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ABSTRACT: Computerized decision support systems (CDSSs) are believed to enhance patient care and reduce healthcare costs; however the current evidence is limited and the cost-effectiveness remains unknown.
To estimate the long-term cost-effectiveness of a CDSS linked to evidence-based treatment recommendations for type 2 diabetes.
Using the Ontario Diabetes Economic Model, changes in factors (eg, HbA1c) from a randomized controlled trial were used to estimate cost-effectiveness. The cost of implementation, development, and maintenance of the core dataset, and projected diabetes-related complications were included. The base case assumed a 1-year treatment effect, 5% discount rate, and 40-year time horizon. Univariate, one-way sensitivity analyses were carried out by altering different parameter values. The perspective was the Ontario Ministry of Health and costs were in 2010 Canadian dollars.
The cost of implementing the intervention was $483,699. The one-year intervention reduced HbA1c by 0.2 and systolic blood pressure by 3.95 mm Hg, but increased body mass index by 0.02 kg/m², resulting in a relative risk reduction of 14% in the occurrence of amputation. The model estimated that the intervention resulted in an additional 0.0117 quality-adjusted life year; the incremental cost-effectiveness ratio was $160,845 per quality-adjusted life-year.
The web-based prototype decision support system slightly improved short-term risk factors. The model predicted moderate improvements in long-term health outcomes. This disease management program will need to develop considerable efficiencies in terms of costs and processes or improved effectiveness to be considered a cost-effective intervention for treating patients with type 2 diabetes.
Journal of the American Medical Informatics Association 11/2011; 19(3):341-5. · 3.61 Impact Factor
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Anne Holbrook,
Eleanor Pullenayegum,
Lehana Thabane,
Sue Troyan,
Gary Foster,
Karim Keshavjee,
David Chan,
Lisa Dolovich,
Hertzel Gerstein,
Catherine Demers,
Greg Curnew
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ABSTRACT: Computerized decision support systems (CDSSs) linked with electronic medical records (EMRs) are promoted as an effective means of improving patient care. However, very few high-quality studies are set in routine, community-based clinical care, and no consistent evidence of an effect on patient outcomes has been found.
A randomized controlled trial among EMR-using primary care practices in Ontario, Canada. Patients 55 years or older with previous vascular events, diabetes mellitus, hypertension, or hypercholesterolemia were randomized to the Computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness (COMPETE III) CDSS intervention or to usual care. The intervention included personally tailored electronic vascular risk monitoring and treatment advice shared between the physician and patient, risk calculation, and a clinical resource. The primary outcome was a composite score of 8 recommended process outcomes at 1 year. Data collectors were blinded to group allocation. Analysis used the intention-to-treat principle with multiple imputation for missing data.
We randomized and included in the analysis 1102 patients in 49 community-based physician practices (53.4% female; mean age, 69.1 years; 28.0% with a previous vascular event). The intervention group (545 [49.5%]) had a significantly greater improvement in mean process composite, with a difference of 4.70 (P < .001) on a 27-point scale. Intervention patients had significantly higher odds of rating their continuity of care (4.18; P < .001) and their ability to improve their vascular health (3.07; P < .001) as improved. Despite this improvement, the clinical outcomes-vascular events, clinical variables, and quality of life-were not improved.
Despite favorable reviews and important improvements in the complex processes required to reduce vascular risk, clinical outcomes remain unchanged.
Archives of internal medicine 10/2011; 171(19):1736-44. · 11.46 Impact Factor
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ABSTRACT: Computerized clinical decision support systems (CCDSSs) for drug therapy management are designed to promote safe and effective medication use. Evidence documenting the effectiveness of CCDSSs for improving drug therapy is necessary for informed adoption decisions. The objective of this review was to systematically review randomized controlled trials assessing the effects of CCDSSs for drug therapy management on process of care and patient outcomes. We also sought to identify system and study characteristics that predicted benefit.
We conducted a decision-maker-researcher partnership systematic review. We updated our earlier reviews (1998, 2005) by searching MEDLINE, EMBASE, EBM Reviews, Inspec, and other databases, and consulting reference lists through January 2010. Authors of 82% of included studies confirmed or supplemented extracted data. We included only randomized controlled trials that evaluated the effect on process of care or patient outcomes of a CCDSS for drug therapy management compared to care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive.
