Health system approaches and experiences implementing the 4Ms: Insights from 3 early adopter health systems.

BACKGROUND: Redesigning the healthcare system to consistently provide effective and tailored care to older adults is needed. The 4Ms (What Matters, Mobility, Medication, and Mentation) offer a framework to guide health systems' efforts to deliver Age-Friendly care. We use an implementation science framework to characterize and assess real-world implementation experiences with the 4Ms across varied health systems. METHODS: With expert input, we selected three health systems that were early adopters of the 4Ms and engaged in different implementation support models through the Institute for Healthcare Improvement. We conducted 29 semi-structured interviews with diverse stakeholders from each site. Stakeholders ranged from hospital leadership to frontline clinicians. Interviews covered each site's approach to and experiences with implementation, including facilitators and barriers. Interviews were recorded, transcribed, and deductively coded using the Consolidated Framework for Implementation Research. We characterized each site's implementation decisions and then inductively identified overarching themes and subthemes with supporting quotes. RESULTS: Health systems varied in their implementation approach, including the implementation order of each of the 4Ms. We identified three overarching themes: (1) the 4Ms offered a compelling conceptual framework for advancing Age-Friendly care, but implementation was complex and fragmented; (2) complete and sustained implementation of the 4Ms required multidisciplinary and multilevel leadership and engagement; (3) strategies that facilitate implementation success and support frontline culture change included top-down communication and infrastructure alongside hands-on clinical education and support. Common barriers are siloed implementation efforts across settings that impeded synergies and scaling; disengaged physicians; and difficulty implementing What Matters in a meaningful way. CONCLUSIONS: Similar to other implementation studies, we identified multifactorial domains impacting 4Ms implementation. To achieve Age-Friendly transformation, health systems must plan for and attend to multiple phases of implementation while ensuring that the work coheres under a unified vision that spans disciplines and settings.

Dispersion in the hospital network of shared patients is associated with less efficient care.

BACKGROUND: There is growing recognition that health care providers are embedded in networks formed by the movement of patients between providers. However, the structure of such networks and its impact on health care are poorly understood. PURPOSE: We examined the level of dispersion of patient-sharing networks across U.S. hospitals and its association with three measures of care delivered by hospitals that were likely to relate to coordination. METHODOLOGY/APPROACH: We used data derived from 2016 Medicare Fee-for-Service claims to measure the volume of patients that hospitals treated in common. We then calculated a measure of dispersion for each hospital based on how those patients were concentrated in outside hospitals. Using this measure, we created multivariate regression models to estimate the relationship between network dispersion, Medicare spending per beneficiary, readmission rates, and emergency department (ED) throughput rates. RESULTS: In multivariate analysis, we found that hospitals with more dispersed networks (those with many low-volume patient-sharing relationships) had higher spending but not greater readmission rates or slower ED throughput. Among hospitals with fewer resources, greater dispersion related to greater readmission rates and slower ED throughput. Holding an individual hospital's dispersion constant, the level of dispersion of other hospitals in the hospital's network was also related to these outcomes. CONCLUSION: Dispersed interhospital networks pose a challenge to coordination for patients who are treated at multiple hospitals. These findings indicate that the patient-sharing network structure may be an overlooked factor that shapes how health care organizations deliver care. PRACTICE IMPLICATIONS: Hospital leaders and hospital-based clinicians should consider how the structure of relationships with other hospitals influences the coordination of patient care. Effective management of this broad network may lead to important strategic partnerships.

Association Between Medicare Policy Reforms and Changes in Hospitalized Medicare Beneficiaries' Severity of Illness.

Importance: The measured severity of illness of hospitalized Medicare beneficiaries has increased. Whether this change is associated with payment reforms, concentrated among hospitalizations with principal diagnoses targeted by payment reform, and reflective of true increases in severity of illness is unknown. Objectives: To assess whether the expansion of secondary diagnosis codes in January 2011 and the incentive payments for health information technology under the US Health Information Technology for Economic and Clinical Health Act were associated with changes in measured severity of illness and whether those changes are reflective of true increases in underlying patient severity. Design, Setting, and Participants: This cohort study of Medicare fee-for-service beneficiary discharges (N = 47 951 443) between January 1, 2008, and August 31, 2015, used a regression-discontinuity design to evaluate changes in measured severity of illness after the expansion of secondary diagnoses. Discharge-level linear regression model with hospital fixed effects was used to evaluate changes in measured severity of illness after hospitals' receipt of incentives for health information technology. The change in predictive accuracy of measured severity of illness on 30-day readmissions after the implementation of both policies was evaluated. Data analysis was performed from November 1, 2018, to March 5, 2019. Main Outcomes and Measures: The primary outcome was patients' measured severity of illness determined by the number of condition categories from secondary discharge diagnosis codes. Measured severity of illness for diagnoses commonly targeted by Medicare policies and untargeted diagnoses was assessed. Results: In total, 47 951 443 discharges at 2850 hospitals were included. In 2008, these beneficiaries included 3 882 672 women (58.5%) with a mean (SD) age of 78.5 (8.4) years. In 2014, the discharges included 3 377 137 women (57.8%) with the mean (SD) age of 78.4 (8.7) years. The Centers for Medicare & Medicaid Services expansion of secondary diagnoses was associated with a 0.348 (95% CI, 0.328-0.367; P < .001) change in condition categories for all diagnoses, 0.445 (95% CI, 0.419-0.470; P < .001) for targeted diagnoses, and 0.321 (95% CI, 0.302-0.341; P < .001) for untargeted diagnoses. Health information technology incentives were associated with a 0.013 (95% CI, 0.004-0.022; P = .005) change in condition categories for all diagnoses, 0.195 (95% CI, 0.184-0.207; P < .001) for targeted diagnoses, and -0.016 (95% CI, -0.025 to -0.007; P < .001) for untargeted diagnoses. Minimal improvements in predictive accuracy were observed. Conclusions and Relevance: Changes in Centers for Medicare & Medicaid Services policies appear to be associated with increases in measured severity of illness; these increases do not appear to reflect substantive changes in true patient severity.