Objective: Two quality measures, initiation and engagement in substance use disorder (SUD) treatment (IET), have been adopted by stakeholders. Further evidence is needed to inform optimal use of these measures to support quality improvement efforts. The objective of this study was to examine variability, reliability, and multilevel correlates of IET. Methods: Retrospective cohort study of members of a large integrated health system in the Pacific Northwest. Descriptive statistics were used to characterize variation in quality. Mixed-effects models with random intercepts were applied to quantify variability and reliability at different health system levels. We examined associations between quality measures and a wide set of patient-, provider-, clinic-, and area-level characteristics. We examined 32,960 care episodes subject to IET quality criteria between 2017 and 2022. Results: Overall, 30.0% and 7.8% of episodes met the criteria for initiation and engagement in SUD treatment, respectively. Little variation in initiation (2.8%) and engagement (3.9%) was collectively explained at the provider and clinic levels. No providers met the threshold for 70% reliability on an annual basis. Across varying assumptions of provider-level annual episode volume, clinics were unlikely to meet reliability thresholds. Factors consistently associated with IET included insurance type and history of behavioral health conditions. Conclusion: A low proportion of variation explained implies a larger number of episodes is needed to ensure reliable estimates. Stakeholders should apply IET measures cautiously, ensuring sufficient sample sizes to support robust measurement. Factors associated with a lower probability of meeting quality criteria can inform specific patients who may benefit from efforts to increase IET.
BACKGROUND:Social adversity contributes to poor health outcomes for children after liver transplantation (LT), including greater morbidity and mortality. Although social risk screening is standard practice in pediatric primary care, its adoption in pediatric LT (pLT) remains limited, despite the significant prevalence of social risks among these families. OBJECTIVES:To evaluate barriers and facilitators to social risk screening implementation in pLT settings across a multicenter learning health network. RESEARCH DESIGN:A mixed-methods study utilizing surveys and semistructured interviews. SUBJECTS:Health care practitioners involved in pLT care across North American centers in the Starzl Network for Excellence in Pediatric Transplantation (SNEPT) Network. Participants included multidisciplinary team members, notably transplant physicians, social workers, and research coordinators. MEASURES:We surveyed center leads to identify center-level implementation challenges. We conducted 1-on-1 interviews with transplant team members to identify barriers and facilitators. We analyzed qualitative data using the Capability, Opportunity, Motivation-Behavior model, an implementation science model for developing targeted interventions. RESULTS:We surveyed 10 centers, of which 40% of liver transplant clinics reported actively screening patients for Social Determinants of Health (SDoH). Most practitioners indicated reliance on social workers and cited limited resources as barriers to implementation. We also conducted interviews with 18 practitioners across 11 centers. Reported barriers included uncertainty about the tool's added value, time and space constraints during patient encounters, and challenges with data entry and sharing across a multicenter network. Facilitators included institutional support, interdisciplinary collaboration, and integration into electronic health records. CONCLUSION:Efforts to increase adoption should focus on improving practitioners' experiences with the tool as well as further assessing and disseminating its potential value in improving outcomes. Strategies for addressing logistical challenges to adapting workflows and simplifying network data management should be considered. This study establishes a foundation for improving screening rates and data capacity.
