Discharge Timing and Associations With Outcomes Following Heart Failure Hospitalization

ABSTRACT

Objectives: To compare all-cause readmission or mortality between patients with heart failure (HF) with discharge ordered before noon (DOBN) and those with discharge ordered after noon (DOAN).

Study Design: A retrospective multicenter study of 14,469 patients hospitalized for acute decompensated HF at 17 hospitals in 4 US states (admitted January 2010-December 2022 and followed through May 2023).

Methods: Patients were grouped by discharge timing: DOBN (00:00-12:00) and DOAN (12:01-23:59). We assessed all-cause readmission or mortality at 7 days, 30 days, and 3 years post discharge.

Results: Of all patients, 2844 (19.7%) were in the DOBN group and 11,625 (80.3%) were in the DOAN group. The DOBN group had higher mortality than the DOAN group at 7 days (2.6% vs 1.3%; HR, 1.39; 95% CI, 1.05-1.86), 30 days (8.9% vs 5.2%; HR, 1.34; 95% CI, 1.15-1.58), and 3 years (50.6% vs 41.4%; HR, 1.13, 95% CI, 1.06-1.21) post discharge. The DOBN group also had a higher readmission rate within 7 days (8.3% vs 6.4%; HR 1.99; 95% CI, 1.61-2.48) post discharge but similar readmission rates to the DOAN group at 30 days (16.0% vs 15.2%; HR, 1.07; 95% CI, 0.97-1.20) and 3 years (48.6% vs 49.7%; HR, 0.96; 95% CI, 0.90-1.02). The differences persisted after categorizing patients into 2 timeline groups (2010-2016 and 2017-2022), with DOBN patients having shorter median times to mortality and readmission than DOAN patients.

Conclusions: In hospitalized patients with HF, DOBN was independently associated with higher all-cause mortality both in the short and long term as well as increased early readmission rates. These findings have implications for discharge policies.

Am J Manag Care. 2025;31(10):In Press

Takeaway Points

• In this retrospective multicenter study, patients hospitalized for heart failure with discharge ordered before noon (DOBN) experienced higher short-term (7 and 30 days) and long-term (3 years) mortality rates than those with discharge ordered after noon (DOAN).
• The DOBN group also had a higher readmission rate within 7 days post discharge but similar readmission rates at 30 days and 3 years compared with the DOAN group.
• These findings suggest that discharge timing may impact patient outcomes, indicating a need to review and potentially adjust discharge policies.

Early discharge of hospitalized patients, especially by noon, is a complex and challenging procedure involving patients, their families, health care providers, clinicians, and various support health care personnel. This approach can create substantial challenges for clinicians responsible for providing care to critically ill patients in intensive care units or general medical wards based on their acuity level, particularly during the morning hours. As a result, clinicians may need to redirect their attention toward tasks associated with patient discharge. Despite these challenges, the discharge-by-noon strategy is increasingly gaining traction within health care systems to improve hospital throughput and alleviate congestion in emergency departments.^1-3

Approximately 20% of patients are discharged to postacute care facilities, which favor discharge before noon for prompt admission.⁴ Consequently, the discharge-by-noon approach is increasingly being implemented for both medical and surgical patients in US hospitals, even though definitive data on downstream benefits are lacking.^5-7 This approach aims to streamline the discharge process so patients can leave the hospital by noon, creating more bed availability and reducing the strain on emergency departments. Although the concept holds promise, health care professionals and administrators need concrete data and research results to determine whether the discharge-by-noon strategy enhances patient care, reduces adverse health outcomes, optimizes resource allocation, and increases overall hospital efficiency.

