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Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre.
@article{Kostopoulou:2021:jamia/ocab025,
author = {Kostopoulou, O and Tracey, C and Delaney, B},
doi = {jamia/ocab025},
journal = {Journal of the American Medical Informatics Association},
title = {Can decision support combat incompleteness and bias in routine primary care data?},
url = {http://dx.doi.org/10.1093/jamia/ocab025},
year = {2021}
}
TY - JOUR
AB - Objective: Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.Materials and Methods: We used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting di- agnoses, the DSS facilitates data coding. We compared the documentation from consultations with the elec- tronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician’s final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations re- lated to their diagnosis, while in supported consultations, they would also document other observations as a re- sult of exploring more diagnoses and/or ease of coding.Results: Supported documentation contained significantly more codes (incidence rate ratio [IRR] 1⁄4 5.76 [4.31, 7.70] P < .001) and less free text (IRR 1⁄4 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b 1⁄4 0.08 [0.11, 0.05] P < .001) in the supported consultations, and this was the case for both codes and free text.Conclusions: We provide evidence that data entry in the EHR is incomplete and reflects physicians’ cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.
AU - Kostopoulou,O
AU - Tracey,C
AU - Delaney,B
DO - jamia/ocab025
PY - 2021///
SN - 1067-5027
TI - Can decision support combat incompleteness and bias in routine primary care data?
T2 - Journal of the American Medical Informatics Association
UR - http://dx.doi.org/10.1093/jamia/ocab025
ER -
