BACKGROUND: Direct oral anticoagulants (DOACs) have complex dosing regimens and are often incorrectly prescribed. We evaluated a nationwide DOAC population management dashboard rollout whose purpose includes pharmacist review and correction of off-label dosing prescriptions.
RESULTS: Using data from Veterans Health Affairs, we identified all patients prescribed DOACs for atrial fibrillation or venous thromboembolism between August 2015 and December 2019. Sites were grouped on the basis of the timing of moderate-high usage of the DOAC population management tool dashboard. Effectiveness was defined as the monthly rate of off-label DOAC prescribing and the rate of clinical adverse events (bleeding, composite of stroke or venous thromboembolism). Implementation was evaluated as the percentage of off-label DOAC prescriptions changed within 7 days. Among the 128 652 patients receiving DOAC therapy at 123 centers, between 6.9% and 8.6% had off-label DOAC prescriptions. Adoption of the DOAC population management tool dashboard before July 2018 was associated with a decline in off-label dosing prescriptions (8.7%-7.6%). Only 1 group demonstrated a significant reduction in monthly rates of bleeding following implementation. All sites experienced a reduction in the composite of venous thromboembolism or stroke following dashboard adoption. There was no difference in the implementation outcome of DOAC prescription change within 7 days in any of the adoption groups.
CONCLUSIONS: Early adoption of the DOAC population management tool dashboard was associated with decreased rates of off-label DOAC dosing prescription and reduced bleeding. Following adoption of the DOAC population management tool dashboard, all sites experienced reductions in venous thromboembolism and stroke events.

UNASSIGNED: Population health management is increasingly being used to support place-based models of care. This case study provides an account of the use of the Population Health Management - Maturity Index (PHM-MI) tool to inform the future development of a neighbourhood model of care for older people in the Central Coast region of Australia.
UNASSIGNED: The PHM-MI tool comprises a set of six evidence-informed elements known to be important in enabling PHM in practice. As part of a joint strategic needs assessment, 17 selected stakeholders from key regional organizations were invited to undertake the PHM-MI tool survey. Three follow-up workshops were held to interpret the results and determine priority actions.
UNASSIGNED: The PHM-MI scores revealed that the overall maturity of the Central Coast to successfully deliver PHM was low across all six elements, findings that were corroborated through participant workshops. Systemic fragmentations, most pertinently of funding and regulation, incentivised silo-based working. The need to formalise and strengthen regional collaborations, enable data integration, find creative ways to use existing funding streams, and promote community engagement were highlighted as core priorities.
UNASSIGNED: Using the PHM-MI tool was enabled by it being embedded within a pre-existing regional strategic process. The results were used to inform future regional priorities. The PHM-MI tool has the potential for use across regional or national contexts.

This research seeks to assess the potential of regionally integrated health management for specific sub-populations, including the incorporation of self-management initiatives. It will achieve this by conducting a thorough stratification analysis of hospital data, utilizing the Adjusted Clinical Groups (ACG) classification system. The approach involves a retrospective review of healthcare data spanning five years, which includes patient demographics, health outcomes, and healthcare utilization metrics. We intend to use the ACG method to classify the patient population into pertinent groups that mirror their health requirements and resource use. The insights obtained from this analysis will be used to create a localized adaptation of the Kaiser Permanente Pyramid Model of Care. This adaptation aims to identify the distribution of costs among patients treated in the Rivierenland Hospital. We anticipate that stratifying data with the ACG method will identify distinct multimorbid subgroups. These subgroups will have unique healthcare requirements. Early interventions and customized health management strategies, based on these insights, could enhance health outcomes and resource efficiency for high-risk patients. This analysis will serve as a foundation for constructive discussions with hospital management and clinical staff, fostering a deeper comprehension of the patients\' burden of disease. It might also foster multidisciplinary collaboration opportunities between medical specialties as with regional healthcare partners such as general practitioners (GPs), mental health and other long-term care organizations. Moreover, we anticipate that self-care initiatives, supported by customized health information, will encourage increased patient engagement and strategies for enhancing lifestyle improvements. This strategy is expected to enable the personalization of advanced care planning based on individual needs profiles, thereby improving the management of complex and chronic conditions, and encouraging self-care practices. Our anticipated findings highlight the potential benefits of a data-informed approach to advancing healthcare outcomes and present opportunities for future investigations to refine and implement such integrated care models across the region.

