BACKGROUND: Maintaining therapeutic concentrations of drugs with a narrow therapeutic window is a complex task. Several computer systems have been designed to help doctors determine optimum drug dosage. Significant improvements in health care could be achieved if computer advice improved health outcomes and could be implemented in routine practice in a cost-effective fashion. This is an updated version of an earlier Cochrane systematic review, first published in 2001 and updated in 2008.
OBJECTIVES: To assess whether computerized advice on drug dosage has beneficial effects on patient outcomes compared with routine care (empiric dosing without computer assistance).
SEARCH METHODS: The following databases were searched from 1996 to January 2012: EPOC Group Specialized Register, Reference Manager; Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Ovid; EMBASE, Ovid; and CINAHL, EbscoHost. A "top up" search was conducted for the period January 2012 to January 2013; these results were screened by the authors and potentially relevant studies are listed in Studies Awaiting Classification. The review authors also searched reference lists of relevant studies and related reviews.
SELECTION CRITERIA: We included randomized controlled trials, non-randomized controlled trials, controlled before-and-after studies and interrupted time series analyses of computerized advice on drug dosage. The participants were healthcare professionals responsible for patient care. The outcomes were any objectively measured change in the health of patients resulting from computerized advice (such as therapeutic drug control, clinical improvement, adverse reactions).
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, anti-rejection 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.
Background. Computerized decision support systems (CDSS) are believed to have the potential to improve the quality of health care delivery, although results from high quality studies have been mixed. We conducted a systematic review to evaluate whether certain features of prescribing decision support systems (RxCDSS) predict successful implementation, change in provider behaviour, and change in patient outcomes. Methods. A literature search of Medline, EMBASE, CINAHL and INSPEC databases (earliest entry to June 2008) was conducted to identify randomized controlled trials involving RxCDSS. Each citation was independently assessed by two reviewers for outcomes and 28 predefined system features. Statistical analysis of associations between system features and success of outcomes was planned. Results. Of 4534 citations returned by the search, 41 met the inclusion criteria. Of these, 37 reported successful system implementations, 25 reported success at changing health care provider behaviour, and 5 noted improvements in patient outcomes. A mean of 17 features per study were mentioned. The statistical analysis could not be completed due primarily to the small number of studies and lack of diversity of outcomes. Descriptive analysis did not confirm any feature to be more prevalent in successful trials relative to unsuccessful ones for implementation, provider behaviour or patient outcomes. Conclusion. While RxCDSSs have the potential to change health care provider behaviour, very few high quality studies show improvement in patient outcomes. Furthermore, the features of the RxCDSS associated with success (or failure) are poorly described, thus making it difficult for system design and implementation to improve.
CONTEXT: Computerized physician order entry (CPOE) with clinical decision support (CDS) has been promoted as an effective strategy to prevent the development of a drug injury defined as an adverse drug event (ADE). OBJECTIVE: To systematically review studies evaluating the effects of CPOE with CDS on the development of an ADE as an outcome measure. Data Sources PUBMED versions of MEDLINE (from inception through March 2007) were searched to identify relevant studies. Reference lists of included studies were also searched. METHODS: We searched for original investigations, randomized and nonrandomized clinical trials, and observational studies that evaluated the effect of CPOE with CDS on the rates of ADEs. The studies identified were assessed to determine the type of computer system used, drug categories being evaluated, types of ADEs measured, and clinical outcomes assessed. RESULTS: Of the 543 citations identified, 10 studies met our inclusion criteria. These studies were grouped into categories based on their setting: <i>hospital or ambulatory</i>; no studies related to the <i>long-term care</i> setting were identified. CPOE with CDS contributed to a statistically significant (<i>P</i> ≤ .05) decrease in ADEs in 5 (50.0%) of the 10 studies. Four studies (40.0%) reported a nonstatistically significant reduction in ADE rates, and 1 study (10.0%) demonstrated no change in ADE rates. CONCLUSIONS: Few studies have measured the effect of CPOE with CDS on the rates of ADEs, and none were randomized controlled trials. Further research is needed to evaluate the efficacy of CPOE with CDS across the various clinical settings. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
BACKGROUND: Older adults take multiple medications and are at high risk for adverse drug effects. OBJECTIVE: This systematic review was conducted to describe the impact of computer decision support (CDS) interventions designed to improve the quality of medication prescribing in older adults. METHODS: PubMed and EMBASE databases were searched from January 1980 through July 2007 (English-language only); studies were eligible if they described a CDS intervention intended to improve medication prescribing in adults aged > or =60 years. Studies were retained if they were observational or experimental in design and reported > or =1 process or clinical outcome measurement related to medication prescribing. In the main analysis, study characteristics and major outcome results were extracted. A combination of searches was performed using relevant medical subject headings: aged; drug therapy, computer-assisted; medication errors; medication errors/prevention and control; decision making, computer-assisted; decision support systems, clinical; and clinical pharmacy information systems. RESULTS: After review of study abstracts, 10 articles met the eligibility criteria. Of those 10 studies testing CDS interventions, 8 showed at least modest improvements (median number needed to treat, 33) in prescribing, as measured by minimizing drugs to avoid, optimizing drug dosage, or more generally improving prescribing choices in older adults (according to each study's intervention protocols). Findings for the impact of CDS interventions on clinical outcomes were mixed and were reported for only 2 studies. CONCLUSIONS: Various types of CDS interventions may be effective in improving medication prescribing in older adults, but few studies reported clinical outcomes related to changes in medication prescribing. Data from this study should help to guide refinement and testing of future CDS interventions that specifically target older adult populations that are taking multiple medications.
