Automated Diabetes Case Identification Using Electronic Health Record Data at a Tertiary Care Facility. Academic Article uri icon



  • Objective: To develop and validate a phenotyping algorithm for the identification of patients with type 1 and type 2 diabetes mellitus (DM) preoperatively using routinely available clinical data from electronic health records. Patients and Methods: We used first-order logic rules (if-then-else rules) to imply the presence or absence of DM types 1 and 2. The "if" clause of each rule is a conjunction of logical and, or predicates that provides evidence toward or against the presence of DM. The rule includes International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes, outpatient prescription information, laboratory values, and positive annotation of DM in patients' clinical notes. This study was conducted from March 2, 2015, through February 10, 2016. The performance of our rule-based approach and similar approaches proposed by other institutions was evaluated with a reference standard created by an expert reviewer and implemented for routine clinical care at an academic medical center. Results: A total of 4208 surgical patients (mean age, 52 years; males, 48%) were analyzed to develop the phenotyping algorithm. Expert review identified 685 patients (16.28% of the full cohort) as having DM. Our proposed method identified 684 patients (16.25%) as having DM. The algorithm performed well-99.70% sensitivity, 99.97% specificity-and compared favorably with previous approaches. Conclusion: Among patients undergoing surgery, determination of DM can be made with high accuracy using simple, computationally efficient rules. Knowledge of patients' DM status before surgery may alter physicians' care plan and reduce postsurgical complications. Nevertheless, future efforts are necessary to determine the effect of first-order logic rules on clinical processes and patient outcomes.

publication date

  • April 28, 2017


PubMed Central ID

  • PMC6135013

Digital Object Identifier (DOI)

  • 10.1016/j.mayocpiqo.2017.04.005

PubMed ID

  • 30225406

Additional Document Info


  • 1


  • 1