Population Health Management最新文献

{"title":"A New Approach to Identifying Avoidable Emergency Department Visits Among High-Need High-Cost Medicaid Patients.","authors":"Nadereh Pourat, Xiao Chen, Connie Lu, Menbere Haile, Helen Yu-Lefler, Troyana Benjamin, Benjamin Picillo, Minh Wendt, Alek Sripipatana","doi":"10.1177/19427891261490010","DOIUrl":"https://doi.org/10.1177/19427891261490010","url":null,"abstract":"<p><p>Patients with high need and high costs (HNHCs) are frequent users of emergency departments (EDs), and some of their visits may be avoidable with better access to primary care. A better understanding of the reasons for ED use of HNHC patients can inform cost containment and quality improvement efforts. Our goal was to identify reasons for ED visits based on categories of services received by HNHC patients during the visit and to examine the likely role of different categories of primary care providers (PCPs) in reducing potentially preventable visits. We used the 2018 California Medicaid enrollment and claims data for HNHC patients with at least 1 PCP visit and 1 ED visit followed by discharge. These patients generated 539,798 ED visits. We conducted latent class analysis to classify visits and logistic regressions to examine associations between PCP type, visit, and patient characteristics with ED classes. We identified 6 classes of ED visits and considered 2 as acute care and 4 as nonacute care classes. The latter included a large class of ED visits (51%) by patients who received the fewest procedures. Patients within this group were more likely to have chronic and mental health conditions, weekend ED visits, live in rural locations, and attend group practices and health centers funded by the Health Resources and Services Administration. Our findings suggest that expanding after-hours services, providing medical emergency hotlines, and delivering comprehensive services within primary care may help circumvent costly and frequent nonacute ED visits among HNHC patients.</p>","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"19427891261490010"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Point-of-Care A1C and Time from Test to Communication of Results in Primary Care.","authors":"Cecilia E W Nicol, Joselyne E Aucapina, Cordelia Lee, Pamela Mariano, Judy Tung, Jennifer I Lee, Mangala Rajan, Andrea Card, Monika M Safford, Lisa M Kern","doi":"10.1177/19427891261456847","DOIUrl":"10.1177/19427891261456847","url":null,"abstract":"<p><p>Although patients with uncontrolled diabetes (A1C ≥ 9%) frequently visit their primary care physicians' offices, their diabetes medication is not always changed or intensified at those visits, a pattern described as \"therapeutic inertia.\" One potential solution is the use of point-of-care fingerstick hemoglobin A1C (A1C), which provides results in minutes, rather than venous A1C, which takes days. However, point-of-care A1C is not consistently used in current practice. The authors sought to determine the effectiveness of a systematic nurse-based intervention for implementing point-of-care A1C testing among patients with uncontrolled diabetes. They conducted a before-and-after experimental trial to determine the effect of point-of-care A1C testing on therapeutic inertia in a hospital-based clinic in New York, NY, in 2024-2025. They compared the frequency of any clinical action taken and the time to communication of results to patients before vs. after intervention implementation, using chi-squared and <i>t</i>-tests, respectively. The study included 203 patients, of whom 40.4% were Black or African American and 38.9% were Hispanic. Systematic collection of point-of-care A1C did not change the frequency of clinical actions taken overall (79.0% pre-intervention vs. 73.8% post-intervention, <i>P</i> = 0.38). However, the intervention resulted in a significantly shorter time from test to communication of results to patients, compared to usual care (0.6 days vs. 7.4 days, <i>P</i> = 0.03). A systematic intervention to increase the utilization of point-of-care A1C is effective in facilitating faster delivery of results to patients, which enables more counseling for patients with uncontrolled diabetes during their visit.