JMIR Publications

ABSTRACT

Background:

Routinely recorded electronic health records (EHRs) from general practitioners (GPs) are increasingly available and provide valuable data for estimating incidence and prevalence rates of diseases in the population.

Objective:

This paper describes how we developed an algorithm to construct episodes of illness based on EHR data to calculate morbidity rates.

Methods:

The algorithm was developed in discussion rounds with two expert groups and tested with data from NIVEL Primary Care Database, which consisted of a representative sample of 219 general practices, covering a total population of 867,140 listed patients in 2012. Morbidity data were used from EHRs in the period 2010-2012, including recorded ICPC coded episodes of care, encounters and prescriptions.

Results:

All 685 symptoms and diseases of ICPC-1 were categorized as acute symptoms/diseases, long-lasting reversible diseases, and chronic diseases. Based on knowledge of the duration of a disease, for each category an algorithm was developed to construct episodes of illness. Compared with recorded episodes of care, for acute and long-lasting diseases, applying the algorithm resulted in a reduction of both the number and average duration of the episodes up to 53% and 94%, respectively. On the other hand, for chronic diseases, the algorithm resulted in a slight increase in the number of episodes as well as the episode duration.

Conclusions:

An algorithm was developed to construct episodes of illness based on routinely recorded EHR data to estimate morbidity rates. The algorithm constitutes a simple and uniform way of using EHR data and can easily be applied in other registries.