Acta Marisiensis - Seria Medica

AbstractObjectiveThis study aims to analyze hospitals' adoption and integration of electronic health record (EHR) metadata into their management processes.DesignThe study compares the rates of EHR metadata utilization across various hospitals over time. Hospitals' self‐reported use of EHR metadata is drawn from the AHA‐IT Supplements from 2018 to 2020. An analysis of metadata utilization by EHR vendors is also provided.MethodThe study uses Bass diffusion modeling to estimate EHR adoption parameters by fitting adoption rate data from 2018 to 2020, using Excel Solver to minimize prediction errors. The estimated internal and external influence coefficients reveal which factor primarily drives adoption, while the diffusion model enables future projection of tipping point and adoption level.ResultsAnalysis of EHR metadata utilization rates from 2018 to 2020 find a significant trend towards the integration of this data into hospital management practices. Among health systems responding to the items of interest, 69% of them are already using EHR metadata, and it is projected that nearly all will do so by 2035. Further, metadata use varied significantly depending on the vendor.DiscussionThe study underscores that hospital managers' intrinsic motivations, rather than external demands, are driving EHR metadata. As innovations with greater intrinsic appeal spread more rapidly and have greater staying power, EHR metadata use will continue to grow. These trends are indicative of the growing importance of EHR metadata in management decision‐making, clinical quality improvement, and optimizing workforce efficiency.ConclusionsEHR metadata holds great promise as a managerial and health service research source. The tools' utilities would be enhanced if EHR vendors created uniform metrics.

AbstractIntroductionTo accelerate transformation toward value‐based, equitable care, health systems need a viable approach for engaging and aligning multiple stakeholders to promote innovation. Building and sustaining learning communities offers one possible solution.MethodsWe describe how one learning community has leveraged the collective strengths, assets, and expertise of multiple stakeholders to improve care value for subpopulations who experience low‐quality, high‐cost, and/or inequitable health outcomes.ResultsBy providing critical infrastructure and support, UPMC's Learning Community was able to (1) accelerate adoption of risk‐based payment models that promote shared accountability among providers, payers, and families/caregivers for the total costs of care of children and adolescents with medically complex conditions, (2) drive widespread practice change for improving physical and mental wellness for individuals with serious mental illness, and (3) increase access to evidence‐based treatment and improve outcomes for individuals with opioid and substance use disorders.ConclusionsLearning communities can serve as important catalyzers for the payment, practice, and service delivery innovation necessary to achieve a high‐value, equitable health system.

AbstractIntroductionLearning health system (LHS) development has been described in the highest resource settings, which could suggest that resources are a precondition to LHS formation.MethodWe reviewed literature surrounding LHSs in low‐resource contexts and used this to inform an argument that LHS activity is critically important in these circumstances.ResultsWe focus on three key points. First, when resources are scarce, they should not be squandered. Second, local knowledge artifacts have advantages. Finally, LHS emphasis on lasting sociotechnical infrastructure addresses sustainability concerns.ConclusionWe believe LHS formation and activity is more important in low‐resource contexts than in their higher resource counterparts. Less path dependence in many low‐resource contexts forecasts that LHSs may see their greatest success there.

AbstractIntroductionIn 2021, the Learning Health Systems Rehabilitation Research Network (LeaRRn) developed and administered a needs assessment survey, based on the Agency on Healthcare Research and Quality's (AHRQ's) original seven domains of learning health systems (LHS) researcher core competencies, to identify knowledge and interest in LHS research competencies among rehabilitation researchers. In 2022, the AHRQ added a new health and healthcare equity and justice (HE) domain to the existing seven domains for LHS researcher core competencies.MethodsLeaRRn utilized methods similar to those employed in the development of their original needs assessment survey to generate and refine competency items for the HE domain. In this report, we describe the methods used to develop these HE competency items.Results & ConclusionsOther training programs and LHS researchers may use the competency items developed for this needs assessment survey to identify training opportunities in the HE domain.

