Assessing the implementation of a clinical decision support tool in primary care for diabetes prevention: a qualitative interview study using the Consolidated Framework for Implementation Science.

BACKGROUND: In this paper we describe the use of the Consolidated Framework for Implementation Research (CFIR) to study implementation of a web-based, point-of-care, EHR-linked clinical decision support (CDS) tool designed to identify and provide care recommendations for adults with prediabetes (Pre-D CDS). METHODS: As part of a large NIH-funded clinic-randomized trial, we identified a convenience sample of interview participants from 22 primary care clinics in Minnesota, North Dakota, and Wisconsin that were randomly allocated to receive or not receive a web-based EHR-integrated prediabetes CDS intervention. Participants included 11 clinicians, 6 rooming staff, and 7 nurse or clinic managers recruited by study staff to participate in telephone interviews conducted by an expert in qualitative methods. Interviews were recorded and transcribed, and data analysis was conducted using a constructivist version of grounded theory. RESULTS: Implementing a prediabetes CDS tool into primary care clinics was useful and well received. The intervention was integrated with clinic workflows, supported primary care clinicians in clearly communicating prediabetes risk and management options with patients, and in identifying actionable care opportunities. The main barriers to CDS use were time and competing priorities. Finally, while the implementation process worked well, opportunities remain in engaging the care team more broadly in CDS use. CONCLUSIONS: The use of CDS tools for engaging patients and providers in care improvement opportunities for prediabetes is a promising and potentially effective strategy in primary care settings. A workflow that incorporates the whole care team in the use of such tools may optimize the implementation of CDS tools like these in primary care settings. Trial registration Name of the registry: Clinicaltrial.gov. TRIAL REGISTRATION NUMBER: NCT02759055. Date of registration: 05/03/2016. URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT02759055 Prospectively registered.

Clinician perceptions of a clinical decision support system to reduce cardiovascular risk among prediabetes patients in a predominantly rural healthcare system.

BACKGROUND: The early detection and management of uncontrolled cardiovascular risk factors among prediabetes patients can prevent cardiovascular disease (CVD). Prediabetes increases the risk of CVD, which is a leading cause of death in the United States. CVD clinical decision support (CDS) in primary care settings has the potential to reduce cardiovascular risk in patients with prediabetes while potentially saving clinicians time. The objective of this study is to understand primary care clinician (PCC) perceptions of a CDS system designed to reduce CVD risk in adults with prediabetes. METHODS: We administered pre-CDS implementation (6/30/2016 to 8/25/2016) (n = 183, 61% response rate) and post-CDS implementation (6/12/2019 to 8/7/2019) (n = 131, 44.5% response rate) independent cross-sectional electronic surveys to PCCs at 36 randomized primary care clinics participating in a federally funded study of a CVD risk reduction CDS tool. Surveys assessed PCC demographics, experiences in delivering prediabetes care, perceptions of CDS impact on shared decision making, perception of CDS impact on control of major CVD risk factors, and overall perceptions of the CDS tool when managing cardiovascular risk. RESULTS: We found few significant differences when comparing pre- and post-implementation responses across CDS intervention and usual care (UC) clinics. A majority of PCCs felt well-prepared to discuss CVD risk factor control with patients both pre- and post-implementation. About 73% of PCCs at CDS intervention clinics agreed that the CDS helped improve risk control, 68% reported the CDS added value to patient clinic visits, and 72% reported they would recommend use of this CDS system to colleagues. However, most PCCs disagreed that the CDS saves time talking about preventing diabetes or CVD, and most PCCs also did not find the clinical domains useful, nor did PCCs believe that the clinical domains were useful in getting patients to take action. Finally, only about 38% reported they were satisfied with the CDS. CONCLUSIONS: These results improve our understanding of CDS user experience and can be used to guide iterative improvement of the CDS. While most PCCs agreed the CDS improves CVD and diabetes risk factor control, they were generally not satisfied with the CDS. Moreover, only 40-50% agreed that specific suggestions on clinical domains helped patients to take action. In spite of this, an overwhelming majority reported they would recommend the CDS to colleagues, pointing for the need to improve upon the current CDS. TRIAL REGISTRATION: NCT02759055 03/05/2016.

