ABSTRACT
Background
Post‐acute care (PAC) is designed to help older adults recover functional independence following hospitalization. Frailty is a predictor of adverse health outcomes in hospitalized older adults, but the impact on PAC remains unclear. The objective of this study was to investigate whether (1) frailty is associated with hospital discharge location and (2) the impact of frailty and discharge location on health outcomes.
Methods
We conducted a retrospective cohort study from a single US‐based healthcare system from January 2021 to September 2023 of adults aged ≥ 65 years with an electronic frailty index (eFI) frailty score and hospital admission for at least 24 h. We evaluated the odds of hospital discharge location (home, home health services [HHS], and skilled nursing facility [SNF]) and health outcomes including hospital readmission and mortality, using logistic regression and Cox‐regression models adjusted for demographics and healthcare system factors.
Results
A cohort of 23,407 older adult patients was included, with a mean age of 76.7 years (SD 7.7), 53% female sex, and 19% non‐White race/ethnicity. Participant frailty was categorized as 22% non‐frail, 46% pre‐frail, 23% frail, and 8% severely frail. Patients with a higher degree of frailty were more likely to discharge with PAC (SNF or HHS) compared to home, adjusted odds ratio OR [95% CI], frail: OR 1.20 [1.09–1.33]. Frailty was a risk factor for 90‐day hospital readmissions (OR 1.91 [1.67–2.20], p < 0.001) and mortality (HR 2.56 [2.10–3.13], p < 0.001) compared to non‐frail individuals. In addition, patients discharged to SNF had higher mortality (HR 5.46 [4.43–6.73], p < 0.001), but did not have increased readmissions compared to individuals discharged home without HHS.
Conclusions
Frailty in older adults was a risk factor for discharge to PAC, higher mortality, and readmissions.
Automated frailty assessment can be used to identify high‐risk patients, facilitating targeted interventions across the PAC continuum.
Keywords: care transitions, frailty, hospital readmissions, post‐acute care
Summary.
- Key points
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○Frailty in hospitalized older adults, calculated from an EHR embedded eFI, is associated with increased risk of post‐acute care discharge, hospital readmissions, and mortality.
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○Opportunities to address the higher mortality rate of patients discharged to skilled nursing facilities need to be explored.
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○Automated frailty identification supported by the eFI can be used to develop population health interventions for frail older adults.
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- Why does this paper matter?
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○This study confirms the impact of frailty on hospital discharge destination and outcomes of readmissions and mortality. Its findings support the use of automated frailty assessment to identify high‐risk patients in post‐hospital care transitions.
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1. Introduction
The use of post‐acute care (PAC) is common among older adults, with approximately 20% requiring rehabilitation services after hospital discharge [1]. PAC services aim to facilitate functional recovery through rehabilitation and return to community living [2]. Each year, over 3 million older Americans enter skilled nursing facilities (SNF), most often for PAC after hospitalization [3]. Unfortunately, recovery is often incomplete; only 60% return to baseline function, and 10% remain in long‐term care [4, 5]. There is a clear need for evidence‐based approaches in PAC to improve outcomes during this transitional period.
PAC encompasses various rehabilitative health services, with SNFs and home health services (HHS) being the primary settings in the US. Medicare claims data suggest similar functional outcomes between SNF and HHS; however, HHS incurs lower costs [6]. While many patients expect to return home after SNF rehab, frail patients have longer stays and persistent physical impairments [7]. Frailty is characterized by impaired physical and functional reserve, predisposing individuals to worse health outcomes, including hospital readmission, institutionalization, and mortality [8, 9, 10, 11, 12]. Though frailty likely impacts recovery potential, optimal PAC service selection for frail older adults remains unclear. This study investigates the association of frailty on hospital discharge location and health outcomes.
2. Methods
This retrospective cohort study was approved by the Wake Forest University School of Medicine institutional review board IRB00085605. Informed consent was waived given the use of de‐identified patient data. We examined hospitalization data from five hospitals comprising an academic health system over a period between January 2021 and September 2023. Eligible participants were at least 65‐years‐old with a hospital admission of 24 h or longer. If a patient had multiple hospitalizations, their first hospital stay during the study period was considered the primary hospitalization for this analysis. Excluded participants were below the age criteria or deceased during the primary hospitalization. Frailty was categorized using an electronic health record (EHR)‐based frailty index [12]. The eFI captures a simple proportion of 54 total age‐related health deficits present for each individual across domains including diagnosis codes, medications, laboratory studies, biometrics, and functional measurements [10]. Participants were categorized as non‐frail (eFI < 0.1), pre‐frail (0.10 ≤ eFI ≤ 0.21), frail (0.21 < eFI ≤ 0.3), and severely frail (eFI > 0.3) at the most recent outpatient visit prior to hospitalization; or were excluded if there was not sufficient EHR data to calculate the eFI.
