title: Clinician time used for decision making: a best case workflow study using cardiovascular risk assessments and Ask Mayo Expert algorithmic care process models.
abstract: Risk calculation is increasingly used in lipid management, congestive heart failure, and atrial fibrillation. The risk scores are then used for decisions about statin use, anticoagulation, and implantable defibrillator use. Calculating risks for patients and making decisions based on these risks is often done at the point of care and is an additional time burden for clinicians that can be decreased by automating the tasks and using clinical decision-making support.
  
  
  
  Using Morae Recorder software, we timed 30 healthcare providers tasked with calculating the overall risk of cardiovascular events, sudden death in heart failure, and thrombotic event risk in atrial fibrillation. Risk calculators used were the American College of Cardiology Atherosclerotic Cardiovascular Disease risk calculator (AHA-ASCVD risk), Seattle Heart Failure Model (SHFM risk), and CHA2DS2VASc. We also timed the 30 providers using Ask Mayo Expert care process models for lipid management, heart failure management, and atrial fibrillation management based on the calculated risk scores. We used the Mayo Clinic primary care panel to estimate time for calculating an entire panel risk.
  
  
  
  Mean provider times to complete the CHA2DS2VASc, AHA-ASCVD risk, and SHFM were 36, 45, and 171 s respectively. For decision making about atrial fibrillation, lipids, and heart failure, the mean times (including risk calculations) were 85, 110, and 347 s respectively.
  
  
  
  Even under best case circumstances, providers take a significant amount of time to complete risk assessments. For a complete panel of patients this can lead to hours of time required to make decisions about prescribing statins, use of anticoagulation, and medications for heart failure. Informatics solutions are needed to capture data in the medical record and serve up automatically calculated risk assessments to physicians and other providers at the point of care.
authors.affiliation: Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA. north.frederick@mayo.edu.
authors.forename: Frederick
authors.identifier: 0000-0002-3696-4595
authors.initials: F
authors.lastname: North
authors.affiliation: Office of Information and Knowledge Management, Mayo Clinic, Rochester, MN, USA.
authors.forename: Samuel
authors.initials: S
authors.lastname: Fox
authors.affiliation: Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA.|Office of Information and Knowledge Management, Mayo Clinic, Rochester, MN, USA.
authors.forename: Rajeev
authors.initials: R
authors.lastname: Chaudhry
mesh_terms: D000328:Adult; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000175:diagnosis*; D000066491:Clinical Decision-Making / Q000379:methods*; D020000:Decision Support Systems, Clinical; D006801:Humans; D009722:Nurse Practitioners / Q000706:statistics & numerical data*; D010820:Physicians / Q000706:statistics & numerical data*; D018570:Risk Assessment / Q000379:methods*; D013997:Time Factors; D057188:Workflow
keywords: Algorithm; Ask Mayo Expert; Atrial fibrillation; Care process; Clinical burden; Clinical decision support; Heart failure; Lipid management; Risk calculation; Risk factor assessment