The American journal of managed care
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Transitional care management (TCM) services after hospital discharge are critical for continuity of care, and the COVID-19 pandemic accelerated the shift to telehealth modes of delivery. This study examined the shift from face-to-face to telehealth care around the start of the pandemic (April-July 2020) compared with the same months in 2019 and 2021 and the corresponding 30-day readmission rates. We compared the rates of face-to-face and telehealth TCM as well as face-to-face and telehealth non-TCM services and observed a dramatic shift to telehealth in 2020 with a slight drop-off in 2021. ⋯ These data indicate that this dramatic systemwide shift from face-to-face to telehealth TCM was not accompanied by concurrent changes in either 30-day readmission or mortality rates. Although the findings may be subject to ecologic bias, the data at hand did not allow for reliable estimation of differences in effects of patient-level service delivery type on readmission risk or mortality due to the extremely low volume of face-to-face visits during the pandemic periods. Future research would be needed to conduct such comparisons.
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This article reviews the book Artificial Intelligence for Improved Patient Outcomes: Principles for Moving Forward With Rigorous Science by Daniel W. Byrne.
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Understanding how the COVID-19 pandemic affected cardiovascular disease (CVD) risk monitoring in primary care may inform new approaches for addressing modifiable CVD risks. This study examined how pandemic-driven changes in primary care delivery affected CVD risk management processes. ⋯ After pandemic onset, appointment completion rates were higher, time to appointment was shorter, HbA1c documentation increased, and BP documentation decreased. Future research should explore the advantages of using VC for CVD risk management while continuing to monitor for unintended consequences.
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To understand patient perceptions of specific applications of predictive models in health care. ⋯ Patients are more comfortable with clinical applications of predictive models than administrative ones. Privacy protections and transparency about how health care systems protect patient data may facilitate patient comfort with these technologies. However, larger inequities and negative experiences in health care remain important for how patients perceive administrative applications of prediction.
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Limited research has assessed how virtual care (VC) affects cardiovascular disease (CVD) risk management, especially in community clinic settings. This study assessed change in community clinic patients' CVD risk management during the COVID-19 pandemic and CVD risk factor control among patients who had primarily in-person or primarily VC visits. ⋯ Among community clinic patients with CVD risk, receiving a majority of care in person vs a majority of care via VC was not significantly associated with longitudinal trends in reversible CVD risk score or key CVD risk factors.