Applied Clinical Informatics最新文献

{"title":"Effect of an Electronic Health Record-Based Intervention on Documentation Practices.","authors":"Shreya Shah, Michael Bedgood, Anna Devon-Sand, Cathriona Dolphin-Dempsey, Venkata Cherukuri, Kirsti Weng, Steven Lin, Christopher Sharp","doi":"10.1055/a-2367-8564","DOIUrl":"10.1055/a-2367-8564","url":null,"abstract":"<p><strong>Background: </strong> Documentation burden is one of the largest contributors to physician burnout. Evaluation and Management (E&M) coding changes were implemented in 2021 to alleviate documentation burden.</p><p><strong>Objectives: </strong> We used this opportunity to develop documentation best practices, implement new electronic health record (EHR) tools, and study the potential impact on provider experiences with documentation related to these 2021 E&M changes, documentation length, and time spent documenting at an academic medical center.</p><p><strong>Methods: </strong> Five actionable best practices, developed through a consensus-driven, multidisciplinary approach in November 2020, led to the creation of two new ambulatory note templates, one for E&M visits (implemented in January 2021) and another for preventative visits (implemented in May 2021). As part of a quality-improvement initiative at nine faculty primary care clinics, surveys were developed utilizing a 5-point Likert scale to assess provider perceptions and deidentified EHR metadata (Signal, Epic Systems) were analyzed to measure changes in EHR use metrics between a pre-E&M changes timeframe (August 2020-December 2020) and a post-E&M change timeframe (August 2021-December 2021). A subgroup analysis was conducted comparing EHR use metrics among note template utilizers versus nonutilizers. Any provider who used one of the note templates at least once was categorized as a utilizer.</p><p><strong>Results: </strong> Between January 2021 and December 2021, the adoption of the E&M visit template was 31,480 instances among 120 unique ambulatory providers, and adoption of the preventative visit template was 1,464 instances among 22 unique ambulatory providers. Survey response rate among faculty primary care providers was 82% (88/107): 55% (48/88) believed the 2021 E&M changes provided an opportunity to reduce documentation burden, and 28% reported favorable satisfaction with time spent documenting. Among providers who reported using one or both of the new note templates, 81% (35/43) of survey respondents reported favorable satisfaction with new note templates. EHR use metric analyses revealed a small, yet significant reduction in time in notes per appointment (<i>p</i> = 0.004) with no significant change in documentation length of notes (<i>p</i> = 0.45). Note template utilization was associated with a statistically significant reduction in documentation length (<i>p</i> = 0.034).</p><p><strong>Conclusion: </strong> This study shows modest progress in improving EHR use measures of documentation length and time spent documenting following the 2021 E&M changes, but without great improvement in perceived documentation burden. Additional tools are needed to reduce documentation burden and further research is needed to understand the impact of these interventions.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141635154","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 Advanced Cardiac Life Support Application Improves Performance during Simulated Cardiac Arrest.","authors":"Michael Senter-Zapata, Dylan V Neel, Isabella Colocci, Afaf Alblooshi, Faten Abdullah M AlRadini, Brian Quach, Samuel Lyon, Maxwell Coll, Andrew Chu, Katharine W Rainer, Beth Waters, Christopher W Baugh, Roger D Dias, Haipeng Zhang, Andrew Eyre, Eric Isselbacher, Jared Conley, Narath Carlile","doi":"10.1055/s-0044-1788979","DOIUrl":"10.1055/s-0044-1788979","url":null,"abstract":"<p><strong>Objectives: </strong> Variability in cardiopulmonary arrest training and management leads to inconsistent outcomes during in-hospital cardiac arrest. Existing clinical decision aids, such as American Heart Association (AHA) advanced cardiovascular life support (ACLS) pocket cards and third-party mobile apps, often lack comprehensive management guidance. We developed a novel, guided ACLS mobile app and evaluated user performance during simulated cardiac arrest according to the 2020 AHA ACLS guidelines via randomized controlled trial.</p><p><strong>Methods: </strong> Forty-six resident physicians were randomized to lead a simulated code team using the AHA pockets cards (<i>N</i> = 22) or the guided app (<i>N</i> = 24). The primary outcome was successful return of spontaneous circulation (ROSC). Secondary outcomes included code leader stress and confidence, AHA ACLS guideline adherence, and errors. A focus group of 22 residents provided feedback. Statistical analysis included two-sided <i>t</i>-tests and Fisher's exact tests.</p><p><strong>Results: </strong> App users showed significantly higher ROSC rate (50 vs. 18%; <i>p</i> = 0.024), correct thrombolytic administration (54 vs. 23%; <i>p</i> = 0.029), backboard use (96 vs. 27%; <i>p</i> < 0.001), end-tidal CO2 monitoring (58 vs. 27%; <i>p</i> = 0.033), and confidence compared with baseline (1.0 vs 0.3; <i>p</i> = 0.005) compared with controls. A focus group of 22 residents indicated unanimous willingness to use the app, with 82% preferring it over AHA pocket cards.