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

{"title":"Erratum to: The complex case of EHRs: examining the factors impacting the EHR user experience","authors":"Michael A. Tutty, Lindsey E. Carlasare, Stacy Lloyd, C. Sinsky","doi":"10.1093/jamia/ocz129","DOIUrl":"https://doi.org/10.1093/jamia/ocz129","url":null,"abstract":"","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116827291","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":"Patient preferences for visualization of longitudinal patient-reported outcomes data","authors":"S. Stonbraker, Tiffany Porras, Rebecca Schnall","doi":"10.1093/jamia/ocz189","DOIUrl":"https://doi.org/10.1093/jamia/ocz189","url":null,"abstract":"OBJECTIVE\u0000The study sought to design symptom reports of longitudinal patient-reported outcomes data that are understandable and meaningful to end users.\u0000\u0000\u0000MATERIALS AND METHODS\u0000We completed a 2-phase iterative design and evaluation process. In phase I, we developed symptom reports and refined them according to expert input. End users then completed a survey containing demographics, a measure of health literacy, and items to assess visualization preferences and comprehension of reports. We then collected participants' perspectives on reports through semistructured interviews and modified them accordingly. In phase II, refined reports were evaluated in a survey that included demographics, validated measures of health and graph literacy, and items to assess preferences and comprehension of reports. Surveys were administered using a think-aloud protocol.\u0000\u0000\u0000RESULTS\u0000Fifty-five English- and Spanish-speaking end users, 89.1% of whom had limited health literacy, participated. In phase I, experts recommended improvements and 20 end users evaluated reports. From the feedback received, we added emojis, changed date and font formats, and simplified the y-axis scale of reports. In phase II, 35 end users evaluated refined designs, of whom 94.3% preferred reports with emojis, the favorite being a bar graph combined with emojis, which also promoted comprehension. In both phases, participants literally interpreted reports and provided suggestions for future visualizations.\u0000\u0000\u0000CONCLUSIONS\u0000A bar graph combined with emojis was participants' preferred format and the one that promoted comprehension. Target end users must be included in visualization design to identify literal interpretations of images and ensure final products are meaningful.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128987917","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":"Imputation and characterization of uncoded self-harm in major mental illness using machine learning","authors":"Praveen Kumar, A. Nestsiarovich, S. J. Nelson, B. Kerner, D. Perkins, Christophe Gerard Lambert","doi":"10.1093/jamia/ocz173","DOIUrl":"https://doi.org/10.1093/jamia/ocz173","url":null,"abstract":"Abstract Objective We aimed to impute uncoded self-harm in administrative claims data of individuals with major mental illness (MMI), characterize self-harm incidence, and identify factors associated with coding bias. Materials and Methods The IBM MarketScan database (2003-2016) was used to analyze visit-level self-harm in 10 120 030 patients with ≥2 MMI codes. Five machine learning (ML) classifiers were tested on a balanced data subset, with XGBoost selected for the full dataset. Classification performance was validated via random data mislabeling and comparison with a clinician-derived “gold standard.” The incidence of coded and imputed self-harm was characterized by year, patient age, sex, U.S. state, and MMI diagnosis. Results Imputation identified 1 592 703 self-harm events vs 83 113 coded events, with areas under the curve >0.99 for the balanced and full datasets, and 83.5% agreement with the gold standard. The overall coded and imputed self-harm incidence were 0.28% and 5.34%, respectively, varied considerably by age and sex, and was highest in individuals with multiple MMI diagnoses. Self-harm undercoding was higher in male than in female individuals and increased with age. Substance abuse, injuries, poisoning, asphyxiation, brain disorders, harmful thoughts, and psychotherapy were the main features used by ML to classify visits. Discussion Only 1 of 19 self-harm events was coded for individuals with MMI. ML demonstrated excellent performance in recovering self-harm visits. Male individuals and seniors with MMI are particularly vulnerable to self-harm undercoding and may be at risk of not getting appropriate psychiatric care. Conclusions ML can effectively recover unrecorded self-harm in claims data and inform psychiatric epidemiological and observational studies.