Statement of principal findings
Measuring length of stay using spells can lead to substantial underestimates of nearly 25% for some conditions. The typical patient pathway often differs between areas. Under a spell-based analysis this can impair benchmarking and lead regions to appear efficient simply because they transfer a large proportion of patients for rehabilitation. In general, the time spent in rehabilitation spells has increased over time which could undermine examination of temporal trends in LOS under a spell-based analysis. Each of these issues were most profound for stroke and fractured proximal femur, as patients were frequently transferred to a separate hospital for rehabilitation, however important disparities also existed for conditions with simpler pathways (e.g. ENT infections, dehydration and gastroenteritis).
Strengths and weaknesses
This analysis addresses an important, but often overlooked, methodological issue when using the HES dataset to calculate LOS nationally, compare between regions, or investigate temporal trends. By including a diverse range of conditions we have identified the circumstances under which these biases are largest, and when they are perhaps tolerable. We have used bootstrapping methods to calculate sampling distributions around key parameters (e.g. ranking of PCTs) which provides an objective measure of uncertainty.
The main weakness in our study lies in its potential lack of generalisability beyond those using the HES dataset; nevertheless it seems likely that similar issues will exist in any country where administrative data is collected within hospitals and used to guide decision making. The HES dataset is widely used for research and audit purposes [
5] meaning that our findings have extremely important implications for NHS policymaking. Our decision to exclude spells censored by death may have introduced a small bias into our results. It is possible that more advanced competing-risk survival models could be used to overcome this [
18]. The spell classifications used within our analysis may be too simplistic to differentiate between the myriad of pathways a patient my take during a hospital stay. Future investigation into the causes and consequences of variable hospital pathways may require a more comprehensive system. For example, it may be useful to delineate ‘new condition’ spells related to medical error from those which are unpreventable.
Implications for clinicians and policymakers
Accurate calculation of LOS is extremely important for a wide range of audit and research purposes. Benchmarking has been identified as a key tool to drive productivity savings in the National Health Service [
21], however our analysis demonstrates this can be completely undermined when using spell LOS. This could severely limit the ability of NHS organisations to identify and act on improvement opportunities. Cross-sectional studies investigating the effect of patient or hospital characteristics on LOS have been commonly used to identify the most important drivers of LOS, and develop interventions to reduce discharge delays. However these factors (e.g. condition volume [
22], clinical guidelines [
23]) could appear to be strongly associated with spell LOS, when a relationship doesn’t actually exist with the total time spent in hospital, if they are correlated with the probability of hospital transfer. Similarly, before and after studies have been commonly utilised to investigate the effect of healthcare policies (e.g. payment by results [
10], centralisation of stroke care [
11]) on LOS however, when using a spell-based methodology, changes in LOS could be due to evolving patient pathways (e.g. more transfers for stroke rehabilitation) rather than any true difference in the time spent within hospital. It is unlikely that the deficiencies of a spell-based analysis could be overcome by statistical adjustment, as the causes of differing hospital pathways are likely to be complex and, in many cases, intangible. Our analysis empirically describes the potential bias of a spell-based analysis for the first time, and should provide a stimulus for improved methodological rigour.
Despite the pitfalls of calculating LOS using spells, this methodology is widely employed by NHS organisations and academic researchers. The HSCIC, which is the national provider of data to analysts and commissioners, presents national-level LOS using spells [
24]. Perhaps of even greater concern, given the results in our study, is that several NHS benchmarking tools including the NHS Better Care, Better Value Indicators [
25], NHS Compendium of Information [
26], and the RightCare Atlas of Variation [
27] base at least some of their outputs on spell LOS. Inaccurate benchmarking analysis could lead to vital improvement opportunities being missed, or costly investigations being launched to solve problems that don’t actually exist. The academic literature also contains many studies using spell LOS which could undermine their conclusions [
6‐
10]. For example, a study finding an association between the introduction of payment by results and reduced spell LOS [
10] might be confounded by an increasing proportion of hospital transfers over time. Similarly, another study finding inter-hospital variation in four common types of surgery may simply reflect differences in patient pathways rather than true disparities in the time spent in hospital [
6]. Several studies did not provide sufficient detail on the methodology used to calculate LOS [
28‐
30] which prevents readers from determining the robustness of their results.
Our results highlight the need for a step change in how LOS is calculated and reported. National data providers, such as the HSCIC, have sufficient resources to routinely report CIPS-based LOS and should switch to this methodology. Higher quality data could lead to more robust decisions and improved patient outcomes. Similarly, publishers of LOS benchmarking tools should ensure these are based on CIPS as spell-based comparisons are unreliable, even for conditions where care is typically provided by a single hospital. At the very least, spell-based LOS comparisons should explicitly acknowledge the weaknesses of this approach and advise caution when interpreting the results. It is perhaps understandable that small research teams with limited resources sometimes forgo the complex procedure of creating CIPS, and instead opt to use spell LOS. Our results suggest that this may be defensible providing they do not compare across areas and are solely interested in clinical areas where care is typically provided within a single hospital. Such analyses should always be accompanied by a report on the proportion of spells which end with a hospital transfer. However, CIPS-based analysis is always preferable and should be conducted when possible.