< PreviousA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES 30 JULY | AUGUST 2021 the value examiner Optimism. CC&O used two different tests to evaluate analysts’ forecast optimism. The first test was based on the relative accuracy score for pessimism (RAS p ), which simply measures the percentage of times that analyst forecasts are less than the actuals within each size-year group. Hence, the smaller the RAS p , the lower the pessimism, and the bigger the optimism. Since this test is only based on counts, it is robust to outlier observations. However, this test based on RAS p , does not capture the magnitude of optimism. Based on the RAS p test, both sales and profit margin display optimistic forecast bias for the three smallest size groups (groups 1, 2, and 3), but not for groups 4 and 5. Moreover, a comparison of the RAS p between sales and profit margin reveals that forecasts for sales tend to be less optimistically biased than forecasts for profit margin. For the second test, CC&O use a method that quantifies the bias by assigning a weight (K) to adjust analyst forecasts to be closer to the realizations, such that the percentage of the forecasts that is larger than the realizations is approximately 50 percent in each size-year group. Specifically, CC&O used an iterative method to estimate for each size-year group, where the goal is to minimize the absolute value of RAS p – 50, where RAS p is based on K × PM t+1 AF > PM t+1 ~50% or K × S t+1 AF > S t+1 ~50%. In the test based on K (which is used to iteratively adjust the forecasts to minimize the deviations from the realizations), the results for sales forecasts show average K ranging from 0.93 to 0.99 for size-groups 1 to 3, with a K essentially equal to 1 for the largest groups. For profit margin, the averages range from 0.65 to 0.90 for groups 1 to 3, and similar to 1 for the largest size- group. A comparison of the Ks across sales and profit margin forecasts suggests that sales forecasts are less optimistically biased than profit margin forecasts for groups 1 to 3. In short, the reported results indicate that analysts’ forecasts of sales as well as of profit margin exhibit optimistic bias in the three smallest size groups. Forecast accuracy relative to a benchmark model. CC&O argue that while current sales growth tends to continue into the future, trends in profit margin are likely to be less persistent due to profit margin’s mean reverting tendency. As a result, two different benchmark models are used for sales and profit margin. Specifically, the benchmark model for profit margin is a simple random walk model, where the realization in period t (PM t ) is used as the benchmark model forecast for period t+1 (PM t+1 BM ). In contrast, the benchmark model for sales includes a factor for growth (g t ), such that current sales (S t ) are used to forecast future sales (S t+1 BM ) as follows: S t+1 BM = S t × (1 + g t ), where CC&O rather arbitrarily define g t as a weighted average of sales growth from the peer size group and the firm’s own past sales growth (i.e., 40 percent of the most recent median sales growth in the same size-year group + 60 percent of the firm’s recent sales growth). The results are qualitatively similar if an industry growth rate is used instead. In this context, the relative accuracy score (RAS A ) for forecast accuracy counts the percentage of times an analyst forecast is more accurate than the corresponding benchmark model forecast. Specifically, it counts the instances in which the analyst forecast for sales (S t+1 AF ) is closer to actual sales than S t+1 BM or the instances in which the analyst forecast for profit margin (PM t+1 AF ) is closer to the actual profit margin than PM t+1 BM . The reported results indicate that analysts’ forecasts for sales and profit margin are more accurate than the corresponding forecasts based on the benchmark models, i.e., all the RAS A are significantly larger than 50 percent. For sales, averages of RAS A are between 52.7 percent and 68.6 percent, while for profit margin averages of RAS A are 58.7 percent to 65.4 percent. The superior accuracy of analysts’ forecasts is more pronounced for large firms. A comparison of RAS A for sales with RAS A for profit margin suggests that relative to the benchmark forecasts, analysts’ sales forecasts are relatively more accurate than the corresponding forecasts for profit margin, except for the smallest size group. Suboptimality of forecasts. In this context, CC&O use the term suboptimality to refer to the extent to