Literature DB >> 27013297

The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes.

Michael Staff1, Christopher Roberts2, Lyn March3.   

Abstract

AIM: To describe the completeness of routinely collected primary care data that could be used by computer models to predict clinical outcomes among patients with Type 2 Diabetes (T2D).
METHODS: Data on blood pressure, weight, total cholesterol, HDL-cholesterol and glycated haemoglobin levels for regular patients were electronically extracted from the medical record software of 12 primary care practices in Australia for the period 2000-2012. The data was analysed for temporal trends and for associations between patient characteristics and completeness. General practitioners were surveyed to identify barriers to recording data and strategies to improve its completeness.
RESULTS: Over the study period data completeness improved up to around 80% complete although the recording of weight remained poorer at 55%. T2D patients with Ischaemic Heart Disease were more likely to have their blood pressure recorded (OR 1.6, p=0.02). Practitioners reported not experiencing any major barriers to using their computer medical record system but did agree with some suggested strategies to improve record completeness.
CONCLUSION: The completeness of routinely collected data suitable for input into computerised predictive models is improving although other dimensions of data quality need to be addressed.
Copyright © 2016 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Computer modelling; Data completeness; Electronic records; Primary care

Mesh:

Substances:

Year:  2016        PMID: 27013297     DOI: 10.1016/j.pcd.2016.02.002

Source DB:  PubMed          Journal:  Prim Care Diabetes        ISSN: 1878-0210            Impact factor:   2.459


  4 in total

1.  A longitudinal follow-up study of a type 2 diabetes "lost to follow-up" cohort - positive effect on glycaemic control after changes in medication.

Authors:  Timo Kauppila; Merja K Laine; Mikko Honkasalo; Marko Raina; Johan G Eriksson
Journal:  Int J Circumpolar Health       Date:  2020-01-01       Impact factor: 1.228

2.  A basic model for assessing primary health care electronic medical record data quality.

Authors:  Amanda L Terry; Moira Stewart; Sonny Cejic; J Neil Marshall; Simon de Lusignan; Bert M Chesworth; Vijaya Chevendra; Heather Maddocks; Joshua Shadd; Fred Burge; Amardeep Thind
Journal:  BMC Med Inform Decis Mak       Date:  2019-02-12       Impact factor: 2.796

3.  Limitations for health research with restricted data collection from UK primary care.

Authors:  Helen Strongman; Rachael Williams; Wilhelmine Meeraus; Tarita Murray-Thomas; Jennifer Campbell; Lucy Carty; Daniel Dedman; Arlene M Gallagher; Jessie Oyinlola; Antonis Kousoulis; Janet Valentine
Journal:  Pharmacoepidemiol Drug Saf       Date:  2019-04-16       Impact factor: 2.890

4.  Blood pressure and cholesterol measurements in primary care: cross-sectional analyses in a dynamic cohort.

Authors:  Annemarijn R de Boer; Monika Hollander; Ineke van Dis; Frank Lj Visseren; Michiel L Bots; Ilonca Vaartjes
Journal:  BJGP Open       Date:  2022-08-30
  4 in total

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