OBJECTIVE: To examine the association between computerization of physician orders and prescribing medication errors. Data Sources. Studies published in English language were identified through MEDLINE (1990 through December 2005), Cochrane Central Register of Controlled Trials, and bibliographies of retrieved articles. Of 252 identified in the search, 12 (4.8 percent) original investigations that compared rates of prescribing medication errors with handwritten and computerized physician orders were included. DATA COLLECTION: Information on study design, participant characteristics, clinical settings, and outcomes rates were abstracted independently by two investigators using a standardized protocol. PRINCIPAL FINDINGS: Compared with handwritten orders, 80 percent of studies (8/10 studies) reported a significant reduction in total prescribing errors, 43 percent in dosing errors (3/7 studies), and 37.5 percent in adverse drug events (3/8 studies). The use of computerized orders was associated with a 66 percent reduction in total prescribing errors in adults (odds ratio [OR]=0.34; 95 percent confidence interval [CI] 0.22-0.52) and a positive tendency in children (p for interaction=.028). The benefit of computerized orders was larger when the rate of errors was more than 12 percent with handwritten orders (p for interaction=.022). Significant heterogeneity in the results compromised pooled relative risks. One randomized controlled intervention demonstrated the greatest benefits of computerized orders on total prescribing errors (OR=0.02, 95 percent CI 0.01-0.02) and dosing errors (OR=0.28; 95 percent CI 0.15-0.52) with 775 avoided prescribing errors (95 percent CI 752-811) per 1,000 orders in a pediatric hospital. CONCLUSIONS: Computerization of physicians' orders shows great promise. It will be more effective when linked to other computerized systems to detect and prevent prescribing errors.
OBJECTIVE: To examine the effect of computerized decision support systems (CDSSs) on nursing performance and patient outcomes. METHOD: Fifteen databases, including Medline and CINAHL, were searched up to May 2006 together with reference lists of included studies and relevant reviews. Randomized controlled trials, controlled clinical trials, controlled before and after studies and interrupted time series studies that assessed the effects of CDSS use by nurses in a clinical setting on measurable professional and/or patient outcomes were included. RESULTS: Eight studies, three comparing nurses using CDSS with nurses not using CDSS and five comparing nurses using CDSS with other health professionals not using CDSS, were included. Risk of contamination was a concern in four studies. The effect of CDSS on nursing performance and patient outcomes was inconsistent. CONCLUSION: The introduction of CDSS may not necessarily lead to a positive outcome; further studies are needed in order to identify contexts in which CDSS use by nurses is most effective. CDSS are complex interventions and should be evaluated as such; future studies should explore the impact of the users and the protocol on which the CDSS is based, reporting details of both. Contamination is a significant issue when evaluating CDSS, so it is important that randomization is at the practitioner or the unit level. Future systematic reviews should focus on particular uses of CDSS.
