mortality by reducing SSI incidence.10 16

Chlebicki et al. (2013) conducted a meta-analysis to determine if preoperative bathing with CHG would decrease SSI incidence when compared to a placebo or no bath. Surgical site infections were defined as infections caused by existent dermal organisms or the introduction of organisms in the surgical site intraoperatively.7 Risk factors were bacterial colonization and intraoperative surgical site contamination.7 Clinical presentations and signs and symptoms were suggested to be inflammation, skin breakdown, purulent drainage, fever, and tenderness.7 Within the articles included in the meta-analysis, assessment and diagnosis were presumably guided by the SSI definition and clinical presentation. Treatment for SSIs was not specified. Complications of SSIs were longer hospitalization, increased costs, and increased morbidity and mortality.7 In one trial, five participants experienced redness of the skin after application of CHG, yet five additional participants experienced redness of the skin after the application of the placebo.7 Ultimately, the meta-analysis showed no clear benefit from preoperative bathing with an antiseptic to prevent SSIs.7 Quality care was not addressed, as the researchers focused more on cost effectiveness.

Webster and Osborne (2015) conducted a metaanalysis to determine if preoperative bathing with an antiseptic reduced SSIs when compared to nonantiseptic preparations. Webster and Osborne (2015) defined SSIs as “wound infections that occur after invasive (surgical) procedures” (p. 1). Webster and Osborne (2015) suggested that the cause of SSIs is contamination or flora overgrowth. Signs, symptoms, and clinical presentations were pus, erythema, or tenderness within 30 days postoperatively.1 Assessment and diagnosis methods were based on the definition, clinical presentation, signs, and symptoms. Risk factors were age, nutrition, diabetes, increased body mass, lack of hand sterility of the staff and surgery length or technique, inadequate preoperative preparation, lack of preprocedure antibiotic administration, inadequate body hair removal, and inadequate preoperative bathing.1 Webster and Osborne (2015) found “no clear evidence that washing with chlorhexidine reduced the incidence of SSIs” (p. 19). Complications of SSIs were longer hospital stay, higher health care costs, morbidity, mortality, readmissions, and potential litigation.1 No treatment for SSIs was specified. Webster and Osborne (2015) indicated that antiseptic agents commonly

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cause hypersensitivity reactions.

However, the results of the meta-analysis indicated a small number of hypersensitivity reactions, and more hypersensitivity reactions occurred in the placebo group than the antiseptic group, making their statement questionable in credibility and reliability.

The WHO (2016) conducted a meta-analysis to establish guidelines to prevent SSIs. Surgical site infection was defined as an infection at the incision site within 30 days postoperatively and can involve varying degrees of depth within the tissue.5 Risk factors included being overweight or obese, severe wounds, diabetes mellitus, and lengthy surgical procedures.5 The cause of SSIs was increased bacteria at the incision site.5 Symptoms and clinical presentations varied depending on the study, but the common consensus across most studies was the presence of purulent discharge, tenderness, and fever greater than 38 degrees Celsius. Assessment and diagnosis methods were based on the definition, clinical presentation, and signs and symptoms. Treatment of SSIs was not specified. The WHO (2016) found “moderate quality of evidence that bathing with CHG soap does not significantly reduce SSI rates compared to bathing with plain soap (OR: 0.92; 95% CI: 0.80- 1.04)� (p. 60). Complications of SSIs were noted to be morbidity, mortality, increased hospital length, increased readmissions, and increased health care costs.5 Despite the unintentional nature of SSIs, these surgical complications are perceived by others as an indicator of poor-quality care.5 Franco et al. (2017) conducted a meta-analysis to determine if SSIs after clean surgeries were reduced by preoperative bathing with CHG when compared to a placebo or soap. The definition of SSI also included signs and symptoms and clinical presentations, as all studies defined SSIs as the presence of drainage and purulent discharge.8 Risk factors were surgical contamination, improper hair removal at the incision, surgical length, preoperative antibiotic use, and comorbidities.8 The principle cause of SSIs was bacteria introduced in the surgical site intraoperatively.8 The assessment of SSIs and diagnosis methods were guided by each study’s definition, signs and symptoms, and clinical presentation of SSIs. Treatment of SSIs was not specified. The complications of SSIs were amputations and financial burden.8 Quality care

was not addressed, and this study found no significant difference in SSIs in those who bathed with 4% CHG versus a placebo.8 Wang et al. (2017) conducted a meta-analysis to determine if preoperative bathing with CHG would decrease SSI incidence after total knee arthroplasties.

Wang et al. (2017) defined SSIs as a postoperative complication. Infection risk factors were identified using the NNIS.3 The causes of SSIs were exposure to bacteria in the perioperative period or transient skin flora.3 Signs, symptoms, clinical presentations, and treatment of SSIs were not specified. Assessment of SSIs and diagnosis methods were also not specified but were presumably reviewed in the studies selected for the meta-analysis. Wang et al. (2017) found that preoperative CHG bathing could decrease SSIs in moderate and high-risk patients. Complications of SSIs were prolonged treatment and subsequent surgical revisions.3 Quality care was not addressed, but it was implied that quality of life can be enhanced by eliminating the extended treatment associated with SSIs. Discussion of Findings The results from the ten articles demonstrated conflicting evidence. Five studies found that preoperative bathing with an antiseptic decreased the incidence of SSIs,2,3,6,9,10 and five studies found no significant benefit or difference in SSIs when incorporating the intervention.1,4,5,7,8 However, all studies were congruent in noting that preoperative bathing with an antiseptic agent decreased bacterial colonization on the skin, and SSIs have a devastating impact on patients physically, emotionally, and financially. Health care providers and organizations can be affected by SSIs financially in the form of decreased reimbursements, litigation, or a decrease in patient volume due to the perception of low-quality care. From a risk-benefit perspective, there was substantial evidence of benefit from the intervention, as little to no risk was identified in the articles.

There were limitations in the reviewed articles. Lack of randomization and control was evident in several articles. Participants were not observed applying the preoperative antiseptic to verify adequate application in several studies, which could alter the results due to the lack of intervention control. There was not a speci-

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fied time window between the time of the preoperative bathing and the surgical procedure in several studies, and there was a lack of clear delineation regarding length of time taken to perform bathing with antiseptic agents. Several of the studies had a low compliance rate and smaller sample sizes that compromised statistical significance. The impact of SSIs on quality care was more often implicit than explicit, and each article largely focused on cost-control. When considering that SSI occurrence can be perceived as low-quality care despite being unintentional, studies with a cost-control focus direct attention away from this stigma rather that address it.

An evidence leveling system by Melnyk and FineoutOverholt (2005) was used to grade the evidence from the literature review. Level I evidence included four meta-analyses and systematic reviews and one article with evidence-based guidelines. One article was an RCT, graded level II evidence. Four articles were nonrandomized prospective cohort studies graded as level IV evidence. More studies with higher levels of evidence found no clear benefit from the intervention contributing to reduce SSIs. However, these studies also did not indicate that preoperative bathing with an antiseptic agent caused significant harm, compromised patient safety, or demonstrated poorer patient outcomes. Studies with lower levels of evidence found a clear benefit from the intervention, evidenced by a reduction in SSIs. Recommendations Due to the lack of consensus regarding the effectiveness of the intervention, recommendation for the practice would stem from cost-effectiveness and patient safety. These considerations would include cost effectiveness of using an antiseptic agent preoperatively, resistance to the antiseptic agent over time, and possible skin irritation as a complication. It is unlikely that a global consensus can be achieved demonstrating the cost-effectiveness of the practice, as resources can be either scarce or abundant and are location dependent.

The fact that standard perioperative preparation with an antiseptic agent by the surgical team has not demonstrated evidence of resistance may be justification enough to use preoperative bathing with an antiseptic agent in practice.

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Further, all studies either cited previous evidence or found new evidence that indicated bacterial colonization reduction with preoperative antiseptic bathing.

This could be especially beneficial for vulnerable populations such as immunocompromised persons, the elderly, diabetics, and tobacco users. Should preoperative bathing with an antiseptic agent become standardized, patients should be educated on the length of time to bathe, the areas to bathe, and the number of times to bathe to enhance application quality. Quality improvement measures should be implemented to ensure adequate patient education as well as record the incidence of skin irritation. This would provide continual risk-benefit analysis for the practice.

Several studies supporting the practice lacked randomization, and little focus was given to quality care overall. Future research would benefit from RCTs replicating the studies supporting the practice. These results from RCTs would yield findings most useful for evidence-based practice. Patient perceptions of quality care after experiencing SSIs should also be investigated via qualitative methodology, despite yielding lower quality evidence results. Policymakers should be exposed to the results from both highly controlled studies and studies that humanize the experience of SSIs to promote best practice methods and high-quality care.

Conclusion Holistic health must be considered when contemplating the impact of SSIs. Patients experience physical and psychological effects, and health care should focus on preserving these aspects of health. Some studies continue to debate the efficacy of preoperative bathing with an antiseptic agent. Overall, there is little to no harm caused by the practice and the benefits have been shown to outweigh the risks. Prevention of an SSI is preferable and less costly than treatment for an SSI, thus it is recommended that the practice be used to improve surgical outcomes for patients.

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REFERENCES 1. Webster J, Osborne S. Preoperative bathing or showering with skin antiseptics to prevent surgical site infection. Cochrane Database of Systematic Reviews. 2015;(2):1-51. doi: 10.1002/14651858.

CD004985.pub5.

2. Zywiel MG, Daley JA, Delanois RE, Naziri Q, Johnson AJ, Mont MA. Advance pre-operative chlorhexidine reduces the incidence of surgical site infections in knee arthroplasty. International Orthopedics. 2010;35:1001-1006. doi: 10.1007/ s00264-010- 1078-5 3. Wang Z, Zheng J, Zhao Y, et al. Preoperative bathing with chlorhexidine reduces the incidence of surgical site infections after total knee arthroplasty: A meta-analysis. Medicine. 2017;96(47):1-7. doi:10.1097/MD.0000000000008321 4. Veiga D, Damasceno C, Veiga-Filho J, et al.

Randomized controlled trial of the effectiveness of chlorhexidine showers before elective plastic surgical procedures. Infection Control & Hospital Epidemiology. 2009;30(1):77-79. doi:10.1086/592980 5. World Health Organization. Global guidelines for the prevention of surgical site infection. Geneva: World Health Organization; 2016. http://www. who.int/gpsc/ssi-guidelines/en/ 6. Graling PR, Vasaly FW. Effectiveness of 2% CHG cloth bathing for reducing surgical site infections. AORN Journal. 2013;97(5):547-551. doi:10.1016/j.aorn.2013.02.009

7. Chlebicki MP, Safdar N, O’Horo JC, Maki DG. Preoperative chlorhexidine shower or bath for prevention of surgical site infection: A meta-analysis. American Journal of Infection Control, 2013;41(2):167-173. doi:10.1016/j. ajic.2012.02.014 8. Franco LC, Cota GF, Pinto TS, Ercole FF. Preoperative bathing of the surgical site with chlorhexidine for infection prevention: Systematic review with meta-analysis. American Journal of Infection Control. 2017;45(4):343-349. doi:10.1016/j. ajic.2016.12.003 9. Johnson AJ, Daley JA, Zywiel MG, Delanois RE, Mont MA. Preoperative chlorhexidine preparation and the incidence of surgical site infections after hip arthroplasty. The Journal of Arthroplasty. 2010;25(Suppl. 1):98-102. doi:10.1016/j. arth.2010.04.012 10. Kapadia BH, Johnson AJ, Daley JA, Issa K, Mont MA. Pre-admission cutaneous chlorhexidine preparation reduces surgical site infections in total hip arthroplasty. The Journal of Arthroplasty.

