Measuring quality of routine primary care data

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 diagnoses, the DSS facilitates data
coding. We compared the documentation from consultations with the
electronic 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 related to their diagnosis, while in
supported consultations, they would also document other observations as a
result of exploring more diagnoses and/or ease of coding.
Results: Supported documentation contained significantly more
codes (IRR=5.76 [4.31,
7.70] P<0.001) and less free text (IRR =
0.32 [0.27, 0.40] P<0.001) than baseline
documentation. As expected, the proportion of diagnosis-related data was
significantly lower (b=-0.08 [-0.11, -0.05] P<0.001) 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.