Framework to implement a pharmaceutical decision support system: Detecting and resolving drug-related problems (notice n° 179433)
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| fixed length control field | 02218cam a2200217 4500500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250112041229.0 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | fre |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | dc |
| 100 10 - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Potier, Arnaud |
| Relator term | author |
| 245 00 - TITLE STATEMENT | |
| Title | Framework to implement a pharmaceutical decision support system: Detecting and resolving drug-related problems |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2023.<br/> |
| 500 ## - GENERAL NOTE | |
| General note | 63 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Goals. To present the framework for implementing a pharmaceutical decision support system (PDSS) that improves the detection and resolution of drug-related problems (DRP). The aim is to improve the relevance of the patient’s drug management. Methods. Over 4 years, in 2 health care facilities, pharmacists and IT professionals, supported by the company Keenturtle (France), formalized the PDSS, based on the active triangulation of a clinical decision support system (CDSS). Guidelines for representing knowledge in pharmaceutical algorithms, including human supervision, were defined. A glossary of terms, particularly related to AI-pharmacy, was made available to train PDSS users. Results. The PDSS is operational since 2018; it associates patient health data to pharmacotherapy knowledge in the software Pharmaclass®. A 12-step guideline helps the pharmacist transpose clinical recommendations into 201 pharmaceutical algorithms that model patient situations integrated into the PDSS. A dedicated framework for these pharmaceutical algorithms facilitates the detection and resolution of DRPs. Additionally, 41 terms are defined in a glossary. Conclusion. Defining a framework for implementing and using a PDSS reduces its complexity. The knowledge representation enhances pharmacists’ expertise through its pedagogical aspect, making it a central element of the PDSS. The symbolic artificial intelligence approach will support pharmacists in their practice. |
| 700 10 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ade, Mathias |
| Relator term | author |
| 700 10 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Huguet, Anaïs |
| Relator term | author |
| 700 10 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Pilven, Pierre |
| Relator term | author |
| 700 10 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Jeanjacquot, Audrey |
| Relator term | author |
| 700 10 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dufay, Edith |
| Relator term | author |
| 786 0# - DATA SOURCE ENTRY | |
| Note | Journal de Pharmacie Clinique | 42 | 3 | 2023-07-01 | p. 133-142 | 0291-1981 |
| 856 41 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://shs.cairn.info/journal-journal-de-pharmacie-clinique-2023-3-page-133?lang=en">https://shs.cairn.info/journal-journal-de-pharmacie-clinique-2023-3-page-133?lang=en</a> |
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