2013;28:490-493. doi:10.1016/j.arth.2012.07.015 11. Melnyk BM, Fineout-Overholt E. Evidence-based practice in nursing & healthcare: A guide to best practice. 1st ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2005.

AAMCN Would Like to Recognize Our Corporate Partners Avedro Chamberlain University Exact Sciences Foundation Medicine, Inc. Gilead Sciences, Inc. Guardian Life Insurance Home Instead Senior CareÂŽ Humana, Inc.

Mallinckrodt Pharmaceuticals Morgan Consulting Resources, Inc. Novocure Paratek Pharma TCS Healthcare University of Rochester,

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Call for Articles

Journal of Managed Care Nursing Interested writers are requested to submit their editorial or research articles!

The JMCN publishes topics on managed care and related subjects, like quality & utilization management, patient advocacy, current trends, changing legislature, leadership tips, and more.

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Association of Medicare Spending With Subspecialty Consultation for Elderly Hospitalized Adults Kira L. Ryskina, MD, MS; Yihao Yuan, MS; Rachel M. Werner, MD, PhD

Abstract Importance High use of subspecialty care is an important source of health care spending.

Medical subspecialty care in particular may duplicate the scope of practice of the primary attending physicians for patients hospitalized for medical conditions. Under value-based payments, which aim to control overall spending during an episode of hospitalization (including Part B physician fees), subspecialty consultations may be a target for hospitals working to reduce costs.

Objectives To measure the use of subspecialty consultation for Medicare beneficiaries hospitalized for nonsurgical conditions; to compare payments for consultative and nonconsultative care, adjusted for case mix and demographics; and to measure variation in payments across hospital referral regions (HRRs).

Design, Setting, and Participants This retrospective cross-sectional study included a 15% random sample of Medicare fee-for-service beneficiaries enrolled in Parts A and B and identified all discharges after acute care hospital stays for nonsurgical conditions from January 1 through December 31, 2014. A total of 735 627 discharges were included. The analyses were conducted from December 1, 2017, through February 12, 2019. Total Part B payments were extrapolated to the population of Medicare fee-for-service beneficiaries.

Main Outcomes and Measures Probability of any consultation during a hospitalization was estimated using logistic regression. The number of consultations per stay and the number of consultative visits per hospital day were estimated using Poisson regression. Part B payments for consultative and nonconsultative care were estimated using generalized linear regression with gamma-log link. All models were adjusted for patient demographics and case mix. Payment models also included HRR fixed effects.

Results A total of 735 627 discharges from 4534 hospitals in 2014 were included in the analysis (41.2% men and 58.8% women; mean [SD] age, 79.6 [8.9] years; 84.7% white, 10.1% black, and 5.2% other race). After adjusting for patient case mix and demographics, a 6-fold variation between the top and bottom quintiles of hospitals (relative difference, $401 [95% CI, $368-$434]) and HRRs (relative difference, $363 [95% CI, $337-$389]) was found in payments per stay for consultative care. Part B payments for consultative care by medical subspecialists accounted for 41.3% of payments for physician visits during hospitalization and totaled $1.3 billion in 2014.

Conclusions and Relevance The substantial variation in the use of subspecialty consultative care suggests potential opportunities for cost savings.

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Key Points Question Does inpatient consultative care by medical subspecialists represent a potential opportunity for cost savings during an episode of hospitalization for nonsurgical conditions?

Findings In this cross-sectional study of 735 627 hospital discharges among Medicare beneficiaries 65 years or older extrapolated to the population of Medicare fee-for-service beneficiaries, substantial variation in Medicare payments for medicine subspecialty consultations suggested inefficiencies. Subspecialty consultative care accounted for more than $1.3 billion dollars of Medicare spending in 2014.

Meaning Although whether patients have improved outcomes as a result of these consultations remains unclear, efforts to constrain the unnecessary use of consultative care during a hospitalization represent a potential area for savings.

Introduction Value-based payment models are based on the premise that health care professionals can deliver better care at a lower cost.1 High use of subspecialty care is one important source of health care spending.2,3 In the hospital setting, subspecialty consultations are requested at the discretion of the attending physician to provide organ-system or procedural expertise.4 Under value-based payment models that focus on inpatient care, which aim to control overall spending during an episode of hospitalization (including Part B physician fees), subspecialty consultations may be a target for hospitals working to reduce costs.5 However, the costs of consultative care provided to hospitalized patients by subspecialty physicians have not been well described.

Primary care or generalist physicians have increasingly delegated more care to subspecialist physicians.6-11 In the ambulatory setting, the number of referrals from primary care physicians to subspecialists grew 94% between 1999 and 2009.10 Whether this increase reflects a necessary specialization of clinical practice in response to patient complexity or duplicative care in response to nonclinical pressures such as malpractice or productivity is not clear.12 Consultative care is even more common in the inpatient setting than in the ambulatory care setting. A 2010 study13 found that 90% of hospitalizations included at least 1 subspecialty consultation, and each hospitalization involved 2.6 subspecialties in addition to the attending or the operating physician’s specialty.

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That study13 included medical and surgical hospitalizations as well as consultations with medical and surgical specialists. However, consultations with internal medicine subspecialists for patients hospitalized for medical conditions may represent a distinct category of consultative care from surgical consultations for two main reasons. First, internal medicine subspecialists and generalists share similar backgrounds, including residency training and patient population, whereas general surgeons and surgical subspecialists are more narrowly focused in terms of procedures performed and patient population. Thus, medical subspecialty care may be duplicative with the scope of practice of the primary attending physician for patients hospitalized for medical conditions. Second, reimbursement to surgeons is derived largely from procedures rather than cognitive services such as consultative care. For patients who undergo surgery during their hospitalization, surgical visits are typically rolled into a global payment for the procedure and individual patient visits would not be captured in claims.

We conducted this study to evaluate inpatient consultative care by internal medicine subspecialists as a potential opportunity for cost-savings during an episode of hospitalization. Our objectives were to measure the use of subspecialty consultation for elderly adults hospitalized for nonsurgical conditions; to compare payments for consultative and nonconsultative care, adjusted for case mix and patient demographics; and to measure variation in payments for consultative care across hospitals and hospital referral regions (HRRs).

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Methods The study was approved by the institutional review board of the University of Pennsylvania and the privacy board of the Centers for Medicare & Medicaid Services. Owing to the use of retrospective records, informed consent was waived. The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.14 Data and Sample From a 15% random sample of Medicare fee-forservice beneficiaries enrolled in Parts A and B, we identified all discharges after an acute care hospital stay for nonsurgical conditions from January 1 through December 31, 2014, among beneficiaries 65 years and older. Discharges after hospitalization for surgical conditions were excluded using the diagnosis related group (DRG) code in the hospitalization claim.15 The study sample included 735 627 discharges from 4534 hospitals in 2014. Professional claims were linked to hospitalization claims using dates of service and place of service codes. Hospital characteristics were obtained from the Medicare Provider of Service file.

Identification of Subspecialty Consultations We used two specifications to measure consultative care. The first specification included only those stays where the attending physician was a generalist (internal medicine, general/family practice, or geriatrics) and defined any visits by subspecialists during the hospital stay as consultative care. We first identified those hospitalizations where the attending physician was a generalist based on the specialty code for the evaluation and management claims corresponding to the hospital visit, including claims for patients admitted under observational care.16,17 For those hospitalizations, we identified all evaluation and management services performed by subspecialists (in the fields of allergy/ immunology, cardiology, gastroenterology, pulmonology, nephrology, infectious disease, endocrinology, rheumatology, critical care, hematology, hematology/ oncology, preventive medicine, and medical oncology) during the hospital stay. Hospital stays without any generalist visits were excluded from this analysis (9.4% of discharges).

Although most attending physicians for patients hospitalized for medical conditions are generalists, patients can be hospitalized on a subspecialist service where

the attending physician is a subspecialist who could consult other subspecialists. To account for this possibility, we conducted a second set of analyses that included hospitalizations where the attending physician was a specialist. To do this, we identified hospitalizations without any generalist visits and counted the number of visits by each medicine subspecialty. Next, we assigned the hospitalization to the subspecialty that submitted a plurality of claims for patient visits as the attending subspecialty. Physicians who belonged to other subspecialties were considered consultants, and their services and payments were summed to measure use of consultative care and costs for these hospitalizations.

Variables To measure the use of subspecialty consultation, we counted the number of hospital visits by physicians in each subspecialty. This variable was defined as any line items for hospital-based evaluation and management services submitted during the hospital stay by physicians in each subspecialty. We measured use of consultative care in two ways. First, we measured the number of consultations per hospital stay as the number of distinct subspecialties with at least one consultation day during a hospital stay. Second, because generalist and subspecialty physicians have broad discretion about how frequently they see patients during the hospital stay, we calculated the number of consultative visits per hospital day as the total number of consultation days for all specialties divided by the length of hospital stay.

We also measured Medicare payments associated with consultative care. To do so, Part B payments associated with the consultative visits were aggregated at the hospital-stay level. Nonconsultative visits and Part B payments for the primary attending services were similarly measured at the hospital-day and hospitalstay levels. In the first set of analyses, generalists provided nonconsultative services by definition. In the second set of analyses, which included stays where the primary attending physician was a subspecialist, nonconsultative services were provided by a generalist or by the subspecialist with the plurality of claims.

Statistical Analysis The analyses were conducted from December 1, 2017, through February 12, 2019. We compared discharge characteristics using χ2 tests for categorical variables

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and unpaired t tests for continuous variables. P < .05 was considered statistically significant; all hypothesis tests were 2-sided. The total payments for consultative and nonconsultative care from the 15% random sample were extrapolated to the entire Medicare fee-forservice population of Medicare beneficiaries 65 years and older.

Probability of any consultation during a hospitalization was estimated using logistic regression. The number of consultations per stay and the number of consultative visits per hospital day were estimated using Poisson regression. Payments were estimated using generalized linear regression with gamma-log link. All models were adjusted for patient demographics (age at admission, sex, and race), Elixhauser comorbidities, any time spent in the intensive care unit,18 and whether the patient died in the hospital. We also included dummies for the primary DRG for the hospitalization (DRGs of different severity levels were coded separately). Payment models also included HRR fixed effects to account for geographic differences in reimbursement rates. We omitted observations with 1 or more missing variables from analysis (33 discharges were missing hospital characteristics [<0.001% of observations]). The Huber-White sandwich estimator was used in all regressions to account for clustering of observations within hospitals.19 To describe variation in consultative care by hospital and market characteristics, we used logistic regression to estimate mean adjusted probability of a subspecialty consultation as a function of hospital and market characteristics. In addition to the patient demographic and clinical characteristics, independent variables included in this model were hospital size (<100, 100-399, or >400 beds), ownership (private for-profit, private not-for-profit, religious organization, federal, state, or local government, or other [eg, physician]), rural vs urban location, and teaching status (any residents), as well as 2 market characteristics. Markets were characterized at the HRR level by measuring the health maintenance organization (HMO) penetration rate and hospital market competition within each HRR. The HMO penetration rate was estimated by calculating the proportion of all Medicare beneficiaries who were enrolled in Medicare Advantage using the Medicare Beneficiary Summary file. The hospital market competitiveness within each HRR was measured by calculating the Herfindahl-Hirschman index, which 24

is the sum of the squares of each hospitalâ&#x20AC;&#x2122;s share of patients in the HRR. The HRRs were categorized into quartiles for HMO penetration rate and HerfindahlHirschman index for ease of interpretation. Statistical analyses were performed using Stata software (version 15; StataCorp).

Results A total of 735 627 discharges from 4534 hospitals in 2014 were analyzed (41.2% men and 58.8% women; mean [SD] age, 79.6 [8.9] years; 84.7% white, 10.1% black, and 5.2% other race) (Table 1).