</p><p><strong>Conclusion: </strong> Our guided ACLS app shows potential to improve user confidence and adherence to the AHA ACLS guidelines and may help to standardize in-hospital cardiac arrest management. Further validation studies are essential to confirm its efficacy in clinical practice.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367125","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":"External Validation of an Electronic Phenotyping Algorithm Detecting Attention to High Body Mass Index in Pediatric Primary Care.","authors":"Anya G Barron, Ada M Fenick, Kaitlin R Maciejewski, Christy B Turer, Mona Sharifi","doi":"10.1055/s-0044-1787975","DOIUrl":"10.1055/s-0044-1787975","url":null,"abstract":"<p><strong>Objectives: </strong> The lack of feasible and meaningful measures of clinicians' behavior hinders efforts to assess and improve obesity management in pediatric primary care. In this study, we examined the external validity of a novel algorithm, previously validated in a single geographic region, using structured electronic health record (EHR) data to identify phenotypes of clinicians' attention to elevated body mass index (BMI) and weight-related comorbidities.</p><p><strong>Methods: </strong> We extracted structured EHR data for 300 randomly selected 6- to 12-year-old children with elevated BMI seen for well-child visits from June 2018 to May 2019 at pediatric primary care practices affiliated with Yale. Using diagnosis codes, laboratory orders, referrals, and medications adapted from the original algorithm, we categorized encounters as having evidence of attention to BMI only, weight-related comorbidities only, or both BMI and comorbidities. We evaluated the algorithm's sensitivity and specificity for detecting any attention to BMI and/or comorbidities using chart review as the reference standard.</p><p><strong>Results: </strong> The adapted algorithm yielded a sensitivity of 79.2% and specificity of 94.0% for identifying any attention to high BMI/comorbidities in clinical documentation. Of 86 encounters labeled as \"no attention\" by the algorithm, 83% had evidence of attention in free-text components of the progress note. The likelihood of classification as \"any attention\" by both chart review and the algorithm varied by BMI category and by clinician type (<i>p</i> < 0.001).</p><p><strong>Conclusion: </strong> The electronic phenotyping algorithm had high specificity for detecting attention to high BMI and/or comorbidities in structured EHR inputs. The algorithm's performance may be improved by incorporating unstructured data from clinical notes.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11387092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094006","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":"Patients with Heart Failure: Internet Use and Mobile Health Perceptions.","authors":"Albert Sohn, Anne M Turner, William Speier, Gregg C Fonarow, Michael K Ong, Corey W Arnold","doi":"10.1055/a-2273-5278","DOIUrl":"10.1055/a-2273-5278","url":null,"abstract":"<p><strong>Background: </strong> Heart failure is a complex clinical syndrome noted on approximately one in eight death certificates in the United States. Vital to reducing complications of heart failure and preventing hospital readmissions is adherence to heart failure self-care routines. Mobile health offers promising opportunities for enhancing self-care behaviors by facilitating tracking and timely reminders.</p><p><strong>Objectives: </strong> We sought to investigate three characteristics of heart failure patients with respect to their heart failure self-care behaviors: (1) internet use to search for heart failure information; (2) familiarity with mobile health apps and devices; and (3) perceptions of using activity trackers or smartwatches to aid in their heart failure self-care.</p><p><strong>Methods: </strong> Forty-nine heart failure patients were asked about their internet and mobile health usage. The structured interview included questions adapted from the Health Information National Trends Survey.</p><p><strong>Results: </strong> Over 50% of the patients had utilized the internet to search for heart failure information in the past 12 months, experience using health-related apps, and thoughts that an activity tracker or smartwatch could help them manage heart failure. Qualitative analysis of the interviews revealed six themes: trust in their physicians, alternatives to mobile health apps, lack of need for mobile health devices, financial barriers to activity tracker and smartwatch ownership, benefits of tracking and reminders, and uncertainty of their potential due to lack of knowledge.</p><p><strong>Conclusion: </strong> Trust in their physicians was a major factor for heart failure patients who reported not searching for health information on the internet. While those who used mobile health technologies found them useful, patients who did not use them were generally unaware of or unknowledgeable about them. Considering patients' preferences for recommendations from their physicians and tendency to search for heart failure information including treatment and management options, patient-provider discussions about mobile health may improve patient knowledge and impact their usage.