Sixty-five studies met our inclusion criteria, including 41 new studies since our previous review. Methodological quality was generally high and unchanged with time. CCDSSs improved process of care performance in 37 of the 59 studies assessing this type of outcome (64%, 57% of all studies). Twenty-nine trials assessed patient outcomes, of which six trials (21%, 9% of all trials) reported improvements.
CCDSSs inconsistently improved process of care measures and seldomly improved patient outcomes. Lack of clear patient benefit and lack of data on harms and costs preclude a recommendation to adopt CCDSSs for drug therapy management.
Implementation Science 08/2011; 6:89. · 3.10 Impact Factor
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Diana Sherifali,
Janet L Greb,
Gaya Amirthavasar,
Dereck Hunt,
R Brian Haynes,
William Harper, Anne Holbrook,
Sarah Capes,
Ron Goeree,
Daria O'Reilly,
Eleanor Pullenayegum,
Hertzel C Gerstein
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ABSTRACT: It is unknown whether computer-generated, patient-tailored feedback leads to improvements in glycemic control in people with type 2 diabetes.
We recruited people with type 2 diabetes aged ≥ 40 years with a glycated hemoglobin (A1C) ≥ 7%, living in Hamilton, Canada, who were enrolled in a community-based program (Diabetes Hamilton) that provided regular evidence-based information and listings of community resources designed to facilitate diabetes self-management. After completing a questionnaire, participants were randomly allocated to either receive or not receive periodic computer-generated, evidence-based feedback on the basis of their questionnaire responses and designed to facilitate improved glycemic control and diabetes self-management. The primary outcome was a change in A1C after 1 year.
A total of 465 participants (50% women, mean age 62 years, and mean A1C 7.83%) were randomly assigned, and 12-month A1C values were available in 96% of all participants, at which time the A1C level had decreased by an absolute amount of 0.24 and 0.15% in the intervention and control groups, respectively. The difference in A1C reduction for the intervention versus control group was 0.09% (95% CI -0.08 to 0.26; P = 0.3). No between-group differences in measures of quality of life, diabetes self-management behaviors, or clinical outcomes were observed.
Providing computer-generated tailored feedback to registrants of a generic, community-based program that supports diabetes self-management does not lead to lower A1C levels or a better quality of life than participation in the community-based program (augmented by periodic A1C testing) alone.
Diabetes care 06/2011; 34(8):1794-8. · 8.09 Impact Factor
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ABSTRACT: It is not uncommon for a participant to withdraw from a randomized controlled trial (RCT). The withdrawal of a participant results in missing data and the potential for withdrawal bias. Data withdrawal, or a request from a participant to withdraw all of their previously collected data from a study, is particularly problematic because it leaves little opportunity to characterize or statistically address those that have withdrawn to minimize withdrawal bias. The aim of this commentary is to (1) provide a synthesis of available information on the ethical and methodological issues related to data withdrawal in RCTs and (2) provide some suggestions on how to minimize the impact of data withdrawal during the execution or analysis phases of an RCT. We searched PubMed, EMBASE and JSTOR for published articles on data withdrawal. In addition, we used internet sources as an additional tool to identify content on data withdrawal from research ethics guidelines, legislation, research ethics boards, funding agencies, professional organizations and researchers. We did not find any definitive guidelines for dealing with data withdrawal. We propose recommendations for minimizing the occurrence of data withdrawal, including explicit and clear descriptions in consent forms of how data will be handled after participant withdrawal. We also suggest using imputation techniques to deal with the missing data during analysis. The current commentary can be used to minimize the impact of data withdrawal in RCTs.
Contemporary clinical trials 02/2011; 32(3):318-22. · 1.51 Impact Factor
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ABSTRACT: Multicentre randomized controlled trials (RCTs) routinely use randomization and analysis stratified by centre to control for differences between centres and to improve precision. No consensus has been reached on how to best analyze correlated continuous outcomes in such settings. Our objective was to investigate the properties of commonly used statistical models at various levels of clustering in the context of multicentre RCTs.