which analysts’ forecasts can be improved by incorporating the forecast generated by the corresponding benchmark model (both of which are described above). Specifically, CC&O used a “mixed model” approach, where a weight (w) is used to estimate a mixed model forecast (S t+1 MM or PM t+1 MM ) as follows: S t+1 MM = w × S t+1 BM + (1 − w) × S t+1 AF PM t+1 MM = w × PM t+1 BM + (1 − w) × PM t+1 AFA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES the value examiner JULY | AUGUST 2021 31 The goal of this mixed model approach is to identify a weight (w) so that the mixed model performs better than the analyst. The following results are based on a rather arbitrarily chosen w = 5 percent. 4 In this context, the relative accuracy score (RAS s ) for forecast suboptimality counts the percentage of times the mixed model forecast is more accurate than the corresponding analyst forecasts. Suboptimality suggests that adding the benchmark forecasts with a 5 percent weight and the analyst forecast with a 95 percent weight yields a mixed model forecast with higher accuracy than weighting the analyst forecast at 100 percent. For sales, the mixed model forecast is more accurate than the analysts’ forecast for the smallest size group. For profit margin, the mixed model forecast is more accurate than the analyst forecast for the three smallest size groups. Comparing the RAS s for sales with the RAS s for profit margin suggests that it is more difficult for the mixed model to generate a more accurate sales forecast than a profit margin forecast for firms in the small size groups. In other words, analysts’ profit margin forecasts are more suboptimal than analysts’ sales forecasts. While the robust RAS s tests allow for an examination of the frequency of suboptimality, the following test allows for an examination of the magnitude of the suboptimality. Specifically, CC&O used the equation below to estimate the weight for the benchmark model k BM and the weight for the analyst forecast (k AF ) using the Theil-Sen Method for each size-year group. 5 CC&O hypothesize that the benchmark model contains at least some extra information and that analysts’ forecasts are optimistically biased. Consequently, one would expect k BM > 0 and (k AF ) < 1. PM t+1 = k BM × PM t+1 BM + k AF × PM t+1 AF + noise S t+1 = k BM × S t+1 BM + k AF × S t+1 AF + noise For sales, the smallest size group has a k BM of 0.15 that is significantly larger than 0 and a k AF of 0.77 that is significantly smaller than 1. For the smallest firms, this result indicates that analysts fail to fully incorporate the information from the benchmark model. However, for all size groups 2 to 5, k BM is not significantly larger than zero and k AF is not significantly smaller than 1. For profit margin, the three smallest size groups have a k BM that is significantly larger than 0 and a k AF that is significantly smaller than 1. These findings suggest that while including the benchmark model can only improve sales forecasts for the smallest size firms, profit margin forecasts can be improved by including the benchmark model for size groups 1, 2, and 3. 4 CC&O also present results based on empirically estimated weight. However, since this weight would be estimated with ex-post data, CC&O caution the reader that such a weight might not be optimal for out- of-sample prediction. 5 A brief explanation of the Theil-Sen method can be found in the Appendix, below. For sales, the mixed model forecast is more accurate than the analysts’ forecast for the smallest size group. For profit margin, the mixed model forecast is more accurate than the analyst forecast for the three smallest size groups.A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES 32 JULY | AUGUST 2021 the value examiner Serial correlation of forecast errors. CC&O use a robust approach to assess the serial correlation in analyst forecasts errors. First, they define analyst forecast error (AFE) as follows: AFE = Actual − Forecast). Next, observations are assigned into one of four cells based on the sign of AFE in period t and period t+1. Specifically, the four cells are AFE t + &AFE + t+1 , AFE t + &AFE - t+1 , AFE t − &AFE + t+1 , and AFE t − &AFE t − +1 . To the extent that serial correlation is present in forecast errors, the percentage of observations in cells AFE t + &AFE + t+1 or AFE t − &AFE − t+1 should be significantly larger than 50 percent (i.e., 2 × 25 percent). For both sales and profit margin, the percentages of observations in cells AFE t + &AFE + t+1 or AFE t − &AFE − t+1 