While health care systems continue to explore ways to optimize their operational measures,^1,8,9 it remains imperative to conduct thorough research to ascertain whether the discharge-by-noon strategy can deliver the desired results without compromising patient safety. These results should encompass not only improved hospital efficiency and throughput but also confirm the absence of negative effects on patient-level outcomes. The term discharge before noon lacks clarity, as it could refer to patients physically leaving the hospital before noon or to having discharge ordered before noon (DOBN). This distinction is crucial because the timing of discharge orders does not always coincide with the actual time patients physically leave the hospital. However, previous research has shown that DOBN was highly correlated with the timing of patient discharge,¹⁰ suggesting that issuing discharge orders earlier in the day may be instrumental in ensuring timely patient departures. DOBN, regardless of physical departure, may serve as a valuable indicator of hospital efficiency, helping to streamline patient flow, optimize bed availability, improve capacity management, and enhance coordination with postdischarge services, ultimately benefiting hospital operations and patient satisfaction. These findings support the validity of using physician-recorded discharge times as a reasonable proxy in clinical studies. The association between DOBN and subsequent clinical outcomes in patients hospitalized for heart failure (HF)—one of the most common reasons for hospitalization in the US¹¹—remains insufficiently explored. Understanding this relationship is crucial, as patients with HF often require complex care coordination and postdischarge management.

To address this gap, we investigated the clinical implications of discharge order timing by examining the associations between DOBN and all-cause readmission or mortality rates at 7 days, 30 days, and 3 years following the index hospitalization for acute decompensated HF (ADHF). We aimed to broaden our analysis to determine which subsets of patients with HF, acknowledging the heterogeneity among them,¹² could be safely released before noon. These analyses compare the outcomes of patients with DOBN vs those with discharge ordered after noon (DOAN).

METHODS

Study Design and Population

This retrospective cohort study included consecutive patients admitted with ADHF in 17 Mayo Clinic hospitals in 4 US states (Arizona, Florida, Minnesota, and Wisconsin) from January 1, 2010, to December 31, 2022, with patient follow-up extending to May 23, 2023. Data were sourced from electronic health records (EHRs) using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for HF, as detailed in eAppendix Table 1 (eAppendix available at ajmc.com). The Department of Biostatistics and Informatics at Mayo Clinic carried out the data extraction in a manner consistent with our prior report.¹³ The cohort consisted of patients 18 years and older with a primary discharge diagnosis of ADHF. Excluded from the study were patients with HF as a secondary diagnosis, pregnant women, and individuals who declined participation in EHR-based research. The Mayo Clinic Institutional Review Board approved the study protocol, ensuring adherence to the Declaration of Helsinki and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹⁴

Groups by Discharge Order Timing

We assessed discharge order time logged in the EHR to the precise second. Patients were classified into 2 groups for comparison: (1) DOBN, with discharge order time from 00:00 to 12:00, and (2) DOAN, with discharge order time from 12:01 to 23:59.

HF

The diagnosis of ADHF was determined based on physicians’ documentation in the EHR, supported in most patients by elevated levels of N-terminal pro–brain natriuretic peptide, results from imaging tests, and findings from echocardiographic studies. The study used first ADHF hospitalization as the index event and treated subsequent hospitalizations within the study time frame as readmissions. HF types were classified by left ventricular ejection fraction (LVEF) measured by echocardiogram within a year before or during the index hospitalization. These classifications included HF with preserved ejection fraction (HFpEF; LVEF ≥ 50%), HF with mildly reduced ejection fraction (HFmrEF; LVEF 41%-49%), and HF with reduced ejection fraction (HFrEF; LVEF ≤ 40%).¹⁵

Chronic Conditions

The determination of comorbidities or chronic conditions was prespecified, drawing on findings from our previous research,^13,16 literature reviews,¹⁷ and experts’ recommendations. We selected 25 chronic conditions, including 19 of the 20 specified by the Office of the Assistant Secretary for Health, with the exception of autism spectrum disorder.¹⁸

More information on the comorbidity selection can be found in our previous publication.¹⁶ These comorbidities and their corresponding ICD-10-CM codes are presented in eAppendix Tables 1 and 2.