Compared with stays without consultative care, a larger proportion of hospital stays with consultative care took place in hospitals located in the Northeast (22.1% vs 16.2%; P < .001) and those that were large (38.9% vs 28.3%; P < .001), urban (90.0% vs 71.5%; P < .001), and teaching (42.9% vs 34.0%; P < .001). The median hospital length of stay was 5 days (interquartile range [IQR], 4-8 days) for stays with consultative care and 4 days (IQR, 3-5 days) for stays without consultative care.

In 2014, 52.8% of all discharges after hospitalization for nonsurgical conditions and 54.5% of discharges where the attending was a generalist included at least 1 consultation with a medicine subspecialist. Of all hospitalizations for nonsurgical conditions, 31.7% included a consultation with 1 subspecialty; 13.5%, 2 subspecialties; and 7.6%, 3 or more subspecialties (Table 1).

Table 2 shows the use of specialties consulted per stay, consultative visits per hospital day, and Medicare Part B payments. Substantial variation across stays occurred in the use of consultative care. For stays with a generalist attending, a median of 0.78 different specialties was consulted per hospital stay (IQR, 0.51-1.13), with a median of 0.40 consultative care visits per day (IQR, 0.26-0.57). On the other hand, little variation across stays occurred in the number of nonconsultative care visits per hospital day (median, 0.87; IQR, 0.85-0.88). These finding were stable to including stays where the attending physician was a specialist (Table 2).

Table 3 shows the adjusted probability and odds of consultation by hospital and market characteristics.

After adjusting for case mix, stays at hospitals in the

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Table 1. Patient and Hospital Characteristics of Discharges After Hospitalization for Nonsurgical Conditions by Medicare Fee-for-Service Beneficiaries in 2014a

Abbreviations: IQR, interquartile range; NA, not applicable. a Percentages have been rounded and may not total 100. www.aamcn.org | Vol. 6, No. 2 | Journal of Managed Care Nursing b All comparisons between stays with and without consultative care are significant at P < .001.

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Table 2. Number of Specialists Consulted per Stay, Number of Consultative Visits per Hospital Day, and Medicare Part B Payments, Adjusted for Case Mix and Demographics, 2014

Abbreviations: IQR, interquartile range; NA, not applicable. a Based on a 15%random sample of 666 388 stays in 4528 hospitals where the attending physician was a generalist and 735 627 stays in 4534 hospitals where the attending physician was a generalist or a specialist. b Indicates the number of distinct subspecialties with at least 1 consultation visit during a hospital stay. c Indicates the total number of consultation days for all specialties divided by the length of stay.

Northeast had higher odds of a subspecialty consultation compared with hospitals located in the Midwest (odds ratio [OR], 0.74; 95% CI, 0.67-0.83), South (OR, 0.81; 95% CI, 0.73-0.90), or West (OR, 0.41; 95% CI, 0.36-0.47). Stays at small hospitals had lower odds of a subspecialty consultation (OR, 0.35; 95% CI, 0.31-0.39) compared with large hospitals. Stays at hospitals with private not-for-profit ownership (OR, 0.74; 95% CI, 0.68-0.81) or government hospitals (OR, 0.62; 95% CI, 0.55-0.69) had lower odds of consultation compared with hospitals owned by private for-profit organizations. Stays at hospitals in urban locations had higher odds of consultative care (OR, 2.53; 95% CI, 2.31-2.78) compared with hospitals in rural locations. The odds of consultation were not significantly different between teaching and nonteaching hospitals (OR, 0.97; 95% CI, 0.90-1.05). These findings were generally stable to including stays where the attending was a subspecialist (Table 3).

Hospitalizations in HRRs in the highest 2 quartiles of HMO penetration rate had higher odds of consultative care (OR for the third quartile, 1.19 [95% CI, 1.08-1.32]; OR for the highest quartile, 1.29 [95% CI, 1.17-1.43]) compared with the lowest quartile of HMO penetration rate (Table 3). The highest 2 quartiles of hospital market concentration were associated with lower odds of consultative care (OR for the third quartile, 0.84 [95% CI, 0.77-0.92]; OR for the highest quartile, 0.80 [95% CI, 0.73- 0.87]) compared with the lowest quartile of market concentration (Table 3). These findings were also stable to including stays 26

where a subspecialist was the primary attending physician (Table 3).

Medicare Part B payments were more variable for consultative care than for nonconsultative care. The median adjusted Part B payment for consultative care was $156 (IQR, $75-$324) per stay and $28 per hospital day (IQR, $15-$53) (Table 2). The median adjusted Part B payments for nonconsultative care were $357 (IQR, $294-$441) per stay and $73 per hospital day (IQR, $55-$88) (Table 2). Adjusted for case mix, consultative care accounted for 41.3% of total Medicare Part B payments for internal medicine physician visits during acute care hospitalizations. In 2014, total Part B payments for consultative care provided to Medicare fee-for-service beneficiaries during hospitalization for nonsurgical conditions were $1.3 billion, whereas total Part B payments for nonconsultative care provided during these hospitalizations were $1.8 billion (Figure 1).

Substantial variation occurred across hospitals and HRRs in payments for consultative care. Across HRRs, the risk-adjusted Part B payments for consultative care varied approximately 6-fold between the top and bottom quintiles of payments (Figure 2). The relative difference in payments per stay between the HRRs in the highest vs lowest quintiles of Part B payments for consultative care was $363 (95% CI, $337$389). Payments for consultative care varied similarly (6-fold) between the top and bottom quintiles of hospitals. The relative difference in payments per stay

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Table 3. Probability of Consultation by Hospital and Market Characteristics, Adjusted for Case Mix and Demographics, 2014

Abbreviations: HMO, health maintenance organization; OR, odds ratio. a Adjusted for patient case mix and demographics. b Measured as the proportion of Medicare beneficiaries in each hospital referral region who were enrolled in Medicare Advantage in 2014. The mean rate in each quartile was 13.7% in the lowest quartile, 23.3% in the second quartile, 30.9% in the third quartile, and 43.1% in the highest quartile. c Measured using Herfindahl-Hirschman Index (calculated as the sum of each hospitalâ&#x20AC;&#x2122;s share of the patient population in the hospital referral region). The mean Herfindahl-Hirschman Index in each quartile was 0.04 in the lowest quartile of hospital market concentration, 0.08 in the second quartile, 0.14 in the third quartile, 0.32 in the highest quartile of market 27 www.aamcn.org | Vol. 6, No.and 2 | Journal of Managed Care Nursing concentration.

Figure 1. Difference in Total Part B Spending for Inpatient Consultative vs Nonconsultative Care

Participants include Medicare fee-for-service beneficiaries hospitalized for nonsurgical conditions, adjusted for case mix and demographics, January 1 through December 31, 2014. Total payments are extrapolated to the Medicare feefor-service population. In 2014, total Part B payments for patients who were hospitalized for nonsurgical conditions were $1.3 billion for consultative care and $1.8 billion for nonconsultative care.

between the hospitals in the highest vs lowest quintiles of Part B payments for consultative care was $401 (95% CI, $368-$434).

Discussion We found substantial variation in Medicare payments for medicine subspecialty consultations. The regional variation in the use of consultative care suggests that the practice of consulting subspecialists during a hospitalization could be optimized. Overall, we found that subspecialty consultative care accounted for $1.3 billion dollars of Medicare spending in 2014.

Our estimate of Medicare spending is large but is likely an underestimate, because we include only admissions for medical DRGs. In addition, likely substantial downstream costs are associated with these inpatient subspecialty consultations, including additional diagnostic testing conducted during or after the hospitalization and subsequent outpatient follow-up visits. A recent study found that inpatient consultation after total joint arthroplasty was associated with higher total use of services compared with cases with similar comorbidities, complication rates, and length of stay 28

that did not involve a consultation.20 Furthermore, consultative care may prolong length of stay if patients are required to remain hospitalized while awaiting results of diagnostic tests requested by the consultant and to make arrangements for the additional outpatient follow-up appointments. Aggregating prospective payments that do not account for length of stay does not fully capture costs to hospitals from prolonged hospitalization.

Consistent with prior studies,11 we observed considerable variation in consultation rates by regional and hospital characteristics. However, identifying potentially unnecessary consultations can be challenging due to a lack of universal guidelines for consultation.21,22 Prior literature also suggests that some consultations save money in the long run. For example, inpatient palliative care consultation programs have been shown to save hospitals money and to provide improved care to patients with serious illness.23 Furthermore, some variation in consultation rates may stem from underuse as well as overuse. For instance, regional variation in physician supply results in rural hospitals having trouble attracting physicians in some subspecialties. Consistent with other analyses of regional variation in Medicare spending, regional differences in specialist involvement in care were attenuated after adjusting for patient case-mix.24,25 At the HRR level, higher rates of HMO penetration and more competitive markets for hospital care were associated with the use of consultative care. These findings are somewhat counterintuitive. Medicare Advantage plans receive lump sum payments from Medicare to encourage more efficient use of health care resources, such as consultative care. Similarly, one might expect lower rates of use of consultative care in more competitive markets, as hospitals attempt to reduce costs to increase market share. One possible explanation is that hospitals are primarily reimbursed on a fee-for-service basis measured by DRG. Thus, the incentive to reduce length of stay outweighs other cost considerations. If hospitals (or the attending physicians who act as hospital agents in the use of consultations and length of stay) view consultative care as a potential way to reduce length of stay, then we would expect higher consultation rates in regions with higher HMO penetration and market competition.

In general, physician services are much less costly to hospitals than the opportunity cost of another inpatient

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Figure 2. Mean Medicare Part B Payments for Consultative Care per Stay by Hospital Referral Region

Data include Medicare fee-for-service beneficiaries hospitalized for nonsurgical conditions, January 1 through December 31, 2014. Mean payments by hospital referral region are adjusted for case mix and demographics. www.aamcn.org | Vol. 6, No. 2 | Journal of Managed Care Nursing

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stay. Other possible explanations include variation in physician supply across HRRs, which may confound the association between market characteristics and use of consultative care. Moreover, an inverse relationship may occur between high consultative care use (or overuse) and higher rates of HMO penetration and market competition.

care, quicker diagnostic workup, and more advanced techniques. On other hand, involvement of multiple subspecialty consultants may increase risks associated with care fragmentation and iatrogenic risks owing to prolonged hospitalization. Future studies should evaluate outcomes associated with different levels of use of inpatient consultative care.

Nevertheless, optimizing use of consultative care may be an attractive strategy for hospitals seeking to constrain costs during an episode of hospitalization.

This situation may be particularly true for physicianled accountable care organizations that may be better able to achieve efficiencies by identifying and eliminating unnecessary consultations and optimizing use of subspecialists during an episode of care. Although ambulatory subspecialty care can originate from patient self-referral and is associated in part with patient insurance coverage and income,26 inpatient consultation is typically under the control of the attending physician. More direct involvement of those physicians with value-based payments (such as via physician-led accountable care organizations) may more effectively counteract the considerations that lead to overuse of consultative care (eg, malpractice concerns or clinical inertia).

Limitations This study has several limitations. First, we measured direct payments to physicians and did not consider other costs to the hospital and payers associated with consultative care, such as downstream costs of additional diagnostic testing, secondary consultations, and prolonged length of stay. As a result, our cost estimates of consultative care likely underestimate the total costs. Second, we did not measure quality of the care or associated outcomes and were therefore unable to evaluate the relative value of consultative care during the hospitalization. Third, variation in spending on generalist care was limited in part by Medicare regulations that require attending physicians to submit no more than one visit claim per hospital day, whereas different specialists can bill separately on the same day.

Whether hospitals transitioning to value-based payment can reduce spending on consultative care without detrimentally affecting quality of care is unknown.

