Variability in Transitional Care Outcomes Across Hospitals Discharging Veterans to Skilled Nursing Facilities

Abstract

Background:

The period after transition from hospital to skilled nursing facility (SNF) is high-risk, but variability in outcomes related to transitions across hospitals is not well-known.

Objectives:

Evaluate variability in transitional care outcomes across Veterans Health Administration (VHA) and non-VHA hospitals for Veterans, and identify characteristics of high-performing and low-performing hospitals.

Research Design:

Retrospective observational study using the 2012–2014 Residential History File, which concatenates VHA, Medicare, and Medicaid data into longitudinal episodes of care for Veterans.

Subjects:

Veterans aged 65 or older who were acutely hospitalized in a VHA or non-VHA hospital and discharged to SNF; 1 transition was randomly selected per patient.

Measures:

Adverse “transitional care” outcomes were a composite of hospital readmission, emergency department visit, or mortality within 7 days of hospital discharge.

Results:

Among the 365,942 Veteran transitions from hospital to SNF across 1310 hospitals, the composite outcome rate ranged from 3.3% to 23.2%. In multivariable analysis adjusting for patient characteristics, hospital discharge diagnosis and SNF category, no single hospital characteristic was significantly associated with the 7-day adverse outcomes in either VHA or non-VHA hospitals. Very few high or low-performing hospitals remained in this category across all 3 years. The increased odds of having a 7-day event due to being treated in a low versus high-performing hospital was similar to the odds carried by having an intensive care unit stay during the index admission.

Conclusions:

While variability in hospital outcomes is significant, unmeasured care processes may play a larger role than currently measured hospital characteristics in explaining outcomes.

BACKGROUND

The transition from hospital to skilled nursing facility (SNF) is perilous for older adults. Nearly 1 in 4 older adults are readmitted to the hospital from SNF within 30 days and more than a quarter of these readmissions occur within the first 7 days.^1–3 Recognition of adverse outcomes in transitions from hospital to SNF led to substantial national policy changes that provide financial incentives to improve these outcomes.^4–7 In contrast to this national focus on improving postacute care outcomes in non-Veterans, the outcomes of Veterans transitioning from hospital to SNF are less well described. In prior work we found >1 in 10 Veterans transitioning to SNF experienced a potentially adverse transitional care outcome within 7 days of hospital discharge, including an emergency department (ED) visit, hospital readmission, or death.⁸ Early adverse transitional care outcomes may be particularly important because few hospital clinicians or hospitalized patients would expect one of these events so soon after discharge. In addition, these outcomes are those most likely to be responsive to transitional care interventions.^1,9,10

However, it is unknown how much variability in postacute care outcomes exists across hospitals within 7 days of discharge, nor how to identify high-performing or low-performing hospitals. Identifying and describing high-performing and low-performing hospitals is needed in order to understand how to improve transitional care outcomes for Veterans discharged to SNF, and these lessons may be broadly applicable to the older adult population in the United States.^11–15

In this analysis, we use a sample of Veterans enrolled in the Veterans Health Administration (VHA) discharged from VHA or non-VHA hospitals to SNFs for postacute care. We first assessed the degree of variability in 7-day outcomes for Veterans across VHA and non-VHA hospitals, including how much variability remains after adjusting for patient case mix, discharge diagnosis, and SNF type. We then sought to identify characteristics of high-performing and low-performing hospitals, quantify the effect of being treated in a high-performing and low-performing hospital, and explore consistency of performance over time.

