Applied Clinical Informatics最新文献

{"title":"Special Issue on Informatics Education: Exploring the Impact of GitHub Copilot on Health Informatics Education.","authors":"Sanja Avramovic,Ivan Avramovic,Janusz Wojtusiak","doi":"10.1055/a-2414-7790","DOIUrl":"https://doi.org/10.1055/a-2414-7790","url":null,"abstract":"BACKGROUNDThe use of artificial intelligence-driven code completion tools, particularly the integration of GitHub Copilot with Visual Studio, has potential implications for Health Informatics education, particularly for students learning SQL and Python.OBJECTIVESThis study aims to evaluate the effectiveness of these tools in solving or assisting with the solution of problems found in Health Informatics coursework, ranging from simple to complex.METHODSThe study assesses the performance of GitHub Copilot in generating code for Health Informatics coding assignments from graduate classes, with a focus on the impact of detailed explanations on the tool's effectiveness.RESULTSFindings reveal that GitHub Copilot can generate correct code for straightforward problems. The correctness and effectiveness of solutions decrease with problem complexity, and the tool struggles with the most challenging problems, although performance on complex problems improves with more detailed explanations.CONCLUSIONSThe study underscores the relevance of these tools to programming in Health Informatics education but also highlights the need for critical evaluation by students. It concludes with a call for educators to adapt swiftly to this rapidly evolving technology.","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"32 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265942","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":"Developing PRISM: A Pragmatic Institutional Survey and Bench Marking Tool to Measure Digital Research Maturity of Cancer Centers","authors":"Carlos Berenguer Albiñana, Matteo Pallocca, Hayley Fenton, Will Sopwith, Charlie Van Eden, Olof Akre, Annika Auranen, François Bocquet, Marina Borges, Emiliano Calvo, John Corkett, Serena Di Cosimo, Nicola Gentili, Julien Guérin, Sissel Jor, Tomas Kazda, Alenka Kolar, Tim Kuschel, Maria Julia Lostes, Chiara Paratore, Paolo Pedrazzoli, Marko Petrovic, Jarno Raid, Miriam Roche, Christoph Schatz, Joelle Thonnard, Giovanni Tonon, Alberto Traverso, Andrea Wolf, Ahmed H. Zedan, Piers Mahon","doi":"10.1055/s-0044-1788331","DOIUrl":"https://doi.org/10.1055/s-0044-1788331","url":null,"abstract":"<p>\u0000<b>Background</b> Multicenter precision oncology real-world evidence requires a substantial long-term investment by hospitals to prepare their data and align on common Clinical Research processes and medical definitions. Our team has developed a self-assessment framework to support hospitals and hospital networks to measure their digital maturity and better plan and coordinate those investments. From that framework, we developed PRISM for Cancer Outcomes: <b>PR</b>agmatic <b>I</b>nstitutional <b>S</b>urvey and bench<b>M</b>arking.</p> <p>\u0000<b>Objectives</b> The primary objective was to develop PRISM as a tool for self-assessment of digital maturity in oncology hospitals and research networks; a secondary objective was to create an initial benchmarking cohort of >25 hospitals using the tool as input for future development.</p> <p>\u0000<b>Methods</b> PRISM is a 25-question semiquantitative self-assessment survey developed iteratively from expert knowledge in oncology real-world study delivery. It covers four digital maturity dimensions: (1) Precision oncology, (2) Clinical digital data, (3) Routine outcomes, and (4) Information governance and delivery. These reflect the four main data types and critical enablers for precision oncology research from routine electronic health records.</p> <p>\u0000<b>Results</b> During piloting with 26 hospitals from 19 European countries, PRISM was found to be easy to use and its semiquantitative questions to be understood in a wide diversity of hospitals. Results within the initial benchmarking cohort aligned well with internal perspectives. We found statistically significant differences in digital maturity, with Precision oncology being the most mature dimension, and Information governance and delivery the least mature.</p> <p>\u0000<b>Conclusion</b> PRISM is a light footprint benchmarking tool to support the planning of large-scale real-world research networks. It can be used to (i) help an individual hospital identify areas most in need of investment and improvement, (ii) help a network of hospitals identify sources of best practice and expertise, and (iii) help research networks plan research. With further testing, policymakers could use PRISM to better plan digital investments around the Cancer Mission and European Digital Health Space.