The Effects of On-Screen, Point of Care Computer Reminders on Processes and Outcomes of Care
Director, University of Toronto Centre for Patient Safety, Sunnybrook Health Sciences Centre, Room D474, 2075 Bayview Avenue, Toronto, Ontario, Canada, M4N 3M5. Cochrane database of systematic reviews (Online)
(Impact Factor: 6.03).
02/2009; 3(3):CD001096. DOI: 10.1002/14651858.CD001096.pub2
It is known that doctors do not always provide the care that is recommended or according to the latest research. Many strategies have been tried in an attempt to reduce this gap between what is recommended and what is done. A potentially low cost way to do this could be to use computer systems that remind physicians about important information while they make decisions. For example, a doctor could be ordering antibiotics for a child with an ear infection. At that point, the computer the doctor is working on displays a pop up window with a reminder about the evidence for the best dose and length of time the antibiotics should be prescribed. This review found 28 studies that evaluated the effects of different on-screen computer reminders. The studies tested reminders to prescribe specific medications, to warn about drug interactions, to provide vaccinations, or to order tests. The review found small to moderate benefits. The reminders improved physician practices by a median of 4%. In eight of the studies, patients' health improved by a median of 3%. Although some studies showed larger benefits than these median effects, no specific reminders or features of how they worked were consistently associated with these larger benefits. More research is needed to identify what types of reminders work and when.
Available from: Niels Peek
- "As a result, the efficacy of CDS is modest and highly variable. A recent Cochrane review of " onscreen , point of care computer reminders " demonstrated they improved processes of care by a median of 4.2% (interquartile range [IQR], 0.8% to 18.8%) . Another review found only 58% of trials demonstrated an improvement in either processes of care or patient outcomes . "
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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.
- "In addition to developing capacity for EIDM, the knowledge broker may engage in other strategies to support EIDM. Small and moderate improvements to evidence use among physicians have been found for interventions such as providing reminders, use of opinion leaders, audit and feedback, and decision support mechanisms such as email alerts and computer-generated prompts (Grimshaw et al. 2001; Forsetlund et al. 2009; Shojania et al. 2009; Boaz et al. 2011; Flodgren et al. 2011). While these types of interventions have not been consistently tested among nurses, they may support evidence use – especially if used in a multifaceted intervention. "
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ABSTRACT: Registered nurses with graduate preparation are in a unique position to act as knowledge brokers owing to their extensive clinical experience and ability to be seen as a credible and respected resource by their peers. Nurse knowledge brokers can bridge the gap between research producers and those that need evidence for decision-making and support capacity development for evidence-informed decision-making (EIDM). Knowledge broker competencies include graduate-level education with exposure to research methods; experience with the EIDM process; and established networking skills to bring researchers, decision-makers, stakeholders and policymakers together. For the knowledge broker to be successful, the nurse leader can cultivate an organizational culture supportive of evidence use with advocacy for mandates that require evidence for decisions, structures in place for each stage of the EIDM process, and physical resources such as library services for evidence retrieval.
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Available from: Minna Kaila
- "Such automatic systems combine medical evidence with patient-specific data from the Electronic Patient Record (EPR), which supports clinical decision making [9-11]. According to a Cochrane Review of 28 studies, computer reminders achieved a median improvement in process adherence of 4.2% . Focused computer-generated reminders and alerts work well in a variety of single conditions [13-16] and in preventive care . "
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ABSTRACT: Computer-based decision support systems are a promising method for incorporating research evidence into clinical practice. However, evidence is still scant on how such information technology solutions work in primary healthcare when support is provided across many health problems. In Finland, we designed a trial where a set of evidence-based, patient-specific reminders was introduced into the local Electronic Patient Record (EPR) system. The aim was to measure the effects of such reminders on patient care. The hypothesis was that the total number of triggered reminders would decrease in the intervention group compared with the control group, indicating an improvement in patient care.
From July 2009 to October 2010 all the patients of one health center were randomized to an intervention or a control group. The intervention consisted of patient-specific reminders concerning 59 different health conditions triggered when the healthcare professional (HCP) opened and used the EPR. In the intervention group, the triggered reminders were shown to the HCP; in the control group, the triggered reminders were not shown. The primary outcome measure was the change in the number of reminders triggered over 12 months. We developed a unique data gathering method, the Repeated Study Virtual Health Check (RSVHC), and used Generalized Estimation Equations (GEE) for analysing the incidence rate ratio, which is a measure of the relative difference in percentage change in the numbers of reminders triggered in the intervention group and the control group.
In total, 13,588 participants were randomized and included. Contrary to our expectation, the total number of reminders triggered increased in both the intervention and the control groups. The primary outcome measure did not show a significant difference between the groups. However, with the inclusion of patients followed up over only six months, the total number of reminders increased significantly less in the intervention group than in the control group when the confounding factors (age, gender, number of diagnoses and medications) were controlled for.
Computerized, tailored reminders in primary care did not decrease during the 12 months of follow-up time after the introduction of a patient-specific decision support system.Trial registration: ClinicalTrial.gov NCT00915304.
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