title: Understanding clinical prediction models as 'innovations': a mixed methods study in UK family practice.
abstract: Well-designed clinical prediction models (CPMs) often out-perform clinicians at estimating probabilities of clinical outcomes, though their adoption by family physicians is variable. How family physicians interact with CPMs is poorly understood, therefore a better understanding and framing within a context-sensitive theoretical framework may improve CPM development and implementation. The aim of this study was to investigate why family physicians do or do not use CPMs, interpreting these findings within a theoretical framework to provide recommendations for the development and implementation of future CPMs.
  
  
  
  Mixed methods study in North West England that comprised an online survey and focus groups.
  
  
  
  One hundred thirty eight respondents completed the survey, which found the main perceived advantages to using CPMs were that they guided appropriate treatment (weighted rank [r] = 299; maximum r = 414 throughout), justified treatment decisions (r = 217), and incorporated a large body of evidence (r = 156). The most commonly reported barriers to using CPMs were lack of time (r = 163), irrelevance to some patients (r = 161), and poor integration with electronic health records (r = 147). Eighteen clinicians participated in two focus groups (i.e. nine in each), which revealed 13 interdependent themes affecting CPM use under three overarching domains: clinician factors, CPM factors and contextual factors. Themes were interdependent, indicating the tensions family physicians experience in providing evidence-based care for individual patients.
  
  
  
  The survey and focus groups showed that CPMs were valued when they supported clinical decision making and were robust. Barriers to their use related to their being time-consuming, difficult to use and not always adding value. Therefore, to be successful, CPMs should offer a relative advantage to current working, be easy to implement, be supported by training, policy and guidelines, and fit within the organisational culture.
authors: Health eResearch Centre, Farr Institute, University of Manchester, Manchester, M13 9PL, UK.
authors: Benjamin
authors: B
authors: Brown
authors: NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, University of Manchester, Manchester, UK.
authors: Sudeh
authors: S
authors: Cheraghi-Sohi
authors: Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
authors: Thomas
authors: T
authors: Jaki
authors: School of Dentistry, University of Manchester, Manchester, UK.
authors: Ting-Li
authors: TL
authors: Su
authors: Health eResearch Centre, Farr Institute, University of Manchester, Manchester, M13 9PL, UK.
authors: Iain
authors: I
authors: Buchan
authors: Health eResearch Centre, Farr Institute, University of Manchester, Manchester, M13 9PL, UK. matthew.sperrin@manchester.ac.uk.
authors: Matthew
authors: 0000-0002-5351-9960
authors: M
authors: Sperrin
mesh_terms: D000066491:Clinical Decision-Making / Q000379:methods*; D004739:England; D005194:Family Practice; D017144:Focus Groups; D006801:Humans; D062069:Inventions; D008962:Models, Theoretical; D010821:Physicians, Family / Q000706:statistics & numerical data*; D017410:Practice Guidelines as Topic / Q000592:standards*; D011795:Surveys and Questionnaires
keywords: Attitude of health personnel; Clinical decision support systems; Clinical prediction models; Clinicians; Diagnostic models; Family physicians; Healthcare information technology adoption; Practice patterns; Primary care information systems; Prognostic models; Risk stratification