Assuming no treatment by centre interaction, we compared six methods (ignoring centre effects, including centres as fixed effects, including centres as random effects, generalized estimating equation (GEE), and fixed- and random-effects centre-level analysis) to analyze continuous outcomes in multicentre RCTs using simulations over a wide spectrum of intraclass correlation (ICC) values, and varying numbers of centres and centre size. The performance of models was evaluated in terms of bias, precision, mean squared error of the point estimator of treatment effect, empirical coverage of the 95% confidence interval, and statistical power of the procedure.
While all methods yielded unbiased estimates of treatment effect, ignoring centres led to inflation of standard error and loss of statistical power when within centre correlation was present. Mixed-effects model was most efficient and attained nominal coverage of 95% and 90% power in almost all scenarios. Fixed-effects model was less precise when the number of centres was large and treatment allocation was subject to chance imbalance within centre. GEE approach underestimated standard error of the treatment effect when the number of centres was small. The two centre-level models led to more variable point estimates and relatively low interval coverage or statistical power depending on whether or not heterogeneity of treatment contrasts was considered in the analysis.
All six models produced unbiased estimates of treatment effect in the context of multicentre trials. Adjusting for centre as a random intercept led to the most efficient treatment effect estimation across all simulations under the normality assumption, when there was no treatment by centre interaction.
BMC Medical Research Methodology 02/2011; 11:21. · 2.67 Impact Factor
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ABSTRACT: To determine how patients and physicians balance the perceived benefits and harms of sharing electronic health data for patient care and for secondary purposes.
Before-after survey of patients and providers in practices using electronic medical records (EMRs) enrolled in a clinical trial in Ontario, Canada.
Outcomes were measured using the Health Information Privacy Questionnaire (HIPQ) at baseline and end of study. Thirteen questions in 4 general domains investigated attitudes towards the privacy of EMRs, outsider's use of patient's health information, the sharing of patient's information within the health care system, and the overall perception of benefits versus harms of computerization in health care.
511 patients (mean age 60.3 years, 49.6% female) and 46 physicians (mean age 47.2 years, 37.0% female) participated. Most (>90%) supported the computerized sharing of the patient's health records among their health care professionals and to provide clinical advice. Fewer agreed that the patient's de-identified information should be shared outside of the health care circle (<70%). Only a minority of either group supported the notion that computerized records can be keep more private than paper records (38-50%). Overall, a majority (58% patients, 70% physicians) believed that the benefits of computerization were greater than the risks of confidentiality loss. This was especially true for patients who were frequent computer users.
While these primary care physicians and their patients valued the clinical features of EMRs, a substantial minority have concerns about the secondary use of de-identified information.
International Journal of Medical Informatics 02/2011; 80(2):94-101. · 2.41 Impact Factor
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ABSTRACT: Diabetes mellitus is a complex disease with serious complications. Electronic decision support, providing information that is shared and discussed by both patient and physician, encourages timely interventions and may improve the management of this chronic disease. However, it has rarely been tested in community-based primary care.
In this pragmatic randomized trial, we randomly assigned adult primary care patients with type 2 diabetes to receive the intervention or usual care. The intervention involved shared access by the primary care provider and the patient to a Web-based, colour-coded diabetes tracker, which provided sequential monitoring values for 13 diabetes risk factors, their respective targets and brief, prioritized messages of advice. The primary outcome measure was a process composite score. Secondary outcomes included clinical composite scores, quality of life, continuity of care and usability. The outcome assessors were blinded to each patient's intervention status.
We recruited sequentially 46 primary care providers and then 511 of their patients (mean age 60.7 [standard deviation 12.5] years). Mean follow-up was 5.9 months. The process composite score was significantly better for patients in the intervention group than for control patients (difference 1.27, 95% confidence interval [CI] 0.79-1.75, p < 0.001); 61.7% (156/253) of patients in the intervention group, compared with 42.6% (110/258) of control patients, showed improvement (difference 19.1%, p < 0.001). The clinical composite score also had significantly more variables with improvement for the intervention group (0.59, 95% CI 0.09-1.10, p = 0.02), including significantly greater declines in blood pressure (-3.95 mm Hg systolic and -2.38 mm Hg diastolic) and glycated hemoglobin (-0.2%). Patients in the intervention group reported greater satisfaction with their diabetes care.
