2015
DOI: 10.1186/s12911-016-0239-x
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The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials

Abstract: BackgroundAn increasing number of clinical trials are conducted in primary care settings. Making better use of existing data in the electronic health records to identify eligible subjects can improve efficiency of such studies. Our study aims to quantify the proportion of eligibility criteria that can be addressed with data in electronic health records and to compare the content of eligibility criteria in primary care with previous work.MethodsEligibility criteria were extracted from primary care studies downl… Show more

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Cited by 56 publications
(66 citation statements)
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“…Whereas, both participants with and without NCI were equally likely to use cell phone for a number of activities, such as phone calls, text messaging, watching videos, and recording videos. Overall, these findings lend support to the notion that cell phone could be used as a tool to accommodate NCI through its use as a new modality of monitoring and treatment support that may include text messaging [37, 56, 60], phone calls [57, 60], video-based eHealth interventions [6163], video (or virtually) observed therapy (VOT) [64, 65] targeted specifically for this at-risk populations.…”
Section: Discussionmentioning
confidence: 60%
“…Whereas, both participants with and without NCI were equally likely to use cell phone for a number of activities, such as phone calls, text messaging, watching videos, and recording videos. Overall, these findings lend support to the notion that cell phone could be used as a tool to accommodate NCI through its use as a new modality of monitoring and treatment support that may include text messaging [37, 56, 60], phone calls [57, 60], video-based eHealth interventions [6163], video (or virtually) observed therapy (VOT) [64, 65] targeted specifically for this at-risk populations.…”
Section: Discussionmentioning
confidence: 60%
“…Recall for the structured queries varied widely across topics (Figure 3). There was 100% recall of word-based query relevant patients on 8 of the 56 topics, greater than 50% recall on 35 of the 56 topics, less then 50% recall on 13 of the 56, one topic (48) with no recall of relevant patients, and two topics with no retrieval at all (22,25).…”
Section: Structured Queriesmentioning
confidence: 98%
“…Another thread of work has focused on making querying easier to carry out, typically through development of natural language or other structured interfaces to the patient data [22][23][24][25]. Other approaches focus on normalizing semantic representation of patient data within the EHR itself [26] and applying deep learning to non-topical characteristics of studies and researchers [27].…”
Section: Introductionmentioning
confidence: 99%
“…In the personal health record and health knowledge sharing system design, Lee et al [20] use the Integral Healthcare Enterprise-Cross Enterprise Document Sharing (IHE-XDS) and the W3C Web Ontology Language (OWL) to maintain the Personal Health Records (PHRs) and collate the useful health Web resources related to the personal diseases. Making better use of existing data in the electronic health records to identify eligible subjects can improve efficiency and quality of medical care [21]. Discovering the knowledge from the medical records may support medical personnel in making clinical decisions and also help improve personalized medicine and care [22].…”
Section: Related Workmentioning
confidence: 99%