Well-designed value-based payments align financial incentives across a diverse group of health care providers, including physicians, hospitals, and postâ&#x20AC;&#x201C;acute care providers. Such alignment should encourage health care organizations to shift resources from more expensive settings (eg, hospitals) to less expensive settings (eg, physician offices) and help limit overused services. However, value-based payments based on episodes of care also lead providers to avoid high-risk patients or focus care away from high-cost services.

Recent evaluations found little empirical evidence of adverse effects of value-based payments on the quality of hospital care,27,28 Nevertheless, quality and outcomes of consultative care may be difficult to evaluate. For example, the association between inpatient consultative care use and existing measures of hospital quality, such as 30-day mortality and readmissions, is not well understood. On the one hand, more liberal use of inpatient consultative care may improve these outcomes by expediting patient access to higher-intensity 30

Conclusions Direct costs to Medicare attributable to inpatient subspecialty consultations for medical conditions accounted for $1.3 billion in 2014. Whether patients have improved outcomes as a result of these consultations remains unclear, but the substantial variation in the use of subspecialty consultative care suggests potential opportunities for cost savings. Efforts to identify and constrain the unnecessary use of consultative care during a hospitalization may represent a potential area for savings under value-based reimbursement.

This article first appeared in Health Policy, JAMA Network and is republished here under a Creative Commons license. https://jamanetwork. com/journals/jamanetworkopen/fullarticle/2729802 REFERENCES 1. American Hospital Association. Medicareâ&#x20AC;&#x2122;s bundled payment initiative: considerations for providers. Issue brief. https://www.aha.org/ guidesreports/2016-01-19-issue-brief-medicares-

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bundled-payment-initiatives- considerations-providers. 2017. Accessed October 17, 2017.

2. Orszag PR, Ellis P. The challenge of rising health care costs: a view from the Congressional Budget Office. N Engl J Med. 2007;357(18):1793-1795. doi:10.1056/NEJMp078190 3. Carey TS, Garrett J, Jackman A, McLaughlin C, Fryer J, Smucker DR. The outcomes and costs of care for acute low back pain among patients seen by primary care practitioners, chiropractors, and orthopedic surgeons: the North Carolina Back Pain Project. N Engl J Med. 1995;333(14):913-917. doi:10.1056/NEJM199510053331406 4. Forrest CB. A typology of specialists’ clinical roles. Arch Intern Med. 2009;169(11):1062-1068. doi:10.1001/ archinternmed.2009.114 5. Centers for Medicare and Medicaid Services.

Medicare spending per beneficiary. https://www. cms.gov/Medicare/ Quality-Initiatives-PatientAssessment-Instruments/hospital-value-basedpurchasing/Downloads/Fact-Sheet-MSPB-Spending-Breakdowns-by-Claim-Type-Dec-2014.pdf.

Updated November 2014. Accessed September 17, 2018.

6. Bertakis KD, Callahan EJ, Azari R, Robbins JA. Predictors of patient referrals by primary care residents to specialty care clinics. Fam Med. 2001;33(3):203-209.

7. Borowsky SJ, Rubenstein LV, Skootsky SA, Shapiro MF. Referrals by general internists and internal medicine trainees in an academic medicine practice. Am J Manag Care. 1997;3(11):1679-1687.

8. Cook NL, Hicks LS, O’Malley AJ, Keegan T, Guadagnoli E, Landon BE. Access to specialty care and medical services in community health centers.

Health Aff (Millwood). 2007;26(5):1459-1468. doi:10.1377/hlthaff.26.5.1459 9. Forrest CB, Majeed A, Weiner JP, Carroll K, Bindman AB. Comparison of specialty referral rates in the United Kingdom and the United States: retrospective cohort analysis. BMJ. 2002;325(7360):370-371. doi:10.1136/bmj.

325.7360.370 10. Barnett ML, Song Z, Landon BE. Trends in physician referrals in the United States, 19992009. Arch Intern Med. 2012;172(2):163-170. doi:10.1001/archinternmed.2011.722 11. Forrest CB, Reid RJ. Passing the baton: HMOs’ influence on referrals to specialty care. Health Aff (Millwood). 1997;16(6):157-162. doi:10.1377/

hlthaff.16.6.157 12. Mehrotra A, Forrest CB, Lin CY. Dropping the baton: specialty referrals in the United States.

Milbank Q. 2011; 89(1):39-68. doi:10.1111/j.14680009.2011.00619.x 13. Stevens JP, Nyweide D, Maresh S, et al. Variation in inpatient consultation among older adults in the United States. J Gen Intern Med. 2015;30(7):992999. doi:10.1007/s11606-015-3216-7 14. Vandenbroucke JP, von Elm E, Altman DG, et al; STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.

Ann Intern Med. 2007;147(8): W163-W194. doi:10.7326/0003-4819-147-8-20071016000010-w1 15. Centers for Medicare and Medicaid Services.

Appendix A, List of MS-DRGs, version 28.0.

Draft ICD-10-CM/PCS MS-DRGv28 definitions manual. https://www.cms.gov/icd10manual/fullcode_cms/P0029.html. July 31, 2018. Accessed July 31, 2018.

16. Teno JM, Mitchell SL, Gozalo PL, et al. Hospital characteristics associated with feeding tube placement in nursing home residents with advanced cognitive impairment. JAMA. 2010;303(6):544550. doi:10.1001/jama. 2010.79 17. Baldwin LM, Adamache W, Klabunde CN, Kenward K, Dahlman C, L Warren J. Linking physician characteristics and medicare claims data: issues in data availability, quality, and measurement.

Med Care. 2002;40(8)(suppl): IV-82-IV-95.

18. Weissman GE, Hubbard RA, Kohn R, et al.

Validation of an administrative definition of ICU admission using revenue center codes. Crit Care Med. 2017;45(8):e758-e762. doi:10.1097/ CCM.0000000000002374 19. White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica. 1980;48(4):817-838. doi:10.2307/1912934 20. Baumgartner BT, Karas V, Kildow BJ, et al. Inpatient consults and complications during primary total joint arthroplasty in a bundled care model. J Arthroplasty. 2018;33(4):973-975. doi:10.1016/j. arth.2017.11.042 21. Franks P, Mooney C, Sorbero M. Physician referral rates: style without much substance? Med Care.

2000;38 (8):836-846. doi:10.1097/00005650200008000-00007

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22. Franks P, Zwanziger J, Mooney C, Sorbero M. Variations in primary care physician referral rates.

Health Serv Res. 1999;34(1, pt 2):323-329.

23. Arora S, Panaich SS, Patel N, et al. Coronary atherectomy in the United States (from a Nationwide Inpatient Sample). Am J Cardiol. 2016;117(4):555-562. doi:10.1016/j.amjcard.2015.11.041 24. Reschovsky JD, Hadley J, Oâ&#x20AC;&#x2122;Malley AJ, Landon BE. Geographic variations in the cost of treating condition- specific episodes of care among Medicare patients. Health Serv Res. 2014;49(1):32-51. doi:10.1111/1475-6773.12087 25. Reschovsky JD, Hadley J, Saiontz-Martinez CB, Boukus ER. Following the money: factors associated with the cost of treating high-cost Medicare beneficiaries. Health Serv Res. 2011;46(4):9971021. doi:10.1111/j.1475-6773. 2011.01242.x 26. Shea D, Stuart B, Vasey J, Nag S. Medicare physician referral patterns. Health Serv Res. 1999;34(1, pt 2): 331-348.

27. Navathe AS, Liao JM, Dykstra SE, et al. Association of hospital participation in a Medicare bundled payment program with volume and case mix of lower extremity joint replacement episodes. JAMA. 2018;320(9):901-910. doi:10.1001/ jama.2018.12345 28. Finkelstein A, Ji Y, Mahoney N, Skinner J. Mandatory Medicare bundled payment program for lower extremity joint replacement and discharge to institutional postacute care: interim analysis of the first year of a 5-year randomized trial.

JAMA. 2018;320(9):892-900. doi:10.1001/ jama.2018.12346 ARTICLE INFORMATION Published: April 5, 2019. doi:10.1001/jamanetworkopen.2019.1634 Open Access: This is an open access article distributed under the terms of the CC-BY License. Š 2019 Ryskina KL et al. JAMA Network Open.

Corresponding Author: Kira L. Ryskina, MD, MS, Division of General Internal Medicine, University of Pennsylvania, 423 Guardian Dr, 12-30 Blockley Hall, Philadelphia, PA 19104 (ryskina@pennmedicine.upenn.edu).

Pennsylvania, Philadelphia (Ryskina, Werner); Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania (Werner).

Author Contributions: Dr Ryskina had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Ryskina.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Ryskina.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Ryskina, Yuan.

Obtained funding: Ryskina.

Supervision: Werner.

Conflict of Interest Disclosures: Dr Ryskina reported grants from the National Institute on Aging (NIA) during the conduct of the study. Dr Werner reported personal fees from CarePort Health and National Quality Forum outside the submitted work and grants from the Agency for Healthcare Research and Quality outside the submitted work. No other disclosures were reported.

Funding/Support: This study was supported by Career Development Award (K08-AG052572) from the NIA (Dr Ryskina) and grant K24-AG047908 from the NIA (Dr Werner).

Role of the Funder/Sponsor: The funding source 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 content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Meeting Presentation: Preliminary findings of this study were presented at the 41st Annual Research Meeting of the Society of General Internal Medicine; April 12, 2018; Denver, Colorado; and the 35th Annual Research Meeting of the Academy Health; June 26, 2018; Seattle, Washington.

Supplement: Available online eFigure: Study Sample Selection Flowchart https://jamanetwork.com/journals/jamanetworkopen/ fullarticle/2729802

Author Affiliations: Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia (Ryskina, Yuan, Werner); Division of General Internal Medicine, University of 32

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Comparison of a Potential Hospital Quality Metric With Existing Metrics for Surgical Quality– Associated Readmission Laura A. Graham, PhD, MPH; Hillary J. Mull, PhD, MPP; Todd H. Wagner, PhD; Melanie S. Morris, MD; Amy K. Rosen, PhD; Joshua S. Richman, MD, PhD; Jeffery Whittle, MD, MPH; Edith Burns, MD; Laurel A. Copeland, PhD; Kamal M. F. Itani, MD; Mary T. Hawn, MD, MPH

Abstract Importance The existing readmission quality metric does not meaningfully distinguish readmissions associated with surgical quality from those that are not associated with surgical quality and thus may not reflect the quality of surgical care.

Objective To compare a quality metric that classifies readmissions associated with surgical quality with the existing metric of any unplanned readmission in a surgical population.

Design, Setting, and Participants Cohort study using US nationwide administrative data collected on 4 high-volume surgical procedures performed at 103 Veterans Affairs hospitals from October 1, 2007, through September 30, 2014. Data analysis was conducted from October 1, 2017, to January 24, 2019.

Main Outcomes and Measures Hospital-level rates of unplanned readmission (existing metric) and surgical readmissions associated with surgical quality (new metric) in the 30 days following hospital discharge for an inpatient surgical procedure.

Results The study population included 109 258 patients who underwent surgery at 103 hospitals.

Patients were majority male (94.1%) and white (78.2%) with a mean (SD) age of 64.0 (10.0) years at the time of surgery. After case-mix adjustment, 30-day surgical readmissions ranged from 4.6% (95% CI, 4.5%-4.8%) among knee arthroplasties to 11.1% (95% CI, 10.9%-11.3%) among colorectal resections. The new surgical readmission metric was significantly correlated with facility-level postdischarge complications for all procedures, with ρ coefficients ranging from 0.33 (95% CI, 0.13-0.51) for cholecystectomy to 0.52 (95% CI, 0.38-0.68) for colorectal resection. Correlations between postdischarge complications and the new surgical readmission metric were higher than correlations between complications and the existing readmission metric for all procedures examined (knee arthroplasty: 0.50 vs 0.48; hip replacement: 0.44 vs 0.18; colorectal resection: 0.52 vs 0.42; and cholecystectomy: 0.33 vs 0.10). When compared with using the existing readmission metric, using the new surgical readmission metric could change hip replacement–associated payment penalty determinations in 28.4% of hospitals and knee arthroplasty–associated penalties in 26.0% of hospitals.

