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4CPS-200 Sustaining a pharmaceutical decision support system by determining the clinical risk’s level of detected drug-related problems
  1. J Bouet1,
  2. A Potier1,
  3. C Mongaret2,
  4. B Michel3,
  5. M Cillis4,
  6. A Dony5,
  7. M Ade1,
  8. E Divoux5,
  9. C Viaud1,
  10. E Dufay5
  1. 1Chru Nancy, Pharmacy, Vandœuvre-Lès-Nancy, France
  2. 2Chu Reims, Pharmacy, Reims, France
  3. 3Hu Strasbourg, Pharmacy, Strasbourg, France
  4. 4Chu Uclouvain, Pharmacy, Namur, Belgium
  5. 5Ch Lunéville, Pharmacy, Lunéville, France


Background and Importance Pharmaceutical decision support system (PDSS) is a positive triangulation between patients’ data, modelled situations standing for drug-related problems and a reasoning software sending alerts. So the pharmaceutical interventions better prevent adverse drug events and better reduce healthcare costs. But to be optimal the PDSS has also to link the modelled situations to a clinical well-defined risk. As consequences each pharmaceutical intervention’s impact will be documented and the PDSS’s interest in patients’ safety sustained.

Aim and Objectives To present the results of an e-Delphi study during which health professional experts evaluate the clinical risk’s level of 52 modelled situations standing for drug-related problems or adverse drug events.

Material and Methods Twenty experts across 4 francophone countries were involved because of their clinical skills. Based on their experience, physicians (5) or pharmacists (15) scored the likelihood of occurrence of clinical consequences and its severity for each of the 52 modelled patients’ situations using a five-point Likert scale. These situations were chosen among a panel of 199 one, according to their high frequency in the health facilities. The degree of consensus between participants was defined as the proportion that gave a risk score in the same category as the median. Consensus was obtained if the score was 75% or more. Then the 2 median scores -occurrence and severity- were combined to produce the risk level for each situation. Only 2 Delphi rounds were necessary.

Results After the first round a consensus was reached for 8 situations. Experts agreed on the level of risk associated with 48 out of 52 modelled situations. A high or extreme consensus risk level is determined for 45 modelled situations. These situations represent a variety of drug-related problems. Overdosing was the most frequent situation [12 (22%)]. Cardiovascular, Psychiatric and Endocrinological drug classes are the most common involved in respectively [25 (45%)], [7 (13%)] and [5 (9%)] situations.

Conclusion and Relevance The symbolic artificial intelligence to detect drug-related problems in patients’ medications will be much more shared if pharmaceutical algorithms including the clinical risk are defined through consensus.

References and/or Acknowledgements Health Regional Agency, Innovation Department, Région Grand Est, France

Conflict of Interest No conflict of interest

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