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127667075","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":"Uncovering the relationship between food-related discussion on Twitter and neighborhood characteristics","authors":"V.G.Vinod Vydiswaran, Daniel M. Romero, Xinyan Zhao, D. Yu, Iris N. Gomez-Lopez, Jin Xiu Lu, Bradley E. Iott, A. Baylin, E. Jansen, P. Clarke, V. Berrocal, R. Goodspeed, T. Veinot","doi":"10.1093/jamia/ocz181","DOIUrl":"https://doi.org/10.1093/jamia/ocz181","url":null,"abstract":"Abstract Objective Initiatives to reduce neighborhood-based health disparities require access to meaningful, timely, and local information regarding health behavior and its determinants. We examined the validity of Twitter as a source of information for neighborhood-level analysis of dietary choices and attitudes. Materials and Methods We analyzed the “healthiness” quotient and sentiment in food-related tweets at the census tract level, and associated them with neighborhood characteristics and health outcomes. We analyzed keywords driving the differences in food healthiness between the most and least-affluent tracts, and qualitatively analyzed contents of a random sample of tweets. Results Significant, albeit weak, correlations existed between healthiness and sentiment in food-related tweets and tract-level measures of affluence, disadvantage, race, age, U.S. density, and mortality from conditions associated with obesity. Analyses of keywords driving the differences in food healthiness revealed foods high in saturated fat (eg, pizza, bacon, fries) were mentioned more frequently in less-affluent tracts. Food-related discussion referred to activities (eating, drinking, cooking), locations where food was consumed, and positive (affection, cravings, enjoyment) and negative attitudes (dislike, personal struggles, complaints). Discussion Tweet-based healthiness scores largely correlated with offline phenomena in the expected directions. Social media offer less resource-intensive data collection methods than traditional surveys do. Twitter may assist in informing local health programs that focus on drivers of food consumption and could inform interventions focused on attitudes and the food environment. Conclusions Twitter provided weak but significant signals concerning food-related behavior and attitudes at the neighborhood level, suggesting its potential usefulness for informing local health disparity reduction efforts.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133233112","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 impact of drug order complexity on prospective medication order review and verification time","authors":"David S Dakwa, V. Marshall, B. Chaffee","doi":"10.1093/jamia/ocz188","DOIUrl":"https://doi.org/10.1093/jamia/ocz188","url":null,"abstract":"OBJECTIVE\u0000To assess if the amount of time a pharmacist spends verifying medication orders increases as medication orders become more complex.\u0000\u0000\u0000MATERIALS AND METHODS\u0000The study was conducted by observing pharmacist verification of adult medication orders in an academic medical center. Drug order complexity was prospectively defined and validated using a classification system derived from 3 factors: the degree of order variability, ISMP high-alert classification, and a pharmacist perception survey. Screen capture software was used to measure pharmacist order review time for each classification. The annualized volume of low complexity drug orders was used to calculate the potential time savings if these were verified using an alternate system that did not require pharmacist review.\u0000\u0000\u0000RESULTS\u0000The primary study hypothesis was not achieved. Regression results did not show statistical significance for moderate (n = 30, 23.7 seconds, sd = 23.3) or high complexity (n = 30, 18.6 seconds, sd = 23.1) drugs relative to the low complexity drugs (n = 30, 8.0 seconds, sd = 14.4) nor for moderate vs high complexity; (βmoderate vs low = 15.6, P = .113), (βhigh vs low = 10.3, P = .235), (βmoderate vs high = 5.3, P = .737). The sensitivity analysis showed statistical significance in the high vs low comparison (βhigh vs low = 13.8, P = .017).\u0000\u0000\u0000DISCUSSION\u0000This study showed that verifying pharmacists spent less time than projected to verify medication orders of different complexities, but the time did not correlate with the classifications used in our complexity scale. Several mitigating factors, including operational aspects associated with timing antimicrobial orders, likely influenced order verification time. These factors should be evaluated in future studies which seek to define drug order complexity and optimize pharmacist time spent in medication order verification.\u0000\u0000\u0000CONCLUSION\u0000The findings suggest that there may be other factors involved in pharmacist decision-making that should be considered when categorizing drugs by perceived complexity.