Browsing by Subject "Healthcare delivery"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Agile implementation of alcohol screening in primary care
    (Springer Nature, 2024-07-11) Summanwar, Diana; Ropert, Chelan; Barton, James; Hiday, Rachael; Bishop, Dawn; Boustani, Malaz; Willis, Deanna; Medicine, School of Medicine
    Background: Despite the United States Preventive Services Task Force recommendation to screen adults for unhealthy alcohol use, the implementation of alcohol screening in primary care remains suboptimal. Methods: A pre and post-implementation study design that used Agile implementation process to increase screening for unhealthy alcohol use in adult patients from October 2021 to June 2022 at a large primary care clinic serving minority and underprivileged adults in Indianapolis. Results: In comparison to a baseline screening rate of 0%, the agile implementation process increased and sustained screening rates above 80% for alcohol use using the Alcohol Use Disorders Identification Test - Consumption tool (AUDIT-C). Conclusions: Using the agile implementation process, we were able to successfully implement evidence-based recommendations to screen for unhealthy alcohol use in primary care.
  • Thumbnail Image
    Item
    Association between social vulnerability index and admission urgency for transcatheter aortic valve replacement
    (Elsevier, 2024) Bolakale-Rufai, Ikeoluwapo Kendra; Shinnerl, Alexander; Knapp, Shannon M.; Johnson, Amber E.; Mohammed, Selma; Brewer, LaPrincess; Torabi, Asad; Addison, Daniel; Mazimba, Sula; Breathett, Khadijah; Medicine, School of Medicine
    Background: Transcatheter aortic valve replacement (TAVR) are not offered equitably to vulnerable population groups. Adequate levels of insurance may narrow gaps among patients with higher social vulnerability index (SVI). Among a national population of individuals with commercial or Medicare insurance, we sought to determine whether SVI was associated with urgency of receipt of TAVR for aortic stenosis. Methods and results: Using Optum's de-identified Clinformatics Data Mart Database (CDM), we identified admissions for TAVR with aortic stenosis between January 2018 and March 2022. Admission urgency was identified by CDM claims codes. SVI was cross-referenced to patient zip codes and grouped into quintiles. Generalized linear mixed effects models were used to predict the probability of a TAVR admission being urgent based on SVI quintiles, adjusting for patient and hospital-level covariates. Results: Among 6680 admissions for TAVR [median age 80 years (interquartile range 75-85), 43.9 % female], 8.5 % (n = 567) were classified as urgent. After adjusting for patient and hospital-level variables, there were no significant differences in the odds of urgent admission for TAVR according to SVI quintiles [OR 5th (greatest social vulnerability) vs 1st quintile (least social vulnerability): 1.29 (95 % CI: 0.90-1.85)]. Conclusions: Among commercial or Medicare beneficiaries with aortic stenosis, SVI was not associated with admission urgency for TAVR. To clarify whether cardiovascular care delivery is improved across SVI with higher paying beneficiaries, future investigation should identify whether relationships between SVI and TAVR urgency vary for Medicaid beneficiaries compared to commercial beneficiaries.
  • Thumbnail Image
    Item
    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.