Conclusions and Relevance In this study, surgical quality–associated readmissions were more correlated with postdischarge complications at a higher rate than were unplanned readmissions.

Thus, a metric based on such readmissions may be a better measure of surgical care quality. This work provides an important step in the development of future value-based payments and promotes evidence-based quality metrics targeting the quality of surgical care. www.aamcn.org | Vol. 6, No. 2 | Journal of Managed Care Nursing

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Key Points Question Does inpatient consultative care by medical subspecialists represent a potential opportunity for cost savings during an episode of hospitalization for nonsurgical conditions?

Findings In this cross-sectional study of 735 627 hospital discharges among Medicare beneficiaries 65 years or older extrapolated to the population of Medicare fee-for-service beneficiaries, substantial variation in Medicare payments for medicine subspecialty consultations suggested inefficiencies. Subspecialty consultative care accounted for more than $1.3 billion dollars of Medicare spending in 2014.

Meaning Although whether patients have improved outcomes as a result of these consultations remains unclear, efforts to constrain the unnecessary use of consultative care during a hospitalization represent a potential area for savings.

Introduction In 2012, the Hospital Readmissions Reduction Program (HRRP) of the Centers for Medicare & Medicaid Services (CMS) targeted admissions for 3 medical conditions: acute myocardial infarction, heart failure, and pneumonia. Payment penalties were imposed on hospitals with high risk-adjusted readmission rates. In 2014, CMS excluded planned readmissions from the program, and in 2015, added 2 surgical procedures— total hip replacement and total knee arthroplasty—to the targeted index admissions.1 No changes were made to the definition of an unplanned readmission. The current metrics include all unplanned readmissions following a surgical procedure and ignore the quality of surgical care. With the addition of 2 new surgical readmission measures, hospital executives called on their surgical staff to reduce readmissions associated with surgery. Current research has shown that surgical readmissions are not fully explained by factors available from the hospital discharge summary.2-8 Since the elimination of planned readmissions from the readmission quality metric, no effort has been made, to our knowledge, to improve the relationship between unplanned readmissions and surgical quality.

Some of our group developed a potential definition of a surgical quality–associated readmission in a prior study using a modified Delphi process.8,9 In the current study, we operationalize and compare the new surgical quality–associated readmission metric to the unplanned readmission metric that is currently in use.

We assess the correlation of both with postdischarge 34

complication rates, a measure that is widely believed to reflect surgical quality. We hypothesize that the new surgery quality–associated readmission metric will be more highly correlated than the existing readmission metric with postdischarge complication rates. Support for our hypothesis would provide evidence that the new quality metric is more representative of the quality of surgical care. As a secondary objective, we examine how the new metric would change hospital payment penalties as assessed by HRRP if it were used in lieu of the current readmission metric.

Methods This study uses a prospective study design and administratively collected data from a national cohort of patients. This article adheres to the current Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.

The study protocol was reviewed and approved by the Veterans Affairs Central Institutional Review Board, which also waived the need to obtain informed consent.

Setting and Participants The study population included patients who underwent surgery at 103 Veterans Health Administration (VHA) hospitals between October 1, 2007, and September 30, 2014. Surgical procedures were identified from the Veterans Administration Surgical Quality Improvement Program (VASQIP). The VASQIP captures approximately 83% of all eligible major noncardiac surgical procedures.10 Only the 4 most common inpatient

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surgical procedures were included. These procedures were (1) knee arthroplasty, (2) total and partial hip replacement, (3) colorectal resection, and (4) cholecystectomy or common bile duct exploration. Patients with a length of stay less than 2 days and patients who died in the hospital were excluded from this analysis.

Defining Readmission Quality Metrics Readmissions were defined as any inpatient admission to a VHA hospital in the 30 days following hospital discharge from the index surgical admission. Inpatient admissions were queried from the VHA Corporate Data Warehouse inpatient domain. Planned readmissions were excluded using the current CMS coding algorithm.1 The final unplanned readmission quality metric was defined as any unplanned readmission to a VHA hospital in the 30 days following hospital discharge from the index surgical admission.

Surgical quality–associated readmissions (surgical readmissions) were identified by International Classification of Diseases, Ninth Revision (ICD-9) codes using a definition reached by consensus of a panel of experts on readmission.8,9 Prior to this analysis, a modified Delphi process was used to assess whether a subsequent 30-day readmission was associated with the quality of a surgical procedure occurring during the index hospitalization. Reasons for readmission were determined by a review of the primary and secondary ICD-9 codes. Panelists were presented with the top 50 reasons for readmission, representing 90% of all reasons for readmission in the sample.

Panelists were asked, “Does the readmission reason reflect possible surgical quality of care problems in the index admission?” Readmissions were considered surgical readmissions if the panel agreed that they were “likely related” or “directly related to index quality.” After 3 rounds, the expert panel identified 16 reasons for readmission that were associated with the quality of the index surgical procedure (eTable 1 in the Supplement). These reasons included woundassociated readmission, hemorrhage or hematoma, ostomy complication, and other infectious outcomes.8,9 After further review by the study team (all authors), readmissions associated with all device complications and device malfunctions were also included in the definition of surgical readmissions for this analysis.

The top 10 readmission reasons that were not classified as associated with surgery are shown in eTable 2

in the Supplement.

Additional Variables Postdischarge complications were used as an indicator of surgical quality for this analysis. Complications were defined as any VASQIP nurse–identified postoperative complication. Since VASQIP only collects postoperative complications up to 30 days after the operation date, a time frame of 14 days postdischarge was chosen to minimize the effect of right censoring among patients with longer postoperative stays.

Patient age, sex, race, functional status, American Society of Anesthesiologist physical status classification, history of acute myocardial infarction, history of peripheral vascular disease, recent diagnosis of depression, and current diabetes were included in the case-mix adjustment to account for variation across patient populations. Operative time, work relative value unit (RVU), and emergent surgery status were also included in the case-mix adjustment to account for variations within procedure type. Work RVU is a measure of surgical procedure complexity defined by CMS for each specific procedure code. When determined by VASQIP, this measure represents the highest work RVU Current Procedural Terminology code coded for the procedure, not just the work RVU for the principal Current Procedural Terminology code.

In addition to the VASQIP-assessed comorbidities and surgery characteristics, the hospital-level excess readmission ratio for each facility was calculated using the CMS HRRP formulas for fiscal year 2014.11 The excess readmission ratio is used to determine CMS payment penalties due to excess readmission rates.

This was calculated as the case-mix adjusted predicted readmissions divided by the case-mix adjusted expected readmissions. The excess readmission ratio was calculated for both the unplanned readmission metric and the surgical readmission metric. Facilities with excess readmission ratios less than 1.00 were assumed to have no CMS payment penalty. Facilities with excess readmission ratios between 1.00 and 1.03 were assumed to have CMS payment penalties equivalent to the excess readmission ratio (eg, 1.021 = 2.1%).

Penalties were capped at 3% for an excess readmission ratio of 1.03 or greater. The potential change in penalties experienced by each facility was determined by comparing the estimated penalty for the hospital when

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the current unplanned readmission metric was used and the estimated penalty for the hospital when the surgical-quality readmission metric was used.

Statistical Analysis All analyses were stratified by surgical procedure type because surgical procedures vary in indication, predictors, and outcomes. All patient and operative characteristics were aggregated to the facility level for each procedure. Years during which facilities had fewer than 25 inpatient surgical procedures were excluded from the analysis, consistent with CMS methods for assessing hospital quality.4 Unadjusted outcomes were calculated as aggregated hospital-level rates of readmission, surgical readmission, and postdischarge complications for each procedure. Case-mix adjusted outcomes were then determined using mixed-effects logistic regression with random intercepts for the hospitals and fixed effects for patient and operative characteristics.

Spearman rank correlation coefficients (ρ) were used to examine associations among the case-mix adjusted metrics. Bootstrapping with 1000 repetitions was used to calculate 95% CIs for the Spearman rank correlation coefficients. Two-sided tests were used throughout all analyses, and an α level of .05 determined statistical significance. Data management was conducted in SAS, version 9.4 (SAS Institute Inc). All statistical analyses and plots were constructed in R, version 3.4.1 (The R Foundation). Mixed models were output using the glmer function in the lme4 package, and plots were constructed using the ggplot2 package.12,13 Data analyses were conducted from October 1, 2017, to January 24, 2019.

Results The final analytic sample consisted of 103 VHA hospitals accruing patients across a 7-year study period.

Thirty-one hospitals were excluded because of low procedure volume across all study years (ie, <25 cases per year). The study population included 109 258 patients who underwent inpatient surgery at these 103 hospitals. Most patients were male (94.1%), white (78.2%), and older (mean [SD] age, 64.0 [10.0] years) individuals. The most prevalent procedures occurred in the orthopedic and general surgery specialties: knee arthroplasty, hip replacement, colorectal resection, 36

and inpatient cholecystectomy. Orthopedic procedures were most prevalent across the 7-year study period with means (SDs) of 477.4 (237.0) knee arthroplasties per hospital and 255.2 (112.1) hip replacements per hospital. There was a mean (SD) of 234.4 (95.9) colorectal resections per hospital and a mean (SD) of 146.0 (66.5) inpatient cholecystectomy procedures per hospital (Table 1).

As shown in Table 1, patient characteristics, comorbidities, and operative characteristics varied not only by procedure type but also within specialty (orthopedic vs general). Patients who underwent colorectal resection were older and had longer operative times and higher work RVUs. By contrast, those who underwent inpatient cholecystectomy were younger with lower work RVU procedures but were more commonly identified as emergent procedures (13.0%) within VASQIP. General surgery procedures (colorectal resection and cholecystectomy) were more likely to be performed on sicker patients as assessed

by American Society of Anesthesiologist classification (classification >3: colorectal resection 11.4% and cholecystectomy 10.0% vs knee arthroplasty 2.6% and hip replacement 5.7%).

Overlap of Readmission Metrics With Postdischarge Complication Rates Although the number of readmissions categorized as surgical quality–associated was always smaller than the number categorized as unplanned, this difference varied by procedure, with small differences for knee arthroplasty (4.6% [95% CI, 4.5%-4.8%] vs 5.0% [95% CI, 4.9%-5.2%]), hip replacement (5.3% [95% CI, 5.2%-5.5%] vs 5.4% [95% CI, 5.3%-5.5%]), and colorectal resection (11.1% [95% CI, 10.9%-11.3%] vs 13.1% [95% CI, 12.8%-13.4%]) but larger differences for cholecystectomy procedures (6.0% [95% CI, 5.9%-6.0%] vs 9.7% [95% CI, 9.6%-9.7%]) (Table 2).

There was substantial overlap between the two definitions. More than two-thirds of unplanned readmissions (71.6%) following surgery were also categorized as surgical quality–associated by our definition, whereas 84.5% of surgical quality–associated readmissions were also categorized as unplanned.

The overall postdischarge complication rate was 3.7%. Most postdischarge complications identified

by VASQIP were wound associated (29.6% superfi-

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Table 1. Characteristics of 109 258 Patients Undergoing the 4 Most Common Inpatient Surgical Procedures

Abbreviations: ASA, American Society of Anesthesiologists; MI,myocardial infarction; PVD, peripheral vascular disease; RVU, relative value unit.

