ObjectivesPre-exposure prophylaxis (PrEP) is a key biomedical intervention for ending the HIV epidemic in the United States, but its uptake is impeded by systemic barriers, including fragmented workflows and ineffective data coordination. This study aims to design PrEPLinker, a blockchain-based, client-centered platform to enhance care to address these challenges by improving care coordination and enabling clients to securely manage their identity and PrEP-related data.Materials and MethodsUsing Houston, Texas, as a use case, we conducted a needs assessment with PrEP collaborators to evaluate existing workflows and identify barriers in the PrEP care continuum. Based on these findings, we designed PrEPinker, a blockchain-based identity framework and digital wallet using self-sovereign identity and verifiable credentials (VCs). These features enable clients to securirely control their identity data and facilitate efficient, privacy-serving data sharing across PrEP service points, such as community testing sites, clinics, and pharmacies.ResultsThe needs assessment identified significant gaps in data exchange for PrEP referrals and follow-up appointments. In response, PrEPLinker was designed to incorporate decentralized identifiers—unique, secure digital identifiers that are not linked to any centralized authority—and VCs for ensuring seamless transfer of digital medical records. Preliminary usability testing with 15 participants showed that over 70% rated the interactive design positively, finding it easy to use, learn, and navigate without technical support. Additionally, more than 80% expressed confidence in using the blockchain based platform to manage sensitive health information securely.Discussion and ConclusionBlockchain technology offers a promising, client-centered solution for addressing systemic barriers in PrEP care by improving data cordination, security, and client control over personal health information. The design of PrEPLinker demorates the potential to streamline PrEP referrals, follow-up processes, and data managent. These advancements in data coordination and secruity could improve PrEP uptake and adherence, supporting efforts to reduce HIV transmission in Houston and beyond.

Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent. Recent studies have highlighted the transformative potential of incorporating the FAIR (Findable, Accessible, Interoperable, and Reusable) principles into workflows, particularly in contexts like software and machine learning model development, to promote open science. This study aims to introduce a comprehensive framework, designed to calibrate existing reporting guidelines against the FAIR principles. The goal is to enhance reproducibility and promote open science by integrating these principles into the scientific reporting process. We employed the "Best fit" framework synthesis approach which involves systematically reviewing and synthesizing existing frameworks and guidelines to identify best practices and gaps. We then proposed a series of defined workflows to align reporting guidelines with FAIR principles. A use case was developed to demonstrate the practical application of the framework. The integration of FAIR principles with established reporting guidelines through the framework effectively bridges the gap between FAIR metrics and traditional reporting standards. The framework provides a structured approach to enhance the findability, accessibility, interoperability, and reusability of scientific data and outputs. The use case demonstrated the practical benefits of the framework, showing improved data management and reporting practices. The framework addresses critical challenges in scientific research, such as data leakage and reproducibility issues. By embedding FAIR principles into reporting guidelines, the framework ensures that scientific outputs are more transparent, reliable, and reusable. This integration not only benefits researchers by improving data management practices but also enhances the overall scientific process by promoting open science and collaboration. The proposed framework successfully combines FAIR principles with reporting guidelines, offering a robust solution to enhance reproducibility and open science. This framework can be applied across various contexts, including software and machine learning model development stages, to foster a more transparent and collaborative scientific environment.

ObjectivesArtificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.Materials and MethodsWe analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle.Results, Discussion, and ConclusionThrough case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders.