Social Determinants of Health

Social determinants of health (SDOH) are crucial factors influencing short- and long-term mortality in patients with HF, yet they remain challenging to accurately assess.¹⁹ For analysis, we selected 4 measures of SDOH from a comprehensive list developed by HHS.^20,21

Living status. We classified living status into 2 groups for analysis: individuals who were married (including those widowed) or living with a long-term partner and those living alone. The living alone category included individuals who were single, never married, divorced, or separated. This classification was used to assess the potential impact of social and household dynamics on the outcomes of interest, as living arrangements often influence access to support systems, emotional well-being, and overall health.

Residential status. Using the 2013 National Center for Health Statistics scheme, we classified US counties into 6 levels of urbanization: large metro, large fringe metro, medium metro, small metro, micropolitan, and noncore. Within this framework, the 4 metropolitan categories were designated as urban and the 2 nonmetropolitan categories were identified as rural.²² Further details about the rural-urban classification are provided in our earlier publication.²³

Cigarette smoking.We categorized smoking status into 2 groups: current smokers and former smokers/never smokers. Current smokers included those actively using tobacco, and former smokers/never smokers comprised individuals who had quit or never smoked.

Substance use. From EHR data, we collected information on self-reported substance use, including alcohol misuse and the use of opioids (natural [morphine, codeine], semisynthetic [hydrocodone, oxycodone, heroin], and synthetic [fentanyl, methadone, tramadol]) and stimulants (cocaine, methamphetamine, ecstasy, and bath salts).

Hospital-Level Characteristics

We analyzed (1) the distribution of patients to specific clinical services upon admission: internal medicine, family medicine, critical care, cardiology, and other specialties (including hematology oncology, surgical specialties, transplant service, pulmonology, gastroenterology, and rheumatology); (2) the proportion of patients, upon discharge, prescribed angiotensin-converting enzyme (ACE) inhibitors/angiotensin receptor blockers (ARBs)/angiotensin receptor–neprilysin inhibitors (ARNIs), HF β-blockers, and/or mineralocorticoid receptor antagonists (MRAs), as recommended by national guidelines²⁴; and (3) the assignment of patients into either skilled nursing facilities (SNFs) or non-SNF destinations upon discharge.

Readmission

Readmission was defined as all-cause repeat hospitalization occurring from the discharge date to the censoring date.

Mortality

Mortality was defined as death from any cause occurring from the date of admission up to the censoring date. We obtained mortality information from Mayo Clinic’s EHR. Mayo Clinic regularly updates its mortality records by tracking clinical care across its hospitals and clinics, reviewing local obituary records, and examining documents from state departments of health and human services as previously described.²⁵

End Points

Primary end points were the occurrences of all-cause readmission and all-cause mortality within 7 days, 30 days, and 3 years post discharge.Secondary end points examined whether the likelihood of readmission or mortality was influenced by factors such as age, sex, race, smoking status, living in rural vs urban settings, presence or absence of key comorbidities (categorized into cardiovascular vs noncardiovascular), HF classification (HFrEF, HFmrEF, HFpEF), or hospital attributes (admitting department, receipt of guideline-directed medical therapy [GDMT] at discharge, and discharge destination). An additional end point was identifying patients for whom discharge timing did not adversely impact outcomes.

Study Subgroups

Predetermined subgroups were as follows: (1) demographic: age (< 65 and ≥ 65 years), sex (female and male), and race (White race or other races); (2) social: marital status (married and nonmarried), cigarette smoking (current smoking and no current smoking), and rurality (classification of patient residency as rural or urban); (3) comorbidities: hypertension, atrial fibrillation, coronary artery disease (CAD), stroke, hyperlipidemia, diabetes, obesity, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), obstructive sleep apnea (OSA), anemia, chronic liver disease, arthritis, cancer, dementia, and depression; (4) HF type by LVEF: HFrEF, HFmrEF, and HFpEF; (5) hospital admission service: internal medicine, family medicine, critical care, cardiology, and other specialties; (6) receipt of GDMT at discharge: ACE inhibitors/ARBs/ARNIs, β-blockers, and MRAs; and (7) discharge destination: SNF and no SNF.

