Assortment Optimization for Patient-Provider Matching

• URL: http://arxiv.org/abs/2502.10353v1
• Date: Fri, 14 Feb 2025 18:32:11 GMT
• Title: Assortment Optimization for Patient-Provider Matching
• Authors: Naveen Raman, Holly Wiberg,
• Abstract summary: Rising provider turnover forces healthcare administrators to frequently rematch patients to available providers. We develop a patient-provider matching model in which we simultaneously offer each patient a menu of providers. By offering assortments upfront, administrators can balance logistical ease and patient autonomy.
• Score: 4.806579822134391
• License:
• Abstract: Rising provider turnover forces healthcare administrators to frequently rematch patients to available providers, which can be cumbersome and labor-intensive. To reduce the burden of rematching, we study algorithms for matching patients and providers through assortment optimization. We develop a patient-provider matching model in which we simultaneously offer each patient a menu of providers, and patients subsequently respond and select providers. By offering assortments upfront, administrators can balance logistical ease and patient autonomy. We study policies for assortment optimization and characterize their performance under different problem settings. We demonstrate that the selection of assortment policy is highly dependent on problem specifics and, in particular, on a patient's willingness to match and the ratio between patients and providers. On real-world data, we show that our best policy can improve match quality by 13% over a greedy solution by tailoring assortment sizes based on patient characteristics. We conclude with recommendations for running a real-world patient-provider matching system inspired by our results.

Related papers

• LLMs Can Simulate Standardized Patients via Agent Coevolution [8.539733225671059]
  Training medical personnel using standardized patients (SPs) remains a complex challenge. EvoPatient is a novel simulated patient framework in which a patient agent and doctor agents simulate the diagnostic process through multi-turn dialogues. Our framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference.
  arXiv  Detail & Related papers  (2024-12-16T12:36:47Z)
• Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning [48.94952630292219]
  We propose a novel client selection strategy designed to emulate the performance achieved with full client participation. In a single round, we select clients by minimizing the gradient-space estimation error between the client subset and the full client set. In multi-round selection, we introduce a novel individual fairness constraint, which ensures that clients with similar data distributions have similar frequencies of being selected.
  arXiv  Detail & Related papers  (2024-05-22T12:27:24Z)
• Predictive and Prescriptive Analytics for Multi-Site Modeling of Frail and Elderly Patient Services [0.0]
  The aim of this research is to assess how various predictive and prescriptive analytical methods contribute to addressing the operational challenges within an area of healthcare that is growing in demand. On the prescriptive side, deterministic and two-stage programs are developed to determine how to optimally plan for beds and ward staff. Our research reveals that healthcare managers should consider using predictive and prescriptive models to make more informed decisions.
  arXiv  Detail & Related papers  (2023-11-13T12:25:45Z)
• Safe and Interpretable Estimation of Optimal Treatment Regimes [54.257304443780434]
  We operationalize a safe and interpretable framework to identify optimal treatment regimes. Our findings support personalized treatment strategies based on a patient's medical history and pharmacological features.
  arXiv  Detail & Related papers  (2023-10-23T19:59:10Z)
• Deep Attention Q-Network for Personalized Treatment Recommendation [1.6631602844999724]
  We propose the Deep Attention Q-Network for personalized treatment recommendations. The Transformer architecture within a deep reinforcement learning framework efficiently incorporates all past patient observations. We evaluated the model on real-world sepsis and acute hypotension cohorts, demonstrating its superiority to state-of-the-art models.
  arXiv  Detail & Related papers  (2023-07-04T07:00:19Z)
• SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization [50.01382938451978]
  We introduce a model-agnostic pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings indicate that our method not only bolsters performance but also effectively curbs variance for various language models.
  arXiv  Detail & Related papers  (2023-03-23T04:47:46Z)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• Aligning with Heterogeneous Preferences for Kidney Exchange [7.858296711223292]
  We propose a methodology for prioritizing patients based on heterogeneous moral preferences. We find that this methodology increases the average rank of matched patients in the sampled preference ordering, indicating better satisfaction of group preferences.
  arXiv  Detail & Related papers  (2020-06-16T21:16:53Z)
• COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching [70.08786840301435]
  We propose CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching.
  arXiv  Detail & Related papers  (2020-06-15T21:01:33Z)
• DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret [59.81290762273153]
  Dynamic treatment regimes (DTRs) are personalized, adaptive, multi-stage treatment plans that adapt treatment decisions to an individual's initial features and to intermediate outcomes and features at each subsequent stage. We propose a novel algorithm that, by carefully balancing exploration and exploitation, is guaranteed to achieve rate-optimal regret when the transition and reward models are linear.
  arXiv  Detail & Related papers  (2020-05-06T13:03:42Z)

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.