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| Name | Class |
|---|---|
| OCHIN, Inc. | OTHER |
| HealthPartners Institute | OTHER |
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This project aims to reduce disparities in cardiovascular disease (CVD) risk factor control and in rates of heart attacks and strokes among the low-income, racially / ethnically diverse Americans who receive primary care at safety net community health centers (CHCs). To achieve this important objective, the investigators will adapt a successful clinical decision support (CDS) system currently used in CVD care at several large, integrated health care systems, to meet the patient needs and workflow processes of 60 CHCs. The investigators will determine if use of this CDS improves CVD care, reduces disparities in CVD care and outcomes, and increases patient engagement in CVD treatment choices, in CHCs. Results of this randomized trial will help accelerate the translation of major investments in health informatics systems into substantial clinical benefits for large numbers of high-risk, low-income patients. Results will also provide a template for CVD care improvement that can be spread to other CHCs and extended to other clinical conditions.
The investigators seek to learn whether clinical decision support (CDS) systems from well-resourced care settings are effective in safety net community health centers (CHCs), and how to enhance such cross-setting implementation. Thus, the investigators propose a clinic-randomized, pragmatic trial of the uptake and impact of the 'CV Wizard' CDS tool in 60 CHCs that share a linked electronic health record. CV Wizard summarizes each patient's reversible CVD risks, generates prioritized, guideline-based care recommendations based on those risks, and shows these in a 'provider view' and a 'patient view,' enabling patient engagement. Use rates and satisfaction with this CDS were high in the large healthcare delivery system where it was developed and tested. The investigators will: study its impact in the CHC setting, assess uptake of the CDS system, assess strategies for integrating it into CHC workflows, and its impact on patients' CVD risk and risk factor management. The investigators hypothesize that this cutting-edge CDS will improve rates of guideline-based CVD preventive care in CHC patients, who experience disparities in CVD risk factors, care and outcomes. The 60 study clinics will be members of OCHIN, Inc., a non-profit health center-controlled network and national leader in health information technology for CHCs. OCHIN's leadership enthusiastically supports the proposed work and will help ensure that recruitment goals are met. The investigators will partner with stakeholders / medical leadership from OCHIN's member clinics at every step, via existing structures. This study addresses gaps in guideline-based CVD care in high-risk populations, using targeted, multi-level strategies; considers setting-specific needs; tests how CDS affects guideline implementation in community clinics; and uses technology to support patient engagement. Results will yield knowledge about providing CHCs with cutting-edge CDS, and associated impacts on CVD disparities. The innovative study is the second trial to implement CDS tools from private care settings in CHCs, and the first to do so with complex CDS tools that address a range of CVD risk management guidelines, make prioritized care recommendations, and facilitate point-of-care patient engagement. Results could lead to substantial improvements in CVD prevention, care, and outcomes in CHCs nationwide.
Our overarching aims are to:
Aim 1. Conduct a clinic-randomized trial of the impact of an evidence-based point-of-care CDS system on (i) overall CVD risk scores, and (ii) control of individual CVD risks (blood pressure; HbA1c, lipid levels; aspirin use; smoking; body mass index), among high CVD risk CHC adult patients. H1: High CVD risk patients in Arm 1 CHCs will have significantly lower overall CVD risk scores over a 12-month post-index visit period, compared to those in Arm 2 CHCs. H2: High CVD risk patients in Arm 1 CHCs who have poor control of specific CVD risk factors at an index visit will have significantly better control of those factors over a 12 month post-index visit period, compared to those in Arm 2 CHCs. H3: Disparities in specific CVD risk factor control between CHC patients' versus national CVD risk factor control rates will be significantly reduced by 18 months post-implementation in each Arm (secondary analysis).
Aim 2. Develop and hone need-based implementation support protocols to help Arm 1 CHCs implement the CV Wizard CDS system into their standard workflows; assess whether use of the protocols developed for Arm 1 CHCs accelerates implementation and adoption of the CDS system in the Arm 2 CHCs. H4: CDS uptake into CHC workflows will be significantly faster in Arm 2 CHCs than in Arm 1 CHCs.
