Applied Clinical Informatics最新文献

{"title":"Improving Discrete Documentation of Cancer Staging-An Alert-Free Approach.","authors":"Renee Potashner, Adam P Yan","doi":"10.1055/a-2594-3722","DOIUrl":"10.1055/a-2594-3722","url":null,"abstract":"<p><p>Cancer staging is integral to ensuring cancer patients receive appropriate risk-adapted therapy. Discrete cancer staging using a structured staging form helps ensure accurate staging, provides a single source of truth for staging information, and allows for reporting to regulatory authorities. Our institution created pediatric oncology specific discrete staging forms that have been shared with the broader Epic community. By November 2023, baseline utilization of the staging form for patients with leukemia or lymphoma was 43%, and the override rate for our existing alert was 99.9%.Improve discrete documentation of cancer stage for patients with leukemia or lymphoma within 60 days following initiation of chemotherapy to >80% by July 2024 as measured by signed staging form.Model for improving plan-do-study-act (PDSA) cycles was implemented, and statistical process control charts were used to evaluate impact. The first intervention was educational training to oncology providers. The second PDSA cycle involved sharing monthly individual completion data with the primary oncologist regarding their personal patient metrics. The third PDSA cycle involved removing the interruptive alert.Within 6 months, documentation of primary oncologist improved from 86 to 100%, and initiation of staging form improved from 57 to 90%. Completion of signed cancer staging form reached 80%. Patients marked as not needing staging increased from 5 to 17%.Completion of a digital cancer staging form is important for continuity of care, and to facilitate reporting to regulatory authorities, though frequent interruptive alerts were an ineffective method for improving documentation. Education and data sharing increased staging completion to near target, with ongoing efforts to reach the goal of 80%.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1005-1013"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Nurse Documentation Time via an Electronic Health Record Documentation Efficiency Tool.","authors":"John Will, Deborah Jacques, Denise Dauterman, Rachelle Torres, Glenn Doty, Kerry O'Brien, Lisa Groom","doi":"10.1055/a-2581-6172","DOIUrl":"10.1055/a-2581-6172","url":null,"abstract":"<p><p>Nursing documentation burden is a growing point of concern in the United States health care system. Documentation in the electronic health record (EHR) is a contributor to perceptions of burden. Efficiency tools like flowsheet macros are one development intended to ease the burden of documentation.This study aimed to evaluate whether flowsheet macros, a documentation efficiency tool in the EHR that consolidates documentation into a single click, reduces the time spent on documentation activities and the EHR overall.Nurses in the health system were encouraged to create and utilize flowsheet macros for their documentation. Flowsheet documentation and time in system data for nurses' first and last shifts in the evaluation period were extracted from the EHR. Linear regression with control variables was utilized to understand if the utilization of flowsheet macros for documentation reduced the time spent in flowsheets or the EHR.The results of linear regression showed a significant, negative relationship between flowsheet macros use and time in flowsheets (adjusted odds ratio [AOR] = -0.291, 95% confidence interval [CI] = -0.342 to -0.240, <i>p</i> < 0.001). Flowsheet macros use and time in system also had a significant, negative relationship (AOR = -0.269, CI = -0.390 to -0.147, <i>p</i> ≤ 0.001). Subgroups for department specialties showed time savings in flowsheet activities for medical surgical, critical care, and obstetrics units, however, a significant relationship was not found in emergency and rehabilitation units.Utilization of flowsheet macros was associated with a decrease in the amount of time a nurse spends in both flowsheets and the EHR. Adoption and timesavings varied by the department setting, suggesting flowsheet macros may not be applicable to all patient types or conditions. Future research should investigate if the time savings from this tool yield benefits in perceptions of nurse documentation burden.