Applied Clinical Informatics最新文献

{"title":"Manual Evaluation of Record Linkage Algorithm Performance in Four Real-World Datasets.","authors":"Agrayan K Gupta, Huiping Xu, Xiaochun Li, Joshua R Vest, Shaun J Grannis","doi":"10.1055/a-2291-1391","DOIUrl":"10.1055/a-2291-1391","url":null,"abstract":"<p><strong>Objectives: </strong> Patient data are fragmented across multiple repositories, yielding suboptimal and costly care. Record linkage algorithms are widely accepted solutions for improving completeness of patient records. However, studies often fail to fully describe their linkage techniques. Further, while many frameworks evaluate record linkage methods, few focus on producing gold standard datasets. This highlights a need to assess these frameworks and their real-world performance. We use real-world datasets and expand upon previous frameworks to evaluate a consistent approach to the manual review of gold standard datasets and measure its impact on algorithm performance.</p><p><strong>Methods: </strong> We applied the framework, which includes elements for data description, reviewer training and adjudication, and software and reviewer descriptions, to four datasets. Record pairs were formed and between 15,000 and 16,500 records were randomly sampled from these pairs. After training, two reviewers determined match status for each record pair. If reviewers disagreed, a third reviewer was used for final adjudication.</p><p><strong>Results: </strong> Between the four datasets, the percent discordant rate ranged from 1.8 to 13.6%. While reviewers' discordance rate typically ranged between 1 and 5%, one exhibited a 59% discordance rate, showing the importance of the third reviewer. The original analysis was compared with three sensitivity analyses. The original analysis most often exhibited the highest predictive values compared with the sensitivity analyses.</p><p><strong>Conclusion: </strong> Reviewers vary in their assessment of a gold standard, which can lead to variances in estimates for matching performance. Our analysis demonstrates how a multireviewer process can be applied to create gold standards, identify reviewer discrepancies, and evaluate algorithm performance.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"620-628"},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11290950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140177354","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":"Interprofessional Evaluation of a Medication Clinical Decision Support System Prior to Implementation.","authors":"Jacqueline Bauer, Marika Busse, Tanja Kopetzky, Christof Seggewies, Martin F Fromm, Frank Dörje","doi":"10.1055/s-0044-1787184","DOIUrl":"10.1055/s-0044-1787184","url":null,"abstract":"<p><strong>Background: </strong> Computerized physician order entry (CPOE) and clinical decision support systems (CDSS) are widespread due to increasing digitalization of hospitals. They can be associated with reduced medication errors and improved patient safety, but also with well-known risks (e.g., overalerting, nonadoption).</p><p><strong>Objectives: </strong> Therefore, we aimed to evaluate a commonly used CDSS containing Medication-Safety-Validators (e.g., drug-drug interactions), which can be locally activated or deactivated, to identify limitations and thereby potentially optimize the use of the CDSS in clinical routine.</p><p><strong>Methods: </strong> Within the implementation process of Meona (commercial CPOE/CDSS) at a German University hospital, we conducted an interprofessional evaluation of the CDSS and its included Medication-Safety-Validators following a defined algorithm: (1) general evaluation, (2) systematic technical and content-related validation, (3) decision of activation or deactivation, and possibly (4) choosing the activation mode (interruptive or passive). We completed the in-depth evaluation for exemplarily chosen Medication-Safety-Validators. Moreover, we performed a survey among 12 German University hospitals using Meona to compare their configurations.</p><p><strong>Results: </strong> Based on the evaluation, we deactivated 3 of 10 Medication-Safety-Validators due to technical or content-related limitations. For the seven activated Medication-Safety-Validators, we chose the interruptive option [\"PUSH-(&PULL)-modus\"] four times (4/7), and a new, on-demand option [\"only-PULL-modus\"] three times (3/7). The site-specific configuration (activation or deactivation) differed across all participating hospitals in the survey and led to varying medication safety alerts for identical patient cases.</p><p><strong>Conclusion: </strong> An interprofessional evaluation of CPOE and CDSS prior to implementation in clinical routine is crucial to detect limitations. This can contribute to a sustainable utilization and thereby possibly increase medication safety.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"15 3","pages":"637-649"},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11290949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861357","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":"Social Media's Lessons for Clinical Decision Support: Strategies to Improve Engagement and Acceptance.","authors":"Christopher Sova, Eric Poon, Robert Clayton Musser, Anand Chowdhury","doi":"10.1055/s-0044-1787648","DOIUrl":"10.1055/s-0044-1787648","url":null,"abstract":"","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"15 3","pages":"528-532"},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11221992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499395","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":"Designing the User Interface of a Nitroglycerin Dose Titration Decision Support System: User-Centered Design Study.","authors":"Navpreet Kamboj, Kelly Metcalfe, Charlene H Chu, Aaron Conway","doi":"10.1055/s-0044-1787755","DOIUrl":"10.1055/s-0044-1787755","url":null,"abstract":"<p><strong>Background: </strong> Nurses adjust intravenous nitroglycerin infusions to provide acute relief for angina by manually increasing or decreasing the dosage. However, titration can pose challenges, as excessively high doses can lead to hypotension, and low doses may result in inadequate pain relief. Clinical decision support systems (CDSSs) that predict changes in blood pressure for nitroglycerin dose adjustments may assist nurses with titration.