Asthma is a common condition associated with significant patient morbidity and health care costs. Although widely accepted evidence-based guidelines for asthma management exist, unnecessary variation in patient care remains. Application of biomedical informatics techniques is one potential way to improve care for asthmatic patients. We performed a systematic literature review to identify computerized applications for clinical asthma care. Studies were evaluated for their clinical domain, developmental stage and study design. Additionally, prospective trials were identified and analyzed for potential study biases, study effects, and clinical study characteristics. Sixty-four papers were selected for review. Publications described asthma detection or diagnosis (18 papers), asthma monitoring or prevention (13 papers), patient education (13 papers), and asthma guidelines or therapy (20 papers). The majority of publications described projects in early stages of development or with non-prospective study designs. Twenty-one prospective trials were identified, which evaluated both clinical and non-clinical impacts on patient care. Most studies took place in the outpatient clinic environment, with minimal study of the emergency department or inpatient settings. Few studies demonstrated evidence of computerized applications improving clinical outcomes. Further research is needed to prospectively evaluate the impact of using biomedical informatics to improve care of asthmatic patients.
OBJECTIVES: To review for acute abdominal pain (AAP), the diagnostic accuracies of combining decision tools (DTs) and doctors aided by DTs compared with those of unaided doctors. Also to evaluate the impact of providing doctors with an AAP DT on patient outcomes, clinical decisions and actions, what factors are likely to determine the usage rates and usability of a DT and the associated costs and likely cost-effectiveness of these DTs in routine use in the UK.
DESIGN: Electronic databases were searched up to 1 July 2003.
REVIEW METHODS: Data from each eligible study were extracted. Potential sources of heterogeneity were extracted for both questions. For the accuracy review, meta-analysis was conducted. Among studies comparing diagnostic accuracies of DTs with unaided doctors, error rate ratios provided estimates of the differences between the false-negative and false-positive rates of the DT and unaided doctors' performance. Pooled error rate ratios and 95% confidence intervals (CIs) for false-negative rates and false-positive rates were computed. Metaregression was used to explore heterogeneity.
RESULTS: Thirty-two studies from 27 articles, all based in secondary care, were eligible for the review of DT accuracies, while two were eligible for the review of the accuracy of hospital doctors aided by DTs. Sensitivities and specificities for DTs ranged from 53 to 99% and from 30 to 99%, respectively. Those for unaided doctors ranged from 64 to 93% and from 39 to 91%, respectively. Thirteen studies reported false-positive and false-negative rates for both DTs and unaided doctors, enabling a direct comparison of their performance. In random effects meta-analyses, DTs had significantly lower false-positive rates (error rate ratio 0.62, 95% CI 0.46 to 0.83) than unaided doctors. DTs may have higher false-negative rates than unaided doctors (error rate ratio 1.34, 95% CI 0.93 to 1.93). Significant heterogeneity was present. Two studies compared the diagnostic accuracies of doctors aided by DTs to unaided doctors. In a multiarm cluster randomised controlled trial (n = 5193), the diagnostic accuracy of doctors not given access to DTs was not significantly worse (sensitivity 28.4% and specificity 96.0%) than that of three groups of aided doctors (sensitivities of 42.4-47.9%, and specificities of 95.5-96.5%, respectively). In an uncontrolled before-and-after study (n = 1484), the sensitivities and specificities of aided and unaided doctors were 95.5% and 91.5% (p = 0.24) and 78.1% and 86.4% (p < 0.001), respectively. The metaregression of DTs showed that prospective test-set validation at the site of the tool's development was associated with considerably higher diagnostic accuracy than prospective test-set validation at an independent centre [relative diagnostic odds ratio (RDOR) 8.2; 95% CI 3.1 to 14.7]. It also showed that the earlier in the year the study was performed the higher the performance (RDOR 0.88, 0.83 to 0.92), that when developers evaluated their own DT there was better performance than when independent evaluators carried out the study (RDOR = 3.0, 1.3 to 6.8), and that there was no evidence of association between other quality indicators and DT accuracy. The one eligible study of the impact study review, a four-arm cluster randomised trial (n = 5193), showed that hospital admission rates of patients by doctors not allocated to a DT (42.8%) were significantly higher than those by doctors allocated to three combinations of decision support (34.2-38.5%) (p < 0.001). There was no evidence of a difference between perforation rates (p = 0.19) and negative laparotomy rates in the four trial arms (p = 0.46). Usage rates of DTs by doctors in accident and emergency departments ranged from 10 to 77% in the six studies that reported them. Possible determinants of usability include the reasoning method used, the number of items used and the output format. A deterministic cost-effectiveness comparison demonstrated that a paper checklist is likely to be 100-900 times more cost-effective than a computer-based DT, under stated assumptions.
