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The Association Between Telemedicine Use and Changes in Health Care Usage and Outcomes in Patients With Congestive Heart Failure: Retrospective Cohort Study

The Association Between Telemedicine Use and Changes in Health Care Usage and Outcomes in... sex, and number of hospitalizations owing to CHF in the 3 Database, which records all inpatient hospital admissions; (3) months prior to the index date. To ensure that matching was the National Ambulatory Care Reporting System, which contains successful, the distribution of characteristics in both groups was data on all hospital- and community-based ambulatory care then compared, and standardized differences greater than 0.1 (including emergency department [ED] visits); (4) Ontario Drug were considered imbalanced. The following covariates were Benefit, which includes data on prescription claims for patients incorporated into the model that was used to generate individual aged >65 years; (5) the Registered Persons Database, which propensity scores: income quintile, rural residence, number of contains demographic information of all patients covered under ED visits owing to heart failure in 12 months prior to the index OHIP; and (6) the CHF database, an Institute for Clinical date, prescription claims for select medication classes in 100 Evaluative Sciences (ICES) database that uses validated days prior to the index date (angiotensin-converting enzyme algorithms to identify patients ever diagnosed with CHF, and https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al inhibitors or angiotensin II receptor blockers, antiplatelets, defined as the slope for the telemedicine group divided by the beta-blockers, aldosterone receptor antagonists, statins, diuretics, slope for the unexposed group, was also calculated to compare nitrates, and digoxin), Charlson comorbidity index in 3 years whether the rate of change over time significantly differed prior, number of outpatient primary care and cardiology visits between groups. A ratio of slopes greater than 1 implies that in the year prior, diabetes diagnosis any time prior, hypertension there was higher usage over time in the telemedicine group than diagnosis any time prior, hospitalization for acute myocardial in the unexposed group. Absolute rates of usage per 100 infarction in 3 years prior, peripheral vascular disease within 3 person-months over the 15-month period were also calculated years prior, history of coronary artery disease in 3 years prior, for each outcome, along with rate differences to compare and atrial fibrillation diagnosis in 3 years prior (Table S3 in between groups. The rate of the unexposed group was subtracted Multimedia Appendix 1). from that of the telemedicine group; therefore, a positive rate difference indicates a higher rate in the telemedicine group. All Outcomes analyses were performed in SAS (version 9.4; SAS Institute). We enumerated the following health care usage outcomes Ethics Approval monthly, 12 months before the index date, and over the 90-day period post the index date: number of hospitalizations owing Use of these databases for the purposes of this study was to CHF, hospitalizations owing to cardiovascular disease, authorized under §45 of Ontario’s Personal Health Information all-cause hospitalizations, all-cause ED visits, outpatient primary Protection Act, which does not require review by a research care visits, repeat outpatient cardiology visits, outpatient ethics board. An exemption was also received from the cardiology visits with any cardiologist, laboratory claims (ie, Women’s College Hospital Research Ethics Board (reference hemoglobin A , lipid profile, complete blood count, and number: (REB # 2020-0106-E). 1c creatinine), cardiac diagnostic tests (transthoracic Results echocardiogram, cardiac stress test, cardiac catheterization, and Holter monitoring), and new prescription claims. Patient Characteristics Statistical Analysis Prior to matching, we identified 12,741 eligible patients with We developed a generalized estimating equation (GEE) model CHF in the unexposed group and 33,250 patients with CHF in for each outcome based on the independent variables time, the telemedicine group (Table 1), and after propensity score exposure group, and the time×group interaction. We accounted matching, 11,131 pairs were identified. Table 1 shows the for correlation due to matching as the GEE could only distribution of baseline patient characteristics in the unexposed incorporate one level of clustering. An exchangeable correlation versus telemedicine group before and after matching (49% were structure was used. Rate ratios, also known as the slope of male; mean age 78.9, SD 12.0 years). Matching successfully change over the 15-month period, were calculated for both balanced characteristics between the 2 groups, as demonstrated unexposed and telemedicine groups for each outcome. A rate by standardized differences of <0.10 for all measured baseline ratio, or slope, greater than 1 implies that there was a general characteristics. increase in usage over time for that group. A ratio of the slopes, https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Table 1. Baseline characteristics of patients before and after propensity score matching (with standardized differences). Variables Before propensity score matching After propensity score matching Unexposed group Telemedicine group Standardized Unexposed Telemedicine group Standardized (n=12,741) (n=33,250) difference group (n=11,131) difference (n=11,131) Sex, n (%) Female 6703 (52.6) 16,111 (48.5) 0.08 5677 (51.0) 5677 (51.0) 0 Male 6038 (47.4) 17,139 (51.5) 0.08 5454 (49.0) 5454 (49.0) 0 Age (years), mean (SD) 79.7 (12.3) 76.9 (11.6) 78.9 (12.0) 78.9 (12.0) 0 0.23 Charlson comorbidity index, n (%) 0 1469 (11.5) 3828 (11.5) 0 1325 (11.9) 1297 (11.7) 0.01 1 2959 (23.2) 7007 (21.1) 0.05 2550 (22.9) 2619 (23.5) 0.01 ≥2 8313 (65.2) 22,415 (67.4) 0.05 7256 (65.2) 7215 (64.8) 0.01 Congestive heart failure admis- 964 (7.6) 3224 (9.7) 0.08 706 (6.3) 706 (6.3) 0 sion in 3 months prior, n (%) Congestive heart failure admis- 3595 (28.2) 10,513 (31.6) 0.07 3128 (28.1) 2919 (26.2) 0.04 sion in 1 year prior, n (%) Emergency department visit for 4228 (33.2) 12,901 (38.8) 3745 (33.6) 3708 (33.3) 0.01 0.12 congestive heart failure