</p> ","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199666","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":"Issue on Teaching and Training Future Health Informaticians:Partnering with Students to Develop a Capstone for a Graduate Health Informatics Program.","authors":"Rita Jezrawi,Stephanie Zahorka Derka,Elizabeth Warnick,Jasmine Foley,Vritti Patel,Neethu Pavithran,Thérèse Bernier,Nicole Wagner,Neil G Barr,Vincent Maccio,Margaret Leyland,Cynthia Lokker","doi":"10.1055/a-2412-3535","DOIUrl":"https://doi.org/10.1055/a-2412-3535","url":null,"abstract":"OBJECTIVETo assess the desirability, feasibility, and sustainability of integrating a project-based capstone course with the course-based curriculum of an interdisciplinary MSc health informatics program guided with a student-partnered steering committee and student-centered approach.METHODSWe conducted an online cross-sectional survey (n=87) and three semi-structured focus groups (n=18) of health informatics students and alumni. Survey data was analyzed descriptively. Focus groups were audio-recorded and transcribed verbatim and then analyzed using a general inductive and classic analysis approach.RESULTSMost students were supportive of including a capstone project but desired an option to work independently or within a group. Students perceived several benefits to capstone courses while concerned over perceived challenges to capstone implementation, evaluation, and managing group processes. Themes identified were: 1) professional development, identity, and career advancement; 2) emulating the real world and learning beyond the classroom, 3) embracing new, full circle learning, 4) anticipated course structure, delivery, and preparation, 5) balancing student choice, interests, and priorities, and 6) concerns over group dynamics, limitations, and support.CONCLUSIONSThis study demonstrates the value of having students as partners at each stage in the process from methods conception to course curriculum design. With the steering committee and the curriculum developer, we codeveloped a student-centered course that integrates foundational digital health-related project knowledge acquisition with an inquiry-based project which can be completed independently or in small groups. This study demonstrates the potential benefits and challenges that health informatics educators may consider when (re)-designing capstone courses.","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"44 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199635","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":"Enhancing Suicide Risk Prediction Models with Temporal Clinical Note Features.","authors":"Kevin Krause,Sharon Davis,Zhijun Yin,Katherine Schafer,Trent Rosenbloom,Colin Walsh","doi":"10.1055/a-2411-5796","DOIUrl":"https://doi.org/10.1055/a-2411-5796","url":null,"abstract":"OBJECTIVEThe objective of this study was to investigate the impact of enhancing a structured-data-based suicide attempt risk prediction model with temporal Concept Unique Identifiers (CUIs) derived from clinical notes. We aimed to examine how different temporal schemes, model types, and prediction ranges influenced the model's predictive performance. This research sought to improve our understanding of how the integration of temporal information and clinical variable transformation could enhance model predictions.MATERIALS AND METHODSWe identified modeling targets using diagnostic codes for suicide attempts within 30, 90, or 365 days following a temporally grouped visit cluster. Structured data included medications, diagnoses, procedures, and demographics, while unstructured data consisted of terms extracted with regular expressions from clinical notes. We compared models trained only on structured data (controls) to hybrid models trained on both structured and unstructured data. We used two temporalization schemes for clinical notes: fixed 90-day windows and flexible epochs. We trained and assessed random forests and hybrid LSTM neural networks using AUPRC and AUROC, with additional evaluation of sensitivity and PPV at 95% specificity.RESULTSThe training set included 2,364,183 visit clusters with 2,009 30-day suicide attempts, and the testing set contained 471,936 visit clusters with 480 suicide attempts. Models trained with temporal CUIs outperformed those trained with only structured data. The window-temporalized LSTM model achieved the highest AUPRC (0.056 ± 0.013) for the 30-day prediction range. Hybrid models generally showed better performance compared to controls across most metrics.DISCUSSION AND CONCLUSIONThis study demonstrated that incorporating EHR-derived clinical note features enhanced suicide attempt risk prediction models, particularly with window-temporalized LSTM models. Our results underscored the critical