</p>","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"330-339"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13374563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148176634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Colorectal Cancer Screening and Follow-up: Results of a Multi-Organization Quality Improvement Collaborative.","authors":"Earlean S Chambers, Erin Leaver Schmidt, Cori Rattelman, Elizabeth L Ciemins, John W Kennedy, Danielle F Casanova, Theodore R Levin","doi":"10.1177/19427891261491274","DOIUrl":"https://doi.org/10.1177/19427891261491274","url":null,"abstract":"<p><p>Colorectal cancer (CRC) screening rates in the United States remain below national targets, and follow-up after abnormal stool-based testing is frequently incomplete. This study evaluated changes in CRC screening rates, age-based screening disparities, and 90-day follow-up colonoscopy completion among health care organizations participating in a national quality improvement learning collaborative. Using a mixed-methods, pre-test post-test design, 18 health care organizations serving approximately 3.9 million patients aged 45-75 years reported quarterly aggregate data from the third quarter of 2023 through the first quarter of 2025. Up-to-date (UTD) screening rates and 90-day follow-up colonoscopy completion after abnormal non-colonoscopy results were stratified by age group (45-49, 50-64, and 65-75 years). Group averages and within-organization changes were assessed using chi-square tests and paired <i>t-</i>tests, and qualitative implementation data were analyzed using the Consolidated Framework for Implementation Research. The group average UTD screening rate increased from 61.3% to 66.2%, a 4.9-percentage-point improvement (<i>P</i> < 0.001), corresponding to an estimated 133,538 additional patients screened. Ninety-day follow-up colonoscopy completion increased from 36.2% to 42.9% (<i>P</i> = 0.005). The screening gap between patients aged 45-49 years and those aged 65-75 years narrowed from 35.7 to 26.3 percentage points (<i>P</i> < 0.001). High-impact implementation strategies included readiness assessment, facilitation, data infrastructure development, coalition-building, and performance feedback. Participation in a structured national learning collaborative was associated with meaningful improvements in screening, follow-up, and equity across diverse organizations, supporting collaborative, data-driven approaches to advancing preventive care in population health management. Together, these findings constitute statistically meaningful results across the full screening continuum.</p>","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"19427891261491274"},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Community Linkages into Care Management Workflows to Address Contextual Drivers of Health: Lessons from a Pilot Intervention.","authors":"Rose Gunn, Maura Pisciotta, Constance Owens-Jasey, Rachel Gold, Connie Fee, Brenda M McGrath, Hannah L Fein, Jenna Donovan, Anna Templeton, Molly Volk-Britton, Wei Huang, Shirley Stowe, Nicole Cook","doi":"10.1177/19427891261491171","DOIUrl":"10.1177/19427891261491171","url":null,"abstract":"<p><p>Adverse contextual drivers (eg, food, housing, transportation insecurity) influence health outcomes, particularly among individuals with chronic conditions. Electronic health record (EHR)-based care management tools can support screening and referrals for contextual drivers, but under-resourced health centers often lack support to integrate these tools into routine workflows. This pilot study assessed the feasibility of a multicomponent implementation support model to improve care managers' use of EHR tools to address contextual drivers. Three health centers within a national network using a shared EHR participated in a 3-month intervention that included individualized training, practice facilitation, peer support, and audit and feedback. Qualitative data were collected through semi-structured interviews and periodic reflections with care management staff and analyzed thematically. Participants valued structured engagement, protected time for workflow refinement, and individualized guidance from practice coaches and technical experts. They also reported increased confidence using EHR tools to document referrals and coordinate care. Challenges included confusion about audit and feedback data sources, insufficient training on report generation, and variable value of peer discussions. Findings informed intervention refinements, including earlier training, revised peer support structures, enhanced technical assistance, and improved data transparency and accessibility. Results support the feasibility and acceptability of tailored implementation support for integrating contextual driver workflows into care management. The refined intervention will be tested in a stepped wedge cluster randomized trial to evaluate effectiveness, scalability, and impact on care teams.