Harnessing Data Warehousing for Precision in Off-Label Prescription Detection in Psychiatry (PSYHAMM): Retrospective...

Off-label drug prescribing is prevalent across medicine, including psychiatry, often due to unmet therapeutic needs and inadequate responses to standard treatments. The PSYHAMM (Psychotropes Hors Autorisation de Mise sur le Marché) project, funded by the French Research Agency, investigates these practices. To support this research, a clinical data warehouse (CDW) with advanced data analysis tools was developed and deployed. This system integrates both structured and unstructured data from electronic health records, facilitating comprehensive data analysis. The goal is to improve understanding, regulation, and safety of off-label drug use in psychiatry by providing insights into prescribing patterns and their impacts, ultimately contributing to better clinical guidelines and patient care.

This study aimed to evaluate the precision (positive predictive value) of a CDW in identifying candidate off-label prescriptions in psychiatry among the cases automatically flagged by the system, rather than its overall accuracy, sensitivity, or specificity.

The PSYHAMM data analysis involved a retrospective study of pathology-medication pairs to evaluate the precision of a computerized system among system-flagged cases. This system was compared with manual checks performed by a psychiatrist. The evaluation process included verifying if the condition identified by PSYHAMM was documented in the medical record, assessing diagnostic agreement with tolerance for schizoaffective disorders, and ensuring the identified treatment was current or prescribed in the past. Precision was measured as the number of relevant documents retrieved divided by the total number of documents proposed and was computed for the precise diagnosis, the broad diagnosis, and the identified treatment among the flagged cases.

The study analyzed 197 records, identifying 14 unique drug-pathology combinations. Bipolar disorder treated with sodium valproate represented the most cases (108/197, 54.8%), followed by schizophrenia treated with sodium valproate (37/197, 18.8%). The overall precision for detecting off-label situations was 51.3% (101/197). The precise diagnosis achieved a precision of 75.6% (149/197), while the broad diagnosis showed a higher precision of 84.8% (167/197). The identified treatment had a precision of 61.4% (121/197). The primary challenge was temporal discrepancies, such as distinguishing between acute and chronic conditions, which accounted for most of the 48.7% (96/197) of cases that were incorrectly classified.

As a single-center, proof-of-concept evaluation, the PSYHAMM project demonstrates the potential of automated systems to support the identification of off-label prescriptions in psychiatry as a sensitive prescreening step requiring expert validation. The relatively high false-positive rate was driven mainly by temporal discrepancies (drugs prescribed before the index stay, discontinued during the stay, or only hypothetically mentioned) rather than by semantic errors. Future research should focus on integrating real-time data analytics and expanding to multiple institutions to improve the utility of off-label detection systems.