in 1 year prior, n (%) Neighborhood income quintile, n (%) 1 3585 (28.1) 8231 (24.8) 0.08 3027 (27.2) 3041 (27.3) 0 2 2860 (22.4) 7464 (22.4) 0 2527 (22.7) 2545 (22.9) 0 3 2365 (18.6) 6703 (20.2) 0.04 2109 (18.9) 2066 (18.6) 0.01 4 2031 (15.9) 5632 (16.9) 0.03 1780 (16.0) 1812 (16.3) 0.01 5 1812 (14.2) 5085 (15.3) 0.03 1626 (14.6) 1595 (14.3) 0.01 Rurality, n (%) Rural 1550 (12.2) 2691 (8.1) 1231 (11.1) 1253 (11.3) 0.01 0.14 Urban 10,895 (85.5) 30,195 (90.8) 9696 (87.1) 9682 (87.0) 0 0.16 Prior diabetes, n (%) 6585 (51.7) 19,122 (57.5) 5941 (53.4) 5863 (52.7) 0.01 0.12 Prior hypertension, n (%) 11,620 (91.2) 30,759 (92.5) 0.05 10,188 (91.5) 10,194 (91.6) 0 Acute myocardial infarction ad- 954 (7.5) 2551 (7.7) 0.01 859 (7.7) 849 (7.6) 0 mission in 3 years prior, n (%) Peripheral vascular disease in 3 936 (7.3) 2722 (8.2) 0.03 843 (7.6) 854 (7.7) 0 years prior, n (%) Coronary artery disease in 3 1694 (13.3) 5366 (16.1) 0.08 1576 (14.2) 1568 (14.1) 0 years prior, n (%) Atrial fibrillation in 3 years prior 6790 (53.3) 18,330 (55.1) 0.04 5967 (53.6) 5934 (53.3) 0.01 Outpatient primary care visits in 3.5 (4.5) 5.9 (5.6) 3.9 (4.6) 3.9 (4.5) 0 0.47 1 year prior, mean (SD) Outpatient visits with same cardi- 0.5 (1.2) 1.0 (1.7) 0.5 (1.2) 0.6 (1.3) 0.02 0.34 ologist in 1 year prior, mean (SD) Outpatient visits with any cardi- 0.9 (1.5) 1.6 (2.0) 1.0 (1.5) 1.0 (1.5) 0.02 0.41 ologist in 1 year prior, mean (SD) Prescriptions in 100 days prior, n (%) https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Variables Before propensity score matching After propensity score matching Unexposed group Telemedicine group Standardized Unexposed Telemedicine group Standardized (n=12,741) (n=33,250) difference group (n=11,131) difference (n=11,131) Angiotensin-converting en- 3703 (29.1) 10,919 (32.8) 0.08 3362 (30.2) 3339 (30.0) 0 zyme inhibitor or an- giotensin II receptor blocker Antithrombotic 2419 (19.0) 6754 (20.3) 0.03 2119 (19.0) 2179 (19.6) 0.01 Beta-blocker 6504 (51.0) 17,944 (54.0) 0.06 5760 (51.7) 5824 (52.3) 0.01 Diuretic 5837 (45.8) 15,515 (46.7) 0.02 5137 (46.2) 5180 (46.5) 0.01 Calcium channel blocker or 7524 (59.1) 21,934 (66.0) 6855 (61.6) 6951 (62.4) 0.02 0.14 statin Nitrate 1142 (9.0) 2687 (8.1) 0.03 967 (8.7) 969 (8.7) 0 Aldosterone receptor antag- 8473 (66.5) 22,416 (67.4) 0.02 7385 (66.3) 7450 (66.9) 0.01 onist Digoxin 1718 (13.5) 4559 (13.7) 0.01 1473 (13.2) 1493 (13.4) 0.01 Standardized difference>0.1. the unexposed group. The average monthly decrease in CHF Hospitalizations and ED Visits admissions over the 15-month observation period was –5.2% Figure 1 illustrates the adjusted rates of hospitalizations and in the unexposed group versus –1.7% in the telemedicine group ED visits across time in both the unexposed and telemedicine and –4.7% in the unexposed group versus –2.2% in the groups. During the 15-month period starting 12 months before telemedicine group for cardiovascular admissions. Similarly, their index visit, which was defined as their first in-person or both groups saw declines in monthly all-cause ED visits over telemedicine visit during the pandemic, to 3 months post the the observation period (–3.6% for the unexposed group vs –0.6% index date, both groups had a significant reduction in CHF and for the telemedicine group). cardiovascular admissions, though the decrease was greater in Figure 1. Rate of hospitalizations and emergency department visits by exposure group. CHF: congestive heart failure; ED: emergency department. https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Table 2 reports the rate ratio (slope) and ratio of slope estimates cardiovascular admissions (RRR 1.03, 95% CI 1.02-1.04), from the GEE model, as well as the absolute rates and all-cause admissions (RRR 1.03, 95% CI 1.02-1.04), and accompanying rate differences. The ratio of the slopes indicates any-cause ED visits (RRR 1.03, 95% CI 1.03-1.04). The a steeper decline in the unexposed group in CHF admissions absolute rate differences were –0.12, –0.15, –0.08, and 0.67 (ratio of rate ratio [RRR] 1.02, 95% CI 1.02-1.03), admissions per 100 person-months, respectively. Table 2. Absolute and relative rates by virtual care user group. a b Outcomes Absolute rate per 100 person- Rate Rate ratio or slope (95% CI) Ratio of slopes month (95% CI) difference Unexposed group Telemedicine group Unexposed Telemedicine group group Hospitalizations and emergency department visits c c c Congestive heart failure admis- 2.47 2.36 –0.12 0.95 (0.94-0.96) 0.98 (0.97-0.98) 1.02 (1.02-1.03) sion c c c Cardiovascular admission 3.39 3.24 –0.15 0.95 (0.95-0.96) 0.98 (0.97-0.99) 1.03 (1.02-1.04) c c Any-cause admission 1.00 (1.00-1.01) 7.46 7.38 –0.08 0.98 (0.97-0.98) 1.03 (1.02-1.04) c c c Any-cause emergency depart- 17.17 17.84 0.67 0.96 (0.96-0.96) 0.99 (0.99-0.99) 1.03 (1.03-1.04) ment visits Physician visits c c c Primary care visits 28.07 27.49 –0.58 0.93 (0.92-0.93) 0.92 (0.92-0.92) 0.99 (0.99-1.00) c c Visits with the same cardiologist 1.01 (1.00-1.02) 3.92 4.13 0.22 0.93 (0.92-0.93) 0.93 (0.93-0.94) c c c Visits with any cardiologist 6.74 7.06 0.32 0.92 (0.92-0.93) 0.93 (0.93-0.94) 1.01 (1.01-1.02) Other health care usage c c c Total laboratory tests 58.48 71.32 12.84 0.97 (0.96-0.97) 0.99 (0.99-0.99) 1.02 (1.02-1.03) c c c Total diagnostic tests 10.67 12.10 1.43 0.94 (0.94-0.95) 0.98 (0.98-0.99) 1.04 (1.03-1.05) c c c New prescriptions (age>65 22.53 21.59 –0.94 0.94 (0.93-0.94) 0.96 (0.95-0.96) 1.02 (1.01-1.03) years) A rate ratio or slope of greater than 1 implies a general increase in health care usage over time, and vice versa. Ratio of the slopes is defined as the slope for the telemedicine group divided by the slope for the unexposed group. A value greater than 1 implies that there was higher usage over time in the telemedicine group than in the unexposed group. Statistically significant (95% CI does not include 1, or P<.05). When comparing the 2 groups, the decline in the rate of visits Physician Visits with any cardiologist was steeper in the unexposed group than Figure 2 shows the trends in physician visit rates for the in the telemedicine group (RRR 1.01, 95% CI 1.01-1.02) with unexposed and telemedicine groups. Over the 15-month study an absolute difference of 0.32 visits per 100 person-months; period, both groups had a significant monthly decline in primary