JMIR Publications

ABSTRACT

Background:

Since the use of electronic health records (EHRs) in an automatized way, pharmacovigilance or pharmacoepidemiology studies have been trying to characterize the therapy using different algorithms. Although progress has been made in this area for monotherapy, in exposure to combinations of two or more drugs the challenge to characterize the treatment increases significantly and more research is needed.

Objective:

To develop and describe a novel algorithm that automatically returns the most likely therapy of one drug or combinations of two or more drugs over time.

Methods:

We used the Information System for Research in Primary Care (SIDIAP) as our reference EHRs platform for the smooth algorithm development. The algorithm is inspired by statistical methods based on moving averages and depends on a parameter Wt, a flexible window that determines the level of smoothing. The effect of Wt was evaluated in a simulation study with different window lengths on the same dataset. To understand the algorithm performance in a clinical or pharmacological perspective, we conducted a validation study. We designed four different pharmacological scenarios and asked to four independent professionals to compare a standard algorithm with the smooth algorithm. Then, the data from the simulation and the validation studies was analysed.

Results:

The Wt parameter has an impact over the raw data. As we increased the window length, the number of smoothed patients augmented, although we hardly see changes bigger than five percent of the total data. In the validation study, significant differences were obtained in the performance of the smooth algorithm against the standard method. These differences were consistent across pharmacological scenarios.

Conclusions:

The smooth algorithm is an automatic approach that standardizes, simplifies, and improves the data processing in drug exposition studies using EHRs. This algorithm can be generalized to almost any pharmacological medication and facilitates the detection of treatment switches, discontinuations and/or terminations throughout the study period.