Effect of point-of-care computer reminders on physician behavior: A systematic review. Canadian Medical Association Journal, 182, E216-E225

University of Toronto, Toronto, Ontario, Canada
Canadian Medical Association Journal (Impact Factor: 5.96). 03/2010; 182(5):E216-25. DOI: 10.1503/cmaj.090578
Source: PubMed


The opportunity to improve care using computer reminders is one of the main incentives for implementing sophisticated clinical information systems. We conducted a systematic review to quantify the expected magnitude of improvements in processes of care from computer reminders delivered to clinicians during their routine activities.
We searched the MEDLINE, Embase and CINAHL databases (to July 2008) and scanned the bibliographies of retrieved articles. We included studies in our review if they used a randomized or quasi-randomized design to evaluate improvements in processes or outcomes of care from computer reminders delivered to physicians during routine electronic ordering or charting activities.
Among the 28 trials (reporting 32 comparisons) included in our study, we found that computer reminders improved adherence to processes of care by a median of 4.2% (interquartile range [IQR] 0.8%-18.8%). Using the best outcome from each study, we found that the median improvement was 5.6% (IQR 2.0%-19.2%). A minority of studies reported larger effects; however, no study characteristic or reminder feature significantly predicted the magnitude of effect except in one institution, where a well-developed, "homegrown" clinical information system achieved larger improvements than in all other studies (median 16.8% [IQR 8.7%-26.0%] v. 3.0% [IQR 0.5%-11.5%]; p = 0.04). A trend toward larger improvements was seen for reminders that required users to enter a response (median 12.9% [IQR 2.7%-22.8%] v. 2.7% [IQR 0.6%-5.6%]; p = 0.09).
Computer reminders produced much smaller improvements than those generally expected from the implementation of computerized order entry and electronic medical record systems. Further research is required to identify features of reminder systems consistently associated with clinically worthwhile improvements.

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    • "When delivered outside clinical consultations and at the population-level, they are are typically described as audit and feedback (A&F). Systematic reviews of both types of intervention suggest they are moderately effective at ensuring patients receive improved care [3] [4] [5]. However, the reviews also suggest interventions are highly variable: sometimes the interventions work very well, and sometimes they do not [3– 5]. "
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    ABSTRACT: Many patients do not receive care consistent with best practice. Health informatics interventions often attempt to address this problem by comparing care provided to patients (e.g., from electronic health record data) to quality standards (e.g., described in clinical guidelines) and feeding this information back to clinicians. Traditionally these interventions are delivered at the patient-level as computerized clinical decision support (CDS) or at the population level as audit and feedback (A&F). Both CDS and A&F can improve care for patients but are variably effective; the challenge is to understand how the efficacy can be maximized. Although CDS and A&F are traditionally considered separate approaches, we argue that the systems share common mechanisms, and efficacy may be improved by cross-fertilizing relevant features and concepts. We draw on the Health Informatics and Implementation Science literature to argue that common mechanisms include functions typically associated with the other system, in addition to other features that may prove fruitful for further research.
    Studies in health technology and informatics 08/2015; 216:419-23.
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    • "Several different types of intervention studies have been conducted in an effort to improve prescription adequacy in clinical practice. These can be grouped into three major types of intervention: 1) feedback or audits [19], in which information about clinical practice is a posteriori; 2) alerts or reminders [20], which suggest an action such as preventive care at the time of the patient visit or a priori; and 3) clinical decision support systems (CDSS) [21], which are more complex systems that guide practitioners and provide recommendations on clinical decisions to be made, based on information added to the clinical record. "
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    ABSTRACT: Background Although hypercholesterolemia is considered a cardiovascular risk factor, in isolation it is not necessarily sufficient cause for a cardiovascular event. To improve event prediction, cardiovascular risk calculators have been developed; the REGICOR calculator has been validated for use in our population. The objective of this project is to develop an intervention with general practitioners (GPs) and evaluate its impact on prescription adequacy of cholesterol-lowering drugs in primary prevention of cardiovascular disease and in controlling the costs associated with this disease. Methods This nonblinded, cluster-randomized clinical trial analyzes data from primary care electronic medical records (ECAP) and other databases. Inclusion criteria are patients aged 35 to 74 years with no known cardiovascular disease and a new prescription for cholesterol-lowering drugs during the 2-year study period. Dependent variables include the following: RETIRA, defined as new cholesterol-lowering drugs initiated during the year preceding the intervention, considered inadequate, and withdrawn during the study period; EVITA, defined as new cholesterol-lowering drugs initiated during the study period and considered inadequate; COST, defined as the total cost of inadequate new treatments prescribed; and REGISTER, defined as the recording of cardiovascular risk factors. Independent variables include the GP’s quality-of-care indicators and randomly assigned study group (intervention vs control), patient demographics, and clinical variables. Aggregated descriptive analysis will be done at the GP level and multilevel analysis will be performed to estimate the intervention effect, adjusted for individual and GP variables. Discussion The study objective is to generate evidence about the effectiveness of implementing feedback information programs directed to GPs in the context of Primary Care. The goal is to improve the prescription adequacy of lipid-lowering therapies for primary prevention. Trial registration Identifier: NCT01997671. November 28, 2013.
    BMC Family Practice 07/2014; 15(1):135. DOI:10.1186/1471-2296-15-135 · 1.67 Impact Factor
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    • "CDS can be based on best available clinical evidence or guidelines, derived from evidence-based medicine [9]. However, when Shojania et al. [10] carried out a systematic review of point of care computer reminders on physician behaviour they found that such reminders produced smaller improvements (in the order 4-5%) than those generally expected from the implementation of computerized order entry and electronic medical record systems. CDS can also utilize predictions based on data mining of previous cases as has been proposed for pathology ordering [11]. "
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    ABSTRACT: We previously described a methodology for converting a large set of confidential data records into a set of summaries of similar patients. They claimed that the resulting patient types could "capture important trends and patterns in the data set without disclosing the information in any of the individual data records." In this paper we examine the predictive validity of an initial set of patient types developed in our earlier research. We ask the following question: To what extent can the summarized data derived from each cluster (patient type) be as informative as the original case level data (individuals) from which the clusters were inferred? We address this question by assessing how well predictions made with summarized data matched predictions made with original data. After reviewing relevant literature, and explaining how data is summarized in each cluster of similar patients, we compare the results of predicting death in the ICU 1 using both summarized (regression analysis) and original case data (discriminant analysis and logistic regression analysis). When multiple clusters were used, prediction based on regression analysis of the summarized data was found to be better than prediction using either logistic regression or discriminant analysis on the raw data. We hypothesize that this result is due to segmentation of a heterogenous multivariate space into more homogeneous subregions. We see the present results as an important step towards the development of generalized health data search engines that can utilize non-confidential summarized data passed through health data repository firewalls.
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