Browsing by Author "Solid, Craig A."

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    CMS Practice Assessment Tool Validity for Alternative Payment Models
    (MJH Life Science, 2023-02) Boustani, Malaz A.; Perkins, Anthony J.; Davis-Ajami, Mary L.; Simon, Kosali I.; Chang, Chiang-Hua; Solid, Craig A.; Monahan, Patrick O.; Medicine, School of Medicine
    Objectives: To study the predictive validity of the CMS Practice Assessment Tool (PAT) among 632 primary care practices. Study Design: Retrospective observational study. Methods: The study included primary care physician practices recruited by the Great Lakes Practice Transformation Network (GLPTN), 1 of 29 CMS-awarded networks, and used data from 2015 to 2019. At enrollment, trained quality improvement advisers scored each of the PAT’s 27 milestones by its degree of implementation based on interviews with staff, review of documents, direct observation of practice activity, and professional judgment. The GLPTN also tracked each practice’s status regarding alternative payment model (APM) enrollment. Exploratory factor analysis (EFA) was used to identify summary scores; mixed-effects logistic regression was used to assess the relationship between derived scores with APM participation. Results: EFA revealed that the PAT’s 27 milestones could be summed into 1 overall score and 5 secondary scores. By the end of the 4-year project, 38% of practices were enrolled in an APM. A baseline overall score and 3 secondary scores were associated with increased odds of joining an APM (overall score: odds ratio [OR], 1.06; 95% CI, 0.99-1.12; P = .061; data-driven care quality score: OR, 1.11; 95% CI, 1.00-1.22; P = .040; efficient care delivery score: OR, 1.08; 95% CI, 1.03-1.13; P = .003; collaborative engagement score: OR, 0.88; 95% CI, 0.80-0.96; P = .005). Conclusions: These results demonstrate that the PAT has adequate predictive validity for APM participation.
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    Developing the Agile Implementation Playbook for Integrating Evidence-Based Health Care Services into Clinical Practice
    (Wolters Kluwer, 2018-10) Boustani, Malaz A.; van der Marck, Marjolein A.; Adams, Nadia; Azar, Jose M.; Holden, Richard J.; Vollmar, Horst C.; Wang, Sophia; Williams, Christopher; Alder, Catherine; Suarez, Shelley; Khan, Babar; Zarzaur, Ben; Fowler, Nicole R.; Overley, Ashley; Solid, Craig A.; Gatmaitan, Alfonso; Medicine, School of Medicine
    Problem: Despite the more than $32 billion the National Institutes of Health has invested annually, evidence-based health care services are not reliably implemented, sustained, or distributed in health care delivery organizations, resulting in suboptimal care and patient harm. New organizational approaches and frameworks that reflect the complex nature of health care systems are needed to achieve this goal. Approach: To guide the implementation of evidence-based health care services at their institution, the authors used a number of behavioral theories and frameworks to develop the Agile Implementation (AI) Playbook, which was finalized in 2015. The AI Playbook leverages these theories in an integrated approach to selecting an evidence-based health care service to meet a specific opportunity, rapidly implementing the service, evaluating its fidelity and impact, and sustaining and scaling up the service across health care delivery organizations. The AI Playbook includes an interconnected eight-step cycle: (1) identify opportunities; (2) identify evidence-based health care services; (3) develop evaluation and termination plans; (4) assemble a team to develop a minimally viable service; (5) perform implementation sprints; (6) monitor implementation performance; (7) monitor whole system performance; and (8) develop a minimally standardized operating procedure. Outcomes: The AI Playbook has helped to improve care and clinical outcomes for intensive care unit survivors and is being used to train clinicians and scientists in AI to be quality improvement advisors. Next Steps: The authors plan to continue disseminating the details of the AI Playbook and illustrating how health care delivery organizations can successfully leverage it.
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    A Profile in Population Health Management: The Sandra Eskenazi Center for Brain Care Innovation
    (2019) Boustani, Malaz; Yourman, Lindsey; Holden, Richard J.; Pang, Peter S.; Solid, Craig A.; Medicine, School of Medicine
    This article describes how key aspects of the Sandra Eskenazi Center for Brain Care Innovation's (SECBCI) care model can inform other entities on the development of new models of population health management, through a framework that emphasizes social, behavioral, and environmental determinants of health, as well as biomedical aspects. The SECBCI is a collaboration with Eskenazi Health and community-based organizations such as the Central Indiana Council on Aging Area Agency on Aging and the Greater Indianapolis Chapter of the Alzheimer's Association in Central Indiana.
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    The impact of antipsychotic adherence on acute care utilization
    (BMC, 2023-01-24) Perkins, Anthony J.; Khandker, Rezaul; Overley, Ashley; Solid, Craig A.; Chekani, Farid; Roberts, Anna; Dexter, Paul; Boustani, Malaz A.; Hulvershorn, Leslie; Medicine, School of Medicine
    Background: Non-adherence to psychotropic medications is common in schizophrenia and bipolar disorders (BDs) leading to adverse outcomes. We examined patterns of antipsychotic use in schizophrenia and BD and their impact on subsequent acute care utilization. Methods: We used electronic health record (EHR) data of 577 individuals with schizophrenia, 795 with BD, and 618 using antipsychotics without a diagnosis of either illness at two large health systems. We structured three antipsychotics exposure variables: the proportion of days covered (PDC) to measure adherence; medication switch as a new antipsychotic prescription that was different than the initial antipsychotic; and medication stoppage as the lack of an antipsychotic order or fill data in the EHR after the date when the previous supply would have been depleted. Outcome measures included the frequency of inpatient and emergency department (ED) visits up to 12 months after treatment initiation. Results: Approximately half of the study population were adherent to their antipsychotic medication (a PDC ≥ 0.80): 53.6% of those with schizophrenia, 52.4% of those with BD, and 50.3% of those without either diagnosis. Among schizophrenia patients, 22.5% switched medications and 15.1% stopped therapy. Switching and stopping occurred in 15.8% and 15.1% of BD patients and 7.4% and 20.1% of those without either diagnosis, respectively. Across the three cohorts, non-adherence, switching, and stopping therapy were all associated with increased acute care utilization, even after adjusting for baseline demographics, health insurance, past acute care utilization, and comorbidity. Conclusion: Non-continuous antipsychotic use is common and associated with high acute care utilization.