METHODS

Data Source

We used the Residential History File (RHF) from January 1, 2012 to December 31, 2014.¹⁶ The RHF includes VHA hospital and SNF care, VHA-paid SNF care in the community, Medicare fee-for-service claims, and Medicaid fee-for-service claims for Veterans enrolled in the VHA. Although the RHF does not include commercial payers, Medicare fee-for-service is the predominant payer for SNF care in the United States.¹⁷

Population Description

We began with every Veteran age enrolled in the VHA who had an acute hospitalization followed by a stay in a SNF (n = 985,577 transitions). Swing bed stays, hospice care, and psychiatric or inpatient rehabilitation stays were excluded (n = 132,757 excluded transitions). We also excluded Veterans less than age 65 or older than 100 (n = 87,558). We further restricted the sample to hospitals that averaged at least 1 Veteran discharge per week to SNF in each year of our sample, to avoid unstable estimates (n = 189,320 Veteran transitions excluded). We had a small amount of missing data about SNFs or relevant patient characteristics and excluded those records (n = 5390). We then randomly selected 1 record per Veteran, so Veterans with multiple stays only contributed information to a single transition type (n = 204,610 excluded). We conducted a sensitivity analysis including all stays which did not change our results, and report the randomly selected results to avoid potential problems with independence across Veterans in our sample.

Predictor and Outcome Variables

Our primary outcome was a composite of death, ED visit, or rehospitalization within 7 days of discharge from the hospital. While Veterans could have >1 outcome during the 7-day period, the composite is a binary measure.

We adjusted for predictors that have been linked to adverse transitional care outcomes in other studies.^1,3,18 Veteran demographic data included age, sex, race, and ethnicity, median income in their ZIP code of residence from the 2015 American Community Survey, and individual comorbidities that constitute the Charlson-Deyo score (identified using International Classification of Diseases-9 diagnosis codes within 1 year before the initial hospital admission).¹⁹ We identified the number of prior hospitalizations Veterans had in the year before the index admission, and characteristics of their index admission [hospital length of stay and whether they had an Intensive Care Unit (ICU) stay]. We also identified and adjusted for the primary discharge diagnosis of the index hospitalization, aggregating International Classification of Diseases-9 codes using Agency for Health Research and Quality Clinical Classification Score codes.²⁰

While Minimum Data Set (MDS) admission information can be useful in predicting adverse outcomes,²¹ we found a >0% rate of missing data in this cohort. Since missingness could be related to patient outcomes (patients who had an outcome would not have an MDS assessment) we did not include MDS variables.

Hospital characteristics included number of beds, affiliation with a medical school or residency program, urban/rural location, ownership, and whether the hospital was capable of performing organ transplants (a marker of its patient population and relative complexity of services offered). We also identified the number of SNFs used by a particular hospital over the entire 3-year period, as well as the largest proportion (or concentration) of discharges sent to the most commonly used SNF.^22–24 Non-VHA hospital characteristics were identified from the Medicare Provider of Services file. VHA hospital urban/rural status was taken from the Medicare Provider of Services file. Number of VHA hospital operating beds was taken from VHA Support Services Center. VHA hospital teaching status and organ transplant information was taken from the VHA Office of Productivity, Efficiency, and Staffing.

We previously found that the SNF type (including VHA vs. non-VHA SNFs) is associated with 7-day outcome rates⁸ and adjusted for type of SNF in the analysis.

Statistical Analysis

We first described characteristics of the Veteran population in VHA and non-VHA hospitals discharged to SNF, as well as characteristics of the hospitals themselves (Supplemental Digital Content, Table 1, http://links.lww.com/MLR/B931). We calculated unadjusted 7-day outcome rates in both VHA and non-VHA hospitals over each year of the time period. We used a mixed logistic regression model with hospital random effects, stratified by hospital type (VHA or non-VHA) to identify hospital characteristics associated with high versus low performance. We displayed the stratified adjusted outcome rate across hospitals using box and whisker plots. We also calculated the ability of our model to accurately predict outcomes by measuring the area under the receiver operating characteristic curve, and report a c-statistic. We quantified the effect of being at a low (10th percentile) versus high-performing (90th percentile) hospital on outcomes, using the percentiles from the distribution of random effects in the model. We analyzed the proportion of high-performing and low-performing hospitals in 2012 (categorized by top and bottom quartile, respectively, of adjusted 7-day outcomes) who consistently remained in this category in subsequent years. The study was approved by the Corporal Crescenz VHA Medical Center IRB.

