Comparative effectiveness of long-term acute care hospital versus skilled nursing facility transfer

Caregiver Engagement in Serious Illness Communication in a Long-Term Acute Care Hospital Setting

CONTEXT

Prolonged management of critical illnesses in long-term acute care hospitals (LTACH) makes serious illness communication (SIC), a clinical imperative. SIC in LTACH is challenging as clinicians often lack training and patients are typically unable to participate-making caregivers central.

OBJECTIVES

This qualitative descriptive study characterized caregiver engagement in SIC encounters, while considering influencing factors, following the implementation of Ariadne Labs' SIC training at a LTACH in the Northeastern United States.

METHODS

Clinicians' documented SIC notes (2019-2020) were analyzed using directed content analysis. Codes were grouped into four categories generated from two factors that influence SIC-evidence of prognostic understanding (yes/no) and documented preferences (yes/no)-and caregiver engagement themes identified within each category.

RESULTS

Across 125 patient cases, 251 SIC notes were analyzed. In the presence of prognostic understanding and documented preferences, caregivers acted as upholders of patients' wishes (29%). With prognostic understanding but undocumented preferences, caregivers were postponers of healthcare decision-making (34%). When lacking prognostic understanding but having documented preferences, caregivers tended to be searchers, intent on identifying continued treatment options (13%). With poor prognostic understanding and undocumented preferences, caregivers were strugglers, having difficulty with the clinicians or family unit over healthcare decision-making (21%).

CONCLUSION

The findings suggest that two factors-prognostic understanding and documented preferences-are critical factors clinicians can leverage in tailoring SIC to meet caregivers' SIC needs in the LTACH setting. Such strategies shift attention away from SIC content alone toward factors that influence caregivers' ability to meaningfully engage in SIC to advance healthcare decision-making.

Patient Outcomes After Long-Term Acute Care Hospital Closures

Importance

Long-term acute care hospitals (LTCHs) are common sites of postacute care for patients recovering from severe respiratory failure requiring mechanical ventilation (MV). However, federal payment reform led to the closure of many LTCHs in the US, and it is unclear how closure of LTCHs may have affected upstream care patterns at short-stay hospitals and overall patient outcomes.

Objective

To estimate the association between LTCH closures and short-stay hospital care patterns and patient outcomes.

Design, Setting, and Participants

This retrospective, national, matched cohort study used difference-in-differences analysis to compare outcomes at short-stay hospitals reliant on LTCHs that closed during 2012 to 2018 with outcomes at control hospitals. Data were obtained from the Medicare Provider Analysis and Review File, 2011 to 2019. Participants included Medicare fee-for-service beneficiaries aged 66 years and older receiving MV for at least 96 hours in an intensive care unit (ie, patients at-risk for prolonged MV) and the subgroup also receiving a tracheostomy (ie, receiving prolonged MV). Data were analyzed from October 2022 to June 2023.

Exposure

Admission to closure-affected hospitals, defined as those discharging at least 60% of patients receiving a tracheostomy to LTCHs that subsequently closed, vs control hospitals.

Main Outcomes and Measures

Upstream hospital care pattern outcomes were short-stay hospital do-not-resuscitate orders, palliative care delivery, tracheostomy placement, and discharge disposition. Patient outcomes included hospital length of stay, days alive and institution free within 90 days, spending per days alive within 90 days, and 90-day mortality.

Results

Between 2011 and 2019, 99 454 patients receiving MV for at least 96 hours at 1261 hospitals were discharged to 459 LTCHs; 84 LTCHs closed. Difference-in-differences analysis included 8404 patients (mean age, 76.2 [7.2] years; 4419 [52.6%] men) admitted to 45 closure-affected hospitals and 45 matched-control hospitals. LTCH closure was associated with decreased LTCH transfer rates (difference, -5.1 [95% CI -8.2 to -2.0] percentage points) and decreased spending-per-days-alive (difference, -$8701.58 [95% CI, -$13 323.56 to -$4079.60]). In the subgroup of patients receiving a tracheostomy, there was additionally an increase in do-not-resuscitate rates (difference, 10.3 [95% CI, 4.2 to 16.3] percentage points) and transfer to skilled nursing facilities (difference, 10.0 [95% CI, 4.2 to 15.8] percentage points). There was no significant association of closure with 90-day mortality.

Conclusions and Relevance

In this cohort study, LTCH closure was associated with changes in discharge patterns in patients receiving mechanical ventilation for at least 96 hours and advanced directive decisions in the subgroup receiving a tracheostomy, without change in mortality. Further studies are needed to understand how LTCH availability may be associated with other important outcomes, including functional outcomes and patient and family satisfaction.

Rapid Response Systems at a Long-Term Acute Care Hospital

Rapid Response Systems (RRS) improve patient outcomes at large medical centers. Little is known about how RRS are used in other medical settings. The purpose of this exploratory study was to describe RRS events at a long-term acute care hospital (LTACH). We conducted a retrospective review of 71 RRS event records at an urban 50-bed Midwestern LTACH. Measures included demographic data, triggering mechanisms, contextual factors, mechanism factors, and clinical outcomes. Of patients who experienced a RRS event, median age was 71 (62, 80) years; 52.1% were female; most (n = 49, 69%) were "full code." Most (n = 41, 58%) events occurred during the daytime. The most common trigger was "mental status changes/unresponsiveness." Registered nurses were the most frequent activator (n = 19, 26.8%) and responders (n = 63, 60.6%). Median duration of RRS events was 14 (6, 25) minutes. Most patients stabilized and their condition improved (n = 54, 76.1%). RRS can be expanded and modified to the LTACH population.

