ArticleApplied clinical informatics2015
Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs.
Article in Applied clinical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Crowdsourcing Electronic Health Record Improvements at Scale across an Integrated Health Care Delivery System.Applied clinical informatics · 2023Article
- OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.BMC medical informatics and decision making · 2018Article
- New Problems - New Solutions: A Never Ending Story. Findings from the Clinical Information Systems Perspective for 2015.Yearbook of medical informatics · 2016Article
- Data-mining to build a knowledge representation store for clinical decision support. Studies on curation and validation based on machine performance in multiple choice medical licensing examinations.Computers in biology and medicine · 2016Article
- Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study.JMIR research protocols · 2016Article
- Crowdsourcing Diagnosis for Patients With Undiagnosed Illnesses: An Evaluation of CrowdMed.Journal of medical Internet research · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundClinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as clinical summarization. However, developing these knowledge bases can be challenging.
objectiveWe sought to validate a previously developed crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large, non-university health care system with a widely used, commercially available electronic health record.
methodsWe first retrieved medications and problems entered in the electronic health record by clinicians during routine care during a six month study period. Following the previously published approach, we calculated the link frequency and link ratio for each pair then identified a threshold cutoff for estimated problem-medication pair appropriateness through clinician review; problem-medication pairs meeting the threshold were included in the resulting knowledge base. We selected 50 medications and their gold standard indications to compare the resulting knowledge base to the pilot knowledge base developed previously and determine its recall and precision.
resultsThe resulting knowledge base contained 26,912 pairs, had a recall of 62.3% and a precision of 87.5%, and outperformed the pilot knowledge base containing 11,167 pairs from the previous study, which had a recall of 46.9% and a precision of 83.3%.
conclusionsWe validated the crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large non-university health care system with a widely used, commercially available electronic health record, indicating that the approach may be generalizable across healthcare settings and clinical systems. Further research is necessary to better evaluate the knowledge, to compare crowdsourcing with other approaches, and to evaluate if incorporating the knowledge into electronic health records improves patient outcomes.
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