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

{"title":"Microaggression clues from social media: revealing and counteracting the suppression of women's health care.","authors":"Hyeyoung Ryu,&nbsp;Wanda Pratt","doi":"10.1093/jamia/ocab208","DOIUrl":"https://doi.org/10.1093/jamia/ocab208","url":null,"abstract":"<p><strong>Objective: </strong>The purpose of this study was to demonstrate how analyzing social media posts can uncover microaggressions and generate new cultural insights. We explore why Korean women hesitate to seek recommended gynecological care and how microaggressions visible in social media reveal insights for counteracting such harmful messaging.</p><p><strong>Materials and methods: </strong>We scraped the posts and responses on social media related to unmarried women's uncomfortableness or unpleasantness in receiving gynecological care. We conducted content analyses of the posts and responses with the microaggression framework to identify both the types of microaggressions occurring within and outside the clinic as well as the responsible perpetrators. With an open-coding and subsequent deductive coding approach, we further investigated the socio-cultural context for receiving gynecological care as an unmarried woman in South Korea.</p><p><strong>Results: </strong>Our analysis uncovered that mothers, male partners, and superficially supportive social media responders contribute to pre- and post-visit microaggressions toward unmarried women seeking gynecological care whereas healthcare providers contribute to only mid-visit microaggressions. We also exposed how social media was not only revealing but also reinforcing the suppression of women's health care.</p><p><strong>Discussion: </strong>Mid-visit microaggressions are currently addressed by cultural competence education, but pre- and post-visit microaggressions are overlooked. We uncover the gaps in current practices of informatics and public health methods and suggest ways to counteract online and offline microaggressions.</p><p><strong>Conclusions: </strong>Social media provides valuable information about the cultural context of health care and should be used as a source of insights for targeted interventions to improve health care, in this case for unmarried Korean women.</p>","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"257-270"},"PeriodicalIF":6.4,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8757297/pdf/ocab208.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39594966","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":"Promoting health equity for deaf patients through the electronic health record.","authors":"Tyler G James,&nbsp;Meagan K Sullivan,&nbsp;Joshua D Butler,&nbsp;Michael M McKee","doi":"10.1093/jamia/ocab239","DOIUrl":"https://doi.org/10.1093/jamia/ocab239","url":null,"abstract":"<p><p>Language status can be conceptualized as an equity-relevant variable, particularly for non-English-speaking populations. Deaf and hard-of-hearing (DHH) individuals who use American Sign Language (ASL) to communicate comprise one such group and are understudied in health services research. DHH individuals are at high-risk of receiving lower-quality care due to ineffective patient-provider communication. This perspective outlines barriers to health equity research serving DHH ASL-users due to systems developed by large-scale informatics networks (eg, the Patient-Centered Clinical Outcomes Research Network), and institutional policies on self-serve cohort discovery tools. We list potential to help adequate capture of language status of DHH ASL-users to promote health equity for this population.</p>","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"213-216"},"PeriodicalIF":6.4,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714292/pdf/ocab239.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39594962","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":"Development and validation of a prediction model for actionable aspects of frailty in the text of clinicians' encounter notes.","authors":"Jacob A Martin,&nbsp;Andrew Crane-Droesch,&nbsp;Folasade C Lapite,&nbsp;Joseph C Puhl,&nbsp;Tyler E Kmiec,&nbsp;Jasmine A Silvestri,&nbsp;Lyle H Ungar,&nbsp;Bruce P Kinosian,&nbsp;Blanca E Himes,&nbsp;Rebecca A Hubbard,&nbsp;Joshua M Diamond,&nbsp;Vivek Ahya,&nbsp;Michael W Sims,&nbsp;Scott D Halpern,&nbsp;Gary E Weissman","doi":"10.1093/jamia/ocab248","DOIUrl":"https://doi.org/10.1093/jamia/ocab248","url":null,"abstract":"<p><strong>Objective: </strong>Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes.</p><p><strong>Materials and methods: </strong>We used an active learning strategy to select notes from the EHR and annotated each sentence for 4 actionable aspects of frailty: respiratory impairment, musculoskeletal problems, fall risk, and nutritional deficiencies. We compared the performance of regression, tree-based, and neural network models to predict the labels for each sentence. We evaluated performance with the scaled Brier score (SBS), where 1 is perfect and 0 is uninformative, and the positive predictive value (PPV).</p><p><strong>Results: </strong>We manually annotated 155 952 sentences from 326 patients. Elastic net regression had the best performance across all 4 frailty aspects (SBS 0.52, 95% confidence interval [CI] 0.49-0.54) followed by random forests (SBS 0.49, 95% CI 0.47-0.51), and multi-task neural networks (SBS 0.39, 95% CI 0.37-0.42). For the elastic net model, the PPV for identifying the presence of respiratory impairment was 54.8% (95% CI 53.3%-56.6%) at a sensitivity of 80%.</p><p><strong>Discussion: </strong>Classification models using EHR notes can effectively identify actionable aspects of frailty among patients living with chronic lung disease. Regression performed better than random forest and neural network models.