Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990-2007)

Computerised clinical decision support systems (CDSSs) are used widely to improve quality of care and patient outcomes. This systematic review evaluated the impact of CDSSs in targeting specific aspects of prescribing, namely initiating, monitoring and stopping therapy. We also examined the influence of clinical setting (institutional vs ambulatory care), system- or user-initiation of CDSS, multi-faceted vs stand alone CDSS interventions and clinical target on practice changes in line with the intent of the CDSS. We searched Medline, Embase and PsychINFO for publications from 1990-2007 detailing CDSS prescribing interventions. Pairs of independent reviewers extracted the key features and prescribing outcomes of methodologically adequate studies (experiments and strong quasi-experiments). 56 studies met our inclusion criteria, 38 addressing initiating, 23 monitoring and three stopping therapy. At the time of initiating therapy, CDSSs appear to be somewhat more effective after, rather than before, drug selection has occurred (7/12 versus 12/26 studies reporting statistically significant improvements in favour of CDSSs on = 50% of prescribing outcomes reported). CDSSs also appeared to be effective for monitoring therapy, particularly using laboratory test reminders (4/7 studies reporting significant improvements in favour of CDSSs on the majority of prescribing outcomes). None of the studies addressing stopping therapy demonstrated impacts in favour of CDSSs over comparators. The most consistently effective approaches used system-initiated advice to fine-tune existing therapy by making recommendations to improve patient safety, adjust the dose, duration or form of prescribed drugs or increase the laboratory testing rates for patients on long-term therapy. CDSSs appeared to perform better in institutional compared to ambulatory settings and when decision support was initiated automatically by the system as opposed to user initiation. CDSSs implemented with other strategies such as education were no more successful in improving prescribing than stand alone interventions. Cardiovascular disease was the most studied clinical target but few studies demonstrated significant improvements on the majority of prescribing outcomes. Our understanding of CDSS impacts on specific aspects of the prescribing process remains relatively limited. Future implementation should build on effective approaches including the use of system-initiated advice to address safety issues and improve the monitoring of therapy

Computerized advice on drug dosage to improve prescribing practice

International audienceComputerized advice on drug dosage to improve prescribing practice (Review) 1 Copyright © 2013 The Cochrane Collaboration. Published by JohnWiley & Sons, Ltd. Data collection and analysis Two review authors independently extracted data and assessed study quality.We grouped the results from the included studies by drug used and the effect aimed at for aminoglycoside antibiotics, amitriptyline, anaesthetics, insulin, anticoagulants, ovarian stimulation, anti-rejection drugs and theophylline. We combined the effect sizes to give an overall effect for each subgroup of studies, using a random-effects model. We further grouped studies by type of outcome when appropriate (i.e. no evidence of heterogeneity). Main results Forty-six comparisons (from 42 trials) were included (as compared with 26 comparisons in the last update) including a wide range of drugs in inpatient and outpatient settings. All were randomized controlled trials except two studies. Interventions usually targeted doctors, although some studies attempted to influence prescriptions by pharmacists and nurses. Drugs evaluated were anticoagulants, insulin, aminoglycoside antibiotics, theophylline, anti-rejection drugs, anaesthetic agents, antidepressants and gonadotropins. Although all studies used reliable outcome measures, their quality was generally low. This update found similar results to the previous update and managed to identify specific therapeutic areas where the computerized advice on drug dosage was beneficial compared with routine care: 1. it increased target peak serum concentrations (standardized mean difference (SMD) 0.79, 95% CI 0.46 to 1.13) and the proportion of people with plasma drug concentrations within the therapeutic range after two days (pooled risk ratio (RR) 4.44, 95% CI 1.94 to 10.13) for aminoglycoside antibiotics; 2. it led to a physiological parameter more often within the desired range for oral anticoagulants (SMD for percentage of time spent in target international normalized ratio +0.19, 95% CI 0.06 to 0.33) and insulin (SMD for percentage of time in target glucose range: +1.27, 95% CI 0.56 to 1.98); 3. it decreased the time to achieve stabilization for oral anticoagulants (SMD -0.56, 95% CI -1.07 to -0.04); 4. it decreased the thromboembolism events (rate ratio 0.68, 95% CI 0.49 to 0.94) and tended to decrease bleeding events for anticoagulants although the difference was not significant (rate ratio 0.81, 95%CI 0.60 to 1.08). It tended to decrease unwanted effects for aminoglycoside antibiotics (nephrotoxicity: RR 0.67, 95% CI 0.42 to 1.06) and anti-rejection drugs (cytomegalovirus infections: RR 0.90, 95% CI 0.58 to 1.40); 5. it tended to reduce the length of time spent in the hospital although the difference was not significant (SMD -0.15, 95% CI -0.33 to 0.02) and to achieve comparable or better cost-effectiveness ratios than usual care; 6. there was no evidence of differences in mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, antirejection drugs and antidepressants. For all outcomes, statistical heterogeneity quantified by I2 statistics was moderate to high. Authors’ conclusions This review update suggests that computerized advice for drug dosage has some benefits: it increases the serum concentrations for aminoglycoside antibiotics and improves the proportion of people for which the plasma drug is within the therapeutic range for aminoglycoside antibiotics. It leads to a physiological parameter more often within the desired range for oral anticoagulants and insulin. It decreases the time to achieve stabilization for oral anticoagulants. It tends to decrease unwanted effects for aminoglycoside antibiotics and anti-rejection drugs, and it significantly decreases thromboembolism events for anticoagulants. It tends to reduce the length of hospital stay compared with routine care while comparable or better cost-effectiveness ratios were achieved. However, there was no evidence that decision support had an effect on mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimize the effect of computerized advice. Taking into account the high risk of bias of, and high heterogeneity between, studies, these results must be interpreted with caution. P L A I N L A N G U A G E S U M M A R Y Computerized advice on drug dosage to improve prescribing practice (Review) 2 Copyright © 2013 The Cochrane Collaboration. Published by JohnWiley & Sons, Ltd. Computerized advice on drug dosage to improve prescribing practice Background Physicians and other healthcare professionals often prescribe drugs that will only work at certain concentrations. These drugs are said to have a narrow therapeutic window. This means that if the concentration of the drug is too high or too low, they may cause serious side effects or not provide the benefits they should. For example, blood thinners (anticoagulants) are prescribed to thin the blood to prevent clots. If the concentration is too high, people may experience excessive bleeding and even death. In contrast, if the concentration is too low, a clot could form and cause a stroke. For these types of drugs, it is important that the correct amount of the drug be prescribed. Calculating and prescribing the correct amount can be complicated and time-consuming for healthcare professionals. Sometimes determining the correct dose can take a long time since healthcare professionals may not want to prescribe high doses of the drugs initially because they make mistakes in calculations. Several computer systems have been designed to do these calculations and assist healthcare professionals in prescribing these types of drugs. Study characteristics We sought clinical trial evidence from scientific databases to evaluate the effectiveness of these computer systems. The evidence is current to January 2012. We found data from 42 trials (40 randomized controlled trials (trials that allocate people at random to receive one of a number of drugs or procedures) and two non-randomized controlled trials). Key results Computerized advice for drug dosage can benefit people taking certain drugs compared with empiric dosing (where a dose is chosen based on a doctor’s observations and experience)without computer assistance.When using the computer system, healthcare professionals prescribed appropriately higher doses of the drugs initially for aminoglycoside antibiotics and the correct drug dose was reached more quickly for oral anticoagulants. It significantly decreased thromboembolism (blood clotting) events for anticoagulants and tended to reduce unwanted effects for aminoglycoside antibiotics and anti-rejection drugs (although not an important difference). It tended to reduce the length of hospital stay compared with routine care with comparable or better cost-effectiveness. There was no evidence of effects on death or clinical side events for insulin (low blood sugar (hypoglycaemia)), anaesthetic agents, anti-rejection drugs (drugs taken to prevent rejection of a transplanted organ) and antidepressants. Quality of evidence The quality of the studies was low so these results must be interpreted with caution

