Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial

Sperl-Hillen, JoAnn M.; Crain, A. Lauren; Margolis, Karen L.; Ekström, Heidi; Appana, Deepika; Amundson, Gerald; Sharma, Rashmi; Desai, Jay; O’Connor, Patrick J.

doi:10.1093/jamia/ocy085

Cited by 52 publications

(87 citation statements)

References 22 publications

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“…Other qualitative feedback responses were obtained by focus groups or workshops (n=5) [43,57,61,67,68]. The alert firing rate (n=12) [43,55,62,[69][70][71][72][73][74][75][76][77] and alert acceptance rate (n=15) [27,29,43,58,61,62,69,74,[78][79][80][81][82][83][84] were also common metrics used to understand alarm fatigue and acceptability. Although metrics of relevance such as sensitivity, specificity, and positive predictive value (PPV) were referenced several times [77,[84][85][86][87][88], only one study evaluated these metrics as pre-specified outcomes [49].…”

Section: How and How Much -Methods And Metricsmentioning

confidence: 99%

“…One RCT of CDS found no difference in length of stay or mortality, though a modest increase in alert resolution [80]. Another RCT of a CDS system to assist in the management of patients with cardiovascular risk showed a 2.24% improvement in the surrogate outcome of annual change in predicted 10-year cardiovascular risk [82]. Of the 11 studies that examined CDS effects on end-users as well as patient health outcomes, only two [46,82] showed benefit in some domains.…”

Section: Ends Justify the Means -Measuring Outcomesmentioning

confidence: 99%

“…In one study, non-physician care-coordinators were responsible for data entry to support CDS [46]. In another, nurses rooming patients triggered the CDS [82]. Alerts tailored by role could also be used in cases where the ownership of an alert would otherwise be ambiguous, such as results of a genomic test [37].…”

Section: Fix It -Improving Cdsmentioning

confidence: 99%

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Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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Objectives: This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout. Methods: We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science™. Themes were manually extracted from publications that met inclusion criteria. Results: Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS. Conclusions: CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.

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Section: How and How Much -Methods And Metricsmentioning

confidence: 99%

Section: Ends Justify the Means -Measuring Outcomesmentioning

confidence: 99%

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Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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“…However, it is worth noting that in a series of randomized trials, Priorities Wizard has been shown to improve BP control, glucose control, smoking cessation, and cardiovascular risk, reduce clinical inertia, and improve recognition and diagnosis of diabetes in those who meet clinical criteria for the disease. [16][17][18][19][20] Moreover, a formal cost-effectiveness analysis shows a base-case estimate of $3017 per quality adjusted life year gained, and the CDS system is likely to be cost saving to payers at scale. 21 Should Diabetes CDS Stand Alone, or Be Integrated with CDS for Other Conditions?…”

Section: Priorities Wizard: a Current Diabetes Clinical Decision Suppmentioning

confidence: 99%

“…In our experience to date, high CDS use rates can be achieved when targeting up to 30% of all adult care visits for CDS use. 17 What Are the Costs of Developing, Implementing, and Maintaining Outpatient Diabetes CDS Systems?…”

Section: S2-30 O'connor and Sperl-hillenmentioning

confidence: 99%

Current Status and Future Directions for Electronic Point-of-Care Clinical Decision Support to Improve Diabetes Management in Primary Care

O’Connor

Sperl-Hillen

2019

Diabetes Technology & Therapeutics

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In the past decade there have been major improvements in the design, use, and effectiveness of point-of-care clinical decision support (CDS) systems to improve quality of care for patients with diabetes and related conditions. Advances in data exchange, data security, and human factors research have driven these improvements. Current diabetes CDS systems have high use rates, high clinician/user satisfaction rates, and have measurably improved glucose control, blood pressure control, and cardiovascular risk trajectories in adults with diabetes. As diabetes care increasingly relies on complex biomarker-driven risk prediction methods to optimize care goals and prioritize treatment options based on potential benefit to an individual patient, CDS systems will become indispensable tools to guide clinician and patient decision-making. In this study we describe specific challenges that must be addressed further to improve the design, implementation, and effectiveness of primary care diabetes CDS systems in coming years.

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Effect of Clinical Decision Support at Community Health Centers on the Risk of Cardiovascular Disease

et al. 2022

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IMPORTANCE Management of cardiovascular disease (CVD) risk in socioeconomically vulnerablepatients is suboptimal; better risk factor control could improve CVD outcomes. OBJECTIVE To evaluate the impact of a clinical decision support system (CDSS) targeting CVD risk in community health centers (CHCs). DESIGN, SETTING, AND PARTICIPANTSThis cluster randomized clinical trial included 70 CHC clinics randomized to an intervention group (42 clinics; 8 organizations) or a control group that received no intervention (28 clinics; 7 organizations) from September 20, 2018, to March 15, 2020. Randomization was by CHC organization accounting for organization size. Patients aged 40 to 75 years with (1) diabetes or atherosclerotic CVD and at least 1 uncontrolled major risk factor for CVD or (2) total reversible CVD risk of at least 10% were the population targeted by the CDSS intervention. INTERVENTIONS A point-of-care CDSS displaying real-time CVD risk factor control data and personalized, prioritized evidence-based care recommendations. MAIN OUTCOMES AND MEASURES One-year change in total CVD risk and reversible CVD risk (ie, the reduction in 10-year CVD risk that was considered achievable if 6 key risk factors reached evidence-based levels of control). RESULTS Among the 18 578 eligible patients (9490 [51.1%] women; mean [SD] age, 58.7 [8.8] years), patients seen in control clinics (n = 7419) had higher mean (SD) baseline CVD risk (16.6% [12.8%]) than patients seen in intervention clinics (n = 11 159) (15.6% [12.3%]; P < .001); baseline reversible CVD risk was similarly higher among patients seen in control clinics. The CDSS was used at 19.8% of 91 988 eligible intervention clinic encounters. No population-level reduction in CVD risk was seen in patients in control or intervention clinics; mean reversible risk improved significantly more among patients in control (−0.1% [95% CI, −0.3% to −0.02%]) than intervention clinics (0.4%[95% CI, 0.3% to 0.5%]; P < .001). However, when the CDSS was used, both risk measures decreased more among patients with high baseline risk in intervention than control clinics; notably, mean reversible risk decreased by an absolute 4.4% (95% CI, −5.2% to −3.7%) among patients in intervention clinics compared with 2.7% (95% CI, −3.4% to −1.9%) among patients in control clinics (P = .001). CONCLUSIONS AND RELEVANCEThe CDSS had low use rates and failed to improve CVD risk in the overall population but appeared to have a benefit on CVD risk when it was consistently used for patients with high baseline risk treated in CHCs. Despite some limitations, these results provide (continued) Key Points Question Does a clinical decision support system (CDSS) proven to reduce cardiovascular risk in integrated care settings also reduce cardiovascular risk in community health centers? Findings In this cluster randomized clinical trial of 18 578 eligible patients, although CDSS adoption rates were low, CDSS use was associated with significantly improved reversible risk of cardiovascular disease among patients with the hig...

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Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial

Abstract: This EHR-integrated, web-based outpatient CDS system significantly improved 10-year CV risk trajectory in targeted adults.

Cited by 52 publications

References 22 publications

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Current Status and Future Directions for Electronic Point-of-Care Clinical Decision Support to Improve Diabetes Management in Primary Care

Effect of Clinical Decision Support at Community Health Centers on the Risk of Cardiovascular Disease

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