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Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial.

ABSTRACT: Accurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date.To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation.Inference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009-5/2010) and intervention (5/2010-11/2010) periods.17,043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions.Problem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement.ClinicalTrials.gov: NCT01105923.

SUBMITTER: Wright A

PROVIDER: S-EPMC3384110 | biostudies-literature | 2012 Jul-Aug

REPOSITORIES: biostudies-literature

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Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial.

Wright Adam A Pang Justine J Feblowitz Joshua C JC Maloney Francine L FL Wilcox Allison R AR McLoughlin Karen Sax KS Ramelson Harley H Schneider Louise L Bates David W DW

Journal of the American Medical Informatics Association : JAMIA 20120103 4

<h4>Background</h4>Accurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date.<h4>Objective</h4>To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation.<h4>Study design and methods</h4>Inference rules for 17 conditions were constructed and an electron ...[more]

PMID: 22215056

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Project description:ImportanceInappropriate use of antibiotics for diarrheal illness can result in adverse effects and increase in antimicrobial resistance.ObjectiveTo determine whether the diarrheal etiology prediction (DEP) algorithm, which uses patient-specific and location-specific features to estimate the probability that diarrhea etiology is exclusively viral, impacts antibiotic prescriptions in patients with acute diarrhea.Design, setting, and participantsA randomized crossover study was conducted to evaluate the DEP incorporated into a smartphone-based electronic clinical decision-support (eCDS) tool. The DEP calculated the probability of viral etiology of diarrhea, based on dynamic patient-specific and location-specific features. Physicians were randomized in the first 4-week study period to the intervention arm (eCDS with the DEP) or control arm (eCDS without the DEP), followed by a 1-week washout period before a subsequent 4-week crossover period. The study was conducted at 3 sites in Bangladesh from November 17, 2021, to January 21, 2021, and at 4 sites in Mali from January 6, 2021, to March 5, 2021. Eligible physicians were those who treated children with diarrhea. Eligible patients were children between ages 2 and 59 months with acute diarrhea and household access to a cell phone for follow-up.InterventionsUse of the eCDS with the DEP (intervention arm) vs use of the eCDS without the DEP (control arm).Main outcomes and measuresThe primary outcome was the proportion of children prescribed an antibiotic.ResultsA total of 30 physician participants and 941 patient participants (57.1% male; median [IQR] age, 12 [8-18] months) were enrolled. There was no evidence of a difference in the proportion of children prescribed antibiotics by physicians using the DEP (risk difference [RD], -4.2%; 95% CI, -10.7% to 1.0%). In a post hoc analysis that accounted for the predicted probability of a viral-only etiology, there was a statistically significant difference in risk of antibiotic prescription between the DEP and control arms (RD, -0.056; 95% CI, -0.128 to -0.01). No known adverse effects of the DEP were detected at 10-day postdischarge.Conclusions and relevanceUse of a tool that provides an estimate of etiological likelihood did not result in a significant change in overall antibiotic prescriptions. Post hoc analysis suggests that a higher predicted probability of viral etiology was linked to reductions in antibiotic use.Trial registrationClinicaltrials.gov Identifier: NCT04602676.