Impact of the COVID-19 Pandemic on Type 2 Diabetes Care Delivery.

The coronavirus disease 2019 (COVID-19) pandemic instigated major changes in care delivery, but our understanding of how the rapid transition from in-person to telehealth encounters affected the care of patients with chronic conditions such as type 2 diabetes remains incomplete. This study examined changes in primary care encounters, A1C testing rates, and the likelihood of meeting A1C guidelines before and during the first 9 months of the COVID-19 pandemic in a large health care system. It found significant decreases in utilization and testing rates and the likelihood of meeting A1C guidelines, primarily driven by missing A1C tests. Patients who had all telehealth encounters or no encounters, who identified as racial or ethnic minorities, or had Medicaid or no insurance were significantly more likely to miss A1C tests.

The impact of personalized clinical decision support on primary care patients' views of cancer prevention and screening: a cross-sectional survey.

BACKGROUND: Few studies have assessed the impact of clinical decision support (CDS), with or without shared decision-making tools (SDMTs), on patients' perceptions of cancer screening or prevention in primary care settings. This cross-sectional survey was conducted to understand primary care patient's perceptions on cancer screening or prevention. METHODS: We mailed surveys (10/2018-1/2019) to 749 patients aged 18 to 75 years within 15 days after an index clinical encounter at 36 primary care clinics participating in a clinic-randomized control trial of a CDS system for cancer prevention. All patients were overdue for cancer screening or human papillomavirus vaccination. The survey compared respondents' answers by study arm: usual care; CDS; or CDS + SDMT. RESULTS: Of 387 respondents (52% response rate), 73% reported having enough time to discuss cancer prevention options with their primary care provider (PCP), 64% reported their PCP explained the benefits of the cancer screening choice very well, and 32% of obese patients reported discussing weight management, with two-thirds reporting selecting a weight management intervention. Usual care respondents were significantly more likely to decide on colorectal cancer screening than CDS respondents (p < 0.01), and on tobacco cessation than CDS + SDMT respondents (p = 0.02) and both CDS and CDS + SDMT respondents (p < 0.001). CONCLUSIONS: Most patients reported discussing cancer prevention needs with PCPs, with few significant differences between the three study arms in patient-reported cancer prevention care. Upcoming research will assess differences in screening and vaccination rates between study arms during the post-intervention follow-up period. TRIAL REGISTRATION: clinicaltrials.gov , NCT02986230 , December 6, 2016.

Primary care physician perspectives on using team care in clinical practice.

BACKGROUND: Primary care physicians were prompted to refer eligible patients with uncontrolled hypertension (HTN) to a program that offered home blood pressure telemonitoring and pharmacist care management. Understanding attitudes, barriers and facilitators, and use of team care in this program provides insight into how physicians incorporate team care into their practice. OBJECTIVE: To understand physician attitudes and use of team care in the context of a study intervention that included telehealth care with pharmacist care management. METHODS: Clinicians who were part of the telehealth intervention arm of the Hyperlink 3 study and had at least 20 opportunities to refer an eligible patient with HTN to a clinical pharmacist were invited to be interviewed. Nine physician interviews were conducted, recorded, and transcribed. Each interview lasted approximately 30 minutes and followed an interview guide, allowing for some variation and deeper dives into content on the basis of the clinician response. Three research staff coded each interview and sorted coded text to identify patterns at the physician level and then identified themes across interviews using a comparative process. RESULTS: Physicians had an overall positive attitude about team care. Communication, access, trust, and perceived role competency of team members influenced physician engagement in team care. Individualized practice styles influenced how physicians used team care and which care team members they involved most often. All physicians felt that their individual style best achieved high-quality care. CONCLUSION: For health care teams to be most effective, an understanding of how a physician's practice style influences their use of team care is likely to be more successful than a one-size-fits-all approach. Incorporating practice style into the key factors necessary for high-functioning teams, such as communication, access, and trust, is necessary for health care teams to thrive.

Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system.

BACKGROUND: Cancer is a leading cause of death in the United States. Primary care providers (PCPs) juggle patient cancer prevention and screening along with managing acute and chronic health problems. However, clinical decision support (CDS) may assist PCPs in addressing patients' cancer prevention and screening needs during short clinic visits. In this paper, we describe pre-implementation study design and cancer screening and prevention CDS changes made to maximize utilization and better fit a healthcare system's goals and culture. We employed the Consolidated Framework for Implementation Research (CFIR), useful for evaluating the implementation of CDS interventions in primary care settings, in understanding barriers and facilitators that led to those changes. METHODS: In a three-arm, pragmatic, 36 clinic cluster-randomized control trial, we integrated cancer screening and prevention CDS and shared decision-making tools (SDMT) into an existing electronic medical record-linked cardiovascular risk management CDS system. The integrated CDS is currently being tested within a predominately rural upper Midwestern healthcare system. Prior to CDS implementation, we catalogued pre-implementation changes made from 2016 to 2018 based on: pre-implementation site engagement; key informant interviews with healthcare system rooming staff, providers, and leadership; and pilot testing. We identified influential barriers, facilitators, and changes made in response through qualitative content analysis of meeting minutes and supportive documents. We then coded pre-implementation changes made and associated barriers and facilitators using the CFIR. RESULTS: Based on our findings from system-wide pre-implementation engagement, pilot testing, and key informant interviews, we made changes to accommodate the needs of the healthcare system based on barriers and facilitators that fell within the Intervention Characteristics, Inner Setting, and Outer Setting CFIR domains. Changes included replacing the expansion of medical assistant roles in one intervention arm with targeted SDMT, as well as altering cancer prevention CDS and study design elements. CONCLUSIONS: Pre-implementation changes to CDS may help meet healthcare systems' evolving needs and optimize the intervention by being responsive to real-world implementation barriers and facilitators. Frameworks like the CFIR are useful tools for identifying areas where pre-implementation barriers and facilitators may result in design changes, both to research studies and CDS systems. TRIAL REGISTRATION: NCT02986230.

Barriers and facilitators to implementing cancer prevention clinical decision support in primary care: a qualitative study.

BACKGROUND: In the United States, primary care providers (PCPs) routinely balance acute, chronic, and preventive patient care delivery, including cancer prevention and screening, in time-limited visits. Clinical decision support (CDS) may help PCPs prioritize cancer prevention and screening with other patient needs. In a three-arm, pragmatic, clinic-randomized control trial, we are studying cancer prevention CDS in a large, upper Midwestern healthcare system. The web-based, electronic health record (EHR)-linked CDS integrates evidence-based primary and secondary cancer prevention and screening recommendations into an existing cardiovascular risk management CDS system. Our objective with this study was to identify adoption barriers and facilitators before implementation in primary care. METHODS: We conducted semi-structured interviews guided by the Consolidated Framework for Implementation Research (CFIR) with 28 key informants employed by the healthcare organization in either leadership roles or the direct provision of clinical care. Transcribed interviews were analyzed using qualitative content analysis. RESULTS: EHR, CDS workflow, CDS users (providers and patients), training, and organizational barriers and facilitators were identified related to Intervention Characteristics, Outer Setting, Inner Setting, and Characteristics of Individuals CFIR domains. CONCLUSION: Identifying and addressing key informant-identified barriers and facilitators before implementing cancer prevention CDS in primary care may support a successful implementation and sustained use. The CFIR is a useful framework for understanding pre-implementation barriers and facilitators. Based on our findings, the research team developed and instituted specialized training, pilot testing, implementation plans, and post-implementation efforts to maximize identified facilitators and address barriers. TRIAL REGISTRATION: clinicaltrials.gov , NCT02986230 , December 6, 2016.

Understanding primary care providers' perceptions of cancer prevention and screening in a predominantly rural healthcare system in the upper Midwest.

