Easy Medication Reconciliation at Hospital Admission: The EzMedRec Decision Support System.
| Autor: | Seroussi B; Sorbonne Universite, Inserm, Universite Sorbonne Paris Nord, LIMICS UMR_S 1142, Paris, France.; Assistance Publique-Hopitaux de Paris, Hopital Tenon, Paris, France., Ghomari MB; Sorbonne Universite, Inserm, Universite Sorbonne Paris Nord, LIMICS UMR_S 1142, Paris, France., Guezennec G; Sorbonne Universite, Inserm, Universite Sorbonne Paris Nord, LIMICS UMR_S 1142, Paris, France., Federspiel F; Assistance Publique-Hopitaux de Paris, Hopital Tenon, Paris, France., Debrix I; Assistance Publique-Hopitaux de Paris, Hopital Tenon, Paris, France., Bouaud J; Assistance Publique-Hopitaux de Paris, DRCI, Paris, France.; Sorbonne Universite, Inserm, Universite Sorbonne Paris Nord, LIMICS UMR_S 1142, Paris, France. |
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| Jazyk: | angličtina |
| Zdroj: | AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2021 Jan 25; Vol. 2020, pp. 1110-1119. Date of Electronic Publication: 2021 Jan 25 (Print Publication: 2020). |
| Abstrakt: | Medication reconciliation (MR) aims at preventing medication errors at care transitions. It is a complex, time-consuming, cognitively demanding pharmacological task. We have developed a decision support system, EzMedRec, to assist retroactive MR at hospital admission. EzMedRec compares the best possible medication history (BPMH), i.e., all medications taken by the patient before hospitalization, to the list of admission medication orders (AMO). The process includes (i) the decomposition of BPMH and AMO drugs into their active ingredients (AIs), (ii) the detection of medication discontinuations and additions, and (iii) the identification of modified medication orders. The ATC classification is used to semantically enrich MR by comparing discontinued AIs and added AIs and suggesting a potential intentional drug substitution serving the same therapeutic objective. EzMedRec has been evaluated on a sample of 52 actual MRs involving 822 medication order lines, 406 in BPMHs, and 416 in AMOs with a global accuracy of 98,3%. (©2020 AMIA - All rights reserved.) |
| Databáze: | MEDLINE |
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