Publications

PURPOSE

Primary care needs new models to facilitate advance care planning conversations. These conversations focus on preferences regarding serious illness and may involve patients, decision makers, and health care providers. We describe the feasibility of the first primary care-based group visit model focused on advance care planning.

METHODS

We conducted a pilot demonstration of an advance care planning group visit in a geriatrics clinic. Patients were aged at least 65 years. Groups of patients met in 2 sessions of 2 hours each facilitated by a geriatrician and a social worker. Activities included considering personal values, discussing advance care planning, choosing surrogate decision-makers, and completing advance directives. We used the RE-AIM framework to evaluate the project.

RESULTS

Ten of 11 clinicians referred patients for participation. Of 80 patients approached, 32 participated in 5 group visit cohorts (a 40% participation rate) and 27 participated in both sessions (an 84% retention rate). Mean age was 79 years; 59% of participants were female and 72% white. Most evaluated the group visit as better than usual clinic visits for discussing advance care planning. Patients reported increases in detailed advance care planning conversations after participating (19% to 41%, P = .02). Qualitative analysis found that older adults were willing to share personal values and challenges related to advance care planning and that they initiated discussions about a broad range of relevant topics.

CONCLUSION

A group visit to facilitate discussions about advance care planning and increase patient engagement is feasible. This model warrants further evaluation for effectiveness in improving advance care planning outcomes for patients, clinicians, and the system.

BACKGROUND

Hospitals that have robust financial performance may have improved publicly reported outcomes.

OBJECTIVES

To assess the relationship between hospital financial performance and publicly reported outcomes of care, and to assess whether improved outcome metrics affect subsequent hospital financial performance.

DESIGN

Observational cohort study.

SETTING AND PATIENTS

Hospital financial data from the Office of Statewide Health Planning and Development in California in 2008 and 2012 were linked to data from the Centers for Medicare and Medicaid Services Hospital Compare website.

MEASUREMENTS

Hospital financial performance was measured by net revenue by operations, operating margin, and total margin. Outcomes were 30-day risk-standardized mortality and readmission rates for acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia (PNA).

RESULTS

Among 279 hospitals, there was no consistent relationship between measures of financial performance in 2008 and publicly reported outcomes from 2008 to 2011 for AMI and PNA. However, improved hospital financial performance (by any of the 3 measures) was associated with a modest increase in CHF mortality rates (ie, 0.26% increase in CHF mortality rate for every 10% increase in operating margin [95% confidence interval: 0.07%-0.45%]). Conversely, there were no significant associations between outcomes from 2008 to 2011 and subsequent financial performance in 2012 (P > 0.05 for all).

CONCLUSIONS

Robust financial performance is not associated with improved publicly reported outcomes for AMI, CHF, and PNA. Financial incentives in addition to public reporting, such as readmissions penalties, may help motivate hospitals with robust financial performance to further improve publicly reported outcomes. Reassuringly, improved mortality and readmission rates do not necessarily lead to loss of revenue. Journal of Hospital Medicine 2016;11:481-488. © 2016 Society of Hospital Medicine.

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.