Reviewer-suggested additional subgroups were as follows: (1) timeline groups: group 1, January 1, 2010, to December 31, 2016; and group 2, January 1, 2017, to December 31, 2022; and (2) hospital setting groups: academic hospitals (urban; n = 4) vs community hospitals (rural; n = 13).

Statistical Analysis

Statistical analyses were performed using R 4.3.1 (R Foundation for Statistical Computing) and SAS Viya (SAS Institute Inc). Descriptive statistics were presented as the mean and SD, and categorical variables were described with percentages. We considered a 2-tailed P value less than .05 as significant. Time to readmission or death was calculated from the date of discharge following hospitalization until censored.

We employed logistic regression models, adjusting for age, sex, and race, to estimate the ORs and 95% CIs for each comorbid condition and hospital-level characteristics across the DOBN and DOAN groups. We used unadjusted Kaplan-Meier estimates and adjusted Cox proportional hazards models to compare DOBN and DOAN groups for readmission or mortality within 30 days and 3 years. Cox regression models were adjusted to account for demographics (age, sex, and race), social indicators (marital, cigarette smoking, and rural vs urban statuses), comorbid conditions, receipt of GDMT at discharge, and discharge destination (SNF vs no SNF). Additionally, we broadened the Cox regression-based comparative analysis between the DOBN and DOAN groups at 7 days, 30 days, and 3 years to evaluate the consistency of the association across predefined subgroups.

A total of 38 covariates from various domains included in the multivariable analysis are detailed in eAppendix Table 1. Three hierarchical models, similar to those in our previous report,¹³ were constructed to assess readmissions and mortality at 7 days, 30 days, and 3 years in study groups, using Cox regression analyses. Model 1 (employed for 7-day readmission and mortality) was adjusted for age, sex, and race. Model 2 (employed for 30-day readmission and mortality) was adjusted for variables in model 1 and rurality (patient residential status), cigarette smoking, HF types, hypertension, CAD, atrial fibrillation, stroke, COPD, hyperlipidemia, diabetes, CKD, anemia, cancer, depression, clinical service, discharge disposition, and receipt of ACE inhibitors/ARBs/ARNIs, β-blockers, and MRAs. Model 3 (employed for 3-year readmission and mortality) was adjusted for variables in model 2 and marital status, asthma, other lung conditions, OSA, obesity, malnutrition, osteoporosis, liver disease, other gastrointestinal conditions, CKD, arthritis, other rheumatic conditions, dementia, substance use, and other psychiatric conditions.

RESULTS

Baseline Characteristics

Figure 1 presents the STROBE flow diagram illustrating the selection process for study patients. Of 14,469 patients hospitalized for ADHF, 2844 (20%) were identified as DOBN and 11,625 (80%) as DOAN based on their discharge timings documented in the EHR. The Table illustrates that DOBN patients compared with DOAN patients were predominantly older, female, White, unmarried, and rural residents and had a higher prevalence of HFpEF. Of the 25 comorbidities analyzed, dementia and depression were more common in the DOBN group, whereas prior transplants were more prevalent in the DOAN group; the other comorbidities showed similar distribution across the 2 groups (Figure 2). Hospital-level characteristics varied considerably, with DOBN patients showing higher internal medicine and fewer cardiology admissions, similar receipt of GDMT, and increased discharges to SNFs compared with DOAN patients. All patients were followed up at 7 days and 30 days; 10,844 patients had complete follow-up for 3 years.

Collinearity

We evaluated the pairwise correlation coefficients among all predictor variables and assessed the variance inflation factor (VIF) for each. The VIF values ranged from 1.02 to 1.30, which are well below the threshold of 10, indicating no significant multicollinearity.

