The American journal of managed care
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To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data. ⋯ This study indicates that it is possible to risk-stratify patients' risk of utilization without interacting with the patient or collecting information beyond the patient's age, gender, race, and address. The implications of this application are wide and have the potential to positively affect health systems by facilitating targeted patient outreach with specific, individualized interventions to tackle detrimental SDH at not only the individual level but also the neighborhood level.
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Observational Study
Does machine learning improve prediction of VA primary care reliance?
The Veterans Affairs (VA) Health Care System is among the largest integrated health systems in the United States. Many VA enrollees are dual users of Medicare, and little research has examined methods to most accurately predict which veterans will be mostly reliant on VA services in the future. This study examined whether machine learning methods can better predict future reliance on VA primary care compared with traditional statistical methods. ⋯ The modest gains in performance from the best-performing model, gradient boosting machine, are unlikely to outweigh inherent drawbacks, including computational complexity and limited interpretability compared with traditional logistic regression.
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Implementation of retail health consumer tactics in primary care poses challenges for primary care doctors that must be recognized and addressed.
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To mark the 25th anniversary of the journal, each issue in 2020 will include an interview with a healthcare thought leader. Because January is our annual health information technology issue, we turned to Eric Topol, MD, of Scripps Research.
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To (1) assess whether hospitals in states requiring explicit patient consent ("opt-in") for health information exchange (HIE) are more likely to report regulatory barriers to HIE and (2) analyze whether these policies correlate with hospital volume of HIE. ⋯ Our findings suggest that opt-in consent laws may carry greater administrative burdens compared with opt-out policies. However, less technologically advanced hospitals may bear more of this burden. Furthermore, opt-in policies may not affect HIE volume for hospitals that have already achieved a degree of technological sophistication. Policy makers should carefully consider the incidence of administrative burdens when crafting laws pertaining to HIE.