title: Applying preventable drug-related morbidity indicators to the electronic patient record in UK primary care: methodological development.
abstract: OBJECTIVE
  Measuring and assessing the quality of health care services is an issue of high international importance. Providing data can be reliably extracted, making use of the electronic patient record (EPR) could help practitioners fulfil clinical governance obligations and ultimately improve the quality of patient care. The objective of this paper is to describe (i) the process used to apply a series of clinical indicators for preventable drug-related morbidity (PDRM) in the EPR, (ii) problems encountered and (iii) our attempts to resolve them.
  
  
  METHODS
  The PDRM indicators were applied retrospectively in the EPR of all patients aged 18 years and over in nine general practices using the Morbidity Information and Query Export Syntax (MIQUEST) computer software programme.
  
  
  RESULTS
  Issues identified as requiring attention when attempting to extract data from the EPR include considering the ranges to be used for age and biochemical test results, accuracy of diagnosis and drug coding, the level of complexity of the information needed, and how best to manipulate the resulting data. Practical difficulties encountered were ensuring the query coding schemes were sufficiently robust and comprehensive to secure reliable data extraction, the number of MIQUEST queries required to express each indicator, the time-consuming nature of the stages involved in the data manipulation process.
  
  
  CONCLUSIONS
  Despite some practical difficulties, we have successfully used MIQUEST to identify potential preventable drug-related morbidities from the EPR. The quality of information that can be extracted from the EPR is obviously limited by the accuracy and completeness of the data on the system and the ability of the enquirer to reliably extract and manipulate that data.
  
  
  CONCLUSIONS
  Although some of the problems encountered were specific to the MIQUEST software, many, including considering appropriate ranges for age and biochemical test results and paying careful attention to the reliability of drug and diagnosis coding, are relevant whenever data are extracted from the EPR for any purpose.
authors.affiliation: Division of Primary Care, University of Nottingham, Nottingham, UK. vicky.hammersley@ed.ac.uk
authors.forename: V S
authors.initials: VS
authors.lastname: Hammersley
authors.forename: C J
authors.initials: CJ
authors.lastname: Morris
authors.forename: S
authors.initials: S
authors.lastname: Rodgers
authors.forename: J A
authors.initials: JA
authors.lastname: Cantrill
authors.forename: A J
authors.initials: AJ
authors.lastname: Avery
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D019411:Clinical Laboratory Techniques; D016208:Databases, Factual; D011307:Drug Prescriptions; D064420:Drug-Related Side Effects and Adverse Reactions; D005260:Female; D006801:Humans; D006947:Hyperkalemia / Q000139:chemically induced / Q000175:diagnosis; D008297:Male; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D011188:Potassium / Q000097:blood; D011320:Primary Health Care / Q000706:statistics & numerical data*; D011787:Quality of Health Care; D012189:Retrospective Studies; D006113:United Kingdom
chemical_list: D011188:Potassium