Aim 3. Conduct a mixed methods process evaluation, guided by the Technology Acceptance Model, to identify and address patient, provider, and delivery system barriers to uptake / impact of this CDS in CHCs.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Arm 1: Immediate implementation | Active Comparator | 30 safety net community health centers (CHCs) will be randomized to implement the sophisticated CV Wizard clinical decision support (CDS) system at the start of study year 2. Arm 1 CHCs will receive implementation support that will be pragmatically iterated to address any barriers to adoption / sustained use of the CDS that are identified through study activities. The investigators will apply these learnings to improve adoption rates in Arm 2. |
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| Arm 2: Delayed implementation | Active Comparator | 30 safety net community health centers (CHCs) will be randomized to implement the sophisticated CV Wizard clinical decision support (CDS) system 18 months later than Arm 1. The investigators will measure the intervention's impact on CVD risk factor control in CHCs. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CV WIZARD | Behavioral | This project will determine whether a sophisticated CDS system will be effective in CHCs. The innovative, point-of-care, web-based CDS system we will test ("CV Wizard") generates a guideline-based prioritized summary of each patient's major CVD risk factors, then presents patient and provider 'views' of this summary, with individualized care recommendations. |
| Measure | Description | Time Frame |
|---|---|---|
| 1-year Change in 10-year CVD Risk | 10-year CVD risk was estimated using the American College of Cardiology/American Heart Association pooled risk equations, which include age, race and ethnicity, sex, systolic BP, total cholesterol level, high-density lipoprotein cholesterol level, and diabetes, smoking, and antihypertensive medication status. | 12 months |
| 1-year Change in Reversible CVD Risk | Reversible CVD risk was calculated as follows: Standardized equations estimated the potential reduction in CVD risk if a patient's uncontrolled risk factors reached evidence-based thresholds. Change was calculated by subtracting reversible risk at follow-up from that at index visit; negative values represent favorable changes. | 12 months |
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Inclusion Criteria:
Exclusion Criteria:
-Children aged younger than 18
Note: The investigators are not enrolling patients for this clinic-randomized study, but rather studying the uptake and impact of a set of EHR-based clinical decision support tools into regular care at the participating clinics. In this clinic-randomized trial, the intervention / randomization are clinic level.
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| Name | Affiliation | Role |
|---|---|---|
| Rachel Gold, PhD, MPH | Kaiser Permanente | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Kaiser Permanente Center for Health Research | Portland | Oregon | 97227 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37704387 | Derived | Boston D, Larson AE, Sheppler CR, O'Connor PJ, Sperl-Hillen JM, Hauschildt J, Gold R. Does Clinical Decision Support Increase Appropriate Medication Prescribing for Cardiovascular Risk Reduction? J Am Board Fam Med. 2023 Oct 11;36(5):777-788. doi: 10.3122/jabfm.2022.220391R2. Epub 2023 Sep 13. | |
| 36909848 | Derived |
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41 organizations (761 clinics) were assessed for eligibility. Of those, 26 organizations (691 clinics) either did not meet inclusion criteria (n=556 clinics) or declined participation/did not volunteer (n=135 clinics). A total of 15 organizations (70 clinics) were randomized. While individual patients were not enrolled for this clinic-randomized study, patient data from enrolled clinics was used in analyses. Clinic staff interviews not included in primary outcome analyses, so not reflected here.
| ID | Title | Description |
|---|---|---|
| FG000 | Arm 1: Intervention (Immediate Implementation) | 42 safety net community health centers (CHCs) from 8 organizations were randomized to implement the sophisticated CV Wizard clinical decision support (CDS). Arm 1 CHCs received implementation support that was pragmatically iterated to address any barriers to adoption/sustained use of the CDS that were identified through study activities. The investigators will apply the learnings from Arm 1 implementation with the goal of improving adoption rates in Arm 2. |
| FG001 | Arm 2: Control (Delayed Implementation) | 28 safety net community health centers (CHCs) from 7 organizations were randomized to implement the sophisticated CV Wizard clinical decision support (CDS) system 18 months later than Arm 1 clinics. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| ID | Title | Description |
|---|---|---|
| BG000 | Arm 1: Intervention (Immediate Implementation) | 42 safety net community health centers (CHCs) from 8 organizations were randomized to implement the sophisticated CV Wizard clinical decision support (CDS). Arm 1 CHCs received implementation support that was pragmatically iterated to address any barriers to adoption/sustained use of the CDS that were identified through study activities. The investigators will apply the learnings from Arm 1 implementation with the goal of improving adoption rates in Arm 2. |
| Units | Counts |
|---|---|
| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 1-year Change in 10-year CVD Risk | 10-year CVD risk was estimated using the American College of Cardiology/American Heart Association pooled risk equations, which include age, race and ethnicity, sex, systolic BP, total cholesterol level, high-density lipoprotein cholesterol level, and diabetes, smoking, and antihypertensive medication status. | Posted | Mean | 95% Confidence Interval | percent risk of CVD | 12 months |
|
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Adverse events were not monitored or assessed. The recommendations generated by CV Wizard are evidence-based and operationalize current national and regional standards of care; therefore, the risk of adverse consequences was considered minimal. Moreover, this potential risk is present in every clinical encounter in the healthcare system. Therefore, there were no adverse risks to patients involved in this study beyond data management risks (e.g. breach of confidentiality).