are significantly larger than 50 percent for all size groups, with the effect being most pronounced for the smallest size group (approximately 70 percent). Consistent with the previous results, the serial correlation of forecast errors is less pronounced for sales forecasts, relative to profit margin forecasts. Conclusion Previous literature has documented a set of properties for earnings forecasts made by sell-side analysts following publicly traded U.S. companies. However, earnings can be decomposed into sales and profit margin (E = S × PM). CC&O show that several of the properties of earnings forecasts also apply to sales and profit margin. Specifically, CC&O show that four forecast properties—optimistic forecast bias, relative accuracy with respect to benchmark model forecasts, forecast suboptimality, and serial correlation of forecast errors—apply to both sales and profit margin forecasts. However, sales forecasts seem to be less affected by these four properties than profit margin forecasts. Using market cap as a proxy for the richness of a firm’s information environment, CC&O also show that forecasts for larger firms tend to be less affected by these four forecast properties than forecasts for smaller firms with a poorer information environment. CC&O provide good reasons why one should decompose earnings forecasts into forecasts for sales and profit margin. Based on this decomposition, CC&O have shown that it is difficult to improve upon the accuracy of analysts’ sales forecasts using statistical models, especially for larger firms. Appendix: The Theil-Sen (TS) Estimation Method 6 TS was first proposed by Theil in 1950 and extended by Sen in 1968. 7 It has been acknowledged in several popular textbooks on nonparametric and robust statistics 6 This brief description of the Theil-Sen method is based on James A. Ohlson and Seil Kim, “Linear valuation without OLS: The Theil-Sen estimation approach,” Review of Accounting Studies 20, no. 1 (March 2015): 395–435, and Philipp Schaberl and Keith Sellers, “Theil-Sen Estimation for the Market Approach,” The Value Examiner (March/April 2017): 6–12. 7 See H. Theil, “A rank-invariant method of linear and polynomial regression analysis,” Proceedings of the Koninklijke Nederlandse Akademie Wetenchappen, Series A (1950), 53, 386–392, and Pranab Kumar Sen, “Estimates of the Regression Coefficient Based on Kendall’s Tau,” Journal of the American Statistical Association 63, no. 324 (1968): 1379–1389. Previous literature has documented a set of properties for earnings forecasts made by sell- side analysts following publicly traded U.S. companies.A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES the value examiner JULY | AUGUST 2021 33 as a robust alternative to ordinary least squares (OLS) regression in the presence of heteroscedasticity or outliers and is widely recognized in the field of statistics. 8 The methodology underlying TS is straightforward. Rather than running a single OLS regression using all available observations in the sample, repeated OLS regressions are run using thousands of small sub-samples of n randomly selected observations, where n equals the number of estimated parameters (intercept and coefficients). Hence, each sub-sample yields a set of n parameters, some of which may be heavily influenced by outliers. The final TS parameter estimates are calculated as the medians across the thousands of parameter estimates generated from the small sub-samples. Using medians across thousands of sub-samples removes the undue influence of outliers. Figure 1: TS vs. OLS Figure 1 is a simple example from Ohlson and Kim (2015, p. 434) that nicely illustrates the difference between TS and OLS. The following data points (X, Y) are plotted in Figure 1: (5, 10), (2, 7), (1, 3), (7, 15), and the outlier, (19, 1). Based on any combination of the first four data points, one can readily infer a positive slope. Only the outlier suggests a negative slope. By minimizing the sum of squared residuals, OLS estimates a slope of -0.26. It is hard to argue that the OLS line shown in Figure 1 is a good 8 See, e.g., Xin Dang et al., “Theil-Sen Estimators in a Multiple Linear Regression Model” (2009), available at http://home.olemiss.edu/~xdang/papers/MTSE.pdf. representation of the “typical” relationship between X and Y. In contrast, a TS approach that estimates a slope for each of the 10 possible pairs of data points estimates a slope of 1.3. Note that for most approaches, it is possible that any given observation may or may not be included in any of the random samples. Hence, some observations are likely to be included in more than one sub-sample regression, especially if the number of regressions is large. 