however, the decline in primary care visit rates was steeper in care visits (–6.1% for the unexposed group vs –6.5% for the the telemedicine group (RRR 0.99, 95% CI 0.99-1.00) with an telemedicine group), visits with the same cardiologist as the absolute difference of –0.58 visits per 100 person-months. There index visit (–5.4% for the unexposed group vs –4.8% for the was no significant difference between low and high users in telemedicine group), and visits with any cardiologist (–6.4% their slopes for visits with the same cardiologist. for the unexposed group vs –5.1% in the telemedicine group). https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Figure 2. Rate of physician visits by exposure group. group), and new prescriptions among those aged 65 years and Laboratory Testing, Diagnostic Imaging, and older (–7.1% for the unexposed group vs –5.9% for the Medication Usage telemedicine group). The unexposed group showed a steeper Figure 3 displays the monthly ordering rates of laboratory decline in laboratory testing (RRR 1.02, 95% CI 1.02-1.03), testing, imaging, and medication prescriptions over time. Both diagnostic testing (RRR 1.04, 95% CI 1.03-1.05), and new the unexposed and telemedicine groups reported a significant prescriptions (RRR 1.02, 95% CI 1.01-1.03) than the decrease across the 15-month observation period in the monthly telemedicine group. The corresponding absolute differences rates of total laboratory tests (–2.1% for the unexposed group were 12.84, 1.43, and –0.94 tests or claims per 100 vs –0.2% for the telemedicine group), total diagnostic tests person-months, respectively. (–3.9% for the unexposed group vs –0.8% for the telemedicine https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Figure 3. Rate of laboratory tests, diagnostic tests, and prescription claims by exposure group. weeks of the first wave of the pandemic [3], with over 90% of Discussion the visits being facilitated by telephone. Telemedicine was widely seen as a temporary emergency measure designed to Principal Findings quickly provide care to patients with chronic disease while In this large, population-based study, we aimed to evaluate the reducing infection risk [2]. Despite initial concerns that impact of telemedicine use on changes in health care usage and telemedicine would compromise the quality of care, our findings outcomes on patients with CHF during the first wave of the demonstrate small, albeit significant differences in COVID-19 pandemic. Both the telemedicine and unexposed hospitalization and ED visit rates, which were generally higher groups showed significant reductions in health service use in over time within telemedicine compared to in-person care. Prior the months leading up to and during the pandemic. Patients with studies of telemedicine and CHF have reported mixed results, CHF in the unexposed group saw steeper reductions in with Klersy et al [14] and Chaudhry et al [15] having failed to hospitalization and ED usage rates than those in the telemedicine demonstrate improvements in CHF outcomes in a large, group. In addition, patients in the unexposed group had steeper randomized controlled trial of a telemonitoring solution; reductions in testing and medication prescriptions. In contrast, however, the more recent Telemedical Interventional the rate of decrease in primary care physician visits was higher Management in Heart Failure II study [5] demonstrated in the telemedicine group. To further supplement our findings, significant reductions in hospitalizations and mortality. These we also report difference-in-difference ratios comparing the studies, however, were mostly conducted before the pandemic pre- and postindex rates between exposure groups (Table S4 in and assessed telemonitoring systems that are adjunctive to Multimedia Appendix 1). These results show that the rate physician visits, of which the majority of visits in these studies comparisons before and during the pandemic between groups were conducted in person. This study assessed telemedicine are consistent with our main findings. While the differences we visits as a substitute to in-person physician visits. It is possible found were significant, the absolute differences between the 2 that frequent telemedicine visits, which are more easily groups were mostly small, and the clinical significance of these accessible for frail patients with CHF, may have brought patients findings are uncertain. However, these results highlight the fact to medical attention and facilitated hospitalization. It is also that patients with higher telemedicine usage also seem to have possible that patients who had more frequent telemedicine visits higher usage of many other health care services. were likely to be acutely decompensating, requiring an ED visit for assessment, particularly when access to in-person care was Comparison to Prior Work limited. In contrast to our findings, a few international studies The COVID-19 pandemic led to widespread telemedicine have evaluated telemedicine use in the population of patients adoption in a very short time frame, with rates of telemedicine with heart failure during the COVID-19 pandemic and found usage ranging from 1% before the pandemic to over 70% within https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al that those accessing telemedicine saw a decrease or no difference Limitations in hospitalizations during this time [16,17]. The results of this study should be contextualized by some significant limitations. First, although we propensity The American College of Cardiology’s CHF guidelines score–matched high-frequency and low-frequency users or recommend recording volume status and vital signs as part of nonusers of telemedicine based on a number of important every clinical assessment [4]. Telemedicine visits limit the baseline characteristics, there still exists the potential for ability to conduct a physical examination; hence, some suspected unmeasured confounders as administrative data do not account that telemedicine visits would lead to higher use of diagnostic for vital signs, laboratory values, or other markers of disease testing in lieu of a clinical examination. Our results suggest acuity. Second, these user definitions may not be as applicable higher usage of laboratory and diagnostic testing in the as we enter a