JAMIA Open最新文献

{"title":"Retrieval augmented scientific claim verification.","authors":"Hao Liu, Ali Soroush, Jordan G Nestor, Elizabeth Park, Betina Idnay, Yilu Fang, Jane Pan, Stan Liao, Marguerite Bernard, Yifan Peng, Chunhua Weng","doi":"10.1093/jamiaopen/ooae021","DOIUrl":"10.1093/jamiaopen/ooae021","url":null,"abstract":"<p><strong>Objective: </strong>To automate scientific claim verification using PubMed abstracts.</p><p><strong>Materials and methods: </strong>We developed CliVER, an end-to-end scientific <b>Cl</b>a<b>i</b>m <b>VER</b>ification system that leverages retrieval-augmented techniques to automatically retrieve relevant clinical trial abstracts, extract pertinent sentences, and use the PICO framework to support or refute a scientific claim. We also created an ensemble of three state-of-the-art deep learning models to classify rationale of support, refute, and neutral. We then constructed CoVERt, a new <b>CO</b>VID <b>VER</b>ifica<b>t</b>ion dataset comprising 15 PICO-encoded drug claims accompanied by 96 manually selected and labeled clinical trial abstracts that either support or refute each claim. We used CoVERt and SciFact (a public scientific claim verification dataset) to assess CliVER's performance in predicting labels. Finally, we compared CliVER to clinicians in the verification of 19 claims from 6 disease domains, using 189 648 PubMed abstracts extracted from January 2010 to October 2021.</p><p><strong>Results: </strong>In the evaluation of label prediction accuracy on CoVERt, CliVER achieved a notable F1 score of 0.92, highlighting the efficacy of the retrieval-augmented models. The ensemble model outperforms each individual state-of-the-art model by an absolute increase from 3% to 11% in the F1 score. Moreover, when compared with four clinicians, CliVER achieved a precision of 79.0% for abstract retrieval, 67.4% for sentence selection, and 63.2% for label prediction, respectively.</p><p><strong>Conclusion: </strong>CliVER demonstrates its early potential to automate scientific claim verification using retrieval-augmented strategies to harness the wealth of clinical trial abstracts in PubMed. Future studies are warranted to further test its clinical utility.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 1","pages":"ooae021"},"PeriodicalIF":2.5,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10919922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140060751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.","authors":"Janick Weberpals, Sudha R Raman, Pamela A Shaw, Hana Lee, Bradley G Hammill, Sengwee Toh, John G Connolly, Kimberly J Dandreo, Fang Tian, Wei Liu, Jie Li, José J Hernández-Muñoz, Robert J Glynn, Rishi J Desai","doi":"10.1093/jamiaopen/ooae008","DOIUrl":"10.1093/jamiaopen/ooae008","url":null,"abstract":"<p><strong>Objectives: </strong>Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions.</p><p><strong>Materials and methods: </strong>We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR.</p><p><strong>Results: </strong>smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data.</p><p><strong>Conclusions: </strong>The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 1","pages":"ooae008"},"PeriodicalIF":2.5,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10833461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139673099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: a case study of the MIMIC-IV Emergency Department module.","authors":"Philip van Damme, Matthias Löbe, Nirupama Benis, Nicolette F de Keizer, Ronald Cornet","doi":"10.1093/jamiaopen/ooae002","DOIUrl":"10.1093/jamiaopen/ooae002","url":null,"abstract":"<p><strong>Objectives: </strong>To provide a real-world example on how and to what extent Health Level Seven Fast Healthcare Interoperability Resources (FHIR) implements the Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles for scientific data. Additionally, presents a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR.</p><p><strong>Materials and methods: </strong>A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department (MIMIC-ED) dataset, a deidentified clinical dataset converted into FHIR. The FAIRness of this dataset was assessed using a set of common FAIR assessment indicators.</p><p><strong>Results: </strong>The FHIR distribution of MIMIC-ED, comprising an implementation guide and demo data, was more FAIR compared to the non-FHIR distribution. The FAIRness score increased from 60 to 82 out of 95 points, a relative improvement of 37%. The most notable improvements were observed in interoperability, with a score increase from 5 to 19 out of 19 points, and reusability, with a score increase from 8 to 14 out of 24 points. A total of 14 FAIR implementation choices were identified.</p><p><strong>Discussion: </strong>Our work examined how and to what extent the FHIR standard contributes to FAIR data. Challenges arose from interpreting the FAIR assessment indicators. This study stands out for providing a real-world example of a dataset that was made more FAIR using FHIR.</p><p><strong>Conclusion: </strong>To the best of our knowledge, this is the first study that formally assessed the conformance of a FHIR dataset to the FAIR principles. FHIR improved the accessibility, interoperability, and reusability of MIMIC-ED. Future research should focus on implementing FHIR in research data infrastructures.