چکیده (انگلیسی):

Background:Electronic Medical Records (EMRs) are increasingly used in the provision of primary care and have
been compiled into databases which can be utilized for surveillance, research and informing practice. The primary
purpose of these records is for the provision of individual patient care; validation and examination of underlying
limitations is crucial for use for research and data quality improvement. This study examines and describes the
validity of chronic disease case definition algorithms and factors affecting data quality in a primary care EMR
database.
Methods:A retrospective chart audit of an age stratified random sample was used to validate and examine
diagnostic algorithms applied to EMR data from the Manitoba Primary Care Research Network (MaPCReN), part of
the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). The presence of diabetes, hypertension,
depression, osteoarthritis and chronic obstructive pulmonary disease (COPD) was determined by review of the
medical record and compared to algorithm identified cases to identify discrepancies and describe the underlying
contributing factors.
Results:The algorithm for diabetes had high sensitivity, specificity and positive predictive value (PPV) with all
scores being over 90%. Specificities of the algorithms were greater than 90% for all conditions except for
hypertension at 79.2%. The largest deficits in algorithm performance included poor PPV for COPD at 36.7% and
limited sensitivity for COPD, depression and osteoarthritis at 72.0%, 73.3% and 63.2% respectively. Main sources of
discrepancy included missing coding, alternative coding, inappropriate diagnosis detection based on medications
used for alternate indications, inappropriate exclusion due to comorbidity and loss of data.
Conclusions:Comparison to medical chart review shows that at MaPCReN the CPCSSN case finding algorithms are
valid with a few limitations. This study provides the basis for the validated data to be utilized for research and
informs users of its limitations. Analysis of underlying discrepancies provides the ability to improve algorithm
performance and facilitate improved data quality.