BACKGROUND: Cancer is the leading cause of death in the United States, with the burden expected to rise in the coming decades, increasing the need for effective cancer prevention and screening options. The United States Preventive Services Task Force has suggested that a shared decision-making process be used when clinicians and patients discuss cancer screening. The electronic medical record (EMR) often provides only reminders or alerts to primary care providers (PCPs) when screenings are due, a strategy with limited efficacy. METHODS: We administered a cross-sectional electronic survey to PCPs (n = 165, 53% response rate) at 36 Essentia Health primary care clinics participating in a large, National Cancer Institute-funded study on a cancer prevention clinical decision support (CDS) tool. The survey assessed PCP demographics, perceptions of the EMR's ability to help assess and manage patients' cancer risk, and experience and comfort level discussing cancer screening and prevention with patients. RESULTS: In these predominantly rural clinics, only 49% of PCPs thought the EMR was well integrated to help assess and manage cancer risk. Both advanced care practitioners and physicians agreed that cancer screening and informed discussion of cancer risks are important; however, only 53% reported their patients gave cancer screening a high priority relative to other health issues. CONCLUSIONS: The impact of EMR-linked CDS delivered to both patients and PCPs may improve cancer screening, but only if it is easy to use and saves PCPs time.

Key components of success in a randomized trial of blood pressure telemonitoring with medication therapy management pharmacists.

OBJECTIVES: The Hyperlink trial tested a 12-month intervention of home blood pressure (BP) telemonitoring with pharmacist case management in adults with uncontrolled hypertension. The intervention resulted in improved BP control compared with usual care at both 6 (72% vs. 45%; P < 0.001) and 12 months (71% vs. 53%; P = 0.005). We sought to investigate factors contributing to intervention success. DESIGN: Mixed-methods analysis of process of care data, patient focus groups, and pharmacist interviews. PARTICIPANTS: Data from 228 intervention patients were examined from the original 450 patients randomly assigned from 16 primary care clinics. Five patient focus groups and 4 pharmacist interviews were conducted to ascertain the patient and pharmacist perspective. Focus group and interview data were coded, and themes relevant to pharmacists were identified. OUTCOME MEASURES: Home BP readings of less than 135/85 mm Hg and patient focus group and pharmacist interview themes. RESULTS: Mean BP at the intake visit was 148/85 mm Hg. Antihypertensive medications were adjusted in 10% of patients at the initial in-person visit, 33% at phone visit 1, 36% at phone visit 2, and 19% at phone visit 3. Thereafter, medication changes declined. The mean home BP for patients at the first phone visit was 136/80 mm Hg, 126/74 mm Hg at 3 months, and 123/73 mm Hg at 5 months, with little change thereafter. Key components of success from patient and pharmacist interviews included a strong patient-pharmacist relationship, individualized treatment plans, and frequent phone contact with the pharmacist. CONCLUSION: Frequent adjustments to the antihypertensive treatment regimen based on home BP telemonitoring resulted in rapid lowering of BP. Our results suggest that an intensive telephone-based intervention with the key components of medication adjustments, a strong patient and pharmacist relationship, and individualized treatment plans can achieve BP control in only 3 months in many patients with uncontrolled hypertension.

Understanding Primary Care Provider Attitudes and Behaviors Regarding Cardiovascular Disease Risk and Diabetes Prevention in the Northern Midwest.

IN BRIEF We sought to fill critical gaps in understanding primary care providers' (PCPs') beliefs regarding diabetes prevention and cardiovascular disease risk in the prediabetes population, including through comparison of attitudes between rural and non-rural PCPs. We used data from a 2016 cross-sectional survey sent to 299 PCPs practicing in 36 primary clinics that are part of a randomized control trial in a predominately rural northern Midwestern integrated health care system. Results showed a few significant, but clinically marginal, differences between rural and non-rural PCPs. Generally, PCPs agreed with the importance of screening for prediabetes and thoroughly and clearly discussing CV risk with high-risk patients.

