To identify studies conductedusing the direct, electronic extraction of electronic health record (EHR) datato electronic data capture (EDC) systems, also known as eSource, and toidentify any gaps or limitations present for promoting standardized healthinformation exchange in clinical research.Materials and Methods:Articleswere included only if the solution described (1) utilized eSource to directlyexchange data electronically from EHR-to-EDC and (2) was relevant to aprospective clinical study use case.Results:Intotal, 20 relevant articles were identified, describing a total of 15 uniqueeSource interventions. Of the 15interventions, 12 were single-site, single-EHR (SS-SE) implementations and 3were multi-site, multi-EHR (MS-ME) implementations. All 15 implementationsmentioned the use of standards, but nearly all referenced older data exchangestandards. Discussion:Following the trajectory of work towardsdirect EHR-to-EDC, eSource data collection, we appear to have arrived at thepoint where information systems leveraging data standards can offer efficiencyand increased quality in clinical research. However, these methods need to betested for effectiveness and acceptance in the context of real multicenterclinical trials. Several early studies using a single source of data forresearch and patient care appeared over a decade ago. Since that time,implementations and evaluations have been scarce and almost always confined tosingle-EHR, single-EDC, single-institution implementations.Conclusion:These results only further emphasize the observation thatthe clinical trial use case continues to be the most difficult and leastdemonstrated eSource-related initiative. Thus, additional work is criticallyneeded in this area to address the gaps identified from the literature.

Prescription drug misuse is a global problem, especially in the United States (US). Clinician involvement is necessary in this crisis, and prescription drug monitoring programs (PDMPs) are a recommended tool for the prevention, recognition, and management of prescription opioid misuse. However, because of the plethora of differences between different PDMPs, research on their effects is mixed. Yet, despite varied evidence, policy on PDMP use is trending stricter and more comprehensive. We aimed to identify patterns in the research to inform clinicians and policy. Through a systematic review of four literature databases (CINAHL, Cochrane Database, Embase, and Medline/OVID), we found 56 experimental and quasi-experimental studies published between 2016 and 2023 evaluating PDMP effects on clinician behavior. To address study heterogeneity, we categorized studies by type of intervention and study outcome. The review suggests that more comprehensive PDMP legislation is associated with decreases in the number of opioid prescriptions overall and the number of risky prescriptions prescribed or dispensed. However, this review shows that much is still unknown, encourages improvements to PDMPs and policies, and suggests further research.

Background: Previous AI literacy studies have been limited to clinical students and professionals and included subjective reporting. This survey study explored the extent of AI knowledge in health information professionals with subjective and objective questions. The objective of the study was to inform the International Federation of Health Information Management Associations (IFHIMA) and national health information bodies about current AI literacy levels and the education needs of their members. Methods: A descriptive survey was adapted from two validated, previously-published study instruments. Survey data were collected between December 5, 2024, and February 28, 2025 using a self-administered Qualtrics (Seattle, WA) online survey link distributed by email to IFHIMA members. The survey link was also distributed on the LinkedIn professional networking platform (LinkedIn Corp; Sunnyvale, CA) by multiple IFHIMA members. Results were analyzed using Chi-square, ANOVA, and Tukey HSD post-hoc tests to assess the associations between the categorical response variables and the subjective survey question measures. Results: A total of 176 participants began the survey. Data were cleaned to exclude 48 incomplete responses, leaving 128 complete and valid responses for analysis. AI knowledge varied by demographics; country of employment or residence and professional association membership were shown to influence familiarity with AI. Many health information professionals reported limited or no AI experience, and those with practical AI experience performed better on foundational AI knowledge questions, suggesting that experiential learning scaffolds AI literacy. Most respondents understood emerging AI-related threats. However, regardless of experiences with everyday AI tools, they struggled with AI modeling and product development. Conclusions: The study results identified a major gap in AI knowledge, and the authors provide input for educators aiming to align educational programs with job market demand by increasing AI knowledge content, addressing gaps through targeted curriculum development and educator training.

In the United States about 100,000 people die annually from healthcare-associated infections (HAIs). Catheter-associated urinary tract infections (CAUTI) are the second most common type of HAI in the US, accounting for about 15% of HAIs. CAUTIs pose a significant treat to patients, which consequences could be as minor as an extra day in the hospital or as serious as death. Although studies show that implementing best practices and educating healthcare workers can help prevent HAIs, CAUTI prevention continues to pose a challenge to healthcare quality. Therefore, decreasing CAUTI rates has been targeted as one of the focus areas for several healthcare national initiatives. This survey paper considers research published from 2004 to 2015 on risk factors associated with CAUTIs and current preventive interventions. The goal of the survey is to provide a comprehensive taxonomy that aggregates and compare studies on CAUTIs and to identify future research opportunities for the development of system interventions that can be used to manage CAUTIs.

Objective: This study investigated how well-suited the International Classification of Diseases, 11th Revision, for Mortality and Morbidity Statistics, (ICD-11 MMS) is for 2 morbidity use cases, patient safety and quality, examining the level of detail captured, and evaluating the necessity for the development of a US clinical modification (CM). Materials and Methods: Utilizing the 5 NCVHS-specified perspectives plus the consumer perspective, a framework was created of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) use cases. Analysis yielded candidate source criteria for use in case evaluation. Patient safety and quality were chosen because they are relevant across all perspectives. Granularity differences and content coverage of ICD-11 MMS entities were assessed pre- and post-coordination to determine suitability for the 2 use cases. Pressure ulcers, a common condition across 3 patient safety applications, became the focus for comparing ICD-10-CM codes to ICD-11 MMS codes. For 3 electronic clinical quality measures (eCQMs), the evaluation centered on specified value sets for ischemic stroke, hypertension, and diabetes. Results: For pressure ulcers, the ICD-11 MMS was found to exceed ICD-10-CM capabilities via post-coordinated extension codes. For the 3 eCQM value sets explored, the ICD-11 MMS fully represented the disease concepts when post-coordinated code clusters were used. Conclusions: The examples from the patient safety and quality use cases evaluated in this study are appropriate for ICD-11 MMS. It captures greater detail than ICD-10-CM, and ICD-11 MMS specificity would benefit both use cases. The authors believe this preliminary study indicates the US should invest resources to explore adopting the WHO ICD-11 MMS and tooling and guidelines to implement post-coordination.

Current methods for addressing catheter-associated urinary tract infections (CAUTIs) are mostly focused on the biological causes, emphasizing the adherence to prevention guidelines, collecting data and disseminating the results, as well as identifying emerging threats from infectious organisms. However, there is a need for tools that can help organizations to implement interventions in ways that maximize the benefits while minimizing the impact upon efficient operations. This research extends the current methods for addressing CAUTIs to a new level beyond the current state of practice by introducing a new discrete-event simulation model for system assessment and determination of the clinical efficacy of CAUTI preventive interventions. The simulation model provides a platform where proposed clinical interventions can be studied and analyzed; giving an idea of possible expectations before clinical research is conducted; saving time, money, and minimizing risks. The computational results provide useful insights into patient service management for CAUTI prevention and show that operational factors such as the nurse:bed ratio, catheter daily removal chance, and late maintenance risk have a significant impact in the intensive care unit CAUTI rate.