Real-time clinical note monitoring to detect conditions for rapid follow-up: A case study of clinical trial enrollment in drug-induced torsades de pointes and Stevens-Johnson syndrome

DeLozier, Sarah; Speltz, Peter; Brito, Jason; Tang, Leigh Anne; Wang, Janey; Smith, Joshua; Giuse, Dario A.; Phillips, Elizabeth; Williams, Kristina; Strickland, Teresa; Davogustto, Giovanni; Roden, Dan M.; Denny, Joshua C.

doi:10.1093/jamia/ocaa213

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…We anticipate that future applications of our important finding would encourage real-time monitoring of non-adherent instances within clinical research studies in order to intervene early and prevent withdrawal from the study. The concept of real-time monitoring stems from successful remote monitoring to prevent and document adverse events, especially in phase 1, 2, and 3 clinical trials [19][20][21][22]. We specifically identified that the "adherence audit" should occur after the first non-adherence to identify the barriers of the participant or the coordinating team to implement immediate solutions.…”

Section: Discussionmentioning

confidence: 99%

Predictors of participants’ retention—socioeconomic factors or nonadherence: insights from a urological clinical prospective study

Wheeler¹,

Garg²,

Kaushik³

et al. 2022

Trials

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Background To investigate various patient-level variables, specifically socioeconomic status, as risk factors for withdrawal in a recently completed clinical study. We specifically investigated a non-interventional prospective study assessing the role of novel imaging as a biomarker for cancer upgradation in prostate cancer for this objective. Methods In this retrospective analysis, we assessed the association between various patient-level factors including clinic-demographic factors, socioeconomic status, and the number of non-adherences with the participants’ retention or withdrawal from the study. For socioeconomic status (SES), we used the zip code–based Economic Innovation Group Distressed Community Index (DCI) which classifies into five even distress tiers: prosperous, comfortable, mid-tier, at-risk, or distressed. Low SES was defined as those with a DCI Distress tier of at-risk or distressed. We compared values between the two retention and withdrawal groups using t-test, chi-square test, and logistic regression analysis. Results Of 273 men screened, 123 men were enrolled. Among them, 86.2% (106/123) retained through the study whereas 13.8% (17/123) withdrew from the study. The mean (SD) age was 64 (6.4) years. Overall, 31.7% (39/123) were Hispanics and 24.3% (30/123) were African Americans. The median (IQR) DCI score was 34 (10.3, 68.1) and 30.8% (38/123) of patients belonged to low SES. The median DCI score in participants who retained in the study was statistically similar to those who withdrew from the study (p=0.4). Neither the DCI tiers (p=0.7) nor the low SES (p=0.9) were associated with participants’ retention or withdrawal of the study. In terms of non-adherence, all participants in the withdrawn group had at least one non-adherent event compared to 48.1% in the retained group (p<0.001). Repetitive non-adherence was significantly higher in participants who withdrew from the study vs those who retained in the study [88.2% vs 16.9%, p <0.001]. On multivariate logistic regression analysis, the number of non-adherences (OR=12.5, p<0.001) and not DCI (OR=0.99, p=0.7) appeared to be an independent predictor for participants’ retention or withdrawal from the study. Conclusions Expanding diverse inclusion and limiting withdrawal with real-time non-adherence monitoring will lead to more efficient clinical research and greater generalizability of results.

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Section: Discussionmentioning

confidence: 99%

Predictors of participants’ retention—socioeconomic factors or nonadherence: insights from a urological clinical prospective study

Wheeler¹,

Garg²,

Kaushik³

et al. 2022

Trials

View full text Add to dashboard Cite

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“…Federman et al [53], 2017; Goehler et al [54], 2019; Horton et al [55], 2018; Howell et al [56], 2014; Klang et al [57], 2021; Li et al [58], 2020; Lindholm et al [59], 2010; Melnick et al [60], 2022; Milne Adult et al [61], 2020; Mitchell et al [62], 2022; Mulhem et al [63], 2020; Park et al [64], 2021; Ritchey et al [65], 2016; Rose et al [66], 2018; Ryan et al [67], 2021; Shelley et al [68], 2017; Sroujieh et al [69], 2016; Toscos et al [70], 2020; Toscos et al [71], 2020; Willis et al [72], 2022 Del Fiol et al [73], 2020; DeLozier [74], 2021; Jones et al [75], 2022; Jose et al [76], 2020; Keizur et al [77], 2022; Kolb et al [78], 2016; Lilih et al [79], 2017; Nikolic et al [80], 2017; Ray et al [81], 2018; Adult and pediatric Shelden et al [82], 2021; Siff and Emerman [83], 2016; Sonstein et al [84], 2014; Straub et al [85], 2013; Stutz et al [86], 2018; Tham et al [87], 2016; Wendel et al [88], 2023; Zorn et al [89], 2022 Tsai et al [90], 2015 Not specified Goldberg et al [91], 2016; Hojat et al [92], 2020; Simon et al [93], 2023; Tham et al…”

Section: Clinical Decision Supportmentioning

confidence: 99%

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

Chishtie,

Sapiro,

Wiebe

et al. 2023

J Med Internet Res

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Background Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research. Objective The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences. Methods We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results. Results We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized. Conclusions The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

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“…Many investigators built clinical NLP systems using the UMLS within their institutions, such as KnowledgeMap and Apache cTAKES [ 19 , 20 ]. Recently, such systems were leveraged to provide real-time NLP-based support for serious rare adverse drug events (Steven Johnson Syndrome and torsade de pointes) with known genetic influences [ 21 ].…”

Section: Electronic Health Records - a Real World Platform To Enable ...mentioning

confidence: 99%

The U.S. National Library of Medicine’s impact on precision and genomic medicine

Huan

Denny

2022

ISU

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Precision medicine offers the potential to improve health through deeper understandings of the lifestyle, biological, and environmental influences on health. Under Dr. Donald A. B. Lindberg’s leadership, the U.S. National Library of Medicine (NLM) has developed the central reference resources for biomedical research and molecular laboratory medicine that enable precision medicine. The hosting and curation of biomedical knowledge repositories and data by NLM enable quality information reachable for providers and researchers throughout the world. NLM has been supporting the innovation of electronic health record systems to implement computability and secondary use for biomedical research, producing the scale of linked health and molecular datasets necessary for precision medicine discovery.

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Real-time clinical note monitoring to detect conditions for rapid follow-up: A case study of clinical trial enrollment in drug-induced torsades de pointes and Stevens-Johnson syndrome

Cited by 7 publications

References 15 publications

Predictors of participants’ retention—socioeconomic factors or nonadherence: insights from a urological clinical prospective study

Predictors of participants’ retention—socioeconomic factors or nonadherence: insights from a urological clinical prospective study

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

The U.S. National Library of Medicine’s impact on precision and genomic medicine

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