9 One key aspect of TS is the very large number of samples that are typically run. This aspect can make the implementation of TS challenging for users without programming skills. For example, Schaberl and Sellers (2017) used the results from 50,000 randomly drawn sub-samples per year, totaling 450,000 individual OLS regressions. 10 VE Philipp Schaberl, PhD, is an associate professor of accounting and chair of the Department of Accounting and Computer Information Systems at the Monfort College of Business at the University of Northern Colorado. He earned his MS and PhD in accounting from the University of Cincinnati and his MS in business and economics from the Johannes Kepler University in Linz, Austria. He teaches financial accounting and data analytics to undergraduate, MACC, and MBA students in the U.S. and internationally. Dr. Schaberl has published his work on capital markets, valuation, and financial analysis in journals such as Financial Management, European Financial Management, Advances in Accounting, and The Value Examiner. Email: philipp. schaberl@unco.edu. 9 Studies indicate that the TS method is robust with or without replacement of the selected observations, and in Schaberl and Sellers (2017) we performed our tests allowing replacement. 10 The large number of repeated regressions may appear to be overkill. However, the number of possible combinations for even a relatively small set of data can be staggering. For example, avid poker players are probably aware that there are 2,598,960 distinct five-card poker hands. There would be an identical number of potential discrete samples from 52 comparable firms when using five independent variables (calculate as N!/{K!(N–K)!, where N is the number of comparable firms and K-1 is the number of independent variables).The Authority in Matters of Value ® Join Us | www.NACVA.com/30Years The first business valuation credential for the accounting profession Certified Valuation Analyst ® (CVA ® ) Years of Firsts The first financial forensics credential for the accounting profession Master Analyst in Financial Forensics ® (MAFF ® ) ISO/IEC 17024 Personnel Certification Program #8937 The first and only dually accredited business valuation credential, the CVA National Commission for Certifying Agencies ® (NCCA ® ) and American National Standards Institute ® (ANSI ® ) The first organization to have certified over 10,000 business valuation professionals The first globalization of the profession through Global Association of Certified Valuators and Analysts ™ (GACVA ™ ) international chaptersA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES the value examiner JULY | AUGUST 2021 35 /////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// T elemedicine has rapidly advanced over the past couple of decades, and its advancement has been significantly accelerated since the COVID-19 pandemic struck the U.S. These virtual services have the potential to allow greater access to, and quality of, care, while also resulting in significant cost savings. However, the technology also has numerous challenges, such as infrastructure gaps, capital requirements, and knowledge barriers among patients. The first installment in this five-part series on the valuation of telemedicine provides a description of telemedicine, an overview of its role during the COVID-19 public health emergency (PHE), and the potential challenges and opportunities it may face in the future. Defining Telemedicine and Telehealth The National Institutes of Health (NIH) broadly defines telehealth as the “use of communications technologies to provide health care at a distance.” 1 Telehealth can be used to describe the monitoring of medical devices, health status data collection and analysis via smart devices, or virtual visits between physicians and patients. 2 The World Health Organization (WHO) defines telemedicine as “the delivery of health care services, where distance is a critical factor… using information and communication technologies…for diagnosis, treatment and prevention of disease and injuries, research and evaluation, and for the continuing education of health care providers.” 3 1 “Telehealth,” National Institutes of Health, accessed June 1, 2021, https:// www.nibib.nih.gov/science-education/science-topics/telehealth. 