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11357730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139933747","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":"What Do We Mean by Sharing of Patient Data? DaSH - A Data Sharing Hierarchy of Privacy and Ethical Challenges.","authors":"Richard Schreiber, Ross Koppel, Bonnie Kaplan","doi":"10.1055/a-2373-3291","DOIUrl":"https://doi.org/10.1055/a-2373-3291","url":null,"abstract":"<p><strong>Background: </strong>Sharing of clinical data is common and necessary for patient care, research, public health, and innovation. The term \"data sharing,\" however, is often ambiguous in its many facets and complexities-each of which involves ethical, legal, and social issues. To our knowledge there is no extant hierarchy of data sharing that assesses these issues.</p><p><strong>Objective: </strong>Develop a hierarchy explicating the risks and ethical complexities of data sharing with particular focus on patient data privacy.</p><p><strong>Methods: </strong>We surveyed the available peer-reviewed and gray literature, and with our combined extensive experience in bioethics and medical informatics, created this hierarchy.</p><p><strong>Results: </strong>We present six ways data are shared and provide a tiered Data Sharing Hierarchy (DaSH) of risks, showing increasing threats to patients' privacy and to clinicians and organizations as one progresses up the hierarchy from data sharing for direct patient care, public health and safety, scientific research, commercial purposes, complex combinations of the preceding efforts, and among networked third parties. We offer recommendations to enhance benefits of data sharing while mitigating risks and protecting patients' interests by: improving consenting; developing better policies and procedures; clarifying, simplifying, and updating regulation to include all health-related data regardless of source; expanding the scope of bioethics for information technology; and increasing ongoing monitoring and research.</p><p><strong>Conclusions: </strong>Data sharing, while essential for patient care, is increasingly complex, opaque, and perhaps perilous for patients, clinicians and healthcare institutions. Risks increase with advances in technology and with more encompassing patient data from wearables and artificial intelligence database mining.</p><p><strong>Clinical significance: </strong>Data sharing places responsibilities on all parties: patients, clinicians, researchers, educators, risk managers, attorneys, informaticists, bioethicists, institutions, and policy makers.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue on Teaching and Training Future Health Informaticians: Increasing generative artificial intelligence competency among students enrolled in doctoral nursing research coursework.","authors":"Meghan Reading Turchioe, Liesbet Van Bulck, Sergey Kisselev, Suzanne Bakken","doi":"10.1055/a-2373-3151","DOIUrl":"https://doi.org/10.1055/a-2373-3151","url":null,"abstract":"<p><strong>Background: </strong>Generative AI tools may soon be integrated into healthcare practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way.</p><p><strong>Objective: </strong>The objective of this study was to develop and evaluate a brief intervention to increase PhD nursing students' knowledge of appropriate applications for using generative AI tools in healthcare.</p><p><strong>Methods: </strong>We created didactic lectures and laboratory-based activities to introduce generative AI to students enrolled in a nursing PhD data science and visualization course. Students were provided with a subscription to Chat GPT 4.0, a general-purpose generative AI tool, for use in and outside the class. During the didactic portion, we described generative AI and its current and potential future applications in healthcare, including examples of appropriate and inappropriate applications. In the laboratory sessions, students were given three tasks representing different use cases of generative AI in healthcare practice and research (clinical decision support, patient decision support, and scientific communication) and asked to engage with ChatGPT on each. Students (n=10) independently wrote a brief reflection for each task evaluating safety (accuracy, hallucinations) and usability (ease of use, usefulness, and intention to use in the future). Reflections were analyzed using directed content analysis.</p><p><strong>Results: </strong>Students were able to identify the strengths and limitations of ChatGPT in completing all three tasks and developed opinions on whether they would feel comfortable using ChatGPT for similar tasks in the future. They also all reported increasing their self-rated competency in generative AI by one to two points on a 5-point rating scale.</p><p><strong>Conclusions: </strong>This brief educational intervention supported doctoral nursing students in understanding the appropriate uses of ChatGPT, which may support their ability to appraise and use these tools in their future work.