Browsing by Author "Vest, Joshua"

Now showing 1 - 5 of 5
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    Electronic Health Record (EHR) Data Quality and Type 2 Diabetes Mellitus Care
    (2022-06) Wiley, Kevin Keith, Jr.; Vest, Joshua; Blackburn, Justin; De Groot, Mary; Menachemi, Nir; Mendonca, Eneida
    Due to frequent utilization, high costs, high prevalence, and negative health outcomes, the care of patients managing type 2 diabetes mellitus (T2DM) remains an important focus for providers, payers, and policymakers. The challenges of care delivery, including care fragmentation, reliance on patient self-management behaviors, adherence to care management plans, and frequent medical visits are well-documented in the literature. T2DM management produces numerous clinical data points in the electronic health record (EHR) including laboratory test values and self-reported behaviors. Recency or absence of these data may limit providers’ ability to make effective treatment decisions for care management. Increasingly, the context in which these data are being generated is changing. Specifically, telehealth usage is increasing. Adoption and use of telehealth for outpatient care is part of a broader trend to provide care at-a-distance, which was further accelerated by the COVID-19 pandemic. Despite unknown implications for patients managing T2DM, providers are increasingly using telehealth tools to complement traditional disease management programs and have adapted documentation practices for virtual care settings. Evidence suggests the quality of data documented during telehealth visits differs from that which is documented during traditional in-person visits. EHR data of differential quality could have cascading negative effects on patient healthcare outcomes. The purpose of this dissertation is to examine whether and to what extent levels of EHR data quality are associated with healthcare outcomes and if EHR data quality is improved by using health information technologies. This dissertation includes three studies: 1) a cross-sectional analysis that quantifies the extent to which EHR data are timely, complete, and uniform among patients managing T2DM with and without a history of telehealth use; 2) a panel analysis to examine associations between primary care laboratory test ages (timeliness) and subsequent inpatient hospitalizations and emergency department admissions; and 3) a panel analysis to examine associations between patient portal use and EHR data timeliness.
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    LaPorte County Opioid Addiction Needs Assessment
    (Richard M. Fairbanks School of Public Health, 2018) Greene, Marion; Kooreman, Harold; Vest, Joshua; Balio, Casey
    The opioid epidemic continues to affect the lives of many Americans. Paralleling the rise in prescription opioid (pain reliever) sales over the past two decades, the country also experienced an increase in opioid-related morbidity and mortality. Between 2000 and 2015, more than half a million Americans died from drug overdoses, the majority of which were attributable to opioids. The costs, in terms of human suffering and economic impact, are considerable. Based on findings from the LaPorte County survey and key informant interviews that were conducted in the community, respondents cited lack of or limited access to treatment services, especially detoxification, inpatient services, and medication-assisted treatment, as a major challenge in dealing with the epidemic. Furthermore, socio-economic factors, such as poverty and unemployment, and limited awareness or acceptance of the problem within the larger community, together with insufficient funding to address the problem, were described as contributing factors.
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    Time to Leverage Health System Collaborations: Supporting Nursing Facilities Through the COVID-19 Pandemic
    (Wiley, 2020-05-11) Unroe, Kathleen T.; Vest, Joshua; Medicine, School of Medicine
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    Traumatic Brain Injury Surveillance and Research with Electronic Health Records: Building New Capacities
    (2023-03) McFarlane, Timothy D.; Dixon, Brian E.; Malec, James; Vest, Joshua; Wessel, Jennifer
    Between 3.2 and 5.3 million U.S. civilians live with traumatic brain injury (TBI)-related disabilities. Although the post-acute phase of TBI has been recognized as both a discrete disease process and risk factor for chronic conditions, TBI is not recognized as a chronic disease. TBI epidemiology draws upon untimely, incomplete, cross-sectional, administrative datasets. The adoption of electronic health records (EHR) may supplement traditional datasets for public health surveillance and research. Methods Indiana constructed a state-wide clinical TBI registry from longitudinal (2004-2018) EHRs. This dissertation includes three distinct studies to enhance, evaluate, and apply the registry: 1) development and evaluation of a natural language processing algorithm for identification of TBI severity within free-text notes; 2) evaluation and comparison of the performance of the ICD-9-CM and ICD-10-CM surveillance definitions; and 3) estimating the effect of mild TBI (mTBI) on the risk of post-acute chronic conditions compared to individuals without mTBI. Results Automated extraction of Glasgow Coma Scale from clinical notes was feasible and demonstrated balanced recall and precision (F-scores) for classification of mild (99.8%), moderate (100%), and severe (99.9%) TBI. We observed poor sensitivity for ICD-10-CM TBI surveillance compared to ICD-9-CM (0.212 and 0.601, respectively), resulting in potentially 5-fold underreporting. ICD-10-CM was not statistically equivalent to ICD-9-CM for sensitivity (𝑑𝑑𝑑𝑑̂=0.389, 95% CI [0.388,0.405]) or positive predictive value (𝑑𝑑𝑑𝑑̂=-0.353, 95% CI [-0.362,-0.344]). Compared to a matched cohort, individuals with mTBI were more likely to be diagnosed with mental health, substance use, neurological, cardiovascular, and endocrine conditions. Conclusion ICD-9-CM and ICD-10-CM surveillance definitions were not equivalent, and the transition resulted in a underreporting incidence for mTBI. This has direct implications on existing and future TBI registries and the Report to Congress on Traumatic Brain Injury in the United States. The supplementation of state-based trauma registries with structured and unstructured EHR data is effective for studying TBI outcomes. Our findings support the classification of TBI as a chronic disease by funding bodies, which may improve public funding to replace legacy systems to improve standardization, timeliness, and completeness of the epidemiology and post-acute outcomes of TBI.
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    The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression
    (2018-05-30) Kasthurirathne, Suranga N.; Jones, Josette; Grannis, Shaun; Biondich, Paul; Purkayastha, Saptarshi; Vest, Joshua
    Depression is the most commonly occurring mental illness the world over. It poses a significant health and economic burden across the individual and community. Not all occurrences of depression require the same level of treatment. However, identifying patients in need of advanced care has been challenging and presents a significant bottleneck in providing care. We developed a knowledge-driven depression taxonomy comprised of features representing clinical, behavioral, and social determinants of health (SDH) that inform the onset, progression, and outcome of depression. We leveraged the depression taxonomy to build decision models that predicted need for referrals across: (a) the overall patient population and (b) various high-risk populations. Decision models were built using longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH into each model. For each patient population under study, we built additional decision models that incorporated a wide range of patient and aggregate-level SDH and compared their performance against the original models. Models that incorporated SDH yielded high predictive performance. However, use of SDH did not yield statistically significant performance improvements. Our efforts present significant potential to identify patients in need of advanced care using a limited number of clinical and behavioral features. However, we found no benefit to incorporating additional SDH into these models. Our methods can also be applied across other datasets in response to a wide variety of healthcare challenges.