Towards a fairer reimbursement system for burn patients using cost-sensitive classification

• URL: http://arxiv.org/abs/2107.00531v1
• Date: Thu, 1 Jul 2021 15:23:21 GMT
• Title: Towards a fairer reimbursement system for burn patients using cost-sensitive classification
• Authors: Chimdimma Noelyn Onah, Richard Allmendinger, Julia Handl, Ken W. Dunn
• Abstract summary: The adoption of the Prospective Payment System (PPS) in the UK has led to the creation of Health Resource Groups (HRGs) HRGs aim to identify groups of clinically similar patients that share similar resource usage for reimbursement purposes. We propose a data-driven model and the inclusion of patient-level costing to improve homogeneity in resource usage and severity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The adoption of the Prospective Payment System (PPS) in the UK National Health Service (NHS) has led to the creation of patient groups called Health Resource Groups (HRG). HRGs aim to identify groups of clinically similar patients that share similar resource usage for reimbursement purposes. These groups are predominantly identified based on expert advice, with homogeneity checked using the length of stay (LOS). However, for complex patients such as those encountered in burn care, LOS is not a perfect proxy of resource usage, leading to incomplete homogeneity checks. To improve homogeneity in resource usage and severity, we propose a data-driven model and the inclusion of patient-level costing. We investigate whether a data-driven approach that considers additional measures of resource usage can lead to a more comprehensive model. In particular, a cost-sensitive decision tree model is adopted to identify features of importance and rules that allow for a focused segmentation on resource usage (LOS and patient-level cost) and clinical similarity (severity of burn). The proposed approach identified groups with increased homogeneity compared to the current HRG groups, allowing for a more equitable reimbursement of hospital care costs if adopted.

Related papers

• An AI-Guided Data Centric Strategy to Detect and Mitigate Biases in Healthcare Datasets [32.25265709333831]
  We generate a data-centric, model-agnostic, task-agnostic approach to evaluate dataset bias by investigating the relationship between how easily different groups are learned at small sample sizes (AEquity) We then apply a systematic analysis of AEq values across subpopulations to identify and manifestations of racial bias in two known cases in healthcare. AEq is a novel and broadly applicable metric that can be applied to advance equity by diagnosing and remediating bias in healthcare datasets.
  arXiv  Detail & Related papers  (2023-11-06T17:08:41Z)
• Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources [47.57108369791273]
  Scarcity of health care resources could result in the unavoidable consequence of rationing. There is no universally accepted standard for health care resource allocation protocols. We propose a transformer-based deep Q-network to integrate the disease progression of individual patients and the interaction effects among patients.
  arXiv  Detail & Related papers  (2023-09-15T17:28:06Z)
• Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN [70.76142503046782]
  We propose supplementing machine learning-based (ML) healthcare tools for bias with SLOGAN, an automatic tool for capturing local biases in a clinical prediction task. LOGAN adapts an existing tool, LOcal Group biAs detectioN, by contextualizing group bias detection in patient illness severity and past medical history. On average, SLOGAN identifies larger fairness disparities in over 75% of patient groups than LOGAN while maintaining clustering quality.
  arXiv  Detail & Related papers  (2022-11-16T08:04:12Z)
• Identifying Undercompensated Groups Defined By Multiple Attributes in Risk Adjustment [0.09137554315375918]
  We evaluate the risk adjustment formulas used in the U.S. health insurance Marketplaces and Medicare. We find that groups with multiple chronic conditions are undercompensated in the Marketplaces risk adjustment formula. No complex groups were found to be consistently under- or overcompensated in the Medicare risk adjustment formula.
  arXiv  Detail & Related papers  (2021-05-18T13:09:13Z)
• Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients [50.48904066814385]
  In this work we apply deep semi-supervised embedded clustering to determine data-driven patient subgroups of heart failure. We find clinically relevant clusters from an embedded space derived from heterogeneous data. The proposed algorithm can potentially find new undiagnosed subgroups of patients that have different outcomes.
  arXiv  Detail & Related papers  (2020-12-24T12:56:46Z)
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression [97.88605060346455]
  We develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-06-15T20:48:43Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)
• Bayesian Nonparametric Cost-Effectiveness Analyses: Causal Estimation and Adaptive Subgroup Discovery [0.0]
  We develop a nonparametric Bayesian model for joint cost-survival distributions in the presence of censoring. We use our model to assess the cost-efficacy of endometrial chemotherapy versus radiation adjuvant therapy for treating cancer in the SEER-Medicare database.
  arXiv  Detail & Related papers  (2020-02-11T21:51:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.