Applied Clinical Informatics最新文献

{"title":"Surveying the Efficacy of an Open Access Biomedical Informatics Boot Camp.","authors":"Skyler Resendez, Gillian Franklin, Crystal Tomlin, Rachel Stephens, Heather Maness, Srikar Chamala, Ross Koppel, Peter Elkin","doi":"10.1055/a-2547-5208","DOIUrl":"https://doi.org/10.1055/a-2547-5208","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the efficacy of a biomedical informatics boot camp with regards to improving the skill sets of its participants.</p><p><strong>Methods: </strong>The University at Buffalo hosts a free, virtual biomedical informatics boot camp annually. Lectures covering various subject matters are offered, e.g., general programming, machine learning, natural language processing, and clinical decision support. Once the 2023 boot camp had concluded, an anonymous, voluntary survey was distributed.</p><p><strong>Results: </strong>70% of the survey respondents indicated that they agreed their expectations were met. 82% of the respondents indicated that our JupyterHub and the associated educational coding materials are useful tools for learning. Free response answers showed a desire for additional hands-on courses over theoretical lectures.</p><p><strong>Conclusions: </strong>The results were overwhelmingly positive. Most respondents felt they expanded upon their knowledge of informatics. The study also pointed out challenges, including keeping difficulty levels appropriate for an audience with diverse educational backgrounds.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531018","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 CDS failures: Challenges to reduce alert burden using current decision support infrastructure in two commercial EHR systems: lessons learned and path forward.","authors":"Tiago K Colicchio, David El Halta, Guilherme Del Fiol, Kensaku Kawamoto, Howard R Strasberg, James J Cimino","doi":"10.1055/a-2546-5954","DOIUrl":"https://doi.org/10.1055/a-2546-5954","url":null,"abstract":"<p><strong>Background: </strong>Despite the proven usefulness of appropriate clinical decision support (CDS) alerts, many CDS systems fire excessive, clinically irrelevant alerts that are often ignored by clinicians. We have developed a method to suppress false-positive alerts based on prior drug tolerance but encountered substantial barriers to integrate the method into widely adopted commercial electronic health record (EHR) systems.</p><p><strong>Objective: </strong>To describe the challenges faced while attempting to integrate our method into the CDS infrastructure of two commercial EHR systems and provide recommendations for future research and CDS design.</p><p><strong>Methods: </strong>Using a multifaceted approach, we investigated 1) the use of emergent CDS standards (e.g., CDS Hooks) to create a scalable solution to augment off-the-shelf EHR-based alerts with patient-specific custom alerts, 2) customize CDS rules of commercial medication knowledge bases (MKBs) to reduce false-positive alerts, and 3) manually inactivate allergy documentation in patients with prior drug tolerance.</p><p><strong>Results: </strong>We were unable to implement the standards-based approach because support for CDS Hooks was found to be tailored to specific scenarios that involve creation of new drug allergy alerts (DAAs) but not the suppression of vendor-supplied DAAs. Likewise, we were unable to suppress alerts imported from MKBs into the EHR systems investigated because these systems do not support discrete clinical documentation changes that drive DAAs. Lastly, we determined that although manually inactivating allergy documentation in patients with prior drug tolerance is possible, doing so requires the impractical solution of creating and maintaining individual rules for each drug at the ingredient level.</p><p><strong>Conclusion: </strong>We describe the barriers that precluded implementation of a novel method to suppress clinically irrelevant CDS alerts in two commercial EHR systems. Overcoming these barriers will require a more flexible CDS infrastructure, as well as collaboration and shared responsibility across diverse stakeholders.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143532115","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":"Optimizing Wound Culture Ordering in Electronic Health Records: A Case Report.","authors":"John J Hanna, Jenny L Weon, Kathryn Kelton, Ellis Haskell, Mary J Ramirez, Bonnie Greene, Marcus Kouma, Donald Storey, Shelby D Melton, Toby Gray, William Scott, Rafael Ortiz-Colberg, David Truong, James Blaine, Richard J Medford, Christoph Ulrich Lehmann, Bishnu Devkota, Jeffrey Hastings","doi":"10.1055/a-2546-5868","DOIUrl":"https://doi.org/10.1055/a-2546-5868","url":null,"abstract":"<p><strong>Background: </strong>At a large quaternary health system, tissue specimens were frequently sent to the microbiology laboratory with an incorrect wound culture order meant for swab specimens due to poor electronic health record menu design. Wound cultures were also requested in chronic wound cases with a low index of suspicion for acute infection.