2 Ibid. 3 Nicol Turner Lee, Jack Karsten, and Jordan Roberts, Removing regulatory barriers to telehealth before and after COVID-19, Brookings Institution, May 6, 2020, https://www.brookings.edu/research/removing-regulatory-barriers-to- telehealth-before-and-after-covid-19/, 4–5. The terms “telehealth” and “telemedicine” are distinguished by some in the healthcare industry. The WHO, for example, differentiates telemedicine, which is limited to services administered by physicians, from telehealth, which describes services administered by nurses, pharmacists, or other healthcare professionals. 4 In contrast, the American Telemedicine Association (ATA) considers the terms to be synonymous and largely interchangeable. 5 For purposes of this series, the terms will be considered synonymous, with the term “telemedicine” used for the sake of consistency. The three main forms of telemedicine are: 1. Store-and-forward or “asynchronous” telemedicine, where information—such as medical histories, reports, or other data—is sent to a specialist for diagnosis and treatment; 2. Remote patient monitoring, where a patient’s clinical status is evaluated continuously through video monitoring, images, or remotely reviewing tests; and 3. Real-time or “synchronous” telemedicine, which consists of a live conversation between the patient and provider. 6 The Rise of Telemedicine Although utilization of telemedicine technology has been relatively low historically, provider use of telemedicine services has grown considerably in recent years as the technology 4 Ibid., 5. 5 Ibid., 5. 6 Oren Mechanic, Yudy Persaud, and Alexa Kimball, Telehealth Systems, StatPearls, September 18, 2020, https://www.ncbi.nlm.nih.gov/books/ NBK459384/. By Todd Zigrang, MBA, MHA, FACHE, CVA, ASA, and Jessica Bailey-Wheaton, Esq. Valuation of Telemedicine: Introduction (Part I of V) HEALTHCARE INSIGHTSA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES 36 JULY | AUGUST 2021 the value examiner becomes more readily available and affordable. 7 Advancements in telemedicine technology and its infrastructure have allowed otherwise unserved or underserved patients to receive healthcare services. Payors and providers (including physician practices and hospitals) alike have been adopting telemedicine technologies at a rapid pace in an attempt to reduce avoidable hospitalizations, improve in-facility care, and decrease costs. 8 As healthcare reimbursement has continued to shift from volume-based to value-based, healthcare providers have increasingly looked to telemedicine to expand their services and better support patients before and after their visits. 9 Telemedicine can also be a more appealing option for patients who face difficulty accessing care or leaving their residences. This technology means that healthcare services can be delivered either at a closer facility or in the comfort of the patient’s home. 10 While telemedicine utilization has been on the rise over the past decade, it was not until the 2020 COVID-19 PHE that the technology became widely adopted and utilized by a variety of patients and providers. Telemedicine and the COVID-19 PHE Since the beginning of the COVID-19 PHE in March 2020, all states and medical specialties have seen unprecedented increases in telemedicine utilization. 11 Several policies and developments have jump-started this rapid expansion. Following the declaration of COVID-19 as a PHE, the Centers for Medicare & Medicaid Services (CMS) announced a number of relaxations and flexibilities for telemedicine reimbursement and coverage. This emergency declaration allowed beneficiaries to receive care wherever they were located—even from out-of-state providers—and did not penalize providers who, while acting in good faith, violated the Health Insurance Portability and Accountability Act (HIPAA) by using unencrypted video programs, such as Skype or FaceTime, to conduct telemedicine visits during the COVID-19 PHE. 12 These measures represented dramatic changes from previous policies, which only covered telemedicine for rural patients and had stringent restrictions on the originating site for the care and only allowed physicians to care for established patients in the same state in which they were licensed. 