The Relationship Between Patient-Specific Factors and Discharge Destination After COVID-19 Hospitalization

OBJECTIVE

The aim of this study was to determine the discharge destinations and associated patient-specific factors among patients hospitalized with COVID-19.

DESIGN

A retrospective cohort study was carried out at a single-site tertiary acute care hospital.

RESULTS

Among 2872 patients, discharge destination included home without services ( n = 2044, 71.2%), home with services ( n = 379, 13.2%), skilled nursing facility (117, 4.1%), long-term acute care hospital ( n = 39, 1.3%), inpatient rehabilitation facility ( n = 97, 3.4%), acute care facility ( n = 23, 0.8%), hospice services ( n = 20, 0.7%), or deceased during hospitalization ( n = 153, 5.3%). Adjusting by covariates, patients had higher odds of discharge to a rehabilitation facility (skilled nursing facility, long-term acute care hospital, or inpatient rehabilitation facility) than home (with or without services) when they were older (odds ratio [OR], 2.37; 95% confidence interval [CI], 1.80-3.11; P < 0.001), had a higher Charlson Comorbidity Index score (3-6: OR, 2.36; 95% CI, 1.34-4.15; P = 0.003; ≥7: OR, 2.76; 95% CI, 1.56-4.86; P < 0.001), were intubated or required critical care (OR, 2.15; 95% CI, 1.48-3.13; P < 0.001), or had a longer hospitalization (3-7 days: OR, 12.48; 95% CI, 3.77-41.32; P < 0.001; 7-14 days: OR, 28.14; 95% CI, 8.57-92.43; P < 0.001). Patients were less likely to be discharged to a rehabilitation facility if they received remdesivir (OR, 0.44; 95% CI, 0.31-0.64; P < 0.001).

CONCLUSIONS

Patient-specific factors associated with COVID-19 hospitalization should be considered by physicians when prognosticating patient rehabilitation.

Factors Influencing Admission Decisions in Skilled Nursing Facilities: A Retrospective Quantitative Study (Preprint)

BACKGROUND

Occupancy rates within skilled nursing facilities (SNFs) in the United States have reached a record low. Understanding drivers of occupancy, including admission decisions, is critical for assessing the recovery of the long-term care sector as a whole. We provide the first comprehensive analysis of financial, clinical, and operational factors that impact whether a patient referral to an SNF is accepted or denied, using a large health informatics database.

OBJECTIVE

Our key objectives were to describe the distribution of referrals sent to SNFs in terms of key referral- and facility-level features; analyze key financial, clinical, and operational variables and their relationship to admission decisions; and identify the key potential reasons behind referral decisions in the context of learning health systems.

METHODS

We extracted and cleaned referral data from 627 SNFs from January 2020 to March 2022, including information on SNF daily operations (occupancy and nursing hours), referral-level factors (insurance type and primary diagnosis), and facility-level factors (overall 5-star rating and urban versus rural status). We computed descriptive statistics and applied regression modeling to identify and describe the relationships between these factors and referral decisions, considering them individually and controlling for other factors to understand their impact on the decision-making process.

RESULTS

When analyzing daily operation values, no significant relationship between SNF occupancy or nursing hours and referral acceptance was observed (P>.05). By analyzing referral-level factors, we found that the primary diagnosis category and insurance type of the patient were significantly related to referral acceptance (P<.05). Referrals with primary diagnoses within the category "Diseases of the Musculoskeletal System" are least often denied whereas those with diagnoses within the "Mental Illness" category are most often denied (compared with other diagnosis categories). Furthermore, private insurance holders are least often denied whereas "medicaid" holders are most often denied (compared with other insurance types). When analyzing facility-level factors, we found that the overall 5-star rating and urban versus rural status of an SNF are significantly related to referral acceptance (P<.05). We found a positive but nonmonotonic relationship between the 5-star rating and referral acceptance rates, with the highest acceptance rates found among 5-star facilities. In addition, we found that SNFs in urban areas have lower acceptance rates than their rural counterparts.

CONCLUSIONS

While many factors may influence a referral acceptance, care challenges associated with individual diagnoses and financial challenges associated with different remuneration types were found to be the strongest drivers. Understanding these drivers is essential in being more intentional in the process of accepting or denying referrals. We have interpreted our results using an adaptive leadership framework and suggested how SNFs can be more purposeful with their decisions while striving to achieve appropriate occupancy levels in ways that meet their goals and patients' needs.

Hospital-free Days: A Pragmatic and Patient-centered Outcome for Trials Among Critically and Seriously Ill Patients

Hospital-free days (HFDs), alternatively known as "days alive and outside the hospital," is increasingly used as a primary or secondary outcome in randomized trials among critically and seriously ill patients. This novel outcome measure addresses an existing gap in the availability of patient-centered, reliably obtained outcome measures among patients with acute respiratory failure, advanced lung diseases, lung transplantation, and other serious and critical illnesses. Traditional outcomes such as mortality, organ-failure-free days, and longitudinal patient-reported measures have distinct drawbacks that limit their suitability as endpoints in trials of patients with serious illness, particularly those trials with pragmatic designs. By contrast, HFDs provides a summary measure of important health events and is easily calculated from administrative or electronic health record data, thereby balancing the goals of patient-centeredness and pragmatic measurement. However, before HFDs can be widely adopted as an endpoint in trials of patients with respiratory and critical illnesses, several questions must be addressed regarding the optimal definition, measurement, and analysis of HFDs. In this perspective, we outline important considerations relevant to the use of HFDs as a trial endpoint and suggest directions for further development of the measure.