Validation of a novel Artificial Pharmacological Intelligence (API) system for the management of patients with polypharmacy

Dorit Dil-Nahlieli; Arie Ben-Yehuda; Daniel Souroujon; Eytan Hyam; Sigal Shafran-Tikvah

doi:10.1016/j.sapharm.2024.04.003

Validation of a novel Artificial Pharmacological Intelligence (API) system for the management of patients with polypharmacy

Res Social Adm Pharm. 2024 Apr 10:S1551-7411(24)00117-7. doi: 10.1016/j.sapharm.2024.04.003. Online ahead of print.

Authors

Dorit Dil-Nahlieli¹, Arie Ben-Yehuda², Daniel Souroujon³, Eytan Hyam⁴, Sigal Shafran-Tikvah⁵

Affiliations

¹ Department of Research and Development, MDI Health Technologies, Ramat Gan, Israel. Electronic address: Dorit@mdihealth.com.
² Department of Medicine, Hadassah Medical Center Faculty of Medicine, Hebrew University, Jerusalem, Israel.
³ School of Public Health, Tel Aviv University, Tel Aviv, Israel.
⁴ Department of Research and Development, MDI Health Technologies, Ramat Gan, Israel.
⁵ Nursing Division, Hadassah University Medical Center & Jerusalem College of Technology, Jerusalem, Israel.

PMID: 38637208
DOI: 10.1016/j.sapharm.2024.04.003

Abstract

Objective: Medication management of patients with polypharmacy is highly complex. We aimed to validate a novel Artificial Pharmacological Intelligence (API) algorithm to optimize the medication review process in a comprehensive, personalized, and scalable way.

Materials and methods: The study was conducted on anonymized retrospective electronic health records (EHR) of 49 patients. Each patient's file was reviewed by the API system, a clinical pharmacist, and a judging committee. Validation was assessed by comparing the overall agreement of the judging committee (as the gold standard, blinded to the identity of the analyzer) to both the API system and clinical pharmacists' conclusions. Five medication-related problem (MRP) categories were assessed: duplication of therapy, age-related issues, incorrect dose, current side effects and future side effects' risk. For each category the overall validity parameters, agreement, positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity were analyzed.

Results: The agreement between the API system and the judging committee was 93.5 % (95 % CI 92.7-94.4), while the agreement between the clinical pharmacists and the judging committee was 73.9 % (95 % CI 72.5-75.3). The PPV was 92.2 % (90.9-93.5) and NPV was 94.2 % (93.1-95.2) for the API system and 76.3 % (69.8-82.8) and 73.5 % (72.3-74.8) respectively for the clinical pharmacists.

Discussion: AI systems can equip clinicians with sophisticated tools and scale manual processes such as comprehensive medication reviews, thus reducing MRPs and drug-related hospitalizations related to multidrug treatments. The API system validated in this study provided comprehensive, multidrug, multilayered analysis intended to bridge the innate complexity of personalized polypharmacy treatment.

Conclusions: The API system was validated as a tool for providing actionable clinical insights non-inferior to a manual clinical review of a clinical pharmacist. The API system showed promising results in reducing MRPs.

Keywords: Clinical decision-support system; Drug-related problem; Medication optimization; Medication risks; Personalized medicine; Polypharmacy.