BACKGROUND: Patients with diabetes at risk of food insecurity face cost barriers to healthy eating and, as a result, poor health outcomes. Population health management strategies are needed to improve food security in real-world health system settings. We seek to test the effect of a prescription produce program, \'Eat Well\' on cardiometabolic health and healthcare utilization. We will also assess the implementation of an automated, affirmative outreach strategy.
METHODS: We will recruit approximately 2400 patients from an integrated academic health system in the southeastern United States as part of a two-arm parallel hybrid type 1 pragmatic randomized controlled trial. Patients with diabetes, at risk for food insecurity, and a recent hemoglobin A1c reading will be eligible to participate. The intervention arm receives, \'Eat Well\', which provides a debit card with $80 (added monthly) for 12 months valid for fresh, frozen, or canned fruits and vegetables across grocery retailers. The control arm does not. Both arms receive educational resources with diabetes nutrition and self-management materials, and information on existing care management resources. Using an intent-to-treat analysis, primary outcomes include hemoglobin A1C levels and emergency department visits in the 12 months following enrollment. Reach and fidelity data will be collected to assess implementation.
CONCLUSIONS: Addressing food insecurity, particularly among those at heightened cardiometabolic risk, is critical to equitable and effective population health management. Pragmatic trials provide important insights into the effectiveness and implementation of \'Eat Well\' and approaches like it in real-world settings.
BACKGROUND: ClinicalTrials.gov Identifier: NCT05896644; Clinical Trial Registration Date: 2023-06-09.

Population Health Management - often abbreviated to PHM - is a relatively new approach for healthcare planning, requiring the application of analytical techniques to linked patient level data. Despite expectations for greater uptake of PHM, there is a deficit of available solutions to help health services embed it into routine use. This paper concerns the development, application and use of an interactive tool which can be linked to a healthcare system\'s data warehouse and employed to readily perform key PHM tasks such as population segmentation, risk stratification, and deriving various performance metrics and descriptive summaries. Developed through open-source code in a large healthcare system in South West England, and used by others around the country, this paper demonstrates the importance of a scalable, purpose-built solution for improving the uptake of PHM in health services.

This manuscript examines the synergistic potential of prospective real-world/time data/evidence (RWTD/E) and randomized controlled trials (RCTs) to enrich healthcare research and operational insights, with a particular focus on its impact within the sarcoma field. Through exploring RWTD/E\'s capability to provide real-world/time, granular patient data, it offers an enriched perspective on healthcare outcomes and delivery, notably in the complex arena of sarcoma care. Highlighting the complementarity between RWTD/E\'s expansive real-world/time scope and the structured environment of RCTs, this paper showcases their combined strength, which can help to foster advancements in personalized medicine and population health management, exemplified through the lens of sarcoma treatment. The manuscript further outlines methodological innovations such as target trial emulation and their significance in enhancing the precision and applicability of RWTD/E, underscoring the transformative potential of these advancements in sarcoma care and beyond. By advocating for the strategic incorporation of prospective RWTD/E into healthcare frameworks, it aims to create an evidence-driven ecosystem that significantly improves patient outcomes and healthcare efficiency, with sarcoma care serving as a pivotal domain for these developments.

