Evaluation Of Results

Evaluation Of Results Of In-Person Vs. Virtual Vs. Combined Visits For Diabetes And Obesity Care

By: Dr. Michelle D Chaplin Haley Clark Megan Triplett Dr. Edward T. Chiyaka

BACKGROUND

Telehealth has been an option for providing healthcare for many years; however, utilization has significantly increased over the COVID-19 pandemic. Many medical practices began to include telehealth visits regularly in patient care plans. These shifted ways of operation daily, with lockdowns causing a need for appointments to be conducted virtually, either by phone call or video chat, to maintain the health and safety of patients and providers alike. According to the CDC, during the first quarter of 2020, the number of telehealth visits increased by 50 percent when compared to the same period in 2019.1 It was also noted that during the last week of March 2020, the number of telehealth visits increased by 154 percent compared to 2019.1 Over the course of the COVID-19 pandemic, changes were made to billing terminologies to allow for proper reimbursement. For example, before the pandemic, virtual visits were not reimbursed at the same level as in-person visits; however, the Centers for Medicare and Medicaid Services (CMS) recognized the critical need to shift towards virtual care when the pandemic began.2 With the integration of telehealth visits being inevitable, billing terminologies were revised to allow virtual visits and vaccine administration to be reimbursable as long as coded correctly, thus also providing vital public health data throughout the pandemic.3 With the increasing utility of telehealth appointments, patients are offered flexibility and convenience, which can be efficiently integrated into lifestyles and break down some barriers to access; however, more data is needed to support that the quality of care being provided is maintained now that pandemic lockdowns have lifted.

Emerging data from the COVID-19 pandemic has shown that patients with diabetes or obesity are more likely to experience complications from SARS-CoV-2 infection. One study showed that 72 percent of patients with comorbidities, including diabetes, required admission to the intensive care unit (ICU). In contrast, only 37 percent of patients without comorbidities required ICU admission.4 Results from another study looking at obese patients under the age of 60 in the United States showed patients with a body mass index (BMI) of 30-35 kg/m2 and a BMI greater than 35 kg/m2 were 1.8 and 3.6 times more likely to need ICU level care, respectively.5 Due to the increasing prevalence of diabetes and obesity in the American population and the increased risk generated by COVID-19, adequate disease state management is critical. Current guidelines recommend that patients diagnosed with diabetes or obesity benefit from frequent follow-up visits with their providers. The 2021 American Diabetes Association Guidelines (ADA) acknowledge that lifestyle intervention programs should be intensive, with frequent follow-ups to improve clinical and personal goals.6

Pharmacists have an increasing presence in medical practices offering diabetes and obesity education and management services. Ambulatory care pharmacists aid in chronic disease state

management, promote overall health and well-being, and provide patient education.7 There is significant evidence that pharmacists provide health benefits, as shown in an article published by Shane-McWhorter and colleagues that looked at the value pharmacists add to interprofessional teams through a study evaluating pharmacist-provided diabetes care via a telemonitoring approach.8 A significant 2 percent reduction was noted in HbA1c values from baseline to follow-up in the intervention group, while only a 0.66 percent decrease was noted in the control group. Some limitations in this study included a primarily Spanish-speaking population and timely troubleshooting of the telemonitoring technology used.8

Another study by Ross and colleagues looked specifically at weight loss in patients provided the “gold standard” of care in person compared to telehealth delivery of the same material.9 The study found that 70 percent of participants lost 5 percent or more of their baseline body weight while 26.3 percent lost 10 percent or more. Results yielded a 7.37-kilogram average loss from baseline (7.2 percent from baseline), thus falling within the margin for noninferiority (p < 0.001). A major gap still left in research after this study included generalizability to a larger population as most participants included here were middle-aged females of the non-Hispanic white race who were highly educated.9 Both studies show telehealth’s value in helping patients achieve their clinical goals. With the increasing prevalence of pharmacists in the primary care setting, pharmacists have a growing opportunity to improve patient outcomes through the telehealth platform.

Two previous studies looking at one pharmacist-provided disease state management service in an outpatient clinic showed clinically significant improvements in clinical markers relevant to diabetes and obesity when conducted in person.9,10 These studies were completed by the same pharmacist at the same clinic with a patient population similar to the one described below.

Due to the necessity of integrating telehealth visits during the COVID-19 pandemic, the primary objective of this study was to evaluate changes in A1c and BMI of patients who had appointments with a pharmacist in person, virtually, or combined since April 2020. This data will help to establish whether telehealth should be continued due to similar efficacy in treatment or identify if in-person visits should be prioritized.

