Answers to questions posed during daily patient care are more likely to be answered by UpToDate than PubMed.

BACKGROUND: UpToDate and PubMed are popular sources for medical information. Data regarding the efficiency of PubMed and UpToDate in daily medical care are lacking. OBJECTIVE: The purpose of this observational study was to describe the percentage of answers retrieved by these information sources, comparing search results with regard to different medical topics and the time spent searching for an answer. METHODS: A total of 40 residents and 30 internists in internal medicine working in an academic medical center searched PubMed and UpToDate using an observation portal during daily medical care. The information source used for searching and the time needed to find an answer to the question were recorded by the portal. Information was provided by searchers regarding the topic of the question, the situation that triggered the question, and whether an answer was found. RESULTS: We analyzed 1305 patient-related questions sent to PubMed and/or UpToDate between October 1, 2005 and March 31, 2007 using our portal. A complete answer was found in 594/1125 (53%) questions sent to PubMed or UpToDate. A partial or full answer was obtained in 729/883 (83%) UpToDate searches and 152/242 (63%) PubMed searches (P < .001). UpToDate answered more questions than PubMed on all major medical topics, but a significant difference was detected only when the question was related to etiology (P < .001) or therapy (P = .002). Time to answer was 241 seconds (SD 24) for UpToDate and 291 seconds (SD 7) for PubMed. CONCLUSIONS: Specialists and residents in internal medicine generally use less than 5 minutes to answer patient-related questions in daily care. More questions are answered using UpToDate than PubMed on all major medical topics.

Analysis of queries sent to PubMed at the point of care: observation of search behaviour in a medical teaching hospital.

BACKGROUND: The use of PubMed to answer daily medical care questions is limited because it is challenging to retrieve a small set of relevant articles and time is restricted. Knowing what aspects of queries are likely to retrieve relevant articles can increase the effectiveness of PubMed searches. The objectives of our study were to identify queries that are likely to retrieve relevant articles by relating PubMed search techniques and tools to the number of articles retrieved and the selection of articles for further reading. METHODS: This was a prospective observational study of queries regarding patient-related problems sent to PubMed by residents and internists in internal medicine working in an Academic Medical Centre. We analyzed queries, search results, query tools (Mesh, Limits, wildcards, operators), selection of abstract and full-text for further reading, using a portal that mimics PubMed. RESULTS: PubMed was used to solve 1121 patient-related problems, resulting in 3205 distinct queries. Abstracts were viewed in 999 (31%) of these queries, and in 126 (39%) of 321 queries using query tools. The average term count per query was 2.5. Abstracts were selected in more than 40% of queries using four or five terms, increasing to 63% if the use of four or five terms yielded 2-161 articles. CONCLUSION: Queries sent to PubMed by physicians at our hospital during daily medical care contain fewer than three terms. Queries using four to five terms, retrieving less than 161 article titles, are most likely to result in abstract viewing. PubMed search tools are used infrequently by our population and are less effective than the use of four or five terms. Methods to facilitate the formulation of precise queries, using more relevant terms, should be the focus of education and research.

External validation of EPICON: a grouping system for estimating morbidity rates using electronic medical records.

OBJECTIVE: To externally validate EPICON, a computerized system for grouping diagnoses from EMRs in general practice into episodes of care. These episodes can be used for estimating morbidity rates. DESIGN: Comparative observational study. MEASUREMENTS: Morbidity rates from an independent dataset, based on episode-oriented EMRs, were used as the gold standard. The EMRs in this dataset contained diagnoses which were manually grouped by GPs. The authors ungrouped these diagnoses and regrouped them automatically into episodes using EPICON. The authors then used these episodes to estimate morbidity rates that were compared to the gold standard. The differences between the two sets of morbidity rates were calculated and the authors analyzed large as well as structural differences to establish possible causes. RESULTS: In general, the morbidity rates based on EPICON deviate only slightly from the gold standard. Out of 675 diagnoses, 36 (5%) were considered to be deviating diagnoses. The deviating diagnoses showed differences for two main reasons: "differences in rules between the two methods of episode construction" and "inadequate performance of EPICON." CONCLUSION: The EPICON system performs well for the large majority of the morbidity rates. We can therefore conclude that EPICON is useful for grouping episodes to estimate morbidity rates using EMRs from general practices. Morbidity rates of diseases with a broad range of symptoms should, however, be interpreted cautiously.

Development of a case-based system for grouping diagnoses in general practice.

INTRODUCTION: This article describes the development of EPICON; an application to group ICPC-coded diagnoses from electronic medical records in general practice into episodes of care. These episodes can be used to estimate prevalence and incidence rates. METHODS: We used data from 89 practices that participated in the Dutch National Survey of General Practice. Additionally, we held interviews with seven experts, and studied documentation to establish the requirements of the application and to develop the design. We then performed a formative evaluation by assessing incorrectly grouped diagnoses. RESULTS: EPICON is based on a combination of logical expressions, a decision table, and information extracted from individual cases by case-based reasoning. EPICON is able to group all diagnoses in the selected 89 practices, and groups 95% correctly. CONCLUSION: The results cautiously indicate that EPICONs performance will probably be adequate for the purpose of estimating morbidity rates in general practice.