{"doi":"10.1111/acem.14972","title":"Hospital‐free days: A novel measure to study outcomes for emergency department care","abstract":null,"journal":"Academic Emergency Medicine","year":2024,"id":614727,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":901086,"name":"M. Kit Delgado","orcid":"0000-0002-2248-8401","position":1,"is_corresponding":false},{"id":648463,"name":"Catherine L. Auriemma","orcid":"0000-0003-4803-0375","position":2,"is_corresponding":false},{"id":504792,"name":"Austin S. Kilaru","orcid":"0000-0002-3141-1772","position":3,"is_corresponding":false},{"id":278045,"name":"Ari B. Friedman","orcid":"0000-0003-0412-6754","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Hospital‐free days: A novel measure to study outcomes for emergency department care","abstract":"Traditionally, researchers have evaluated the quality of emergency care using subsequent adverse events, like mortality or return hospital visits. The selection of these well-defined, single events as outcomes for epidemiological studies has strengths, including ease of interpretation. Yet any particular outcome only captures part of the story of how patients fare after receiving care in the emergency department (ED). For example, mortality is a clinically significant outcome. It fails, however, to capture quality of life decrements that may be important to patients and families.1, 2 Furthermore, its rarity necessitates prohibitively large sample sizes. By contrast, composite outcome measures such as major adverse cardiac events require less statistical power but assume that all component parts (e.g., death and hospitalization) are valued equally by patients. Hospital-free days (HFD) offer a potential solution, naturally combining morbidity and mortality to create an easily calculated, patient-centered measure sensitive to high-quality emergency care. Essentially synonymous with days alive and out of hospital (DAOH), and similar in concept to other metrics including healthy days at home (HDAH), HFD is gaining acceptance in health services, epidemiological, and comparative effectiveness research.3, 4 However, these metrics have only recently been used in emergency medicine research.5 In this commentary, we outline the HFD approach, discuss unique considerations to using HFD to study emergency medicine outcomes, and propose development of standardized approaches for emergency medicine research. Unlike event counts such as mortality rates and revisit rates, the HFD approach aims to measure a conceptual construct of health health rather than illness. Like some longstanding measures, such as disability-adjusted life year, HFD begins with an idealized amount of “full health,” a quota from which illness and death are subtracted. To calculate HFD, a time period after an anchor event (e.g., presentation to the ED or discharge from the ED or hospital) must be selected. For ED encounters, 30 days has often been used to measure mortality or readmissions after ED visits and may also represent an appropriate window to calculate HFD.6, 7 For outcomes expected to be highly responsive to ED treatment, a 9-day time window may be most appropriate.8 Depending on the specific research question and context, time frames of 60 or 90 days might also be appropriate. Next, the number of days in which patients are neither alive nor hospitalized are then subtracted from the total days in the chosen time period. Importantly, there is no single definition for which types of services should be counted, highlighting the importance of transparent reporting of how HFD, HDAH, or DAOH are defined for any individual study as well as an opportunity to incorporate patient perspectives into these definitions. Nearly all definitions subtract days for which patients have expired or spend in an acute care hospital, including long-term acute care. The HFD measure typically does not subtract days spent in post–acute care, such as skilled nursing facilities or inpatient rehabilitation, while HDAH does subtract those days.3, 4 Neither measure typically subtracts time spent in residential nursing homes or hospice. Studies vary with regard to classification of subsequent ED visits that do not result in hospital admission, either ignoring these encounters entirely or considering each ED visit as a half or a full day.9, 10 Finally, some definitions of HDAH subtract days that patients receive postacute home health visits while others do not.11 Measures including these contacts with the health care system will be more sensitive to morbidity relative to mortality. HFD captures a more nuanced, and potentially more accurate, picture of patients' overall health than alternatives. For example, a study evaluating a clinical pathway for patients discharged from the ED with undifferentiated abdominal pain may report that 20% of patients were admitted over the subsequent 30 days. These readmissions might represent a mix of brief observation stays, patients who experience prolonged hospitalization due to a missed serious diagnosis, and patients who expire during their readmission. Focusing on readmission rates alone does not consider illness severity, duration of hospitalization, or number of readmissions, while focusing on mortality may only consider patients with very severe illness. HFD offers one solution, therefore, that incorporates magnitude into an otherwise binary outcome. Depending on the context, HFD