Advances in health information science and practice最新文献

{"title":"Clinical Document Improvement in Surgical Residency Training.","authors":"Jane Tian, Jonathan Mejia, Alexander Schaal, Philippe Rousseau, Kazi Islam, Esther Espinoza, Kellyann Inniss, Martine A Louis, Nageswara Mandava","doi":"10.63116/PMCY2154","DOIUrl":"10.63116/PMCY2154","url":null,"abstract":"<p><strong>Background: </strong>Accurate clinical documentation is essential for patient care, interdisciplinary communication, quality metrics reporting, and hospital reimbursement. Many hospitals face significant revenue losses due to inadequate or incomplete documentation.</p><p><strong>Methods: </strong>A retrospective chart review was conducted at a 250-bed community hospital in Queens, NY, analyzing surgical coding queries from January 2021 to December 2023. Before 2021, clinical documentation improvement (CDI) queries were randomly assigned to residents or attendings. In 2021, a quality improvement initiative centralized queries to the administrative chief resident, who facilitated resolutions. Data, including query volume and response times, were extracted from 3M CDI software.</p><p><strong>Results: </strong>A total of 701 charts were reviewed for the study period. Prior to the intervention, CDI queries averaged 18 per month with a 67% response rate within 24 hours. Following centralization in 2021, query volume decreased to 9 per month by 2023, and the 24-hour response rate improved to 97%. The implementation of standardized templates and real-time coding reviews contributed to these improvements.</p><p><strong>Conclusions: </strong>Centralizing CDI queries and integrating structured interventions into surgical training programs significantly improved documentation accuracy and response times. Enhanced collaboration between residents and CDI specialists positively impacted workflow efficiency, patient care, and hospital financial outcomes.</p>","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"PMCY2154"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flesch-Kincaid Grade Level Readability Scores to Evaluate Readability of Clinical Documentation During an Electronic Health Record Transition.","authors":"Douglas W Challener, Andrew Wen, Jungwei W Fan, Hongfang Liu, John O'Horo, Mark Nyman","doi":"10.63116/VBWY7913","DOIUrl":"10.63116/VBWY7913","url":null,"abstract":"<p><strong>Background: </strong>Electronic health records (EHRs) serve as a critical tool for clinical documentation, yet their complexity has raised concerns about readability and usability for healthcare providers and patients. Readability scores, such as the Flesch-Kincaid (FK) Grade Level, offer a quantitative method to assess documentation clarity. In this study, the authors evaluated the impact of an EHR transition on the readability of clinical notes across multiple medical specialties.</p><p><strong>Methods: </strong>All clinical notes recorded between 2016 and 2019 at a large healthcare organization were evaluated across specialties. The organization transitioned EHR systems during the study period, in May 2018. FK Grade Level scores were calculated for each clinical consultation note and stratified by specialty and EHR.</p><p><strong>Results: </strong>Across 10 medical specialties, 630,246 clinical notes were evaluated. The median FK grade levels from notes composed in the new EHR were similar to those composed in the previous EHR (8.228 vs 8.240; p=0.58). The variation in FK grade level as measured by standard deviation was lower in the new EHR compared with the old EHR (1.405 vs 1.764; p <0.01).</p><p><strong>Conclusions: </strong>Calculation of FK grade level of clinical notes is a feasible method to assess clinical note readability. Study results showed that clinical documentation readability may not be strongly associated with underlying EHR. Migrating to a new EHR alone should not be expected to improve readability of clinical notes.</p>","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"VBWY7913"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Transformative Innovation to Clinical Coding for Better Data and Less Burden.","authors":"Michael Pine, Kathy Giannangelo, Charles Hobson, Christopher Tompkins","doi":"10.63116/FQRG7761","DOIUrl":"10.63116/FQRG7761","url":null,"abstract":"<p><strong>Background: </strong>The importance of codes for documenting healthcare concepts has expanded from a historical need to collect statistics to today's wide array of clinical and other secondary uses. The most deeply embedded system for coding diagnoses is the World Health Organization's (WHO) International Classification of Diseases (ICD). WHO's eleventh revision, ICD-11, was evaluated to determine how best to meet the needs of stakeholders concerned about clinical quality, healthcare decision-making, and other secondary use cases.