The University of Wisconsin Neighborhood Health Partnerships Program used electronic health record and influenza vaccination data to estimate COVID-19 relative mortality risk and potential barriers to vaccination in Wisconsin ZIP Code Tabulation Areas. Data visualization revealed four groupings to use in planning and prioritizing vaccine outreach and communication based on ZIP Code Tabulation Area characteristics. The program provided data, visualization, and guidance to health systems, health departments, nonprofits, and others to support planning targeted outreach approaches to increase COVID-19 vaccination uptake. (Am J Public Health. 2021;111(12):2111–2114. https://doi.org/10.2105/AJPH.2021.306524 )

Background During the COVID‐19 pandemic, patients with chronic illnesses avoided regular medical care, raising concerns about long‐term complications. Our objective was to identify a population of older patients with chronic conditions who may be at risk from delayed or missed care (DMC) and follow their non‐COVID outcomes during the pandemic. Methods We used a retrospective matched cohort design using Medicare claims and electronic health records at a large health system with community and academic clinics. Participants included 14,406 patients over 65 years old with two or more chronic conditions who had 1 year of baseline data and up to 9 months of postpandemic follow‐up from March 1, 2019 to December 31, 2020; and 14,406 matched comparison patients from 1 year prior. Risk from DMC was defined by 13 indicators, including chronic conditions, frailty, disability affecting the use of telehealth, recent unplanned acute care, prior missed outpatient care, and social determinants of health. Outcomes included mortality, inpatient events, Medicare payments, and primary care and specialty care visits (in‐person and telehealth). Results A total of 25% of patients had four or more indicators for risk from DMC. Per 1000 patients annually, those with four or more indicators had increased mortality of 19 patients (95% confidence interval, 4 to 32) and decreased utilization, including unplanned events (−496 events, −611 to −381) and primary care visits (−1578 visits, −1793 to −1401). Conclusions Older patients who had four or more indicators for risk from DMC had higher mortality and steep declines in inpatient and outpatient utilization during the pandemic.

BACKGROUND Impactability modeling promises to help solve the nationwide crisis in caring for high-need high-cost patients by matching specific case management programs with patients using a “benefit” or “impactability” score, but there are limitations in tailoring each model to a specific program and population. OBJECTIVE We evaluated the impact on Medicare ACO savings from developing a benefit score for patients enrolled in an historic case management program, then prospectively implementing the score and evaluating the results in a new case management program. METHODS We conducted a longitudinal cohort study of 76,140 patients in a Medicare ACO with multiple before-and-after measures of the outcome using linked electronic health records and Medicare claims data from 2012 to 2019. There were 489 patients in the historic case management program and 1,550 matched comparison patients; 830 patients in the new program with 2,368 matched comparisons. The historic program targeted high-risk patients and assigned a centrally-located registered nurse and social worker to each patient. The new program targets high- and moderate-risk patients and assigns a nurse physically located in a primary care clinic. Our primary outcomes were any unplanned hospital events (admissions, observation stays, and ED visits), count of event-days, and Medicare payments. RESULTS In the historic program, as expected, high-benefit patients enrolled in case management had fewer events, fewer event-days, and an average $1.15 million reduction in Medicare payments per 100 patients over the subsequent year when compared to matched comparisons. For the new program, high-benefit high-risk patients enrolled in case management had fewer events, while high-benefit moderate-risk patients enrolled in case management did not differ from matched comparisons. CONCLUSIONS Although there was evidence that a benefit score could be extended to a new case management program for similar (i.e., high-risk) patients, there was no evidence that it could be extended to a moderate-risk population. Extending a score to a new program and population should include evaluation of program outcomes within key subgroups. With the increased attention to value-based care, policy makers and measure developers should consider ways to incorporate impactability modeling into program design and evaluation. CLINICALTRIAL N/A

Background Impactability modeling promises to help solve the nationwide crisis in caring for high-need high-cost patients by matching specific case management programs with patients using a “benefit” or “impactability” score, but there are limitations in tailoring each model to a specific program and population. Objective We evaluated the impact on Medicare accountable care organization savings from developing a benefit score for patients enrolled in a historic case management program, prospectively implementing the score, and evaluating the results in a new case management program. Methods We conducted a longitudinal cohort study of 76,140 patients in a Medicare accountable care organization with multiple before-and-after measures of the outcome, using linked electronic health records and Medicare claims data from 2012 to 2019. There were 489 patients in the historic case management program, with 1550 matched comparison patients, and 830 patients in the new program, with 2368 matched comparison patients. The historic program targeted high-risk patients and assigned a centrally located registered nurse and social worker to each patient. The new program targeted high- and moderate-risk patients and assigned a nurse physically located in a primary care clinic. Our primary outcomes were any unplanned hospital events (admissions, observation stays, and emergency department visits), count of event-days, and Medicare payments. Results In the historic program, as expected, high-benefit patients enrolled in case management had fewer events, fewer event-days, and an average US $1.15 million reduction in Medicare payments per 100 patients over the subsequent year when compared with the findings in matched comparison patients. For the new program, high-benefit high-risk patients enrolled in case management had fewer events, while high-benefit moderate-risk patients enrolled in case management did not differ from matched comparison patients. Conclusions Although there was evidence that a benefit score could be extended to a new case management program for similar (ie, high-risk) patients, there was no evidence that it could be extended to a moderate-risk population. Extending a score to a new program and population should include evaluation of program outcomes within key subgroups. With increased attention on value-based care, policy makers and measure developers should consider ways to incorporate impactability modeling into program design and evaluation.

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