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 1","pages":"ooae002"},"PeriodicalIF":2.5,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10822118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139571801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and evaluation of an electronic prospective medication order review system for medication orders in the inpatient setting.","authors":"Pooja Ojha, Benjamin J Anderson, Evan W Draper, Susan M Flaker, Mark H Siska, Kristin C Mara, Brian D Kennedy, Diana J Schreier","doi":"10.1093/jamiaopen/ooae003","DOIUrl":"10.1093/jamiaopen/ooae003","url":null,"abstract":"<p><strong>Objectives: </strong>Since the 1970s, a plethora of tools have been introduced to support the medication use process. However, automation initiatives to assist pharmacists in prospectively reviewing medication orders are lacking. The review of many medications may be protocolized and implemented in an algorithmic fashion utilizing discrete data from the electronic health record (EHR). This research serves as a proof of concept to evaluate the capability and effectiveness of an electronic prospective medication order review (EPMOR) system compared to pharmacists' review.</p><p><strong>Materials and methods: </strong>A subset of the most frequently verified medication orders were identified for inclusion. A team of clinical pharmacist experts developed best-practice EPMOR criteria. The established criteria were incorporated into conditional logic built within the EHR. Verification outcomes from the pharmacist (human) and EPMOR (automation) were compared.</p><p><strong>Results: </strong>Overall, 13 404 medication orders were included. Of those orders, 13 133 passed pharmacist review, 7388 of which passed EPMOR. A total of 271 medication orders failed pharmacist review due to order modification or discontinuation, 105 of which passed EPMOR. Of the 105 orders, 19 were duplicate orders correctly caught by both EPMOR and pharmacists, but the opposite duplicate order was rejected, 51 orders failed due to scheduling changes.</p><p><strong>Discussion: </strong>This simulation was capable of effectively discriminating and triaging orders. Protocolization and automation of the prospective medication order review process in the EHR appear possible using clinically driven algorithms.</p><p><strong>Conclusion: </strong>Further research is necessary to refine such algorithms to maximize value, improve efficiency, and minimize safety risks in preparation for the implementation of fully automated systems.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 1","pages":"ooae003"},"PeriodicalIF":2.1,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10822119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139571803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of cross-vendor linkage of ophthalmic images with electronic health record data: an analysis from the IRIS Registry^®.","authors":"Michael Mbagwu, Zhongdi Chu, Durga Borkar, Alex Koshta, Nisarg Shah, Aracelis Torres, Hylton Kalvaria, Flora Lum, Theodore Leng","doi":"10.1093/jamiaopen/ooae005","DOIUrl":"10.1093/jamiaopen/ooae005","url":null,"abstract":"<p><strong>Purpose: </strong>To link compliant, universal Digital Imaging and Communications in Medicine (DICOM) ophthalmic imaging data at the individual patient level with the American Academy of Ophthalmology IRIS^® Registry (Intelligent Research in Sight).</p><p><strong>Design: </strong>A retrospective study using de-identified EHR registry data.</p><p><strong>Subjects participants controls: </strong>IRIS Registry records.</p><p><strong>Materials and methods: </strong>DICOM files of several imaging modalities were acquired from two large retina ophthalmology practices. Metadata tags were extracted and harmonized to facilitate linkage to the IRIS Registry using a proprietary, heuristic patient-matching algorithm, adhering to HITRUST guidelines. Linked patients and images were assessed by image type and clinical diagnosis. Reasons for failed linkage were assessed by examining patients' records.</p><p><strong>Main outcome measures: </strong>Success rate of linking clinicoimaging and EHR data at the patient level.</p><p><strong>Results: </strong>A total of 2 287 839 DICOM files from 54 896 unique patients were available. Of these, 1 937 864 images from 46 196 unique patients were successfully linked to existing patients in the registry. After removing records with abnormal patient names and invalid birthdates, the success linkage rate was 93.3% for images. 88.2% of all patients at the participating practices were linked to at least one image.</p><p><strong>Conclusions and relevance: </strong>Using identifiers from DICOM metadata, we created an automated pipeline to connect longitudinal real-world clinical data comprehensively and accurately to various imaging modalities from multiple manufacturers at the patient and visit levels. The process has produced an enriched and multimodal IRIS Registry, bridging the gap between basic research and clinical care by enabling future applications in artificial intelligence algorithmic development requiring large linked clinicoimaging datasets.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 1","pages":"ooae005"},"PeriodicalIF":2.5,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10811449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139571868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation.","authors":"Ilia Rattsev, Vered Stearns, Amanda L Blackford, Daniel L Hertz, Karen L Smith, James M Rae, Casey Overby Taylor","doi":"10.1093/jamiaopen/ooae006","DOIUrl":"10.1093/jamiaopen/ooae006","url":null,"abstract":"<p><strong>Objectives: </strong>Early discontinuation is common among breast cancer patients taking aromatase inhibitors (AIs). Although several predictors have been identified, it is unclear how to simultaneously consider multiple risk factors for an individual. We sought to develop a tool for prediction of AI discontinuation and to explore how predictive value of risk factors changes with time.