Identifying patients with diabetes and the earliest date of diagnosis in real time: An electronic health record case-finding algorithm

Background: Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date. Methods. The source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard. Results: The electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date. Conclusions: A diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management. © 2013 Makam et al.; licensee BioMed Central Ltd

Identifying patients with diabetes and the earliest date of diagnosis in real time: An electronic health record case-finding algorithm

Background: Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date. Methods. The source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard. Results: The electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date. Conclusions: A diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management. © 2013 Makam et al.; licensee BioMed Central Ltd

Biomarkers: Novel troponin immunoassay for early ACS rule-out

The introduction of a new high-sensitivity troponin immunoassay might revolutionize the way acute coronary syndrome is diagnosed. The high analytical sensitivity of the assay enables earlier and more accurate identification of myocardial injury than with currently used methods, and also allows for the safe discharge of patients without myocardial ischaemia

Underuse and Overuse of Osteoporosis Screening in a Regional Health System: a Retrospective Cohort Study

BACKGROUND: The United States Preventive Services Task Force (USPSTF) recommends screening for osteoporosis with dual-energy x-ray absorptiometry (DXA) for women aged ≥ 65 years and younger women with increased risk. “Choosing Wisely” initiatives advise avoiding DXA screening in women younger than 65 years without osteoporosis risk factors. OBJECTIVE: We aimed to determine the extent to which DXA screening is used in accordance with USPSTF recommendations within a regional health system. DESIGN: This was a retrospective longitudinal cohort study within 13 primary care clinics in the Sacramento, CA region. PATIENTS: The study included 50,995 women aged 40–85 years without prior osteoporosis screening, diagnosis, or treatment attending primary care visits from 2006 to 2012, observed for a mean of 4.4 years. MAIN MEASURES: We examined incidence of DXA screening. Covariates included age, race/ethnicity, and osteoporosis risk factors (body mass index < 20, glucocorticoid use, secondary osteoporosis, prior high-risk facture, rheumatoid arthritis, alcohol abuse, and current smoking). KEY RESULTS: Among previously unscreened women for whom the USPSTF recommends screening, 7-year cumulative incidence of DXA screening was 58.8 % among women aged 60–64 years with ≥ 1 risk factor (95 % CI: 51.9–65.8 %), 57.8 % for women aged 65–74 years (95 % CI: 55.6–60.0 %), and 42.7 % for women aged ≥ 75 years (95 % CI: 38.7–46.7 %). Among women for whom the USPSTF does not recommend screening, 7-year cumulative incidence was 45.5 % among women aged 50–59 years (95 % CI 44.1–46.9 %) and 58.6 % among women aged 60–64 years without risk factors (95 % CI 55.9–61.4 %). CONCLUSIONS: DXA screening was underused in women at increased fracture risk, including women aged ≥ 65 years. Meanwhile, DXA screening was common among women at low fracture risk, such as younger women without osteoporosis risk factors. Interventions may be needed to augment the value of population screening for osteoporosis