OpenEvidence is a popular artificial intelligence (AI) based medical search engine that generates evidence-based answers. It includes a quick search engine method (OE) that takes only seconds to respond, along with a limited number of references. In mid-2025, the platform introduced “Deep Consult” (DC), which takes several minutes to respond and provides more comprehensive answers with additional references. OpenEvidence scored 100% on USMLE-type multiple-choice questions, but it has not been tested on more complex medical scenarios. We tested the OE and DC models using questions primarily derived from medical specialty board exams, specifically, the MedXpertQA dataset. In a prior published study, this dataset was evaluated with eleven large language models (LLMs), and the results indicated poor accuracy (14-46%) for all LLMs. We evaluated the performance of OpenEvidence on a sample of the MedXpertQA dataset, comprising 100 medical subspecialty scenarios and using two independent evaluators. The highest accuracy for DC was 41%, and for OE, 34%. Repeatability testing revealed an evaluator concordance rate of 77% for OE and 72% for DC.

Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.

PurposeMany states in the US are predominately rural and frequently depend on critical access hospitals (CAHs) for medical care. Due to the small size of many of these facilities, they often do not have staff adequately trained in health information technology (HIT), informatics or analytics. Their specific needs are often unknown and may vary. In this study, we determined the Nebraska CAH's health informatics workforce needs in order to design an applied curriculum in health informatics.MethodsWe developed and administered a survey to quantify workforce needs related to HIT, informatics and analytics. Hospital leaders from fourteen of 63 CAHs in Nebraska responded to a survey with an overall response rate of 22%. Both closed-ended questions and free text comments were analyzed.ResultsAround half of the senior hospital leaders reported that their staff needed additional education and training in several areas to meet informatics needs. Specifically, more than 50% of respondents reported the demand for education and training in the Analytical Tools domain, Organization Learning domain, Decision Support Systems domain, and Interoperability domain. Less than 50% of respondents indicated their hospital personnel could benefit from education and training, especially in the Electronic Health Records domain, Management of Health Information Systems and Health Information Management domain.ConclusionCAHs in Nebraska need ongoing workforce training and education in the areas of data analysis, data-driven organizational improvement/learning, decision support systems, and interoperability. Training modules addressing these areas are needed for rural CAH staff.

BACKGROUND:The field of health information management (HIM) focuses on the protection and management of health information from a variety of sources. The American Health Information Management Association (AHIMA) Council for Excellence in Education (CEE) determines the needed skills and competencies for this field. AHIMA's HIM curricula competencies are divided into several domains among the associate, undergraduate, and graduate levels. Moreover, AHIMA's career map displays career paths for HIM professionals. What is not known is whether these competencies and the career map align with industry demands.OBJECTIVE:The primary aim of this study is to analyze HIM job postings on a US national job recruiting website to determine whether the job postings align with recognized HIM domains, while the secondary aim is to evaluate the AHIMA career map to determine whether it aligns with the job postings.METHODS:A national job recruitment website was mined electronically (web scraping) using the search term "health information management." This cross-sectional inquiry evaluated job advertisements during a 2-week period in 2021. After the exclusion criteria, 691 job postings were analyzed. Data were evaluated with descriptive statistics and natural language processing (NLP). Soft cosine measures (SCM) were used to determine correlations between job postings and the AHIMA career map, curricular competencies, and curricular considerations. ANOVA was used to determine statistical significance.RESULTS:Of all the job postings, 29% (140/691) were in the Southeast, followed by the Midwest (140/691, 20%), West (131/691,19%), Northeast (94/691, 14%), and Southwest (73/691, 11%). The educational levels requested were evenly distributed between high school diploma (219/691, 31.7%), associate degree (269/691, 38.6%), or bachelor's degree (225/691, 32.5%). A master's degree was requested in only 8% (52/691) of the postings, with 72% (42/58) preferring one and 28% (16/58) requiring one. A Registered Health Information Technologist (RHIT) credential was the most commonly requested (207/691, 29.9%) in job postings, followed by Registered Health Information Administrator (RHIA; 180/691, 26%) credential. SCM scores were significantly higher in the informatics category compared to the coding and revenue cycle (P=.006) and data analytics categories (P<.001) but not significantly different from the information governance category (P=.85). The coding and revenue cycle category had a significantly higher SCM score compared to the data analytics category (P<.001). Additionally, the information governance category was significantly higher than the data analytics category (P<.001). SCM scores were significantly different between each competency category, except there were no differences in the average SCM score between the information protection and revenue cycle management categories (P=.96) and the information protection and data structure, content, and information governance categories (P=.31).CONCLUSIONS:Industry job postings primarily sought degrees, with a master's degree a distant fourth. NLP analysis of job postings suggested that the correlation between the informatics category and job postings was higher than that of the coding, revenue cycle, and data analytics categories.

