The American journal of managed care
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In this issue of Evidence-Based Oncology™ we see a foreshadowing of what the future of cancer care innovation could look like and how we may learn to move forward, safely, at an ever-accelerating pace.
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Observational Study
Growth of electronic consultations in the Veterans Health Administration.
To evaluate the growth and variation of electronic consultation, or e-consult, use in the Veterans Health Administration (VHA) across regions and specialties. ⋯ Use of e-consults in the VHA grew substantially between 2012 and 2018, with variability across specialties. In-person follow-up after an e-consult was low, suggesting that e-consults may substitute for in-person visits and reduce considerable patient travel burden.
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Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs), making measurement and analysis difficult. This study aims to extract and classify, from EHR notes, interventions intended to address patients' social needs using natural language processing (NLP) and machine learning (ML) algorithms. ⋯ NLP and ML can be utilized for automated identification and classification of SW interventions documented in EHRs. Health care administrators can leverage this automated approach to gain better insight into the most needed social interventions in the patient population served by their organizations. Such information can be applied in managerial decisions related to SW staffing, resource allocation, and patients' social needs.
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The telehealth policy changes enacted for short-term control of the coronavirus disease 2019 (COVID-19) pandemic present an opportunity to address the fundamental gap in health care underutilization.
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Palliative care has been demonstrated to have positive effects for patients, families, health care providers, and health systems. Early identification of patients who are likely to benefit from palliative care would increase opportunities to provide these services to those most in need. This study predicted all-cause mortality of patients as a surrogate for patients who could benefit from palliative care. ⋯ LSTM models can effectively predict mortality by using a combination of EHR data and administrative claims data. The model could be used as a promising clinical tool to aid clinicians in early identification of appropriate patients for palliative care consultations.