Primary Care's Effects on Costs in the US Veterans Health Administration, 2016–2019: an Observational Cohort Study
Abstract: Enhancing primary care is a promising strategy for improving the efficiency of health care. Previous studies of primary care’s effects on health expenditures have mostly relied on ecological analyses comparing region-wide expenditures rather than spending for individual patients. To compare overall medical expenditures for individual patients enrolled vs. those not enrolled in primary care in the Veterans Health Administration (VHA). Research design included a cohort study with stratification for clinical risk and multivariable linear regression models adjusted for clinical and demographic confounders of expenditures. Participants were, in total, 6,009,973 VHA patients in fiscal year (FY) 2019—5,410,034 enrolled with a primary care provider (PCP) and 599,939 without a PCP—and similar numbers in FYs 2016–2018. Total annual cost per patient to the VHA (including VHA payments to non-VHA providers) stratified by a composite health risk score previously shown to predict VHA expenditures, and multivariate models additionally adjusted for VHA regional differences, patients’ demographic characteristics, non-VHA insurance coverage, and driving time to the nearest VHA facility. Sensitivity analyses explored different modeling strategies and risk adjusters, as well as the inclusion of expenditures by the Medicare program that covers virtually all elderly VHA patients for care not paid for by the VHA. Within each health-risk decile, non-PCP patients had higher outpatient, inpatient, and total costs than those with a PCP. After adjustment for health risk and other factors, lack of a PCP was associated 27.4% higher VHA expenditures, $3274 per patient annually (p < .0001). Sensitivity analyses using different risk adjusters and including Medicare’s spending for VHA patients yielded similar results. In the VHA system, primary care is associated with substantial cost savings. Investments in primary care in other settings might also be cost-effective.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.