METHODS

The research was approved by the Wingate University research review board and the hospital system review board. A retrospective medical chart review was conducted on all adult patients with diabetes or obesity seen by a pharmacist clinic from March 2020 through August 2021. The pharmacist clinic acquires patients through referrals from other healthcare providers based on disease state, most commonly for diabetes and obesity management. Virtual visits were required of all patients from March through June 2020 due to the COVID-19 pandemic, but subsequent months allowed individual patients to choose between in-person or virtual appointments.

Inclusion criteria included patients seen by the pharmacist clinic from March 2020 through August 2021. Exclusion criteria included patients that were referred and treated for gestational diabetes. All patients were classified according to the type of visit. Data collected included pertinent lab values, vital signs, number and type of visit, medication usage, and demographic data. HbA1c was assessed at baseline, the 3–6-month mark, and the 9–12-month mark. BMI was assessed at three months, six months, and nine months after baseline. ANOVA was used to compare the changes in HbA1c and BMI between and within groups.

RESULTS

In total, 350 patient charts were reviewed, and 316 patients were included in the review, with 34 exclusions related to gestational diabetes. The baseline characteristics are included in Table 1. Most patients were female (63 percent) and Caucasian (89 percent). All patients were classified according to the type of visit (45 in-person, 170 virtual, and 101 combined). Table 2 shows the mean scores at baseline and follow-up times for each visit type. Also included are the p-values from a paired sample t-test between the baseline and follow-up scores. All p-values less than 0.05 indicate that the difference between the baseline and follow-up scores was statistically significant. Patients who had

combined visits had the largest decrease in HbA1c (1.24 SD 1.83), and patients who had in-person visits had the least decrease (0.85, SD 1.06).

However, there was no significant difference in the mean difference between the baseline measure and the end-of-study measure across the three groups, indicating that outcomes were similar whether visits were virtual, in person, or combined. Similar patterns were observed for BMI. In addition to no significant differences between groups, there were statistically significant decreases in the averages (p<0.05) from baseline for HbA1c and BMI within groups at all time points, except BMI at the 9-month mark in the in-person group, indicating that the services were effective at improving overall health outcomes. Only the mean difference between the BMI at baseline and BMI at nine months for the in-person group did not show any statistical significance. Overall, the mean A1c scores declined over the 12 months under review. The most significant reduction was in the combined group during the first six months.

DISCUSSION

The expansion of billable services authorized for telehealth by CMS allowed extended care to patients during the COVID-19 pandemic on a temporary and emergency basis. As the country moves away from emergency status, it is important to establish the efficacy of telehealth treatment to sustain its use. search demonstrates that health outcomes for two common and costly disease states, obesity, and diabetes, could be improved irrespective of the type of visit conducted. For diabetes, the decrease in HbA1c was clinically significant as most patients would have an HbA1c goal of less than 7% per the ADA guidelines.5 Before referral, most patients were above goal, and after referral, they were below goal based on the reduction. The change in BMI was less significant, and no established change in BMI was used to demonstrate clinical significance. Based on averages, a change in BMI by 1 point indicates about 8-10 pounds lost, which can be clinically significant. The effectiveness was also not changed by the number of medication changes or chronic disease states per a subanalysis. As many patients have scheduling conflicts, transportation, or financial barriers with attending in-person visits, telehealth broadens the avenue to provide care and can improve access.

Limitations of this study include that it was done at one health system by one clinical pharmacist, so it is unclear whether results would be replicated elsewhere. The population was also mainly Caucasian and female and is not representative of all patients with obesity and diabetes. Data was also collected retrospectively from an electronic medical record and depended upon the accuracy of the information within the medical chart. While improving certain barriers, telehealth also requires using telephones and internet access, which may be an issue for patients with financial concerns or in rural areas. As previously mentioned, this was also conducted at a time when CMS approved billing for telehealth visits which may not be extended in a postCOVID environment.2

CONCLUSION

Although telehealth was not new before the COVID-19 pandemic, the significant expansion in use could lead to sustained and improved access for a variety of patients to receive high-level healthcare while eliminating some barriers. Although limited in scope, the data from this research shows that effective care related to diabetes and obesity can be achieved, and it is as effective as in-person visits. Continued research would include expanded patient population size and other disease state outcomes. For telehealth to continue to be an appropriate treatment choice, payment for providers and telephone and internet access for patients in all regions must be supported.

Authors: Michelle D Chaplin, PharmD, BCACP, CDCES, is an Associate Professor, Assistant Dean, and Clinical Pharmacist at Wingate University School of Pharmacy in Hendersonville, NC. m.chaplin@wingate.edu. Haley Clark is a 2023 PharmD candidate at Wingate University School of Pharmacy in Hendersonville, NC. Megan Triplett, BA, is a 2023 PharmD candidate at Wingate University School of Pharmacy in Hendersonville, NC. Edward T. Chiyaka, PhD, MSc, and Assistant Professor at Wingate University School of Pharmacy in Wingate, NC.

References: 1. Koonin LM, Hoots B, Tsang CA, et al.