</p><p><strong>Objective: </strong> This study aimed to design a user interface for a CDSS for nitroglycerin dose titration (<i>Nitro</i>glycerin Dose Titration <i>D</i>ecision <i>S</i>upport <i>S</i>ystem [<i>nitro DSS</i>]).</p><p><strong>Methods: </strong> A user-centered design (UCD) approach, consisting of an initial qualitative study with semistructured interviews to identify design specifications for prototype development, was used. This was followed by three iterative rounds of usability testing. Nurses with experience titrating nitroglycerin infusions in coronary care units participated.</p><p><strong>Results: </strong> A total of 20 nurses participated, including 7 during the qualitative study and 15 during usability testing (2 nurses participated in both phases). Analysis of the qualitative data revealed four themes for the interface design to be (1) clear and consistent, (2) vigilant, (3) interoperable, and (4) reliable. The major elements of the final prototype included a feature for viewing the predicted and actual blood pressure over time to determine the reliability of the predictions, a drop-down option to report patient side effects, a feature to report reasons for not accepting the prediction, and a visual alert indicating any systolic blood pressure predictions below 90 mm Hg. Nurses' ratings on the questionnaires indicated excellent usability and acceptability of the final <i>nitro DSS</i> prototype.</p><p><strong>Conclusion: </strong> This study successfully applied a UCD approach to collaborate with nurses in developing a user interface for the <i>nitro DSS</i> that supports the clinical decision-making of nurses titrating nitroglycerin.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"15 3","pages":"583-599"},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11268987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761947","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":"Resident-Driven Clinical Decision Support Governance to Improve the Utility of Clinical Decision Support","authors":"Kristin N. Sheehan, Anthony L. Cioci, Tomas M. Lucioni, Sean M. Hernandez","doi":"10.1055/s-0044-1786682","DOIUrl":"https://doi.org/10.1055/s-0044-1786682","url":null,"abstract":"<p>\u0000<b>Objectives</b> This resident-driven quality improvement project aimed to better understand the known problem of a misaligned clinical decision support (CDS) strategy and improve CDS utilization.</p> <p>\u0000<b>Methods</b> An internal survey was sent to all internal medicine (IM) residents to identify the most bothersome CDS alerts. Survey results were supported by electronic health record (EHR) data of CDS firing rates and response rates which were collected for each of the three most bothersome CDS tools. Changes to firing criteria were created to increase utilization and to better align with the five rights of CDS. Findings and proposed changes were presented to our institution's CDS Governance Committee. Changes were approved and implemented. Postintervention firing rates were then collected for 1 week.</p> <p>\u0000<b>Results</b> Twenty nine residents participated in the CDS survey and identified sepsis alerts, lipid profile reminders, and telemetry renewals to be the most bothersome alerts. EHR data showed action rates for these CDS as low as 1%. We implemented changes to focus emergency department (ED)-based sepsis alerts to the right provider, better address the right information for lipid profile reminders, and select the right time in workflow for telemetry renewals to be most effective. With these changes we successfully eliminated ED-based sepsis CDS reminders for IM providers, saw a 97% reduction in firing rates for the lipid profile CDS, and noted a 55% reduction in firing rates for telemetry CDS.</p> <p>\u0000<b>Conclusion</b> This project highlighted that alert improvements spearheaded by resident teams can be completed successfully using robust CDS governance strategies and can effectively optimize interruptive alerts.</p> ","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140828538","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":"Physicians' Perspectives with the E-prescribing System in Five Teaching Hospitals.","authors":"Jahanpour Alipour, Abolfazl Payandeh, Aida Hashemi, Ali Aliabadi, Afsaneh Karimi","doi":"10.1055/s-0044-1786872","DOIUrl":"10.1055/s-0044-1786872","url":null,"abstract":"<p><strong>Objectives: </strong> Despite the evidence suggesting the potential of electronic prescribing (e-prescribing), this system also faces challenges that can lead to inefficiency and even failure. This study aimed to evaluate physicians' perspectives on the efficiency, effectiveness, opportunities, and challenges associated with the e-prescribing system.</p><p><strong>Methods: </strong> In 2023, a descriptive analytics cross-sectional study was carried out. Due to the finite population size, all the physicians from five studied hospitals who agreed to participate in the study were included through the census method (<i>n</i> = 195). Data collection was conducted using a validated questionnaire. Data were analyzed using descriptive (mean, standard deviation, and frequency) and analytical (Pearson's correlation coefficient, two-sample <i>t</i>-test, one-way analysis of variance (ANOVA), and linear multiple regression model) statistics.