Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review.
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ABSTRACT:
SUBMITTER: Varghese J
PROVIDER: S-EPMC7646949 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
Publications
Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review.
Varghese Julian J Kleine Maren M Gessner Sophia Isabella SI Sandmann Sarah S Dugas Martin M
Journal of the American Medical Informatics Association : JAMIA 20180501 5
<h4>Objectives</h4>To systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care.<h4>Materials and methods</h4>Medline, Cochrane Trials, and Cochrane Reviews were searched for CDSS studies that assessed patient outcomes in inpatient settings. For each study, 2 physicians independently mapped patient outcome effects to a predefined medical effect score to assess the clinical impact of reported outcome effects. Disagreements were measure ...[more]
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Project description:BACKGROUND:Computerized decision support systems (CDSSs) are software programs that support the decision making of practitioners and other staff. Other reviews have analyzed the relationship between CDSSs, practitioner performance, and patient outcomes. These reviews reported positive practitioner performance in over half the articles analyzed, but very little information was found for patient outcomes. OBJECTIVE:The purpose of this review was to analyze the relationship between CDSSs, practitioner performance, and patient medical outcomes. PubMed, CINAHL, Embase, Web of Science, and Cochrane databases were queried. METHODS:Articles were chosen based on year published (last 10 years), high quality, peer-reviewed sources, and discussion of the relationship between the use of CDSS as an intervention and links to practitioner performance or patient outcomes. Reviewers used an Excel spreadsheet (Microsoft Corporation) to collect information on the relationship between CDSSs and practitioner performance or patient outcomes. Reviewers also collected observations of participants, intervention, comparison with control group, outcomes, and study design (PICOS) along with those showing implicit bias. Articles were analyzed by multiple reviewers following the Kruse protocol for systematic reviews. Data were organized into multiple tables for analysis and reporting. RESULTS:Themes were identified for both practitioner performance (n=38) and medical outcomes (n=36). A total of 66% (25/38) of articles had occurrences of positive practitioner performance, 13% (5/38) found no difference in practitioner performance, and 21% (8/38) did not report or discuss practitioner performance. Zero articles reported negative practitioner performance. A total of 61% (22/36) of articles had occurrences of positive patient medical outcomes, 8% (3/36) found no statistically significant difference in medical outcomes between intervention and control groups, and 31% (11/36) did not report or discuss medical outcomes. Zero articles found negative patient medical outcomes attributed to using CDSSs. CONCLUSIONS:Results of this review are commensurate with previous reviews with similar objectives, but unlike these reviews we found a high level of reporting of positive effects on patient medical outcomes.
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Project description:Acute medical care often demands timely, accurate decisions in complex situations. Computerized clinical decision support systems (CCDSSs) have many features that could help. However, as for any medical intervention, claims that CCDSSs improve care processes and patient outcomes need to be rigorously assessed. The objective of this review was to systematically review the effects of CCDSSs on process of care and patient outcomes for acute medical care.We conducted a decision-maker-researcher partnership systematic review. MEDLINE, EMBASE, Evidence-Based Medicine Reviews databases (Cochrane Database of Systematic Reviews, DARE, ACP Journal Club, and others), and the Inspec bibliographic database were searched to January 2010, in all languages, for randomized controlled trials (RCTs) of CCDSSs in all clinical areas. We included RCTs that evaluated the effect on process of care or patient outcomes of a CCDSS used for acute medical care compared with 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.Thirty-six studies met our inclusion criteria for acute medical care. The CCDSS improved process of care in 63% (22/35) of studies, including 64% (9/14) of medication dosing assistants, 82% (9/11) of management assistants using alerts/reminders, 38% (3/8) of management assistants using guidelines/algorithms, and 67% (2/3) of diagnostic assistants. Twenty studies evaluated patient outcomes, of which three (15%) reported improvements, all of which were medication dosing assistants.The majority of CCDSSs demonstrated improvements in process of care, but patient outcomes were less likely to be evaluated and far less likely to show positive results.