</p><p><strong>Materials and methods: </strong>Survival machine learning was used to predict time-to-discontinuation of AIs in 181 women who enrolled in a prospective cohort. Models were evaluated via time-dependent area under the curve (AUC), c-index, and integrated Brier score. Feature importance was analysis was conducted via Shapley Additive Explanations (SHAP) and time-dependence of their predictive value was analyzed by time-dependent AUC. Personalized survival curves were constructed for risk communication.</p><p><strong>Results: </strong>The best-performing model incorporated genetic risk factors and changes in patient-reported outcomes, achieving mean time-dependent AUC of 0.66, and AUC of 0.72 and 0.67 at 6- and 12-month cutoffs, respectively. The most significant features included variants in <i>ESR1</i> and emergent symptoms. Predictive value of genetic risk factors was highest in the first year of treatment. Decrease in physical function was the strongest independent predictor at follow-up.</p><p><strong>Discussion and conclusion: </strong>Incorporation of genomic and 3-month follow-up data improved the ability of the models to identify the individuals at risk of AI discontinuation. Genetic risk factors were particularly important for predicting early discontinuers. This study provides insight into the complex nature of AI discontinuation and highlights the importance of incorporating genetic risk factors and emergent symptoms into prediction models.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 1","pages":"ooae006"},"PeriodicalIF":2.1,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10799747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139514103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health.","authors":"Leonard Ruocco, Yuqian Zhuang, Raymond Ng, Richard J Munthali, Kristen L Hudec, Angel Y Wang, Melissa Vereschagin, Daniel V Vigo","doi":"10.1093/jamiaopen/ooae001","DOIUrl":"10.1093/jamiaopen/ooae001","url":null,"abstract":"<p><strong>Objectives: </strong>To design a novel artificial intelligence-based software platform that allows users to analyze text data by identifying various coherent topics and parts of the data related to a specific research theme-of-interest (TOI).</p><p><strong>Materials and methods: </strong>Our platform uses state-of-the-art unsupervised natural language processing methods, building on top of a large language model, to analyze social media text data. At the center of the platform's functionality is BERTopic, which clusters social media posts, forming collections of words representing distinct topics. A key feature of our platform is its ability to identify whole sentences corresponding to topic words, vastly improving the platform's ability to perform downstream similarity operations with respect to a user-defined TOI.</p><p><strong>Results: </strong>Two case studies on mental health among university students are performed to demonstrate the utility of the platform, focusing on signals within social media (Reddit) data related to depression and their connection to various emergent themes within the data.</p><p><strong>Discussion and conclusion: </strong>Our platform provides researchers with a readily available and inexpensive tool to parse large quantities of unstructured, noisy data into coherent themes, as well as identifying portions of the data related to the research TOI. While the development process for the platform was focused on mental health themes, we believe it to be generalizable to other domains of research as well.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 1","pages":"ooae001"},"PeriodicalIF":2.5,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10799551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139514512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topic modeling on clinical social work notes for exploring social determinants of health factors.","authors":"Shenghuan Sun, Travis Zack, Christopher Y K Williams, Madhumita Sushil, Atul J Butte","doi":"10.1093/jamiaopen/ooad112","DOIUrl":"10.1093/jamiaopen/ooad112","url":null,"abstract":"<p><strong>Objective: </strong>Existing research on social determinants of health (SDoH) predominantly focuses on physician notes and structured data within electronic medical records. This study posits that social work notes are an untapped, potentially rich source for SDoH information. We hypothesize that clinical notes recorded by social workers, whose role is to ameliorate social and economic factors, might provide a complementary information source of data on SDoH compared to physician notes, which primarily concentrate on medical diagnoses and treatments. We aimed to use word frequency analysis and topic modeling to identify prevalent terms and robust topics of discussion within a large cohort of social work notes including both outpatient and in-patient consultations.</p><p><strong>Materials and methods: </strong>We retrieved a diverse, deidentified corpus of 0.95 million clinical social work notes from 181 644 patients at the University of California, San Francisco. We conducted word frequency analysis related to ICD-10 chapters to identify prevalent terms within the notes. We then applied Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion, which was further stratified by note types and disease groups.</p><p><strong>Results: </strong>Word frequency analysis primarily identified medical-related terms associated with specific ICD10 chapters, though it also detected some subtle SDoH terms. In contrast, the LDA topic modeling analysis extracted 11 topics explicitly related to social determinants of health risk factors, such as financial status, abuse history, social support, risk of death, and mental health. The topic modeling approach effectively demonstrated variations between different types of social work notes and across patients with different types of diseases or conditions.