</p><p><strong>Results: </strong> The mean scores of efficiency and effectiveness were 47.47 ± 14.46 and 36.09 ± 10.67 out of 95 and 65, respectively. Removing the illegibility of the prescriptions (<i>n</i> = 22) was the most frequent opportunity and internet connectivity problem (<i>n</i> = 37) was the most frequent challenge associated with the e-prescribing system. There was a strong positive significant correlation between efficiency and effectiveness (r = 0.850, <i>p</i> <i><</i> 0.01). Moreover, age was found to have a significant negative correlation with efficiency (B =  -7.261, <i>p</i> = 0.004) and effectiveness (B = - 5.784, <i>p</i> = 0.002).</p><p><strong>Conclusion: </strong> Physicians believe that e-prescribing enhances the efficiency and effectiveness of their work. There are many opportunity and challenges to the use of e-prescription. Assessing the needs of physicians, actively participating and training them in the stages of design and implementation, and conducting regular evaluations of the e-prescribing system are crucial to overcome the challenges. Our finding offers insightful information about how doctors see the e-prescribing system at teaching hospitals and provide a basis for managers and policy makers at the local and national levels to support the implementation of this system and plan for improvement of its shortcomings.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"15 3","pages":"428-436"},"PeriodicalIF":2.9,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11136528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141176516","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":"Preparing Future Pediatric Care Providers with a Clinical Informatics Elective.","authors":"Tiranun Rungvivatjarus, Mario Bialostozky, Amy Z Chong, Jeannie S Huang, Cynthia L Kuelbs","doi":"10.1055/s-0044-1786977","DOIUrl":"10.1055/s-0044-1786977","url":null,"abstract":"<p><strong>Background: </strong> Clinical informatics (CI) has reshaped how medical information is shared, evaluated, and utilized in health care delivery. The widespread integration of electronic health records (EHRs) mandates proficiency among physicians and practitioners, yet medical trainees face a scarcity of opportunities for CI education.</p><p><strong>Objectives: </strong> We developed a CI rotation at a tertiary pediatric care center to teach categorical pediatric, pediatric-neurology, and medicine-pediatric residents foundational CI knowledge and applicable EHR skills.</p><p><strong>Methods: </strong> Created in 2017 and redesigned in 2020, a CI rotation aimed to provide foundational CI knowledge, promote longitudinal learning, and encourage real-world application of CI skills/tools. Led by a team of five physician informaticist faculty, the curriculum offers personalized rotation schedules and individual sessions with faculty for each trainee. Trainees were tasked with completing an informatics project, knowledge assessment, and self-efficacy perception survey before and after rotation. Paired <i>t</i>-test analyses were used to compare pre- and postcurriculum perception survey.</p><p><strong>Results: </strong> Thirty-one residents have completed the elective with their projects contributing to diverse areas such as medical education, division-specific initiatives, documentation improvement, regulatory compliance, and operating plan goals. The mean knowledge assessment percentage score increased from 77% (11.6) to 92% (10.6; p ≤ 0.05). Residents' perception surveys demonstrated improved understanding and confidence across various informatics concepts and tools (p ≤ 0.05).</p><p><strong>Conclusion: </strong> Medical trainees are increasingly interested in CI education and find it valuable. Our medical education curriculum was successful at increasing residents' understanding, self-efficacy, and confidence in utilizing CI concepts and EHR tools. Future data are needed to assess the impact such curricula have on graduates' proficiency and efficiency in the use of CI tools in the clinical workplace.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"15 3","pages":"437-445"},"PeriodicalIF":2.9,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11152768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263121","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":"Leveraging Novel Clinical Decision Support to Improve Preferred Language Documentation in a Neonatal Intensive Care Unit.","authors":"Osvaldo Mercado, Alex Ruan, Bolu Oluwalade, Matthew Devine, Kathleen Gibbs, Leah Carr","doi":"10.1055/a-2332-5843","DOIUrl":"10.1055/a-2332-5843","url":null,"abstract":"<p><strong>Background: </strong> Recognition of the patient and family's diverse backgrounds and language preference is critical for communicating effectively. In our hospital's electronic health record, a patient or family's identified language for communication is documented in a discrete field known as \"preferred language.\" This field serves as an interdepartmental method to identify patients with a non-English preferred language, creating a bolded banner for non-English speakers easily identifiable by health care professionals. Despite the importance of documenting preferred language to facilitate equitable care, this field is often blank.</p><p><strong>Objectives: </strong> Using the Institute for Healthcare Improvement's Model for Improvement, our team sought to increase preferred language documentation within the neonatal intensive care unit (NICU) from a baseline of 74% in September 2021 to above 90% within 6 months.