AbstractBackgroundThere is increasing demand for health systems to reduce greenhouse gas emissions and invest in climate‐resilient health care. Coordinating organizational structures and processes for reducing health system emissions presents challenges. Learning health systems, defined as systems that seek to continuously generate and apply evidence, innovation, quality, and value in health care, can guide health systems with planning organizational structures and processes to advance environmentally sustainable healthcare. The purpose of this research is to gather in‐depth insight from key health system leaders and healthcare professionals to identify challenges and recommendations for planning environmentally sustainable learning health systems.MethodsEnvironmental scan methods were used, comprising jurisdictional literature review and informal discussions with key informants at one tertiary care center in Nova Scotia, Canada. Key informants were asked to describe challenges of coordinating environmentally sustainable health system structures and processes, and recommendations to advance planning for environmentally sustainable learning health systems. Deductive thematic analysis was used to categorize challenges and recommendations into seven characteristics of a learning health system framework.ResultsInformal discussions with 16 key informants provide detailed descriptions of 7 challenges and recommendations for planning and coordinating organizational structures and processes to advance environmentally sustainable learning health systems. Health system challenges include limited patient and community engagement, no systematic approach to measuring and monitoring emissions data, and limited knowledge of sustainability co‐benefits and strategies for mobilizing sustainable organizational change. Recommendations include engaging patients and communities in co‐creation of sustainable healthcare, monitoring of emissions data identifying high‐impact areas for action, and well‐coordinated leadership supporting sustainable policies, procedures, and decision‐making in practice.ConclusionLearning health systems provide structure for establishing critical processes to adapt to routinely collected data through rapid cycle improvements, and operationalization of value‐based health care that prioritizes health outcomes, reduction of costs, and mitigating environmental impacts.

AbstractIntroductionLearning health systems (LHSs) are systems that seamlessly embed continuous quality improvement based on real‐world data. To establish LHSs, several infrastructures need to be in place. Registries already have part(s) of this infrastructure and could therefore be leveraged to establish LHSs. This study aims to identify key factors facilitating the transition of registries into LHS to support continuous learning from real‐world data.MethodsEleven interviews with 12 stakeholders, including medical specialists and nonmedical stakeholders, were conducted in the context of a prostate cancer registry. Findings were coded deductively based on seven previously identified facilitators for learning: complexity, relative advantage, compatibility, credibility, social impact, actionability, and resource match. These facilitators cover technical, social, and organizational aspects. An inductive phase followed to pinpoint factors for continuous learning and LHSs. Subsequently, two focus groups were conducted to ensure accurate interpretation of findings, and five expert panels to provide additional context.ResultsComplexity within healthcare systems emerged as a significant challenge, attributed to multiple stakeholders and the rapidly changing healthcare landscape. The advantage of LHSs is the timely availability of population‐based data for real‐time care adjustments. Compatibility of the system with stakeholders' needs was considered pivotal requiring a relatively flexible infrastructure. Credibility of data and results was supported by creating transparent processes in which stakeholders could review data from their own patient population. Social influences, including interpersonal trust and engaged leadership, fostered collaboration within LHSs. Actionability of the findings and resource match were vital for knowledge translation and sustainability.ConclusionOur findings provide practical recommendations to support registries in transitioning towards LHSs by leveraging and expanding their infrastructure for continuous learning. We identified technical, interpersonal, and organizational factors that facilitate continuous and rapid learning using real‐world data, create transparent and collaborative infrastructures, and help to navigate the complexity of the healthcare system.

AbstractIntroductionThe acute care system faced significant challenges in managing healthcare emergencies due to a lack of coordination between emergency services and logistical support. This disorganization undermined collaboration and response efficiency.MethodsTaiwan's Presidential Hackathon introduced an innovative approach to improving the trauma system by integrating digital pipeline science through public–private partnerships (PPPs). This initiative specifically addressed inefficiencies and complexities in the acute care ecosystem, brought to light by the catastrophic 2014 gas explosion in Kaohsiung City.ResultsThe hackathon led to the development of a unified digital platform for emergency data management. This platform significantly enhanced communication, data sharing, and coordination across healthcare sectors, culminating in the implementation of a digital pre‐hospital emergency care system across multiple administrative regions.ConclusionOur experience demonstrated the effectiveness of leveraging digital technologies, PPPs, and the hackathon model to revolutionize emergency healthcare management and response systems through cross‐sector collaboration.