</p><p><strong>Methods: </strong>This study explored the capability of ICD-10-CM, the standard ICD-11 for Mortality and Morbidity Statistics (ICD-11-MMS), and an enhanced ICD-11-MMS prototype (ICD-11-CCL) to represent clinical patient profiles for two natural clinical language scenarios. Systems were evaluated for granularity, comprehensiveness, accuracy, precision, relevancy, and their capability to provide user-friendly coding support.</p><p><strong>Results: </strong>Both the standard and enhanced ICD-11-MMS produced code clusters that captured more clinical details and insight than ICD-10-CM's codes. However, ICD-11-MMS was less comprehensive than ICD-11-CCL and introduced occasional new inaccuracies. Unlike ICD-10-CM and ICD-11-MMS, ICD-11-CCL enabled clinicians to designate their clinical perspectives directly. While ICD-11-MMS produced numerous synonymous code clusters, the enhanced syntax of ICD-11-CCL resulted in a unique code cluster for each scenario.</p><p><strong>Conclusions: </strong>Adoption of an enhanced ICD-11-MMS such as ICD-11-CCL could provide clinically nuanced data to support important secondary data applications more effectively than standard ICD-11-MMS, while retaining ICD-11-MMS's capability to support international comparative analyses such as mortality and morbidity statistics. With automated coding tools, enhanced ICD-11-MMS could also reduce the burden on humans to select, review, memorize or decipher codes. Enhanced ICD-11-MMS represents a substantial improvement over customized modifications of ICD-10 and, with further development and testing, could be a viable option for future adoption.</p>","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"FQRG7761"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Accuracy of ChatGPT in Answering FAQs, Making Clinical Recommendations, and Categorizing Patient Symptoms: A Literature Review.","authors":"John Geracitano, Brittney Anderson, Melissa Coffel, Myles Rosenzweig, Spencer D Dorn, Saif Khairat, Jamie Conklin","doi":"10.63116/VXUL2925","DOIUrl":"10.63116/VXUL2925","url":null,"abstract":"<p><strong>Background: </strong>ChatGPT is a popular open-source large language model (LLM) that uses supervised learning to create human-like queries. In recent years, ChatGPT has generated excitement in the medical field. However, its accuracy must be carefully evaluated to determine its usefulness in patient care. In this literature review, the authors examine whether ChatGPT can accurately answer frequently asked questions (FAQs) from patients, make clinical recommendations, and effectively categorize patient symptoms.</p><p><strong>Methods: </strong>A database search in PubMed was conducted using the search terms \"ChatGPT,\" \"accuracy,\" and \"clinical decision-making,\" yielding 122 unique references. Two screening stages resulted in 9 studies that met the evaluation criteria for this review.</p><p><strong>Results: </strong>Analysis of 9 studies showed that while ChatGPT can answer FAQs, offer recommendations, and categorize symptoms in less complicated scenarios, its clinical accuracy ranged from 20% to 95%. ChatGPT may be helpful in specific clinical scenarios; however, its variable accuracy makes it unsuitable as a stand-alone point-of-care product.</p><p><strong>Conclusions: </strong>ChatGPT is only adept at providing generalized recommendations when individual patient care is more suitable. Further research is needed to identify where ChatGPT delivers the most accurate responses and how it can supplement traditional care.</p>","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"VXUL2925"},"PeriodicalIF":0.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Patient Literacy in the Health Information Space: What Does Release of Information Tell Us?","authors":"Darin Challacombe, Elizabeth N McElhiney","doi":"10.63116/XLNW5463","DOIUrl":"10.63116/XLNW5463","url":null,"abstract":"<p><strong>Background: </strong>Some research on social determinants of health (SDOH) has suggested that patient health literacy (HL) or comprehension can influence health outcomes, while other recent studies have not observed strong connections between those variables. Previous research has utilized various screening tools to assess patient HL, with varying success.</p><p><strong>Methods: </strong>In this three-phase study, the authors assessed the \"readability\" of various common patient health information documents and forms used by prominent US-based healthcare organizations and requestors, including authorizations and privacy notices, according to the Flesch-Kincaid and Flesch Reading Ease scales.</p><p><strong>Results: </strong>The results showed that the documents examined had readability scores higher than the average American's literacy level.</p><p><strong>Conclusions: </strong>Confirming anecdotal data, this research supports the conclusion that many forms signed by American patients are above their average reading level and therefore they may not have a full understanding of what they are signing, even when requesting that records to be sent to others.