Doctor of Philosophy

dissertationAlthough Electronic Health Record (EHR) systems have recently achieved widespread adoption in the U.S., our understanding of their impact on care outcomes is still limited. Current literature has produced mixed results due to the use of non-standardized measurements and weak research designs. In this dissertation, 4 studies are conducted to develop a systematic methodology for detecting near real-time performance changes during EHR implementations. It also explores factors that can affect outcomes during a commercial EHR implementation. The first study assesses the current state of the literature on health IT adoption to identify the most commonly reported outcome measures and proposes a taxonomy to classify these measurements. The second study expands the first study by identifying additional measures through semistructured interviews with experienced clinical and administrative leaders from a large care delivery system. We also collect input from national informatics experts who suggested additional relevant measures. The third study is a robust longitudinal analysis including several measures from our larger inventory that were used for monitoring a large-scale commercial EHR implementation and detected patterns of impact and mixed time-sensitive effects across geographically dispersed settings from an integrated care delivery system. The fourth study is a qualitative analysis guided by the quantitative results of the third study. We identified several factors that may have contributed to performance changes detected by our methodology. In summary, this dissertation will help the broader medical and informatics communities by informing what and how to continuously monitor future similar implementations. First, it contributes to the identification of relevant outcomes likely impacted by health IT interventions. Second, it combines these outcome measures with a robust interrupted time-series design, producing a systematic methodology that allows earlier and potentially more precise detection of unexpected effects, and implementation of effective response to mitigate negative impacts. Last, the identification of factors that may impact outcomes during and following an EHR implementation and covariates to measure them will empower researchers in charge of future evaluations, hopefully increasing the understanding of the full impact of health IT interventions

Medical-Data-Models.org:A collection of freely available forms (September 2016)

MDM-Portal (Medical Data-Models) is a meta-data repository for creating, analysing, sharing and reusing medical forms, developed by the Institute of Medical Informatics, University of Muenster in Germany. Electronic forms for documentation of patient data are an integral part within the workflow of physicians. A huge amount of data is collected either through routine documentation forms (EHRs) for electronic health records or as case report forms (CRFs) for clinical trials. This raises major scientific challenges for health care, since different health information systems are not necessarily compatible with each other and thus information exchange of structured data is hampered. Software vendors provide a variety of individual documentation forms according to their standard contracts, which function as isolated applications. Furthermore, free availability of those forms is rarely the case. Currently less than 5 % of medical forms are freely accessible. Based on this lack of transparency harmonization of data models in health care is extremely cumbersome, thus work and know-how of completed clinical trials and routine documentation in hospitals are hard to be re-used. The MDM-Portal serves as an infrastructure for academic (non-commercial) medical research to contribute a solution to this problem. It already contains more than 4,000 system-independent forms (CDISC ODM Format, www.cdisc.org, Operational Data Model) with more than 380,000 dataelements. This enables researchers to view, discuss, download and export forms in most common technical formats such as PDF, CSV, Excel, SQL, SPSS, R, etc. A growing user community will lead to a growing database of medical forms. In this matter, we would like to encourage all medical researchers to register and add forms and discuss existing forms