Defining Potentially Inappropriate Prescriptions for Hypoglycaemic Agents to Improve Computerised Decision Support: A Study Protocol

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 antidia...

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Autores principales: Paul Quindroit, Nicolas Baclet, Erwin Gerard, Laurine Robert, Madleen Lemaitre, Sophie Gautier, Chloé Delannoy-Rousselière, Bertrand Décaudin, Anne Vambergue, Jean-Baptiste Beuscart
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
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Acceso en línea:https://doaj.org/article/bec2e1891000414c81f06c4114983ed8
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Sumario: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.