Clinical decision support for high-cost imaging: A randomized clinical trial.
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ABSTRACT:
SUBMITTER: Doyle J
PROVIDER: S-EPMC6419998 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
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Clinical decision support for high-cost imaging: A randomized clinical trial.
Doyle Joseph J Abraham Sarah S Feeney Laura L Reimer Sarah S Finkelstein Amy A
PloS one 20190315 3
There is widespread concern over the health risks and healthcare costs from potentially inappropriate high-cost imaging. As a result, the Centers for Medicare and Medicaid Services (CMS) will soon require high-cost imaging orders to be accompanied by Clinical Decision Support (CDS): software that provides appropriateness information at the time orders are placed via a best practice alert for targeted (i.e. likely inappropriate) imaging orders, although the impacts of CDS in this context are uncl ...[more]
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Project description:ImportanceAppendicitis is the most common pediatric surgical emergency. Efforts to improve efficiency and quality of care have increased reliance on computed tomography (CT) and ultrasonography (US) in children with suspected appendicitis.ObjectiveTo evaluate the effectiveness of an electronic health record-linked clinical decision support intervention, AppyCDS, on diagnostic imaging, health care costs, and safety outcomes for patients with suspected appendicitis.Design, setting, and participantsIn this parallel, cluster randomized trial, 17 community-based general emergency departments (EDs) in California, Minnesota, and Wisconsin were randomized to the AppyCDS intervention group or usual care (UC) group. Patients were aged 5 to 20 years, presenting for an ED visit with right-sided or diffuse abdominal pain lasting 5 days or less. We excluded pregnant patients, those with a prior appendectomy, those with selected comorbidities, and those with traumatic injuries. The trial was conducted from October 2016 to July 2019.InterventionsAppyCDS prompted data entry at the point of care to estimate appendicitis risk using the pediatric appendicitis risk calculator (pARC). Based on pARC estimates, AppyCDS recommended next steps in care.Main outcomes and measuresPrimary outcomes were CT, US, or any imaging (CT or US) during the index ED visit. Safety outcomes were perforations, negative appendectomies, and missed appendicitis. Costs were a secondary outcome. Ratio of ratios (RORs) for primary and safety outcomes and differences by group in cost were used to evaluate effectiveness of the clinical decision support tool.ResultsWe enrolled 3161 patients at intervention EDs and 2779 patients at UC EDs. The mean age of patients was 11.9 (4.6) years and 2614 (44.0%) were boys or young men. RORs for CT (0.94; 95% CI, 0.75-1.19), US (0.98; 95% CI, 0.84-1.14), and any imaging (0.96; 95% CI, 0.86-1.07) did not differ by study group. In an exploratory analysis conducted in 1 health system, AppyCDS was associated with a reduction in any imaging (ROR, 0.82; 95% CI, 0.73- 0.93) for patients with pARC score of 15% or less and a reduction in CT (ROR, 0.58; 95% CI, 0.45-0.74) for patients with a pARC score of 16% to 50%. Perforations, negative appendectomies, and cases of missed appendicitis by study phase did not differ significantly by study group. Costs did not differ overall by study group.Conclusions and relevanceIn this study, AppyCDS was not associated with overall reductions in diagnostic imaging; exploratory analysis revealed more appropriate use of imaging in patients with a low pARC score.Trial registrationClinicalTrials.gov Identifier: NCT02633735.
| S-EPMC7873779 | biostudies-literature
Project description:OBJECTIVES:Although blood pressure (BP) is routinely measured in outpatient visits, elevated BP and hypertension are often not recognized. We evaluated whether an electronic health record-linked clinical decision support (CDS) tool could improve the recognition and management of hypertension in adolescents. METHODS:We randomly assigned 20 primary care clinics within an integrated care system to CDS or usual care. At intervention sites, the CDS displayed BPs and percentiles, identified incident hypertension on the basis of current or previous BPs, and offered tailored order sets. The recognition of hypertension was identified by an automated review of diagnoses and problem lists and a manual review of clinical notes, antihypertensive medication prescriptions, and diagnostic testing. Generalized linear mixed models were used to test the effect of the intervention. RESULTS:Among 31?579 patients 10 to 17 years old with a clinic visit over a 2-year period, 522 (1.7%) had incident hypertension. Within 6 months of meeting criteria, providers recognized hypertension in 54.9% of patients in CDS clinics and 21.3% of patients in usual care (P ? .001). Clinical recognition was most often achieved through visit diagnoses or documentation in the clinical note. Within 6 months of developing incident hypertension, 17.1% of CDS subjects were referred to dieticians or weight loss or exercise programs, and 9.4% had additional hypertension workup versus 3.9% and 4.2%, respectively (P = .001 and .046, respectively). Only 1% of patients were prescribed an antihypertensive medication within 6 months of developing hypertension. CONCLUSIONS:The CDS had a significant, beneficial effect on the recognition of hypertension, with a moderate increase in guideline-adherent management.