</p><p><strong>Conclusions: </strong>NLP-based models offer promising support to population health management programs that seek to identify and refer community-dwelling patients with frailty for evidence-based interventions.</p>","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"109-119"},"PeriodicalIF":6.4,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714261/pdf/ocab248.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39743835","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 cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data.","authors":"Ziyan Yin,&nbsp;Jiayi Tong,&nbsp;Yong Chen,&nbsp;Rebecca A Hubbard,&nbsp;Cheng Yong Tang","doi":"10.1093/jamia/ocab222","DOIUrl":"https://doi.org/10.1093/jamia/ocab222","url":null,"abstract":"<p><strong>Objectives: </strong>Electronic health records (EHR) are commonly used for the identification of novel risk factors for disease, often referred to as an association study. A major challenge to EHR-based association studies is phenotyping error in EHR-derived outcomes. A manual chart review of phenotypes is necessary for unbiased evaluation of risk factor associations. However, this process is time-consuming and expensive. The objective of this paper is to develop an outcome-dependent sampling approach for designing manual chart review, where EHR-derived phenotypes can be used to guide the selection of charts to be reviewed in order to maximize statistical efficiency in the subsequent estimation of risk factor associations.</p><p><strong>Materials and methods: </strong>After applying outcome-dependent sampling, an augmented estimator can be constructed by optimally combining the chart-reviewed phenotypes from the selected patients with the error-prone EHR-derived phenotype. We conducted simulation studies to evaluate the proposed method and applied our method to data on colon cancer recurrence in a cohort of patients treated for a primary colon cancer in the Kaiser Permanente Washington (KPW) healthcare system.</p><p><strong>Results: </strong>Simulations verify the coverage probability of the proposed method and show that, when disease prevalence is less than 30%, the proposed method has smaller variance than an existing method where the validation set for chart review is uniformly sampled. In addition, from design perspective, the proposed method is able to achieve the same statistical power with 50% fewer charts to be validated than the uniform sampling method, thus, leading to a substantial efficiency gain in chart review. These findings were also confirmed by the application of the competing methods to the KPW colon cancer data.</p><p><strong>Discussion: </strong>Our simulation studies and analysis of data from KPW demonstrate that, compared to an existing uniform sampling method, the proposed outcome-dependent method can lead to a more efficient chart review sampling design and unbiased association estimates with higher statistical efficiency.</p><p><strong>Conclusion: </strong>The proposed method not only optimally combines phenotypes from chart review with EHR-derived phenotypes but also suggests an efficient design for conducting chart review, with the goal of improving the efficiency of estimated risk factor associations using EHR data.</p>","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"52-61"},"PeriodicalIF":6.4,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714286/pdf/ocab222.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39829709","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":"Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic.","authors":"Peter Hong,&nbsp;Joshua C Herigon,&nbsp;Colby Uptegraft,&nbsp;Bassem Samuel,&nbsp;D Levin Brown,&nbsp;Jonathan Bickel,&nbsp;Jonathan D Hron","doi":"10.1093/jamia/ocab231","DOIUrl":"https://doi.org/10.1093/jamia/ocab231","url":null,"abstract":"<p><strong>Objective: </strong>This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019 (COVID-19).</p><p><strong>Materials and methods: </strong>We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process.</p><p><strong>Results: </strong>Among 233 patients with COVID-19, our EHR Report identified 4116 patient-HCW pairs, including 2365 (30.0%) of the 7890 pairs detected by the CTT. The EHR Report also revealed 1751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall.</p><p><strong>Conclusions: </strong>Automated methods to screen HCWs for potential exposures to patients with COVID-19 using clinical event data from the EHR (1) are likely to improve epidemiological surveillance by contact tracing programs and (2) represent a viable and readily available strategy that should be considered by other institutions.</p>","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"142-148"},"PeriodicalIF":6.4,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714290/pdf/ocab231.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39497445","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":"My mom got diagnosed with cancer through the MyChart app.","authors":"Aaron Shapiro","doi":"10.1093/jamia/ocab193","DOIUrl":"https://doi.org/10.1093/jamia/ocab193","url":null,"abstract":"","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"217"},"PeriodicalIF":6.4,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714269/pdf/ocab193.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39530101","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":"The quality of social determinants data in the electronic health record: a systematic review.","authors":"Lily A Cook,&nbsp;Jonathan Sachs,&nbsp;Nicole G Weiskopf","doi":"10.1093/jamia/ocab199","DOIUrl":"https://doi.org/10.1093/jamia/ocab199","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH).</p><p><strong>Materials and methods: </strong>We conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process.</p><p><strong>Results: </strong>The most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully.</p><p><strong>Discussion: </strong>The type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups.</p><p><strong>Conclusion: </strong>Consideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.</p>","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"187-196"},"PeriodicalIF":6.4,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1a/f3/ocab199.PMC8714289.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39530102","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":"Response to \"My mom got diagnosed with cancer through the MyChart app\".","authors":"Andrew Gettinger,&nbsp;Teresa Zayas-Cabán","doi":"10.1093/jamia/ocab223","DOIUrl":"https://doi.org/10.1093/jamia/ocab223","url":null,"abstract":"","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"218-219"},"PeriodicalIF":6.4,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714291/pdf/ocab223.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39531695","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":"Dynamics of social corrections to peers sharing COVID-19 misinformation on WhatsApp in Brazil.","authors":"Santosh Vijaykumar,&nbsp;Daniel T Rogerson,&nbsp;Yan Jin,&nbsp;Mariella Silva de Oliveira Costa","doi":"10.1093/jamia/ocab219","DOIUrl":"https://doi.org/10.1093/jamia/ocab219","url":null,"abstract":"<p><strong>Objective: </strong>Online COVID-19 misinformation is a serious concern in Brazil, home to the second-largest WhatsApp user base and the second-highest number of COVID-19 deaths. We examined the extent to which WhatsApp users might be willing to correct their peers who might share COVID-19 misinformation.</p><p><strong>Materials and methods: </strong>We conducted a cross-sectional online survey using Qualtrics among 726 Brazilian adults to identify the types of social correction behaviors (SCBs) and health and technological factors that shape the performance of these behaviors.</p><p><strong>Results: </strong>Brazil's WhatsApp users expressed medium to high levels of willingness to engage in SCBs. We discovered 3 modes of SCBs: correction to the group, correction to the sender only, and passive or no correction. WhatsApp users with lower levels of educational attainment and from younger age groups were less inclined to provide corrections. Lastly, the perceived severity of COVID-19 and the ability to critically evaluate a message were positively associated with providing corrections to either the group or the sender.</p><p><strong>Discussion: </strong>The demographic analyses point to the need to strengthen information literacy among population groups that are younger with lower levels of educational attainment. These efforts could facilitate individual-level contributions to the global fight against misinformation by the World Health Organization in collaboration with member states, social media companies, and civil society.</p><p><strong>Conclusion: </strong>Our study suggests that Brazil's WhatsApp users might be willing to actively respond with feedback when exposed to COVID-19 misinformation by their peers on small-world networks like WhatsApp groups.</p>","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"33-42"},"PeriodicalIF":6.4,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39537699","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":"Evaluation of the International Classification of Health Interventions (ICHI) in the coding of common surgical procedures.","authors":"Kin Wah Fung,&nbsp;Julia Xu,&nbsp;Filip Ameye,&nbsp;Lisa Burelle,&nbsp;Janice MacNeil","doi":"10.1093/jamia/ocab220","DOIUrl":"https://doi.org/10.1093/jamia/ocab220","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the International Classification of Health Interventions (ICHI) in the clinical and statistical use cases.</p><p><strong>Materials and methods: </strong>We identified 300 most-performed surgical procedures as represented by their display names in an electronic health record. For comparison with existing coding systems, we coded the procedures in ICHI, SNOMED CT, International Classification of Diseases (ICD)-10-PCS, and CCI (Canadian Classification of Health Interventions), using postcoordination (modification of existing codes by adding other codes), when applicable. Failure analysis was done for cases where full representation was not achieved. The ICHI encoding was further evaluated for adequacy to support statistical reporting by the Organisation for Economic Co-operation and Development (OECD) and European Union (EU) categories of surgical procedures.</p><p><strong>Results: </strong>After deduplication, 229 distinct procedures remained. Without postcoordination, ICHI achieved full representation in 52.8%. A further 19.2% could be fully represented with postcoordination. SNOMED CT was the best performing overall, with 94.3% full representation without postcoordination, and 99.6% with postcoordination. Failure analysis showed that \"method\" and \"target\" constituted most of the missing information for ICHI encoding. For all OECD/EU surgical categories, ICHI coding was adequate to support statistical reporting. One OECD/EU category (\"Hip replacement, secondary\") required postcoordination for correct assignment.</p><p><strong>Conclusion: </strong>In the clinical use case of capturing information in the electronic health record, ICHI was outperformed by the clinically oriented procedure coding systems (SNOMED CT and CCI), but was comparable to ICD-10-PCS. Postcoordination could be an effective and efficient means of improving coverage. ICHI is generally adequate for the collection of international statistics.</p>","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":" ","pages":"43-51"},"PeriodicalIF":6.4,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714263/pdf/ocab220.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39513485","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}