Defining Potentially Inappropriate Prescriptions for Hypoglycaemic Agents to Improve Computerised Decision Support: A Study Protocol.
Paul QuindroitNicolas BacletErwin GerardLaurine RobertMadleen LemaitreSophie GautierChloé Delannoy-RousselièreBertrand DécaudinAnne VambergueJean-Baptiste BeuscartPublished in: Healthcare (Basel, Switzerland) (2021)
In France, around 5% of the general population are taking drug treatments for diabetes mellitus (mainly type 2 diabetes mellitus, T2DM). Although the management of T2DM has become more complex, most of these patients are managed by their general practitioner and not a diabetologist for their antidiabetics treatments; this increases the risk of potentially inappropriate prescriptions (PIPs) of hypoglycaemic agents (HAs). Inappropriate prescribing can be assessed by approaches that are implicit (expert judgement based) or explicit (criterion based). In a mixed, multistep process, we first systematically reviewed the published definitions of PIPs for HAs in patients with T2DM. The results will be used to create the first list of explicit definitions. Next, we will complete the definitions identified in the systematic review by conducting a qualitative study with two focus groups of experts in the prescription of HAs. Lastly, a Delphi survey will then be used to build consensus among participants; the results will be validated in consensus meetings. We developed a method for determining explicit definitions of PIPs for HAs in patients with T2DM. The resulting explicit definitions could be easily integrated into computerised decision support tools for the automated detection of PIPs.
Keyphrases
- glycemic control
- systematic review
- end stage renal disease
- study protocol
- meta analyses
- clinical practice
- ejection fraction
- chronic kidney disease
- randomized controlled trial
- newly diagnosed
- type diabetes
- primary care
- peritoneal dialysis
- emergency department
- high throughput
- machine learning
- cardiovascular disease
- prognostic factors
- deep learning
- metabolic syndrome
- insulin resistance
- single cell
- skeletal muscle
- cardiovascular risk factors