PubMed · 15681995
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Kathryn H Bowles. Sharpen decision-making with computerized support tools.. https://doi.org/10.1097/00024665-200501000-00011
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OBJECTIVE: Computerized physician order entry with clinical decision support has been shown to improve medication safety in adult inpatients, but few data are available regarding its usefulness in the long-term care setting. The objective of this study was to examine opportunities for improving medication safety in that clinical setting by determining the proportion of medication orders that would generate a warning message to the prescriber via a computerized clinical decision support system and assessing the extent to which these alerts would affect prescribers' actions. DESIGN: The study was set within a randomized controlled trial of computerized clinical decision support conducted in the long-stay units of a large, academically-affiliated long-term care facility. In March 2002, a computer-based clinical decision support system (CDSS) was added to an existing computerized physician order entry (CPOE) system. Over a subsequent one-year study period, prescribers ordering drugs for residents on three resident-care units of the facility were presented with alerts; these alerts were not displayed to prescribers in the four control units. MEASUREMENTS: We assessed the frequency of drug orders associated with various categories of alerts across all participating units of the facility. To assess the impact of actually receiving an alert on prescriber behavior during drug ordering, we calculated separately for the intervention and control units the proportion of the alerts, within each category, that were followed by an appropriate action and estimated the relative risk of an appropriate action in the intervention units compared to the control units. RESULTS: During the 12 months of the study, there were 445 residents on the participating units of the facility, contributing 3,726 resident-months of observation time. During this period, 47,997 medication orders were entered through the CPOE system-approximately 9 medication orders per resident per month. 9,414 alerts were triggered (2.5 alerts per resident-month). The alert categories most often triggered were related to risks of central nervous system side-effects such as over-sedation (20%). Alerts for risk of drug-associated constipation (13%) or renal insufficiency/electrolyte imbalance (12%) were also common. Twelve percent of the alerts were related to orders for warfarin. Overall, prescribers who received alerts were only slightly more likely to take an appropriate action (relative risk 1.11, 95% confidence interval 1.00, 1.22). Alerts related to orders for warfarin or central nervous system side effects were most likely to engender an appropriate action, such as ordering a recommended laboratory test or canceling an ordered drug. CONCLUSION: Long-term care facilities must implement new system-level approaches with the potential to improve medication safety for their residents. The number of medication orders that triggered a warning message in this study suggests that CPOE with a clinical decision support system may represent one such tool. However, the relatively low rate of response to these alerts suggests that further refinements to such systems are required, and that their impact on medication errors and adverse drug events must be carefully assessed.
OBJECTIVE: Research on clinical reasoning has been useful in developing expert systems. These tools are based on Artificial Intelligence techniques which assist the physician in the diagnosis of complex diseases. The development of these systems is based on a cognitive model extracted through the identification of the clinical reasoning patterns applied by experts within the clinical decision-making context. This study describes the method of knowledge acquisition for the identification of the triggering symptoms used in the reasoning of three experts for the diagnosis of schizophrenia. METHOD: Three experts on schizophrenia, from two University centers in Sao Paulo, were interviewed and asked to identify and to represent the triggering symptoms for the diagnosis of schizophrenia according to the graph methodology. RESULTS: Graph methodology showed a remarkable disagreement on how the three experts established their diagnosis of schizophrenia. They differed in their choice of triggering-symptoms for the diagnosis of schizophrenia: disorganization, blunted affect and thought disturbances. CONCLUSIONS: The results indicate substantial differences between the experts as to their diagnostic reasoning patterns, probably under the influence of different theoretical tendencies. The disorganization symptom was considered to be the more appropriate to represent the heterogeneity of schizophrenia and also, to further develop an expert system for the diagnosis of schizophrenia.
BACKGROUND: Context - the combined effect of factors such as physician type, clinical setting, and guideline characteristics - influences the ability of automated decision support (ADS) to improve physician compliance with clinical practice guidelines (CPGs). Our goal was to determine whether research about the utility of ADS for promoting CPG compliance is contextually applicable to United States physicians. METHODS: We extracted information about physicians, settings, and guidelines from all articles published in the last 10 years that describe original research about the use of ADS to promote CPG compliance. The extracted information was compared to the range of practice contexts seen in the United States. RESULTS: Nearly two-thirds (65.3%) of papers described studies conducted in an academic setting, but only 11% of physicians report academic affiliations (p<0.0001). Physician reimbursement structure is often not reported. Salaried physicians were explicitly included as subjects in 14% of articles, but make up 45% of US physicians (p<0.0001). There are little data about the generalizability of ADS research to emergency care settings (6% of articles), and nursing home or skilled nursing facilities (10% of articles). Finally, ADS has not been studied at all in several epidemiologically important disease categories. CONCLUSION: The literature does not adequately address some physician, setting, and guideline contexts. Before making policy or spending decisions based on the effectiveness of ADS, additional research is needed to determine whether ADS research can be generalized to under-represented contexts.