We abstracted additional information including demographics, patient‐specific factors, and health system factors from the EHR. Baseline demographic data included age, gender, race/ethnicity, and whether patients resided in a rural area according to Rural–Urban Commuting Area Code designations [13]. Patient‐specific factors included prior diagnosis of dementia or hospital‐acquired diagnosis of delirium. Healthcare system factors included length of hospital stay, number of hospitalizations in the prior 12 months, discharge service line, and insurance program: Medicare under an Accountable Care Organization (ACO), managed Medicaid, or non‐ACO Medicare, in a healthcare system ACO that includes only fee‐for‐service, traditional Medicare.
We used multivariable models including frailty category, demographics, and healthcare system factors to estimate associations with outcomes of interest: hospital discharge location (home, HHS, or SNF) and health outcomes (hospital readmission rates and post‐discharge mortality rates). Mortality data from the cohort were confirmed with the North Carolina state vital records index. Models for health outcomes included interaction terms between frailty and discharge location. Associations with readmission and discharge location were based on logistic regression, while analyses of mortality were based on Cox proportional hazard models to account for censoring. Data was analyzed with R statistical software version 4.2.3 (R Foundation, Vienna, Austria).
3. Results
3.1. Patient Characteristics
A total of 34,237 older adult patients were discharged over the study period. Of those, 23,407 (68.7%) were included with eFI values, a mean age of 76.7 (SD 7.7), 53% female sex, and 81% non‐Hispanic White race/ethnicity (Table 1). Participant frailty was categorized as 22% non‐frail, 46% pre‐frail, 23% frail, and 8% severely frail. Older persons, females, and Black persons tended to have higher eFI frailty values (p < 0.001) (Table 1). Healthcare system factors associated with a higher degree of frailty included longer hospital length of stay, more prior hospitalizations, discharge from the hospitalist service, and being on ACO‐contract insurance (all p < 0.001). Individuals without eFI frailty values were more likely to be male, living in rural zip codes, and had less recorded healthcare system factors and outcomes (Supporting Information S1).
TABLE 1.
Patient characteristics by frailty category abbreviations: ACO, accountable care organization; IQR, interquartile range; SD, standard deviation.
| Overall | Fit/non‐frail | Pre‐frail | Frail | Severely frail | p | |
|---|---|---|---|---|---|---|
| N = 23,407 | N = 5125 | N = 10,867 | N = 5445 | N = 1970 | ||
| Demographics | ||||||
| Age, mean (SD) | 76.7 (7.7) | 75.3 (7.3) | 76.4 (7.7) | 77.8 (7.9) | 78.6 (7.8) | < 0.001 |
| Female sex, n (%) | 12,335 (53) | 2513 (49) | 5644 (52) | 3032 (56) | 1146 (58) | < 0.001 |
| Race, n (%) | < 0.001 | |||||
| White | 19,008 (81) | 4343 (85) | 8742 (80) | 4314 (79) | 1609 (82) | |
| Black | 3436 (15) | 533 (10) | 1676 (15) | 917 (17) | 310 (16) | |
| Hispanic | 407 (1.7) | 104 (2.0) | 176 (1.6) | 103 (1.9) | 24 (1.2) | |
| Other | 556 (2.4) | 145 (2.8) | 273 (2.5) | 111 (2.0) | 27 (1.4) | |
| Rural zip code, n (%) | 6497 (28) | 1671 (33) | 3151 (29) | 1328 (24) | 347 (18) | < 0.001 |
| Healthcare system factors | ||||||
| Dementia history, n (%) | 2576 (11) | 140 (2.7) | 1038 (9.6) | 889 (16) | 509 (26) | < 0.001 |
| Hospital delirium, n (%) | 1970 (8.4) | 270 (5.3) | 878 (8.1) | 573 (11) | 249 (13) | < 0.001 |
| Hospital admissions (past 12 months), n (%) | < 0.001 | |||||
| 0 | 15,619 (67) | 4269 (83) | 7643 (70) | 2977 (55) | 730 (37) | |
| 1 | 4613 (20) | 666 (13) | 2148 (20) | 1299 (24) | 500 (25) | |
| 2 | 3175 (14) | 190 (3.7) | 1076 (9.9) | 1169 (21) | 740 (38) | |
