Literature Collection

BACKGROUND: Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system. METHODS: We used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs' feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances. RESULTS: The backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term "high risk" of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs' workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability. CONCLUSIONS: The final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.

INTRODUCTION: Interprofessional teamwork is the key issue of delivering integrated hospital care; however, measuring interprofessional collaboration for auditing is fragmented. In this study, a questionnaire to measure InterProfessional collaborative Practice for Integrated Hospital care (IPPIH) has been developed and validated. METHODS: A four-step iterative process was conducted: (1) literature search to find suitable questionnaires; (2) semistructured stakeholder interviews (individual and in focus groups) to discuss the topics and questions (face validity), (3) pretesting the prototype of the questionnaire in two different integrated care pathways for feasibility, usability, and internal consistency, and (4) testing (content and construct validity and responsiveness) of the revised questionnaire in eight integrated care pathways; the validation and responsiveness was tested by means of exploratory factor analysis, calculation of Cronbach alpha, item analysis, and linear mixed model analysis. RESULTS: Based on six questionnaires and the opinion of direct stakeholders, the questionnaire IPPIH comprised 27 items. Five different domains could be distinguished: own skills, culture, coordination and collaboration, practical support, and appreciation with the Cronbach alpha varied from 0.91 to 0.48. The self-reported intensity of the collaboration within a specific care pathway significantly influenced the outcome ( P = .000). DISCUSSION: The product is a questionnaire, IPPIH, which can measure the degree of interprofessional collaborative practice in integrated hospital care pathways. The IPPIH was initially developed for quality assurance. However, the IPPIH also seems to be suitable as a self-assessment tool for directors to monitor and improve the interprofessional collaboration and the quality of their integrated care pathway.

BACKGROUND: Practical and motivational barriers can deter people from engaging in substance use disorder (SUD) treatment, even those who seek treatment. Care navigation is a psychosocial intervention that seeks to facilitate patients' timely access to care by identifying and intervening upon barriers. Few trials have tested the effectiveness of care navigation when embedding in real-world healthcare, and no trials have studied the process of implementing care navigation into clinical practice. This protocol describes a study that will evaluate whether care navigation can increase treatment engagement among patients seeking SUD treatment. METHODS: The Addressing Barriers to Care for Substance Use Disorder (ABC-SUD) study is a hybrid type I cluster-randomized effectiveness-implementation trial. It is conducted in a mental health access center of an integrated healthcare system in Washington state. Within this center, licensed mental health clinicians assess patient needs and use shared decision-making to establish SUD treatment plans for patients (usual care). This study tests whether an added care navigation intervention can improve patient engagement in SUD treatment. Care navigation begins after a treatment plan is made and provides up to 7 weeks of support focused on enhancing patient motivation to initiate and engage in treatment, problem-solving barriers (e.g., transportation logistics), and accommodating patient preferences (e.g., preferred language of care, cultural preferences). This trial uses a two period, two sequence crossover design. Clinicians are randomized to offer care navigation to patients during the first or second study period (i.e., clinicians are assigned to an initial study condition and switch conditions halfway through the trial). Care navigation is implemented with several strategies: leadership engagement, clinical workflow specifications, electronic health record (EHR) tools, training, performance improvement, and electronic learning collaborative. The primary outcome-obtained from EHRs and insurance claims-is engagement in SUD treatment, defined as ≥3 SUD treatment visits within 48 days of a treatment plan. This study uses standardized measures of implementation climate and outcomes to examine mechanisms with which the intervention strategies exert their impact on implementation and effectiveness outcomes. DISCUSSION: The ABC-SUD study will test whether care navigation improves SUD treatment engagement while concurrently generating information about its implementation in healthcare. TRIAL REGISTRATION: This study was prospectively registered at www. CLINICALTRIALS: gov (NCT06729957) on December 9, 2024.

BACKGROUND: This study explores the facilitating and inhibiting factors in the design/development, implementation, and applicability/evaluation of value-based payment models of integrated care. The Delphi technique was used to reach consensus among a panel of (inter)national experts on these factors. METHODS: An expert panel of 15 members participated in a three-round Delphi study. Factors from experts and literature were used to compile a list of 40 facilitators and 40 inhibitors. Afterwards, experts were asked to rate the importance of these factors using a 5-point Likert scale. RESULTS: Eight facilitating (e.g., transparency, communication, and trust among involved stakeholders) and seven inhibiting factors (e.g., lack of motivation and engagement among involved stakeholders) achieved full consensus. Timely availability of data and an integrated information technology system for data registration (a facilitator) were the only factors achieving full consensus through a very high agreement. CONCLUSIONS: Adequate outcome measures, targets, benchmarks, and incentives are important in value-based payment models. The less quantifiable items, such as strong leadership, transparency, communication and trust, and motivation and engagement of the involved stakeholders, are also important for successful adoption of these models and promote high-quality care at lower or equal costs.

The opioid epidemic was declared a national public health emergency in 2017. In Georgia, standing orders for the opioid antagonist, naloxone, have been implemented to reduce mortality from opioid overdoses. Service industry workers in the Atlanta, Georgia, inner-city community of Little Five Points (L5P) have access to naloxone, potentially expanding overdose rescue efforts in the community setting. To explore the issues facing L5P, our research brings together qualitative descriptive inquiry, ethnography, community-based research, a community advisory board, and a local artist to maximize community dissemination of research findings through a graphic novel that describes encountering an opioid overdose. This format was chosen due to the ethical responsibility to disseminate in participants' language and for its potential to empower and educate readers. This article describes the process of working on this study with the community and a local artist to create sample pages that will be tested for clarity of the message in a later phase. Working with an artist has revealed that while dissemination and implementation for collaboration begin before findings are ready, cross-collaboration with the artist requires early engagement, substantial funding, artist education in appropriate content, and member checking to establish community acceptability altering illustrations that reinforce negative stereotypes. By sharing the experiences of actions taken during an opioid overdose in L5P through a graphic novel, we can validate service industry workers' experiences, acknowledge their efforts to contribute to harm reduction, and provide much-needed closure to those who encounter opioid overdoses in the community.

Parkinson's Disease (PD) is a progressive neurodegenerative condition that requires timely intervention to manage symptoms and prevent deterioration. This study investigates the essential requirements for a speech-based decision support tool to monitor PD progression in a community-integrated care setting. While still at an early design stage, the envisioned tool may take the form of mobile or desktop software accessible to patients, carers, and professionals in home and clinical settings. A mixed-methods approach, including surveys (n=31), focus groups (n=12), interviews (n=11), and policy document analysis, was used to gather insights from health and social care staff in Wales. Four major themes emerged: essential technical requirements (reliability, ease of use), workforce needs (training, analytic transparency), patient considerations (preferences, privacy), and systemic integration (interoperability, funding). Findings highlight the potential of speech-based AI systems for early, objective detection of PD deterioration. However, clinician trust, digital literacy, and user-centered design remain critical for adoption. Co-design with people with PD (PwPD), carers, and staff is strongly recommended for future development and evaluation. This study contributes to the growing field of intelligent systems in digital health and decision support.