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shared Access to Adults' Patient Portals: A Secret Shopper Exercise.","authors":"Deborah Wachenheim, Isabel Hurwitz, Vadim Dukhanin, Jennifer Wolff, Catherine M DesRoches","doi":"10.1055/a-2370-2220","DOIUrl":"https://doi.org/10.1055/a-2370-2220","url":null,"abstract":"<p><strong>Background: </strong>Millions of Americans manage their healthcare with the help of a trusted individual. Shared access to a patient's online patient portal is one tool that can assist their care partner(s) in gaining access to the patient's health information and allow for easy information exchange with the patient's care team. Shared access provides care partners with a validated and secure method for accessing the patient's portal account using their own login credentials. Shared access provides extra privacy protection and control to the patient, who designates which individuals can view their record. It also reduces confusion for the care team when interacting with the care partner via the portal. Shared access is underutilized among adult patients' care partners.</p><p><strong>Objective: </strong>Investigate the process of granting or receiving shared access at multiple healthcare organizations in the U.S. to learn about barriers and facilitators experienced by patients and care partners.</p><p><strong>Methods: </strong>The Shared Access Learning Collaborative undertook a \"Secret Shopper\" exercise. Participants attempted to give or gain shared access to another adult's portal account. After each attempt they completed a 14-question survey with a mix of open and closed-ended questions.</p><p><strong>Results: </strong>Eighteen participants attempted to grant or receive shared access a total of 24 times. Fifteen attempts were successful. Barriers to success included requiring paper forms with signatures, lack of knowledgeable staff, lack of access to technical support, and difficult-to-navigate technology. Facilitators included easy-to-navigate online processes and accessible technical help. Participants who were successful in gaining shared access reported feeling more informed and able to engage in shared decision-making.</p><p><strong>Conclusions: </strong>The outcomes of our secret shopper exercise underscore the importance of collaboration aimed at learning from diverse encounters and disseminating best practices. This is essential to address technical, informational, and organizational obstacles that may impede the widespread and accessible adoption of shared access.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Medical Student-Led Multi-Pronged Initiative to Close the Digital Divide in Outpatient Primary Care.","authors":"Yilan Jiangliu, Hannah Tang Kim, Michelle Lazar, Eileen Liu, Saaz Mantri, Edwin Qiu, Megan Berube, Himani Sood, Anika S Walia, Breanne Biondi, Andres Miguel Mesias Gonzalez, Rebecca Grochow Mishuris, Pablo Buitron de la Vega","doi":"10.1055/a-2370-2298","DOIUrl":"https://doi.org/10.1055/a-2370-2298","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic accelerated the use of telehealth. However, this also exacerbated healthcare disparities for vulnerable populations.</p><p><strong>Objective: </strong>To explore the feasibility and effectiveness of a medical student-led initiative to identify and address gaps in patient access to digital health resources in adult primary care clinics at a safety-net academic center.</p><p><strong>Methods: </strong>Medical students used an online HIPAA-compliant resource directory to screen for digital needs, connect patients with resources, and track outcome metrics. Through a series of Plan-Do-Study-Act (PDSA) cycles, the program grew to offer services such as information and registration for subsidized internet and phone services via the Affordable Connectivity Program (ACP) and Lifeline, assistance setting up and utilizing MyChart (an online patient portal for access to electronic health records), orientation to telehealth applications, and connection to community-based digital literacy training.</p><p><strong>Results: </strong>Between November 2021 and March 2023, the program received 608 assistance requests. The most successful intervention was MyChart help, resulting in 83% of those seeking assistance successfully signing up for MyChart accounts and 79% feeling comfortable navigating the portal. However, subsidized internet support, digital literacy training, and telehealth orientation had less favorable outcomes. The PDSA cycles highlighted numerous challenges such as inadequate patient outreach, time-consuming training, limited in-person support, and unequal language assistance. To overcome these barriers, the program evolved to utilize clinic space for outreach, increase flier distribution, standardize training, and enhance integration of multilingual resources.</p><p><strong>Conclusion: </strong>This study is, to the best of our knowledge, the first time a medical student-led initiative addresses the digital divide with a multi-pronged approach. We outline a system that can be implemented in other outpatient settings to increase patients' digital literacy and promote health equity, while also engaging students in important aspects of non-clinical patient care.