Factors Associated With the Utilization of Outpatient Virtual Clinics: Retrospective Observational Study Using Multilevel Analysis

Abstract

Background Although the COVID-19 pandemic has accelerated the adoption of telemedicine and virtual consultations worldwide, complex factors that may affect the use of virtual clinics are still unclear. Objective This study aims to identify factors associated with the utilization of virtual clinics in the experience of virtual clinic service implementation in Taiwan. Methods We retrospectively analyzed a total of 187,742 outpatient visits (176,815, 94.2%, in-person visits and 10,927, 5.8%, virtual visits) completed at a large general hospital in Taipei City from May 19 to July 31, 2021, after rapid implementation of virtual outpatient clinic visits due to the COVID-19 pandemic. Data of patients’ demographic characteristics, disease type, physicians’ features, and specialties/departments were collected, and physicians’ opinions regarding virtual clinics were surveyed and evaluated using a 5-point Likert scale. Multilevel analysis was conducted to determine the factors associated with the utilization of virtual clinics. Results Patient-/visit-, physician-, and department-level factors accounted for 67.5%, 11.1%, and 21.4% of the total variance in the utilization of virtual clinics, respectively. Female sex (odds ratio [OR] 1.27, 95% CI 1.22-1.33, P<.001); residing at a greater distance away from the hospital (OR 2.36, 95% CI 2.15-2.58 if distance>50 km, P<.001; OR 3.95, 95% CI 3.11-5.02 if extensive travel required, P<.001); reimbursement by the National Health Insurance (NHI; OR 7.29, 95% CI 5.71-9.30, P<.001); seeking care for a major chronic disease (OR 1.33, 95% CI 1.24-1.42, P<.001); the physician’s positive attitude toward virtual clinics (OR 1.50, 95% CI 1.16-1.93, P=.002); and visits within certain departments, including the heart center, psychiatry, and internal medicine (OR 2.55, 95% CI 1.46-4.46, P=.004), were positively associated with the utilization of virtual clinics. The patient’s age, the physician’s age, and the physician’s sex were not associated with the utilization of virtual clinics in our study. Conclusions Our results show that in addition to previously demonstrated patient-level factors that may influence telemedicine use, including the patient’s sex and distance from the hospital, factors at the visit level (insurance type, disease type), physician level (physician’s attitude toward virtual clinics), and department level also contribute to the utilization of virtual clinics. Although there was a more than 300-fold increase in the number of virtual visits during the pandemic compared with the prepandemic period, the majority (176,815/187,742, 94.2%) of the outpatient visits were still in-person visits during the study period. Therefore, it is of great importance to understand the factors impacting the utilization of virtual clinics to accelerate the implementation of telemedicine. The findings of our study may help direct policymaking for expanding the use of virtual clinics, especially in countries struggling with the development and promotion of telemedicine virtual clinic services.

Mapping Factors That Affect the Uptake of Digital Therapeutics Within Health Systems: Scoping Review

“…Demographic characteristics are associated with patients’ uptake of digital therapeutics. Younger age [ 5 , 19 , 40 , 43 , 49 , 52 - 54 , 63 , 83 , 100 , 101 , 126 , 135 , 139 , 175 , 177 , 181 , 182 , 196 , 203 , 206 , 235 , 236 , 256 , 259 ], sex (male or female depending on the circumstances) [ 17 , 53 , 62 , 63 , 101 , 181 , 182 , 235 , 256 ], higher language skills [ 19 , 53 , 126 ], higher education level [ 5 , 17 , 43 , 49 , 54 , 62 , 63 , 83 , 100 , 101 , 107 , 135 , 175 , 177 , 181 , 182 ], higher health literacy [ 40 , 47 ], being employed [ 5 , 17 , 62 , 100 , 101 ], higher income level [ 17 , 40 , 52 , ...…”

“…With respect to structure measures, the demographic characteristics of health professionals play a role. Female and younger health professionals were reported to favor the prescription of digital therapeutics in Germany and the Netherlands [ 147 , 196 ], as did health professionals located in rural environments in Australia, the United States, and Taiwan [ 62 , 151 , 155 , 236 ]. The digital connectivity and literacy of health professionals were widely recognized as key components in determining their willingness toward and likelihood of prescribing digital therapeutics across countries [ 1 , 5 , 17 , 42 - 46 , 62 , 70 , 76 , 80 , 81 , 90 , 100 , 101 , 104 , 106 , 116 , 117 , 135 , 145 , 146 , 162 , 174 , 183 , 184 , 187 , 189 , 191 , 192 , 194 , 196 , 197 , 199 - 201 , 203 , 210 , 224 , 225 , 228 , 231 ], as were their personal attitudes toward, familiarity with, and trust in digital therapeutics [ 1 , 42 , 43 , 62 , 70 , 104 , 105 , 116 , 139 , 145 - 147 , 183 ...…”

“…Approval from the institutional or social environment was also reported as an important indicator of the uptake and use of digital therapeutics by health professionals [ 19 , 42 , 62 , 104 , 112 , 115 , 139 , 156 , 159 , 174 , 200 , 202 , 203 ]. Finally, certain medical specialties (eg, psychology, psychiatry, and neurology) may be more amenable to adopting digital therapeutics than other specialties (eg, ophthalmology, dermatology, and surgery) [ 140 , 145 , 146 , 162 , 167 , 183 , 196 , 213 , 235 , 236 , 247 ], which may be explained by the varying degrees of availability or applicability of digital therapeutics for different medical specialties [ 183 , 196 ].…”

“…Our search strategy yielded 49,192 1). Out of 244 documents, our final sample comprised 60 (24.6%) literature reviews [19,; 49 (20.1%) viewpoints, commentaries, and editorials [11,13,17,; 43 (17.6%) cross-sectional studies [83,; 39 (16%) qualitative research articles [1,15,; 13 (5.3%) case reports [4,5,12,[224][225][226][227][228][229][230][231][232][233]; 11 (4.5%) longitudinal studies [234][235][236][237][238][239][240][241][242][243][244]; 9 (3.7%) mixed methods studies [245][246][247][248][249][250][251][252][253]; 5 (2%) economic evaluations [254][255][256][257][258]; ...…”

“…Younger age [5,19,40, [49,62,71,82,86,115,135,142,159,193,195,235]; ethnic background [40,101,102,107,142,175,193,195,235]; being unhealthy or living with a disability [11,17,40,52,62,80,[100][101][102][103]107,181,190,206,236,259,270]; patient-specific needs, attitudes, and habits [1,80,102,109,110,193,195]; distance to health services [152,171,185,236], risk of contracting a disease…”