</p>","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"19427891261491171"},"PeriodicalIF":2.1,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accountable Care Organization Medicare SNF 3-Day Waiver Improves Subacute Rehabilitation Access and Decreases Cost of Care.","authors":"Alicia Jacobs, Carrie Weigand","doi":"10.1177/19427891261491154","DOIUrl":"https://doi.org/10.1177/19427891261491154","url":null,"abstract":"<p><p>The Medicare Skilled Nursing Facility 3-Day Rule Waiver Benefit Enhancement (SNF Waiver) has been in use in Vermont since 2018 through a Centers for Medicare and Medicaid Services (CMS) waiver with the OneCare Vermont Accountable Care Organization (ACO). This article describes the usage of the waiver in 2024-2025 and calculates a cost savings from eliminating the Medicare SNF 3-Day Rule. OneCare conducted a usage and cost savings analysis from the implementation of the SNF Waiver. Hospitalization costs and number of hospital days were estimated. The use of the SNF Waiver on an annual basis nearly doubled absolutely from the reporting period of 2018-2019 to 2023-2025, which was a 55% relative increase of use in the state of Vermont. The SNF Waiver was broadly used across many different specialties. The analysis determined that use of the SNF Waiver saved at least $2 million in hospital expenses in gross savings in 2024 in Vermont alone. This study demonstrates that utilizing ACO waivers allows patients to receive <i>the right care, in the right place, at the right time.</i> Further, the study shows considerable health care cost savings when applying an average of $3000 savings per patient per hospital day. If the SNF Waiver were to be adopted nationally, roughly 275,000 inpatient days could be avoided annually, which represents more than $800 million in potential cost savings. This study, combined with others, supports the recommendation that the CMS consider permanent reform by <i>eliminating</i> the SNF 3-Day Rule.</p>","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"19427891261491154"},"PeriodicalIF":2.1,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Border Care Coordination as a Value-Based Strategy: A Point of View for Population Health Leaders.","authors":"Ronald W Adkins","doi":"10.1177/19427891261491117","DOIUrl":"https://doi.org/10.1177/19427891261491117","url":null,"abstract":"","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"19427891261491117"},"PeriodicalIF":2.1,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149025949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weed Was Right Decades Ago: Do We Have the Will to Support Augmented Intelligence for Population Health?","authors":"Richard G Stefanacci","doi":"10.1177/19427891261491164","DOIUrl":"https://doi.org/10.1177/19427891261491164","url":null,"abstract":"","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"19427891261491164"},"PeriodicalIF":2.1,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149025930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Care Management Targeting: A Machine Learning Comparison of Regression, Classification, and Ranking Approaches for Identifying Future High-Cost Patients.","authors":"Chunhui Mo","doi":"10.1177/19427891261488894","DOIUrl":"https://doi.org/10.1177/19427891261488894","url":null,"abstract":"<p><p>Effective care management programs depend on accurately identifying patients who are likely to account for a disproportionate share of future health care expenditures. Unlike traditional risk-adjustment models, which emphasize population-level predictive accuracy, care management targeting is fundamentally a top-k decision problem focused on maximizing expenditure capture. This study used 2000-2023 MEPS data to compare regression-, classification-, and learning-to-rank machine learning approaches for predicting next-year health care expenditures. Primary outcomes included precision-at-k, cost capture-at-k, weighted cost capture-at-k, and AUC. Models were benchmarked against a persistence-based floor baseline using prior-year expenditures and an oracle upper bound representing perfect future information. Classification-based models consistently achieved the strongest operational targeting performance. Neural network and XGBoost classifiers outperformed regression and ranking approaches in identifying future high-cost individuals and maximizing expenditure capture. Differences in cost capture were statistically significant across yearly evaluations. More complex modeling extensions provided little additional benefit. Prior-year utilization and expenditure variables contributed substantially to predictive accuracy; however, models retained approximately 80%-90% of full-model performance after removal of these features, indicating meaningful predictive signal from sociodemographic characteristics, chronic disease burden, functional limitations, and self-reported health status. For care management programs operating under fixed enrollment constraints, classification-based prediction approaches appear better aligned with operational objectives than regression or ranking. Additional performance gains are more likely to arise from richer clinical and longitudinal data than from increased model complexity alone. Future AI-enabled care management systems may further improve resource allocation by integrating cost-risk prediction with estimates of patient engagement and intervention impactability.