ObjectivePopulation health management (PHM) is a promising data-driven approach to address the challenges faced by health care systems worldwide. Although Business Intelligence (BI) systems are known to be relevant for a data-driven approach, the usage for PHM is limited in its elaboration. To explore available scientific publications, a systematic review guided by PRISMA was conducted of mature BI initiatives to investigate their decision contexts and BI capabilities.Materials and MethodsPubMed, Embase, and Web of Science were searched for articles published from January 2012 through November 2023. Articles were included if they described a (potential) BI system for PHM goals. Additional relevant publications were identified through snowballing. Technological Readiness Levels were evaluated to select mature initiatives from the 29 initiatives found. From the 11 most mature systems the decision context (eg, patient identification, risk stratification) and BI capabilities (eg, data warehouse, linked biobank) were extracted.ResultsThe initiatives found are highly fragmented in decision context and BI capabilities. Varied terminology is used and much information is missing. Impact on population’s health is currently limited for most initiatives. Care Link, CommunityRx, and Gesundes Kinzigtal currently stand out in aligning BI capabilities with their decision contexts.Discussion and ConclusionPHM is a data-driven approach that requires a coherent data strategy and understanding of decision contexts and user needs. Effective BI capabilities depend on this understanding. Designing public-private partnerships to protect intellectual property while enabling rapid knowledge development is crucial. Development of a framework is proposed for systematic knowledge building.

ObjectiveThis study investigates the concordance of patient information collected using a medical history app compared to in-person interviews.Materials and MethodsIn this cross-sectional study we used an app to collect medical data from patients in family practice in Germany. Collected information included age, height, weight, perceived severity of complaints, and 38 current complaints. Subsequently, in-person interviews based on the query structure of the app were conducted with patients directly after the patient finished filling out the app. Concordance was assessed as exact matches between the data collected app-based and in-person interviews, with the in-person interview as a reference. Regression analysis examined which patient characteristics were associated with mismatching and underreporting of complaints.ResultsThree hundred ninety-nine patients were included in the study. Concordance of reported age, weight, and height, as well as perceived severity of complaints ranged from 76.2% to 96.7%. Across all 38 complaints, 64.4% of participants showed completely identical complaint selection in app-based and in-person interviews; 18.5% of all participants overreported; and 17.0% underreported at least 1 complaint when using the app. Male sex, higher age, and higher number of stated complaints were associated with higher odds of underreporting at least one complaint in the app.DiscussionApp-collected data regarding age, weight, height, and perceived severity of complaints showed high concordance. The discordance shown concerning various complaints should be examined regarding their potential for medical errors.ConclusionThe introduction of apps for gathering information on complaints can improve the efficiency and quality of care but must first be improved.Trial registrationThe study was registered at the German Clinical Trials Register No. DRKS00026659 registered November 3, 2021. World Health Organization Trial Registration Data Set, https://trialsearch.who.int/Trial2.aspx?TrialID=DRKS00026659

ObjectiveThis survey aims to understand frontline healthcare professionals’ perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions.Materials and MethodsWe conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants’ familiarity and experience with AI on the survey results.ResultsFrom 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits (r = 0.825) and Trust in AI Mechanisms (r = 0.777). Perceived risk negatively correlated with Intention to Use AI (r = −0.718). There was no difference in perceptions between AI experienced and AI naïve subgroups.DiscussionThe findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals’ intention to use AI, regardless of their AI familiarity.ConclusionThe study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals’ perspective. These insights can guide strategies for successful AI implementation in healthcare.

ObjectiveThis study uses electronic health record (EHR) data to predict 12 common cancer symptoms, assessing the efficacy of machine learning (ML) models in identifying symptom influencers.Materials and MethodsWe analyzed EHR data of 8156 adults diagnosed with cancer who underwent cancer treatment from 2017 to 2020. Structured and unstructured EHR data were sourced from the Enterprise Data Warehouse for Research at the University of Iowa Hospital and Clinics. Several predictive models, including logistic regression, random forest (RF), and XGBoost, were employed to forecast symptom development. The performances of the models were evaluated by F1-score and area under the curve (AUC) on the testing set. The SHapley Additive exPlanations framework was used to interpret these models and identify the predictive risk factors associated with fatigue as an exemplar.ResultsThe RF model exhibited superior performance with a macro average AUC of 0.755 and an F1-score of 0.729 in predicting a range of cancer-related symptoms. For instance, the RF model achieved an AUC of 0.954 and an F1-score of 0.914 for pain prediction. Key predictive factors identified included clinical history, cancer characteristics, treatment modalities, and patient demographics depending on the symptom. For example, the odds ratio (OR) for fatigue was significantly influenced by allergy (OR = 2.3, 95% CI: 1.8-2.9) and colitis (OR = 1.9, 95% CI: 1.5-2.4).DiscussionOur research emphasizes the critical integration of multimorbidity and patient characteristics in modeling cancer symptoms, revealing the considerable influence of chronic conditions beyond cancer itself.ConclusionWe highlight the potential of ML for predicting cancer symptoms, suggesting a pathway for integrating such models into clinical systems to enhance personalized care and symptom management.