OBJECTIVE: Despite increased availability of methodologies to identify algorithmic bias, the operationalization of bias evaluation for healthcare predictive models is still limited. Therefore, this study proposes a process for bias evaluation through an empirical assessment of common hospital readmission models. The process includes selecting bias measures, interpretation, determining disparity impact and potential mitigations.
METHODS: This retrospective analysis evaluated racial bias of four common models predicting 30-day unplanned readmission (i.e., LACE Index, HOSPITAL Score, and the CMS readmission measure applied as is and retrained). The models were assessed using 2.4 million adult inpatient discharges in Maryland from 2016 to 2019. Fairness metrics that are model-agnostic, easy to compute, and interpretable were implemented and apprised to select the most appropriate bias measures. The impact of changing model\'s risk thresholds on these measures was further assessed to guide the selection of optimal thresholds to control and mitigate bias.
RESULTS: Four bias measures were selected for the predictive task: zero-one-loss difference, false negative rate (FNR) parity, false positive rate (FPR) parity, and generalized entropy index. Based on these measures, the HOSPITAL score and the retrained CMS measure demonstrated the lowest racial bias. White patients showed a higher FNR while Black patients resulted in a higher FPR and zero-one-loss. As the models\' risk threshold changed, trade-offs between models\' fairness and overall performance were observed, and the assessment showed all models\' default thresholds were reasonable for balancing accuracy and bias.
CONCLUSIONS: This study proposes an Applied Framework to Assess Fairness of Predictive Models (AFAFPM) and demonstrates the process using 30-day hospital readmission model as the example. It suggests the feasibility of applying algorithmic bias assessment to determine optimized risk thresholds so that predictive models can be used more equitably and accurately. It is evident that a combination of qualitative and quantitative methods and a multidisciplinary team are necessary to identify, understand and respond to algorithm bias in real-world healthcare settings. Users should also apply multiple bias measures to ensure a more comprehensive, tailored, and balanced view. The results of bias measures, however, must be interpreted with caution and consider the larger operational, clinical, and policy context.

OBJECTIVE: Maternal mortality and morbidity disproportionately affect birthing people from racialized populations. Unfortunately, researchers can often compound these poor outcomes through a lack of authentic community engagement in research beyond the role of the research subject, leading to ineffective strategies for improving care and increasing equity. This article details the real-life strategies utilized to develop a community-engaged research project of a phased federally funded grant employing community engagement principles of co-leadership and co-creation. It also includes reflections from the researchers and advisory board on promising practices and lessons learned for equitably engaging patients and community partners in research.
METHODS: This article details the application of principles of community-engaged research in a federally funded phased research project focused on understanding disparities in maternal sepsis to develop better clinical and community interventions. Specifically, it discusses early steps in the research partnership to create a sustainable partnership with a Community Leadership Board guided by the principles of transparency, respect, compensation, and increasing research justice.
RESULTS: Based on the authors\' experience, recommendations are provided for funders, researchers, and institutions to improve the quality and outcomes of communityengaged research. This work adds to community-based participatory and community-engaged research literature by providing concrete and practical steps for equitably engaging in research partnerships with a variety of collaborators.
CONCLUSIONS: In conclusion, integrated patient and community co-leadership enhances research by providing insight, access to communities for education and dissemination of information, and identifying critical areas needing change. This report may help others address fundamental principles in this journey.

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The recruitment and retention of professionals in indirectly managed and privately managed health care institutions is governed by a different regulatory framework than in directly managed institutions. That legal framework is the Workers\' Statute, which contains its own regulatory elements in terms of bargaining power and general basic conditions, among others. The regulatory framework of the Workers\' Statute allows for a broad capacity for management, negotiation and agreement in the field of human resources management, and specifically in the processes of recruitment, selection and retention, but for some years now basic legislation and interventions by public control bodies have been incorporated which have modified this discretionarily for indirect management entities, bringing them closer and closer to the system of administrative management for civil servants/statutory employees, and consequently limiting the capacity for decision making and adaptation typical of business/private management. This article attempts to explain the similarities and differences between the different areas of management and to explore the weaknesses and opportunities of each of them in terms of recruitment, selection, and retention policies, offering a specific reflection on the selection of executives and managers, as well as an analysis and assessment of the retention of professionals in healthcare institutions.