Risk Indicators Improve the Prescription Quality of Drugs with Anticholinergic Properties in Nursing Homes.

AIM: The objective of this study was to assess the impact of a collaborative therapeutic optimization program on the rate of potentially inappropriate prescription of drugs with anticholinergic properties in nursing homes. METHODS: Quasi-experimental study in 37 nursing homes in France. The intervention included the use of quality indicators for prescriptions combined with educational sessions and dedicated materials for nursing home staff (unlimited access to study material for staff, including nurses, general practitioners, pharmacists). Indicators were calculated based on routine data collected from an electronic pill dispenser system. The primary outcome was the presence of at least one prescription containing ≥1 drug from a list of 12 drugs with anticholinergic properties. A difference-in-differences analysis was conducted at 18 months as well as propensity score weighting to minimize any potential indication bias. A generalized estimating equation model estimated the probability of being prescribed at least one target drug at any time during a 9-month period for each resident. RESULTS: In total, 33 nursing homes (intervention group: n = 10; control group: n = 23) were included, totalling 8137 residents. There was a decrease in the use of drugs with anticholinergic properties over time in both groups, as well as a decline in the intervention group compared to the control group (Odds Ratio: 0.685, 95% CI: 0.533, 0.880; p < 0.01) that was attributable to the intervention. An estimated 49 anticholinergic properties drug prescriptions were avoided by the intervention. CONCLUSION: This study found that an intervention based on indicators derived from routine prescription data was effective in reducing use of drugs with anticholinergic properties prescriptions in nursing homes.

UMLF: a unified medical lexicon for French.

Medical Informatics has a constant need for basic medical language processing tasks, e.g. for coding into controlled vocabularies, free text indexing and information retrieval. Most of these tasks involve term matching and rely on lexical resources: lists of words with attached information, including inflected forms and derived words, etc. Such resources are publicly available for the English language with the UMLS Specialist Lexicon, but not in other languages. For the French language, several teams have worked on the subject and built local lexical resources. The goal of the present work is to pool and unify these resources and to add extensively to them by exploiting medical terminologies and corpora, resulting in a unified medical lexicon for French (UMLF). This paper exposes the issues raised by such an objective, describes the methods on which the project relies and illustrates them with experimental results.