TY - JOUR

T1 - What does validation of cases in electronic record databases mean?

T2 - The potential contribution of free text

AU - Nicholson, Amanda

AU - Tate, Anne Rosemary

AU - Koeling, Rob

AU - Cassell, Jackie A

N1 - Copyright © 2011 John Wiley & Sons, Ltd.

PY - 2011/3

Y1 - 2011/3

N2 - Electronic health records are increasingly used for research. The definition of cases or endpoints often relies on the use of coded diagnostic data, using a pre-selected group of codes. Validation of these cases, as 'true' cases of the disease, is crucial. There are, however, ambiguities in what is meant by validation in the context of electronic records. Validation usually implies comparison of a definition against a gold standard of diagnosis and the ability to identify false negatives ('true' cases which were not detected) as well as false positives (detected cases which did not have the condition). We argue that two separate concepts of validation are often conflated in existing studies. Firstly, whether the GP thought the patient was suffering from a particular condition (which we term confirmation or internal validation) and secondly, whether the patient really had the condition (external validation). Few studies have the ability to detect false negatives who have not received a diagnostic code. Natural language processing is likely to open up the use of free text within the electronic record which will facilitate both the validation of the coded diagnosis and searching for false negatives.

AB - Electronic health records are increasingly used for research. The definition of cases or endpoints often relies on the use of coded diagnostic data, using a pre-selected group of codes. Validation of these cases, as 'true' cases of the disease, is crucial. There are, however, ambiguities in what is meant by validation in the context of electronic records. Validation usually implies comparison of a definition against a gold standard of diagnosis and the ability to identify false negatives ('true' cases which were not detected) as well as false positives (detected cases which did not have the condition). We argue that two separate concepts of validation are often conflated in existing studies. Firstly, whether the GP thought the patient was suffering from a particular condition (which we term confirmation or internal validation) and secondly, whether the patient really had the condition (external validation). Few studies have the ability to detect false negatives who have not received a diagnostic code. Natural language processing is likely to open up the use of free text within the electronic record which will facilitate both the validation of the coded diagnosis and searching for false negatives.

KW - Databases, Factual

KW - Disease

KW - Electronic Health Records

KW - Forms and Records Control

KW - Natural Language Processing

KW - Validation Studies as Topic

U2 - 10.1002/pds.2086

DO - 10.1002/pds.2086

M3 - Journal article

C2 - 21351316

VL - 20

SP - 321

EP - 324

JO - Pharmacoepidemiology and Drug Safety

JF - Pharmacoepidemiology and Drug Safety

SN - 1099-1557

IS - 3

ER -