Clinician Attitudes Toward and Use of Electronic Problem Lists: A Thematic Analysis

Background: The clinical problem list is an important tool for clinical decision making, quality measurement and clinical decision support; however, problem lists are often incomplete and provider attitudes towards the problem list are poorly understood. Methods: An ethnographic study of healthcare providers conducted from April 2009 to January 2010 was carried out among academic and community outpatient medical practices in the Greater Boston area across a wide range of medical and surgical specialties. Attitudes towards the problem list were then analyzed using grounded theory methods. Results: Attitudes were variable, and dimensions of variations fit into nine themes: workflow, ownership and responsibility, relevance, uses, content, presentation, accuracy, alternatives, support/education and one cross-cutting theme of culture. Conclusions: Significant variation was observed in clinician attitudes towards and use of the electronic patient problem list. Clearer guidance and best practices for problem list utilization are needed

An automated technique for identifying associations between medications, laboratory results and problems

AbstractBackgroundThe patient problem list is an important component of clinical medicine. The problem list enables decision support and quality measurement, and evidence suggests that patients with accurate and complete problem lists may have better outcomes. However, the problem list is often incomplete.ObjectiveTo determine whether association rule mining, a data mining technique, has utility for identifying associations between medications, laboratory results and problems. Such associations may be useful for identifying probable gaps in the problem list.DesignAssociation rule mining was performed on structured electronic health record data for a sample of 100,000 patients receiving care at the Brigham and Women’s Hospital, Boston, MA. The dataset included 272,749 coded problems, 442,658 medications and 11,801,068 laboratory results.MeasurementsCandidate medication-problem and laboratory-problem associations were generated using support, confidence, chi square, interest, and conviction statistics. High-scoring candidate pairs were compared to a gold standard: the Lexi-Comp drug reference database for medications and Mosby’s Diagnostic and Laboratory Test Reference for laboratory results.ResultsWe were able to successfully identify a large number of clinically accurate associations. A high proportion of high-scoring associations were adjudged clinically accurate when evaluated against the gold standard (89.2% for medications with the best-performing statistic, chi square, and 55.6% for laboratory results using interest).ConclusionAssociation rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches

Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial

Background: Accurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date. Objective: To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation. Study Design and Methods: Inference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009–5/2010) and intervention (5/2010–11/2010) periods. Results: 17,043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6,277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions. Conclusion: Problem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement

A novel MAP3K7CL-ERG fusion in a molecularly confirmed case of dermatofibrosarcoma protuberans with fibrosarcomatous transformation

Dermatofibrosarcoma protuberans (DFSP) is a translocation-associated, low-grade sarcoma with fibroblastic differentiation. It is the most common superficial sarcoma, almost exclusively arising within the dermis. In a minority of cases, there is a transition from the conventional morphology to a fibrosarcomatous pattern, known as a fibrosarcomatous DFSP (FS-DFSP). Although a number of different molecular alterations have been described to account for this transformation, it remains poorly understood. Herein we report the first case of a FS-DFSP with a fusion between ERG, an ETS family transcription factor, and MAP3K7CL, a kinase gene rarely observed in fusion gene events