Trends in the Use of Telehealth During the Emergence of the COVID-19 Pandemic. MMWR Morb Mortal Wkly Report. 2020;69(43):1595-1599. doi: 10.15585/ mmwr.mm6943a3 2. Fact sheet medicare telemedicine health care provider fact sheet. CMS. https:// www.cms.gov/newsroom/fact-sheets/ medicare-telemedicine-health-care-provider-fact-sheet. Accessed May 24, 2022. 3. Marwaha JS, Halamka JD, Brat GA,

Gordon WJ. Repurposing Billing and

Administrative Terminologies As

Instruments Of Public Health: Lessons from the COVID-19 Pandemic.

Health Affairs blog. October 5, 2021.

Accessed April 20, 2022. https://www. healthaffairs.org/do/10.1377/forefront.20210929.605951/full/ 4. Alberti A, Schuelter-Trevisol F, Moehlecke Iser BP, et al. Obesity in people with diabetes in COVID-19 times: Im-

portant considerations and precautions to be taken. World J Clin Cases. 2021 Jul 16;9(20):5358-5371. doi: 10.12998/ wjcc.v9.i20.5358 5. Lighter J, Phillips M, Hochman S, et al. Obesity in patients younger than 60 years is a risk factor for Covid-19 hospital admission. Clin Infect Dis. 2020;71:896-897. doi: 10.1093/cid/ciaa 6. American Diabetes Association; Standards of Medical Care in Diabetes—2022

Abridged for Primary Care Providers.

Clin Diabetes 1 January 2022; 40 (1): 10–38. https://doi.org/10.2337/cd22as01 7. Ashjian E, Dzwierzynski E, Padilla M, et al. Ambulatory Care Career Tools.

ASHP. 2017. https://www.ashp.org/-/ media/assets/pharmacy-practice/ resource-centers/ambulatory-care/ambulatory-care-career-tool.ashx. Accessed

April 20, 2022. 8. Shane-McWhorter L, McAdam-Marx

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Assoc (2003). 2015;55:516-526. doi: 10.1331/JAPhA.2015.14285 9. Ross KM, Carpenter CA, Arroyo KM, et al. Impact of transition from faceto-face to telehealth on behavioral obesity treatment during the COVID-19 pandemic. Obesity: A Research Journal. 2022;30(4):858-863. doi: 10.1002/ oby.23383 10. DeGeeter M, Taylor SR, Okarlton E, Ellex

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Table 1: Baseline characteristics for in-person, virtual, and combined visit groups (standard deviation)

In-person (n=45) Virtual (n=170) Combined Visits (n=101)

Age (years)

56.04 (13.1) 62.52 (13.94) 59.95 (13.93)

Race

Gender

White 41 (91.1) 151 (88.8) 90 (89.1) Non-White 4 (8.9) 19 (11.2) 11 (10.9)

Male 14 (31.1) 69 (40.6) 34 (33.7) Female 31 (68.9) 101 (59.4) 67 (66.3)

BMI (kg/m2)

34.25 (6.57) 34.63 (8.44) 35.86 (8.92) HgbA1c (%) 7.54 (1.91) 7.47 (1.87) 8.36 (2.11) Chronic conditions (#) 12.09 (8.4) 10.88 (7.05) 13.27 (9.58) Weight (lbs) 215.26 (49.86) 217.78 (52.55) 224.06 (64.16)

Table 2: Mean A1c and BMI scores at Baseline and Follow-up

Outcome Scores

A1c

In-person, Mean (SD)

Virtual, Mean (SD)

Combined, Mean (SD)

BMI

In-person, Mean (SD)

Virtual, Mean (SD)

Combined, Mean (SD)

Baseline 3 to 6 months 9 to 12 months

7.54 (1.91) 7.06 (1.16) diff = 1.14, p=0.0012 7.47 (1.87) 6.67 (0.92) diff=0.97 p<0.0001 8.36 (2.11) 7.17 (1.43) diff=1.29 p<0.0001

6.86 (1.12) diff=0.85, p=.0012 6.59 (1.09) diff=0.95 p<.0001 7.00 (1.84) diff=1.24 p<.0001

Baseline 3 months 6 months 9 months

34.25 (6.57) 34.37 (5.88) diff=0.97 p=0.0012 32.58 (6.74) diff=1.18 p=0.0014 30.78 (5.32) diff=0.87 p=0.0897

34.62 (8.44) 34.45 (8.91) diff=0.65 p<0.0001 33.91 (9.27) diff=0.75 p<0.0001 33.42 (7.19) diff=0.91 p=0.0028

35.86 (8.92) 34.36 (7.05) diff=0.64 p=0.0027 34.60 (7.84) diff=1.17 p=<0.0001 34. 59 (9.08) diff=0.92 p=0.0111