A Successful Multifaceted Trial to Improve Hypertension Control in Primary Care: Why Did it Work?

BACKGROUND: It is important to understand which components of successful multifaceted interventions are responsible for study outcomes, since some components may be more important contributors to the intervention effect than others. OBJECTIVE: We conducted a mediation analysis to determine which of seven factors had the greatest effect on change in systolic blood pressure (BP) after 6 months in a trial to improve hypertension control. DESIGN: The study was a preplanned secondary analysis of a cluster-randomized clinical trial. Eight clinics in an integrated health system were randomized to provide usual care to their patients (n = 222), and eight were randomized to provide a telemonitoring intervention (n = 228). PARTICIPANTS: Four hundred three of 450 trial participants completing the 6-month follow-up visit were included. INTERVENTIONS: Intervention group participants received home BP telemonitors and transmitted measurements to pharmacists, who adjusted medications and provided advice to improve adherence to medications and lifestyle modification via telephone visits. MAIN MEASURES: Path analytic models estimated indirect effects of the seven potential mediators of intervention effect (defined as the difference between the intervention and usual care groups in change in systolic BP from baseline to 6 months). The potential mediators were change in home BP monitor use, number of BP medication classes, adherence to BP medications, physical activity, salt intake, alcohol use, and weight. KEY RESULTS: The difference in change in systolic BP was 11.3 mmHg. The multivariable mediation model explained 47 % (5.3 mmHg) of the intervention effect. Nearly all of this was mediated by two factors: an increase in medication treatment intensity (24 %) and increased home BP monitor use (19 %). The other five factors were not significant mediators, although medication adherence and salt intake improved more in the intervention group than in the usual care group. CONCLUSIONS: Most of the explained intervention effect was attributable to the combination of self-monitoring and medication intensification. High adherence at baseline and the relatively low intensity of resources directed toward lifestyle change may explain why these factors did not contribute to the improvement in BP.

Protocol of a patient randomized clinical trial to improve medication adherence in primary care.

BACKGROUND: Enhanced awareness of poor medication adherence could improve patient care. This article describes the original and adapted protocols of a randomized trial to improve medication adherence for cardiometabolic conditions. METHODS: The original protocol entailed a cluster randomized trial of 28 primary care clinics allocated to either (i) medication adherence enhanced chronic disease care clinical decision support (eCDC-CDS) integrated within the electronic health record (EHR) or (ii) usual care (non-enhanced CDC-CDS). Enhancements comprised (a) electronic interfaces printed for patients and clinicians at primary care encounters that encouraged discussion about specific medication adherence issues that were identified, and (b) pharmacist phone outreach. Study subjects were individuals who at an index visit were aged 18-74 years and not at evidence-based care goals for hypertension (HTN), diabetes mellitus (DM), or lipid management, along with low medication adherence (proportion of days covered [PDC] <80%) for a corresponding medication. The primary study outcomes were improved medication adherence and clinical outcomes (BP and A1C) at 12 months. Protocol adaptation became imperative in response to major implementation challenges: (a) the availability of EHR system-wide PDC calculations that superseded our ability to limit PDC adherence information solely to intervention clinics; (b) the unforeseen closure of pharmacies committed to conducting the pharmacist outreach; and (c) disruptions and clinic closures due to the Covid-19 pandemic. CONCLUSION: This manuscript details the protocol of a study to assess whether enhanced awareness of medication adherence issues in primary care settings could improve patient outcomes. The need for protocol adaptation arose in response to multiple implementation challenges.

Current status and future prospects for electronic point-of-care clinical decision support in diabetes care.

Early efforts to use point-of-care clinical decision support (CDS) were limited to the use of prompts and reminders, which improved test ordering but not intermediate outcomes of care, such as glucose, blood pressure, or lipid levels. More sophisticated diabetes CDS tools are now available that use electronic medical record data to provide patient-specific advice on medication use on the basis of previous treatment, distance from goal, and other clinical data. These tools have shown modest but significant improvement in glucose and blood pressure control. Promising next-generation developments will include prioritizing clinical actions that have maximum benefit to a given patient at the point of care and developing effective methods to communicate CDS information to patients to better incorporate patient preferences in care decisions.