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Project description:BackgroundComputerized clinical decision support systems (CCDSSs) are claimed to improve processes and outcomes of primary preventive care (PPC), but their effects, safety, and acceptance must be confirmed. We updated our previous systematic reviews of CCDSSs and integrated a knowledge translation approach in the process. The objective was to review randomized controlled trials (RCTs) assessing the effects of CCDSSs for PPC on process of care, patient outcomes, harms, and costs.MethodsWe conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews Database, Inspec, and other databases, as well as reference lists through January 2010. We contacted authors to confirm data or provide additional information. We included RCTs that assessed the effect of a CCDSS for PPC on process of care and patient outcomes 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.ResultsWe added 17 new RCTs to our 2005 review for a total of 41 studies. RCT quality improved over time. CCDSSs improved process of care in 25 of 40 (63%) RCTs. Cumulative scientifically strong evidence supports the effectiveness of CCDSSs for screening and management of dyslipidaemia in primary care. There is mixed evidence for effectiveness in screening for cancer and mental health conditions, multiple preventive care activities, vaccination, and other preventive care interventions. Fourteen (34%) trials assessed patient outcomes, and four (29%) reported improvements with the CCDSS. Most trials were not powered to evaluate patient-important outcomes. CCDSS costs and adverse events were reported in only six (15%) and two (5%) trials, respectively. Information on study duration was often missing, limiting our ability to assess sustainability of CCDSS effects.ConclusionsEvidence supports the effectiveness of CCDSSs for screening and treatment of dyslipidaemia in primary care with less consistent evidence for CCDSSs used in screening for cancer and mental health-related conditions, vaccinations, and other preventive care. CCDSS effects on patient outcomes, safety, costs of care, and provider satisfaction remain poorly supported.
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Project description:ObjectiveIn 2004, results from The Texas Medication Algorithm Project (TMAP) showed better clinical outcomes for patients whose physicians adhered to a paper-and-pencil algorithm compared to patients who received standard clinical treatment for major depressive disorder (MDD). However, implementation of and fidelity to the treatment algorithm among various providers was observed to be inadequate. A computerized decision support system (CDSS) for the implementation of the TMAP algorithm for depression has since been developed to improve fidelity and adherence to the algorithm.MethodThis was a 2-group, parallel design, clinical trial (one patient group receiving MDD treatment from physicians using the CDSS and the other patient group receiving usual care) conducted at 2 separate primary care clinics in Texas from March 2005 through June 2006. Fifty-five patients with MDD (DSM-IV criteria) with no significant difference in disease characteristics were enrolled, 32 of whom were treated by physicians using CDSS and 23 were treated by physicians using usual care. The study's objective was to evaluate the feasibility and efficacy of implementing a CDSS to assist physicians acutely treating patients with MDD compared to usual care in primary care. Primary efficacy outcomes for depression symptom severity were based on the 17-item Hamilton Depression Rating Scale (HDRS(17)) evaluated by an independent rater.ResultsPatients treated by physicians employing CDSS had significantly greater symptom reduction, based on the HDRS(17), than patients treated with usual care (P < .001).ConclusionsThe CDSS algorithm, utilizing measurement-based care, was superior to usual care for patients with MDD in primary care settings. Larger randomized controlled trials are needed to confirm these findings.Trial registrationclinicaltrials.gov Identifier: NCT00551083.