postpandemic era and away from a “virtual-first” telemedicine group, though the reason for that difference is not model of care. The study took place within the first wave of the easy to ascertain from the data. One possible explanation is that, COVID-19 pandemic, when in-person services were being as stated previously, more diagnostic testing was ordered to significantly curtailed, which limits the generalizability of the augment clinical assessment. Another possible explanation, study. Third, we are unable to determine the type of telemedicine similar to the explanation around ED visits, is that patients with platform used—telephone or video—in these encounters, CHF who were more acute received telemedicine visits and although anecdotal evidence from patients and providers consequently received more diagnostic tests and medication suggests that the majority of visits based in Ontario were prescriptions. It is interesting that there were only marginal conducted over the telephone. Finally, we are also unable to differences in physician visit trends between the 2 groups, ascertain whether other adjunctive devices, such as wearable however, suggesting that differences in testing and medication devices, were used as part of the telemedicine visit, although ordering were beyond merely increased access to physicians. those devices were not part of common practice. Despite these It is possible that because these patients were more unstable, limitations, our results provide important observations regarding physicians ordered more testing in advance but only scheduled the use of telemedicine and subsequent health care system usage a visit if the test results indicated an issue for follow-up. and patient outcomes. The findings of this study have important implications for the Conclusions long-term sustainability of telemedicine in a postpandemic era. While telemedicine during the pandemic was mainly used to In this population-based retrospective cohort study of patients reduce infection risk and conserve PPE [18], the long-term with CHF in Ontario, Canada, we found that telemedicine sustainable PPE supply and readily available COVID-19 patients had significantly higher use of health care services over vaccines necessitate telemedicine use to align with the quadruple time than low-frequency users or nonusers of telemedicine, aim of improved patient and provider experience, improved although clinically significant differences were minimal for health outcomes, and value for money. Prior studies on most outcomes. As telemedicine becomes a more widespread telemedicine in CHF seem to demonstrate improved patient and permanent form of care delivery, future research is needed satisfaction and potentially improved health outcomes; however, to rigorously assess the optimal use of telemedicine—such as these studies were not population-based [19]. Importantly, CHF which clinical situations would telemedicine derive the most telemedicine programs need to integrate fully into the normal benefit—and quality of care provided during these interactions delivery of CHF care, including in-person visits, to be effective in order to determine the sustainability of telemedicine as it is [18]. integrated into the health system in a post–COVID-19 era. Acknowledgments We thank IQVIA Solutions Canada Inc for use of their Drug Information File. We also thank Dr Onil Bhattacharyya and Dr Kaveh Shojania for their contributions to this study. This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care. Parts of this material are based on data and information compiled and provided by the Ontario MOH and the Canadian Institute for Health Information (CIHI). This study was funded by the Ontario MOH and Women’s College Hospital. The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred. Data Availability The data sets generated during or analyzed in this study are not publicly available owing to restricted data sharing agreements with Institute for Clinical Evaluative Sciences (ICES) and the Canadian Institute for Health Information (CIHI), but access to the data may be granted by contacting ICES. 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Heart Fail Rev 2020 Mar 24;25(2):231-243 [FREE Full text] [doi: 10.1007/s10741-019-09801-5] [Medline: 31197564] https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Abbreviations CHF: congestive heart failure CIHI: Canadian Institute for Health Information ED: emergency department GEE: generalized estimating equation ICES: Institute for Clinical Evaluative Sciences MOH: Ministry of Health OHIP: Ontario Health Insurance Plan PPE: personal protective equipment RRR: ratio of rate ratio Edited by T Leung; submitted 14.01.22; peer-reviewed by G Mason, BJ Nievas-Soriano; comments to author 14.07.22; revised version received 21.07.22; accepted 21.07.22; published 04.08.22 Please cite as: Chu C, Stamenova V, Fang J, Shakeri A, Tadrous M, Bhatia RS The Association Between Telemedicine Use and Changes in Health Care Usage and Outcomes in Patients With Congestive Heart Failure: Retrospective Cohort Study JMIR Cardio 2022;6(2):e36442 URL: https://cardio.jmir.org/2022/2/e36442 doi: 10.2196/36442 PMID: 35881831 ©Cherry Chu, Vess Stamenova, Jiming Fang, Ahmad Shakeri, Mina Tadrous, R Sacha Bhatia. Originally published in JMIR Cardio (https://cardio.jmir.org), 04.08.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cardio, is properly cited. The complete bibliographic information, a link to the original publication on https://cardio.jmir.org, as well as this copyright and license information must be included. https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 11 (page number not for citation purposes) XSL FO RenderX http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JMIR Cardio JMIR Publications

The Association Between Telemedicine Use and Changes in Health Care Usage and Outcomes in Patients With Congestive Heart Failure: Retrospective Cohort Study

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Abstract