Project description:BackgroundAcute diarrhoeal disease management often requires rehydration alone without antibiotics. However, non-indicated antibiotics are frequently ordered and this is an important driver of antimicrobial resistance. The mHealth Diarrhoea Management (mHDM) trial aimed to establish whether electronic decision support improves rehydration and antibiotic guideline adherence in resource-limited settings.MethodsA cluster randomised controlled trial was done at ten district hospitals in Bangladesh. Inclusion criteria were patients aged 2 months or older with uncomplicated acute diarrhoea. Admission orders were observed without intervention in the pre-intervention period, followed by randomisation to electronic (rehydration calculator) or paper formatted WHO guidelines for the intervention period. The primary outcome was rate of intravenous fluid ordered as a binary variable. Generalised linear mixed-effect models, accounting for hospital clustering, served as the analytical framework; the analysis was intention to treat. The trial is registered with ClinicalTrials.gov (NCT03154229) and is completed.FindingsFrom March 11 to Sept 10, 2018, 4975 patients (75·6%) of 6577 screened patients were enrolled. The intervention effect for the primary outcome showed no significant differences in rates of intravenous fluids ordered as a function of decision-support type. Intravenous fluid orders decreased by 0·9 percentage points for paper electronic decision support and 4·2 percentage points for electronic decision support, with a 4·2-point difference between decision-support types in the intervention period (paper 98·7% [95% CI 91·8-99·8] vs electronic 94·5% [72·2-99·1]; pinteraction=0·31). Adverse events such as complications and mortality events were uncommon and could not be statistically estimated.InterpretationAlthough intravenous fluid orders did not change, electronic decision support was associated with increases in the volume of intravenous fluid ordered and decreases in antibiotics ordered, which are consistent with WHO guidelines.FundingUS National Institutes of Health.

Project description:Rationale & objectiveMost adults with chronic kidney disease (CKD) in the United States are cared for by primary care providers (PCPs). We evaluated the feasibility and preliminary effectiveness of an electronic clinical decision support system (eCDSS) within the electronic health record with or without pharmacist follow-up to improve the management of CKD in primary care.Study designPragmatic cluster-randomized trial.Setting & participants524 adults with confirmed creatinine-based estimated glomerular filtration rates of 30 to 59mL/min/1.73m2 cared for by 80 PCPs at the University of California San Francisco. Electronic health record data were used for patient identification, intervention deployment, and outcomes ascertainment.InterventionsEach PCP's eligible patients were randomly assigned as a group into 1 of 3 treatment arms: (1) usual care; (2) eCDSS: testing of creatinine, cystatin C, and urinary albumin-creatinine ratio with individually tailored guidance for PCPs on blood pressure, potassium, and proteinuria management, cardiovascular risk reduction, and patient education; or (3) eCDSS plus pharmacist counseling (eCDSS-PLUS).OutcomesThe primary clinical outcome was change in blood pressure over 12 months. Secondary outcomes were PCP awareness of CKD and use of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker and statin therapy.ResultsAll 80 eligible PCPs participated. Mean patient age was 70 years, 47% were nonwhite, and mean estimated glomerular filtration rate was 56±0.6mL/min/1.73m2. Among patients receiving eCDSS with or without pharmacist counseling (n=336), 178 (53%) completed laboratory measurements and 138 (41%) had laboratory measurements followed by a PCP visit with eCDSS deployment. eCDSS was opened by the PCP for 102 (74%) patients, with at least 1 suggested order signed for 83 of these 102 (81%). Changes in systolic blood pressure were-2.1±1.5mm Hg with usual care, -2.8±1.8mm Hg with eCDSS, and -1.1±1.1 with eCDSS-PLUS (P=0.7). PCP awareness of CKD was 16% with usual care, 26% with eCDSS, and 32% for eCDSS-PLUS (P=0.09). In as-treated analyses, PCP awareness of CKD was significantly greater with eCDSS and eCDSS-PLUS (73% and 69%) versus usual care (47%; P=0.002).LimitationsRecruitment of smaller than intended sample size and limited uptake of the testing component of the intervention.ConclusionsAlthough we were unable to demonstrate the effectiveness of eCDSS to lower blood pressure and uptake of the eCDSS was limited by low testing rates, eCDSS use was high when laboratory measurements were available and was associated with higher PCP awareness of CKD.FundingGrants from government (National Institutes of Health) and not-for-profit (American Heart Association) entities.Trial registrationRegistered at ClinicalTrials.gov with study number NCT02925962.