CONCLUSIONS: With their significantly greater specificity and lower false-positive rates than doctors, DTs are potentially useful in confirming a diagnosis of acute appendicitis, but not in ruling it out. The clinical use of well-designed, condition-specific paper or computer-based structured checklists is promising as a way to improve impact on patient outcomes, subject to further research.
OBJECTIVES: To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit. DATA SOURCES: We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information. STUDY SELECTION: We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes. DATA EXTRACTION: Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes. DATA SYNTHESIS: One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001). CONCLUSIONS: Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
BACKGROUND: Clinical decision support systems (CDSS) are computer-based information systems used to integrate clinical and patient information to provide support for decision-making in patient care. They may be useful in aiding the diagnostic process, the generation of alerts and reminders, therapy critiquing/planning, information retrieval, and image recognition and interpretation. CDSS for use in adult patients have been evaluated using randomised control trials and their results analysed in systematic reviews. There is as yet no systematic review on CDSS use in neonatal medicine.
OBJECTIVES: To examine whether the use of clinical decision support systems has an effect on
1. the mortality and morbidity of newborn infants and
2. the performance of physicians treating them
SEARCH STRATEGY: The standard search method of the Cochrane Neonatal Review Group was used. Searches were made of the Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library, Issue 2, 2007), MEDLINE (from 1966 to July 2007), EMBASE (1980 - July 2007), CINAHL (1982 to July 2007) and AMED (1985 to July 2007).
SELECTION CRITERIA: Randomised or quasi-randomised controlled trials which compared the effects of CDSS versus no CDSS in the care of newborn infants. Trials which compared CDSS against other CDSS were also considered. The eligible interventions were CDSS for computerised physician order entry, computerised physiological monitoring, diagnostic systems and prognostic systems.
DATA COLLECTION AND ANALYSIS: Studies were assessed for eligibility using a standard pro forma. Methodological quality was assessed independently by the different investigators.
MAIN RESULTS: Two studies fitting the selection criteria were found for computer aided prescribing and one study for computer aided physiological monitoring.
Computer-aided prescribing: one study (Cade 1997) examined the effects of computerised prescribing of parenteral nutrition ordering. No significant effects on short-term outcomes were found and longer term outcomes were not studied. The second study (Balaguer 2001) investigated the effects of a database program in aiding the calculation of neonatal drug dosages. It was found that the time taken for calculation was significantly reduced and there was a significant reduction in the number of calculation errors.
Computer-aided physiological monitoring: one eligible study (Cunningham 1998) was found which examined the effects of computerised cot side physiological trend monitoring and display. There were no significant effects on mortality, volume of colloid infused, frequency of blood gases sampling (samples per day) or severe intraventricular haemorrhage (Papile Grade IV). Published data did not permit us to analyse effects on long-term neurodevelopmental outcome.
AUTHORS' CONCLUSIONS: There are very limited data from randomised trials on which to assess the effects of clinical decision support systems in neonatal care. Further evaluation of CDSS using randomised controlled trials is warranted.
Maintaining therapeutic concentrations of drugs with a narrow therapeutic window is a complex task. Several computer systems have been designed to help doctors determine optimum drug dosage. Significant improvements in health care could be achieved if computer advice improved health outcomes and could be implemented in routine practice in a cost-effective fashion. This is an updated version of an earlier Cochrane systematic review, first published in 2001 and updated in 2008.
OBJECTIVES:
To assess whether computerized advice on drug dosage has beneficial effects on patient outcomes compared with routine care (empiric dosing without computer assistance).
SEARCH METHODS:
The following databases were searched from 1996 to January 2012: EPOC Group Specialized Register, Reference Manager; Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Ovid; EMBASE, Ovid; and CINAHL, EbscoHost. A "top up" search was conducted for the period January 2012 to January 2013; these results were screened by the authors and potentially relevant studies are listed in Studies Awaiting Classification. The review authors also searched reference lists of relevant studies and related reviews.
SELECTION CRITERIA:
We included randomized controlled trials, non-randomized controlled trials, controlled before-and-after studies and interrupted time series analyses of computerized advice on drug dosage. The participants were healthcare professionals responsible for patient care. The outcomes were any objectively measured change in the health of patients resulting from computerized advice (such as therapeutic drug control, clinical improvement, adverse reactions).
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, anti-rejection 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.