Table 2. Variation in Outcomes by Surgical Procedure Type Across 103 Facilities

Case-mix adjusted outcomes were determined using mixed-effects logistic regression models with random intercepts for the hospitals, and with patient and operative characteristics as the independent variables. Models were adjusted for patient age, sex, race/ethnicity, functional status, American Society of Anesthesiologist classification, history of acute myocardial infarction, history of peripheral vascular disease, recent diagnosis of depression, current diabetes diagnosis, operative time, work relative value unit, and emergent surgery status. www.aamcn.org | Vol. 6, No. 2 | Journal of Managed Care Nursing 37

cial infections, 12.4% wound dehiscence, 8.1% deep wound infection, and 9.2% organ/space surgical site infections). Postdischarge complication rates varied widely across procedure types, from a mean (95% CI) of 1.8% (1.6%-2.0%) for knee arthroplasty to 7.9% (7.2%-8.6%) for colorectal resection (Table 2). Casemix adjustment decreased the within-procedure variation as shown by the smaller confidence intervals, but variation across surgical procedure types remained, highlighting the importance of stratified analyses.

Figure 1 displays the overlap between the readmission metrics and postdischarge complication rates after case-mix adjustment. Overall, both the unplanned and surgical readmission metrics were correlated with 14-day postdischarge complication rates (surgical: ρ = 0.40; 95% CI, 0.23-0.56; unplanned: ρ = 0.35; 95% CI, 0.17-0.53). The new surgical readmission metric was significantly correlated with 14-day postdischarge complication rates for all procedures, with ρ coefficients ranging from 0.33 (95% CI, 0.13-0.51) for cholecystectomy to 0.52 (95% CI, 0.38-0.68) for colorectal resection. The correlation between the surgical-quality readmission metric and 14-day postdischarge complication rates was consistently higher than the correlation between the current unplanned readmission metric and 14-day postdischarge complication rates for all types of procedures (knee arthroplasty: 0.50 vs 0.48; hip replacement: 0.44 vs 0.18; colorectal resection: 0.52 vs 0.42; and cholecystectomy: 0.33 vs 0.10). This was particularly notable for hip replaceFigure 1. Magnitude of Correlation Between Readmission Metrics and 14-Day Postdischarge Complications by Procedure Type

Data points represent mean; error bars, 95%CIs. 38

ment procedures and cholecystectomy procedures, in which 14-day postdischarge complication rates were not significantly associated with the current unplanned readmission metric (hip replacement: ρ = 0.18; 95% CI, −0.03 to 0.40; P = .08 and cholecystectomy: ρ = 0.10; 95% CI, −0.09 to 0.29; P = .33) (Figure 1).

We also observed significant correlations between both readmission metrics for knee arthroplasty (ρ = 0.77; 95% CI, 0.69-0.87) and colorectal resection (ρ = 0.82; 95% CI, 0.75-0.90) and a significant correlation between these metrics for hip replacement (ρ = 0.39; 95% CI, 0.22-0.58). For cholecystectomy procedures, there was no significant correlation between the two readmission metrics (ρ = 0.17; 95% CI, −0.04 to 0.39; P = .09). For every procedure, postdischarge complications were significantly correlated with surgicalquality readmission rates (Figure 1).

Changes to Hospital Payment Penalties Figure 2 displays scatterplots of the final HRRP excess readmission ratios for each readmission quality metric.

These were examined to understand the distribution and variation of excess readmission rates across all hospitals. There was large variation across hospitals in the ratio of excess unplanned readmissions for both knee arthroplasty and colorectal resection. In addition, few facilities had excess surgical readmission ratios more than 2 SDs outside the excess unplanned readmission ratio distribution for these procedures (4.2% for knee arthroplasty and 0.0% for colorectal resection, Figure 2). By contrast, there was very little variation across facilities in excess unplanned readmissions for hip replacement procedures but large variation in the excess surgical readmission ratio. The estimated excess readmission ratios for hip replacement procedures differed by more than 2 SDs in 28.4% of facilities. Trends among cholecystectomy procedures were similar to those among hip replacement procedures except for smaller variation in excess surgical readmissions (Figure 2).

Payment penalties experienced by facilities varied by the readmission quality metric used (Table 3).

Changes were more frequent for cholecystectomy procedures (65.0%) compared with knee arthro- plasty procedures (26.0%), hip replacement procedures (28.4%), and colorectal resections (18.4%). Among cholecystectomy procedures, 30.1% of hospitals

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Figure 2. Scatterplots for Adjusted Facility-Level Observed/Expected Readmission Ratios

Orange line indicates perfect correlation; black lines, agreement to within 1 SD; blue lines, agreement to within 2 SDs.

Table 3. Changes to Hospital Readmissions Reduction Program Penalties If the Surgical Qualityâ&#x20AC;&#x201C;Associated Readmissions Metric Is Used in Lieu of the Current Unplanned Readmission QualityMetric

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would see a decreased penalty if the surgical readmission metric were used in lieu of the current unplanned readmission metric, and 34.9% would see an increased or added penalty. Twenty facilities (39.1%) with no cholecystectomy-associated penalty under the current readmission metric would be penalized if the surgical readmission metric were used. Among the 52 facilities that would be penalized for excess unplanned readmission rates following chole- cystectomy, 31 facilities (59.6%) would see a reduction in the penalty amount if the surgical readmission metric were used in lieu of the current unplanned readmission metric.

Discussion The current unplanned readmission metric used by the HRRP was developed for a medical population but is being applied to a surgical population. Although providing some indication of the overall quality of inpatient care, the existing unplanned readmission metric overestimates the contribution of readmissions to surgical quality. Our proposed surgical quality–associated readmission metric was correlated with postdischarge surgical complications in all procedures. The correlation was higher than the correlation between the current readmission metric and postdischarge complications for all procedures assessed. This finding indicates that our new surgical readmission metric may be a better measure of surgical quality than the current readmission metric. This study highlights some of the issues with using the existing readmission metric to gauge the quality of surgical care.

Consistent with prior research,14,15 we found variation in surgical readmissions across procedures types, within surgical specialties, and across hospitals, further stressing the importance of accurate case-mix adjustment and procedure-specific models. Our findings of an unplanned readmission rate of 5.0% for knee arthroscopy and 5.4% for hip replacement match the 2014 CMS HRRP–reported unplanned readmission rate for knee arthroscopy or hip replacement (5.3%).16 Although no studies to our knowledge have examined the overlap of the current unplanned readmission metric with surgical quality, a few studies have attempted to estimate the number of unplanned readmissions that are potentially associated with the index surgical procedure.1,17-21 We found that 71.6% of unplanned readmissions were associated with the index surgical 40

procedure. Rosen and colleagues estimated a smaller number (ie, 42%) that were deemed “clinically related.”17 Another recent study in an English population found that only 53% of unplanned readmissions were “surgical readmissions” associated with the index hip replacement or knee arthroscopy.22 Compared with that for current research, our surgical readmission metric is more conservative, classifying more readmissions as surgical quality–associated.

This brings to light an important limitation of the current research: the lack of a consistent definition of surgical readmission. To accurately assess and improve the quality of surgical care, surgical quality metrics should be appropriately designed. There is little guidance on how to define a surgical quality–associated readmission.23 The previous Delphi panel identified nonquality readmission reasons, such as mental health diagnoses, cardiac arrhythmias, peripheral vascular disease, and hematuria.9 Despite this rigorous method, we still overestimate the proportion of surgical readmissions compared with other studies.17,18,22 Designing a surgical quality–associated readmission metric is necessary before additional surgical procedures are targeted by the CMS HRRP. Without a readmission metric tailored to surgical quality, we are only measuring the same outcome in different settings. This contributes to the increasing burden of quality metric assessment experienced by our health care professionals without shedding light on potential areas for improvement in services.24 The current definition of any unplanned readmission following surgery may also be contributing to our inability to accurately predict readmissions after surgery.

As with all predictive models, outcome specification is extremely important. Consistent with the HRRP hospital quality metric, surgical readmission studies typically identify any unplanned readmission after discharge from surgery as a surgical readmission.25 This is not the case, as our results and other studies show.

Before building accurate models to predict surgical readmissions, it is important that we correctly identify the subset of readmissions that are associated with the quality of care delivered during the index surgery (ie, “surgical quality–associated readmissions”). As our findings suggest, the effect of this definition may vary across surgical procedure types, necessitating procedure-specific methods.

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Limitations As with all studies in the VHA, we are limited in generalizability outside of the veteran population owing to the unique population characteristics. Our definition of readmission is also limited to readmissions to VHA hospitals. We suspect that we are missing approximately 10% to 15% of readmissions occurring in this cohort when the patient is readmitted to a non-VHA hospital.26 It is also important to keep in mind that we have limited our sample of eligible surgical procedures to inpatient procedures with a length of stay greater than 2 days and in which the patient was discharged alive. Although cholecystectomy procedures and common bile duct explorations were the fourth most frequent procedure in our cohort, these procedures are often performed with a length of stay shorter than 2 days; thus, the results for cholecystectomies suggest that those performed in our sample likely represented a more complicated procedure or sicker patients than is typical of cholecystectomies.

Although not a limitation, it is important to keep in mind that our definition of surgical qualityâ&#x20AC;&#x201C; associated readmission is the result of a consensus-building process and still requires further development.9 To ensure the most accurate definition, we convened a group of 14 medical and surgical professionals that our study team identified as experts in surgical readmissions.

The resulting readmission reasons determined to be likely associated or directly associated with the quality of surgical care are provided in eTable 1 in the Supplement. The top 10 reasons for readmissions that were not associated with surgical quality in 4 procedure samples are presented in eTable 2 in the Supplement.

Although we used rigorous qualitative methods to develop this definition, further work may refine it.

Conclusions Postdischarge complication rates were more highly correlated with the new surgical-quality readmission metric than with the current readmission metric for all procedures assessed. These findings suggest that the currently used readmission metric may not be an appropriate measure of the quality of surgical care for these procedures, two of which are already being assessed by the CMS HRRP. These findings have implications for the design, use, and interpretation of read-

mission- targeted quality metrics when assessing the quality of surgical care. They represent the next steps in appropriately measuring surgical quality in our mission to provide value-based care. Future research on surgical readmissions should focus on defining readmissions following discharge from surgery in association with the surgery or the episode of hospitalization associated with the surgery.

This article first appeared in Health Policy, JAMA Network and is republished here under a Creative Commons license. https://jamanetwork.com/journals/ jamanetworkopen/fullarticle/2730777 REFERENCES 1. Sacks GD, Dawes AJ, Russell MM, et al. Evaluation of hospital readmissions in surgical patients: do administrative data tell the real story? JAMA Surg. 2014;149(8):759-764. doi:10.1001/jamasurg.2014.18 2. Hicks CW, Bronsert M, Hammermeister KE, et al.

Operative variables are better predictors of postdischarge infections and unplanned readmissions in vascular surgery patients than patient characteristics. J Vasc Surg. 2017; 65(4):1130-1141.e9. doi:10.1016/j.jvs.2016.10.086 3. Glebova NO, Bronsert M, Hammermeister KE, et al. Drivers of readmissions in vascular surgery patients. J Vasc Surg. 2016;64(1):185-194.e3. doi:10.1016/j.jvs.2016.02.024 4. Centers for Medicare & Medicaid Services.

All-cause hospital-wide measure updates and specifications report: hospital-level 30-day riskstandardized readmission measure, version 6.0.

2017. https://www.cms.gov/Medicare/ Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/Measure-Methodology.html. Accessed February 25, 2019.

