A Clustering-Based Patient Grouper for Burn Care
Summary (2 min read)
- Imbursement purposes, these groups should be clinically meaningful and share similar resource usage during their hospital stay.
- In the UK National Health Service (NHS) these groups are known as health resource groups (HRGs), and are predominantly derived based on expert advice and checked for homogeneity afterwards, typically using length of stay (LOS) to assess similarity in resource consumption.
- Also, with complex patient groups such as those encountered in burn care, expert advice will often reflect average patients only, therefore not capturing the complexity and severity of many patients’ injury profile.
- The data-driven development of a grouper may support the identification of features and segments that more accurately account for patient complexity and resource use.
- The authors argue that a data-driven approach minimises bias in feature selection.
- The NHS serves a wide population with varied demographic and medical histories, with the aim of providing health interventions to the population who need them.
- In contrast, prospective payment systems (PPSs) determine the provider's payment rates ex ante without any link to the real costs of the individual provider .
- HRGs are generated using nationally mandated patient-level data, which primarily includes age, complications and comorbidities, diagnosis and procedures.
- The authors core hypothesis is that in-depth analysis of the available data should be used in conjunction with expert input to develop an evidence-based model that comprehensively captures the complexity of care provided by such services, and accurately classifies patients into homogeneous groups with respect to costs and patient characteristics.
- Burn services are to be open regardless of the number of patients admitted, with a minimum number of staff, and they rely on the use of highly specialist equipment and interventions.
- This study uses comprehensive anonymized patient-level data that is nationally mandated for all burn units in England and Wales.
- This includes features such as demographic characteristics (age, gender), burn characteristics (depth, total burn surface area, burn site, locality, type, source, category and injury group), pre-existing conditions (self-harm, alcohol usage, asthma, clotting disorder etc.), time from injury to admission, patient-level cost, LOS and index of multiple deprivation (IMD).
- To highlight current variation in HRGs and as a benchmark for model performance, the authors use the 2017/18 average patient-level cost by HRG open data released by NHS Improvement.
- This is limited to one year as PLICS adoption was introduced just in 2017/18 data collection cycle.
2.2 Analysis Pipeline
- Selecting relevant features and cases, also known as Step 1.
- Linear discriminant analysis (LDA), a supervised approach to dimensionality reduction, is adopted.
- The target feature is then generated using k-means clustering algorithm (k = 38, same as number of HRGs) to partition the two-dimensional target space defined by adjusted LOS and patient-level cost.
- The current grouper splits the data into young patients (<16 years old) and older patients (>=16 years old).
- This reflects the burn care pathway, designed to treat pediatrics separately from adults as young age is identified as a significant complicator.
3 Results and Analysis
- The authors explore the patient-level cost by HRG, as generated by the National Casemix office.
- The wider the boxplot, the more variable are the costs within that group.
- When comparing the clusters Adult3 and Adult12, these have very similar average age, but Adult3 has the more severe burns (TBSA), higher LOS and cost, and so the necessity to have separate groups.
- Child5 and Child10 though with similar adjusted LOS, Child5 has a higher TBSA, higher score with respect to the severity of existing disorders and thus a higher average patient-level cost.
- These results highlight the effectiveness of the datadriven HAC grouper in generating groups with homogenous patient characteristics.
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Frequently Asked Questions (2)
Q1. What have the authors stated for future works in "A clustering-based patient grouper for burn care" ?
The collection of patient-level cost, at a national scale, has created the possibility of generating improved data-driven groups. Future work will be aimed at exploring changes to their analytical model, including the consideration of different approaches to dimensionality reduction and cluster analysis, as well as the inclusion of expert opinion in feature selection and group validation. The authors have been able to highlight that improvements can be made in identifying patient case mix suitable for payment rate derivation. There could be further reduction in within cluster variance with the use of state-ofthe-art clustering algorithms that simultaneously consider Step 2, 3 and 4 of their analysis.
Q2. What are the contributions in "A clustering-based patient grouper for burn care" ?
In this paper, the authors describe the development of such a grouper using established techniques for dimensionality reduction and cluster analysis. Using a registry of patients from 23 burn services in England and Wales, the authors demonstrate a reduction of within cluster cost-variation in the identified groups, when compared to the original casemix.