Table 3.

Barriers to Using HFDs as an Outcome Measure, Conceptual Arguments, and Proposed Solutions

Key Considerations When Using HFDs	Conceptual Argument(s)	Proposed Solution(s) or Opportunity for Future Inquiry
HFDs might miss days spent under “observation status.”	From the patient’s point of view, days spent under observation compared to inpatient status are likely equivalent.	When using claims or EHR data, account for inpatient days spent under observation as hospital days.
HFDs consider all days spent in any acute care setting as equally bad.	Patients may not value time spent in each of these locations equivalently, particularly among populations with long hospitalizations in LTACHs. Some hospitalization days may also be of very high value, particularly if necessary for the relief of symptoms, such as refractory dyspnea.	Exploration of patient and family perspectives on how days spent in an acute care hospital compare to an ambulatory setting.
		Development and validation of methods to quality-weight days spent in hospital settings.
Not all HFDs will be spent “at home,” and some of those days will involve the use of home health services.	For many underserved populations, the home may not be sufficiently resourced or comfortable to be preferred over an institutional setting. The use of home health services may be driven by patient preferences and differential ability to recover at home based on resources and access.	Empiric work to understand how the use HFDs, home time, or HDAH might propagate or create disparities.
		Exploration of patient and family perspectives on the value of days spent in various post-acute care settings.
All HFDs are considered equally good.	Patients may not value all days spent outside of a hospital equivalently, particularly if there is variation in their level of comfort, function, or quality of life.	Development and validation of methods to quality-weight HFDs.
Optimal duration of follow-up is unknown, but we recommend at least 90 d and often longer.	If an intervention decreases mortality among the sickest patients, it may paradoxically increase hospital length of stay and thus HFDs if using shorter follow-up time.	Consideration of using multiple endpoints, all prespecified and determined purposefully based on anticipated impact of intervention.
		Empiric work to determine how different durations of follow-up affect the patient centeredness and statistical power of HFDs.
Distribution of HFDs, with peaks at 0 or near maximum values, present statistical modeling and interpretive challenges.	Interventions may have differential impact on likelihood of survival, index hospitalization length of stay, and risk of recurrent hospitalization during follow-up.	Simulation studies to understand how different modeling strategies might impact results and statistical power.
		Testing of strategies to quality-weight HFDs to improve statistical power.

Definition of abbreviations: EHR = electronic health record; HDAH = healthy days at home; HFDs = hospital-free days; LTACH = long-term acute-care hospital.