Trends in telehealth use by Medicare fee-for-service beneficiaries and its impact on overall volume of healthcare services (preprint)

ABSTRACT Introduction At the start of the COVID-19 public health emergency, the federal government made temporary Medicare policy changes to expand telehealth coverage, resulting in a surge in telehealth use. As federal and state policymakers currently consider permanent telehealth policy options, it is important to understand the trends in telehealth use during 2021 and whether telehealth has led to an increase in the overall volume of healthcare services. Methods Our analysis was conducted using Part B claims for 100% of Medicare fee-for-service beneficiaries. We identified all outpatient evaluation and management (E&M) services received by beneficiaries from January 1, 2019 through December 31, 2021. We then calculated the monthly proportion of outpatient E&M services that were performed in-person and through telehealth. Results The total number of all outpatient E&M services was 289.0 million in 2019, 255.2 million in 2020 (11.7% lower than 2019), and 260.7 million in 2021 (9.8% lower than 2019). Monthly telehealth services peaked at 7.2 million (or 50.7% of monthly E&M services) in April 2020, followed by a slow decline through the end of 2021. During the second half of 2021, telehealth services made up 8.5-9.5% of monthly E&M services. Conclusion From April 2020 through December 2021, the monthly volume of telehealth services slowly declined and has plateaued between 8.5-9.5% of all outpatient E&M services received by Medicare fee-for-service beneficiaries. Importantly, the total volume of outpatient E&M services was lower in 2020 and 2021, suggesting that the COVID-19 telehealth flexibilities have not increased the overall volume of outpatient E&M services received by Medicare beneficiaries. These findings should mitigate some concerns about the impact of telehealth on overall healthcare utilization. At the start of the COVID-19 public health emergency, the federal government made temporary Medicare policy changes to expand telehealth coverage, resulting in a surge in telehealth use. 1,2 While telehealth was a necessary substitute for in-person care during first few months of the pandemic, there was a decline in the use of telehealth during the second half of 2020. 3 As federal and state policymakers currently consider permanent telehealth policy options, it is important to understand the trends in telehealth use during 2021 and whether telehealth has led to an increase in the overall volume of healthcare services.

Savable but lost lives when ICU is overloaded: a model from 733 patients in epicenter Wuhan, China (preprint)

Background. Coronavirus Disease (COVID-19) causes a sudden turn over to bad at some check-point and thus needs intervention of intensive care unit (ICU). This resulted in urgent and large needs of ICUs posed great risks to the medical system. Estimating the mortality of critical in-patients who were not admitted to the ICU (MI-mortality) will be valuable to optimize the management and assignment of ICU.Methods. Retrospective, of the 733 in-patients diagnosed with COVD-19 at Huangpi Hospital of Traditional Chinese Medicine (Wuhan, China), as of March 18, 2020. This study aims to estimate the MI-mortality and build a model to identify the critical in-patients. Demographic, clinical and laboratory results were collected and analyzed. The mortality rate for the patients who failed to receive ICU and unfortunately died was analyzed. To this end, the key factors for prognostic of patients who may need ICU care were found. A prognostic classification model using machine learning was built to identify the patient who may need ICU. Results. Considering the shortage of ICU beds at the beginning of disease emergence, we defined the mortality for those patients who were predicted to be in needing of ICU treatment yet they did not as MI-mortality. Patients who entered the ICU and died were defined as ICU-mortality. To estimate MI-mortality, a prognostic classification model was built to identify the in-patients who may need ICU care based on the medical factors collected in-hospital. Its predictive accuracies on whole patient set (733 [25 708]), training set (586 [20 566]) and testing set (147 [5 142]) dataset were 0.8513, 0.8935 and 0.8288, with the AUC of 0.8844, 0.8941 and 0.9120, respectively. Our analysis had shown that the MI-mortality is 41% and the ICU-mortality is 32%, implying that enough bed of ICU in treating patients in critical conditions. Conclusions. On our cohort of 733 patients, 25 in-patients were admitted to ICU, among them 8 patients died. 25 in-patients who have been predicted by our model that they should need ICU care, yet they did not enter ICU due to lack of shorting ICU wards. The MI-mortality is 41%.