Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data.

OBJECTIVE: To create surveillance algorithms to detect diabetes and classify type 1 versus type 2 diabetes using structured electronic health record (EHR) data. RESEARCH DESIGN AND METHODS: We extracted 4 years of data from the EHR of a large, multisite, multispecialty ambulatory practice serving ∼700,000 patients. We flagged possible cases of diabetes using laboratory test results, diagnosis codes, and prescriptions. We assessed the sensitivity and positive predictive value of novel combinations of these data to classify type 1 versus type 2 diabetes among 210 individuals. We applied an optimized algorithm to a live, prospective, EHR-based surveillance system and reviewed 100 additional cases for validation. RESULTS: The diabetes algorithm flagged 43,177 patients. All criteria contributed unique cases: 78% had diabetes diagnosis codes, 66% fulfilled laboratory criteria, and 46% had suggestive prescriptions. The sensitivity and positive predictive value of ICD-9 codes for type 1 diabetes were 26% (95% CI 12-49) and 94% (83-100) for type 1 codes alone; 90% (81-95) and 57% (33-86) for two or more type 1 codes plus any number of type 2 codes. An optimized algorithm incorporating the ratio of type 1 versus type 2 codes, plasma C-peptide and autoantibody levels, and suggestive prescriptions flagged 66 of 66 (100% [96-100]) patients with type 1 diabetes. On validation, the optimized algorithm correctly classified 35 of 36 patients with type 1 diabetes (raw sensitivity, 97% [87-100], population-weighted sensitivity, 65% [36-100], and positive predictive value, 88% [78-98]). CONCLUSIONS: Algorithms applied to EHR data detect more cases of diabetes than claims codes and reasonably discriminate between type 1 and type 2 diabetes.

Integrating clinical practice and public health surveillance using electronic medical record systems.

Electronic medical record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers' EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. We describe a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.

Integrating clinical practice and public health surveillance using electronic medical record systems.

Electronic medical record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers' EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. The current paper describes a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.

Harnessing electronic health records for public health surveillance.

Electronic medical record (EMR) systems are a rich potential source for detailed, timely, and efficient surveillance of large populations. We created the Electronic medical record Support for Public Health (ESP) system to facilitate and demonstrate the potential advantages of harnessing EMRs for public health surveillance. ESP organizes and analyzes EMR data for events of public health interest and transmits electronic case reports or aggregate population summaries to public health agencies as appropriate. It is designed to be compatible with any EMR system and can be customized to different states' messaging requirements. All ESP code is open source and freely available. ESP currently has modules for notifiable disease, influenza-like illness syndrome, and diabetes surveillance. An intelligent presentation system for ESP called the RiskScape is under development. The RiskScape displays surveillance data in an accessible and intelligible format by automatically mapping results by zip code, stratifying outcomes by demographic and clinical parameters, and enabling users to specify custom queries and stratifications. The goal of RiskScape is to provide public health practitioners with rich, up-to-date views of health measures that facilitate timely identification of health disparities and opportunities for targeted interventions. ESP installations are currently operational in Massachusetts and Ohio, providing live, automated surveillance on over 1 million patients. Additional installations are underway at two more large practices in Massachusetts.