| S-EPMC5810603 | biostudies-literature
Project description:ImportanceChronic kidney disease (CKD) affects 37 million adults in the United States, and for patients with CKD, hypertension is a key risk factor for adverse outcomes, such as kidney failure, cardiovascular events, and death.ObjectiveTo evaluate a computerized clinical decision support (CDS) system for the management of uncontrolled hypertension in patients with CKD.Design, setting, and participantsThis multiclinic, randomized clinical trial randomized primary care practitioners (PCPs) at a primary care network, including 15 hospital-based, ambulatory, and community health center-based clinics, through a stratified, matched-pair randomization approach February 2021 to February 2022. All adult patients with a visit to a PCP in the last 2 years were eligible and those with evidence of CKD and hypertension were included.InterventionThe intervention consisted of a CDS system based on behavioral economic principles and human-centered design methods that delivered tailored, evidence-based recommendations, including initiation or titration of renin-angiotensin-aldosterone system inhibitors. The patients in the control group received usual care from PCPs with the CDS system operating in silent mode.Main outcomes and measuresThe primary outcome was the change in mean systolic blood pressure (SBP) between baseline and 180 days compared between groups. The primary analysis was a repeated measures linear mixed model, using SBP at baseline, 90 days, and 180 days in an intention-to-treat repeated measures model to account for missing data. Secondary outcomes included blood pressure (BP) control and outcomes such as percentage of patients who received an action that aligned with the CDS recommendations.ResultsThe study included 174 PCPs and 2026 patients (mean [SD] age, 75.3 [0.3] years; 1223 [60.4%] female; mean [SD] SBP at baseline, 154.0 [14.3] mm Hg), with 87 PCPs and 1029 patients randomized to the intervention and 87 PCPs and 997 patients randomized to usual care. Overall, 1714 patients (84.6%) were treated for hypertension at baseline. There were 1623 patients (80.1%) with an SBP measurement at 180 days. From the linear mixed model, there was a statistically significant difference in mean SBP change in the intervention group compared with the usual care group (change, -14.6 [95% CI, -13.1 to -16.0] mm Hg vs -11.7 [-10.2 to -13.1] mm Hg; P = .005). There was no difference in the percentage of patients who achieved BP control in the intervention group compared with the control group (50.4% [95% CI, 46.5% to 54.3%] vs 47.1% [95% CI, 43.3% to 51.0%]). More patients received an action aligned with the CDS recommendations in the intervention group than in the usual care group (49.9% [95% CI, 45.1% to 54.8%] vs 34.6% [95% CI, 29.8% to 39.4%]; P < .001).Conclusions and relevanceThese findings suggest that implementing this computerized CDS system could lead to improved management of uncontrolled hypertension and potentially improved clinical outcomes at the population level for patients with CKD.Trial registrationClinicalTrials.gov Identifier: NCT03679247.
| S-EPMC10928544 | biostudies-literature
Project description:RationaleClinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials.ObjectivesTo assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT).MethodsThis was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups.MeasurementsIntervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management.Main resultsOut of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374-1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2-5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3-15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82-1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups.ConclusionsWhile A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians' burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings.Trial registrationClinicalTrials.gov Identifier: NCT02865967.
| S-EPMC8328289 | biostudies-literature
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).