AbstractIntroductionA learning health system (LHS) necessitates collaboration to produce translational health research. This experience report examines the integration of Clemson University scholars into clinical departments of Prisma Health–Upstate in South Carolina, highlighting their experiences working alongside clinician mentors to inform and facilitate research translation. Particularly, this study aims to explore the interpersonal and structural factors influencing the success of an embedded scholar program, focusing on enablers and barriers to collaboration, knowledge integration, and mentorship within the LHS.MethodsNine embedded scholar and 12 mentor semi‐structured interviews were conducted. This qualitative study initially used an inductive technique to analyze responses thematically. After thematic saturation was achieved, deductive analysis was utilized to further organize enablers and barriers across the following five categories: (1) Scholar Integration, (2) Scholar Autonomy, (3) Mentor Support, (4) Programmatic Outcomes, and (5) Institutional Dynamics.ResultsWe found 10 major program‐related enablers and barriers to successfully embedding scholars. These were clinical environment adaptation, mentor interaction, research management, balance of independence, role clarity, resource provision, research application and quality, scholar development, organizational support, and policy and procedure alignment. Findings reveal that effective mentorship, organizational alignment, and resource availability are critical enablers of program success, while misaligned expectations, limited institutional support, and insufficient scholar integration into clinical environments are barriers.ConclusionEvaluating specific components of embedded scholar programs can uncover best practices and innovation opportunities in the LHS. These provide a great opportunity to enhance the mentorship mechanisms between clinical mentors and embedded researchers. As research on embedded scholars in a LHS progresses, fostering structured mentoring relationships may serve as an impetus to bridge the gap between research and clinical practice. Further study is needed to operationalize these relationships effectively.

AbstractIntroductionStanford Medicine is working to better coordinate care across the Stanford healthcare system, as well as improve patient and provider experiences in seeking and receiving care. This study aimed to explore the complexities of moving from a fragmented to an integrated academic healthcare system and to identify and explain factors (e.g., facilitators and barriers) of the implementation of three interventions meant to improve patient experience, reduce staff burden, and integrate health care systems across faculty and community settings.MethodsWe conducted qualitative semi‐structured interviews via Zoom with faculty and community physicians. Interviews were audio‐recorded, professionally transcribed, and analyzed using the Consolidated Framework for Implementation Research (CFIR) and open coding. Using consensus coding approaches, researchers met regularly to discuss themes and adaptations to CFIR.ResultsWe analyzed transcripts from interviews with physicians (n = 26). Factors impacting integration included the following: (1) physicians supported the interventions, promoting mission alignment; (2) physicians were motivated for change, reporting the existing system was intolerable; (3) physicians reported different priorities between clinics: faculty versus community and primary care versus specialty; (4) physicians prioritized interpersonal versus system solutions; (5) specialists were wary of unintended consequences of integration, specifically inappropriate bookings or patients being redirected to other clinics. Broadly speaking, facilitator factors 1–2 focused on the openness to, and tension for, change; and barrier factors 3–5 promoted or sustained variation across specialties and faculty/community clinics.ConclusionsOur results illustrate the challenges and opportunities of moving from a fragmented to an integrated healthcare system and emphasize the importance of building shared culture, collaboration, and coordinated actions across and within an integrated healthcare network.

AbstractIntroductionMost change interventions to address quality of care and lower costs focus on technical aspects of the work through process improvements, which have not consistently delivered the anticipated impact for healthcare organizations. This study aims to (1) understand how relational interventions including shared huddles and cross‐role shadowing opportunities, impact team dynamics and functioning and (2) describe the challenges and opportunities associated with implementing relational interventions at an Academic Medical Center in a large metropolitan city in the United States.MethodsThis paper is a mixed method, pre–post‐intervention study in which data were collected using a validated survey, observations, interviews, and one focus group. Relational coordination survey data were analyzed within and across eight interdependent workgroups on three inpatient medical units at baseline and 16 months post‐intervention. Qualitative data were coded and analyzed for themes.ResultsWhile there were some improvements in overall relational coordination between baseline and post‐intervention measures, the findings were not statistically significant. Qualitative data reveal four themes, highlighting the strengths and barriers to the intervention: (1) incomplete fidelity to the relational coordination framework, (2) leadership, (3) meeting structure and participation, and (4) stakeholder engagement.ConclusionsWithin the healthcare context, this study contributes to our learning about implementing and measuring relational interventions. We offer insights for future research and practice on change initiative overload and operational constraints, socializing relational interventions, and balancing core and non‐core roles in the intervention strategy.