value of unstructured data in suicidality prediction, aligning with previous findings. Future research should focus on integrating more sophisticated methods to continue improving prediction accuracy, which will enhance the effectiveness of future intervention.","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"4 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199654","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 L Wolff, Catherine M DesRoches","doi":"10.1055/a-2370-2220","DOIUrl":"10.1055/a-2370-2220","url":null,"abstract":"<p><strong>Background: </strong> Millions of Americans manage their health care 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 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>Objectives: </strong> Investigate the process of granting or receiving shared access at multiple health care organizations in the United States 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>Conclusion: </strong> The outcomes of our secret shopper exercise underscore the importance of collaboration aimed at learning from diverse encounters and disseminating the 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":" ","pages":"817-823"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464159/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fellows of the American Medical Informatics Association (FAMIA): Looking Back and Looking Ahead.","authors":"Laura Heermann Langford, Kate Fultz Hollis, Margo Edmunds, Allison B McCoy, Eric S Hall, Jeffrey A Nielson, Sarah Collins Rosetti","doi":"10.1055/s-0044-1788658","DOIUrl":"10.1055/s-0044-1788658","url":null,"abstract":"<p><strong>Background: </strong> Over the past 30 years, the American Medical Informatics Association (AMIA) has played a pivotal role in fostering a collaborative community for professionals in biomedical and health informatics. As an interdisciplinary association, AMIA brings together individuals with clinical, research, and computer expertise and emphasizes the use of data to enhance biomedical research and clinical work. The need for a recognition program within AMIA, acknowledging applied informatics skills by members, led to the establishment of the Fellows of AMIA (FAMIA) Recognition Program in 2018.</p><p><strong>Objectives: </strong> To outline the evolution of the FAMIA program and shed light on its origins, development, and impact. This report explores factors that led to the establishment of FAMIA, considerations affecting its development, and the objectives FAMIA seeks to achieve within the broader context of AMIA.</p><p><strong>Methods: </strong> The development of FAMIA is examined through a historical lens, encompassing key milestones, discussions, and decisions that shaped the program. Insights into the formation of FAMIA were gathered through discussions within AMIA membership and leadership, including proposals, board-level discussions, and the involvement of key stakeholders. Additionally, the report outlines criteria for FAMIA eligibility and the pathways available for recognition, namely the Certification Pathway and the Long-Term Experience Pathway.</p><p><strong>Results: </strong> The FAMIA program has inducted five classes, totaling 602 fellows. An overview of disciplines, roles, and application pathways for FAMIA members is provided. A comparative analysis with other fellow recognition programs in related fields showcases the unique features and contributions of FAMIA in acknowledging applied informatics.</p><p><strong>Conclusion: </strong> Now in its sixth year, FAMIA acknowledges the growing influence of applied informatics within health information professionals, recognizing individuals with experience, training, and a commitment to the highest level of applied informatics and the science associated with it.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"15 4","pages":"650-659"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11305823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903325","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 Standard Approach to Project-Based Learning in a Clinical Informatics Fellowship.","authors":"Michael G Leu, Angad P Singh, Christopher W Lewis, B Jane Fellner, Theresa B Kim, Yu-Hsiang Lin, Paul R Sutton, Andrew A White, Peter Tarczy-Hornoch","doi":"10.1055/s-0044-1788980","DOIUrl":"10.1055/s-0044-1788980","url":null,"abstract":"<p><strong>Background: </strong> The Accreditation Council for Graduate Medical Education suggests that Clinical Informatics (CI) fellowship programs foster broad skills, which include collaboration and project management. However, they do not dictate how to best accomplish these learning objectives.</p><p><strong>Objectives: </strong> This study aimed to describe a standard approach to project-based learning for CI, to share its implementation, and to discuss lessons learned.