sex, and number of hospitalizations owing to CHF in the 3 Database, which records all inpatient hospital admissions; (3) months prior to the index date. To ensure that matching was the National Ambulatory Care Reporting System, which contains successful, the distribution of characteristics in both groups was data on all hospital- and community-based ambulatory care then compared, and standardized differences greater than 0.1 (including emergency department [ED] visits); (4) Ontario Drug were considered imbalanced. The following covariates were Benefit, which includes data on prescription claims for patients incorporated into the model that was used to generate individual aged >65 years; (5) the Registered Persons Database, which propensity scores: income quintile, rural residence, number of contains demographic information of all patients covered under ED visits owing to heart failure in 12 months prior to the index OHIP; and (6) the CHF database, an Institute for Clinical date, prescription claims for select medication classes in 100 Evaluative Sciences (ICES) database that uses validated days prior to the index date (angiotensin-converting enzyme algorithms to identify patients ever diagnosed with CHF, and https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al inhibitors or angiotensin II receptor blockers, antiplatelets, defined as the slope for the telemedicine group divided by the beta-blockers, aldosterone receptor antagonists, statins, diuretics, slope for the unexposed group, was also calculated to compare nitrates, and digoxin), Charlson comorbidity index in 3 years whether the rate of change over time significantly differed prior, number of outpatient primary care and cardiology visits between groups. A ratio of slopes greater than 1 implies that in the year prior, diabetes diagnosis any time prior, hypertension there was higher usage over time in the telemedicine group than diagnosis any time prior, hospitalization for acute myocardial in the unexposed group. Absolute rates of usage per 100 infarction in 3 years prior, peripheral vascular disease within 3 person-months over the 15-month period were also calculated years prior, history of coronary artery disease in 3 years prior, for each outcome, along with rate differences to compare and atrial fibrillation diagnosis in 3 years prior (Table S3 in between groups. The rate of the unexposed group was subtracted Multimedia Appendix 1). from that of the telemedicine group; therefore, a positive rate difference indicates a higher rate in the telemedicine group. All Outcomes analyses were performed in SAS (version 9.4; SAS Institute). We enumerated the following health care usage outcomes Ethics Approval monthly, 12 months before the index date, and over the 90-day period post the index date: number of hospitalizations owing Use of these databases for the purposes of this study was to CHF, hospitalizations owing to cardiovascular disease, authorized under §45 of Ontario’s Personal Health Information all-cause hospitalizations, all-cause ED visits, outpatient primary Protection Act, which does not require review by a research care visits, repeat outpatient cardiology visits, outpatient ethics board. An exemption was also received from the cardiology visits with any cardiologist, laboratory claims (ie, Women’s College Hospital Research Ethics Board (reference hemoglobin A , lipid profile, complete blood count, and number: (REB # 2020-0106-E). 1c creatinine), cardiac diagnostic tests (transthoracic Results echocardiogram, cardiac stress test, cardiac catheterization, and Holter monitoring), and new prescription claims. Patient Characteristics Statistical Analysis Prior to matching, we identified 12,741 eligible patients with We developed a generalized estimating equation (GEE) model CHF in the unexposed group and 33,250 patients with CHF in for each outcome based on the independent variables time, the telemedicine group (Table 1), and after propensity score exposure group, and the time×group interaction. We accounted matching, 11,131 pairs were identified. Table 1 shows the for correlation due to matching as the GEE could only distribution of baseline patient characteristics in the unexposed incorporate one level of clustering. An exchangeable correlation versus telemedicine group before and after matching (49% were structure was used. Rate ratios, also known as the slope of male; mean age 78.9, SD 12.0 years). Matching successfully change over the 15-month period, were calculated for both balanced characteristics between the 2 groups, as demonstrated unexposed and telemedicine groups for each outcome. A rate by standardized differences of <0.10 for all measured baseline ratio, or slope, greater than 1 implies that there was a general characteristics. increase in usage over time for that group. A ratio of the slopes, https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Table 1. Baseline characteristics of patients before and after propensity score matching (with standardized differences). Variables Before propensity score matching After propensity score matching Unexposed group Telemedicine group Standardized Unexposed Telemedicine group Standardized (n=12,741) (n=33,250) difference group (n=11,131) difference (n=11,131) Sex, n (%) Female 6703 (52.6) 16,111 (48.5) 0.08 5677 (51.0) 5677 (51.0) 0 Male 6038 (47.4) 17,139 (51.5) 0.08 5454 (49.0) 5454 (49.0) 0 Age (years), mean (SD) 79.7 (12.3) 76.9 (11.6) 78.9 (12.0) 78.9 (12.0) 0 0.23 Charlson comorbidity index, n (%) 0 1469 (11.5) 3828 (11.5) 0 1325 (11.9) 1297 (11.7) 0.01 1 2959 (23.2) 7007 (21.1) 0.05 2550 (22.9) 2619 (23.5) 0.01 ≥2 8313 (65.2) 22,415 (67.4) 0.05 7256 (65.2) 7215 (64.8) 0.01 Congestive heart failure admis- 964 (7.6) 3224 (9.7) 0.08 706 (6.3) 706 (6.3) 0 sion in 3 months prior, n (%) Congestive heart failure admis- 3595 (28.2) 10,513 (31.6) 0.07 3128 (28.1) 2919 (26.2) 0.04 sion in 1 year prior, n (%) Emergency department visit for 4228 (33.2) 12,901 (38.8) 3745 (33.6) 3708 (33.3) 0.01 0.12 congestive heart failure in 1 year prior, n (%) Neighborhood income quintile, n (%) 1 3585 (28.1) 8231 (24.8) 0.08 3027 (27.2) 3041 (27.3) 0 2 2860 (22.4) 7464 (22.4) 0 2527 (22.7) 2545 (22.9) 0 3 2365 (18.6) 6703 (20.2) 0.04 2109 (18.9) 2066 (18.6) 0.01 4 2031 (15.9) 5632 (16.9) 0.03 1780 (16.0) 1812 (16.3) 0.01 5 1812 (14.2) 5085 (15.3) 0.03 1626 (14.6) 1595 (14.3) 0.01 Rurality, n (%) Rural 1550 (12.2) 2691 (8.1) 1231 (11.1) 1253 (11.3) 0.01 0.14 Urban 10,895 (85.5) 30,195 (90.8) 9696 (87.1) 9682 (87.0) 0 0.16 Prior diabetes, n (%) 6585 (51.7) 19,122 (57.5) 5941 (53.4) 5863 (52.7) 0.01 0.12 Prior hypertension, n (%) 11,620 (91.2) 30,759 (92.5) 