Clinician time used for decision making: a best case workflow study using cardiovascular risk assessments and Ask Mayo Expert algorithmic care process models

North, Frederick; Fox, Samuel C; Chaudhry, Rajeev

doi:10.1186/s12911-016-0334-z

Cited by 13 publications

(14 citation statements)

References 19 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Physicians recommended shortening and reorganizing the alert content for clarity and embedding a link to an order set specific for FH so as to reduce the number of clicks. Physicians also recommended including a link to AskMayoExpert, a Mayo Clinic knowledge resource with a topic module dedicated to FH that provides information on clinical presentation, diagnostic criteria and management [ 44 ]. AskMayoExpert also has links to additional knowledge modules, thereby enabling providers to access a number of relevant topics without workflow disruption [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

An Implementation Science Framework to Develop a Clinical Decision Support Tool for Familial Hypercholesterolemia

Bangash

Pencille

Gundelach

et al. 2020

JPM

View full text Add to dashboard Cite

Electronic health record (EHR)-based clinical decision support (CDS) can address the low awareness and undertreatment of familial hypercholesterolemia (FH), a disorder associated with a markedly increased risk of coronary heart disease. We aimed to incorporate provider perspectives into the development and implementation of a CDS tool for FH. An implementation science framework and a user-centered design process were used to create a CDS tool for FH. Primary care physicians and specialist physicians participated in qualitative interviews, usability testing and an implementation survey. The CDS was configured in two formats—a best practice alert (BPA) and an in-basket message and subsequently deployed in the EHR in silent mode. The key themes that emerged from the analysis of interview transcripts included understanding and awareness of FH, clinical workflow, physician preferences and value of CDS tools, perspectives on patient needs and values and dissemination and implementation. Recommendations related to usability included preferred CDS format and placement, content, timing and frequency, and level of alert urgency/prioritization. In response to the survey, 84.6% of physicians agreed that the CDS would improve early FH diagnosis and 92.3% agreed that it would help them identify and manage FH patients. Physician feedback led to iterative CDS refinement. In summary, we developed a CDS tool for FH using an implementation science framework and physician feedback. Initial deployment revealed a significant burden of FH and the potential for the CDS tool to have a large impact.

show abstract

Section: Discussionmentioning

confidence: 99%

An Implementation Science Framework to Develop a Clinical Decision Support Tool for Familial Hypercholesterolemia

Bangash

Pencille

Gundelach

et al. 2020

JPM

View full text Add to dashboard Cite

show abstract

“…In our study, only 10% of providers indicated that they currently use AME for PAD risk modification. Although AME provides knowledge-based care process models, 17 71% of respondents perceived greater potential benefit of individualized risk score calculators and guideline-recommended strategies at the point of care. Importantly, care process models are not individualized for use at the point of care.…”

Section: Discussionmentioning

confidence: 99%

“…Initial questions assessed provider demographic characteristics, including role, primary work area, years in practice, and the number of patients seen with PAD. Subsequent questions investigated how helpful providers considered the current version of Ask Mayo Expert (AME), a Mayo Clinic online knowledge-based care process model 17 for secondary prevention in patients with PAD as well as provider comfort level in caring for patients with PAD. The final questions investigated provider opinions on the potential helpfulness of an advanced CDS system that would automatically display secondary prevention strategies for patients with PAD and an automated risk score for mortality for these patients at the point of care.…”

Section: Methodsmentioning

confidence: 99%

Innovative Informatics Approaches for Peripheral Artery Disease: Current State and Provider Survey of Strategies for Improving Guideline-Based Care