RESULTS

Among the 365,942 Veteran transitions from hospital to SNF across 1310 hospitals, the composite outcome rate ranged from 3.3% to 23.2%, with an interquartile range (IQR) of 8.9%–12.7%. The variability around the median was similar at VHA and non-VHA hospitals, and these estimates were largely stable over time (Table 1). The 117 VHA hospitals in our sample discharged patients to a median of 89 different SNFs (IQR, 51–139), while the 1193 non-VHA hospitals discharged patients to a median of 31 SNFs (IQR, 22–47). VHA hospitals had a higher median proportion of referrals to a single SNF (median, 29%; IQR, 15%–49% for VHA hospitals; median of 22%, IQR 15%–32% for non-VHA hospitals). VHA and non-VHA hospitals differed in the primary discharge diagnoses of patients being discharged to SNF (Table 2 lists outcome rates for the most prevalent diagnoses across hospitals; Supplemental Digital Content, Table 2, http://links.lww.com/MLR/B931 lists the prevalence of discharge diagnoses by hospital type).

TABLE 1.

Variability in Outcome Rates Across VHA and Non-VHA Hospitals

	Median	Minimum	Maximum	Lower Quartile	Upper Quartile
All hospitals, all years	10.7	3.3	23.2	8.9	12.7
VHA hospital (n = 117), all years	9.2	4.5	20.0	7.8	10.7
2012	9.5	1.4	21.6	7.7	11.7
2013	9.4	3.2	18.5	7.3	11.7
2014	9.0	2.2	20.8	7.0	10.4
Non-VHA hospital (n = 1193), all years	10.9	3.3	23.2	9.1	12.8
2012	11.0	1.3	36.4	8.5	14.1
2013	10.2	0.0	36.0	7.5	13.2
2014	10.7	1.6	30.0	8.2	13.6

All values listed are percentages. The composite outcome includes hospitalizations, emergency department visits, and mortality.

VHA indicates Veterans Health Administration.

TABLE 2.

Outcomes by Principle Discharge Diagnosis Stratified by VHA or Non-VHA Hospital

Primary Discharge Diagnosis	VHA Hospital Patients With Diagnosis (%)	Outcome Rate (95% CI)	Non-VHA Hospital Patients With Diagnosis (%)	Outcome Rate (95% CI)
Osteoarthritis	5.3	4.3 (3.6–4.9)	5.5	4.9 (4.5–5.2)
Pneumonia	4.7	10.4 (9.4–11.5)	4.5	11.7 (11.1–12.2)
Hip fracture	3.2	8.3 (7.2–9.5)	4.2	9.1 (8.6–9.6)
Fatigue	0.7	6.9 (4.6–9.3)	4.1	10.0 (9.5–10.5)
Sepsis	3.9	12.0 (10.8–13.2)	3.1	14.1 (13.3–14.8)
Heart failure	4.3	13.2 (12.0–14.4)	3.0	12.8 (12.1–13.6)
Urinary tract infection	4.5	8.8 (7.8–9.8)	2.4	8.2 (7.6–8.8)
Cerebrovascular accident	3.0	8.2 (7.1–9.4)	2.5	13.5 (12.7–14.3)
Syncope	0.6	4.3 (2.4–6.1)	2.6	8.7 (8.1–9.3)
Back problem	1.5	7.2 (5.6–8.7)	2.3	8.6 (7.9–9.3)

The most common 10 principle hospital discharge diagnoses are listed with prevalence (second and fourth columns) and unadjusted diagnosis-specific primary outcome rate (third and fifth columns).

CI indicates confidence interval; VHA, Veterans Health Administration.