</p>","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"19427891261488894"},"PeriodicalIF":2.1,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic Care Management and Diabetes Complications in Medicare Patients with Diabetes and Multiple Chronic Conditions.","authors":"Mariétou H Ouayogodé, Yingying Zheng","doi":"10.1177/19427891261481302","DOIUrl":"https://doi.org/10.1177/19427891261481302","url":null,"abstract":"<p><p>Chronic care management (CCM) services may improve disease management and reduce complications for chronically ill patients with multimorbidity. This retrospective study analyzed Medicare claims data (2015-2020) for diabetes patients with multimorbidity, with evaluation and management visits in four U.S. states (Illinois, Iowa, Minnesota, and Wisconsin). The authors assessed differences in diabetes complications among patients receiving CCM services (CCM patients) compared with those not receiving such services (non-CCM patients). The study described patients' characteristics and diabetes-related complications by CCM receipt and identified the frequency of CCM service types (complex and noncomplex). Using propensity-score bounding, covariate and weight-adjusted probabilities of complications were compared across CCM and non-CCM patients. Heterogeneity in estimated probabilities was assessed across states and age categories. Of 5,465,138 visits analyzed, 38,403 visits included CCM services (with noncomplex CCM representing 95.4% of all CCM visits). Regression analyses showed that CCM patients had comparable estimated probability of developing neurological (0.048 vs. 0.046, <i>P</i> = 1.240) complications, lower probability of renal (0.014 vs. 0.017, <i>P</i> < 0.001) and ophthalmical (the least common complication, 0.009 vs. 0.010, <i>P</i> = 0.066) complications, but relatively higher probability of hypoglycemia (the most common complication, 0.070 vs. 0.061, <i>P</i> < 0.001) and circular/vascular (0.034 vs. 0.027 in non-CCM patients, <i>P</i> < 0.001 for the difference) complications than non-CCM patients. There were differences in estimated probability of complications across states and age categories. Non-complex CCM may not suffice to reduce the development of diabetes-related complications among diabetes patients with multimorbidity.</p>","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"19427891261481302"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148875929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Influence to Impact: Supporting Quality Improvement in Independent Primary Care Practices.","authors":"Nancy Maulick, John Stoeckle, Wayne Stephens, Cathryn Kelly, Pamela Fisher, Jennifer Stephens","doi":"10.1177/19427891261481310","DOIUrl":"https://doi.org/10.1177/19427891261481310","url":null,"abstract":"<p><p>Clinically integrated networks (CINs) and physician-hospital organizations (PHOs) participating in value-based care (VBC) are accountable for quality across both employed and independent practices. Employed practices benefit from shared electronic health records (EHRs), standardized workflows, and centralized oversight. Independent practices present distinct challenges, including EHR heterogeneity, variable billing workflows, limited administrative infrastructure, and lack of direct operational control. Despite representing a substantial proportion of attributed lives, structured approaches to quality improvement (QI) in these settings remain limited. The authors designed and implemented a structured, practice-level QI intervention for independent primary care practices within a large PHO. The model was evaluated using the RE-AIM framework, with the Consolidated Framework for Implementation Research (CFIR) applied to characterize implementation. Core components included individualized practice assessment, a staged workflow approach (retrospective catch-up, real-time visit optimization, and clinician engagement), personalized performance feedback, and relationship-based support adaptable across heterogeneous EHR environments. Twenty-two practices were included; 15 engaged in the intervention over 21 months (March 2024-November 2025). Engaged practices improved mean composite quality scores from 42.9% (SD: 24.1) to 51.8% (SD: 28.6; +9.0 points; <i>P</i> = 0.005). Nonengaged practices declined from 23.3% (SD: 18.3) to 18.0% (SD: 11.2; -5.4 points; <i>P</i> = 0.65). The between-group effect size was large (Cohen's <i>d</i> = 0.85). Improvements occurred across all nonintegrated EHR systems. A relationship-centered, staged, and tailored approach can improve quality performance in independent practices despite structural constraints. This model provides a scalable framework for integrated delivery networks to drive performance across both employed and independent settings.</p>","PeriodicalId":20396,"journal":{"name":"Population Health Management","volume":" ","pages":"19427891261481310"},"PeriodicalIF":2.1,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}