Readmission

As shown in Figure 3, the rates of all-cause readmission following discharge were higher within 7 days in the DOBN group compared with the DOAN group (event rate, 8.3% vs 6.4%; HR, 1.99; 95% CI, 1.61-2.48; P < .001). These rates were similar in both groups at 30 days (event rate, 16.0% vs 15.2%; HR, 1.07; 95% CI, 0.97-1.20; P = .171) and 3 years (event rate, 48.6% vs 49.7%; HR, 0.96; 95% CI, 0.91-1.02; P = .213). However, the time to readmission was significantly shorter for the DOBN group than for DOAN group at 7 days (median [IQR], 0 [0-4] vs 2 [0-4] days; P < .001), 30 days (7 [0-16] vs 10 [3-19] days; P < .001), and 3 years (81 [16-269] vs 85 [22-270] days; P < .001) post discharge.

Mortality

The DOBN group exhibited significantly higher all-cause mortality compared with the DOAN group at 7 days (2.6% vs 1.3%; HR, 1.39; 95% CI, 1.05-1.86; P = .023), 30 days (8.9% vs 5.2%; HR, 1.34; 95% CI, 1.15-1.58; P = .002), and 3 years (50.6% vs 41.4%; HR, 1.13; 95% CI, 1.06-1.21; P < .001) post discharge, as illustrated in Figure 3. Subgroup analysis showed higher mortality rates in DOBN patients within 30 days post discharge, persisting across all 32 subgroups, compared with those in the DOAN group (all interaction P > .05) (Figure 4). Subgroup analysis showed higher mortality rates in DOBN patients within 30 days post discharge persisting within 32 subgroups compared to those in the DOAN patients (all interaction P-value >0.05) as illustrated in Figure 4. Similarly, the association of DOBN with 3-year postdischarge mortality was similar within all subgroups except for patient residential status (rural vs urban; P for interaction for HR = .040), smoking status (smokers vs nonsmokers; P for interaction for HR = .020), or presence or absence of OSA (P for interaction for HR = .025), as shown in Figure 4.

Moreover, in time-to-mortality analysis, the DOBN group had shorter times to mortality than the DOAN group, with median (IQR) times of 12 (6-19) vs 14 (7-23) days at 30 days (P < .001) and 209 (55-518) vs 241 (71-558) days at 3 years (P < .001). There was no between-group difference in time to death within 7 days of follow-up (median [IQR], 3 [2-6] vs 3 [2-6]; P = .987).

Additional subgroup analyses showed that 5867 patients (40.5%) were admitted to academic hospitals and 8602 patients (59.5%) to community hospitals. The patterns of readmission and mortality aligned with the primary analysis, with patients in the DOBN group demonstrating significantly higher 7-day, 30-day, and 3-year mortality compared with the DOAN group across both academic and community hospitals. However, the difference in 7-day readmission observed in the overall cohort between the DOBN and DOAN groups was evident only among patients admitted to community hospitals. eAppendix Figure 1 presents a forest plot illustrating the HRs and 95% CI associated with these results. As shown in eAppendix Figure 2, 4168 patients (28.8%) were admitted during 2010-2016 (timeline group 1) and 10,301 patients (71.2%) during 2017-2022 (timeline group 2). The patterns of readmission and mortality were consistent with the overall cohort analysis, with DOBN patients experiencing higher 7-day readmission rates and greater 7-day, 30-day, and 3-year mortality compared with the DOAN group across both time frames. The STROBE checklist is provided in eAppendix Table 3.

DISCUSSION

This multicenter study, conducted over 13 years across major academic centers and community hospitals in 4 US states (Arizona, Florida, Minnesota, and Wisconsin), revealed that hospitalized patients with ADHF in the DOBN group had elevated all-cause mortality within 7 days post discharge compared with those in the DOAN group, a disparity that persisted for up to 3 years after discharge. The elevated mortality in the DOBN group, independent of sociodemographic factors, HF type, comorbidities, GDMT use, or discharge practices, equated to 1 additional death at 7 days, 30 days, and 3 years post discharge for every 76.9, 32.3, and 10.9 patients, respectively. These findings emphasize the importance of assessing how discharge timing influences both short- and long-term outcomes in patients with ADHF. Additionally, DOBN patients demonstrated a higher rate of 7-day readmission compared with the DOAN group, highlighting the critical importance of discharge strategies in minimizing early adverse events following hospitalization for ADHF.