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Arm 1: Intervention (Immediate Implementation) | 42 safety net community health centers (CHCs) from 8 organizations were randomized to implement the sophisticated CV Wizard clinical decision support (CDS). Arm 1 CHCs received implementation support that was pragmatically iterated to address any barriers to adoption/sustained use of the CDS that were identified through study activities. The investigators will apply the learnings from Arm 1 implementation with the goal of improving adoption rates in Arm 2. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Rachel Gold, PhD MPH | Kaiser Permanente Center for Health Research | 503-335-2400 | rachel.gold@kpchr.org |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Jul 28, 2022 | Aug 13, 2024 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D009203 | Myocardial Infarction |
| D020521 | Stroke |
| D002318 | Cardiovascular Diseases |
| ID | Term |
|---|---|
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D014652 | Vascular Diseases |
| D007238 | Infarction |
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|
| Hauschildt J, Lyon-Scott K, Sheppler CR, Larson AE, McMullen C, Boston D, O'Connor PJ, Sperl-Hillen JM, Gold R. Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers. JAMIA Open. 2023 Mar 10;6(1):ooad012. doi: 10.1093/jamiaopen/ooad012. eCollection 2023 Apr. |
| 35119463 | Derived | Gold R, Larson AE, Sperl-Hillen JM, Boston D, Sheppler CR, Heintzman J, McMullen C, Middendorf M, Appana D, Thirumalai V, Romer A, Bava J, Davis JV, Yosuf N, Hauschildt J, Scott K, Moore S, O'Connor PJ. Effect of Clinical Decision Support at Community Health Centers on the Risk of Cardiovascular Disease: A Cluster Randomized Clinical Trial. JAMA Netw Open. 2022 Feb 1;5(2):e2146519. doi: 10.1001/jamanetworkopen.2021.46519. |
| BG001 | Arm 2: Control (Delayed Implementation) | 28 safety net community health centers (CHCs) from 7 organizations were randomized to implement the sophisticated CV Wizard clinical decision support (CDS) system 18 months later than Arm 1 clinics. |
| BG002 | Total | Total of all reporting groups |
| years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Race/Ethnicity, Customized | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
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| 10-year CVD risk | Patients newly diagnosed with diabetes during the follow-up period (n=347) were excluded from CVD risk analyses because diabetes may be diagnosed more often with CDSS use. | Mean | Standard Deviation | percent risk of CVD |
|
| Reversible CVD Risk | Patients newly diagnosed with diabetes during the follow-up period (n=347) were excluded from CVD risk analyses because diabetes may be diagnosed more often with CDSS use. | Mean | Standard Deviation | percent reversible risk of CVD |
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| OG001 | Arm 2: Control (Delayed Implementation) | 28 safety net community health centers (CHCs) from 7 organizations were randomized to implement the sophisticated CV Wizard clinical decision support (CDS) system 18 months later than Arm 1 clinics. |
|
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| Primary | 1-year Change in Reversible CVD Risk | Reversible CVD risk was calculated as follows: Standardized equations estimated the potential reduction in CVD risk if a patient's uncontrolled risk factors reached evidence-based thresholds. Change was calculated by subtracting reversible risk at follow-up from that at index visit; negative values represent favorable changes. | Posted | Mean | 95% Confidence Interval | percent reversible risk of CVD | 12 months |
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| 0 |
| 0 |
| 0 |
| 0 |
| 0 |
| 0 |
| EG001 | Arm 2: Control (Delayed Implementation) | 28 safety net community health centers (CHCs) from 7 organizations were randomized to implement the sophisticated CV Wizard clinical decision support (CDS) system 18 months later than Arm 1 clinics. | 0 | 0 | 0 | 0 | 0 | 0 |
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| D007511 |
| Ischemia |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D009336 | Necrosis |
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |