Email updates

Keep up to date with the latest news and content from BMC Medical Informatics and Decision Making and BioMed Central.

Open Access Highly Accessed Research article

Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer

Gautier Defossez1*, Alexandre Rollet1, Olivier Dameron2 and Pierre Ingrand13

Author Affiliations

1 Unité d’épidémiologie, biostatistique et registre général des cancers de Poitou-Charentes, Faculté de médecine, Centre Hospitalier Universitaire de Poitiers, Université de Poitiers, 6, rue de la milétrie, Poitiers, Cedex BP 199 86034, France

2 Université de Rennes 1, IRISA UMR6074, Rennes, France

3 INSERM, Poitiers CIC 802, France

For all author emails, please log on.

BMC Medical Informatics and Decision Making 2014, 14:24  doi:10.1186/1472-6947-14-24

Published: 2 April 2014

Abstract

Background

Ensuring that all cancer patients have access to the appropriate treatment within an appropriate time is a strategic priority in many countries. There is in particular a need to describe and analyse cancer care trajectories and to produce waiting time indicators. We developed an algorithm for extracting temporally represented care trajectories from coded information collected routinely by the general cancer Registry in Poitou-Charentes region, France. The present work aimed to assess the performance of this algorithm on real-life patient data in the setting of non-metastatic breast cancer, using measures of similarity.

Methods

Care trajectories were modeled as ordered dated events aggregated into states, the granularity of which was defined from standard care guidelines. The algorithm generates each state from the aggregation over a period of tracer events characterised on the basis of diagnoses and medical procedures. The sequences are presented in simple form showing presence and order of the states, and in an extended form that integrates the duration of the states. The similarity of the sequences, which are represented in the form of chains of characters, was calculated using a generalised Levenshtein distance.

Results

The evaluation was performed on a sample of 159 female patients whose itineraries were also calculated manually from medical records using the same aggregation rules and dating system as the algorithm. Ninety-eight per cent of the trajectories were correctly reconstructed with respect to the ordering of states. When the duration of states was taken into account, 94% of the trajectories matched reality within three days. Dissimilarities between sequences were mainly due to the absence of certain pathology reports and to coding anomalies in hospitalisation data.

Conclusions

These results show the ability of an integrated regional information system to formalise care trajectories and automatically produce indicators for time-lapse to care instatement, of interest in the planning of care in cancer. The next step will consist in evaluating this approach and extending it to more complex trajectories (metastasis, relapse) and to other cancer localisations.

Keywords:
Epidemiology; Evaluation; Care trajectory; Temporal reasoning; Data integration; Cancer