</p><p><strong>Discussion: </strong>Our findings highlight LDA topic modeling's effectiveness in extracting SDoH-related themes and capturing variations in social work notes, demonstrating its potential for informing targeted interventions for at-risk populations.</p><p><strong>Conclusion: </strong>Social work notes offer a wealth of unique and valuable information on an individual's SDoH. These notes present consistent and meaningful topics of discussion that can be effectively analyzed and utilized to improve patient care and inform targeted interventions for at-risk populations.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 1","pages":"ooad112"},"PeriodicalIF":2.5,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10788143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139466752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of possible errors in natural language processing-derived data on downstream epidemiologic analysis.","authors":"Zhou Lan, Alexander Turchin","doi":"10.1093/jamiaopen/ooad111","DOIUrl":"10.1093/jamiaopen/ooad111","url":null,"abstract":"<p><strong>Objective: </strong>To assess the impact of potential errors in natural language processing (NLP) on the results of epidemiologic studies.</p><p><strong>Materials and methods: </strong>We utilized data from three outcomes research studies where the primary predictor variable was generated using NLP. For each of these studies, Monte Carlo simulations were applied to generate datasets simulating potential errors in NLP-derived variables. We subsequently fit the original regression models to these partially simulated datasets and compared the distribution of coefficient estimates to the original study results.</p><p><strong>Results: </strong>Among the four models evaluated, the mean change in the point estimate of the relationship between the predictor variable and the outcome ranged from -21.9% to 4.12%. In three of the four models, significance of this relationship was not eliminated in a single of the 500 simulations, and in one model it was eliminated in 12% of simulations. Mean changes in the estimates for confounder variables ranged from 0.27% to 2.27% and significance of the relationship was eliminated between 0% and 9.25% of the time. No variables underwent a shift in the direction of its interpretation.</p><p><strong>Discussion: </strong>Impact of simulated NLP errors on the results of epidemiologic studies was modest, with only small changes in effect estimates and no changes in the interpretation of the findings (direction and significance of association with the outcome) for either the NLP-generated variables or other variables in the models.</p><p><strong>Conclusion: </strong>NLP errors are unlikely to affect the results of studies that use NLP as the source of data.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 4","pages":"ooad111"},"PeriodicalIF":2.1,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10752385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139049459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of implementing patient-reported outcome measures into routine breast cancer care delivery using a novel collection and reporting platform.","authors":"Elena Tsangaris, Colby Hyland, George Liang, Joanna O'Gorman, Dany Thorpe Huerta, Ellen Kim, Maria Edelen, Andrea Pusic","doi":"10.1093/jamiaopen/ooad108","DOIUrl":"10.1093/jamiaopen/ooad108","url":null,"abstract":"<p><strong>Objectives: </strong>imPROVE is a new Health Information Technology platform that enables systematic patient-reported outcome measure (PROM) collection through a mobile phone application. The purpose of this study is to describe our initial experience and approach to implementing imPROVE among breast cancer patients treated in breast and plastic surgery clinics.</p><p><strong>Materials and methods: </strong>We describe our initial implementation in 4 phases between June 2021 and February 2022: preimplementation, followed by 3 consecutive implementation periods (P1, P2, P3). The Standards for Reporting Implementation Studies statement guided this study. Iterative Plan-Do-Study-Act (PDSA) cycles supported implementation, and success was evaluated using the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework.</p><p><strong>Results: </strong>Qualitative interviews conducted during the preimplementation phase elicited 4 perceived implementation barriers. Further feedback collected during each phase of implementation resulted in the development of brochures, posters in clinic spaces, and scripts for clinic staff to streamline discussions with patients, and the resolution of technical issues concerning patient login capabilities, such as compatibility with cell phone software and barriers to downloading imPROVE. Feedback also generated ideas for facilitating provider interpretation of PROM results. By the end of P3, 2961 patients were eligible, 1375 (46.4%) downloaded imPROVE, and 1070 (36.1% of those eligible, 78% of those who downloaded) completed at least 1 PROM.</p><p><strong>Discussion and conclusion: </strong>Implementation efforts across 2 surgical departments at 2 academic teaching hospitals enabled collaboration across clinical specialties and longitudinal PROM reporting for patients receiving breast cancer care; the implementation effort also highlighted patient difficulties with mobile app-based PROM collection, particularly around initial engagement.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 4","pages":"ooad108"},"PeriodicalIF":2.1,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10750814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139040597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}