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Project description:Importance:Sophisticated evidence-based information resources can filter medical evidence from the literature, integrate it into electronic health records, and generate recommendations tailored to individual patients. Objective:To assess the effectiveness of a computerized clinical decision support system (CDSS) that preappraises evidence and provides health professionals with actionable, patient-specific recommendations at the point of care. Design, Setting, and Participants:Open-label, parallel-group, randomized clinical trial among internal medicine wards of a large Italian general hospital. All analyses in this randomized clinical trial followed the intent-to-treat principle. Between November 1, 2015, and December 31, 2016, patients were randomly assigned to the intervention group, in which CDSS-generated reminders were displayed to physicians, or to the control group, in which reminders were generated but not shown. Data were analyzed between February 1 and July 31, 2018. Interventions:Evidence-Based Medicine Electronic Decision Support (EBMEDS), a commercial CDSS covering a wide array of health conditions across specialties, was integrated into the hospital electronic health records to generate patient-specific recommendations. Main Outcomes and Measures:The primary outcome was the resolution rate, the rate at which medical problems identified and alerted by the CDSS were addressed by a change in practice. Secondary outcomes included the length of hospital stay and in-hospital all-cause mortality. Results:In this randomized clinical trial, 20 563 patients were admitted to the hospital. Of these, 6480 (31.5%) were admitted to the internal medicine wards (study population) and randomized (3242 to CDSS and 3238 to control). The mean (SD) age of patients was 70.5 (17.3) years, and 54.5% were men. In total, 28 394 reminders were generated throughout the course of the trial (median, 3 reminders per patient per hospital stay; interquartile range [IQR], 1-6). These messages led to a change in practice in approximately 4 of 100 patients. The resolution rate was 38.0% (95% CI, 37.2%-38.8%) in the intervention group and 33.7% (95% CI, 32.9%-34.4%) in the control group, corresponding to an odds ratio of 1.21 (95% CI, 1.11-1.32; P < .001). The length of hospital stay did not differ between the groups, with a median time of 8 days (IQR, 5-13 days) for the intervention group and a median time of 8 days (IQR, 5-14 days) for the control group (P = .36). In-hospital all-cause mortality also did not differ between groups (odds ratio, 0.95; 95% CI, 0.77-1.17; P = .59). Alert fatigue did not differ between early and late study periods. Conclusions and Relevance:An international commercial CDSS intervention marginally influenced routine practice in a general hospital, although the change did not statistically significantly affect patient outcomes. Trial Registration:ClinicalTrials.gov identifier: NCT02577198.
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Project description:ObjectiveThe study sought to summarize research literature on nursing decision support systems (DSSs ); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes.Materials and methodsWe conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects. Information about the stages of automation (information acquisition, information analysis, decision and action selection, and action implementation), NCP, and effects was assessed.ResultsOf 1019 articles retrieved, 28 met the inclusion criteria, each studying a unique DSS. Most DSSs were concerned with two NCP steps: assessment (82%) and intervention (86%). In terms of automation, all included DSSs automated information analysis and decision selection. Five DSSs automated information acquisition and only one automated action implementation. Effects on decision making, care delivery, and patient outcome were mixed. DSSs improved compliance with recommendations and reduced decision time, but impacts were not always sustainable. There were improvements in evidence-based practice, but impact on patient outcomes was mixed.ConclusionsCurrent nursing DSSs do not adequately support the NCP and have limited automation. There remain many opportunities to enhance automation, especially at the stage of information acquisition. Further research is needed to understand how automation within the NCP can improve nurses' decision making, care delivery, and patient outcomes.
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Project description:To deal with complexity in cancer care, computerized clinical decision support systems (CDSSs) are developed to support quality of care and improve decision-making. We performed a systematic review to explore the value of CDSSs using automated clinical guidelines, Artificial Intelligence, datamining or statistical methods (higher level CDSSs) on the quality of care in oncology. The search strategy combined synonyms for 'CDSS' and 'cancer.' Pubmed, Embase, The Cochrane Library, Institute of Electrical and Electronics Engineers, Association of Computing Machinery digital library and Web of Science were systematically searched from January 2000 to December 2019. Included studies evaluated the impact of higher level CDSSs on process outcomes, guideline adherence and clinical outcomes. 11,397 studies were selected for screening, after which 61 full-text articles were assessed for eligibility. Finally, nine studies were included in the final analysis with a total population size of 7985 patients. Types of cancer included breast cancer (63.1%), lung cancer (27.8%), prostate cancer (4.1%), colorectal cancer (3.1%) and other cancer types (1.9%). The included studies demonstrated significant improvements of higher level CDSSs on process outcomes and guideline adherence across diverse settings in oncology. No significant differences were reported for clinical outcomes. Higher level CDSSs seem to improve process outcomes and guidelines adherence but not clinical outcomes. It should be noticed that the included studies primarily focused on breast and lung cancer. To further explore the impact of higher level CDSSs on quality of care, high-quality research is required.