Objectives:To report quantitative and qualitative analyses of features, functionalities, organizational, training, clinical specialties, and other factors that impact electronic health record (EHR) experience based on a survey by two large healthcare systems.Materials and Methods:A total of 816 clinicians-352 (43 percent) physicians, 96 (12 percent) residents/fellows, 177 (22 percent) nurses, 96 (12 percent) advanced practice providers, and 95 (12 percent) allied health professionals-completed surveys on different EHRs. Responses were analyzed for quantitative and qualitative factors. The measured outcome was calculated as a net EHR experience.Results:Net EHR experience represents overall satisfaction that clinicians report with the EHR and its usability. EHR experience for Virginia Commonwealth University Medical Center and University of Chicago Medicine was low. There were noticeable differences in physician and nursing experiences with EHRs at both universities. EHR personalization, years of practice, impact on efficiency, quality of care, and satisfaction with EHR training contributed significantly to the net EHR experience. Satisfaction of certain specialty practitioners such as endocrinology, family medicine, infectious disease, nephrology, neurology, and pulmonology was noted to be especially low. Ability to use a split-screen function to view labs, follow-up training from other providers rather than vendors, reduced documentation time burden, fewer click boxes, more customizable order sets, improved messaging, e-prescribing, and improved integration were the most common desired EHR improvements requested on qualitative analysis.Discussion:EHR experience was low regardless of the system and may be improved by better EHR training, increased utilization of personalization tools, reduced documentation burden, and enhanced EHR design and functionality. There was a difference between provider and nursing experiences with the EHR.Conclusion:Designing better EHR training, increasing utilization of personalization tools, enhancing functionality, and decreasing documentation burden may lead to a better EHR experience.

Objectives To describe the education, experience, skills, and knowledge required for health informatics jobs in the United States. Methods Health informatics job postings ( n =206) from Indeed.com on April 14, 2020 were analyzed in an empirical analysis, with the abstraction of attributes relating to requirements for average years and types of experience, minimum and desired education, licensure, certification, and informatics skills. Results A large percentage (76.2%) of posts were for clinical informaticians, with 62.1% of posts requiring a minimum of a bachelor's education. Registered nurse (RN) licensure was required for 40.8% of posts, and only 7.3% required formal education in health informatics. The average experience overall was 1.6 years (standard deviation=2.2), with bachelor's and master's education levels increasing mean experience to 3.5 and 5.8 years, respectively. Electronic health record support, training, and other clinical systems were the most sought-after skills. Conclusion This cross-sectional study revealed the importance of a clinical background as an entree into health informatics positions, with RN licensure and clinical experience as common requirements. The finding that informatics-specific graduate education was rarely required may indicate that there is a lack of alignment between academia and industry, with practical experience preferred over specific curricular components. Clarity and shared understanding of terms across academia and industry are needed for defining and advancing the preparation for and practice of health informatics.

Objective: To discuss and illustrate the utility of two open collaborative data science platforms, and how they would benefit data science and informatics education. Methods and Materials: The features of two online data science platforms are outlined. Both are useful for new data projects and both are integrated with common programming languages used for data analysis. One platform focuses more on data exploration and the other focuses on containerizing, visualization, and sharing code repositories. Results: Both data science platforms are open, free, and allow for collaboration. Both are capable of visual, descriptive, and predictive analytics Discussion: Data science education benefits by having affordable open and collaborative platforms to conduct a variety of data analyses. Conclusion: Open collaborative data science platforms are particularly useful for teaching data science skills to clinical and nonclinical informatics students. Commercial data science platforms exist but are cost-prohibitive and generally limited to specific programming languages.

Electronic health records (EHRs) have been adopted by most hospitals and medical offices in the United States. Because of the rapidity of implementation, health care providers have not been able to leverage the full potential of the EHR for enhancing clinical care, learning, and teaching. Physicians are spending an average of 49% of their working hours on EHR documentation, chart review, and other indirect tasks related to patient care, which translates into less face time with patients. The purpose of this article is to provide a preliminary framework to guide the use of EHRs in teaching and evaluation of residents. First we discuss EHR educational capabilities that have not been reviewed in sufficient detail in the literature and expand our discussion for each educational activity with examples. We emphasize quality improvement of clinical notes as a basic foundational skill using a spreadsheet-based application as an assessment tool. Next, we integrate the six Accreditation Council for Graduate Medical Education (ACGME) Core Competencies and Milestones (CCMs) framework with the Reporter-Interpreter-Manager-Educator (RIME) model to expand our assessments of other areas of resident performance related to EHR use. Finally, we discuss how clinical utility, clinical outcome, and clinical reasoning skills can be assessed in the EHR. We describe a pilot conceptual framework—CCM framework—to guide and demonstrate the use of the EHR for education in a clinical setting. As EHRs and other supporting technologies evolve, medical educators should continue to look for new opportunities within the EHR for education. Our framework is flexible to allow adaptation and use in most training programs. Future research should assess the validity of such methods on trainees’ education.

BACKGROUND:We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes.OBJECTIVE:To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs.METHODS:The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets.RESULTS:Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix.CONCLUSIONS:IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise.

: This historical note outlines the development of the Robert E. Mitchell Center (REMC) for Prisoner of War (POW) Studies and summarizes the pivotal role CAPT Mitchell, MC, USN played in its establishment. His singular vision and sheer audacity enabled the center to provide an unbroken 41 years of dedicated service to those who suffered as a POW. CAPT Mitchell s dedication is the reason that today the REMC for POW studies stands as the only program which continued its care of U.S. repatriated prisoners of war (RPWs) well beyond the expiration of the original 1973-1978 charter. Without CAPT Mitchell s unwavering dedication the many lessons learned would not be possible. Today the REMC is recognized for its subject matter expertise.

Automobile insurance availability is a serious issue for motorists, regulators and the insurance industry. The costs imposed on the system by uninsured motorists are not trivial. In order to minimize these costs it is necessary to understand the factors that lead motorists to drive without insurance. This paper uses data reported to the California Department of Insurance, as well as demographic data collected at the ZIP code level, to analyze the demand for auto insurance in areas that the California Department of Insurance has designated as underserved. The results show that areas – as measured by ZIP codes – that are saddled with high poverty and areas that are predominately urban are more likely to have lower demand for automobile insurance. However, the fact that a certain area is predominately minority does not alone make it more likely to exhibit lower demand for automobile insurance.