Journal of the American Medical Informatics Association : JAMIA最新文献

{"title":"Development and validation of predictive models for COVID-19 outcomes in a safety-net hospital population","authors":"Boran Hao, Yang Hu, Shahabeddin Sotudian, Zahra Zad, W. Adams, S. Assoumou, Heather E. Hsu, Rebecca G Mishuris, I. Paschalidis","doi":"10.1093/jamia/ocac062","DOIUrl":"https://doi.org/10.1093/jamia/ocac062","url":null,"abstract":"Abstract Objective To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs. Materials and Methods Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied. A score based on a recurrent neural network and a transformer architecture was developed to capture the dynamic evolution of vital signs. Combined with patient characteristics, clinical variables, and hospital occupancy measures, this dynamic vital score was used to train predictive models. Results Hospitalizations can be predicted with an area under the receiver-operating characteristic curve (AUC) of 92% using symptoms, hospital occupancy, and patient characteristics, including social determinants of health. Parsimonious models to predict intensive care, mechanical ventilation, and mortality that used the most recent labs and vitals exhibited AUCs of 92.7%, 91.2%, and 94%, respectively. Early predictive models, using labs and vital signs closer to admission had AUCs of 81.1%, 84.9%, and 92%, respectively. Discussion The most accurate models exhibit racial bias, being more likely to falsely predict that Black patients will be hospitalized. Models that are only based on the dynamic vital score exhibited accuracies close to the best parsimonious models, although the latter also used laboratories. Conclusions This large study demonstrates that COVID-19 severity may accurately be predicted using a score that accounts for the dynamic evolution of vital signs. Further, race, social determinants of health, and hospital occupancy play an important role.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123388526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset","authors":"K. Bradwell, J. Wooldridge, B. Amor, T. Bennett, A. Anand, C. Bremer, Y. J. Yoo, Zhenglong Qian, Steven G. Johnson, E. Pfaff, A. Girvin, A. Manna, Emily Niehaus, Stephanie S. Hong, X. Zhang, R. Zhu, Mark Bissell, N. Qureshi, J. Saltz, M. Haendel, C. Chute, H. Lehmann, R. Moffitt","doi":"10.1093/jamia/ocac054","DOIUrl":"https://doi.org/10.1093/jamia/ocac054","url":null,"abstract":"Abstract Objective The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. Materials and Methods The National COVID Cohort Collaborative (N3C) table of laboratory measurement data—over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. Results Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors’ records lacked units). Discussion The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. Conclusion The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124252536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inviting patients to identify diagnostic concerns through structured evaluation of their online visit notes","authors":"Traber Davis, D. T. Choi, Divvy K. Upadhyay, Saritha Korukonda, Taylor M. T. Scott, C. Spitzmueller, C. Schuerch, Dennis Torretti, Hardeep Singh","doi":"10.1093/jamia/ocac036","DOIUrl":"https://doi.org/10.1093/jamia/ocac036","url":null,"abstract":"Abstract Background The 21st Century Cures Act mandates patients’ access to their electronic health record (EHR) notes. To our knowledge, no previous work has systematically invited patients to proactively report diagnostic concerns while documenting and tracking their diagnostic experiences through EHR-based clinician note review. Objective To test if patients can identify concerns about their diagnosis through structured evaluation of their online visit notes. Methods In a large integrated health system, patients aged 18–85 years actively using the patient portal and seen between October 2019 and February 2020 were invited to respond to an online questionnaire if an EHR algorithm detected any recent unexpected return visit following an initial primary care consultation (“at-risk” visit). We developed and tested an instrument (Safer Dx Patient Instrument) to help patients identify concerns related to several dimensions of the diagnostic process based on notes review and recall of recent “at-risk” visits. Additional questions assessed patients’ trust in their providers and their general feelings about the visit. The primary outcome was a self-reported diagnostic concern. Multivariate logistic regression tested whether the primary outcome was predicted by instrument variables. Results Of 293 566 visits, the algorithm identified 1282 eligible patients, of whom 486 responded. After applying exclusion criteria, 418 patients were included in the analysis. Fifty-one patients (12.2%) identified a diagnostic concern. Patients were more likely to report a concern if they disagreed with statements “the care plan the provider developed for me addressed all my medical concerns” [odds ratio (OR), 2.65; 95% confidence interval [CI], 1.45–4.87) and “I trust the provider that I saw during my visit” (OR, 2.10; 95% CI, 1.19–3.71) and agreed with the statement “I did not have a good feeling about my visit” (OR, 1.48; 95% CI, 1.09–2.01). Conclusion Patients can identify diagnostic concerns based on a proactive online structured evaluation of visit notes. This surveillance strategy could potentially improve transparency in the diagnostic process.