A shared electronic decision-support system to support the primary care of diabetes improved the process of care and some clinical markers of the quality of diabetes care. (ClinicalTrials.gov trial register no. NCT00813085.).
Canadian Medical Association Journal 08/2009; 181(1-2):37-44. · 8.22 Impact Factor
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ABSTRACT: 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.
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.
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.
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.
BMC Medical Informatics and Decision Making 03/2009; 9:11. · 1.48 Impact Factor
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Anne Holbrook
ACP journal club 12/2008; 149(5):5.
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ABSTRACT: OBJECTIVE All electronic health (e-health) interventions require validation as health information technologies, ideally in randomized controlled trial settings. However, as with other types of complex interventions involving various active components and multiple targets, health informatics trials often experience problems of design, methodology, or analysis that can influence the results and acceptance of the research. Our objective was to review selected key methodologic issues in conducting and reporting randomized controlled trials in health informatics, provide examples from a recent study, and present practical recommendations. DESIGN For illustration, we use the COMPETE III study, a large randomized controlled clinical trial investigating the impact of a shared decision-support system on the quality of vascular disease management in Ontario, Canada. RESULTS We describe a set of methodologic, logistic, and statistical issues that should be considered when planning and implementing trials of complex e-health interventions, and provide practical recommendations for health informatics trialists. CONCLUSIONS Our recommendations emphasize validity and pragmatic considerations and would be useful for health informaticians conducting or evaluating e-health studies.
Journal of the American Medical Informatics Association 07/2008; 15(5):575-80. · 3.61 Impact Factor
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JAMIA. 01/2008; 15:575-580.
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ABSTRACT: Dosage adjustments are often used to manage HIV drug interactions, but little is known about their clinical significance. We examined patients from the Ontario HIV Cohort Study to assess the effects of dosage adjustments on plasma viral load. A significant reduction (0.67 log10 copies/mL) in viral load was associated with adjustments to manage efavirenz-based interactions (95% confidence interval, -1.33 to -0.01) but was not observed after adjustments to manage rifabutin-based (difference in viral load, 0.03 log10 copies/mL; 95% confidence interval, -0.71 to 0.77) or nevirapine-based interactions (difference in viral load, 0.09 log10 copies/mL; 95% confidence interval, -0.83 to 1.01).
Clinical Infectious Diseases 11/2007; 45(7):933-6. · 9.15 Impact Factor
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ABSTRACT: Decision aids have been shown to be useful in selected situations to assist patients in making treatment decisions. Important features such as the format of decision aids and their graphic presentation of data on benefits and harms of treatment options have not been well studied.
In a randomized trial with a 3 x 2 factorial design, we investigated the effects of decision aid format (decision board, decision booklet with audiotape, or interactive computer program) and graphic presentation of data (pie graph or pictogram) on patients' comprehension and choices of 3 treatments for anticoagulation, identified initially as "treatment A" (warfarin), "treatment B" (acetylsalicylic acid) and "treatment C" (no treatment). Patients aged 65 years or older without known atrial fibrillation and not currently taking warfarin were included. The effect of blinding to the treatment name was tested in a before-after comparison. The primary outcome was change in comprehension score, as assessed by the Atrial Fibrillation Information Questionnaire. Secondary outcomes were treatment choice, level of satisfaction with the decision aid, and decisional conflict.
Of 102 eligible patients, 98 completed the study. Comprehension scores (maximum score 10) increased by an absolute mean of 3.1 (p < 0.01) after exposure to the decision aid regardless of the format or graphic presentation. Overall, 96% of the participants felt that the decision aid helped them make their treatment choice. Unblinding of the treatment name resulted in 36% of the participants changing their initial choice (p < 0.001).
The decision aid led to significant improvement in patients' knowledge regardless of the format or graphic representation of data. Revealing the name of the treatment options led to significant shifts in declared treatment preferences.
Canadian Medical Association Journal 06/2007; 176(11):1583-7. · 8.22 Impact Factor
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ABSTRACT: To critically review the body of literature that refutes or supports the role of antimalarials in the exacerbation of psoriasis.
MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were reviewed to identify English-language publications from 1966-2005 examining the role of antimalarials in the exacerbation of psoriasis. A total of 374 articles were identified, of which 32 studies met the inclusion criteria. All available clinical trials or reported cases of the use of antimalarials for patients with psoriasis were included. Data from clinical studies were summarized according to the level of evidence and the outcome of the study. Data were entered into a standardized data extraction form by two independent reviewers.
No randomized trial evidence was found. Only one cohort study was available for review. A total of 31 case series and case reports were obtained. There is no strong evidence to refute or support the role of antimalarials in the exacerbation of psoriasis. Controlled trials of antimalarial therapy and its effect on psoriasis are warranted.
American Journal of Clinical Dermatology 02/2006; 7(4):249-57. · 1.71 Impact Factor
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07/2005; , ISBN: 9780470011812
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ABSTRACT: Every health policy jurisdiction is endeavoring to enhance its ability to evaluate drug effectiveness, safety and cost in the real world (pharmacosurveillance).
A nominal group consensus conference of stakeholders finalized data items deemed necessary for pharmacosurveillance. Large administrative datasets (LADs), electronic health records (EHRs) and electronic patient registries (PRs), were investigated as sources of this information and for their vulnerability to methodologic bias. Health data privacy legislation and research guidelines were systematically reviewed for their constraint to linked data resource analyses.
More than 129 data items were strongly recommended for routine pharmacosurveillance. LADs had very complete information, but restricted to a small number of required data items. EHRs, especially with e-pharmacy links, offer by far the most complete set of health information domains but data entry completeness is highly variable. Adjustment methods for channeling bias are inadequate to mimic randomized trials. Anonymized, linked data held within a secure academic research environment, poses the least privacy concerns.
Notwithstanding major technical, methodologic and privacy challenges, individual-level linkage of health data resources poses the best option for pharmacosurveillance today. In future, drug regulators and reimbursement agencies should consider mandatory post-marketing randomized trials.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 02/2005;
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AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 02/2005;
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ABSTRACT: To discuss the potential use of data mining and knowledge discovery in databases for detection of adverse drug events (ADE) in pharmacovigilance.
A literature search was conducted to identify articles, which contained details of data mining, signal generation or knowledge discovery in relation to adverse drug reactions or pharmacovigilance in medical databases.
ADEs are common and result in significant mortality, and despite existing systems drugs have been withdrawn due to ADEs many years after licensing. Knowledge discovery in databases (KDD) is a technique which may be used to detect potential ADEs more efficiently. KDD involves the selection of data variables and databases, data preprocessing, data mining and data interpretation and utilization. Data mining encompasses a number of statistical techniques including cluster analysis, link analysis, deviation detection and disproportionality assessment which can be utilized to determine the presence of and to assess the strength of ADE signals. Currently the only data mining methods to be used in pharmacovigilance are those of disproportionality, such as the Proportional Reporting Ratio and Information Component, which have been used to analyse the UK Yellow Card Scheme spontaneous reporting database and the WHO Uppsala Monitoring Centre database. The association of pericarditis with practolol but not with other beta-blockers, the association of captopril and other angiotensin-converting enzymes with cough, and the association of terfenadine with heart rate and rhythm disorders could be identified by mining the WHO database.
In view of the importance of ADEs and the development of massive data storage systems and powerful computer systems, the use of data mining techniques in knowledge discovery in medical databases is likely to be of increasing importance in the process of pharmacovigilance as they are likely to be able to detect signals earlier than using current methods.
British Journal of Clinical Pharmacology 03/2004; 57(2):127-34. · 2.96 Impact Factor
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ABSTRACT: Placebos remain highly controversial therapies largely because of their widespread use in research as a comparator rather than a focus of analysis. While a recent systematic review of placebo versus no therapy arms in trials found no difference, the placebo effect in some areas of drug trial research is large and increasing. We attempt to explain this paradox and suggest how clinicians may use the placebo effect to advantage.
The Canadian journal of clinical pharmacology = Journal canadien de pharmacologie clinique 02/2004; 11(1):e39-40.
