ABSTRACT
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has been accompanied by significant reductions in patient volumes for non-COVID-19-related conditions ranging from acute coronary syndrome to ischemic strokes to acute trauma. However, the impact of the COVID-19 pandemic on patient volumes for a broad range of orthopedic conditions remains unknown. The purpose of this study was to investigate the association of the COVID-19 pandemic with changes in patient volumes of 35 emergent (e.g. dislocations, open fractures), urgent (e.g. fractures), and nonurgent orthopedic conditions (e.g. osteoarthritis, sprains). METHODS: A retrospective interrupted time-series analysis of patient volumes was conducted for 35 orthopedic conditions based on ICD-10 diagnosis codes. Patient hospitalizations and new problem visits were aggregated across two institutions in New York state, including one urban tertiary care orthopedic hospital, one urban academic medical center, and all state outpatient facilities affiliated with the orthopedic institution. Patient volumes in the COVID-19 peak period (03/2020-05/2020) and COVID-19 recovery period (06/2020-10/2020) were compared against pre-COVID-19 vol (01/2018-02/2020). RESULTS: Overall, 169,047 cases were included in the analysis across 35 conditions with 3775 emergent cases, 6376 urgent cases, and 158,896 nonurgent cases. During the COVID-19 peak period, patient caseloads for 1 out of 7 emergent conditions (p = 0.02) and 26 out of 28 urgent and nonurgent conditions (p < 0.05) were significantly reduced compared to the pre-COVID-19 period. During the COVID-19 recovery period, patient volumes in 3 out of 13 emergent and urgent conditions (p < 0.03) and 11 out of 22 nonurgent conditions (p < 0.04) were decreased compared to pre-COVID-19 vol. CONCLUSIONS: This study found that the pandemic was associated with considerable changes in patient patterns for non-COVID-19 orthopedic conditions. The long-term effects of patient volume reductions on both patient outcomes and orthopedic health systems remain to be seen. LEVEL OF EVIDENCE: Cohort study; level of evidence IV.
ABSTRACT
Introduction: The coronavirus disease 2019 (COVID-19) heralded an unprecedented increase in telemedicine utilization. Our objective was to assess patient satisfaction with telemedicine during the COVID-19 era. Methods: Telemedicine visit data were gathered from Stanford Health Care (Stanford) and the Hospital for Special Surgery (HSS). Patient satisfaction data from HSS were captured from a Press-Ganey questionnaire between April 19, 2020, and December 12, 2020, whereas Stanford data were taken from a novel survey instrument that was distributed to all patients between June 22, 2020, and November 1, 2020. Participants: There were 60,550 telemedicine visits at Stanford, each linked with a postvisit survey. At HSS, there were 66,349 total telemedicine visits with 7,348 randomly linked with a postvisit survey. Main Outcomes and Measures: Two measures of patient satisfaction were used for this study: (1) a patient's "overall visit score" and (2) whether the patient indicated the highest possible "likelihood to recommend" (LTR) score (LTR top box score). Results: The LTR top box percentage at Stanford increased from 69.6% to 74.0% (p = 0.0002), and HSS showed no significant change (p = 0.7067). In the multivariable model, the use of a cell phone (adjusted odds ratio [aOR]: 1.18; 95% confidence interval [CI]: 1.12-1.23) and tablet (aOR: 1.15; 95% CI: 1.07-1.23) was associated with higher overall scores, whereas visits with interrupted connections (aOR: 0.49; 95% CI: 0.42-0.57) or help required to connect (aOR: 0.49; 95% CI: 0.42-0.56) predicted lower patient satisfaction. Conclusions: We present the largest published description of patient satisfaction with telemedicine, and we identify important telemedicine-specific factors that predict increased overall visit score. These include the use of cell phones or tablets, phone reminders, and connecting before the visit was scheduled to begin. Visits with poor connectivity, extended wait times, or difficulty being seen, examined, or understood by the provider were linked with reduced odds of high scores. Our results suggest that attention to connectivity and audio/visual definition will help optimize patient satisfaction with future telemedicine encounters.