0.05 10,188 (91.5) 10,194 (91.6) 0 Acute myocardial infarction ad- 954 (7.5) 2551 (7.7) 0.01 859 (7.7) 849 (7.6) 0 mission in 3 years prior, n (%) Peripheral vascular disease in 3 936 (7.3) 2722 (8.2) 0.03 843 (7.6) 854 (7.7) 0 years prior, n (%) Coronary artery disease in 3 1694 (13.3) 5366 (16.1) 0.08 1576 (14.2) 1568 (14.1) 0 years prior, n (%) Atrial fibrillation in 3 years prior 6790 (53.3) 18,330 (55.1) 0.04 5967 (53.6) 5934 (53.3) 0.01 Outpatient primary care visits in 3.5 (4.5) 5.9 (5.6) 3.9 (4.6) 3.9 (4.5) 0 0.47 1 year prior, mean (SD) Outpatient visits with same cardi- 0.5 (1.2) 1.0 (1.7) 0.5 (1.2) 0.6 (1.3) 0.02 0.34 ologist in 1 year prior, mean (SD) Outpatient visits with any cardi- 0.9 (1.5) 1.6 (2.0) 1.0 (1.5) 1.0 (1.5) 0.02 0.41 ologist in 1 year prior, mean (SD) Prescriptions in 100 days prior, n (%) https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Variables Before propensity score matching After propensity score matching Unexposed group Telemedicine group Standardized Unexposed Telemedicine group Standardized (n=12,741) (n=33,250) difference group (n=11,131) difference (n=11,131) Angiotensin-converting en- 3703 (29.1) 10,919 (32.8) 0.08 3362 (30.2) 3339 (30.0) 0 zyme inhibitor or an- giotensin II receptor blocker Antithrombotic 2419 (19.0) 6754 (20.3) 0.03 2119 (19.0) 2179 (19.6) 0.01 Beta-blocker 6504 (51.0) 17,944 (54.0) 0.06 5760 (51.7) 5824 (52.3) 0.01 Diuretic 5837 (45.8) 15,515 (46.7) 0.02 5137 (46.2) 5180 (46.5) 0.01 Calcium channel blocker or 7524 (59.1) 21,934 (66.0) 6855 (61.6) 6951 (62.4) 0.02 0.14 statin Nitrate 1142 (9.0) 2687 (8.1) 0.03 967 (8.7) 969 (8.7) 0 Aldosterone receptor antag- 8473 (66.5) 22,416 (67.4) 0.02 7385 (66.3) 7450 (66.9) 0.01 onist Digoxin 1718 (13.5) 4559 (13.7) 0.01 1473 (13.2) 1493 (13.4) 0.01 Standardized difference>0.1. the unexposed group. The average monthly decrease in CHF Hospitalizations and ED Visits admissions over the 15-month observation period was –5.2% Figure 1 illustrates the adjusted rates of hospitalizations and in the unexposed group versus –1.7% in the telemedicine group ED visits across time in both the unexposed and telemedicine and –4.7% in the unexposed group versus –2.2% in the groups. During the 15-month period starting 12 months before telemedicine group for cardiovascular admissions. Similarly, their index visit, which was defined as their first in-person or both groups saw declines in monthly all-cause ED visits over telemedicine visit during the pandemic, to 3 months post the the observation period (–3.6% for the unexposed group vs –0.6% index date, both groups had a significant reduction in CHF and for the telemedicine group). cardiovascular admissions, though the decrease was greater in Figure 1. Rate of hospitalizations and emergency department visits by exposure group. CHF: congestive heart failure; ED: emergency department. https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Table 2 reports the rate ratio (slope) and ratio of slope estimates cardiovascular admissions (RRR 1.03, 95% CI 1.02-1.04), from the GEE model, as well as the absolute rates and all-cause admissions (RRR 1.03, 95% CI 1.02-1.04), and accompanying rate differences. The ratio of the slopes indicates any-cause ED visits (RRR 1.03, 95% CI 1.03-1.04). The a steeper decline in the unexposed group in CHF admissions absolute rate differences were –0.12, –0.15, –0.08, and 0.67 (ratio of rate ratio [RRR] 1.02, 95% CI 1.02-1.03), admissions per 100 person-months, respectively. Table 2. Absolute and relative rates by virtual care user group. a b Outcomes Absolute rate per 100 person- Rate Rate ratio or slope (95% CI) Ratio of slopes month (95% CI) difference Unexposed group Telemedicine group Unexposed Telemedicine group group Hospitalizations and emergency department visits c c c Congestive heart failure admis- 2.47 2.36 –0.12 0.95 (0.94-0.96) 0.98 (0.97-0.98) 1.02 (1.02-1.03) sion c c c Cardiovascular admission 3.39 3.24 –0.15 0.95 (0.95-0.96) 0.98 (0.97-0.99) 1.03 (1.02-1.04) c c Any-cause admission 1.00 (1.00-1.01) 7.46 7.38 –0.08 0.98 (0.97-0.98) 1.03 (1.02-1.04) c c c Any-cause emergency depart- 17.17 17.84 0.67 0.96 (0.96-0.96) 0.99 (0.99-0.99) 1.03 (1.03-1.04) ment visits Physician visits c c c Primary care visits 28.07 27.49 –0.58 0.93 (0.92-0.93) 0.92 (0.92-0.92) 0.99 (0.99-1.00) c c Visits with the same cardiologist 1.01 (1.00-1.02) 3.92 4.13 0.22 0.93 (0.92-0.93) 0.93 (0.93-0.94) c c c Visits with any cardiologist 6.74 7.06 0.32 0.92 (0.92-0.93) 0.93 (0.93-0.94) 1.01 (1.01-1.02) Other health care usage c c c Total laboratory tests 58.48 71.32 12.84 0.97 (0.96-0.97) 0.99 (0.99-0.99) 1.02 (1.02-1.03) c c c Total diagnostic tests 10.67 12.10 1.43 0.94 (0.94-0.95) 0.98 (0.98-0.99) 1.04 (1.03-1.05) c c c New prescriptions (age>65 22.53 21.59 –0.94 0.94 (0.93-0.94) 0.96 (0.95-0.96) 1.02 (1.01-1.03) years) A rate ratio or slope of greater than 1 implies a general increase in health care usage over time, and vice versa. Ratio of the slopes is defined as the slope for the telemedicine group divided by the slope for the unexposed group. A value greater than 1 implies that there was higher usage over time in the telemedicine group than in the unexposed group. Statistically significant (95% CI does not include 1, or P<.05). When comparing the 2 groups, the decline in the rate of visits Physician Visits with any cardiologist was steeper in the unexposed group than Figure 2 shows the trends in physician visit rates for the in the telemedicine group (RRR 1.01, 95% CI 1.01-1.02) with unexposed and telemedicine groups. Over the 15-month study an absolute difference of 0.32 visits per 100 person-months; period, both groups had a significant monthly decline in primary however, the decline in primary care visit rates was steeper in care visits (–6.1% for the unexposed group vs –6.5% for the the telemedicine group (RRR 0.99, 95% CI 0.99-1.00) with an telemedicine group), visits with the same cardiologist as the absolute difference of –0.58 visits per 100 person-months. There index visit (–5.4% for the unexposed group vs –4.8% for the was no significant difference between low and high users in telemedicine group), and visits with any cardiologist (–6.4% their slopes for visits with the same cardiologist. for the unexposed group vs –5.1% in the telemedicine group). https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Figure 