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131920951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Codesigning a community-based participatory research project to assess tribal perspectives on privacy and health data sharing: A report from the Strong Heart Study","authors":"Cynthia Triplett, Burgundy J Fletcher, Riley Taitingfong, Ying Zhang, T. Ali, L. Ohno-Machado, C. Bloss","doi":"10.1093/jamia/ocac038","DOIUrl":"https://doi.org/10.1093/jamia/ocac038","url":null,"abstract":"Abstract Broad health data sharing raises myriad ethical issues related to data protection and privacy. These issues are of particular relevance to Native Americans, who reserve distinct individual and collective rights to control data about their communities. We sought to gather input from tribal community leaders on how best to understand health data privacy and sharing preferences in this population. We conducted a workshop with 14 tribal leaders connected to the Strong Heart Study to codesign a research study to assess preferences concerning health data privacy for biomedical research. Workshop participants provided specific recommendations regarding who should be consulted, what questions should be posed, and what methods should be used, underscoring the importance of relationship-building between researchers and tribal communities. Biomedical researchers and informaticians who collect and analyze health information from Native communities have a unique responsibility to safeguard these data in ways that align to the preferences of specific communities.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117034209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A tribute to Karen Greenwood and her contributions to the American Medical Informatics Association","authors":"C. Lehmann, P. Brennan, D. Detmer, G. Jackson, L. Ohno-Machado, C. Safran, J. Williamson, E. Shortliffe","doi":"10.1093/jamia/ocac039","DOIUrl":"https://doi.org/10.1093/jamia/ocac039","url":null,"abstract":"After 25 years of service to the American Medical Informatics Association (AMIA), Ms Karen Greenwood, the Executive Vice President and Chief Operating Officer, is leaving the organization. In this perspective, we reflect on her accomplishments and her effect on the organization and the field of informatics nationally and globally. We also express our appreciation and gratitude for Ms Greenwood's role at AMIA.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121470209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing People-Like-Me and linear mixed model predictions of functional recovery following knee arthroplasty","authors":"J. Graber, A. Kittelson, E. Juarez-colunga, Xin Jin, M. Bade, J. Stevens-Lapsley","doi":"10.1101/2022.03.09.22271922","DOIUrl":"https://doi.org/10.1101/2022.03.09.22271922","url":null,"abstract":"Objective Prediction models can be useful tools for monitoring patient status and personalizing treatment in health care. The goal of this study was to compare the relative strengths and weaknesses of two different approaches for predicting functional recovery after knee arthroplasty: a neighbors-based People Like Me (PLM) approach and a linear mixed model (LMM) approach. Materials and Methods We used two distinct datasets to train and then test PLM and LMM prediction approaches for functional recovery following knee arthroplasty. We used Timed Up and Go (TUG), a commonly used test of mobility, to operationalize physical function. Both approaches used patient characteristics and baseline postoperative TUG values to predict TUG recovery from days 1-425 following surgery. We compared the accuracy and precision of PLM and LMM predictions in the testing dataset. Results A total of 317 patient records with 1379 TUG observations were used to train PLM and LMM approaches, and 456 patient records with 1244 TUG observations were used to test the predictions. The approaches performed similarly in terms of mean squared error and bias, but the PLM approach provided more accurate and precise estimates of prediction uncertainty. Discussion and Conclusion Overall, the PLM approach more accurately and precisely predicted TUG recovery following knee arthroplasty. These results suggest PLM predictions may be more clinically useful for monitoring recovery and personalizing care following knee arthroplasty. However, clinicians and organizations seeking to use predictions in practice should consider additional factors (e.g., resource requirements) when selecting a prediction approach.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115846226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic review and narrative synthesis of computerized audit and feedback systems in healthcare","authors":"J. Y. Tsang, N. Peek, I. Buchan, S. Veer, Benjamin Brown","doi":"10.1093/jamia/ocac031","DOIUrl":"https://doi.org/10.1093/jamia/ocac031","url":null,"abstract":"Abstract Objectives (1) Systematically review the literature on computerized audit and feedback (e-A&F) systems in healthcare. (2) Compare features of current systems against e-A&F best practices. (3) Generate hypotheses on how e-A&F systems may impact patient care and outcomes. Methods We searched MEDLINE (Ovid), EMBASE (Ovid), and CINAHL (Ebsco) databases to December 31, 2020. Two reviewers independently performed selection, extraction, and quality appraisal (Mixed Methods Appraisal Tool). System features were compared with 18 best practices derived from Clinical Performance Feedback Intervention Theory. We then used realist concepts to generate hypotheses on mechanisms of e-A&F impact. Results are reported in accordance with the PRISMA statement. Results Our search yielded 4301 unique articles. We included 88 studies evaluating 65 e-A&F systems, spanning a diverse range of clinical areas, including medical, surgical, general practice, etc. Systems adopted a median of 8 best practices (interquartile range 6–10), with 32 systems providing near real-time feedback data and 20 systems incorporating action planning. High-confidence hypotheses suggested that favorable e-A&F systems prompted specific actions, particularly enabled by timely and role-specific feedback (including patient lists and individual performance data) and embedded action plans, in order to improve system usage, care quality, and patient outcomes. Conclusions e-A&F systems continue to be developed for many clinical applications. Yet, several systems still lack basic features recommended by best practice, such as timely feedback and action planning. Systems should focus on actionability, by providing real-time data for feedback that is specific to user roles, with embedded action plans. Protocol Registration PROSPERO CRD42016048695.