</p>","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"XLNW5463"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneity in Online Medical Record Use Based on the Intersectionality of Community and Racioethnicity: A National Study Using Health Information National Trends Survey Data.","authors":"Athena Stamos, Humza Asgher, Naleef Fareed","doi":"10.63116/YVLD1335","DOIUrl":"10.63116/YVLD1335","url":null,"abstract":"<p><strong>Background: </strong>An individual's racioethnicity can affect their use of digital health tools and access to digital resources. In this study, the authors examine the relationship between community Social Determinants of Health (cSDoH) and racioethnicity with access and use of online medical records.</p><p><strong>Methods: </strong>Data were used from the Health Information National Trends Survey (HINTS)-specifically, HINTS 5 Cycle 1 (2017), HINTS 5 Cycle 2 (2018), HINTS 5 Cycle 3 (2019), HINTS 5 Cycle 4 (2020). Predicted probabilities of accessing online medical records based on interactions of cSDoH and racioethnicity were estimated using adjusted weighted logistic regression. Among online medical record users, the authors explored how they accessed the Internet by racioethnicity and cSDoH.</p><p><strong>Results: </strong>A combined analytical dataset was created with 16,092 pooled observations from all four HINTS 5 cycles, including 14,103 respondents who answered a question about whether they accessed their online medical record (with 5,766 responding yes). From a range of predicted probabilities between 0 and 1, Hispanic, Non-Hispanic (NH) Black, and NH White communities had predicted probabilities for accessing online medical records of 0.20, 0.33, and 0.38, respectively, in the most disinvested neighborhoods. We also found differences in how individuals accessed their online medical record by racioethnicity and cSDoH.</p><p><strong>Conclusions: </strong>Results suggest that incorporating cSDoH and racioethnicity in digital health decision making may help bridge the digital divide. Further research should explore the complex interplay between cSDoH, racioethnicity, online medical record access and use to promote digital health equity.</p>","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"YVLD1335"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introducing Advances in Health Information Science and Practice.","authors":"Susan Fenton","doi":"10.63116/YVDO4052","DOIUrl":"10.63116/YVDO4052","url":null,"abstract":"","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"YVDO4052"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interprofessional Education Readiness with Undergraduate Nursing and Health Information Management Students at the University of Louisiana at Lafayette.","authors":"Tierney Beebe, Elizabeth Franklin, Anita Hazelwood","doi":"10.63116/QEKZ6869","DOIUrl":"10.63116/QEKZ6869","url":null,"abstract":"<p><strong>Background: </strong>The need for interprofessional education (IPE) in the health professions has been recognized for many years with the intent to move related health disciplines toward more collaborative efforts.</p><p><strong>Methods: </strong>This explanatory sequential mixed methods study evaluated IPE readiness in Bachelor of Science in Nursing (BSN) and Health Information Management (HIM) students at the University of Louisiana at Lafayette before and after an introductory IPE workshop titled \"IPE is Key.\" Of the total 1,003 BSN and HIM students, 109 voluntarily participated in the workshop, with 78 completing the pre- and post-assessment for a response rate of 71.6% of attendees and 7.8% of all BSN and HIM students who fit the inclusion criteria.</p><p><strong>Results: </strong>Analysis of pre- and post-workshop assessment scores revealed a statistically-significant increase in IPE readiness of participants (p<0.001). Findings support the implementation of an annual introductory workshop for early BSN and HIM students, with potential expansion to other health professions. Findings of the study were utilized to develop the \"IPE is Key\" Instructor's Guide, which provides educators the necessary information to deliver engaging IPE activities shown to enhance students' readiness to learn collaboratively.</p><p><strong>Conclusions: </strong>This study's findings highlight the need for an annual introductory IPE workshop for early BSN and HIM learners, with the potential to include other health related professions students at UL Lafayette. The findings also suggest that the introductory workshop may improve readiness for and satisfaction with the Fall-semester IPHE 310 interprofessional academic course, which includes BSN and HIM students.