5. Parina RP, Chang DC, Rose JA, Talamini MA. Is a low readmission rate indicative of a good hospital? J Am Coll Surg. 2015;220(2):169-176. doi:10.1016/j.jamcollsurg.2014.10.020 6. Stefan MS, Pekow PS, Nsa W, et al. Hospital performance measures and 30-day readmission rates. J Gen Intern Med. 2013;28(3):377-385. doi:10.1007/s11606-012-2229-8 7. Morris MS, Graham LA, Richman JS, et al.

Postoperative 30-day readmission: time to fo-

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cus on what happens outside the hospital.

Ann Surg. 2016;264(4):621-631. doi:10.1097/ SLA.0000000000001855 8. Copeland LA, Graham LA, Richman JS, et al. A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology. BMC Health Serv Res. 2017;17(1):198. doi:10.1186/s12913-017-2134-2 9. Mull HJ, Graham LA, Morris MS, et al. Association of postoperative readmissions with surgical quality using a Delphi consensus process to identify relevant diagnosis codes. JAMA Surg. 2018;153(8):728-737. doi:10.1001/ jamasurg.2018.0592 10. National Surgery Office, Veterans Health Administration. Annual Surgery Report: 2016. Washington, DC: Veterans Health Administration; 2016.

11. Centers for Medicare & Medicaid Services. Hospital Readmissions Reduction Program (HRRP). https://www.cms. gov/Medicare/Medicare-Feefor-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html. Accessed October 17, 2017.

12. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67 (1):1-48. doi:10.18637/jss.v067. i01 13. Wickham H. GGPLOT2: Elegant Graphics for Data Analysis. New York, NY: Springer; 2009. doi:10.1007/978-0- 387-98141-3 14. Wahl TS, Hawn MT. How to predict 30-day readmission. Adv Surg. 2018;52(1):101-111. doi:10.1016/j.yasu.2018. 03.015 15. Gani F, Lucas DJ, Kim Y, Schneider EB, Pawlik TM. Understanding variation in 30-day surgical readmission in the era of accountable care: effect of the patient, surgeon, and surgical subspecialties. JAMA Surg. 2015;150(11):1042-1049. doi:10.1001/jamasurg.2015.2215 16. QualityNet. Measure methodology reports: readmission measures. https://www.qualitynet.org/dcs/ ContentServer?cid=1219069855841&pagenam e=QnetPublic%2FPage%2FQnetTier4&c=Page.

Published 2018. Accessed March 1, 2019.

17. Rosen AK, Chen Q, Shin MH, et al. Medical and surgical readmissions in the Veterans Health Administration: what proportion are related to the index hospitalization? Med Care. 2014;52(3):243249. doi:10.1097/MLR. 0000000000000081 42

18. Weinberg DS, Kraay MJ, Fitzgerald SJ, Sidagam V, Wera GD. Are readmissions after THA preventable? Clin Orthop Relat Res. 2017;475(5):14141423. doi:10.1007/s11999-016-5156-x 19. Pollock FH, Bethea A, Samanta D, Modak A, Maurer JP, Chumbe JT. Readmission within 30 days of discharge after hip fracture care. Orthopedics. 2015;38(1):e7-e13. doi:10.3928/0147744720150105-53 20. Fu MC, Young K, Cody E, Schairer WW, Demetracopoulos CA, Ellis SJ. Most readmissions following ankle fracture surgery are unrelated to surgical site issues: an analysis of 5056 cases. Foot & Ankle Orthopaedics. 2017;2 (2):247301141769525. doi:10.1177/2473011417695254 21. Dawes AJ, Sacks GD, Russell MM, et al. Preventable readmissions to surgical services: lessons learned and targets for improvement. J Am Coll Surg. 2014;219(3):382-389. doi:10.1016/j.jamcollsurg.2014.03.046 22. Bottle A, Loeffler MD, Aylin P, Ali AM. Comparison of 3 types of readmission rates for measuring hospital and surgeon performance after primary total hip and knee arthroplasty. J Arthroplasty. 2018;33(7):2014-2019.e2. doi: 10.1016/j. arth.2018.02.064 23. Khuri SF, Daley J, Henderson WG. The comparative assessment and improvement of quality of surgical care in the Department of Veterans Affairs.

Arch Surg. 2002;137(1):20-27. doi:10.1001/archsurg.137.1.20 24. Casalino LP, Gans D, Weber R, et al. US physician practices spend more than $15.4 billion annually to report quality measures. Health Aff (Millwood). 2016;35(3):401-406. doi:10.1377/ hlthaff.2015.1258 25. Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice.

BMJ. 2009;338:b606. doi:10.1136/bmj.b606 26. Oâ&#x20AC;&#x2122;Brien WJ, Chen Q, Mull HJ, et al. What is the value of adding Medicare data in estimating VA hospital readmission rates? Health Serv Res. 2015;50(1):40-57. doi:10.1111/1475-6773.12207 ARTICLE INFORMATION Published: April 19, 2019. doi:10.1001/jamanetworkopen.2019.1313

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Open Access: This is an open access article distributed under the terms of the CC-BY License. Š 2019 Graham LA et al. JAMA Network Open.

Corresponding Author: Mary T. Hawn, MD, MPH, Department of Surgery, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305 (mhawn@stanford.edu).

Author Affiliations: Health Services Research and Development Unit, Birmingham VA Medical Center, Birmingham, Alabama (Graham, Morris, Richman); Department of Surgery, University of Alabama at Birmingham, Birmingham (Graham, Morris, Richman); Center for Healthcare Organization and Implementation Research, Boston VA Healthcare System, Boston, Massachusetts (Mull, Rosen, Itani); Department of Surgery, Boston University School of Medicine, Boston, Massachusetts (Mull, Rosen, Itani); Veterans Affairs, Palo Alto, Veterans Affairs Medical Center, Palo Alto, California (Wagner, Hawn); Department of Surgery, Stanford University School of Medicine, Palo Alto, California (Wagner, Hawn); Milwaukee Veterans Affairs Medical Center, Milwaukee, Wisconsin (Whittle, Burns); Department of Surgery, Medical College of Wisconsin, Milwaukee (Whittle, Burns); Veterans Affairs Central Western Massachusetts Healthcare System, Leeds (Copeland); University of Massachusetts Medical School, Worcester (Copeland); Harvard University School of Medicine, Boston, Massachusetts (Itani).

cal Center during the conduct of the study. Dr Richman reported receiving grants from the Birmingham VA Medical Center during the conduct of the study. Dr Copeland reported receiving grants from Veterans Health Administration during the conduct of the study and grants from Mallinckrodt Pharmaceuticals outside the submitted work. No other disclosures were reported.

Funding/Support: This project was supported by funding from the US Department of Veterans Affairs Health Services Research & Development (IIR 12-358).

Role of the Funder/Sponsor: The funding source 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 the decision to submit the manuscript for publication.

Supplements: Available online: eTable 1. ICD-9 and ICD-10 Codes for Surgical Quality-Related Readmissions; eTable 2. Top 10 Primary Diagnosis Codes for Non-Surgery Related Readmissions; https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2730777

Author Contributions: Drs Graham and Hawn had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Graham, Mull, Morris, Rosen, Burns, Itani, Hawn.

Acquisition, analysis, or interpretation of data: Graham, Mull, Wagner, Morris, Richman, Whittle, Burns, Copeland, Itani, Hawn.

Drafting of the manuscript: Graham, Rosen.

Critical revision of the manuscript for important intellectual content: Graham, Mull, Wagner, Morris, Richman, Whittle, Burns, Copeland, Itani, Hawn.

Statistical analysis: Graham, Mull, Rosen.

Obtained funding: Graham, Rosen, Hawn.

Administrative, technical, or material support: Graham, Wagner, Whittle, Burns, Itani.

Supervision: Morris, Burns, Itani, Hawn.

Conflict of Interest Disclosures: Dr Wagner reported receiving grants from the US Department of Veterans Affairs during the conduct of the study and grants from the National Institutes of Health, the US Department of Veterans Affairs, and Centers for Disease Control and Prevention outside the submitted work. Dr Morris reported receiving grants from the Birmingham VA Mediwww.aamcn.org | Vol. 6, No. 2 | Journal of Managed Care Nursing

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Managed Care UPDATES

CMS Modernizes Care for Frail, Elderly Individuals Enrolled in PACE Programs of All-Inclusive Care for the Elderly (PACE) Final Rule will provide seamless, customized care to meet individual patients’ needs. The Centers for Medicare & Medicaid Services (CMS) finalized a rule today to update and modernize requirements for the Programs of All-Inclusive Care for the Elderly (PACE). The PACE program provides comprehensive medical and social services to certain frail, elderly individuals who qualify for nursing home care but, at the time of enrollment, can still live safely in the community. The policies finalized in this rule reflect the latest standards in caring for PACE participants – many of whom are “dually eligible” for both Medicare and Medicaid – and will strengthen patient protections, improve care coordination, and provide administrative flexibilities and regulatory relief for PACE organizations. Read more at https://go.cms. gov/2HINH8t CMS Announces New Opportunities to Test Innovative Integrated Care Models for Dually Eligible Individuals Letter to State Medicaid Directors invites states to partner with CMS to improve outcomes for those dually eligible for Medicare and Medicaid. Today, the Centers for Medicare & Medicaid Services (CMS) sent a letter to State Medicaid Directors inviting states to partner with CMS to test innovative approaches to better serve those who are dually eligible for Medicare and Medicaid. Many of the 12 million dually eligible beneficiaries have complex healthcare issues, including multiple chronic conditions, and often have socioeconomic risk factors that can lead to poor outcomes. CMS and states spend over $300 billion per year on the care of dually eligible individuals, yet still do not achieve acceptable health outcomes. Today’s letter opens new ways to address those complex needs, align incentives, encourage marketplace innovation through the private sector, lower costs, and reduce administrative burdens for dually eligible individuals and the providers who serve them.

Read more at https://go.cms.gov/2HINH8t CMS Takes Action to Lower Prescription Drug Prices and Increase Transparency Final rule modernizes the Medicare Advantage & Medicare Part D programs. On May 16, 2019, the Trump Administration finalized improvements to Medicare Advantage and Medicare Part D, which provide seniors with medical and prescription drug coverage through competing private insurance plans. These changes will ensure that patients have greater transparency into the cost of prescription drugs, so patients can compare options and demand value from pharmaceutical companies.

“The improvements we are making to Medicare Advantage and Medicare Part D deliver on the promises in the President’s blueprint to provide more negotiating tools and more transparency for patients,” said HHS Secretary Alex Azar. “They are significant steps toward a Medicare program, a drug pricing marketplace, and a healthcare system where the patient is at the center and in control.” Read more at https://go.cms.gov/2WnwUkm 44

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CMS Advances Agenda to Re-think Rural Health and Unleash Medical Innovation Proposed changes to Inpatient Prospective Payment System (IPPS) and Long-Term Care Hospital (LTCH) Prospective Payment System would improve quality, expand access. On April 23, 2019, the Trump Administration proposed changes that build on the progress made over the last two years and further the agency’s priority to transform the healthcare delivery system through competition and innovation while providing patients with better value and results. The proposed rule would update Medicare payment policies for hospitals under the Inpatient Prospective Payment System (IPPS) and the Long-Term Care Hospital (LTCH) Prospective Payment System (PPS) for fiscal year 2020 and advances two key CMS priorities, “Rethinking Rural Health” and “Unleashing Innovation,” by proposing historic changes to the way Medicare pays hospitals.

Read more at https://go.cms.gov/2DsBcvi FDA approves innovative gene therapy to treat pediatric patients with spinal muscular atrophy, a rare disease and leading genetic cause of infant mortality The U.S. Food and Drug Administration approved Zolgensma (onasemnogene abeparvovec-xioi) on May 24, 2019, the first gene therapy approved to treat children less than two years of age with spinal muscular atrophy (SMA), the most severe form of SMA and a leading genetic cause of infant mortality.