2. Rate of physician visits by exposure group. group), and new prescriptions among those aged 65 years and Laboratory Testing, Diagnostic Imaging, and older (–7.1% for the unexposed group vs –5.9% for the Medication Usage telemedicine group). The unexposed group showed a steeper Figure 3 displays the monthly ordering rates of laboratory decline in laboratory testing (RRR 1.02, 95% CI 1.02-1.03), testing, imaging, and medication prescriptions over time. Both diagnostic testing (RRR 1.04, 95% CI 1.03-1.05), and new the unexposed and telemedicine groups reported a significant prescriptions (RRR 1.02, 95% CI 1.01-1.03) than the decrease across the 15-month observation period in the monthly telemedicine group. The corresponding absolute differences rates of total laboratory tests (–2.1% for the unexposed group were 12.84, 1.43, and –0.94 tests or claims per 100 vs –0.2% for the telemedicine group), total diagnostic tests person-months, respectively. (–3.9% for the unexposed group vs –0.8% for the telemedicine https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Figure 3. Rate of laboratory tests, diagnostic tests, and prescription claims by exposure group. weeks of the first wave of the pandemic [3], with over 90% of Discussion the visits being facilitated by telephone. Telemedicine was widely seen as a temporary emergency measure designed to Principal Findings quickly provide care to patients with chronic disease while In this large, population-based study, we aimed to evaluate the reducing infection risk [2]. Despite initial concerns that impact of telemedicine use on changes in health care usage and telemedicine would compromise the quality of care, our findings outcomes on patients with CHF during the first wave of the demonstrate small, albeit significant differences in COVID-19 pandemic. Both the telemedicine and unexposed hospitalization and ED visit rates, which were generally higher groups showed significant reductions in health service use in over time within telemedicine compared to in-person care. Prior the months leading up to and during the pandemic. Patients with studies of telemedicine and CHF have reported mixed results, CHF in the unexposed group saw steeper reductions in with Klersy et al [14] and Chaudhry et al [15] having failed to hospitalization and ED usage rates than those in the telemedicine demonstrate improvements in CHF outcomes in a large, group. In addition, patients in the unexposed group had steeper randomized controlled trial of a telemonitoring solution; reductions in testing and medication prescriptions. In contrast, however, the more recent Telemedical Interventional the rate of decrease in primary care physician visits was higher Management in Heart Failure II study [5] demonstrated in the telemedicine group. To further supplement our findings, significant reductions in hospitalizations and mortality. These we also report difference-in-difference ratios comparing the studies, however, were mostly conducted before the pandemic pre- and postindex rates between exposure groups (Table S4 in and assessed telemonitoring systems that are adjunctive to Multimedia Appendix 1). These results show that the rate physician visits, of which the majority of visits in these studies comparisons before and during the pandemic between groups were conducted in person. This study assessed telemedicine are consistent with our main findings. While the differences we visits as a substitute to in-person physician visits. It is possible found were significant, the absolute differences between the 2 that frequent telemedicine visits, which are more easily groups were mostly small, and the clinical significance of these accessible for frail patients with CHF, may have brought patients findings are uncertain. However, these results highlight the fact to medical attention and facilitated hospitalization. It is also that patients with higher telemedicine usage also seem to have possible that patients who had more frequent telemedicine visits higher usage of many other health care services. were likely to be acutely decompensating, requiring an ED visit for assessment, particularly when access to in-person care was Comparison to Prior Work limited. In contrast to our findings, a few international studies The COVID-19 pandemic led to widespread telemedicine have evaluated telemedicine use in the population of patients adoption in a very short time frame, with rates of telemedicine with heart failure during the COVID-19 pandemic and found usage ranging from 1% before the pandemic to over 70% within https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al that those accessing telemedicine saw a decrease or no difference Limitations in hospitalizations during this time [16,17]. The results of this study should be contextualized by some significant limitations. First, although we propensity The American College of Cardiology’s CHF guidelines score–matched high-frequency and low-frequency users or recommend recording volume status and vital signs as part of nonusers of telemedicine based on a number of important every clinical assessment [4]. Telemedicine visits limit the baseline characteristics, there still exists the potential for ability to conduct a physical examination; hence, some suspected unmeasured confounders as administrative data do not account that telemedicine visits would lead to higher use of diagnostic for vital signs, laboratory values, or other markers of disease testing in lieu of a clinical examination. Our results suggest acuity. Second, these user definitions may not be as applicable higher usage of laboratory and diagnostic testing in the as we enter a postpandemic era and away from a “virtual-first” telemedicine group, though the reason for that difference is not model of care. The study took place within the first wave of the easy to ascertain from the data. One possible explanation is that, COVID-19 pandemic, when in-person services were being as stated previously, more diagnostic testing was ordered to significantly curtailed, which limits the generalizability of the augment clinical assessment. Another possible explanation, study. Third, we are unable to determine the type of telemedicine similar to the explanation around ED visits, is that patients with platform used—telephone