</p>","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"QEKZ6869"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Standardization and Interoperability: Federated Learning's Impact on EHR Systems and Health Informatics.","authors":"Herat Joshi, Shenson Joseph","doi":"10.63116/UBYM3803","DOIUrl":"10.63116/UBYM3803","url":null,"abstract":"<p><strong>Background: </strong>In electronic health records (EHRs), standardization and interoperability challenges stem from fragmented data across institutions. Federated learning, a distributed machine learning framework through which multiple institutions can collaborate on model development while maintaining patient data privacy, bridges this gap by training shared models while keeping data localized. Therefore, this study focused on the application of federated learning in the biomedical domain, with the aim of addressing statistical challenges, system complexities, and privacy issues.</p><p><strong>Methods: </strong>Following PRISMA guidelines, the authors conducted a comprehensive literature search across PubMed/Medline, Cochrane/EMBASE, PEDro, SCOPUS, MEDLINE, Web of Science, Embase, and arxiv, covering publications from January 2020 to April 2024. The search included terms such as \"electronic health records,\" \"EHR,\" \"electronic medical records,\" \"EMR,\" \"registry/registries,\" \"tabular,\" \"federated learning,\" \"distributed learning,\" and \"distributed algorithms.\" Data were extracted on cohort characteristics, modeling approaches, and federated learning frameworks.</p><p><strong>Results: </strong>After applying inclusion and exclusion criteria to 58 initial results, we analyzed 15 previously-published articles. According to the results described in those articles, federated learning improved data sharing and analysis in various healthcare environments, enhancing EHR standardization and interoperability. Federated learning models typically matched or surpassed localized models, especially when local data was limited or fragmented, and were particularly effective in predicting rare diseases and handling different data types. The use of federated averaging, personalized models, and heterogeneity-aware aggregation methods effectively managed diverse data, ensuring strong performance. Federated learning also maintained privacy and security by keeping patient data local and implementing advanced security protocols like differential privacy.</p><p><strong>Conclusions: </strong>Federated learning represents a transformative advancement in health informatics, addressing the critical need for seamless data exchange in the fragmented US healthcare landscape. By improving patient outcomes and operational efficiencies, federated learning paves the way for leveraging big data analytics on a nationwide scale.</p>","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"UBYM3803"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Professionals' Satisfaction with the Use of Electronic Health Records System for Healthcare Delivery in Ghana.","authors":"Emmanuel K Achampong, Edward Agyemang, Kobina Esia-Donkoh, Addae Boateng Adu-Gyamfi","doi":"10.63116/PBZC1086","DOIUrl":"10.63116/PBZC1086","url":null,"abstract":"<p><strong>Background: </strong>Satisfaction with a given Electronic Health Record (EHR) system has become an important topic in healthcare. As healthcare providers increasingly rely on EHRs to manage patient care, their satisfaction with the system significantly impacts their quality of care. This study analyzed the satisfaction of health professionals who use the EHR called Lightwave Health Information Management System (LHIMS; Lightwave eHealthcare Solutions, Ghana) for healthcare delivery.</p><p><strong>Methods: </strong>A descriptive cross-sectional study design was used for this study. The study employed the stratified probability sampling method. Descriptive statistics were computed to determine the weighted mean score for all the indicators under efficiency. Also, bivariate (chi-square) and multivariate (ordinal logistic regression) analyses were conducted to test the study's hypotheses.</p><p><strong>Results: </strong>Factors such as educational qualification, work experience, training status, duration of the training, and computer efficacy significantly affected health professionals' satisfaction with LHIMS. Sex and age of health professionals did not affect satisfaction levels with LHIMS.</p><p><strong>Conclusions: </strong>Based on the results of this study, health professional participants were satisfied with aspects of LHIMS such as flexibility and consistency, which increases the speed of task execution and enables them to easily retrieve accurate information. Results suggested that training, while necessary, must be tailored to individual user needs. Digital literacy also played a pivotal role in ensuring effective EHR use, with advanced users reporting greater satisfaction.</p>","PeriodicalId":521055,"journal":{"name":"Advances in health information science and practice","volume":"1 1","pages":"PBZC1086"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}