</p><p><strong>Objective: </strong>To present a case report on specific changes to the design of the electronic test menu that resulted in higher numbers of appropriate ordering practices.</p><p><strong>Methods: </strong>\"Wound Culture\" test was renamed to \"Wound Swab Culture\" to distinguish from tissue specimens and \"Tissue Culture\" was added as a new available quick order in the microbiology menu alongside the existing wound culture quick order. In addition, a diagnostic questionnaire was added to \"Wound Swab Culture\" quick orders that inquired about the presence of pus/exudate and erythema and if the wound was a surgical wound to guide and assess the appropriateness of the culture order.</p><p><strong>Results: </strong>The number of tissue specimens erroneously submitted with a wound culture order decreased from 6.6% in July 2022 (pre-intervention) to 0% in July 2023 (post-intervention). The diagnostic questionnaire was utilized in 27.5% of wound culture orders. In 6 out of 98 orders (6.1%) the wound was not surgical and there was absence of pus/exudate and erythema (p = 0.038). Conversely, 92 out of 98 orders (93.9%) had at least one \"Yes\" response. Total numbers of tests six months before and after the test menu design interventions showed that tissue culture orders increased from 228 prior to the intervention to 349 post-intervention. Wound culture orders decreased from 575 to 460 (p < 0.0001).</p><p><strong>Conclusions: </strong>Our case report underscores how targeted electronic health record optimization can be associated with more appropriate microbiology test ordering practices for potential wound infections.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525014","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 CDS Failures: Clinical Decision Support to Reduce Hospital Length-of-Stay for Cancer Patients with Fever and Neutropenia.","authors":"Julia Yarahuan, Swaminathan Kandaswamy, Edwin Ray, Rachael LeRoux, Wayne Liang, Evan Orenstein, Claire L Stokes","doi":"10.1055/a-2540-2349","DOIUrl":"https://doi.org/10.1055/a-2540-2349","url":null,"abstract":"<p><strong>Background: </strong>Pediatric cancer patients with fever and neutropenia are at risk for bacterial sepsis, traditionally requiring extended hospital stays on antibiotics until neutrophil counts recover. According to a new validated scoring system, a subset of these patients is at lower risk and eligible for early discharge and reduced IV antibiotic exposure.</p><p><strong>Objective: </strong>Reduce length of stay (LOS) for febrile neutropenic patients using clinical decision support (CDS) to identify low risk patients.</p><p><strong>Methods: </strong>A CDS system was developed to (1) screen febrile neutropenic patients using a validated clinical decision rule, (2) surface when low risk patients become eligible for discharge, and (3) facilitate close phone follow-up for patients discharged early. The system was implemented in March 2023 and iteratively refined based on usability testing.</p><p><strong>Results: </strong>Post-implementation, LOS did not improve significantly, and uptake of the CDS tool remained low. Though the tool had the potential to reduce LOS, the limited staff engagement was a significant barrier to success. Safety outcomes, including ICU readmissions and mortality, remained unaffected.</p><p><strong>Conclusion: </strong>Despite carefully designed CDS applying an evidence-based scoring system and using human-centered design methodology, the failure to achieve the desired reduction in LOS was primarily due to insufficient uptake by clinical staff. This highlights the need for stronger strategies to ensure clinician engagement and integration into workflows for CDS tools to be effective.