or video—in these encounters, CHF who were more acute received telemedicine visits and although anecdotal evidence from patients and providers consequently received more diagnostic tests and medication suggests that the majority of visits based in Ontario were prescriptions. It is interesting that there were only marginal conducted over the telephone. Finally, we are also unable to differences in physician visit trends between the 2 groups, ascertain whether other adjunctive devices, such as wearable however, suggesting that differences in testing and medication devices, were used as part of the telemedicine visit, although ordering were beyond merely increased access to physicians. those devices were not part of common practice. Despite these It is possible that because these patients were more unstable, limitations, our results provide important observations regarding physicians ordered more testing in advance but only scheduled the use of telemedicine and subsequent health care system usage a visit if the test results indicated an issue for follow-up. and patient outcomes. The findings of this study have important implications for the Conclusions long-term sustainability of telemedicine in a postpandemic era. While telemedicine during the pandemic was mainly used to In this population-based retrospective cohort study of patients reduce infection risk and conserve PPE [18], the long-term with CHF in Ontario, Canada, we found that telemedicine sustainable PPE supply and readily available COVID-19 patients had significantly higher use of health care services over vaccines necessitate telemedicine use to align with the quadruple time than low-frequency users or nonusers of telemedicine, aim of improved patient and provider experience, improved although clinically significant differences were minimal for health outcomes, and value for money. Prior studies on most outcomes. As telemedicine becomes a more widespread telemedicine in CHF seem to demonstrate improved patient and permanent form of care delivery, future research is needed satisfaction and potentially improved health outcomes; however, to rigorously assess the optimal use of telemedicine—such as these studies were not population-based [19]. Importantly, CHF which clinical situations would telemedicine derive the most telemedicine programs need to integrate fully into the normal benefit—and quality of care provided during these interactions delivery of CHF care, including in-person visits, to be effective in order to determine the sustainability of telemedicine as it is [18]. integrated into the health system in a post–COVID-19 era. Acknowledgments We thank IQVIA Solutions Canada Inc for use of their Drug Information File. We also thank Dr Onil Bhattacharyya and Dr Kaveh Shojania for their contributions to this study. This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care. Parts of this material are based on data and information compiled and provided by the Ontario MOH and the Canadian Institute for Health Information (CIHI). This study was funded by the Ontario MOH and Women’s College Hospital. The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred. Data Availability The data sets generated during or analyzed in this study are not publicly available owing to restricted data sharing agreements with Institute for Clinical Evaluative Sciences (ICES) and the Canadian Institute for Health Information (CIHI), but access to the data may be granted by contacting ICES. Conflicts of Interest None declared. https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Multimedia Appendix 1 Tables S1-S4. [DOCX File , 22 KB-Multimedia Appendix 1] References 1. Mehrotra A, Bhatia RS, Snoswell CL. Paying for telemedicine after the pandemic. JAMA 2021 Feb 02;325(5):431-432. [doi: 10.1001/jama.2020.25706] [Medline: 33528545] 2. Bhatia RS, Shojania KG, Levinson W. Cost of contact: redesigning healthcare in the age of COVID. BMJ Qual Saf 2021 Mar 06;30(3):236-239. [doi: 10.1136/bmjqs-2020-011624] [Medline: 32763977] 3. Bhatia RS, Chu C, Pang A, Tadrous M, Stamenova V, Cram P. Virtual care use before and during the COVID-19 pandemic: a repeated cross-sectional study. CMAJ Open 2021 Feb 17;9(1):E107-E114 [FREE Full text] [doi: 10.9778/cmajo.20200311] [Medline: 33597307] 4. 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Heart Fail Rev 2020 Mar 24;25(2):231-243 [FREE Full text] [doi: 10.1007/s10741-019-09801-5] [Medline: 31197564] https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Chu et al Abbreviations CHF: congestive heart failure CIHI: Canadian Institute for Health Information ED: emergency department GEE: generalized estimating equation ICES: Institute for Clinical Evaluative Sciences MOH: Ministry of Health OHIP: Ontario Health Insurance Plan PPE: personal protective equipment RRR: ratio of rate ratio Edited by T Leung; submitted 14.01.22; peer-reviewed by G Mason, BJ Nievas-Soriano; comments to author 14.07.22; revised version received 21.07.22; accepted 21.07.22; published 04.08.22 Please cite as: Chu C, Stamenova V, Fang J, Shakeri A, Tadrous M, Bhatia RS The Association Between Telemedicine Use and Changes in Health Care Usage and Outcomes in Patients With Congestive Heart Failure: Retrospective Cohort Study JMIR Cardio 2022;6(2):e36442 URL: https://cardio.jmir.org/2022/2/e36442 doi: 10.2196/36442 PMID: 35881831 ©Cherry Chu, Vess Stamenova, Jiming Fang, Ahmad Shakeri, Mina Tadrous, R Sacha Bhatia. Originally published in JMIR Cardio (https://cardio.jmir.org), 04.08.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cardio, is properly cited. The complete bibliographic information, a link to the original publication on https://cardio.jmir.org, as well as this copyright and license information must be included. https://cardio.jmir.org/2022/2/e36442 JMIR Cardio 2022 | vol. 6 | iss. 2 | e36442 | p. 11 (page number not for citation purposes) XSL FO RenderX

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JMIR CardioJMIR Publications

Published: Aug 4, 2022

Keywords: telemedicine; telehealth; eHealth; digital health; population; outcomes; health service; health system; utilization; congestive heart failure; cardiology; health outcome; clinical outcome; patient outcome; heart; cardiac; cardiology; ambulatory; COVID-19

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