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Challenges associated with missing data in electronic health records: a case study of a risk prediction model for diabetes using data from Slovenian primary care

Published on: 18th October 2017

Published in 2017

Pub Med ID:

Gregor Stiglic, Primoz Kocbek, Nino Fijacko and Majda Pajnkihar, University of Maribor, Slovenia
Aziz Sheikh, University of Edinburgh


The increasing availability of data stored in electronic health records brings substantial opportunities for advancing patient care and population health. This is, however, fundamentally dependant on the completeness and quality of data in these electronic health records. We sought to use electronic health record data to populate a risk prediction model for identifying patients with undiagnosed type 2 diabetes mellitus. We, however, found substantial (up to 90%) amounts of missing data in some healthcare centres. Attempts at imputing for these missing data or using reduced dataset by removing incomplete records resulted in a major deterioration in the performance of the prediction model. This case study illustrates the substantial wasted opportunities resulting from incomplete records by simulation of missing and incomplete records in predictive modelling process. Government and professional bodies need to prioritise efforts to address these data shortcomings in order to ensure that electronic health record data are maximally exploited for patient and population benefit.

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