Developing A Personal Decision Support Tool for Hospital Capacity Assessment and Querying

• URL: http://arxiv.org/abs/2308.06276v1
• Date: Mon, 31 Jul 2023 22:51:44 GMT
• Title: Developing A Personal Decision Support Tool for Hospital Capacity Assessment and Querying
• Authors: Robert L Burdett, Paul Corry, David Cook, Prasad Yarlagadda
• Abstract summary: This article showcases a personal decision support tool (PDST) called HOPLITE, for performing insightful and actionable quantitative assessments of hospital capacity. The results of extensive development and testing indicate that HOPLITE can automate many nuanced tasks. The functionality that HOPLITE provides may make it easier to calibrate hospitals strategically and/or tactically to demands.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article showcases a personal decision support tool (PDST) called HOPLITE, for performing insightful and actionable quantitative assessments of hospital capacity, to support hospital planners and health care managers. The tool is user-friendly and intuitive, automates tasks, provides instant reporting, and is extensible. It has been developed as an Excel Visual Basic for Applications (VBA) due to its perceived ease of deployment, ease of use, Office's vast installed userbase, and extensive legacy in business. The methodology developed in this article bridges the gap between mathematical theory and practice, which our inference suggests, has restricted the uptake and or development of advanced hospital planning tools and software. To the best of our knowledge, no personal decision support tool (PDST) has yet been created and installed within any existing hospital IT systems, to perform the aforementioned tasks. This article demonstrates that the development of a PDST for hospitals is viable and that optimization methods can be embedded quite simply at no cost. The results of extensive development and testing indicate that HOPLITE can automate many nuanced tasks. Furthermore, there are few limitations and only minor scalability issues with the application of free to use optimization software. The functionality that HOPLITE provides may make it easier to calibrate hospitals strategically and/or tactically to demands. It may give hospitals more control over their case-mix and their resources, helping them to operate more proactively and more efficiently.

Related papers

• Demystifying Large Language Models for Medicine: A Primer [50.83806796466396]
  Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare. This tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice.
  arXiv  Detail & Related papers  (2024-10-24T15:41:56Z)
• MeNTi: Bridging Medical Calculator and LLM Agent with Nested Tool Calling [12.236137157144965]
  We introduce MeNTi, a universal agent architecture for Large Language Models (LLMs) MeNTi integrates a specialized medical toolkit and employs meta-tool and nested calling mechanisms to enhance LLM tool utilization. To assess the capabilities of LLMs for quantitative assessment throughout the clinical process of calculator scenarios, we introduce CalcQA.
  arXiv  Detail & Related papers  (2024-10-17T14:46:22Z)
• Chain of Tools: Large Language Model is an Automatic Multi-tool Learner [54.992464510992605]
  Automatic Tool Chain (ATC) is a framework that enables the large language models (LLMs) to act as a multi-tool user. To scale up the scope of the tools, we next propose a black-box probing method. For a comprehensive evaluation, we build a challenging benchmark named ToolFlow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• An Interactive Decision-Support Dashboard for Optimal Hospital Capacity Management [0.13635858675752993]
  We develop an interactive, user-friendly, electronic dashboard for informing hospital capacity management decisions during surge periods. The dashboard integrates real-time hospital data, predictive analytics, and optimization models. It allows hospital administrators to interactively customize parameters, enabling them to explore a range of scenarios, and provides real-time updates on recommended optimal decisions.
  arXiv  Detail & Related papers  (2024-03-22T22:13:18Z)
• MedAide: Leveraging Large Language Models for On-Premise Medical Assistance on Edge Devices [7.042194397224198]
  Large language models (LLMs) are revolutionizing various domains with their remarkable natural language processing (NLP) abilities. However, deploying LLMs in resource-constrained edge computing and embedded systems presents significant challenges. These challenges include delivering medical assistance in remote areas with limited healthcare facilities and infrastructure.
  arXiv  Detail & Related papers  (2024-02-28T08:30:49Z)
• Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios [93.68764280953624]
  UltraTool is a novel benchmark designed to improve and evaluate Large Language Models' ability in tool utilization. It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving. A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage.
  arXiv  Detail & Related papers  (2024-01-30T16:52:56Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• Large Language Models as Tool Makers [85.00361145117293]
  We introduce a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving. Our approach consists of two phases: 1) tool making: an LLM acts as the tool maker that crafts tools for a set of tasks. 2) tool using: another LLM acts as the tool user, which applies the tool built by the tool maker for problem-solving.
  arXiv  Detail & Related papers  (2023-05-26T17:50:11Z)
• The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling [10.92813727735562]
  We present the BEDS (better elective day of surgery) algorithm, a greedy algorithm for smoothing unit-specific surgical admissions days. BEDS is readily implementable with the limited tools available to most hospitals, does not require reductions to surgeon autonomy or centralized scheduling, and is compatible with changes to hospital capacity or patient volumes. We argue that algorithms generated by this framework retain many of the desirable characteristics of BEDS while being compatible with a wide range of objectives and constraints.
  arXiv  Detail & Related papers  (2022-03-14T04:19:25Z)
• Modular approach to data preprocessing in ALOHA and application to a smart industry use case [0.0]
  The paper addresses a modular approach, integrated into the ALOHA tool flow, to support the data preprocessing and transformation pipeline. To demonstrate the effectiveness of the approach, we present some experimental results related to a keyword spotting use case.
  arXiv  Detail & Related papers  (2021-02-02T06:48:51Z)

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.