Effect of home blood pressure telemonitoring and pharmacist management on blood pressure control: a cluster randomized clinical trial.

IMPORTANCE: Only about half of patients with high blood pressure (BP) in the United States have their BP controlled. Practical, robust, and sustainable models are needed to improve BP control in patients with uncontrolled hypertension. OBJECTIVES: To determine whether an intervention combining home BP telemonitoring with pharmacist case management improves BP control compared with usual care and to determine whether BP control is maintained after the intervention is stopped. DESIGN, SETTING, AND PATIENTS: A cluster randomized clinical trial of 450 adults with uncontrolled BP recruited from 14,692 patients with electronic medical records across 16 primary care clinics in an integrated health system in Minneapolis-St Paul, Minnesota, with 12 months of intervention and 6 months of postintervention follow-up. INTERVENTIONS: Eight clinics were randomized to provide usual care to patients (n = 222) and 8 clinics were randomized to provide a telemonitoring intervention (n = 228). Intervention patients received home BP telemonitors and transmitted BP data to pharmacists who adjusted antihypertensive therapy accordingly. MAIN OUTCOMES AND MEASURES: Control of systolic BP to less than 140 mm Hg and diastolic BP to less than 90 mm Hg (<130/80 mm Hg in patients with diabetes or chronic kidney disease) at 6 and 12 months. Secondary outcomes were change in BP, patient satisfaction, and BP control at 18 months (6 months after intervention stopped). RESULTS: At baseline, enrollees were 45% women, 82% white, mean (SD) age was 61.1 (12.0) years, and mean systolic BP was 148 mm Hg and diastolic BP was 85 mm Hg. Blood pressure was controlled at both 6 and 12 months in 57.2% (95% CI, 44.8% to 68.7%) of patients in the telemonitoring intervention group vs 30.0% (95% CI, 23.2% to 37.8%) of patients in the usual care group (P = .001). At 18 months (6 months of postintervention follow-up), BP was controlled in 71.8% (95% CI, 65.0% to 77.8%) of patients in the telemonitoring intervention group vs 57.1% (95% CI, 51.5% to 62.6%) of patients in the usual care group (P = .003). Compared with the usual care group, systolic BP decreased more from baseline among patients in the telemonitoring intervention group at 6 months (-10.7 mm Hg [95% CI, -14.3 to -7.3 mm Hg]; P<.001), at 12 months (-9.7 mm Hg [95% CI, -13.4 to -6.0 mm Hg]; P<.001), and at 18 months (-6.6 mm Hg [95% CI, -10.7 to -2.5 mm Hg]; P = .004). Compared with the usual care group, diastolic BP decreased more from baseline among patients in the telemonitoring intervention group at 6 months (-6.0 mm Hg [95% CI, -8.6 to -3.4 mm Hg]; P<.001), at 12 months (-5.1 mm Hg [95% CI, -7.4 to -2.8 mm Hg]; P<.001), and at 18 months (-3.0 mm Hg [95% CI, -6.3 to 0.3 mm Hg]; P = .07). CONCLUSIONS AND RELEVANCE: Home BP telemonitoring and pharmacist case management achieved better BP control compared with usual care during 12 months of intervention that persisted during 6 months of postintervention follow-up. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00781365.

Does Clinical Decision Support Increase Appropriate Medication Prescribing for Cardiovascular Risk Reduction?