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450629","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":"An Interactive Voice Response System for Postoperative Care in Primary Total Joint Arthroplasty Patients: A Pilot Study.","authors":"Jose L Ayala-Ortiz, Matthew McCrosson, Roshan Jacob, Aryan Vinoth, Tapan Mehta, Waleed Al-Hardan, Gerald McGwin, Sameer Naranje","doi":"10.1055/a-2539-1283","DOIUrl":"https://doi.org/10.1055/a-2539-1283","url":null,"abstract":"<p><strong>Introduction: </strong>Effective postoperative care is crucial for the success of total joint arthroplasty (TJA) and prevention of unnecessary emergency department (ED) visits. We explore the feasibility and acceptability of utilizing an Interactive Voice Response System (IVRS) to enhance postoperative monitoring in primary TJA patients.</p><p><strong>Methods: </strong>This pilot study was conducted with 24 participants undergoing primary TJA randomly assigned to an intervention group receiving IVRS calls (n=12) or a control group (n=12) receiving standard postoperative follow-up. Feasibility and acceptability were assessed through patient satisfaction and willingness to participate. Outcome measures included the Short Form 36 Health Survey Questionnaire (SF-36), the Hip disability and Osteoarthritis Outcome Score (HOOS), the Knee injury and Osteoarthritis Outcome Score (KOOS), and pain assessments using the Visual Analogue Scale (VAS).</p><p><strong>Results: </strong>The IVRS group exhibited a 92.3% retention rate, with 100% completion of the 12-week study. IVRS calls triggered 10 notifications, addressing pain control and respiratory concerns, leading to timely interventions and preventing ED visits. The IVRS demonstrated an 81% (95% CI, 77.1-86.0%) overall response rate, with high patient satisfaction (92% finding calls 'very useful').</p><p><strong>Conclusion: </strong>This pilot study demonstrates the feasibility and acceptability of an IVRS-based postoperative follow-up system for TJA patients. Patients reported high satisfaction and expressed willingness to use the IVRS service again.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426209","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":"Sharing a Hybrid EHR + FHIR CDS Tool Across Health Systems: Automating Smoking Cessation for Pediatric Caregivers.","authors":"Sameh Nagui Saleh, Eric Kim, Jeritt G Thayer, Emara Nabi, Dean Karavite, Jonathan Winickoff, Alexander Fiks, Brian P Jenssen, Nicholas Riley, Robert W Grundmeier","doi":"10.1055/a-2535-5823","DOIUrl":"https://doi.org/10.1055/a-2535-5823","url":null,"abstract":"<p><p>Objective Experiences sharing complex workflow-integrated clinical decision support (CDS) across health systems are sparse and not well reported. This case study presents the sharing of a hybrid electronic health record (EHR)-native and SMART-compatible CDS tool for automating provision of smoking cessation treatment for caregivers during pediatric visits. Materials & Methods We conducted a comprehensive needs assessment using socio-technical frameworks to identify workflow gaps and technical requirements. A multidisciplinary team of clinical informaticians, software developers, and EHR analysts guided the technology transfer. Iterative testing and feedback informed modifications. The evaluation tracked questionnaire uptake, tobacco use identification rates, and treatment acceptance metrics. Results The needs assessment revealed critical artifacts such as data architecture, source code repositories, and regulatory requirements, which informed adaptations for the recipient health system. In the pre-implementation phase, JXPORT was identified for transferring EHR-native components and the EHR's Active Guidelines Framework was needed to extend the FHIR standard with ordering, posting flowsheet values, and launching activities in the embedded web application. The implementation process resulted in key modifications including same-day nicotine replacement therapy delivery through internal pharmacy services and optimized questionnaire design to improve usability. At the source system, 5.8% (n=3391) of caregivers reported active tobacco use with 46.9% (n=1590) accepting cessation resources. At the recipient system, 24.3% (n=167) of caregivers listed tobacco use and 28.1% (n=47) accepted treatment. Conclusions The cross-system sharing of eCEASE serves as a nascent model for disseminating complex CDS tools and highlighted opportunities for improvement. Future work should focus on creating validated dissemination frameworks and improving use of standards for EHR integration.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400598","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":"Association of an HIV-Prediction Model with Uptake of Pre-Exposure Prophylaxis (PrEP).","authors":"Steven Romero, Kristin Alvarez, Ank E Nijhawan, Arun Nethi, Katie Bistransin, Helen Lynne King","doi":"10.1055/a-2524-4993","DOIUrl":"https://doi.org/10.1055/a-2524-4993","url":null,"abstract":"<p><strong>Background: </strong>Global efforts aimed at ending human immunodeficiency virus (HIV) incidence have adapted and evolved since the turn of the century. The utilization of machine learning incorporated into an electronic health record (EHR) can be refined into prediction models that identify when an individual is at greater HIV infection risk. This can create a novel and innovative approach to identifying patients eligible for preventative therapy.</p><p><strong>Objectives: </strong>This study's aim was to evaluate the effectiveness of an HIV prediction model in clinical workflows. Outcomes included pre-exposure prophylaxis (PrEP) prescriptions generated and the model's ability to identify eligible patients.</p><p><strong>Methods: </strong>A prediction model was developed and implemented at the safety-net hospital in Dallas County. Patients seen in primary care clinics were evaluated between July 2020 to June 2022. The prediction model was incorporated into an existing best practice advisory (BPAs) used to identify potentially eligible PrEP patients. The prior, basic BPA (bBPA) displayed if a prior sexually transmitted infection was documented and the enhanced BPA (eBPA) incorporated the HIV prediction model.</p><p><strong>Results: </strong>A total of 3,218 unique patients received the BPA during the study time period, with 2,346 ultimately included for evaluation. There were 678 patients in the bBPA group and 1,666 in the eBPA group. PrEP prescriptions generated increased in the post-implementation group within the 90-day follow-up period (bBPA:1.48 v. eBPA:3.67 prescriptions per month, p<0.001). Patient demographics also differed between groups, resulting in a higher median age (bBPA:36[IQR 24] v. eBPA:52[QR 19] years, p<0.001) and an even distribution between birth sex in the post-implementation group (female sex at birth bBPA:62.2% v. eBPA:50.2%, p=<0.001).</p><p><strong>Conclusions: </strong>The implementation of a HIV prediction model yielded a higher number of PrEP prescriptions generated and was associated with the identification of twice the number of potentially eligible patients.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143042684","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":"Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010-2021: A Systematic Review.","authors":"Swaminathan Kandaswamy, Lindsey A Knake, Adam Dziorny, Sean Hernandez, Allison B McCoy, Lauren M Hess, Evan Orenstein, Mia S White, Eric S Kirkendall, Matthew Molloy, Philip Hagedorn, Naveen Muthu, Avinash Murugan, Jonathan M Beus, Mark Mai, Brooke Luo, Juan Demetrio Chaparro","doi":"10.1055/a-2521-1508","DOIUrl":"https://doi.org/10.1055/a-2521-1508","url":null,"abstract":"<p><strong>Objective: </strong>To review pediatric artificial intelligence (AI) implementation studies from 2010-2021 and analyze reported performance measures.</p><p><strong>Methods: </strong>We searched PubMed/Medline, Embase CINHAL, Cochrane Library CENTRAL, IEEE and Web of Science with controlled vocabulary.</p><p><strong>Inclusion criteria: </strong>AI intervention in a pediatric clinical setting that learns from data (i.e., data-driven, as opposed to rule-based) and takes actions to make patient-specific recommendations; published between 01/2010 to 10/2021; must have agency (AI must provide guidance that affects clinical care, not merely running in background). We extracted study characteristics, target users, implementation setting, time span, and performance measures.</p><p><strong>Results: </strong>Of 126 articles reviewed as full text, 17 met inclusion criteria. Eight studies (47%) reported both clinical outcomes and process measures, six (35%) reported only process measures, and two (12%) reported only clinical outcomes. Five studies (30%) reported no difference in clinical outcomes with AI, four (24%) reported improvement in clinical outcomes compared to controls, two (12%) reported positive effects on clinical outcomes with use of AI but had no formal comparison or controls, and one (6%) reported poor clinical outcomes with AI. Twelve studies (71%) reported improvement in process measures, while two (12%) reported no improvement. Five (30%) studies reported on at least 1 human performance measure.</p><p><strong>Conclusions: </strong>While there are many published pediatric AI models, the number of AI implementations is minimal with no standardized reporting of outcomes, care processes, or human performance measures. More comprehensive evaluations will help elucidate mechanisms of impact.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013953","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":"Exploring Mixed Reality for Patient Education in Cerebral Angiograms: A Pilot Study.","authors":"Paul Murdock, Snehita Bonthu, Angel Chavez, Yinn Cher Ooi","doi":"10.1055/a-2521-1303","DOIUrl":"https://doi.org/10.1055/a-2521-1303","url":null,"abstract":"<p><strong>Background: </strong>Cerebral aneurysms (CAs) affect 3-5% of the general population, with saccular aneurysms being the most common type. Despite advances in treatment, patient understanding of CAs and associated procedures remains limited, impacting informed consent and treatment outcomes.</p><p><strong>Objectives: </strong>This pilot study aims to evaluate the effectiveness of mixed reality (MR) technology in enhancing patient education and understanding of cerebral angiograms and aneurysm treatment, thereby improving the patient-surgeon communication process.</p><p><strong>Methods: </strong>A non-randomized single-center prospective study was conducted with 16 patients diagnosed with intracranial aneurysms. Participants used a Microsoft HoloLens to view an interactive 3D presentation about cerebral angiograms and aneurysm treatments. Pre- and post-intervention surveys assessed their knowledge and anxiety levels using a 5-point Likert scale. The Wilcoxon signed-rank test was used for statistical analysis.</p><p><strong>Results: </strong>Post-intervention, the total survey scores improved significantly (average increase of 6.7 points, p<0.05). Seven out of eight survey questions showed significant knowledge improvement. The mean perceived ability to explain aneurysm treatment improved by 1.38 points and understanding of access points for procedures increased by 1.31 points (both p<0.05). The question regarding understanding of treatment risks did not show significant change (p>0.05). Anxiety levels decreased, with 75% of participants reporting reduced anxiety post-intervention.</p><p><strong>Conclusions: </strong>MR technology significantly enhances patient understanding and reduces anxiety regarding cerebral angiogram procedures and aneurysm treatments. These findings support the integration of MR in patient education to improve clinical outcomes and patient satisfaction. This approach offers a promising direction for future healthcare communication strategies, especially in complex procedures requiring detailed patient comprehension.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014574","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":"Hospital Health Information Exchange Network Density and Predictors Across U.S. Hospital Referral Regions.","authors":"Sunny C Lin, Karen Joynt Maddox, Daphne Lew, Julia Adler-Milstein, Thomas Kannampallil","doi":"10.1055/a-2516-1692","DOIUrl":"10.1055/a-2516-1692","url":null,"abstract":"<p><p>Objective To develop a measure of Health Information Exchange (HIE) for characterizing the density of inter-hospital HIE connections and identify regional characteristics associated with HIE network density Materials and Methods HIE network density was measured as the proportion of hospital pairs within a region that are connected through HIE. The 2022 American Hospital Association's Information Technology Supplement survey was used to calculate HIE network density for US hospital referral regions (HRRs). Bivariate tests and multivariable regression were used to characterize hospital, electronic health record (EHR) vendor, and resident characteristics associated with HIE network density. Results Data on 2,509 hospitals across 274 HRRs were included in the study, with 92% of hospitals participating in at least 1 HIE. On average, hospitals participated in two HIEs and there were 7 HIEs present in each region. HIE network density ranged from 0.0 to 1.0, with a median of 0.78 and an interquartile range of 0.51 to 1.00. Hospital and vendor characteristics associated with greater HIE network density include: more HIEs per hospital, a higher proportion of non-profit hospitals, greater Epic marketshare, and more concentrated hospital and EHR vendor markets. Resident characteristics associated with greater HIE network density include: higher home values, more educated residents, and higher median household incomes. Conclusion We found that, on average, 7 out of 10 hospital-pairs within a given hospital referral regions are connected via at least one HIE, with lower HIE network density in regions with lower socioeconomic status. This measure can be used to track the impact of the Trusted Exchange Framework and Common Agreement on area-level interoperability.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013646","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}