Comparison of Methods to Define High Use of Inpatient Services Using Population-Based Data

When assessing inpatient system burden, high users by number of inpatient episodes accounted for 47,044 (16.6%) of the 283,204 episodes. High users defined by length of stay accounted for 1,286,539 (46.4%) days of 2,773,561 total days, while high users defined by cost accumulated $1.4 billion (38.9%) of the estimated $3.7 billion in inpatient expenditures. High users defined by cost and length of stay each accounted for comparatively few episode

Using a large population-based cohort of all adults with at least 1 hospitalization in the province of Alberta, Canada, within a 12-month period, we compared 3 commonly used definitions of high inpatient use. The choice of definition had a substantial influence on the types of patients categorized as high use, as well as the proportion of total inpatient utilization that was associated with high users. The definition based on number of inpatient episodes captured a distinct population of high users, while the populations identified using cumulative length of stay or cost were similar.

Differences within and between definitions were especially apparent in age-stratified analyses: Greater length of stay or higher cost among patients aged 18-64 years identifies a large proportion of psychological conditions, while a greater number of inpatient episodes identifies acute conditions and childbirth or labor-related complications. Conversely, definitions based on length of stay and cost in the elderly (65+) identified groups with chronic conditions that result in progressive functional decline (often requiring increasing supportive services upon discharge) or conditions that require significant rehabilitation prior to discharge. Regarding inpatient system burden, high users defined by number of inpatient episodes accounted for a small proportion of total inpatient episodes, while high users defined by length of stay and cost accounted for nearly half of the accumulated hospital days and cost for each. These findings highlight the need for careful consideration of how high use is defined when studying high-user populations and implications for targeting subpopulations for intervention.

Our results add to those from previous studies. A US-based, single-center study of 2566 individuals compared definitions of high inpatient use based on cost and frequency of admission and found that patients defined by cost were predominantly hospitalized for surgical conditions, while those fulfilling the episode-based definition were often hospitalized for medical conditions.¹² The most responsible diagnoses for patient hospitalizations in our study reflect this. We extended this comparison to consider the impact of age on outcomes and inpatient system burden and found that older age was also linked to poorer outcomes and increased burden. We also considered a third definition (cumulative length of stay), which provided another opportunity for comparison. The presence of chronic conditions requiring rehabilitation and possible alternate level of care days within our cohort highlights the utility of this length of stay-based approach when considering definitions. Although there were similarities between patients defined by length of stay and cost, partly due to cost being largely a function of length of stay, there were also important differences in their patient profiles. Those defined by cost tended to have conditions requiring surgical procedures not requiring extended in-hospital rehabilitation. Furthermore, the higher proportion of in-hospital mortality among those defined by cost may also reflect the fact that patients tend to accrue the majority of their healthcare expenditures during the final 120 days of life.²⁴

Each definition of high use identified complex patients; however, the differences between the various types of high users identified by these definitions suggest that they are not interchangeable. Arguably, selection of the most appropriate definition should depend on the objective of measuring high users, particularly if an intervention is planned. Interventions for high users are complex, requiring both medical and nonmedical components. The current literature in this area has often focused on case management programs, collaboration with community-based social support programs, and improving coordination and transitions of care.^25-27 While many of these approaches require considerable involvement outside of the inpatient setting, these interventions can be informed by defining who high users of inpatient services are. Our findings show several possible subgroups of high users, which could be targeted for intervention. For example, an inpatient episode-based definition, which identifies patients with frequent encounters for acute conditions (eg, pneumonia and urinary tract infections), would be informative if an intervention targeted reductions in inpatient use and readmission rates. Alternatively, an intervention designed to improve community-based mental health programs would best be informed by a definition based on length of stay in which high users with underlying mental health conditions were prevalent. Such interventions are rarely mutually exclusive and require multiple perspectives to inform their objectives. A well-designed intervention will not only address the medical characteristics of high users but also the social determinants of health that place patients at risk of high inpatient use.

Our study should be interpreted in light of its limitations. First, measures of disease severity were not available to further characterize similarities and differences across high-use groups. Furthermore, we were unable to account for other social determinants of health that may be relevant to inpatient system usage. Second, direct cost of hospitalizations was estimated based on RIW and is thus reflective of expected rather than actual costs. However, this will have minimal impact on capture, as patients defined by this metric require substantial costs to be included in the top fifth percentile, and thus deviations in individual hospitalization costs will have minimal influence on the cumulative cost. Finally, while inpatient spending makes up a large proportion of healthcare spending, there is likely a number of different high-use profiles found outside of the acute care setting. Despite these limitations, our study includes several key strengths. The use of population-level data allows for analysis that is robust and more generalizable than studies from single centers. Additionally, the comparison of 3 independent definitions allows for a greater comparison of the nuances of each definition. Our study also considers the important impact of age as an effect modifier of inpatient use in the general population and identifies distinct patient profiles that exist across each definition.