| Length of stay (hours), median (IQR) | 78 (48–139) | 74 (46–128) | 77 (48–135) | 82 (50–143) | 91 (53–148) | < 0.001 |
| Discharge service line, n (%) | < 0.001 | |||||
| Hospitalist | 9756 (42) | 1646 (32) | 4537 (42) | 2574 (47) | 999 (51) | |
| Surgery | 2156 (9.2) | 711 (14) | 1017 (9.4) | 359 (6.6) | 69 (3.5) | |
| Other | 11,495 (49) | 2768 (54) | 5313 (49) | 2512 (46) | 902 (46) | |
| Insurance status, n (%) | < 0.001 | |||||
| Traditional medicare (on ACO) | 14,388 (61) | 1722 (34) | 6615 (61) | 4303 (79) | 1748 (89) | |
| Medicare, non‐ACO | 9019 (39) | 3403 (66) | 4252 (39) | 1142 (21) | 222 (11) | |
| Outcomes | ||||||
| Discharge location, n (%) | < 0.001 | |||||
| Home | 14,379 (61) | 3519 (69) | 6824 (63) | 3034 (56) | 1002 (51) | |
| Home health services (HHS) | 3480 (15) | 663 (13) | 1573 (14) | 904 (17) | 340 (17) | |
| SNF | 5548 (24) | 943 (18) | 2470 (23) | 1507 (28) | 628 (32) | |
| Mortality 90‐day, n (%) | 3401 (15) | 462 (9.0) | 1419 (13) | 1033 (19) | 487 (25) | < 0.001 |
| Readmission 90‐d, n (%) | 4910 (21) | 707 (14) | 2130 (20) | 1429 (26) | 644 (33) | < 0.001 |
3.2. Hospital Discharge Location
Overall, 61% of patients were discharged home, 24% were discharged to SNF, and 15% were discharged home with HHS. All estimates are provided as an adjusted odds ratio with a 95% confidence interval (OR [95% CI]). Patients with a higher degree of frailty were more likely to discharge with PAC (SNF or HHS) compared to home (pre‐frail 1.11 [1.02–1.20], frail 1.20 [1.09–1.33], and severely frail 1.17 [1.03–1.34]) (Figure 1). Other factors associated with PAC discharge were older age (1.90 [1.82–1.98] per decade), hospital‐diagnosed delirium (2.67 [2.38–2.98]), longer hospital length of stay (18.4 [16.3–20.7] highest quartile), prior hospitalizations (1.57 [1.43–1.72] for ≥ 2 admissions), and discharge from hospitalist service (1.09 [1.02–1.16]). Patients less likely to discharge to PAC were males (0.70 [0.66–0.75]), Hispanic ethnicity (0.54 [0.42–0.70]), living in rural areas (0.86 [0.80–0.92]), and ACO insurance contracts (0.90 [0.84–0.96]).
FIGURE 1.
Association of frailty with post‐acute care health outcomes. Abbreviations: %, percent of patients observed in each outcome; 95% CI, 95% Confidence Interval; HHS, home health services; HR, hazard ratio; OR, odds ratio; SNF, skilled nursing facility. Forest plot of frailty associations with PAC health outcomes, with reference group of non‐frail patients and home discharge in the interaction analysis. Estimates are displayed on a Log2 scale with error bars representing 95% confidence intervals. The table on the right demonstrates the observed number of events by frailty category (%), and estimates with OR/HR with 95% CI. A higher degree of frailty was associated with increased readmission odds and mortality hazard. The interaction analyses between frailty and discharge location showed reduced readmission odds for pre‐frail and frail patients in HHS. Mortality interaction analysis showed reduced hazard in SNF for pre‐frail, frail, and severely frail patients compared to home discharge. Though this estimate was likely related to the elevated mortality risk in non‐frail individuals, narrowing the increased mortality hazard ratio for frail patients. All models were adjusted for demographics and healthcare system factors.
Frailty was not associated with discharge to SNF compared to HHS (p = 0.30). Patients with older age (1.63 [1.54–1.72] per decade), hospital‐diagnosed delirium (1.63 [1.43–1.87]), longer hospital length of stay (3.06 [2.67–3.52] highest quartile), and prior hospitalizations (1.35 [1.19–1.54] for ≥ 2 admissions) were more likely to discharge to SNF. Patients who were less likely to discharge to SNF were males (0.84 [0.77–0.92]) or other race/ethnicity (0.66 [0.49–0.90]).