| S-EPMC10214648 | biostudies-literature
Project description:Rationale & objectiveThe study aimed to develop, implement, and evaluate a clinical decision support (CDS) system for chronic kidney disease (CKD) in a primary care setting, with the goal of improving CKD care in adults.Study designThis was a cluster randomized trial.Setting & participantsA total of 32 Midwestern primary care clinics were randomly assigned to either receive usual care or CKD-CDS intervention. Between April 2019 and March 2020, we enrolled 6,420 patients aged 18-75 years with laboratory-defined glomerular filtration rate categories of CKD Stage G3 and G4, and 1 or more of 6 CKD care gaps: absence of a CKD diagnosis, suboptimal blood pressure or glycated hemoglobin levels, indication for angiotensin-converting enzyme inhibitor or angiotensin receptor blocker but not prescribed, a nonsteroidal anti-inflammatory agent on the active medication list, or indication for a nephrology referral.InterventionThe CKD-CDS provided personalized suggestions for CKD care improvement opportunities directed to both patients and clinicians at primary care encounters.OutcomesWe assessed the proportion of patients meeting each of 6 CKD-CDS quality metrics representing care gap resolution after 18 months.ResultsThe adjusted proportions of patients meeting quality metrics in CKD-CDS versus usual care were as follows: CKD diagnosis documented (26.6% vs 21.8%; risk ratio [RR], 1.17; 95% CI, 0.91-1.51); angiotensin-converting enzyme inhibitor or angiotensin receptor blocker prescribed (15.9% vs 16.1%; RR, 0.95; 95% CI, 0.76-1.18); blood pressure control (20.4% vs 20.2%; RR, 0.98; 95% CI, 0.84-1.15); glycated hemoglobin level control (21.4% vs 22.1%; RR, 1.00; 95% CI, 0.80-1.24); nonsteroidal anti-inflammatory agent not on the active medication list (51.5% vs 50.4%; RR, 1.03; 95% CI, 0.90-1.17); and referral or visit to a nephrologist (38.7% vs 36.1%; RR, 1.02; 95% CI, 0.79-1.32).LimitationsWe encountered an overall reduction in expected primary care encounters and obstacles to point-of-care CKD-CDS utilization because of the coronavirus disease 2019 pandemic.ConclusionsThe CKD-CDS intervention did not lead to a significant improvement in CKD quality metrics. The challenges to CDS use during the coronavirus disease 2019 pandemic likely influenced these results.FundingNational Institute of Diabetes and Digestive and Kidney Diseases (R18DK118463).Trial registrationclinicaltrials.gov Identifier: NCT03890588.
| S-EPMC10906435 | biostudies-literature
Project description:BackgroundCancer patients often do not make informed decisions regarding clinical trial participation. This study evaluated whether a web-based decision aid (DA) could support trial decisions compared with our cancer center's website.MethodsAdults diagnosed with cancer in the past 6 months who had not previously participated in a cancer clinical trial were eligible. Participants were randomized to view the DA or our cancer center's website (enhanced usual care [UC]). Controlling for whether participants had heard of cancer clinical trials and educational attainment, multivariable linear regression examined group on knowledge, self-efficacy for finding trial information, decisional conflict (values clarity and uncertainty), intent to participate, decision readiness, and trial perceptions.ResultsTwo hundred patients (86%) consented between May 2014 and April 2015. One hundred were randomized to each group. Surveys were completed by 87 in the DA group and 90 in the UC group. DA group participants reported clearer values regarding trial participation than UC group participants reported (least squares [LS] mean = 15.8 vs. 32, p < .0001) and less uncertainty (LS mean = 24.3 vs. 36.4, p = .025). The DA group had higher objective knowledge than the UC group's (LS mean = 69.8 vs. 55.8, p < .0001). There were no differences between groups in intent to participate.ConclusionsImprovements on key decision outcomes including knowledge, self-efficacy, certainty about choice, and values clarity among participants who viewed the DA suggest web-based DAs can support informed decisions about trial participation among cancer patients facing this preference-sensitive choice. Although better informing patients before trial participation could improve retention, more work is needed to examine DA impact on enrollment and retention.Implications for practiceThis paper describes evidence regarding a decision tool to support patients' decisions about trial participation. By improving knowledge, helping patients clarify preferences for participation, and facilitating conversations about trials, decision aids could lead to decisions about participation that better match patients' preferences, promoting patient-centered care and the ethical conduct of clinical research.