</p><p><strong>Methods: </strong> We created a standard approach to project-based learning based on concepts from adult learning theory, the project life cycle framework, the Toyota Production System, and Improvement Science.</p><p><strong>Results: </strong> With this standard approach in place, we learned how best to support fellows in its use. In addition to this approach to supporting needs assessment, risk/change management, implementation, and evaluation/improvement skills, we found the need to develop fellow skills in collaboration, leadership, and time management/managing up. Supported by project-based learning using this standard approach, and with targeted project selection to meet topic-based learning objectives, fellows reached the ability to practice independently in 15 to 21 months.</p><p><strong>Discussion: </strong> Fellows are uniquely positioned to ensure the success of projects due to their increased availability and protected time compared with attendings. They are readily available for project teams to draw upon their expertise with clinical workflows and understanding of technological solutions. Project-based learning addressing organizational priorities complements fellow project management coursework and improves fellows' ability to function successfully in large, complex, and dynamic organizations. Exposing fellows to contemporary problems, then addressing them through projects, provides fellows with up-to-date applied informatics knowledge.</p><p><strong>Conclusion: </strong> Project-based learning can ensure that many general CI learning objectives are supported inherently. It reinforces project management teachings, while providing fellows with a marketable project portfolio to aid with future job applications. Having projects tightly aligned with organizational priorities supports ongoing investment in fellowship programs.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"15 4","pages":"824-832"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394499","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":"Effects of a Pharmacist-Designed Clinical Decision Support System on Antimicrobial Stewardship.","authors":"Miguel Ángel Amor-García, Esther Chamorro-de-Vega, Carmen Guadalupe Rodríguez-González, Irene Iglesias-Peinado, Raquel Moreno-Díaz","doi":"10.1055/a-2341-8823","DOIUrl":"10.1055/a-2341-8823","url":null,"abstract":"<p><strong>Background: </strong> Clinical decision support systems (CDSSs) are computer applications, which can be applied to give guidance to practitioners in antimicrobial stewardship (AS) activities; however, further information is needed for their optimal use.</p><p><strong>Objectives: </strong> Our objective was to analyze the implementation of a CDSS program in a second-level hospital, describing alerts, recommendations, and the effects on consumption and clinical outcomes.</p><p><strong>Methods: </strong> In October 2020, a pharmacist-driven CDSS designed for AS was implemented in a second-level hospital. The program provides a list of alerts related to antimicrobial treatment and microbiology, which were automatized for revision by the AS professionals. To analyze the implementation of the CDSS, a pre-post-intervention, retrospective study was designed. AS-triggered alerts and recommendations (total number and rate of acceptance) were compiled. The effect of the CDSS was measured using antimicrobial consumption, duration of antimicrobial treatments, in-hospital mortality, and length of stay (LOS) for patients admitted for infectious causes.</p><p><strong>Results: </strong> The AS team revised a total of 7,543 alerts and 772 patients had at least one recommendation, with an acceptance rate of 79.3%. Antimicrobial consumption decreased from 691.1 to 656.8 defined daily doses (DDD)/1,000 beds-month (<i>p</i> = 0.04) and the duration of antimicrobial treatment from 3.6 to 3.3 days (<i>p</i> < 0.01). In-hospital mortality decreased from 6.6 to 6.2% (<i>p</i> = 0.46) and mean LOS from 7.2 to 6.2 days (<i>p</i> < 0.01).</p><p><strong>Conclusion: </strong> The implementation of a CDSS resulted in a significant reduction of antimicrobial DDD, duration of antimicrobial treatments, and hospital LOS. There was no significant difference in mortality.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"679-688"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301946","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":"Sociodemographic Differences in Perspectives on Postpartum Symptom Reporting.","authors":"Natalie C Benda, Ruth M Masterson Creber, Roberta Scheinmann, Stephanie Nino de Rivera, Eric Costa Pimentel, Robin B Kalish, Laura E Riley, Alison Hermann, Jessica S Ancker","doi":"10.1055/s-0044-1788328","DOIUrl":"10.1055/s-0044-1788328","url":null,"abstract":"<p><strong>Objective: </strong> The overall goal of this work is to create a patient-reported outcome (PRO) and decision support system to help postpartum patients determine when to seek care for concerning symptoms. In this case study, we assessed differences in perspectives for application design needs based on race, ethnicity, and preferred language.