AbstractBackgroundThe operationalization of learning health system (LHS) principles remains challenging, with minimal guidance currently available to support interprofessional teams on the ground. Consequently, LHS initiatives often fall short of their intended objectives, resulting in wasted resources, delays, and mounting frustration among key stakeholders.MethodsTo bridge this gap, we used design science and participatory action research to co‐develop an operational roadmap for interprofessional LHS teams. Data sources for roadmap design included quantitative and qualitative feedback from interprofessional stakeholders (n = 20) from an academic health system and a pragmatic literature review. Using these data sources, we conducted three design iterations until a final version was reached.ResultsThe resulting roadmap specifies processes to be performed during project‐based LHS initiatives, and provides a self‐assessment tool that enables team members to quantitatively evaluate progress. For generalizability and standardization across settings, we used clinically neutral terminology to describe all elements in the roadmap. We demonstrated content validity through multiple rounds of data collection and analyses with stakeholders. A simulated demonstration is provided to illustrate how the roadmap may be used for team assessments in practice.ConclusionsParticipants considered the roadmap to be an effective tool to assist project management and highly useful for evaluating teams' progress for planning and communication purposes. As a reference model, the roadmap may be re‐utilized across multiple LHS initiatives in any given health system to standardize and streamline LHS development. This research was conducted within a single department in an academic health system, and future research is needed to assess the roadmap's generalizability in other settings. To facilitate development of similar or complementary instruments, the detailed design methodology used in this research may be replicated and/or tailored in other contexts.

AbstractIntroductionIn 2022, the Ohio Department of Medicaid (ODM) launched a Managed Care Population Health and Quality Strategy to improve healthcare quality and equity for Medicaid Managed Care enrollees. Aligned with national quality objectives, the strategy focuses on personalized care, service coordination for complex needs, reducing health disparities, and includes performance incentives for Managed Care Organizations (MCOs) and innovative provider payment models. While Ohio has made progress in quality improvement, challenges remain in addressing statewide health indicators and disparities and helping healthcare providers adapt to performance‐based models. This report outlines a new approach that builds on Ohio's partnership with six colleges of medicine (CoMs) to support provider organizations and engage stakeholders in quality improvement (QI).MethodsODM established Regional QI Hubs within Ohio's CoMs to advance population health initiatives using the Model for Improvement developed by the Associate in Process Improvement. These academically based hubs collaborate with local healthcare clinics, community partners, and payers on QI projects to enhance care, reduce disparities, and strengthen health systems. By engaging stakeholders in designing and testing change ideas using Plan‐Do‐Study‐Act cycles and electronic health record data feedback, QI Hubs further the goals of the learning health system.ResultsKey lessons highlight the benefits of engaging academic institutions to build internal QI capacity and promote health equity. The model required substantial capacity building and commitment on behalf of academic institutions and strengthening of regional partnerships. Collaboration between MCOs and health clinics is focused on standardizing processes to access services and implement best practices. Patient, family, and community engagement efforts aim to improve patient experience and address drivers of health equity. Each partner leverages resources and benefits from the collaboration.ConclusionsOhio's academically based Regional QI Hub Model offers a promising approach to advancing population health. Policymakers are encouraged to consider integrating academic expertise into state quality strategies.

AbstractBackgroundMortality prediction scores for children admitted with diarrhea are unavailable, early identification of at‐risk patients for proper management remains a challenge. This study utilizes machine learning (ML) to develop a highly sensitive model for timelier identification of at‐risk children admitted with acute gastroenteritis (AGE) for better management.MethodsWe used seven ML algorithms to build prognostic models for the prediction of mortality using de‐identified data collected from children aged <5 years hospitalized with AGE at Siaya County Referral Hospital (SCRH), Kenya, between 2010 through 2020. Potential predictors included demographic, medical history, and clinical examination data collected at admission to hospital. We conducted split‐sampling and employed tenfold cross‐validation in the model development. We evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the curve (AUC) for each of the models.ResultsDuring the study period, 12 546 children aged <5 years admitted at SCRH were enrolled in the inpatient disease surveillance, of whom 2271 (18.1%) had AGE and 164 (7.2%) subsequently died. The following features were identified as predictors of mortality in decreasing order: AVPU scale, Vesikari score, dehydration, sunken eyes, skin pinch, maximum number of vomits, unconsciousness, wasting, vomiting, pulse, fever, sunken fontanelle, restless, nasal flaring, diarrhea days, stridor, <90% oxygen saturation, chest indrawing, malaria, and stunting. The sensitivity ranged from 46.3%–78.0% across models, while the specificity and AUC ranged from 71.7% to 78.7% and 56.5%–82.6%, respectively. The random forest model emerged as the champion model achieving 78.0%, 76.6%, 20.6%, 97.8%, and 82.6% for sensitivity, specificity, PPV, NPV, and AUC, respectively.ConclusionsThis study demonstrates promising predictive performance of the proposed algorithm for identifying patients at risk of mortality in resource‐limited settings. However, further validation in real‐world clinical settings is needed to assess its feasibility and potential impact on patient outcomes.