Interstate Telehealth Use By Medicare Beneficiaries Before And After COVID-19 Licensure Waivers, 2017-20.

During the COVID-19 pandemic, all fifty states and Washington, D.C., passed licensure waivers that allowed patients to participate in telehealth visits with out-of-state clinicians (that is, interstate telehealth). Because many of these temporary flexibilities have expired or are set to expire, we analyzed trends in interstate telehealth use by Medicare beneficiaries during 2017-20, which covers the period both directly before and during the first year of the pandemic. Although the volume of interstate telehealth use increased in 2020, out-of-state telehealth made up a small share of all outpatient visits (0.8 percent) and of all telehealth visits (5 percent) overall. For individual states, out-of-state telehealth made up between 0.2 percent and 9.3 percent of all outpatient visits. We found that most out-of-state telehealth use was for established patient care and that a higher percentage of out-of-state telehealth users lived in rural areas compared with beneficiaries who did not receive care outside of their state (28 percent versus 23 percent). Our collective findings suggest that the elimination of pandemic licensure flexibilities will affect different states to varying degrees and will also affect the delivery of care for both established patients and rural patients.

Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction.

The outbreak of coronavirus disease (COVID-19) has been a nightmare to citizens, hospitals, healthcare practitioners, and the economy in 2020. The overwhelming number of confirmed cases and suspected cases put forward an unprecedented challenge to the hospital's capacity of management and medical resource distribution. To reduce the possibility of cross-infection and attend a patient according to his severity level, expertly diagnosis and sophisticated medical examinations are often required but hard to fulfil during a pandemic. To facilitate the assessment of a patient's severity, this paper proposes a multi-modality feature learning and fusion model for end-to-end covid patient severity prediction using the blood test supported electronic medical record (EMR) and chest computerized tomography (CT) scan images. To evaluate a patient's severity by the co-occurrence of salient clinical features, the High-order Factorization Network (HoFN) is proposed to learn the impact of a set of clinical features without tedious feature engineering. On the other hand, an attention-based deep convolutional neural network (CNN) using pre-trained parameters are used to process the lung CT images. Finally, to achieve cohesion of cross-modality representation, we design a loss function to shift deep features of both-modality into the same feature space which improves the model's performance and robustness when one modality is absent. Experimental results demonstrate that the proposed multi-modality feature learning and fusion model achieves high performance in an authentic scenario.

Association Between Primary Care Practice Telehealth Use and Acute Care Visits for Ambulatory Care-Sensitive Conditions During COVID-19.

Importance: During the COVID-19 pandemic, many primary care practices adopted telehealth in place of in-person care to preserve access to care for patients with acute and chronic conditions. The extent to which this change was associated with their rates of acute care visits (ie, emergency department visits and hospitalizations) for these conditions is unknown. Objective: To examine whether a primary care practice's level of telehealth use is associated with a change in their rate of acute care visits for ambulatory care-sensitive conditions (ACSC visits). Design, Setting, and Participants: This retrospective cohort analysis used a difference-in-differences study design to analyze insurance claims data from 4038 Michigan primary care practices from January 1, 2019, to September 30, 2020. Exposures: Low, medium, or high tertile of practice-level telehealth use based on the rate of telehealth visits from March 1 to August 31, 2020, compared with prepandemic visit volumes. Main Outcomes and Measures: Risk-adjusted ACSC visit rates before (June to September 2019) and after (June to September 2020) the start of the COVID-19 pandemic, reported as an annualized average marginal effect. The study examined overall, acute, and chronic ACSC visits separately and controlled for practice size, in-person visit volume, zip code-level attributes, and patient characteristics. Results: A total of nearly 1.5 million beneficiaries (53% female; mean [SD] age, 40 [22] years) were attributed to 4038 primary care practices. Compared with 2019 visit volumes, median telehealth use was 0.4% for the low telehealth tertile, 14.7% for the medium telehealth tertile, and 39.0% for the high telehealth tertile. The number of ACSC visits decreased in all tertiles, with adjusted rates changing from 24.3 to 14.9 per 1000 patients per year (low), 23.9 to 15.3 per 1000 patients per year (medium), and 27.5 to 20.2 per 1000 patients per year (high). In difference-in-differences analysis, high telehealth use was associated with a higher ACSC visit rate (2.10 more visits per 1000 patients per year; 95% CI, 0.22-3.97) compared with low telehealth practices; practices in the middle tertile did not differ significantly from the low tertile. No difference was found in ACSC visits across tertiles when acute and chronic ACSC visits were examined separately. Conclusions and Relevance: In this cohort study that used a difference-in-differences analysis, the association between practice-level telehealth use and ACSC visits was mixed. High telehealth use was associated with a slightly higher overall ACSC visit rate than low telehealth practices. The association of telehealth with downstream care use should be closely monitored going forward.

Use of Telehealth by Surgical Specialties During the COVID-19 Pandemic.