PURPOSE: To assess the impact of a clinical decision support (CDS) system's recommendations on prescribing patterns targeting cardiovascular disease (CVD) when the recommendations are prioritized in order from greatest to least benefit toward overall CVD risk reduction. METHODS: Secondary analysis of trial data from September 20, 2018, to March 15, 2020, where 70 community health center clinics were cluster-randomized to the CDS intervention (42 clinics; 8 organizations) or control group (28 clinics; 7 organizations). Included patients were medication-naïve and aged 40 to 75 years with ≥1 uncontrolled cardiovascular disease risk factor, with known diabetes or cardiovascular disease, or ≥10% 10-year reversible CVD risk. RESULTS: Among eligible encounters with 29,771 patients, the probability of prescribing a medication targeting hypertension was greater at intervention clinic encounters when CDS was used (34.9% [95% CI, 31.5 to 38.3]) versus dismissed (29.6% [95% CI, 26.7 to 32.6]; P < .001), but not when compared with control clinic encounters (34.9% [95% CI, 31.1 to 38.7]; P = .998). Prescribing for dyslipidemia was significantly higher at intervention encounters where the CDS system was used (11.3% [95% CI, 9.3 to 13.3]) compared with dismissed (7.7% [95% CI, 6.1 to 9.3]; P = .003) and to control encounters (8.7% [95% CI, 7.0 to 10.4]; P = .044); smoking cessation medication showed a similar pattern. Except for dyslipidemia, prescribing rates increased according to their prioritization. CONCLUSIONS: Use of this CDS system was associated with significantly higher prescribing targeting most cardiovascular risk factors. These results highlight how displaying prioritized actions to reduce reversible CVD risk could improve risk management. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

Differential Effects of an Intervention to Reduce Cardiovascular Risk for Patients With Bipolar Disorder, Schizoaffective Disorder, or Schizophrenia: A Randomized Clinical Trial.

Objective: To measure the impact of a clinical decision support (CDS) tool on total modifiable cardiovascular risk at 12 months separately for outpatients with 3 subtypes of serious mental illness (SMI) identified via ICD-9 and ICD-10 codes: bipolar disorder, schizoaffective disorder, and schizophrenia.Methods: This cluster-randomized pragmatic clinical trial was active from March 2016 to September 2018; data were analyzed from April 2021 to September 2022. Clinicians and patients from 78 primary care clinics participated. All 8,922 adult patients aged 18-75 years with diagnosed SMI, at least 1 cardiovascular risk factor not at goal, and an index and follow-up visit during the study period were included. The CDS tool provided a summary of modifiable cardiovascular risk and personalized treatment recommendations.Results: Intervention patients had 4% relative reduction in total modifiable cardiovascular risk at 12 months compared to controls (relative risk ratio = 0.96; 95% CI, 0.94 to 0.98), with similar intervention benefits for all 3 SMI subtypes. At index, 10-year cardiovascular risk was higher for patients with schizophrenia (mean [SD] = 11.3% [9.2%]) than for patients with bipolar disorder (8.5% [8.9%]) or schizoaffective disorder (9.4% [8.1%]), while 30-year cardiovascular risk was highest for patients with schizoaffective disorder (44% with 2 or more major cardiovascular risk factors, compared to 40% for patients with schizophrenia and 37% for patients with bipolar disorder). Smoking was highly prevalent (47%), and mean (SD) BMI was 32.7 (7.9).Conclusions: This CDS intervention produced a clinically and statistically significant 4% relative reduction in total modifiable cardiovascular risk for intervention patients versus controls at 12 months, an effect observed across all 3 SMI subtypes and attributable to the aggregate impact of small changes in multiple cardiovascular risk factors.Trial Registration: ClinicalTrials.gov Identifier: NCT02451670.

Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers.

Objective: Electronic health record (EHR)-based shared decision-making (SDM) and clinical decision support (CDS) systems can improve cardiovascular disease (CVD) care quality and risk factor management. Use of the CV Wizard system showed a beneficial effect on high-risk community health center (CHC) patients' CVD risk within an effectiveness trial, but system adoption was low overall. We assessed which multi-level characteristics were associated with system use. Materials and Methods: Analyses included 80 195 encounters with 17 931 patients with high CVD risk and/or uncontrolled risk factors at 42 clinics in September 2018-March 2020. Data came from the CV Wizard repository and EHR data, and a survey of 44 clinic providers. Adjusted, mixed-effects multivariate Poisson regression analyses assessed factors associated with system use. We included clinic- and provider-level clustering as random effects to account for nested data. Results: Likelihood of system use was significantly higher in encounters with patients with higher CVD risk and at longer encounters, and lower when providers were >10 minutes behind schedule, among other factors. Survey participants reported generally high satisfaction with the system but were less likely to use it when there were time constraints or when rooming staff did not print the system output for the provider. Discussion: CHC providers prioritize using this system for patients with the greatest CVD risk, when time permits, and when rooming staff make the information readily available. CHCs' financial constraints create substantial challenges to addressing barriers to improved system use, with health equity implications. Conclusion: Research is needed on improving SDM and CDS adoption in CHCs. Trial Registration: ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