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"796-803"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144008645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Summarize-then-Prompt: A Novel Prompt Engineering Strategy for Generating High-Quality Discharge Summaries.","authors":"Eyal Klang, Jaskirat Gill, Aniket Sharma, Evan Leibner, Moein Sabounchi, Robert Freeman, Roopa Kohli-Seth, Patricia Kovatch, Alexander W Charney, Lisa Stump, David L Reich, Girish N Nadkarni, Ankit Sakhuja","doi":"10.1055/a-2617-6572","DOIUrl":"10.1055/a-2617-6572","url":null,"abstract":"<p><p>Accurate discharge summaries are essential for effective communication between hospital and outpatient providers but generating them is labor-intensive. Large language models (LLMs), such as GPT-4, have shown promise in automating this process, potentially reducing clinician workload and improving documentation quality. A recent study using GPT-4 to generate discharge summaries via concatenated clinical notes found that while the summaries were concise and coherent, they often lacked comprehensiveness and contained errors. To address this, we evaluated a structured prompting strategy, summarize-then-prompt, which first generates concise summaries of individual clinical notes before combining them to create a more focused input for the LLM.The objective of this study was to assess the effectiveness of a novel prompting strategy, summarize-then-prompt, in generating discharge summaries that are more complete, accurate, and concise in comparison to the approach that simply concatenates clinical notes.We conducted a retrospective study comparing two prompting strategies: direct concatenation (M1) and summarize-then-prompt (M2). A random sample of 50 hospital stays was selected from a large hospital system. Three attending physicians independently evaluated the generated hospital course summaries for completeness, correctness, and conciseness using a 5-point Likert scale.The summarize-then-prompt strategy outperformed direct concatenation strategy in both completeness (4.28 ± 0.63 vs. 4.01 ± 0.69, <i>p</i> < 0.001) and correctness (4.37 ± 0.54 vs. 4.17 ± 0.57, <i>p</i> = 0.002) of the summarization of the hospital course. However, the two strategies showed no significant difference in conciseness (<i>p</i> = 0.308).Summarizing individual notes before concatenation improves LLM-generated discharge summaries, enhancing their completeness and accuracy without sacrificing conciseness. This approach may facilitate the integration of LLMs into clinical workflows, offering a promising strategy for automating discharge summary generation and could reduce clinician burden.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1325-1331"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12513772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-Driven Sharing of Health Information: A National Effort to Advance Equitable Interoperability.","authors":"Hannah K Galvin, Jeff Coughlin, Marianne Sharko, Maria A Grando, Mohammad Jafari, Serena Mack, Abigail English, Carolyn Petersen","doi":"10.1055/a-2591-9129","DOIUrl":"https://doi.org/10.1055/a-2591-9129","url":null,"abstract":"<p><p>The goal of national interoperability is to improve care quality and decrease administrative burden and costs. Patients, providers, and other stakeholders are increasingly concerned that indiscriminate sharing of data may have deleterious, permanent consequences, as well as fail to provide granular control over the sharing of individual health data. Data segmentation and consent standards to date have been limited in scope and implementation, which has hindered efforts to scale data sharing preferences. Shift, an independent expert stakeholder task force, has been convened to mature standards, terminologies, and consensus-driven implementation guidance, which are prerequisites for more robust policy drivers needed to support nationwide sensitive data segmentation and consent capabilities. This paper describes Shift's framework and processes as means to advance equitable interoperability.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"951-960"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144975466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing Mobility Indicators Using Machine Learning to Detect Mild Cognitive Impairment: A Systematic Scoping Review.","authors":"Salamah Alshammari, Munirah Alsubaie, Mathieu Figeys, Adriana Ríos Rincón, Victor Ezeugwu, Shaniff Esmail, Christine Daum, Lili Liu, Antonio Miguel Cruz","doi":"10.1055/a-2657-8212","DOIUrl":"10.1055/a-2657-8212","url":null,"abstract":"<p><p>The global aging population is rapidly increasing, and the prevalence of age-related cognitive conditions, such as mild cognitive impairment (MCI), is becoming more common. This condition, which represents intermediate stages between normal aging and dementia, underscores the importance of early detection and timely intervention to address the growing demand for health services. Traditional cognitive assessments have limitations, such as the consistency of results, prompting the need for innovative technology-based solutions.This study aimed to examine how technology-based mobility data collection methods and machine learning algorithms are used to detect MCI in adults.A systematic scoping review was conducted to identify papers that analyzed mobility-related data using machine learning algorithms, focusing on adults aged 18 or older with MCI. Seven databases were searched: MEDLINE, EMBASE, IEEE Xplore, PsycINFO, Scopus, Web of Science, and ACM Digital Library, yielding 2,901 papers.Twenty-four papers met the inclusion criteria, highlighting 116 mobility indicators used to classify or indicate MCI. Wearable devices were the most common data collection method, with mobile applications being the least utilized. The most frequently reported mobility indicator for walking was walking speed. For driving, indicators included the number of hard braking events, the number of night trips, and speed. Logistic regression, random forest, and neural networks were the most used machine learning algorithms. Overall, the mean accuracy, sensitivity, and specificity of all the algorithms were 86.1% (standard deviation [SD] = 6.7%), 84% (SD = 6.5%), and 72.8% (SD = 12%), respectively. The mean area under the curve and the harmonic mean of precision and recall scores (F1) were 0.77 (SD = 0.08) and 0.83 (SD = 0.16), respectively.This review highlights the use of technology-based methods, particularly wearable devices, in assessing mobility and applying machine learning algorithms to detect MCI. However, a notable gap in research on mobile app-based mobility monitoring suggests a promising direction for future studies.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"974-987"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12408119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Informatics Approach to Characterizing Rarely Documented Clinical Information in Electronic Health Records: Spiritual Care as an Exemplar.","authors":"Alaa Albashayreh, Nahid Zeinali, Nanle Joseph Gusen, Yuwen Ji, Stephanie Gilbertson-White","doi":"10.1055/a-2599-6300","DOIUrl":"10.1055/a-2599-6300","url":null,"abstract":"<p><p>Electronic health records (EHRs) contain valuable patient information, yet certain aspects of care remain infrequently documented and difficult to extract. Identifying these rarely documented elements requires advanced informatics approaches to uncover clinical documentation patterns that would otherwise remain inaccessible for research and quality improvement.This study developed and validated an informatics approach using natural language processing (NLP) to detect and characterize rarely documented elements in EHRs, using spiritual care documentation as an exemplar case.Using EHR data from a Midwestern US hospital (2010-2023), we fine-tuned Spiritual-BERT, an NLP model based on Bio-Clinical-BERT. The model was trained on 80% of a manually annotated, gold-standard corpus of EHR notes, and its performance was validated using the remaining 20% of the corpus, alongside 150 synthetic notes generated by GPT-4 and curated by clinical experts. We applied Spiritual-BERT to identify spiritual care documentation and analyzed patterns across diverse patient populations, provider roles, and clinical services.Spiritual-BERT demonstrated high accuracy in capturing spiritual care documentation (F1-scores: 0.938 internal validation, 0.832 external validation). Analysis of nearly 3.6 million EHR notes from 14,729 older adults revealed that 2% of clinical notes contained spiritual care references, while 73% of patients had spiritual care documented in at least one note. Significant variations were observed across provider types: chaplains documented spiritual care in 99.4% of their notes, compared to 1.7% for nurses and 1.2% for physicians. Documentation patterns also varied based on ethnicity, language, and medical diagnosis.This study demonstrates how advanced NLP techniques can effectively identify and characterize rarely documented elements in EHRs that would be challenging to detect through traditional methods. This approach revealed distinct documentation patterns across provider types, clinical settings, and patient characteristics, with promise for analyzing other under-documented clinical information.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1146-1156"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144032537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Rash Decision: Implementing an EHR-Integrated Penicillin Allergy Delabeling Protocol without Adequate Clinician Support.","authors":"Alexander S Plattner, Christine R Lockowitz, Rebecca G Same, Monica Abdelnour, Samuel Chin, Matthew J Cormier, Megan S Daugherty, Alexandra E Grier, Nicholas B Hampton, Mackenzie R Hofford, Sarah S Mehta, Jason G Newland, Kevin S O'Bryan, Matthew M Sattler, Mehr Z Shah, G Lucas Starnes, Valerie Yuenger, Alysa G Ellis, Evan E Facer","doi":"10.1055/a-2595-4849","DOIUrl":"10.1055/a-2595-4849","url":null,"abstract":"<p><p>Approximately 10% of patients have a documented penicillin \"allergy\"; however, up to 95% have subsequent negative testing. These patients may receive suboptimal antibiotics, leading to longer hospitalizations and higher costs, rates of resistant and nosocomial infections, and all-cause mortality. To mitigate these risks in children, we implemented an inpatient penicillin allergy delabeling protocol and integrated it into the electronic health record (EHR) through a mixed methods approach of clinical decision support (CDS).We describe our protocol implementation across three sequential phases: \"Pilot,\" \"Active Antimicrobial Stewardship Program (ASP),\" and \"Mixed CDS.