</p><p><strong>Methods: </strong> A multidisciplinary team was assembled to address preferred language documentation. Our team incorporated guidance regarding preferred language documentation into a novel clinical decision support (CDS) tool aimed at addressing various safety and quality measures within the NICU. Our primary outcome metric was documentation of family's preferred language. Process measures included overall compliance with the CDS tool. A secondary outcome was the assessment of preferred language documentation accuracy.</p><p><strong>Results: </strong> The average rate of preferred language documentation increased from a baseline of 74 to 92% within 6 months and is currently sustained at 96%. Moreover, our follow-up assessments found that 100% of a random sample of contacted patients (<i>n</i> = 88) had their language preferences documented correctly. Overall compliance with the CDS tool remained at 85% throughout the project.</p><p><strong>Conclusion: </strong> Using a quality improvement framework coupled with a CDS initiative, our team was able to successfully and accurately improve preferred language documentation in our NICU. Future projects include strategies for more equitable care for patients with non-English preferences such as improved interpreter usage and discharge instructions in their preferred language.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"629-636"},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11290947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141093915","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":"Machine Alarm Fatigue among Hemodialysis Nurses in 29 Tertiary Hospitals.","authors":"Chaonan Sun, Meirong Bao, Congshan Pu, Xin Kang, Yiping Zhang, Xiaomei Kong, Rongzhi Zhang","doi":"10.1055/a-2297-4652","DOIUrl":"10.1055/a-2297-4652","url":null,"abstract":"<p><strong>Objectives: </strong> To understand the status quo and related influencing factors of machine alarm fatigue of hemodialysis nurses in tertiary hospitals in Liaoning Province.</p><p><strong>Methods: </strong> This cross-sectional study employed convenience sampling to select 460 nurses from 29 tertiary hospitals in Liaoning Province, who are involved in hemodialysis care. Surveys were conducted using the General Information Questionnaire, Alarm Fatigue Scale, National Aeronautics and Space Administration Task Load Index, and Maslach Burnout Inventory Scale.</p><p><strong>Results: </strong> The overall machine alarm fatigue score for 460 hemodialysis nurses from 29 tertiary hospitals in Liaoning Province was 17.04 ± 3.21, indicating a moderate level. The multiple linear regression analysis shows that years of experience in hemodialysis nursing, the number of patients managed per shift, whether specialized nursing training has been received, self-reported health status, emotional exhaustion, and workload have statistically significant associations with alarm fatigue among hemodialysis nurses (<i>p</i> < 0.05). Among them, the years of experience in hemodialysis nursing are negatively correlated with alarm fatigue among hemodialysis nurses, whereas the number of patients managed per shift and workload are positively correlated with alarm fatigue among hemodialysis nurses.</p><p><strong>Conclusion: </strong> This study indicates that certain demographic factors, workload, and occupational burnout are associated with machine alarm fatigue among hemodialysis nurses. Therefore, hemodialysis-related managers should establish a Machine Alarm Management System, implement Personalized Thresholds and Delayed Alarms, ensure reasonable staffing arrangements, improve compassion fatigue, and enhance anticipatory care. Our findings have implications for improving the health and well-being of hemodialysis nurses, providing a conducive environment for professional training in hemodialysis, and ultimately addressing the current situation of machine alarm fatigue among hemodialysis nurses.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"533-543"},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11236447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337404","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 Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.","authors":"Nidhi Soley, Traci J Speed, Anping Xie, Casey Overby Taylor","doi":"10.1055/a-2321-0397","DOIUrl":"10.1055/a-2321-0397","url":null,"abstract":"<p><strong>Background: </strong> Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being.</p><p><strong>Objectives: </strong> This study explored using preoperative electronic health record (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use.</p><p><strong>Methods: </strong> The study cohort consisted of approximately 347 <i>All of Us</i> Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data. We developed four machine learning models and used the Shapley additive explanations (SHAP) technique to identify the most relevant predictors of acute pain and chronic opioid use.</p><p><strong>Results: </strong> The stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve score for severe pain versus other pain was highest (0.88) when predicting acute postoperative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes.</p><p><strong>Conclusion: </strong> SHAP detection of individual risk factors for severe pain can help health care providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":"569-582"},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11290948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877738","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}