In adjusted models, as expected, several patient characteristics (such as comorbidity, ICU stay, discharge diagnosis) as well as SNF type were significantly associated with outcomes. However, no single hospital characteristic was significantly associated with 7-day adverse outcomes in either VHA or non-VHA hospitals (Table 3; Supplemental Digital Content, Table 3, http://links.lww.com/MLR/B931 contains full models). Variability across hospitals remained large after adjustment for patient, hospital, and SNF characteristics (Fig. 1). Less than 10% of high (lowest quartile of performance on 7-day adverse event rates) or low-performing (highest quartile of performance) hospitals remained in this category over time (Fig. 2; Supplemental Digital Content, Tables 4 and 5, http://links.lww.com/MLR/B931). Patients admitted to a SNF after a hospitalization at a low-performing (90th percentile) VHA hospital had 1.4 times the odds of having the composite outcome, compared with a patient admitted to a SNF after a hospitalization at a top-performing VHA hospital (10th percentile). The corresponding odds ratio for non-VHA hospitals was similar (1.3). These odds ratios are similar in magnitude to having an ICU stay during the index admission in terms of increasing odds of a 7-day event (1.4 and 1.2, respectively, at VHA and non-VHA hospitals, Supplemental Digital Content, Table 3, http://links.lww.com/MLR/B931). The c-statistic of our final model was 0.63.

TABLE 3.

Unadjusted and Adjusted Models Evaluating the Relationship of Hospital Characteristics to Event Rates, Stratified by Hospital Type

	VHA Hospitals, OR (95% CI)		Non-VHA Hospitals, OR (95% CI)
	Unadjusted	Adjusted	Unadjusted	Adjusted
Intercept	0.10 (0.07–0.15)	0.03 (0.02–0.05)	0.14 (0.13–0.15)	0.04 (0.03–0.04)
No. beds
< 50	0.85 (0.70–1.03)	0.87 (0.73–1.04)	0.94 (0.70–1.27)	0.99 (0.74–1.32)
51–100	0.94 (0.81–1.10)	0.95 (0.82–1.10)	1.01 (0.91–1.12)	1.05 (0.95–1.16)
101–200	0.96 (0.85–1.10)	0.99 (0.88–1.11)	1.09 (0.96–1.06)	1.04 (0.99–1.09)
Medical school affiliated	1.10 (0.84–1.45)	1.10 (0.85–1.43)	1.00 (0.97–1.04)	1.00 (0.97–1.04)
Urban	0.91 (0.76–1.10)	0.95 (0.80–1.14)	0.85 (0.81–0.90)	0.88 (0.83–0.93)
Transplant-capable	1.03 (0.89–1.19)	1.04 (0.91–1.18)	1.03 (0.99–1.08)	1.03 (0.99–1.07)
Ownership
Private, for-profit	—	—	0.99 (0.93–1.06)	0.98 (0.92–1.05)
Private, not for profit	—	—	0.97 (0.92–1.03)	0.98 (0.93–1.03)
Other	—	—	0.96 (0.90–1.03)	0.97 (0.91–1.03)
Maximum proportion of patients discharged to 1 SNF	0.79 (0.59–1.05)	1.08 (0.83–1.41)	0.84 (0.71–0.99)	0.86 (0.74–1.01)
No. SNFs per 10 patient discharges	1.06 (0.99–1.14)	1.05 (0.98–1.11)	1.04 (1.02–1.06)	1.02 (1.00–1.04)

Maximum proportion of patients discharged to 1 SNF reflects the proportion of a hospital’s patients that go to the single largest SNF by referral volume in their network. Number of SNFs per 10 patient discharges is a marker of how many SNFs a hospital uses adjusted for volume of referrals.

CI indicates confidence interval; OR, odds ratio; SNF, skilled nursing facility; VHA, Veterans Health Administration.

FIGURE 1.

Distribution of adjusted outcome rates across VHA and non-VHA hospitals. Box-and-whisker plots demonstrate the range, 25% and 75% percentile, median, and interquartile range of adjusted 7-day outcomes stratified by VHA or non-VHA hospitals. VHA indicates Veterans Health Administration.

FIGURE 2.