\" We highlight several potential pitfalls that may have contributed to poor clinician adoption.Patients were risk-stratified as nonallergic, low-risk, or high-risk based on history. Process measures included: evaluation rate, oral challenge rate for low-risk, and allergy referral rate for high- or low-risk when oral challenge was deferred. The primary outcome measure was the penicillin allergy delabeling rate among low-risk or nonallergic. Balancing measures included the rate of epinephrine or antihistamine administrations.The pilot and ASP phases used clinician education and an order set, but were mostly manual processes. The mixed CDS phase introduced interruptive alerts, dynamic text in note templates, and patient list columns to guide clinicians, but little education was provided. The mixed CDS phase had the lowest evaluation rate compared with the pilot and active ASP phases (6.4 vs. 25 vs. 15%). However, when the evaluation was performed, the mixed CDS phase had the highest oral challenge rate (33 vs. 26 vs. 13%) and delabeling rate (43 vs. 33 vs. 27%). No adverse events occurred.CDS tools improve clinician decision-making and optimize patient care. However, relying on CDS for complex clinical evaluations can lead to failure when clinicians cannot find the tool or appreciate its importance. Person-to-person communication can be vital in establishing a process and educating intended users for successful CDS implementation.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1095-1103"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12431808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Measurement Science Framework to Optimize CDS for Opioid Use Disorder Treatment in the ED.","authors":"Mark S Iscoe, Carolina Diniz Hooper, Deborah R Levy, John Lutz, Hyung Paek, Christian Rose, Thomas Kannampallil, Daniella Meeker, James D Dziura, Edward R Melnick","doi":"10.1055/a-2595-0317","DOIUrl":"10.1055/a-2595-0317","url":null,"abstract":"<p><p>In the emergency department-initiated buprenorphine for opioid use disorder (EMBED) trial, a clinical decision support (CDS) tool had no effect on rates of buprenorphine initiation in emergency department (ED) patients with opioid use disorder. The Agency for Healthcare Research and Quality (AHRQ) recently released a CDS Performance Measure Inventory to guide data-driven CDS development and evaluation. Through partner co-design, we tailored AHRQ inventory measures to evaluate EMBED CDS performance and drive improvements.Relevant AHRQ inventory measures were selected and adapted using a partner co-design approach grounded in consensus methodology, with three iterative, multidisciplinary partner working group sessions involving stakeholders from various roles and institutions; meetings were followed by postmeeting surveys. The co-design process was divided into conceptualization, specification, and evaluation phases building on the Centers for Medicare and Medicaid Services' measure life cycle framework. Final measures were evaluated in three EDs in a single health system from January 1, 2023, to December 31, 2024.The partner working group included 25 members. During conceptualization, 13 initial candidate metrics were narrowed to 6 priority categories. These were further specified and validated as the following measures, presented with preliminary values based on the use of the current (i.e., preoptimization) EMBED CDS: eligible encounters with CDS engagement, 5.0% (95% confidence interval: 4.3-5.8%); teamwork on ED initiation of buprenorphine, 39.9% (32.5-47.3%); proportion of eligible users who used EMBED, 58.3% (50.9-65.8%); time spent on EMBED, 29.0 seconds (20.4-37.7 seconds); proportion of buprenorphine orders placed through EMBED, 6.5% (3.4-9.6%); and task completion, 13.8% (8.9-18.7%) for buprenorphine order/prescription.A measurement science framework informed by partner co-design was a feasible approach to develop measures to guide CDS improvement. Subsequent research could adapt this approach to evaluate other CDS applications.