Abstract 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.

Background

Social determinants directly contribute to poorer health, and coordination between healthcare and community-based resources is pivotal to addressing these needs. However, our healthcare system remains poorly equipped to address social determinants of health. The potential of health information technology to bridge this gap across the delivery of healthcare and social services remains unrealized.

Objective, design, and participants

We conducted in-depth, in-person interviews with 50 healthcare and social service providers to determine the feasibility of a social-health information exchange (S-HIE) in an urban safety-net setting in Dallas County, Texas. After completion of interviews, we conducted a town hall meeting to identify desired functionalities for a S-HIE.

Approach

We conducted thematic analysis of interview responses using the constant comparative method to explore perceptions about current communication and coordination across sectors, and barriers and enablers to S-HIE implementation. We sought participant confirmation of findings and conducted a forced-rank vote during the town hall to prioritize potential S-HIE functionalities.

Key results

We found that healthcare and social service providers perceived a need for improved information sharing, communication, and care coordination across sectors and were enthusiastic about the potential of a S-HIE, but shared many technical, legal, and ethical concerns around cross-sector information sharing. Desired technical S-HIE functionalities encompassed fairly simple transactional operations such as the ability to view basic demographic information, visit and referral data, and medical history from both healthcare and social service settings.

Conclusions

A S-HIE is an innovative and feasible approach to enabling better linkages between healthcare and social service providers. However, to develop S-HIEs in communities across the country, policy interventions are needed to standardize regulatory requirements, to foster increased IT capability and uptake among social service agencies, and to align healthcare and social service priorities to enable dissemination and broader adoption of this and similar IT initiatives.

Background

Vital sign instability on discharge could be a clinically objective means of assessing readiness and safety for discharge; however, the association between vital sign instability on discharge and post-hospital outcomes is unclear.

Objective

To assess the association between vital sign instability at hospital discharge and post-discharge adverse outcomes.

Design

Multi-center observational cohort study using electronic health record data. Abnormalities in temperature, heart rate, blood pressure, respiratory rate, and oxygen saturation were assessed within 24 hours of discharge. We used logistic regression adjusted for predictors of 30-day death and readmission.

Participants

Adults (≥18 years) with a hospitalization to any medicine service in 2009-2010 at six hospitals (safety-net, community, teaching, and non-teaching) in north Texas.

Main measures

Death or non-elective readmission within 30 days after discharge.

Key results

Of 32,835 individuals, 18.7 % were discharged with one or more vital sign instabilities. Overall, 12.8 % of individuals with no instabilities on discharge died or were readmitted, compared to 16.9 % with one instability, 21.2 % with two instabilities, and 26.0 % with three or more instabilities (p < 0.001). The presence of any (≥1) instability was associated with higher risk-adjusted odds of either death or readmission (AOR 1.36, 95 % CI 1.26-1.48), and was more strongly associated with death (AOR 2.31, 95 % CI 1.91-2.79). Individuals with three or more instabilities had nearly fourfold increased odds of death (AOR 3.91, 95 % CI 1.69-9.06) and increased odds of 30-day readmission (AOR 1.36, 95 % 0.81-2.30) compared to individuals with no instabilities. Having two or more vital sign instabilities at discharge had a positive predictive value of 22 % and positive likelihood ratio of 1.8 for 30-day death or readmission.

Conclusions

Vital sign instability on discharge is associated with increased risk-adjusted rates of 30-day mortality and readmission. These simple vital sign criteria could be used to assess safety for discharge, and to reduce 30-day mortality and readmissions.

Background

Incorporating clinical information from the full hospital course may improve prediction of 30-day readmissions.

Objective

To develop an all-cause readmissions risk-prediction model incorporating electronic health record (EHR) data from the full hospital stay, and to compare "full-stay" model performance to a "first day" and 2 other validated models, LACE (includes Length of stay, Acute [nonelective] admission status, Charlson Comorbidity Index, and Emergency department visits in the past year), and HOSPITAL (includes Hemoglobin at discharge, discharge from Oncology service, Sodium level at discharge, Procedure during index hospitalization, Index hospitalization Type [nonelective], number of Admissions in the past year, and Length of stay).

Design

Observational cohort study.

Subjects

All medicine discharges between November 2009 and October 2010 from 6 hospitals in North Texas, including safety net, teaching, and nonteaching sites.

Measures

Thirty-day nonelective readmissions were ascertained from 75 regional hospitals.

Results

Among 32,922 admissions (validation = 16,430), 12.7% were readmitted. In addition to many first-day factors, we identified hospital-acquired Clostridium difficile infection (adjusted odds ratio [AOR]: 2.03, 95% confidence interval [CI]: 1.18-3.48), vital sign instability on discharge (AOR: 1.25, 95% CI: 1.15-1.36), hyponatremia on discharge (AOR: 1.34, 95% CI: 1.18-1.51), and length of stay (AOR: 1.06, 95% CI: 1.04-1.07) as significant predictors. The full-stay model had better discrimination than other models though the improvement was modest (C statistic 0.69 vs 0.64-0.67). It was also modestly better in identifying patients at highest risk for readmission (likelihood ratio +2.4 vs. 1.8-2.1) and in reclassifying individuals (net reclassification index 0.02-0.06).

Conclusions

Incorporating clinically granular EHR data from the full hospital stay modestly improves prediction of 30-day readmissions. Given limited improvement in prediction despite incorporation of data on hospital complications, clinical instabilities, and trajectory, our findings suggest that many factors influencing readmissions remain unaccounted for. Further improvements in readmission models will likely require accounting for psychosocial and behavioral factors not currently captured by EHRs. Journal of Hospital Medicine 2016;11:473-480. © 2016 Society of Hospital Medicine.