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Posted by / 06-Oct-2020 18:17

METHODS Using a cross-sectional data validation study design, regional and local CPCSSN networks from British Columbia, Alberta (2), Ontario, Nova Scotia, and Newfoundland participated in validating EHR case-finding algorithms.

A random sample of EHR charts were reviewed, oversampling for patients older than 60 years and for those with epilepsy or parkinsonism.

PURPOSE The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is Canada’s first national chronic disease surveillance system based on electronic health record (EHR) data.

The purpose of this study was to develop and validate case definitions and case-finding algorithms used to identify 8 common chronic conditions in primary care: chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes, hypertension, osteoarthritis, parkinsonism, and epilepsy.

Each is unique to the respective chronic condition and includes varying EHR data elements.

CPCSSN data are appropriate for use in public health surveillance, primary care, and health services research, as well as to inform policy for these diseases.

A standardized electronic data abstraction tool was developed to extract anonymous information from patients’ charts and record the reviewers’ assessments in a consistent way.

The manual, training procedures, and abstraction tool were based on those developed for a previous study on data validation in primary care practices.

RESULTS We obtained data from 1,920 charts from 4 different EHR systems (Wolf, Med Access, Nightingale, and PS Suite).

For the total sample, sensitivity ranged from 78% (osteoarthritis) to more than 95% (diabetes, epilepsy, and parkinsonism); specificity was greater than 94% for all diseases; PPV ranged from 72% (dementia) to 93% (hypertension); NPV ranged from 86% (hypertension) to greater than 99% (diabetes, dementia, epilepsy, and parkinsonism).

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The case definitions were constructed with guidance from published evidence and both general and specialist physicians, and required several revisions before validation and implementation using computerized case finding algorithms.

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