Automated identification of molecular effects of drugs (AIMED).

INTRODUCTION: Genomic profiling information is frequently available to oncologists, enabling targeted cancer therapy. Because clinically relevant information is rapidly emerging in the literature and elsewhere, there is a need for informatics technologies to support targeted therapies. To this end, we have developed a system for Automated Identification of Molecular Effects of Drugs, to help biomedical scientists curate this literature to facilitate decision support. OBJECTIVES: To create an automated system to identify assertions in the literature concerning drugs targeting genes with therapeutic implications and characterize the challenges inherent in automating this process in rapidly evolving domains. METHODS: We used subject-predicate-object triples (semantic predications) and co-occurrence relations generated by applying the SemRep Natural Language Processing system to MEDLINE abstracts and ClinicalTrials.gov descriptions. We applied customized semantic queries to find drugs targeting genes of interest. The results were manually reviewed by a team of experts. RESULTS: Compared to a manually curated set of relationships, recall, precision, and F2 were 0.39, 0.21, and 0.33, respectively, which represents a 3- to 4-fold improvement over a publically available set of predications (SemMedDB) alone. Upon review of ostensibly false positive results, 26% were considered relevant additions to the reference set, and an additional 61% were considered to be relevant for review. Adding co-occurrence data improved results for drugs in early development, but not their better-established counterparts. CONCLUSIONS: Precision medicine poses unique challenges for biomedical informatics systems that help domain experts find answers to their research questions. Further research is required to improve the performance of such systems, particularly for drugs in development.

Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base.

BACKGROUND: Correlation of data within electronic health records is necessary for implementation of various clinical decision support functions, including patient summarization. A key type of correlation is linking medications to clinical problems; while some databases of problem-medication links are available, they are not robust and depend on problems and medications being encoded in particular terminologies. Crowdsourcing represents one approach to generating robust knowledge bases across a variety of terminologies, but more sophisticated approaches are necessary to improve accuracy and reduce manual data review requirements. OBJECTIVE: We sought to develop and evaluate a clinician reputation metric to facilitate the identification of appropriate problem-medication pairs through crowdsourcing without requiring extensive manual review. APPROACH: We retrieved medications from our clinical data warehouse that had been prescribed and manually linked to one or more problems by clinicians during e-prescribing between June 1, 2010 and May 31, 2011. We identified measures likely to be associated with the percentage of accurate problem-medication links made by clinicians. Using logistic regression, we created a metric for identifying clinicians who had made greater than or equal to 95% appropriate links. We evaluated the accuracy of the approach by comparing links made by those physicians identified as having appropriate links to a previously manually validated subset of problem-medication pairs. RESULTS: Of 867 clinicians who asserted a total of 237,748 problem-medication links during the study period, 125 had a reputation metric that predicted the percentage of appropriate links greater than or equal to 95%. These clinicians asserted a total of 2464 linked problem-medication pairs (983 distinct pairs). Compared to a previously validated set of problem-medication pairs, the reputation metric achieved a specificity of 99.5% and marginally improved the sensitivity of previously described knowledge bases. CONCLUSION: A reputation metric may be a valuable measure for identifying high quality clinician-entered, crowdsourced data.

Developing an ST-elevation myocardial infarction system of care in Dallas County.

BACKGROUND: The American Heart Association Caruth Initiative (AHACI) is a multiyear project to increase the speed of coronary reperfusion and create an integrated system of care for patients with ST-elevation myocardial infarction (STEMI) in Dallas County, TX. The purpose of this study was to determine if the AHACI improved key performance metrics, that is, door-to-balloon (D2B) and symptom-onset-to-balloon times, for nontransfer patients with STEMI. METHODS: Hospital patient data were obtained through the National Cardiovascular Data Registry Action Registry-Get With The Guidelines, and prehospital data came from emergency medical services (EMS) agencies through their electronic Patient Care Record systems. Initial D2B and symptom-onset-to-balloon times for nontransfer primary percutaneous coronary intervention (PCI) STEMI care were explored using descriptive statistics, generalized linear models, and logistic regression. RESULTS: Data were collected by 15 PCI-capable Dallas hospitals and 24 EMS agencies. In the first 18 months, there were 3,853 cases of myocardial infarction, of which 926 (24%) were nontransfer patients with STEMI undergoing primary PCI. D2B time decreased significantly (P < .001), from a median time of 74 to 64 minutes. Symptom-onset-to-balloon time decreased significantly (P < .001), from a median time of 195 to 162 minutes. CONCLUSION: The AHACI has improved the system of STEMI care for one of the largest counties in the United States, and it demonstrates the benefits of integrating EMS and hospital data, implementing standardized training and protocols, and providing benchmarking data to hospitals and EMS agencies.