may better account for the range of outcomes which patients may experience and may correlate with patient-centered outcomes such as including functional status.12 Another advantage of the HFD approach is the feasibility of data collection. HFD is well suited to pragmatic trials and observational studies that measure outcomes using electronic health record or claims data, since the only data requirements are hospitalization dates (dates of admission and discharge), ED presentation dates, and dates of death. These reduced data requirements offer a pragmatic approach to patient follow-up with considerably lower costs and missing data than survey-based approaches. However, traditional clinical trials or survey-based studies may also easily incorporate HFD as an outcome. To implement this, a clinical study that surveys patients for 30-day outcomes could collect data on not just readmissions but also how many days were spent in different health care settings during that interval or conduct electronic follow-up using regional health exchange and state death record data. HFD may capture a more patient-centered outcome than other metrics. One study specific to older adults suggests this may be the case.13 Ultimately, patients care most about health, which this approach measures as time spent outside of health care facilities. In the example above, patients may be willing to return to the hospital to have their abdominal pain reevaluated if it means that they avoid severe illness. However, only preliminary work has been done to confirm patient perspectives on HFD overall as well as incorporate feedback on methodological choices when using this approach.14, 15 One challenge is the interpretation of HFD, both for clinicians and for patients. A difference in 5 days alive and out of the hospital over a 30-day period is likely to represent a considerable benefit, but interpreting the clinical significance of a 0.5-day improvement in HFD, for example, requires more context. A recent study reporting the minimum clinically important difference (MCID) for 180 days following ICU discharge offers an approach that could be used to determine an MCID for ED patients at shorter time frames more relevant to post–ED discharge.15 Another challenge for studies that seek to use HFD is obtaining data outside of individual health systems. Out-of-hospital death can be available through linkage to the National Death Index or through state vital registration systems. To obtain out-of-hospital ED visitation and hospitalization dates, regional health exchanges can provide data for external health systems, including dates of ED presentation and inpatient admission, enhancing the accuracy of HFD. Administrative claims data sets, such as Medicare, Medicaid, or commercial insurance claims, or state all payer claims databases, also contain sufficient information to calculate HFD. Survey-based approaches can augment or replace these routinely captured data if not available. There are key statistical issues in the use of HFD which remain unresolved. Distribution of the outcome is important to consider depending on the study population and must be considered when selecting statistical tests. Importantly, HFD was initially developed for studying populations with considerable expected health care needs, such as Medicare beneficiaries and survivors of serious illness.3, 4 Healthier patients with low-acuity illness may be expected to have data skewed toward full health and maximal HFD, while patients with severe illness may be skewed in the direction of poor health and relatively few HFD. Analyses of populations with expected high rates of HFD accrual, such as healthy pediatric populations, require additional validation before utilizing this metric. While HFD incorporates quality of life more than existing alternatives, a limitation of HFD is that it cannot fully capture quality of life.3 Two patients may have very different levels of symptoms and function at home but still be considered to have equivalent HFD if those symptoms do not require hospital care. The application of HFD in studies that include patients who are hospitalized following an ED encounter also presents challenges. HFD accounts for hospital and post–acute care length of stay, which make this measure relevant for time-sensitive interventions delivered in the ED that seek to mitigate the severity and length of illness. However, many additional factors may drive HFD that occur following the emergency phase of care. For admitted patients, a clear causal pathway should be established between ED decisions, diagnostics, and management and long-term health outcomes for HFD to be relevant in this context. For these reasons, the choice whether to initiate the follow-up period at the day of ED visitation or the day of hospital discharge may also depend on the goals of the study (Table 1). The time-dependent nature of this outcome also must be considered, particularly when patients expire during the follow-up period. For example, a patient who dies on Day 9 of a 9-day assessment period has the same HFD as a patient who revisits the ED a single time during that period. For a 30-day assessment period, however, the patient's death on Day 9 would cause more than 20 times the reduction in HFD compared to a single ED revisit. In general, shorter periods weight nonmortality outcomes higher relative to longer