Clinical Experience with a Decision Support Computer Program Using Bayes’ Theorem to Diagnose Chest Pain Patients

Aase, O.

doi:10.1159/000006960

Cited by 13 publications

(15 citation statements)

References 5 publications

(9 reference statements)

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“…7,500 references were excluded after screening of titles and abstracts. We reviewed 163 full-text articles, of which 78 [13-90] met criteria for inclusion in the review (see Figure 1). Characteristics of these studies are summarized in Additional file 1: Table S1.…”

Section: Resultsmentioning

confidence: 99%

“…29.5% of the studies occurred in an ICU setting [18,23,31,34-36,39,40,44,47],[51,57-59,63,65,68,73,79,82],[86,88,89], and 15.4% took place in an ED setting [13,20-22,27,29,38,45,75,81],[83,84]. …”

Section: Resultsmentioning

confidence: 99%

“…A large proportion (84.6%) [13-18,20-27,29,30,32-37,39-52,54-58,60],[61,64-70,72-78,82-90] of the studies were quasi-experimental trials, which can be defined as studies that aim to evaluate interventions without the use of randomization [91]. Overall, the most common design used in the studies was the controlled before-after design, wherein researchers used historical controls prior to the implementation of an intervention.…”

Section: Resultsmentioning

confidence: 99%

“…Overall, the most common design used in the studies was the controlled before-after design, wherein researchers used historical controls prior to the implementation of an intervention. Of the 78 studies, 61 (78.2%) were some form of controlled before-after study [13-18,20-27,29,30,32-37,39-51,54,55],[57,60,61,64,65,68-70,72-78,82-90]. Only 12 (15.4%) of the studies were randomized controlled trials (RCTs) [19,28,31,38,53,59,62,63],[71,79-81].…”

Section: Resultsmentioning

confidence: 99%

“…87.1% of the studies in the review solely reported proxy measures as indicators of impact on cost [13-19,21-23,25-32,34,38-61,63,65-67,69],[70,72-90]. The most commonly used type of proxy measure in this group was process measures that were associated with adverse events.…”

Section: Resultsmentioning

confidence: 99%

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Systematic review of clinical decision support interventions with potential for inpatient cost reduction

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Bray

Kawamoto

2013

BMC Med Inform Decis Mak

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BackgroundHealthcare costs are increasing rapidly and at an unsustainable rate in many countries, and inpatient hospitalizations are a significant driver of these costs. Clinical decision support (CDS) represents a promising approach to not only improve care but to reduce costs in the inpatient setting. The purpose of this study was to systematically review trials of CDS interventions with the potential to reduce inpatient costs, so as to identify promising interventions for more widespread implementation and to inform future research in this area.MethodsTo identify relevant studies, MEDLINE was searched up to July 2013. CDS intervention studies with the potential to reduce inpatient healthcare costs were identified through titles and abstracts, and full text articles were reviewed to make a final determination on inclusion. Relevant characteristics of the studies were extracted and summarized.ResultsFollowing a screening of 7,663 articles, 78 manuscripts were included. 78.2% of studies were controlled before-after studies, and 15.4% were randomized controlled trials. 53.8% of the studies were focused on pharmacotherapy. The majority of manuscripts were published during or after 2008. 70.5% of the studies resulted in statistically and clinically significant improvements in an explicit financial measure or a proxy financial measure. Only 12.8% of the studies directly measured the financial impact of an intervention, whereas the financial impact was inferred in the remainder of studies. Data on cost effectiveness was available for only one study.ConclusionsSignificantly more research is required on the impact of clinical decision support on inpatient costs. In particular, there is a remarkable gap in the availability of cost effectiveness studies required by policy makers and decision makers in healthcare systems.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Systematic review of clinical decision support interventions with potential for inpatient cost reduction

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Bray

Kawamoto

2013

BMC Med Inform Decis Mak

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The effects of on-screen, point of care computer reminders on processes and outcomes of care

Shojania

Jennings²,

Mayhew

et al. 2009

Cochrane Database of Systematic Reviews

361

339

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Background The opportunity to improve care by delivering decision support to clinicians at the point of care represents one of the main incentives for implementing sophisticated clinical information systems. Previous reviews of computer reminder and decision support systems have reported mixed effects, possibly because they did not distinguish point of care computer reminders from e-mail alerts, computer-generated paper reminders, and other modes of delivering ‘computer reminders’. Objectives To evaluate the effects on processes and outcomes of care attributable to on-screen computer reminders delivered to clinicians at the point of care. Search methods We searched the Cochrane EPOC Group Trials register, MEDLINE, EMBASE and CINAHL and CENTRAL to July 2008, and scanned bibliographies from key articles. Selection criteria Studies of a reminder delivered via a computer system routinely used by clinicians, with a randomised or quasi-randomised design and reporting at least one outcome involving a clinical endpoint or adherence to a recommended process of care. Data collection and analysis Two authors independently screened studies for eligibility and abstracted data. For each study, we calculated the median improvement in adherence to target processes of care and also identified the outcome with the largest such improvement. We then calculated the median absolute improvement in process adherence across all studies using both the median outcome from each study and the best outcome. Main results Twenty-eight studies (reporting a total of thirty-two comparisons) were included. Computer reminders achieved a median improvement in process adherence of 4.2% (interquartile range (IQR): 0.8% to 18.8%) across all reported process outcomes, 3.3% (IQR: 0.5% to 10.6%) for medication ordering, 3.8% (IQR: 0.5% to 6.6%) for vaccinations, and 3.8% (IQR: 0.4% to 16.3%) for test ordering. In a sensitivity analysis using the best outcome from each study, the median improvement was 5.6% (IQR: 2.0% to 19.2%) across all process measures and 6.2% (IQR: 3.0% to 28.0%) across measures of medication ordering. In the eight comparisons that reported dichotomous clinical endpoints, intervention patients experienced a median absolute improvement of 2.5% (IQR: 1.3% to 4.2%). Blood pressure was the most commonly reported clinical endpoint, with intervention patients experiencing a median reduction in their systolic blood pressure of 1.0 mmHg (IQR: 2.3 mmHg reduction to 2.0 mmHg increase). Authors’ conclusions Point of care computer reminders generally achieve small to modest improvements in provider behaviour. A minority of interventions showed larger effects, but no specific reminder or contextual features were significantly associated with effect magnitude. Further research must identify design features and contextual factors consistently associated with larger improvements in provider behaviour if computer reminders are to succeed on more than a trial and error basis.

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Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

et al. 2017

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Clinical Experience with a Decision Support Computer Program Using Bayes’ Theorem to Diagnose Chest Pain Patients

Cited by 13 publications

References 5 publications

Systematic review of clinical decision support interventions with potential for inpatient cost reduction

Systematic review of clinical decision support interventions with potential for inpatient cost reduction

The effects of on-screen, point of care computer reminders on processes and outcomes of care

Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

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