We developed an electronic case-finding algorithm that accurately identified patients with diabetes at their earliest possible date within a healthcare system using data extracted from an EHR. The performance of our model in identifying patients with diabetes is comparable to other diabetes case-finding algorithms [10–17]. However, the distinct advantage of our automated, real-time algorithm is the timely recognition of diabetes. Relying on only two ICD-9 encounter codes to establish the diagnosis date, a quarter of the cases in our cohort would have been missed and another 11% would have had a delayed diagnosis. By using multiple data elements we were able to identify the date of diagnosis within three months of a physician’s chart review date in 94% of case, with three-quarters of cases having a perfect date match.
Achieving early glycemic control in patients with newly diagnosed diabetes reduces the risk of microvascular complications, myocardial infarction, and all-cause mortality . Attaining the benefits of instituting early treatment requires timely diagnosis. In the ARIC cohort, a population-based prospective study of middle-aged adults, Samuels et al. found that even with an effective screening program the median delay from the onset of diabetes to physician diagnosis was 2.4 years, with more than 7% of incident cases remaining undiagnosed for at least 7.5 years . In addition, delayed diagnoses are more widespread in safety-net settings where patients may have more fragmented, episodic care . Real-time, automated patient identification and tracking can help healthcare systems close this gap and facilitate the delivery of timely, effective therapy at the point-of-care at the earliest possible date .
Improving care for diabetics is increasingly important for healthcare systems in today’s pay-for-performance climate. The high cost, rising prevalence, and documented quality gap has positioned diabetes in the forefront of policies benchmarking performance. To qualify for financial incentives and avoid public scrutiny, healthcare systems are increasingly faced with the challenge to achieve acceptable rates in their diabetic population for targeted metrics such as glycated hemoglobin, low-density lipoprotein, and screening for microalbuminuria. Our electronic case-finding algorithm, which leverages real-time data in the EHR, can enable proactive management of these quality measures. Healthcare systems may additionally apply this algorithm to provide feedback to providers on the quality of their care, generate letters to patients, identify underperforming clinics for quality improvement initiatives, link clinical decision support tools to inform decision making at the point-of-care, and risk stratify diabetic patients to direct limited resources to patients at greatest risk for developing complications.
Our study has several limitations. First, as with other registries, the limitations of miscoding and misclassification prohibited subtype distinction between type 1, type 2, and secondary diabetes . Second, due to limits in study costs, we established an enriched prevalence of diabetes of 50% in our validation cohort to reduce the number of charts required for manual chart review. While this inflated the positive and negative predictive values, the sensitivity and specificity of our algorithm remained unaffected and were comparable to other diabetes case-finding algorithms. Third, by using the e-model algorithm to select our validation cohort, we were unable to evaluate how individual data elements performed in identifying diabetes. Fourth, the direct applicability of our algorithm to other settings is unknown because of differences in practice style, EHR integration across outpatient and inpatient settings, and EHR documentation. Systems with greater accuracy in EHR documentation may need to increase the relative weight of the problem list and past medical history field to maximize the model’s performance. With proper weight adjustments we expect our algorithm to be suitable to a wide range of healthcare settings. Automated machine learning techniques may provide approaches to model adjustment that could minimize manual recalibration and allow larger scales of dissemination. Lastly, in clinical settings transitioning from paper-based records to an EHR, the e-model may not accurately distinguish between newly established versus preexisting cases of diabetes within a healthcare system .