BACKGROUND:Uninteroperable health data impedes care and research-especially for people with multiple chronic conditions (MCC) or from disadvantaged communities. By enabling the sharing of person-centered data, standards-based care plans may improve health outcomes and reduce disparities. OBJECTIVE:Develop and test interoperable electronic e-Care planning tools to collect, aggregate, and share person-centered data for MCC healthcare and research. METHODS:Using participatory and agile design, we developed, implemented, and evaluated e-Care plan tools-including data standards and clinician- and patient/caregiver-facing e-Care plan applications (eCarePlanner and MyCarePlanner). The Consolidated Framework for Implementation Research Process Redesign and the Systems Engineering Initiative for Patient Safety model informed a mixed methods evaluation to gather feedback from patients, caregivers, and clinicians across formative, iterative, and summative stages. RESULTS:The apps successfully connected with 4 electronic health records at 17 institutions. Evaluation participants included 57 patients/caregivers and 15 clinicians. Patients and caregivers were predominantly aged 65 years or older, White, and well-educated. Most participants were comfortable using the app (97%). Most felt app loading was timely (90%), and the app would support complex care coordination (63%). Usability scores were lowest for the ability to improve care team communication (48% agreed/strongly agreed) and for inconsistencies across app sections (37% agreed/strongly agreed). Key themes from interviews included challenges in moving health information across settings and the apps' potential to overcome these challenges, thereby improving the frequency and quality of care planning. CONCLUSION:This project provides a proof-of-concept for standards-based tools to collect, aggregate, and share patient-centered health and social data across health care and research settings.
BACKGROUND:Learning from promising maternal health interventions is limited by the lack of standardized outcome measures needed for their evaluation. We describe the development and utilization of Maryland's severe maternal morbidity (SMM) surveillance by the Maryland Maternal Health Innovation (MDMOM) program. METHODS:To ascertain the development, attributes, and data collected by SMM surveillance, we use 2020-2024 MDMOM program records and 2023 surveillance data. Hospital reports and MDMOM's 2023 maternal health care provider survey data are used to document policy and practice changes attributable to participation in SMM surveillance. Data collected from MDMOM's "Learning from Adverse Events in Maryland" trainees are analyzed to explore knowledge score changes (Wilcoxon signed-rank test) and training satisfaction. RESULTS:SMM surveillance is a simple, flexible, stable, high-quality system for identifying areas of care in need of improvement. Before October 2024, when state legislation mandated all hospitals to participate, 24 of 32 hospitals (80% of the state's annual deliveries) engaged in SMM surveillance. The data are used to assess SMM levels and contributors, disparities (eg, intensity of care practices and outcome differences between patient groups), and preventability. Knowledge scores improved after the training on SMM surveillance findings (P<0.001). Data can be used for quality improvement and to generate PCOR hypotheses. CONCLUSIONS:Maryland established hospital-based SMM surveillance, demonstrated its value, and gained legislative support for statewide scale-up. This model surveillance system can be replicated in other states, aiming to better understand and address SMM and SMM disparities.
BACKGROUND:Out-of-home placement (OOHP) through child welfare, residential substance treatment, or extended inpatient care has life-altering impacts on child health and generates an outsized proportion of pediatric Medicaid costs. Early risk identification may prevent OOHP but is challenged by the difficulty of navigating state and federal data sharing regulations and multiple disconnected data systems. Identifying ways to streamline and simplify this process are critical to OOHP prevention. OBJECTIVES:To describe the development and implementation of an expert-derived data-driven algorithm to identify OOHP risk among Medicaid-enrolled children residing in 2 Ohio counties between 2022 and 2024 (n=27,000), discuss practical considerations for data sharing and linkage across multiple agencies, and identify lessons learned. FINDINGS:A cross-sector team of government and academic partners developed a 3-category OOHP risk stratification algorithm incorporating data from Medicaid claims, child welfare information systems, area-level social determinants of health, and patient-reported health risk assessments as part of Ohio's Integrated Care for Kids Model. Substantial administrative and legal processes were required to link data sources. Lessons learned include: (1) understanding legal, political, and security structures associated with use of administrative data is critical; (2) strong working relationships with data partners can ensure success; (3) as administrative data are dynamic and may be retroactively updated, appropriate lookback periods are necessary for accuracy; and (4) while executing data use agreements can be challenging, the relationships built can have lasting benefits. CONCLUSIONS:Data-driven risk stratification models have the potential to reduce cost, time, and redundancies in identifying families who would benefit from OOHP prevention supports. Balancing data-sharing challenges with the value gained from linking data is vital.