| S-EPMC5153351 | biostudies-literature
Project description:ImportanceContextualizing care is a process of incorporating information about the life circumstances and behavior of individual patients, termed contextual factors, into their plan of care. In 4 steps, clinicians recognize clues (termed contextual red flags), clinicians ask about them (probe for context), patients disclose contextual factors, and clinicians adapt care accordingly. The process is associated with a desired outcome resolution of the presenting contextual red flag.ObjectiveTo determine whether contextualized clinical decision support (CDS) tools in the electronic health record (EHR) improve clinician contextual probing, attention to contextual factors in care planning, and the presentation of contextual red flags.Design, setting, and participantsThis randomized clinical trial was performed at the primary care clinics of 2 academic medical centers with different EHR systems. Participants were adults 18 years or older consenting to audio record their visits and their physicians between September 6, 2018, and March 4, 2021. Patients were randomized to an intervention or a control group. Analyses were performed on an intention-to-treat basis.InterventionsPatients completed a previsit questionnaire that elicited contextual red flags and factors and appeared in the clinician's note template in a contextual care box. The EHR also culled red flags from the medical record, included them in the contextual care box, used passive and interruptive alerts, and proposed relevant orders.Main outcomes and measuresProportion of contextual red flags noted at the index visit that resolved 6 months later (primary outcome), proportion of red flags probed (secondary outcome), and proportion of contextual factors addressed in the care plan by clinicians (secondary outcome), adjusted for study site and for multiple red flags and factors within a visit.ResultsFour hundred fifty-two patients (291 women [65.1%]; mean [SD] age, 55.6 [15.1] years) completed encounters with 39 clinicians (23 women [59.0%]). Contextual red flags were not more likely to resolve in the intervention vs control group (adjusted odds ratio [aOR], 0.96 [95% CI, 0.57-1.63]). However, the intervention increased both contextual probing (aOR, 2.12 [95% CI, 1.14-3.93]) and contextualization of the care plan (aOR, 2.67 [95% CI, 1.32-5.41]), controlling for whether a factor was identified by probing or otherwise. Across study groups, contextualized care plans were more likely than noncontextualized plans to result in improvement in the presenting red flag (aOR, 2.13 [95% CI, 1.38-3.28]).Conclusions and relevanceThis randomized clinical trial found that contextualized CDS did not improve patients' outcomes but did increase contextualization of their care, suggesting that use of this technology could ultimately help improve outcomes.Trial registrationClinicalTrials.gov Identifier: NCT03244033.
| S-EPMC9593230 | biostudies-literature
Project description:We wanted to assess the impact of an electronic health record-based diabetes clinical decision support system on control of hemoglobin A(1c) (glycated hemoglobin), blood pressure, and low-density lipoprotein (LDL) cholesterol levels in adults with diabetes.We conducted a clinic-randomized trial conducted from October 2006 to May 2007 in Minnesota. Included were 11 clinics with 41 consenting primary care physicians and the physicians' 2,556 patients with diabetes. Patients were randomized either to receive or not to receive an electronic health record (EHR)-based clinical decision support system designed to improve care for those patients whose hemoglobin A(1c), blood pressure, or LDL cholesterol levels were higher than goal at any office visit. Analysis used general and generalized linear mixed models with repeated time measurements to accommodate the nested data structure.The intervention group physicians used the EHR-based decision support system at 62.6% of all office visits made by adults with diabetes. The intervention group diabetes patients had significantly better hemoglobin A(1c) (intervention effect -0.26%; 95% confidence interval, -0.06% to -0.47%; P=.01), and better maintenance of systolic blood pressure control (80.2% vs 75.1%, P=.03) and borderline better maintenance of diastolic blood pressure control (85.6% vs 81.7%, P =.07), but not improved low-density lipoprotein cholesterol levels (P = .62) than patients of physicians randomized to the control arm of the study. Among intervention group physicians, 94% were satisfied or very satisfied with the intervention, and moderate use of the support system persisted for more than 1 year after feedback and incentives to encourage its use were discontinued.EHR-based diabetes clinical decision support significantly improved glucose control and some aspects of blood pressure control in adults with type 2 diabetes.
| S-EPMC3022040 | biostudies-literature
Project description: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.
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