3.3. Hospital Readmission
Overall, 21% of patients experienced readmission within 90 days of hospital discharge. Higher odds for readmission were associated with a higher degree of frailty (pre‐frail 1.42 [1.25–1.60], frail 1.91 [1.67–2.20], and severely frail 2.31 [1.93–2.76]), older age (1.07 [1.03–1.12] per decade), male sex (1.11 [1.04–1.18]), Black race (1.23 [1.13–1.35]), longer hospital length of stay (1.48 [1.40–1.63] per 10 days), prior hospitalizations (2.06 [1.88–2.25] for ≥ 2 admissions), and discharge from hospitalist service (1.09 [1.02–1.17]). Those living in rural areas had lower odds of readmission (0.91 [0.84–0.98]).
Patients discharged to HHS (1.66 [1.33–2.05]) had higher odds for readmission; however, those discharged to SNF did not. There was an interaction between frailty and discharge location; pre‐frail and frail individuals discharged with HHS had lower relative odds for readmission (pre‐frail 0.77 [0.60–1.00], p = 0.044; frail 0.66 [0.50–0.86], p = 0.003; and severely frail 0.73 [0.52–1.02], p = 0.063, non‐frail and home discharge reference groups) (Figure 1).
3.4. Mortality Post‐Hospital Discharge
Overall, 15% of patients died within 90 days of hospital discharge. Mortality estimates are reported with hazard ratios (HR [95% CI]) (Figures 1 and 2). Increased mortality was observed in individuals with a higher degree of frailty (pre‐frail 1.70 [1.41–2.05], frail 2.56 [2.10–3.13], severely frail 3.07 [2.42–3.91]), older age (1.32 [1.27–1.38] per decade), and male sex (1.37 [1.28–1.47]) (Figures 1 and 2). Healthcare system factors for greater mortality included hospital‐diagnosed delirium (1.39 [1.27–1.53]), longer hospital length of stay (1.28 [1.21–1.34] per 10 days), and prior hospitalizations (1.78 [1.64–1.95] for ≥ 2 admissions). Patients with lower mortality were discharged from the Hospitalist service (0.93 [0.87–0.99]) and on ACO insurance contracts (0.71 [0.66–0.76]).
FIGURE 2.
Survival over 90 days by discharge location and frailty. Kaplan Meier survival curves of 90 days after hospital discharge. The panel on the left demonstrates survival by discharge location: Home, home with home health services, and SNF. SNF discharge had the steepest drop in mortality in the first 30 days post discharge. The panel on the right demonstrates survival by frailty category: fit/non‐frail, pre‐frail, frail, and severely frail. A step‐wise decrease in survival probability was seen with a higher degree of frailty.
Discharge with PAC was associated with increased mortality hazard for those discharged with HHS (2.06 [1.54–2.77]) and SNF (5.46 [4.43–6.73]) versus home (Figure 2). The 90‐day mortality rates increased with the degree of frailty (non‐frail 27%, pre‐frail 30%, frail 36%, and severely frail 40%) p < 0.001. The interaction between frailty and discharge to SNF revealed individuals with a higher degree of frailty had a lower hazard ratio (pre‐frail 0.69 [0.54–0.87], p = 0.002; frail 0.54 [0.42–0.69], p < 0.001; and severely frail 0.50 [0.37–0.66], p < 0.001, non‐frail and home discharge reference groups) (Figure 1).
4. Discussion
In this large retrospective cohort study of over 23,000 hospitalized older adults, we found that frailty—as measured by an electronic frailty index (eFI)—was significantly associated with both post‐hospital discharge decisions and subsequent health outcomes. Specifically, increasing frailty was associated with a higher likelihood of discharge to PAC, higher rates of hospital readmission, and an increased 90‐day mortality. Our findings confirm and extend previous research suggesting that frailty is a central determinant of post‐hospitalization trajectories [8, 9, 10, 11, 12].