Importance: While telehealth use in surgery has shown to be feasible, telehealth became a major modality of health care delivery during the COVID-19 pandemic. Objective: To assess patterns of telehealth use across surgical specialties before and during the COVID-19 pandemic. Design, Setting, and Participants: Insurance claims from a Michigan statewide commercial payer for new patient visits with a surgeon from 1 of 9 surgical specialties during one of the following periods: prior to the COVID-19 pandemic (period 1: January 5 to March 7, 2020), early pandemic (period 2: March 8 to June 6, 2020), and late pandemic (period 3: June 7 to September 5, 2020). Exposures: Telehealth implementation owing to the COVID-19 pandemic in March 2020. Main Outcomes and Measures: (1) Conversion rate defined as the rate of weekly new patient telehealth visits divided by mean weekly number of total new patient visits in 2019. This outcome adjusts for a substantial decrease in outpatient care during the pandemic. (2) Weekly number of new patient telehealth visits divided by weekly number of total new patient visits. Results: Among 4405 surgeons in the cohort, 2588 (58.8%) performed telehealth in any patient care context. Specifically for new patient visits, 1182 surgeons (26.8%) used telehealth. A total of 109 610 surgical new outpatient visits were identified during the pandemic. The median (interquartile range) age of telehealth patients was 46.8 (34.1-58.4) years compared with 52.6 (38.3-62.3) years for patients who received care in-person. Prior to March 2020, less than 1% (8 of 173 939) of new patient visits were conducted through telehealth. Telehealth use peaked in April 2020 (week 14) and facilitated 34.6% (479 of 1383) of all new patient visits during that week. The telehealth conversion rate peaked in April 2020 (week 15) and was equal to 8.2% of the 2019 mean weekly new patient visit volume. During period 2, a mean (SD) of 16.6% (12.0%) of all new patient surgical visits were conducted via telehealth (conversion rate of 5.1% of 2019 mean weekly new patient visit volumes). During period 3, 3.0% (2168 of 71 819) of all new patient surgical visits were conducted via telehealth (conversion rate of 2.5% of 2019 new patient visit volumes). Mean (SD) telehealth conversion rates varied by specialty with urology being the highest (14.3% [7.7%]). Conclusions and Relevance: Results from this study showed that telehealth use grew across all surgical specialties in Michigan in response to the COVID-19 pandemic. While rates of telehealth use have declined as in-person care has resumed, telehealth use remains substantially higher across all surgical specialties than it was prior to the pandemic.

Direct-To-Consumer Telemedicine Visits For Acute Respiratory Infections Linked To More Downstream Visits.

Use of direct-to-consumer telemedicine-on-demand virtual care for minor medical issues-is growing rapidly. Although it may yield immediate savings by diverting health care from higher-cost settings, these savings could be countered if direct-to-consumer telemedicine increases follow-up care and, therefore, episode costs. Comparing downstream care utilization data from a large, commercial payer for the period 2016-19, we found that patients with initial visits for acute respiratory infection were more likely to obtain follow-up care within seven days after direct-to-consumer telemedicine visits (10.3 percent) than after in-person visits (5.9 percent). In both settings approximately 90 percent of patients did not obtain additional care. The telemedicine cohort had fewer (0.5 percent versus 0.6 percent) emergency department visits-a small but statistically significant difference-but more subsequent office, urgent care, and telemedicine visits. Our findings suggest that potential savings from shifting initial care to a direct-to-consumer telemedicine setting should be balanced against the potential for higher spending on downstream care.

Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.

Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biopsy tool for confirmation of virus infection. However, its accuracy is as low as 60-70%, which is inefficient to uncover the infected. In comparison, the chest CT has been considered as the prior choice in diagnosis and monitoring progress of COVID-19 infection. Although the COVID-19 diagnostic systems based on artificial intelligence have been developed for assisting doctors in diagnosis, the small sample size and the excessive time consumption limit their applications. To this end, this paper proposed a diagnosis prototype system for COVID-19 infection testing. The proposed deep learning model is trained and is tested on 2267 CT sequences from 1357 patients clinically confirmed with COVID-19 and 1235 CT sequences from non-infected people. The main highlights of the prototype system are: (1) no data augmentation is needed to accurately discriminate the COVID-19 from normal controls with the specificity of 0.92 and sensitivity of 0.93; (2) the raw DICOM image is not necessary in testing. Highly compressed image like Jpeg can be used to allow a quick diagnosis; and (3) it discriminates the virus infection within 6 seconds and thus allows an online test with light cost. We also applied our model on 48 asymptomatic patients diagnosed with COVID-19. We found that: (1) the positive rate of RT-PCR assay is 63.5% (687/1082). (2) 45.8% (22/48) of the RT-PCR assay is negative for asymptomatic patients, yet the accuracy of CT scans is 95.8%. The online detection system is available: http://212.64.70.65/covid .