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114721041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The technical landscape for patient-centered CDS: progress, gaps, and challenges","authors":"Prashila Dullabh, Krysta Heaney-Huls, D. Lobach, Lauren S. Hovey, Shana Sandberg, P. Desai, E. Lomotan, James Swiger, M. Harrison, Christine Dymek, Dean F. Sittig, A. Boxwala","doi":"10.1093/jamia/ocac029","DOIUrl":"https://doi.org/10.1093/jamia/ocac029","url":null,"abstract":"Abstract Supporting healthcare decision-making that is patient-centered and evidence-based requires investments in the development of tools and techniques for dissemination of patient-centered outcomes research findings via methods such as clinical decision support (CDS). This article explores the technical landscape for patient-centered CDS (PC CDS) and the gaps in making PC CDS more shareable, standards-based, and publicly available, with the goal of improving patient care and clinical outcomes. This landscape assessment used: (1) a technical expert panel; (2) a literature review; and (3) interviews with 18 CDS stakeholders. We identified 7 salient technical considerations that span 5 phases of PC CDS development. While progress has been made in the technical landscape, the field must advance standards for translating clinical guidelines into PC CDS, the standardization of CDS insertion points into the clinical workflow, and processes to capture, standardize, and integrate patient-generated health data.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124090575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Extraction of Social Determinants of Health from Medical Notes of Chronic Lower Back Pain Patients","authors":"D. Lituiev, Benjamin Lacar, Sang S. Pak, Peter L Abramowitsch, E. D. Marchis, Thomas A. Peterson","doi":"10.1101/2022.03.04.22271541","DOIUrl":"https://doi.org/10.1101/2022.03.04.22271541","url":null,"abstract":"Background. Adverse social determinants of health (SDoH), or social risk factors, such as food insecurity and housing instability, are known to contribute to poor health outcomes and inequities. Our ability to study these linkages is limited because SDoH information is more frequently documented in free-text clinical notes than structured data fields. To overcome this challenge, there is a growing push to develop techniques for automated extraction of SDoH. In this study, we explored natural language processing (NLP) and inference (NLI) methods to extract SDoH information from clinical notes of patients with chronic low back pain (cLBP), to enhance future analyses of the associations between SDoH and low back pain outcomes and disparities. Methods. Clinical notes (n=1,576) for patients with cLBP (n=386) were annotated for seven SDoH domains: housing, food, transportation, finances, insurance coverage, marital and partnership status, and other social support, resulting in 626 notes with at least one annotated entity for 364 patients. We additionally labelled pain scores, depression, and anxiety. We used a two-tier taxonomy with these 10 first-level ontological classes and 68 second-level ontological classes. We developed and validated extraction systems based on both rule-based and machine learning approaches. As a rule-based approach, we iteratively configured a clinical Text Analysis and Knowledge Extraction System (cTAKES) system. We trained two machine learning models (based on convolutional neural network (CNN) and RoBERTa transformer), and a hybrid system combining pattern matching and bag-of-words models. Additionally, we evaluated a RoBERTa based entailment model as an alternative technique of SDoH detection in clinical texts. We used a model previously trained on general domain data without additional training on our dataset. Results. Four annotators achieved high agreement (average kappa=95%, F1=91.20%). Annotation frequency varied significantly dependent on note type. By tuning cTAKES, we achieved a performance of F1=47.11% for first-level classes. For most classes, the machine learning RoBERTa-based NER model performed better (first-level F1=84.35%) than other models within the internal test dataset. The hybrid system on average performed slightly worse than the RoBERTa NER model (first-level F1=80.27%), matching or outperforming the former in terms of recall. Using an out-of-the-box entailment model, we detected many but not all challenging wordings missed by other models, reaching an average F1 of 76.04%, while matching and outperforming the tested NER models in several classes. Still, the entailment model may be sensitive to hypothesis wording and may require further fine tuning. Conclusion. This study developed a corpus of annotated clinical notes covering a broad spectrum of SDoH classes. This corpus provides a basis for training machine learning models and serves as a benchmark for predictive models for named entity recognition f","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122493129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to: The roles of the US National Library of Medicine and Donald A.B. Lindberg in revolutionizing biomedical and health informatics","authors":"R. Miller, E. Shortliffe","doi":"10.1093/jamia/ocac026","DOIUrl":"https://doi.org/10.1093/jamia/ocac026","url":null,"abstract":"","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126916943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}