Project description:BackgroundPotentially Inappropriate Prescriptions (PIPs) are a common cause of morbidity, particularly in the elderly.ObjectiveWe sought to understand how the Screening Tool of Older People's Prescriptions (STOPP) prescribing criteria, implemented in a routinely used primary care Electronic Medical Record (EMR), could impact PIP rates in community (non-academic) primary care practices.MethodsWe conducted a mixed-method, pragmatic, cluster, randomized control trial in research naïve primary care practices. Phase 1: In the randomized controlled trial, 40 fully automated STOPP rules were implemented as EMR alerts during a 16-week intervention period. The control group did not receive the 40 STOPP rules (but received other alerts). Participants were recruited through the OSCAR EMR user group mailing list and in person at user group meetings. Results were assessed by querying EMR data PIPs. EMR data quality probes were included. Phase 2: physicians were invited to participate in 1-hour semi-structured interviews to discuss the results.ResultsIn the EMR, 40 STOPP rules were successfully implemented. Phase 1: A total of 28 physicians from 8 practices were recruited (16 in intervention and 12 in control groups). The calculated PIP rate was 2.6% (138/5308) (control) and 4.11% (768/18,668) (intervention) at baseline. No change in PIPs was observed through the intervention (P=.80). Data quality probes generally showed low use of problem list and medication list. Phase 2: A total of 5 physicians participated. All the participants felt that they were aware of the alerts but commented on workflow and presentation challenges.ConclusionsThe calculated PIP rate was markedly less than the expected rate found in literature (2.6% and 4.0% vs 20% in literature). Data quality probes highlighted issues related to completeness of data in areas of the EMR used for PIP reporting and by the decision support such as problem and medication lists. Users also highlighted areas for better integration of STOPP guidelines with prescribing workflows. Many of the STOPP criteria can be implemented in EMRs using simple logic. However, data quality in EMRs continues to be a challenge and was a limiting step in the effectiveness of the decision support in this study. This is important as decision makers continue to fund implementation and adoption of EMRs with the expectation of the use of advanced tools (such as decision support) without ongoing review of data quality and improvement.Trial registrationClinicaltrials.gov NCT02130895; https://clinicaltrials.gov/ct2/show/NCT02130895 (Archived by WebCite at http://www.webcitation.org/6qyFigSYT).

Project description:BackgroundA clinical diagnostic support system (CDSS) can support medical students and physicians in providing evidence-based care. In this study, we investigate diagnostic accuracy based on the history of present illness between groups of medical students using a CDSS, Google, and neither (control). Further, the degree of diagnostic accuracy of medical students using a CDSS is compared with that of residents using neither a CDSS nor Google.MethodsThis study is a randomized educational trial. The participants comprised 64 medical students and 13 residents who rotated in the Department of General Medicine at Chiba University Hospital from May to December 2020. The medical students were randomly divided into the CDSS group (n = 22), Google group (n = 22), and control group (n = 20). Participants were asked to provide the three most likely diagnoses for 20 cases, mainly a history of a present illness (10 common and 10 emergent diseases). Each correct diagnosis was awarded 1 point (maximum 20 points). The mean scores of the three medical student groups were compared using a one-way analysis of variance. Furthermore, the mean scores of the CDSS, Google, and residents' (without CDSS or Google) groups were compared.ResultsThe mean scores of the CDSS (12.0 ± 1.3) and Google (11.9 ± 1.1) groups were significantly higher than those of the control group (9.5 ± 1.7; p = 0.02 and p = 0.03, respectively). The residents' group's mean score (14.7 ± 1.4) was higher than the mean scores of the CDSS and Google groups (p = 0.01). Regarding common disease cases, the mean scores were 7.4 ± 0.7, 7.1 ± 0.7, and 8.2 ± 0.7 for the CDSS, Google, and residents' groups, respectively. There were no significant differences in mean scores (p = 0.1).ConclusionsMedical students who used the CDSS and Google were able to list differential diagnoses more accurately than those using neither. Furthermore, they could make the same level of differential diagnoses as residents in the context of common diseases.Trial registrationThis study was retrospectively registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (unique trial number: UMIN000042831).

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Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial.

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Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial.

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