</p><p><strong>Methods: </strong> A sample of 446 participants who reported giving birth in the past 12 months was recruited from an existing survey panel. We sampled participants from four self-reported demographic groups: (1) English-speaking panel, Black/African American race, non-Hispanic ethnicity; (2) Spanish-speaking panel, Hispanic-ethnicity; (3) English-speaking panel, Hispanic ethnicity; (4) English-speaking panel, non-Black race, non-Hispanic ethnicity. Participants provided survey-based feedback regarding interest in using the application, comfort reporting symptoms, desired frequency of reporting, reporting tool features, and preferred outreach pathway for concerning symptoms.</p><p><strong>Results: </strong> Fewer Black participants, compared with all other groups, stated that they had used an app for reporting symptoms (<i>p</i> = 0.02), were least interested in downloading the described application (<i>p</i> < 0.05), and found a feature for sharing warning sign information with friends and family least important (<i>p</i> < 0.01). Black and non-Hispanic Black participants also preferred reporting symptoms less frequently as compared with Hispanic participants (English and Spanish-speaking; all <i>p</i> < 0.05). Spanish-speaking Hispanic participants tended to prefer calling their professional regarding urgent warning signs, while Black and English-speaking Hispanic groups tended to express interest in using an online chat or patient portal (all <i>p</i> < 0.05) CONCLUSION:  Different participant groups described distinct preferences for postpartum symptom reporting based on race, ethnicity, and preferred languages. Tools used to elicit PROs should consider how to be flexible for different preferences or tailored toward different groups.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"15 4","pages":"692-699"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11338653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019213","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":"Effect of Ambient Voice Technology, Natural Language Processing, and Artificial Intelligence on the Patient-Physician Relationship.","authors":"Lance M Owens, J Joshua Wilda, Ronald Grifka, Joan Westendorp, Jeffrey J Fletcher","doi":"10.1055/a-2337-4739","DOIUrl":"10.1055/a-2337-4739","url":null,"abstract":"<p><strong>Background: </strong> The method of documentation during a clinical encounter may affect the patient-physician relationship.</p><p><strong>Objectives: </strong> Evaluate how the use of ambient voice recognition, coupled with natural language processing and artificial intelligence (DAX), affects the patient-physician relationship.</p><p><strong>Methods: </strong> This was a prospective observational study with a primary aim of evaluating any difference in patient satisfaction on the Patient-Doctor Relationship Questionnaire-9 (PDRQ-9) scale between primary care encounters in which DAX was utilized for documentation as compared to another method. A single-arm open-label phase was also performed to query direct feedback from patients.</p><p><strong>Results: </strong> A total of 288 patients were include in the open-label arm and 304 patients were included in the masked phase of the study comparing encounters with and without DAX use. In the open-label phase, patients strongly agreed that the provider was more focused on them, spent less time typing, and made the encounter feel more personable. In the masked phase of the study, no difference was seen in the total PDRQ-9 score between patients whose encounters used DAX (median: 45, interquartile range [IQR]: 8) and those who did not (median: 45 [IQR: 3.5]; <i>p</i> = 0.31). The adjusted odds ratio for DAX use was 0.8 (95% confidence interval: 0.48-1.34) for the patient reporting complete satisfaction on how well their clinician listened to them during their encounter.</p><p><strong>Conclusion: </strong> Patients strongly agreed with the use of ambient voice recognition, coupled with natural language processing and artificial intelligence (DAX) for documentation in primary care. However, no difference was detected in the patient-physician relationship on the PDRQ-9 scale.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"660-667"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11305826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141248856","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}