Importantly, we observed interaction effects between frailty and discharge location that inform optimal PAC decisions. Among these findings was that discharge to SNF was associated with the highest overall 90‐day mortality. Although interaction analysis suggested a potential protective effect of SNF discharge for more frail individuals, this was largely driven by markedly higher mortality rates in non‐frail individuals discharged to SNF. In the total population, 25% of severely frail patients died within 90 days, approximately three times the 9% mortality rate of non‐frail patients. However, when focusing on patients discharged to SNF, 40% of severely frail patients died—an alarming figure—but only modestly higher than the 27% mortality among non‐frail patients in SNF. This relative narrowing of mortality differences led to a statistical appearance of protection for frail individuals in the interaction model. In reality, these patterns reflect the significantly elevated mortality risk associated with SNF discharge for all patients, echoing previous research that many individuals discharged to SNFs face a poor clinical trajectory, described by Flint et al. as being “rehabbed to death” [14].
While frailty is a strong predictor for mortality in the population discharged to SNF, frailty assessment does not fully explain the complexity of patients discharged to SNF. Although SNF and HHS are both common PAC destinations, frailty did not independently influence the likelihood of discharge to SNF versus HHS; consistent with prior data showing factors including caregiver support and the degree of functional impairment are more important factors in determining location of PAC services [15, 16].
Notably, patients discharged with HHS showed increased overall readmission risk. However, more nuanced analysis revealed that when compared to non‐frail individuals, frail and pre‐frail individuals discharged to HHS had lower odds of 90‐day readmission relative to individuals with similar frailty discharged home. Unlike the interaction analysis of mortality in SNF, the protective effect of HHS for frail individuals appears accurate, as supported by comparable absolute readmission rates in the total population and HHS subset. This may reflect that frail individuals receiving structured home care may benefit from closer monitoring [17].
Our data also highlight well‐established sociodemographic and healthcare system factors that influence PAC utilization and outcomes. A higher degree of frailty was associated with older age, female sex, and Black race, reflecting both biological vulnerability and potential disadvantages in health care access. Several contributors to poor outcomes, including delirium, are modifiable [18], and underscore the need for adoption of age‐friendly healthcare system practices [19]. The protective effects of ACO‐affiliated insurance suggest that integrated care models may improve post‐discharge outcomes through coordinated transitions and resource allocation [20].
4.1. Limitations
This study has several limitations. First, despite the robust use of EHR data and validated frailty indices, there may be residual confounding from unmeasured social or clinical variables, including functional status, caregiver support, illness severity, or hospital diagnoses, which influence PAC outcomes, such as discharge decisions and mortality [16, 21, 22]. The study was conducted in a single healthcare system in the southeastern US, potentially limiting generalizability. The analyses only included individuals with an eFI score, excluding people not connected to primary care in our system. However, this aligns with the target population for most population health interventions [23]. Finally, the estimates of interaction analyses from logistic regression require careful interpretation, and observational data limit causal inference.
5. Conclusion
Frailty is strongly associated with PAC utilization and post‐discharge outcomes among older adults. Automated frailty assessment at hospital discharge can identify in real time the most vulnerable individuals—severely frail patients discharged to SNF—for pragmatic trials and population health interventions. Discharge with home health services, particularly for frail patients, may mitigate readmission risk. Integrating frailty assessment into discharge planning can support individualized, evidence‐based PAC decisions, improving care transitions and health outcomes for older adults.
Author Contributions
C.T.S., B.N.W., K.E.C., and N.M.P. participated in study design, data analysis and interpretation, and manuscript writing and revision.
Disclosure
Sponsor's Role: The Foundation for Post‐Acute and Long‐Term Care Medicine provided funding to support statistical analysis in this research.
Conflicts of Interest
C.T.S. received grant funding from the Foundation for Post‐Acute and Long‐Term Care Medicine to conduct activities related to this research and receives funding from the Claude D. Pepper OAIC (P30 AG021332) to support his professional effort. The remaining other authors declare no conflicts of interest.
Supporting information
Data S1: Supporting Information.
Semelka C. T., White B. N., Callahan K. E., and Pajewski N. M., “The Association of Frailty With Post‐Hospital Discharge Location and Health Outcomes,” Journal of the American Geriatrics Society 73, no. 11 (2025): 3512–3518, 10.1111/jgs.70034.
Funding: This work was supported by Foundation of Post‐Acute and Long‐Term Care Medicine (2023 Pilot award); Claude Pepper Older Americans Independence Center, Wake Forest School of Medicine, P30 AG021332.
Prior presentations: Oral presentation at the PALTmed Annual 2025 conference March 15, 2025.
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Associated Data
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Supplementary Materials
Data S1: Supporting Information.