Development and implementation of an electronic asthma record for primary care: integrating guidelines into practice

Abstract

Rationale: Evidence-based practice may be enhanced by integrating knowledge translation tools into electronic medical records (EMRs). We examined the feasibility of incorporating an evidence-based asthma care map (ACM) into Primary Care (PC) EMRs, and reporting on performance indicators. Methods: Clinicians and information technology experts selected 69 clinical and administrative variables from the ACM template. Four Ontario PC sites using EMRs were recruited to the study. Certified Asthma Educators used the electronic ACM for patient assessment and management. De-identified data from consecutive asthma patients were automatically transmitted to a secure central server for analysis. Results: Of the four sites recruited, two sites using “stand-alone” EMR systems were able to incorporate the selected ACM variables into an electronic format and participate in the pilot. Data were received on 161 visits by 130 patients aged 36.5 ± 26.9 (mean ± SD) (range 2–93) years. Ninety-four percent (65/69) of the selected ACM variables could be analyzed. Reporting capabilities included: individual patient, individual site and aggregate reports. Reports illustrated the ability to measure performance (e.g. number of patients in control, proportion of asthma diagnoses confirmed by an objective measure of lung function), benchmark and use EMR data for disease surveillance (e.g. number of smokers and the individuals with suspected work-related asthma). Conclusions: Integration of this evidence-based ACM into different EMRs was successful and permitted patient outcomes monitoring. Standardized data definitions and terminology are essential in order for EMR data to be used for performance measurement, benchmarking and disease surveillance.

• Asthma
• electronic medical record
• primary care provider

Acknowledgements

We would like to thank Nicola Thomas RN, BN, CAE and Madonna Ferrone RRT, CRE for subject recruitment and data collection during the study. We would also like to thank Blaine Jenkins for software development and Xuran Jiang, MD, MSc, for assistance with analysis. Also, we thank Karen Jones, RRT, CRE for her input into the study design.