7 Kathleen Klink et al., Family Physicians and Telehealth: Findings from a National Survey (Washington, DC: Robert Graham Center, 2015), 3–4, https://www.graham-center.org/content/dam/rgc/documents/ publications-reports/reports/RGC%202015%20Telehealth%20Report.pdf. 8 “What Are the Advantages of Telehealth Nursing,” Telemedicine Blog, AMD Global Telemedicine, October 1, 2019, http://www.amdtelemedicine.com/blog/article/telemedicine-skilled-nursing-facilities- benefits; Garrison Nord et al., “On-demand synchronous audio video telemedicine visits are cost effective,” The American Journal of Emergency Medicine 37, no. 5 (August 2018): 890; Nnenaya Q. Agochukwu, Ted A. Skolarus, and Daniela Wittmann, “Telemedicine and prostate cancer survivorship: a narrative review,” Mhealth 4, no. 45 (October 2018): 1, 7–8. 9 Brian Eastwood, Telehealth 2018: Vendor Assessment and Market Outlook (Boston: Chilmark Research, 2018), 15–17, https://www.chilmarkresearch.com/chilmark_report/telehealth-2018-vendor-assessment- and-market-outlook/. 10 Nnenaya Q. Agochukwu, Ted A. Skolarus, and Daniela Wittmann, “Telemedicine and prostate cancer survivorship: a narrative review,” Mhealth 4, no. 45 (October 2018): 1, 7–8. 11 “HHS Issues New Report Highlighting Dramatic Trends in Medicare Beneficiary Telehealth Utilization amid COVID-19,” U.S. Department of Health & Human Services, July 28, 2020, https://www. hhs.gov/about/news/2020/07/28/hhs-issues-new-report-highlighting-dramatic-trends-in-medicare- beneficiary-telehealth-utilization-amid-covid-19.html. 12 “Notification of Enforcement Discretion for Telehealth Remote Communications During the COVID-19 Nationwide Public Health Emergency,” U.S. Department of Health & Human Services, January 20, 2021, https://www.hhs.gov/hipaa/for-professionals/special-topics/emergency-preparedness/ notification-enforcement-discretion-telehealth/index.html. This technology means that healthcare services can be delivered either at a closer facility or in the comfort of the patient’s home.A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES the value examiner JULY | AUGUST 2021 37 An additional 144 services, including emergency department visits, were added to the list of covered (and thus reimbursable) services for Medicare beneficiaries. In addition to relaxing the originating site requirements, CMS also expanded the number of services that could be provided through telemedicine. An additional 144 services, including emergency department visits, were added to the list of covered (and thus reimbursable) services for Medicare beneficiaries. 13 While all of these flexibilities and expansions were originally only valid for the length of the PHE, CMS has been looking to extend some expansions in covered services and reimbursement semi-permanently or permanently. For example, CMS’s 2021 Medicare Physician Fee Schedule (MPFS) proposed rule included expansions to reimbursement for telemedicine services, and these expansions were included in the final rule. 14 This final rule permanently implemented and/or expanded coverage for several telemedicine services. 15 Services such as evaluation and management (E/M) and some visits for patients with cognitive impairment were finalized to be permanently covered for telemedicine. 16 CMS also finalized the continued reimbursement for some telemedicine services, such as emergency department visits, only temporarily, until the end of the calendar year in which the COVID-19 PHE officially ends. 17 Under the final rule, seven telemedicine service codes would remain covered permanently, 12 would remain covered temporarily, and 74 would be removed immediately after the PHE ends. 18 Rural providers, as evidenced by their lower rates of telemedicine usage, have not been able to take advantage of the opportunities provided by telemedicine to the same extent as urban providers. 19 However, an executive order issued by President 13 “Trump Administration Finalizes Permanent Expansion of Medicare Telehealth Services and Improved Payment for Time Doctors Spend with Patients,” Centers for Medicare & Medicaid Services, December 1, 2020, https://www.cms.gov/newsroom/press-releases/trump-administration-finalizes- permanent-expansion-medicare-telehealth-services-and-improved-payment. 14 “Proposed Policy, Payment, and Quality Provisions Changes to the Medicare Physician Fee Schedule for Calendar Year 2021,” Centers for Medicare & Medicaid Services, August 3, 2020, https://www.cms.gov/ newsroom/fact-sheets/proposed-policy-payment-and-quality-provisions-changes-medicare-physician- fee-schedule-calendar-year-4; “Final Policy, Payment, and Quality Provisions Changes to the Medicare Physician Fee Schedule for Calendar Year 2021,” Centers for Medicare & Medicaid Services, December 1, 2020, https://www.cms.gov/newsroom/fact-sheets/final-policy-payment-and-quality-provisions-changes- medicare-physician-fee-schedule-calendar-year-1. 