“Today’s approval marks another milestone in the transformational power of gene and cell therapies to treat a wide range of diseases,” said Acting FDA Commissioner Ned Sharpless, M.D. “With each new approval, we see this exciting area of science continue to move beyond the concept phase into reality. The potential for gene therapy products to change the lives of those patients who may have faced a terminal condition, or worse, death, provides hope for the future. The FDA will continue to support the progress in this field by helping to expedite the development of products for unmet medical needs through the use of review pathways designed to advance innovative, safe and effective treatment options.” Read more at https://bit.ly/2VN6hQs FDA approves new treatment for osteoporosis in postmenopausal women at high risk of fracture The U.S. Food and Drug Administration today approved Evenity (romosozumab-aqqg) to treat osteoporosis in postmenopausal women at high risk of breaking a bone (fracture). These are women with a history of osteoporotic fracture or multiple risk factors for fracture, or those who have failed or are intolerant to other osteoporosis therapies.

More than 10 million people in the U.S. have osteoporosis, which is most common in women who have gone through menopause. People with osteoporosis have weakened bones that are more likely to fracture. Read more at https://bit.ly/2Ho01e9 UHS partners with primary-care firm to serve plan enrollees Universal Health Services has become the latest health system to partner with a primary-care specialty company to provide intensive primary care services to its health plan enrollees. The company announced May 29, 2019 that it signed one of its first value-based payment deals with Seattle-based Vera Whole Health to operate two new primary care centers in Reno and Carson City, Nevada, serving members of UHS’ 100,000-member Prominence Health Plan. Prominence serves both Medicare Advantage and commercial members. Vera’s centers in Reno are already up and running. Read more at https://bit.ly/2WoeqzU

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

Lisa D Achim, RN, CCM, CMCN Deanna Adams, RN, CMCN Sandra E Allen, RN, CMCN Ransford Asamoah, RN, CMCN Jorge A Balladares, RN, CMCN Russell Bayer, LPN, CMCN Donna L Belew, MSN, RN, CMCN Pamela Beley-Shaw, RN, CMCN Richard Ryan A Bias, LVN, CMCN Lynne Blount, RN, CMCN Lori-Ann Bowling, RN, CMCN Crystal R Burise, LPN, CMCN Alexandra J Burnett, LVN, CMCN Felicia D Carbin-Brown, LPN, CMCN Eileen M Carroll, RN, CMCN Diana R. Charlton, RN, CMCN Erin Chow, RN, CMCN Keisha Cochrane, LPN, CMCN Leslie A Contino, RN, CMCN Lora Cooper, RN, CMCN Gabriela J Cortez-Trujillo, LVN, CMCN Karen M Daniels, RN, CMCN Robin Dawson, RN, CMCN Tapinder Dhillon, RN, BSN, CMCN Angela Donovan, BSN, RN, CMCN Kristen Downs, RN, CMCN Marisela Duran-Verdugo, RN, BSN, CMCN Deborah E Edwards, RN, CMCN Majiney T Eulingbourgh, LVN, CMCN Julie Flaim, RN, CMCN Ana L Flores, LVN, CMCN Jean Franklin, RN, CMCN Candy W Genco, RN, CMCN Rachel Gomez, RN, CMCN Jacqueline V Gonzalez, RN, CMCN Heather R Gordy, RN, BSN, CMCN Pamela Goring, RN, CMCN Karen Denise Gregory, BSN, RN, CMCN Kimberly H Hannan, RN, CMCN Rhonda Hartman, RN, CMCN Rolanda Haycox, RN, CMCN Kayla Hemling, BSN, RN, CMCN Veronica Hernandez, RN, CMCN Susan A Hicks, RN, CMCN 46

Jill M Hoffmann, RN, CMCN Chaka M Holloway, LVN, CMCN Heather Noel Holman, RN, BSN, CMCN Alissa N Hughes, BSW, CMCP Melissa E Jenna, RN, CMCN Sydell Jolly, RN, CMCN Wendy Jordan, RN, CMCN Angela N Kennedy, LPN, CMCN Emily N Killian, RN, CMCN Desiree Lynn Kimball, RN, CMCN Steven King, LPN, CMCN Kenya P Long, LPN, CMCN Kim Mahoney, RN, CMCN Melania M Marin, LVN, CMCN Ashpley C Matthews, RN, CMCN Susan K McDonald, LPN, MHP, HIA, MMA, HCAFA, PAHM, CMCN Laurianne McHenry, RN, CMCN Madelyn Miller-Thomas, RN, CMCN Sol T. Molen, RN, ASN, CMCN Julie Morgan, RN, CMCN Jennifer A Mount, RN, CMCN Dana Myers, RN, CMCN Maricela Obeso, RN, CMCN Jocelyn E Ong, LVN, CMCN Tayna W Ortego, LPN, CMCN Victoria Pate, BSN, RN, CPN, CMCN Julie A Pepperney, BSN, RN. CMCN Gretchen Pinlac, RN, CMCN Kimberly A Raiford, RN, BSEd, CMCN Jani Ratcliffe, RN, CMCN Tiffany D Recinos, RN, CMCN Teresa M. Riley, CCM, BC, RN, CMCN Kristina N Robinson, RN, CMCN Susan M Rose, RN, BSN, CMCN Melissa Jayne Rumple, RN, CMCN Trinidad E Russell, RN, BSN, CMCN Grace B Salceda, LVN, CMCN Mark Sambilay, RN, CMCN Kristal S. Santana, LPN, CMCN Kaila K Sawatzky, RN, CMCN Jennifer A Shidler, BSN, RN, CMCN Tiffany Smidley, RN, CMCN (List continued on next page)

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Tania Fabiloa Soberanes, LVN, CMCN Kim Solano, LPN, CMCN Elizabeth A.C. Speanburg, RN, COHN, CHC, CMCN Amanda Stachlewitz, RN, BSN, DNC, CMCN Whitney Sylva, RN, CMCN Nathalie A Torres, LVN, CMCN

Andrea Travis-Rinehart, RN, BSN, CMCN Karen B Udenze, RN, CMCN Belia Villegas, RN, CMCN Gabrielle BJ Wilson, LPN, CMCN Jacquelyn M Woods, BS, MBA, RN, CMCN Lori Yuh, RN, CMCN

Welcome New AAMCN Members!

Patricia Abania Akua Aboagye, RN Kristi Adams Bernadette Adkins Joyce Al-Islam, RN Leonor Alvarado, RN Jennifer Asamoah Sylvia Asante, RN Julie Atkinson Therea Avery, RN Chavon Bailey, RN Russell Bayer, LPN, CMCN Maxine Bingham, RN Lynne Blount, RN, CMCN Wendy Boggs, LN Jo Ann Bosco Camille Bourque Marisa Bradbury Elaina Branson Kimberly Brookshear, RN Denise Broomer Christina Brown, RN Gina Browne Jennifer Bucher, RN Diane Butler, RN Randi Butterfield, RN Lori Calio Twyla Camm Fiona Cayer Odania Charles Angela Chen Tracy Christian Cathy Christian-Simmons Stephanie Christy Kathleen Connor, RN Nora Cossett

Tonya Countryman Kerri Craig Bonnie Crowder Maria Nenita Dalmacio Victoria Daughtry, RN Tanya Daurbigny Lorriane Dawkins Shelley Salazar Decker Margaret Dewar Liz Dragolovich Theresa Dunbar-Reid Kathleen Dwyer LeeAnn Eddy, RN Robert Eichorst, RN Katherine Eisan-Ramsey, LPN Cecilia Ekberg Ashley Ellexson, RN Laurie Ellis Theresa Ettienne, RN Sharon Everson, RN Tajudeen Fadairo Melissa Fernandez, LPN Fonda, RN Ferrari Kristi Fleenor Leah Fordham, RN Hilary Fordred Lanita Fortenberry, RN Mirlande Frederic-St.Hilaire Kimberly Gabbard Cara Gallagher, RN Felicia Ganther Tammy Gardner Diana Garza, RN Shirlene Gines, RN Nina Glover, RN Brigham Godfrey

Jennie Golden-Wear, RN Heidi Gonzales Heather Gordy Pamela Goring, RN, CMCN Michelle Graham (List continued on next page) Triverr Gray, RN Carmen Greene Marla Griffith, RN Dawn Grogan Jennifer Gross Carol Hackman, RN Melissa Haddix, RN Mary Hamberger Patricia Hawes Laurie Hawks, RN Christinia Hernandez, RN Tiffany Hinkle, RN Pamela Holladay Chaka Holloway, LVN, CMCN Sharon Holness Wendy Horn, RN Shiela Howard, RN Susan Huber Sandra Huber, RN Antonia Ibarra Namen Iyamu Erin James Ruth Johnson Cynthia Yvonne Johnson Kim Johnson Jaime Jones, RN Wendy Jordan, RN, CMCN Stephanie Karner Laine Kennedy Diane Kilmartin, LPN

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Soonjung Kim, RN Desiree Kimball, RN, CMCN Joan Konzak, RN Mary Ann Kovalchin, RN Bruce Larsen Jody Latimer, RN Ester Lian Lazaro-Mascardo Kimberley Lee, RN Angel Lemoine Bobbie Lewis, RN Melissa Libes Toni LiPira, LPN Gina Locke Claudin Pierre Louis Steffanie Mack Kim Mahoney, RN, CMCN Rhiannon Manzanares, RN Nancy Marini Angelene Martin, RN Guadalupe Martinez Shawn Maselli Mary Patricia Massey Scott Mastran Julia May Gerri McCann Robin McConnell, RN Preciousness McCorvey Kimberly McDonough Michelle McGhee Beth McGovern Kemisha McMullen, RN Cheryl Meaux, RN Kimberly Miller, RN Penny Mobberley, RN, BSN Jane Morey, RN Angie Morgan Jessica MorinHiggins, RN Janet Morrison Charolette Moukouri LeAnn Munoz, RN Ketra Nas, RN Anele Nicholas Vilma Olivencia, RN Jennifer Ollivierre Krystal Orden, RN Eva Ott, LVN Killi Outhouse, RN Elizabeth Parnell Victoria Pate, RN, CMCN 48

Laura Pelletier Luchele Penka, RN Julie Pepperney, CMCN, BSN, RN Katrina Phillips Yuri Popov, RN Barbara Powe, PhD Nancy Powers-Montgomery Jacquelyn Preston-King, LPN Darletta Prillerman Lisa Purcell, RN Pamela Reid Jennifer Remold DaKeitha Rivera Regina Roberts Kathi Rodgers Mary Roller, RN Peggy Rosik Cristina Rouson Marjorie Sander Celia Sanders, RN Kalia Sawatzky, RN, CMCN Jane Schepmann, RN Melanie Schindler Kelly Schubert-Fair, RN Monica Scott, RN Karen Sellers, RN Joia Shafer, RN Sam Shalaby Shelby Shreffler Pamela Shyne Beatrice Smith Deborah Smith Sheila Smith, RN Courtney Snyder, RN Marilyn Soliven Amanda Stachlewitz, RN, CMCN Popovac Stana Tamsen Staniford Mary Sturkey Sharon Sullen, RN Linda Swearingen, RN Karen Sweet Minh Ta Samantha Tapia Corinne Thomas Rumonda Thompson Marcella Timpson Mary Trometer Judy Unekwe

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Jo Vanderpool, RN, BSN Amy Vasconcellos Crystal Vernon, LPN, CMCN Belia Villegas, RN, CMCN Heather Waggoner Jennifer Wahl Shirley Walker Teri Wallis, RN Sharlene Waylon Naydia Williams Robin Winskas, RN Kelley Wood, RN Melissa Worrell Monika Yadrnak, RN Mary Beth Yancy Karen Zeller Cheryle Zillmer, RN

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