Chaudhry

Afzal

Abidian

et al. 2018

Mayo Clinic Proceedings: Innovations, Quality & Outcomes

Self Cite

View full text Add to dashboard Cite

Objective To quantify compliance with guideline recommendations for secondary prevention in peripheral artery disease (PAD) using natural language processing (NLP) tools deployed to an electronic health record (EHR) and investigate provider opinions regarding clinical decision support (CDS) to promote improved implementation of these strategies. Patients and Methods Natural language processing was used for automated identification of moderate to severe PAD cases from narrative clinical notes of an EHR of patients seen in consultation from May 13, 2015, to July 27, 2015. Guideline-recommended strategies assessed within 6 months of PAD diagnosis included therapy with statins, antiplatelet agents, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and smoking abstention. Subsequently, a provider survey was used to assess provider knowledge regarding PAD clinical practice guidelines, comfort in recommending secondary prevention strategies, and potential role for CDS. Results Among 73 moderate to severe PAD cases identified by NLP, only 12 (16%) were on 4 guideline-recommended strategies. A total of 207 of 760 (27%) providers responded to the survey; of these 141 (68%) were generalists and 66 (32%) were specialists. Although 183 providers (88%) managed patients with PAD, 51 (25%) indicated they were uncomfortable doing so; 138 providers (67%) favored the development of a CDS system tailored for their practice and 146 (71%) agreed that an automated EHR-derived mortality risk score calculator for patients with PAD would be helpful. Conclusion Natural language processing tools can identify cases from EHRs to support quality metric studies. Findings of this pilot study demonstrate gaps in application of guideline-recommended strategies for secondary risk prevention for patients with moderate to severe PAD. Providers strongly support the development of CDS systems tailored to assist them in providing evidence-based care to patients with PAD at the point of care.

show abstract

“…Automatically calculated risk scores can be used successfully in population health management. 24 Despite these barriers, an innovative workflow was introduced within the electronic health record which resulted in surpassing our aim of a 10% increase in ASCVD risk score assessment within 12 months. Physician’s barriers to the acceptance of ASCVD risk assessment included: lack of electronic chart alerts and lack of extra time needed to complete the ASCVD risk score assessment during the limited clinic visit while addressing multiple chronic diseases.…”

Section: Resultsmentioning

confidence: 99%

Optimisation of lipids for prevention of cardiovascular disease in a primary care

et al. 2018

View full text Add to dashboard Cite

The 2013 American College of Cardiology/American Heart Association (ACC/AHA) guidelines focus on atherosclerotic cardiovascular disease (ASCVD) risk reduction, using a Pooled Cohort Equation to calculate a patient’s 10-year risk score, which is used to guide initiation of statin therapy. We identified a gap of evidence-based treatment for hyperlipidaemia in the Internal Medicine Clinic. Therefore, the aim of this study was to increase calculation of ASCVD risk scores in patients between the ages of 40 and 75 years from a baseline rate of less than 1% to 10%, within 12 months, for primary prevention of ASCVD. Root cause analysis was performed to identify materials/methods, provider and patient-related barriers. Plan-Do-Study-Act cycles included: (1) creation of customised workflow in electronic health records for documentation of calculated ASCVD risk score; (2) physician education regarding guidelines and electronic health record workflow; (3) refresher training for residents and a chart alert and (4) patient education and physician reminders. The outcome measures were ASCVD risk score completion rate and percentage of new prescriptions for statin therapy. Process measures included lipid profile order and completion rates. Increase in patient wait time, and blood test and medications costs were the balanced measures. We used weekly statistical process control charts for data analysis. The average ASCVD risk completion rate was 14.2%. The mean ASCVD risk completion rate was 4.0%. In eligible patients, the average lipid profile completion rate was 18%. ASCVD risk score completion rate was 33% 1-year postproject period. A team-based approach led to a sustainable increase in ASCVD risk score completion rate. Lack of automation in ASCVD risk score calculation and physician prompts in electronic health records were identified as major barriers. Furthermore, the team identified multiple barriers to lipid blood tests and treatment of increased ASCVD risk based on ACC/AHA guidelines.

show abstract

Clinician time used for decision making: a best case workflow study using cardiovascular risk assessments and Ask Mayo Expert algorithmic care process models

Cited by 13 publications

References 19 publications

An Implementation Science Framework to Develop a Clinical Decision Support Tool for Familial Hypercholesterolemia

An Implementation Science Framework to Develop a Clinical Decision Support Tool for Familial Hypercholesterolemia

Innovative Informatics Approaches for Peripheral Artery Disease: Current State and Provider Survey of Strategies for Improving Guideline-Based Care

Optimisation of lipids for prevention of cardiovascular disease in a primary care

Contact Info

Product

Resources

About