Differences in transitional care processes among high-performing and low-performing hospital-SNF pairs: a rapid ethnographic approach

Abstract

BackgroundDespite the increased focus on improving patient’s postacute care outcomes, best practices for reducing readmissions from skilled nursing facilities (SNFs) are unclear. The objective of this study was to observe processes used to prepare patients for postacute care in SNFs, and to explore differences between hospital-SNF pairs with high or low 30-day readmission rates.DesignWe used a rapid ethnographic approach with intensive multiday observations and key informant interviews at high-performing and low-performing hospitals, and their most commonly used SNF. We used flow maps and thematic analysis to describe the process of hospitals discharging patients to SNFs and to identify differences in subprocesses used by high-performing and low-performing hospitals.Setting and participantsHospitals were classified as high or low performers based on their 30-day readmission rates from SNFs. The final sample included 148 hours of observations with 30 clinicians across four hospitals (n=2 high performing, n=2 low performing) and corresponding SNFs (n=5).FindingsWe identified variation in five major processes prior to SNF discharge that could affect care transitions: recognising need for postacute care, deciding level of care, selecting an SNF, negotiating patient fit and coordinating care with SNF. During each stage, high-performing sites differed from low-performing sites by focusing on: (1) earlier, ongoing, systematic identification of high-risk patients; (2) discussing the decision to go to an SNF as an iterative team-based process and (3) anticipating barriers with knowledge of transitional and SNF care processes.ConclusionIdentifying variations in processes used to prepare patients for SNF provides critical insight into the best practices for transitioning patients to SNFs and areas to target for improving care of high-risk patients.

Skilled Nursing Facility Performance and Readmission Rates Under Value-Based Purchasing

“…Furthermore, because many potential predictors of improvement are not observable in our data set, complementary approaches to identify how SNFsparticularly those with low performance at baseline-were able to improve are needed. [21][22][23] The SNF VBP program is the first national value-based purchasing program in postacute care, but demonstration projects are also under way in other postacute settings, such as home health. 24 Comparing and contrasting findings in these programs may reveal important insights into valuebased purchasing design in postacute care.…”

“…Given the impact of the SNF VBP program on SNF finances, analyses identifying potential unintended consequences of the SNF VBP program on SNF financial performance (and related concerns, such as staffing and closures, which may affect patient access) are needed. Furthermore, because many potential predictors of improvement are not observable in our data set, complementary approaches to identify how SNFs—particularly those with low performance at baseline—were able to improve are needed …”

Postacute care outcomes in home health or skilled nursing facilities in patients with a diagnosis of dementia

“…The decision making process regarding the choice of HH, and between SNF and HH, is understudied, and this is an important potential limitation. However, existing literature suggests the decision making process about postacute care is iterative, with considerable uncertainty about which type of postacute care may be better for patients 22,[36][37][38][39] -leading to substantial variability in use nationally. 40 It is therefore plausible for a sizeable subpopulation of patients that the "correct" postdischarge supports are not clear, and the local supply of SNF or HH could influence decision making.…”

VA nursing home compare metrics as an indicator of skilled nursing facility quality for veterans

“…In all regression models, we adjusted for patient (age, sex, length of hospitalization, number of hospitalizations, ICU stay, history of ESRD, primary diagnosis, Charlson‐Deyo comorbidity score, Barthel Index, CFS), hospital facility (number of beds, medical school affiliation, ownership type, transplant capacity) and CLC facility (number of beds, rural/urban) variables. We included random effects for hospitals to account for potential variations in hospital transitional care processes 30,31 . In all models we used robust standard errors to adjust for clustering of observations within CLCs.…”

“…We included random effects for hospitals to account for potential variations in hospital transitional care processes. 30,31 In all models we used robust standard errors to adjust for clustering of observations within CLCs. Analyses were conducted in SAS version 7.1 (SAS Institute, Inc., Cary, NC).…”