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Project description:BackgroundOvertesting and overdiagnosis of Clostridioides difficile infection are suspected to be common. Reducing inappropriate testing through interventions designed to promote evidence-based diagnostic testing (ie, diagnostic stewardship) may improve C. difficile test utilization. However, the safety of these interventions is not well understood despite the potential risk for missed or delayed diagnoses.MethodsThis retrospective case-control study examined the outcomes of patients admitted to the University of Virginia Medical Center following introduction of a computerized clinical decision support tool without hard-stops designed to reduce inappropriate tests. Outcomes were compared between patients with a prevented C. difficile nucleic acid amplification test and those with a negative result. Chart reviews were performed for patients with a subsequent positive within 7 days, as well as those patients who received C. difficile-active antibiotics after implementation of the computerized clinical decision support tool.ResultsMultivariate analysis of 637 cases (490 negative, 147 prevented) showed that a prevented test was not significantly associated with the primary composite outcome (inpatient mortality or intensive care unit transfer) compared with a negative test (adjusted odds ratio, 0.912; P = .747). Fifty-four of 147 (37%) prevented tests were followed by a completed test within 7 days; 11 of these results were positive, resulting in a potential delay in diagnosis. Individual case reviews found that either clinical changes warranted the delay in testing or no adverse events occurred attributable to C. difficile infection. C. difficile treatment without a positive test was not identified.ConclusionsDiagnostic stewardship of C. difficile testing using computerized clinical decision support may be both safe and effective for reducing inappropriate inpatient testing.
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Project description:Background Interprofessional collaboration (IPC) is seen as the “gold standard” of comprehensive care, but credible evidence concerning the effects on patient-reported outcomes (PRO) is lacking. The aim of this systematic review is to study the effect of IPC on PRO in inpatient care. Methods We systematically searched six electronic databases (PubMed, Web of Science/Social Science Citation Index, CENTRAL (Cochrane Library), Current Contents (LIVIVO), CINAHL, and Embase) for studies published between 1997 and 2021. Additional studies were identified through citation tracking, manually searching the Internet and Google Scholar, and consultation of experts. Risk of bias (RoB) was assessed using the RoB 2 tool for randomized controlled trials (RCTs) and ROBINS-I for non-randomized studies (NRS). The included controlled before-and-after study (CBA) was assessed using both the ROBINS-I and the Effective Practice and Organization of Care (EPOC) quality criteria. Results were synthesized through narrative description, grouping, and thematic analysis of extracted data. Results The search yielded 10,213 records, from which 22 studies (16 RCTs, five NRS, and one CBA) fulfilled the inclusion criteria. In all but five studies, RoB was assessed as being high (RoB 2) resp. critical or serious (ROBINS-I). Within these 22 studies, nine inductively derived outcomes were assessed: (i) quality of life, (ii) coping, (iii) functional ability and health status, (iv) psychiatric morbidity, (v) pain, (vi) managing one’s own health care, (vii) treatment success, (viii) satisfaction, and (ix) therapeutic relationship. While some studies do not report effect estimates, and some of the reported effects appear to be imprecisely estimated, the overall results indicate that IPC may affect PRO positively across all outcomes. Conclusions Due to high clinical heterogeneity and high RoB, the question whether IPC affects PRO cannot be answered conclusively. Methodically rigorous studies are needed in order to answer the question of effectiveness of IPC. Systematic review registration PROSPERO CRD42017073900 Supplementary Information The online version contains supplementary material available at 10.1186/s13643-022-02027-x.
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