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a Digital Scribe: Conversation Summarization for Emergency Department Consultation Calls.","authors":"Emre Sezgin, Joseph Winstead Sirrianni, Kelly Kranz","doi":"10.1055/a-2327-4121","DOIUrl":"10.1055/a-2327-4121","url":null,"abstract":"<p><strong>Objective: </strong>We present a proof-of-concept digital scribe system as an Emergency Department (ED) consultation call-based clinical conversation summarization pipeline to support clinical documentation, and report its performance.</p><p><strong>Materials and methods: </strong>We use four pre-trained large language models to establish the digital scribe system: T5-small, T5-base, PEGASUS-PubMed, and BART-Large-CNN via zero-shot and fine-tuning approaches. Our dataset includes 100 referral conversations among ED clinicians and medical records. We report the ROUGE-1, ROUGE-2, and ROUGE-L to compare model performance. In addition, we annotated transcriptions to assess the quality of generated summaries.</p><p><strong>Results: </strong>The fine-tuned BART-Large-CNN model demonstrates greater performance in summarization tasks with the highest ROUGE scores (F1ROUGE-1=0.49, F1ROUGE-2=0.23, F1ROUGE-L=0.35) scores. In contrast, PEGASUS-PubMed lags notably (F1ROUGE-1=0.28, F1ROUGE-2=0.11, F1ROUGE-L=0.22). BART-Large-CNN's performance decreases by more than 50% with the zero-shot approach. Annotations show that BART-Large-CNN performs 71.4% recall in identifying key information and a 67.7% accuracy rate.</p><p><strong>Discussion: </strong>The BART-Large-CNN model demonstrates a high level of understanding of clinical dialogue structure, indicated by its performance with and without fine-tuning. Despite some instances of high recall, there is variability in the model's performance, particularly in achieving consistent correctness, suggesting room for refinement. The model's recall ability varies across different information categories.</p><p><strong>Conclusion: </strong>The study provides evidence towards the potential of AI-assisted tools in assisting clinical documentation. Future work is suggested on expanding the research scope with additional language models and hybrid approaches, and comparative analysis to measure documentation burden and human factors.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11268986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946262","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":"Predicting Provider Workload Using Predicted Patient Risk Score and Social Determinants of Health in Primary Care Setting.","authors":"Yiqun Jiang, Yu-Li Huang, Alexandra Watral, Renaldo C Blocker, David R Rushlow","doi":"10.1055/s-0044-1787647","DOIUrl":"10.1055/s-0044-1787647","url":null,"abstract":"<p><strong>Background: </strong> Provider burnout due to workload is a significant concern in primary care settings. Workload for primary care providers encompasses both scheduled visit care and non-visit care interactions. These interactions are highly influenced by patients' health conditions or acuity, which can be measured by the Adjusted Clinical Group (ACG) score. However, new patients typically have minimal health information beyond social determinants of health (SDOH) to determine ACG score.</p><p><strong>Objectives: </strong> This study aims to assess new patient workload by first predicting the ACG score using SDOH, age, and gender and then using this information to estimate the number of appointments (scheduled visit care) and non-visit care interactions.</p><p><strong>Methods: </strong> Two years of appointment data were collected for patients who had initial appointment requests in the first year and had the ACG score, appointment, and non-visit care counts in the subsequent year. State-of-art machine learning algorithms were employed to predict ACG scores and compared with current baseline estimation. Linear regression models were then used to predict appointments and non-visit care interactions, integrating demographic data, SDOH, and predicted ACG scores.</p><p><strong>Results: </strong> The machine learning methods showed promising results in predicting ACG scores. Besides the decision tree, all other methods performed at least 9% better in accuracy than the baseline approach which had an accuracy of 78%. Incorporating SDOH and predicted ACG scores also significantly improved the prediction for both appointments and non-visit care interactions. The <i>R</i> ² values increased by 95.2 and 93.8%, respectively. Furthermore, age, smoking tobacco, family history, gender, usage of injection birth control, and ACG were significant factors for determining appointments. SDOH factors such as tobacco usage, physical exercise, education level, and group activities were strongly correlated with non-visit care interactions.</p><p><strong>Conclusion: </strong> The study highlights the importance of SDOH and predicted ACG scores in predicting provider workload in primary care settings.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11221991/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499394","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}