15 “Final Policy, Payment, and Quality Provisions Changes to the Medicare Physician Fee Schedule for Calendar Year 2021,” Centers for Medicare & Medicaid Services, December 1, 2020, https://www.cms.gov/ newsroom/fact-sheets/final-policy-payment-and-quality-provisions-changes-medicare-physician-fee- schedule-calendar-year-1. 16 Ibid. 17 Ibid. 18 Rachel B. Goodman and Nathanial M. Lacktman, “COVID-19: Here’s What CMS Will Do With the Temporary Telemedicine Codes When the PHE Ends,” Foley & Lardner LLP, August 12, 2020, https:// www.foley.com/en/insights/publications/2020/08/covid-19-cms-temporary-telehealth-codes-phe-ends; “Final Policy, Payment, and Quality Provisions Changes to the Medicare Physician Fee Schedule for Calendar Year 2021,” Centers for Medicare & Medicaid Services, December 1, 2020, https://www.cms.gov/ newsroom/fact-sheets/final-policy-payment-and-quality-provisions-changes-medicare-physician-fee- schedule-calendar-year-1. 19 “HHS Issues New Report Highlighting Dramatic Trends in Medicare Beneficiary Telehealth Utilization amid COVID-19,” Department of Health & Human Services, July 28, 2020, https://www. hhs.gov/about/news/2020/07/28/hhs-issues-new-report-highlighting-dramatic-trends-in-medicare- beneficiary-telehealth-utilization-amid-covid-19.html.A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES 38 JULY | AUGUST 2021 the value examiner Trump on August 3, 2020, calls for dramatic functional and reimbursement changes for these rural providers specifically. 20 The executive order also directs CMS to review the 135 services that it originally waived on a temporary basis in March 2020, and orders certain services to be permanently delivered via telemedicine technology going forward. 21 Future Challenges to Implementation Telemedicine’s significant potential to increase quality and access to care and its exponentially expanded popularity during the COVID-19 PHE is countered by a few major barriers experienced by providers seeking to implement and expand telemedicine into their practices. One of the greatest challenges for telemedicine is the limited reimbursement for its services. High up- front technology, administration, and setup costs, without the guarantee of permanent reimbursement (comparable to in-person services), may deter some (and particularly small) providers. In fact, larger organizations of 100 or more clinicians were able to shift an average of 16 percent of their pre-pandemic visits to telemedicine, compared to only about 8 percent for smaller organizations. 22 From the abrupt spike in use through March and April 2020, volume has apparently been decreasing, with the volume of telemedicine visits during the week of June 14, 2020, nearly a third less than in early April. 23 Additionally, there is a lack of integration and interoperability among healthcare organizations’ various electronic health record (EHR) systems, which platforms also may not coordinate with the telemedicine platform. These issues facing the telemedicine industry may result in the provision of costly and inefficient care, and many providers seem to be wary of fully adopting the technology without plans in place for long-term viability. Patient satisfaction with telemedicine services seems to be high, but research also indicates that certain patient populations may not be able to take full advantage of these virtual visits. On one hand, those who have been able to utilize telemedicine and virtual visits seem to be satisfied, with one survey indicating that 96 percent of patients found arranging virtual visits to be either extremely or somewhat easy, and 96 percent were satisfied with the virtual care they received (with 77 percent being 20 “Improving Rural Health and Telehealth Access,” Exec. Order No. 13941, 85 Fed. Reg. 47881 (August 6, 2020), https://www.federalregister.gov/documents/2020/08/06/2020-17364/improving-rural-health- and-telehealth-access. 21 Jack O’Brien, “President Trump Signs Executive Order to Permanently Expand Telehealth Benefits for Medicare Recipients,” HealthLeaders, August 4, 2020, https://www.healthleadersmedia.com/innovation/ president-trump-signs-executive-order-permanently-expand-telehealth-benefits-medicare. 22 Ateev Mehrotra, David Linetsky, and Hilary Hatch, “This is supposed to be telemedicine’s time to shine. Why are doctors abandoning it?,” STAT News, June 25, 2020, https://www.statnews.com/2020/06/25/ telemedicine-time-to-shine-doctors-abandoning-it/. 