Our subgroup analysis revealed that DOBN-associated increased mortality remains consistent and affects a broad HF population across various prespecified subgroups. Specifically, the patterns in readmission and mortality were consistent across both time periods (2010-2016 and 2017-2022), despite advancements in care and evolving clinical practices over time, and across academic (urban) and community (rural) hospitals, suggesting that the findings are broadly generalizable regardless of the health care environment or geographic location. The consistent findings across multiple subgroups underscore the persistent nature of these challenges, highlighting the importance of targeted interventions, strategic resource allocation, and meticulous patient selection for early discharge.

We hypothesize that, aside from patient selection bias, a range of operational, clinical, and system-level factors may explain the differences in mortality and readmission between the groups. For example, patients discharged earlier in the day might receive less comprehensive discharge counseling, experience rushed medication reconciliation, and have suboptimal follow-up scheduling compared with those discharged later. Patients with DOBN also might show subtle decompensation signs that are missed but could be detected with more evaluation time during afternoon discharges, allowing extra observation and stabilization to reduce postdischarge risks. Additional investigation, such as prospective studies or detailed process audits, could help clarify the contributions of these mechanisms.

Clinical Context

DOBN is a feasible strategy and may be implemented to potentially reduce overall throughput and emergency department length of stay.^26,27 Consequently, numerous quality improvement initiatives advocating for early-morning discharges have been proposed.^6,28,29 Notably, incentivizing morning discharges may inadvertently encourage physicians to extend patient stays overnight to enable early-morning releases.³⁰ Nonetheless, most studies on DOBN’s effectiveness have focused on length of stay in the emergency department or hospital and patient flow, yielding mixed results.^{2,7,26,27,29,31,32} There is limited information on postdischarge consequences of the DOBN strategy, making it difficult to assess its short- and long-term effects. Limited studies have been conducted on the effectiveness of the DOBN strategy on readmission following hospitalization, and the outcomes of these studies have been mixed.^26,33 These variations may be due to differences in the targeted patient populations, including a mix of surgical or medical cases and the timing of readmission measurements. To our knowledge, this is the first study to examine how discharge timing affects readmission and mortality rates in patients with ADHF over both short- and long-term follow-ups. The reasons for higher early readmission rates and both short- and long-term mortality with DOBN vs DOAN are not well understood but may include selection bias. However, previous studies on early hospital discharge, not specifically focusing on DOBN, indicated that poor patient outcomes could result from hurried discharges, insufficient patient readiness, medication mistakes, and overlooked SDOH.^34,35

Clinical Implications

This study offers valuable insights for refining discharge protocols for patients with ADHF, focusing on streamlining discharge planning, improving care transitions, and optimizing hospital operations. Patients who are younger than 65 years, of non-White racial backgrounds, married, residing in rural areas, diagnosed with either HFrEF or HFmrEF, without a history of hypertension, receiving care under cardiology or specialized services, and prescribed GDMT may be suitable candidates for having discharge orders placed before noon. This approach not only facilitates efficient transitions of care but also minimizes delays, enhances patient flow, and supports better outcomes for appropriately selected patients. Patients with DOBN had higher rates of discharge to SNFs and greater prevalence of dementia and depression, suggesting many may have originated from nursing homes or long-term care facilities. However, the absence of admission source data in the EHR prevents confirmation. Although discharge timing likely impacts short-term outcomes (eg, 7- and 30-day mortality), we included a 3-year end point to assess whether differences persist. Long-term outcomes are influenced by age, comorbidities, disease progression, and access to care.³⁶ Despite adjusting for key covariates, unmeasured factors such as frailty³⁷ and postdischarge care quality³⁸ likely play a role.

