# Towards a prescribing monitoring system for medication safety evaluation within electronic health records: a scoping review

## TL;DR

This scoping review explores how prediction models can be used to monitor medication safety over time in electronic health records to improve patient care.

## Contribution

The paper introduces a framework for Prescribing Monitoring that emphasizes longitudinal and dynamic prediction models for adverse drug reactions.

## Key findings

- 56 studies were included, highlighting the importance of longitudinal factors in ADR prediction models.
- Three key use cases for Prescribing Monitoring were identified: individualized, longitudinal, and dynamic predictions.
- Hyperkalaemia was used as an example to illustrate the framework for implementing Prescribing Monitoring in EHRs.

## Abstract

Medical care can fail for various reasons: diseases can remain undetected and their severity misjudged, therapies can be incorrectly dosed or ineffective, and therapies can trigger new conditions or adverse drug reactions (ADR). To manage the complexity of changing patient circumstances, data-driven techniques play an increasingly important role in monitoring patient safety and treatment success. Therefore, clinical prediction models need to consider longitudinal factors (“Prescribing Monitoring”) to ensure clinically meaningful results and avoid misclassification in the dynamic health situation of the individual patient.

We have conducted a scoping review (OSF registration: 10.17605/OSF.IO/P93TZ) on prediction models for ADR to collect potential use cases for Prescribing Monitoring. This review identified 2435 relevant studies in English that were published in MEDLINE or EMBASE. Two reviewers screened the records for inclusion, with a third reviewer making the final decision in the event of discrepancies. In order to derive recommendations on the way towards a Prescribing Monitoring system, the following elements were extracted and interpreted: the prediction models used, selection of candidate predictors, use of longitudinal factors, and model performance.

A total of 56 studies were included after the screening process. We identified the main areas of current research in ADR prediction, all covering clinically important outcomes. We identified Prescribing Monitoring use cases based on their potential to (i) make individual predictions considering specific patient characteristics, (ii) make longitudinal predictions in a near time frame, and (iii) make dynamic predictions by updating predictions with previous risk predictions and newly available data. As a further aside, we use hyperkalaemia as an example to discuss the framework for developing Prescribing Monitoring in an electronic health record (EHR).

This scoping review provides an overview of the use of time-varying effects and longitudinal variables in current prediction model research. For application to clinical cases, prediction models should be developed, validated and implemented on this basis, so that time-dependent information can enable continuous monitoring of individual patients.

The online version contains supplementary material available at 10.1186/s12911-025-03096-3.