This article focuses on the role of the electronic health record (EHR) to generate meaningful formative feedback for medical students in the clinical setting. Despite the scores of clinical data housed within the EHR, medical educators have only just begun to tap into this data to enhance student learning. Literature to-date has focused almost exclusively on resident education. Development of EHR auto-logging and triggered notifications are discussed as specific use cases in providing enhanced feedback for medical students. By incorporating predictive and prescriptive analytics into the EHR, there is an opportunity to create powerful educational tools which may also support general clinical activity. This article explores the possibilities of EHR as an educational resource. This serves as a call to action for educators and technology developers to work together on creating health record user-centric tools, acknowledging the ongoing work done to improve student-level attribution to patients. EHR analytics and tools present a novel approach to enhancing clinical clerkship education for medical students.

ObjectivesDespite the proliferation of dashboards that display performance data derived from Qualified Clinical Data Registries (QCDR), the degree to which clinicians and practices engage with such dashboards has not been well described. We aimed to develop a conceptual framework for assessing user engagement with dashboard technology and to demonstrate its application to a rheumatology QCDR.Materials and MethodsWe developed the BDC (Breadth-Depth-Context) framework, which included concepts of breadth (derived from dashboard sessions), depth (derived from dashboard actions), and context (derived from practice characteristics). We demonstrated its application via user log data from the American College of Rheumatology’s Rheumatology Informatics System for Effectiveness (RISE) registry to define engagement profiles and characterize practice-level factors associated with different profiles.ResultsWe applied the BDC framework to 213 ambulatory practices from the RISE registry in 2020-2021, and classified practices into 4 engagement profiles: not engaged (8%), minimally engaged (39%), moderately engaged (34%), and most engaged (19%). Practices with more patients and with specific electronic health record vendors (eClinicalWorks and eMDs) had a higher likelihood of being in the most engaged group, even after adjusting for other factors.DiscussionWe developed the BDC framework to characterize user engagement with a registry dashboard and demonstrated its use in a specialty QCDR. The application of the BDC framework revealed a wide range of breadth and depth of use and that specific contextual factors were associated with nature of engagement.ConclusionGoing forward, the BDC framework can be used to study engagement with similar dashboards.

Clinical research networks facilitate collaborative research, but data sharing remains a common barrier. The TriNetX platform provides real-time access to electronic health record (EHR)-derived, anonymized data from 173 healthcare organizations (HCOs) and tools for queries and analysis. In 2022, 4 pediatric HCOs worked with TriNetX leadership to found the Pediatric Collaboratory Network (PCN), facilitated via a multi-institutional data-use agreement (DUA). The DUA enables collaborative study design and execution, with institutional review board-approved transfer of complete datasets for further analyses on a per-protocol basis. Of the 41.2 million children with TriNetX records, the PCN represents nearly 10%. The PCN assisted several early-career investigators to bring study concepts from conception to an international scientific meeting presentation and journal submission. The PCN facilitates EHR vendor-agnostic multicenter pediatric research on the global TriNetX platform. Continued growth of the PCN will advance knowledge in pediatric health.