Strengths and Limitations

This large multicenter study was conducted over 13 years, involving major academic and community hospitals across 4 geographically dispersed states, which increased diversity and generalizability. The study’s clinical importance and reliability were strengthened by the near completeness and consistent data across varied participating centers as well as 7-day, 30-day, and 3-year follow-ups for readmission and mortality. The study’s findings were supported by applying multivariable models that account for a range of patient and hospital-level factors, summing up to 40 covariates. Moreover, detailed subgroup analyses allowed the study to identify factors or conditions potentially contributing to the disparity in mortality and readmission rates between patients in the DOBN and DOAN groups.

The study, however, has the following significant limitations that should be considered when interpreting the findings: (1) inherent limitations of a retrospective observational study with potential unmeasured confounders, although the study had minimal missing variables; (2) although the study included a diverse set of hospitals, the findings may not be fully generalizable to health care systems in other countries or regions because Mayo Clinic is a highly integrated system with a majority of patients cared for in consultation with specialty services; (3) the study focused on the practice of DOBN in patients with ADHF, which may not be applicable to other disease conditions; and (4) the discharge timing was determined based on the times documented by attending physicians in the EHR. Although these recorded times may not fully capture the actual moment of patient departure due to potential delays from administrative processes, transportation arrangements, or other logistical factors, prior studies have demonstrated a strong correlation between discharge orders documented by the attending physician and the actual time of patient departure.¹⁰ Several systemic factors and SDOH, including insurance status, literacy levels, access to postdischarge care, and structured interventions such as 7-day postdischarge contact recommended by national guidelines, play a crucial role in patient outcomes.^19,20 Many of these critical variables were not captured in our study due to the limitations of relying on EHR data. However, we were able to capture a limited set of variables related to SDOH, including marital status (married vs nonmarried), patients’ residential status (rural vs urban), substance use, and cigarette smoking. These variables were incorporated into our analyses to account for their potential influence on patient outcomes and provide some insight into the broader social and environmental context affecting health; however, they represent only a fraction of the SDOH spectrum.

CONCLUSIONS

This large retrospective multicenter study of patients hospitalized for ADHF indicates that discharge orders placed before noon compared with those placed in the afternoon were independently associated with higher all-cause mortality in both the short and long term as well as an increase in early readmissions. This pattern remained consistent across a range of subgroups, including patients admitted to both academic and community hospitals, as well as across 2 distinct study periods: 2010-2016 and 2017-2022. The persistence of these findings across different hospital types and time frames underscores the robustness and generalizability of the observed association, suggesting that the impact of discharge timing on outcomes is independent of institutional settings or temporal variations in patient care practices.

We identified factors associated with higher mortality in patients with DOBN compared with DOAN, highlighting potential intervention targets. Our findings emphasize evaluating discharge order timing in hospital policies and optimizing discharge procedures and postdischarge care. Further research is needed to extend these findings to other conditions and discharge timings.

Author Affiliations: Department of Hospital Internal Medicine (MY, MWT, ZM) and Department of Endocrine and Metabolism (SB), Mayo Clinic Health System, Austin, MN; Department of Internal Medicine, Eastern Virginia Medical School (RQ), Norfolk, VA; Department of Hospital Internal Medicine, Brigham and Women’s Hospital, Harvard Medical School (EB), Boston, MA; Department of Cardiovascular Medicine, Circulatory Failure, Mayo Clinic (MHY), Jacksonville, FL; Departments of Community Internal Medicine and Geriatrics, Mayo Clinic (PYT), Rochester, MN.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (MY, RQ, SB, MHY); acquisition of data (MY); analysis and interpretation of data (MY, RQ, MWT, EB, ZM, MHY, PYT); drafting of the manuscript (MY, RQ, MWT, EB, SB, MHY); critical revision of the manuscript for important intellectual content (MY, RQ, EB, ZM, SB, MHY, PYT); statistical analysis (MY, RQ, MWT, ZM); provision of patients or study materials (MY); administrative, technical, or logistic support (MY, SB); and supervision (EB, PYT).

Address Correspondence to: Mohammed Yousufuddin, MD, MSc, Mayo Clinic, 1000 1st Dr NW, Austin, MN 55902. Email: yousufuddin.mohammed@mayo.edu.

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