periods. Longer periods may better capture the course of disease and recovery after major illness or procedures. All studies using HFD should also separately report both the rate and the average duration of the component outcomes such as hospitalization, mortality, ED visits, and nursing home days. Future studies should assess the relative tradeoffs of each assessment period to enable researchers to align their choice with the particular context and intervention they are studying. There are few current examples of using HFD and similar approaches in the emergency medicine literature.5-8 However, this approach is likely to gain increasing acceptance in the literature with further exploration of its advantages and disadvantages. Recognizing the potential utility as well as pitfalls of using these outcomes in studies of emergency care, we present key considerations for investigators as well as readers of the literature. We list additional key design and reporting decisions, along with proposed solutions, in Table 1. Finally, technology and structural change in the health care system may accelerate secular trends toward more outpatient and less inpatient care.16 Ongoing monitoring of these trends, as well as longitudinal comparison to metrics that are less likely to be affected by the economics of the health care industry without an underlying change in patient health, such as functional status and mortality, will be necessary to ensure ongoing construct validity. Advantages of hospital-free days to measure clinical outcomes, including feasibility, patient-centeredness, and inclusion of disparate outcomes into a single measure of morbidity and mortality, suggest that this approach has considerable promise. ED-based studies should consider hospital-free days as a primary outcome for both intervention and observational studies, albeit with careful consideration of the consequences and reporting necessary for this approach. Further engagement with ED patients to help inform unresolved questions on the use and interpretation of hospital-free days is also paramount to ensure that methodological approaches are consistent with patient preferences and values. Dr. Friedman is supported by the Public Interest Technology University Network, the Leonard Davis Institute Small Project Grant, and the National Institute on Aging (1R03AG078933-01 and 1K23AG080061). Dr. Delgado is supported by the Patient Centered Outcomes Research Institute (COVID-2020C2-10830), National Institutes of Health, Food and Drug Administration, Centers for Disease Control and Prevention, Department of Transportation, and the Abramson Family Foundation. Dr. Auriemma is supported by the National Heart, Lung, and Blood Institute (1K23HL163402-01). Dr. Kilaru is supported by the Agency for Healthcare Research and Quality (5K12HS026372-04). The authors declare no conflicts of interest.","is_dataset_classified":null,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38991152","pmcid":"PMC11492154","openalex_id":"https://openalex.org/W4399922070","authors":[],"funders":[{"funder_name":"NIA NIH HHS","grant_id":"K23 AG080061","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R03 AG078933","title":null},{"funder_name":"AHRQ HHS","grant_id":"K12 HS026372","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"K23 HL163402","title":null},{"funder_name":"National Institutes of Health","grant_id":"1K23HL163402-01","title":"Developing and Evaluating Quality-Weighted Hospital-Free Days as a Novel, Patient-Centered Outcome for Trials of Patients with Acute Respiratory Failure"},{"funder_name":"National Institutes of Health","grant_id":"1K23AG080061-01A1","title":"Abdominal Pain in Older Patients in Emergency Departments"},{"funder_name":"National Institutes of Health","grant_id":"1R03AG078933-01","title":"Trajectories of Frailty and Cognitive Impairment in Older Adults"},{"funder_name":"National Institutes of Health","grant_id":"5K12HS026372-04","title":"Learning Health Systems Mentored Career Development Program"}],"total_grants":8,"fwci":3.389,"citation_percentile":0.91874534,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":3},{"year":2026,"count":2}],"oa_status":"hybrid","license":"cc-by-nc","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/acem.14972","host_type":"journal"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/acem.14972","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1111/acem.14972","host_type":"publisher"},{"url":"https://doi.org/10.1111/acem.14972","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38991152","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11492154","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11492154/pdf/nihms-2026959.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11492154","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11492154?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1111/acem.14972","host_type":""}],"fields_of_study":["Emergency and Acute Care Studies","Trauma and Emergency Care Studies","Healthcare Policy and Management"],"mesh_terms":[],"keywords":["Medicine","Emergency department","Health care","Emergency medicine","Epidemiology","MEDLINE","Mortality rate","Medical emergency","Adverse effect","Intensive care medicine","Internal medicine","Nursing","Article"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-02T15:44:14.960432Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}