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114958021","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":"Electronic consultations (E-consults) and their outcomes: a systematic review","authors":"Varsha G. Vimalananda, J. Orlander, J. Orlander, M. Afable, M. Afable, B. G. Fincke, Amanda K. Solch, S. Rinne, Eun Ji Kim, Eun Ji Kim, S. Cutrona, Dylan D. Thomas, Judith L. Strymish, Judith L. Strymish, S. Simon, S. Simon","doi":"10.1093/jamia/ocz185","DOIUrl":"https://doi.org/10.1093/jamia/ocz185","url":null,"abstract":"OBJECTIVE\u0000Electronic consultations (e-consults) are clinician-to-clinician communications that may obviate face-to-face specialist visits. E-consult programs have spread within the US and internationally despite limited data on outcomes. We conducted a systematic review of the recent peer-reviewed literature on the effect of e-consults on access, cost, quality, and patient and clinician experience and identified the gaps in existing research on these outcomes.\u0000\u0000\u0000MATERIALS AND METHODS\u0000We searched 4 databases for empirical studies published between 1/1/2015 and 2/28/2019 that reported on one or more outcomes of interest. Two investigators reviewed titles and abstracts. One investigator abstracted information from each relevant article, and another confirmed the abstraction. We applied the GRADE criteria for the strength of evidence for each outcome.\u0000\u0000\u0000RESULTS\u0000We found only modest empirical evidence for effectiveness of e-consults on important outcomes. Most studies are observational and within a single health care system, and comprehensive assessments are lacking. For those outcomes that have been reported, findings are generally positive, with mixed results for clinician experience. These findings reassure but also raise concern for publication bias.\u0000\u0000\u0000CONCLUSION\u0000Despite stakeholder enthusiasm and encouraging results in the literature to date, more rigorous study designs applied across all outcomes are needed. Policy makers need to know what benefits may be expected in what contexts, so they can define appropriate measures of success and determine how to achieve them.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126935425","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":"An augmented estimation procedure for EHR-based association studies accounting for differential misclassification","authors":"Jiayi Tong, Jing Huang, Jessica Chubak, Xuan Wang, J. Moore, R. Hubbard, Yong Chen","doi":"10.1093/jamia/ocz180","DOIUrl":"https://doi.org/10.1093/jamia/ocz180","url":null,"abstract":"OBJECTIVES\u0000The ability to identify novel risk factors for health outcomes is a key strength of electronic health record (EHR)-based research. However, the validity of such studies is limited by error in EHR-derived phenotypes. The objective of this study was to develop a novel procedure for reducing bias in estimated associations between risk factors and phenotypes in EHR data.\u0000\u0000\u0000MATERIALS AND METHODS\u0000The proposed method combines the strengths of a gold-standard phenotype obtained through manual chart review for a small validation set of patients and an automatically-derived phenotype that is available for all patients but is potentially error-prone (hereafter referred to as the algorithm-derived phenotype). An augmented estimator of associations is obtained by optimally combining these 2 phenotypes. We conducted simulation studies to evaluate the performance of the augmented estimator and conducted an analysis of risk factors for second breast cancer events using data on a cohort from Kaiser Permanente Washington.\u0000\u0000\u0000RESULTS\u0000The proposed method was shown to reduce bias relative to an estimator using only the algorithm-derived phenotype and reduce variance compared to an estimator using only the validation data.\u0000\u0000\u0000DISCUSSION\u0000Our simulation studies and real data application demonstrate that, compared to the estimator using validation data only, the augmented estimator has lower variance (ie, higher statistical efficiency). Compared to the estimator using error-prone EHR-derived phenotypes, the augmented estimator has smaller bias.\u0000\u0000\u0000CONCLUSIONS\u0000The proposed estimator can effectively combine an error-prone phenotype with gold-standard data from a limited chart review in order to improve analyses of risk factors using EHR data.