Proportion of VHA and non-VHA hospitals that remained high-performing or low-performing over time. High performance was defined as being in the lowest quartile of 7-day event rates in each year of the study (defined separately for each year); low performance hospitals were those in the highest quartile of 7-day event rates each year. “Other” refers to hospitals not consistently in the top or bottom quartile across years. VHA indicates Veterans Health Administration.

DISCUSSION

While there is significant variability in 7-day outcomes among Veterans discharged from the hospital to SNF, examined hospital characteristics were not associated with high or low performance, and performance was inconsistent over time. It is unknown whether these findings are similar in other populations, such as Medicare beneficiaries, since high performance by hospitals and SNFs are defined separately (ie, by Hospital Compare and Nursing Home Compare). Public reporting (and financial penalties) for 30-day hospital readmissions from SNF may prompt more interest in identifying characteristics of high performers.^4,5 While there is active debate over what proportion of hospital readmissions are preventable in the transition to SNF,^18,25–27 there is clear evidence of adverse outcomes.²

There are several possible explanations for these findings. First, it is possible that the individual components of our composite outcome exhibit different patterns in high-performing versus low-performing hospitals. For example, others have demonstrated that risk-standardized readmission rates and mortality rates are poorly correlated at an individual hospital level.²⁸ In our prior work, we found that differences in our composite outcome across SNFs were driven by ED visits, and to a lesser extent, mortality.⁸ However, investigating each outcome across hospitals requires a larger sample since the number of outcomes becomes small across the >1300 hospitals in our sample. Second, it seems likely that unmeasured hospital characteristics (such as transitional care processes) contribute to variability and performance in SNF transitions. Although it was not our goal to predict patient-level outcomes (we have pursued this Aim in related work),²¹ the relatively modest predictive accuracy of our model when including patient, hospital, and SNF factors suggests many factors of relevance are not currently measured by VHA, Medicare, or Medicaid. Factors such as whether the hospital and SNF share electronic medical record access may be important for improving the safety of care transitions but cannot be assessed using currently available data.^29,30 This is an important gap to address through research (establishing which missing metrics are most useful) and regulation (to ensure valid, reliable measurement). A good model might be “door to balloon” time—a care process metric not routinely captured until its importance in explaining outcomes was measured, leading to it now being standardly measured across all hospitals with cardiac catheterization laboratories in the United States. Third, it is possible that evaluating high-quality transitions from other viewpoints may also be fruitful. For example, it may be important to explore to what extent Veteran and non-Veteran transitional care is similar at non-VHA hospitals. Alternatively, since SNF use is variable across markets,^31,32 analyzing characteristics of high-performing and low-performing markets may hold promise. Fourth, SNF processes may also contribute to outcomes; for example, we have found the timing of physician visits after SNF admission is related to readmission risk in the Medicare population.^12,33 Evaluation of a broader array of SNF factors that contribute to early transitional care outcomes is important in future work. We are unable to assess the impact of measured quality (measured by star ratings, for example) on outcomes since such measurements are not available for VHA facilities during our time period of interest. Prior work has suggested the link may be weak between these quality metrics and readmission rates in the Medicare population.^34,35 Fourth, unmeasured confounding may be present in our patient risk adjustment.³⁶ We adjusted for factors commonly used in studies comparing VHA and non-VHA hospitals.^37,38

Strengths of the study include a comprehensive sample of enrolled Veterans, ability to control for relevant patient and SNF characteristics, and the ability to capture transitional outcomes across VHA, Medicare, and Medicaid data. We are limited in what hospital characteristics can be examined using these data sources, as well as our patient and SNF adjustment for potential confounders. Differences in discharge diagnoses across VHA and non-VHA hospitals may be due to differences in coding practices we were unable to assess. This is an observational study combining data from multiple sources; there is potential for duplicate counting of SNFs depending on how they are labeled in each data source. We sought to eliminate any matching records across claim types and to match ID numbers across datasets to reduce any duplicates.

Significant variability in 7-day outcomes across VHA and non-VHA hospitals may suggest opportunities to improve transitions for this vulnerable population. Complementary studies to identify distinguishing characteristics of high and low performers are urgently needed.