Design of a pragmatic clinical trial to improve screening and treatment for opioid use disorder in primary care.

BACKGROUND: Opioid-related deaths continue to rise in the U.S. A shared decision-making (SDM) system to help primary care clinicians (PCCs) identify and treat patients with opioid use disorder (OUD) could help address this crisis. METHODS: In this cluster-randomized trial, primary care clinics in three healthcare systems were randomized to receive or not receive access to an OUD-SDM system. The OUD-SDM system alerts PCCs and patients to elevated risk of OUD and supports OUD screening and treatment. It includes guidance on OUD screening and diagnosis, treatment selection, starting and maintaining patients on buprenorphine for waivered clinicians, and screening for common comorbid conditions. The primary study outcome is, of patients at high risk for OUD, the percentage receiving an OUD diagnosis within 30 days of index visit. Additional outcomes are, of patients at high risk for or with a diagnosis of OUD, (a) the percentage receiving a naloxone prescription, or (b) the percentage receiving a medication for OUD (MOUD) prescription or referral to specialty care within 30 days of an index visit, and (c) total days covered by a MOUD prescription within 90 days of an index visit. RESULTS: The intervention started in April 2021 and continues through December 2023. PCCs and patients in 90 clinics are included; study results are expected in 2024. CONCLUSION: This protocol paper describes the design of a multi-site trial to help PCCs recognize and treat OUD. If effective, this OUD-SDM intervention could improve screening of at-risk patients and rates of OUD treatment for people with OUD.

Depression and cardiovascular risk in primary care patients.

OBJECTIVE: This study assessed the relationship of both depression diagnosis and clinically significant depressive symptoms with individual cardiovascular risk factors and estimated total cardiovascular risk in primary care patients. METHODS: This study used a cross-sectional and retrospective design. Patients who had a primary care encounter between January 2016 and September 2018 and completed depression screening (PHQ-9) during the year prior to their appointment (N = 70,980) were included in this study. Data examining estimated total cardiovascular risk, specific cardiovascular risk factors, and relevant clinical diagnoses (including depression diagnosis) were extracted from the electronic health record. Patients were categorized into three groups: no depression (PHQ-9 < 10 and no depression diagnosis), controlled depression (PHQ-9 < 10 with previous depression diagnosis), and current depression (PHQ-9 ≥ 10). Groups were compared on estimated total risk and specific cardiovascular risk factors (e.g., body mass index [BMI], smoking status, lipids, blood pressure, and glucose). RESULTS: In adjusted analyses, patients with current depression (n = 18,267) demonstrated significantly higher 10-year and 30-year cardiovascular risk compared to patients with controlled depression (n = 33,383; 10-year: b = 0.59 [95% CI = 0.44,0.74]; 30-year: OR = 1.32 [95% CI = 1.26,1.39]) and patients without depression (n = 19,330; 10-year: b = 0.55 [95% CI = 0.37,0.73]; 30-year: OR = 1.56 [95% CI = 1.48,1.65]). Except for low-density lipoprotein (LDL), patients with current depression had the greatest cardiovascular risk across specific risk factors. CONCLUSIONS: Individuals who had a depression diagnosis and clinically significant depressive symptoms had the greatest cardiovascular risk. Pathways to prevent cardiovascular disease in those with depression might focus on treating depressive symptoms as well as specific uncontrolled cardiovascular risk factors.