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1067-1076"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12431813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144975596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of Mathematical and Simulation Models for Improving Quality of Care in Emergency Departments: A Systematic Literature Review.","authors":"Thamer A Almohaya, James Batchelor, Edilson Arruda","doi":"10.1055/a-2591-3930","DOIUrl":"10.1055/a-2591-3930","url":null,"abstract":"<p><p>The purpose of this systematic literature review is to critically evaluate the use of mathematical and simulation models within emergency departments (EDs) and assess their potential to improve the quality of care. This review emphasizes the critical need for quality enhancement in health care systems, with a specific focus on EDs.This review incorporates studies that have investigated the quality of care provided in ED settings, employing assorted mathematical and simulation models for adult populations. Based on the selected studies, a narrative approach was used to synthesize the findings, focusing on outcome classification, simulation, and modelling. There are six outcome dimensions: safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity.This review analyzed 112 studies, uncovering a distinct focus on a set of key performance measures within ED operations, accounting for 222 instances across these studies. Measures assessing timeliness were most frequent, occurring 111 times, indicative of a strong emphasis on operational efficiency aspects such as waiting times and patient flow. A total of 75 examinations were conducted on efficiency-related measures, with a specific focus on identifying and addressing operational bottlenecks and optimizing resource utilization. On the other hand, safety, patient-centeredness, and effectiveness were not as commonly represented, with only 3, 4, and 29 instances, respectively.This review highlights the considerable potential of mathematical and simulation models to enhance ED operations, particularly regarding timeliness and efficiency. However, aspects such as patient safety, effectiveness, and patient-centeredness were underrepresented, while equity was absent across the studies, indicating a clear need for further research. These findings emphasize the importance of adopting a more thorough approach to evaluating and improving the quality of emergency care. Future research should also concentrate on refining data management practices, incorporating observational studies, and exploring various simulation tools to develop a more balanced and inclusive understanding of these models' applications.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"825-837"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracting Social Determinants of Health from Dental Clinical Notes.","authors":"Farhana Pethani, Alec Chapman, Mike Conway, Xiang Dai, Demiana Bishay, Victor Choh, Alexander He, Su-Elle Lim, Huey Ying Ng, Tanya Mahony, Albert Yaacoub, Sarvnaz Karimi, Heiko Spallek, Adam G Dunn","doi":"10.1055/a-2616-9858","DOIUrl":"10.1055/a-2616-9858","url":null,"abstract":"<p><p>In dentistry, social determinants of health (SDoH) are potentially recorded in the clinical notes of electronic dental records. The objective of this study was to examine the availability of SDoH data in dental clinical notes and evaluate natural language processing methods to extract SDoH from dental clinical notes.A set of 1,000 dental clinical notes was sampled from a dataset of 105,311 patient visits to a dental clinic and manually annotated for information pertaining to sugar, tobacco, alcohol, methamphetamine, housing, and employment. Annotations included temporality, dose, type, duration, and frequency where appropriate. Experiments were to compare extraction using fine-tuned pretrained language models (PLMs) with a rule-based approach. Performance was measured by F1-score.For identifying SDoH, the best-performing PLM method produced F1-scores of 0.75 (sugar), 0.69 (tobacco), 0.67 (alcohol), 0.42 (housing), and 0 (employment). The rule-based method produced F1-scores of 0.70 (sugar), 0.69 (tobacco), 0.53 (alcohol), 0.44 (housing), and 0 (employment). The overall difference between PLMs and rule-based methods was F1-score of 0.04 (95% confidence interval -0.01, 0.09). SDoH were relatively rare in dental clinical notes, from sugar (9.1%), tobacco (3.9%), alcohol (1.2%), housing (1.2%), employment (0.2%), and methamphetamine use (0%).The main challenge of extracting SDoH information from dental clinical notes was the frequency with which they are recorded, and the brevity and inconsistency where they are recorded. Improved surveillance likely needs new ways to standardize how SDoHs are reported in dental clinical notes.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"1281-1291"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12494450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}