23 Ibid. In addition, during the weeks ending June 26–November 6, 2020, the overall percentage of weekly healthcare visits conducted via telemedicine declined by 25 percent, from 35.8 percent to 26.9 percent. See Hanna B. Demeke, et al., “Trends in Use of Telehealth Among Health Centers During the COVID-19 Pandemic—United States, June 26–November 6, 2020,” Centers for Disease Control and Prevention, February 19, 2021, https://www.cdc.gov/mmwr/volumes/70/wr/mm7007a3.htm. One of the greatest challenges for telemedicine is the limited reimbursement for its services.A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES the value examiner JULY | AUGUST 2021 39 completely or very satisfied). 24 Importantly, 86 percent of the surveyed patients were likely to recommend virtual care to others. 25 Another survey found that 83 percent of patients were likely to use telemedicine after the COVID-19 PHE. 26 Other studies examining the ease of telemedicine use for older adults indicate less promising statistics. An August 2020 study found that, across more than 4,500 adults aged 65 or older, over 70 percent showed signs of unreadiness, including difficulty hearing or communicating or inexperience with the technology required. 27 Another study, using pre-COVID-19 data, similarly showed that older age was strongly associated with lower telemedicine utilization. 28 Because these older adults account for approximately 25 percent of all physician office visits, their inability (or unwillingness) to utilize telemedicine could slow the technology’s adoption rate in the near future. 29 However, the quick expansion of telemedicine over the past few years, even before COVID-19, indicates that this technology will continue to expand as usage and adoption rates by physicians and patients alike increase in the future. One way to accomplish this, it seems, is through expanded reimbursement coverage past the COVID-19 PHE. In Part II of this five-part series, we will cover the past, current, and future state of telemedicine reimbursement. VE 24 Jeff Bendix, “Survey: Most patients satisfied with virtual care,” Medical Economics, September 14, 2020, https://www.medicaleconomics.com/view/ survey-most-patients-satisfied-with-virtual-care. 25 Ibid. 26 “Telemedicine Adoption in the Age of COVID-19 and Beyond,” Doctor. com, accessed June 1, 2021, https://cdn2.hubspot.net/hubfs/3053445/Doctor_ Telemedicine_Survey_Infografic.pdf. 27 Kenneth Lam, et al., “Assessing Telemedicine Unreadiness Among Older Adults in the United States During the COVID-19 Pandemic,” Journal of the American Medical Association (August 3, 2020), https://jamanetwork.com/ journals/jamainternalmedicine/fullarticle/2768772. 28 Mary Reed, et al., “Patient Characteristics Associated With Choosing a Telemedicine Visit vs Office Visit With the Same Primary Care Clinicians,” Journal of the American Medical Association (June 17, 2020), https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2767244. 29 Kenneth Lam, et al., “Assessing Telemedicine Unreadiness Among Older Adults in the United States During the COVID-19 Pandemic,” Journal of the American Medical Association (August 3, 2020), https://jamanetwork.com/ journals/jamainternalmedicine/fullarticle/2768772. Todd A. Zigrang, MBA, MHA, FACHE, CVA, ASA, is president of Health Capital Consultants, where he focuses on the areas of valuation and financial analysis for hospitals and other healthcare enterprises. Mr. Zigrang has significant physician integration and financial analysis experience and has participated in the development of a physician owned, multispecialty management service organization and networks involving a wide range of specialties, physician owned hospitals, as well as several limited liability companies for acquiring acute care and specialty hospitals, ASCs, and other ancillary facilities. Email: tzigrang@healthcapital.com. Jessica L. Bailey-Wheaton, Esq., serves as vice president and general counsel of Health Capital Consultants, where she conducts project management and consulting services related to the impact of both federal and state regulations on healthcare exempt organization transactions, and provides research services necessary to support certified opinions of value related to the fair market value and commercial reasonableness of transactions related to healthcare enterprises, assets, and services. Email: jbailey@ healthcapital.com. The quick expansion of telemedicine over the past few years, even before COVID-19, indicates that this technology will continue to expand as usage and adoption rates by physicians and patients alike increase in the future.Next >