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628167","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":"New approaches to cohort selection","authors":"Amber Stubbs, Özlem Uzuner","doi":"10.1093/jamia/ocz174","DOIUrl":"https://doi.org/10.1093/jamia/ocz174","url":null,"abstract":"Cohort selection for clinical trials is a critical component of modern medicine, yet it remains one of the most difficult, time-consuming, and expensive aspects of testing new treatments and interventions. Each clinical trial defines inclusion and exclusion criteria that de-scribe the required patient population for the trial to accurately de-termine efficacy of the treatment. These criteria can be broad, limited only to specific ages or genders, or can be very specific, re-quiring certain medications be taken in a time period, or certain intentions on the parts of the patients (ie, an intention to become pregnant).","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129924435","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":"Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease","authors":"L. Bastarache, J. Hughey, J. Goldstein, Julie A Bastraache, Satya N. Das, Neil Zaki, Chenjie Zeng, Leigh Anne Tang, D. Roden, J. Denny","doi":"10.1093/jamia/ocz179","DOIUrl":"https://doi.org/10.1093/jamia/ocz179","url":null,"abstract":"OBJECTIVE\u0000The Phenotype Risk Score (PheRS) is a method to detect Mendelian disease patterns using phenotypes from the electronic health record (EHR). We compared the performance of different approaches mapping EHR phenotypes to Mendelian disease features.\u0000\u0000\u0000MATERIALS AND METHODS\u0000PheRS utilizes Mendelian diseases descriptions annotated with Human Phenotype Ontology (HPO) terms. In previous work, we presented a map linking phecodes (based on International Classification of Diseases [ICD]-Ninth Revision) to HPO terms. For this study, we integrated ICD-Tenth Revision codes and lab data. We also created a new map between HPO terms using customized groupings of ICD codes. We compared the performance with cases and controls for 16 Mendelian diseases using 2.5 million de-identified medical records.\u0000\u0000\u0000RESULTS\u0000PheRS effectively distinguished cases from controls for all 15 positive controls and all approaches tested (P < 4 × 1016). Adding lab data led to a statistically significant improvement for 4 of 14 diseases. The custom ICD groupings improved specificity, leading to an average 8% increase for precision at 100 (-2% to 22%). Eight of 10 adults with cystic fibrosis tested had PheRS in the 95th percentile prio to diagnosis.\u0000\u0000\u0000DISCUSSION\u0000Both phecodes and custom ICD groupings were able to detect differences between affected cases and controls at the population level. The ICD map showed better precision for the highest scoring individuals. Adding lab data improved performance at detecting population-level differences.\u0000\u0000\u0000CONCLUSIONS\u0000PheRS is a scalable method to study Mendelian disease at the population level using electronic health record data and can potentially be used to find patients with undiagnosed Mendelian disease.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115157202","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":"Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods","authors":"Hans Moen, K. Hakala, Laura-Maria Peltonen, Henry Suhonen, Filip Ginter, T. Salakoski, S. Salanterä","doi":"10.1093/jamia/ocz150","DOIUrl":"https://doi.org/10.1093/jamia/ocz150","url":null,"abstract":"Abstract Objective This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developing a computer system that can assist in incorporating suitable subject headings that reflect the documented topics. Central in this study is performance evaluation of several text classification methods to assess the feasibility of developing such a system. Materials and Methods Seven text classification methods are evaluated using a corpus of approximately 0.5 million nursing notes (5.5 million sentences) with 676 unique headings extracted from a Finnish university hospital. Several of these methods are based on artificial neural networks. Evaluation is first done in an automatic manner for all methods, then a manual error analysis is done on a sample. Results We find that a method based on a bidirectional long short-term memory network performs best with an average recall of 0.5435 when allowed to suggest 1 subject heading per sentence and 0.8954 when allowed to suggest 10 subject headings per sentence. However, other methods achieve comparable results. The manual analysis indicates that the predictions are better than what the automatic evaluation suggests. Conclusions The results indicate that several of the tested methods perform well in suggesting the most appropriate subject headings on sentence level. Thus, we find it feasible to develop a text classification system that can support the use of standardized terminologies and save nurses time and effort on care documentation.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127601670","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}