A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes

• URL: http://arxiv.org/abs/2401.03700v1
• Date: Mon, 8 Jan 2024 07:11:56 GMT
• Title: A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes
• Authors: Hsiao-Ying Lu, Yiran Li, Kwan-Liu Ma
• Abstract summary: Communication among healthcare professionals (HCPs) is crucial for the quality of patient treatment. This paper introduces a visual analytics system designed to study the effectiveness and efficiency of temporal communication networks mediated by the EHR system.
• Score: 24.618192211010612
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Communication among healthcare professionals (HCPs) is crucial for the quality of patient treatment. Surrounding each patient's treatment, communication among HCPs can be examined as temporal networks, constructed from Electronic Health Record (EHR) access logs. This paper introduces a visual analytics system designed to study the effectiveness and efficiency of temporal communication networks mediated by the EHR system. We present a method that associates network measures with patient survival outcomes and devises effectiveness metrics based on these associations. To analyze communication efficiency, we extract the latencies and frequencies of EHR accesses. Our visual analytics system is designed to assist in inspecting and understanding the composed communication effectiveness metrics and to enable the exploration of communication efficiency by encoding latencies and frequencies in an information flow diagram. We demonstrate and evaluate our system through multiple case studies and an expert review.

Related papers

• PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets [21.783005762375417]
  PhenoFlow is a visual analytics system that leverages the collaboration between human and Large Language Models (LLMs) PhenoFlow only utilizes metadata to make inferences and synthesize executable codes, without accessing raw patient data. Our research demonstrates the potential of utilizing LLMs to tackle current challenges in data-driven clinical decision-making for acute ischemic stroke patients.
  arXiv  Detail & Related papers  (2024-07-23T09:25:59Z)
• Assessing the role of clinical summarization and patient chart review within communications, medical management, and diagnostics [0.0]
  This review dives into recent literature and case studies on the significant impacts and outstanding issues of patient chart review on communications, diagnostics, and management. It also discusses recent efforts to integrate artificial intelligence into clinical summarization tasks, and its transformative impact on the clinician's potential.
  arXiv  Detail & Related papers  (2024-06-24T15:31:24Z)
• Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features [50.82725748981231]
  Engagement measurement finds application in healthcare, education, services. Use of physiological and behavioral features is viable, but impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of the unsupervised photoplethysmography (rmography) as an alternative for contact sensors.
  arXiv  Detail & Related papers  (2024-04-05T20:39:16Z)
• Benchmarking Large Language Models on Communicative Medical Coaching: a Novel System and Dataset [26.504409173684653]
  We introduce "ChatCoach", a human-AI cooperative framework designed to assist medical learners in practicing their communication skills during patient consultations. ChatCoachdifferentiates itself from conventional dialogue systems by offering a simulated environment where medical learners can practice dialogues with a patient agent, while a coach agent provides immediate, structured feedback. We have developed a dataset specifically for evaluating Large Language Models (LLMs) within the ChatCoach framework on communicative medical coaching tasks.
  arXiv  Detail & Related papers  (2024-02-08T10:32:06Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• A Unified Approach for Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography [3.7775754350457746]
  We introduce a novel unified framework using a convolutional neural network for comprehensive analysis of spectral and tissue Doppler echocardiography images. The network automatically recognizes key features across various Doppler views, with novel Doppler shape embedding and anti-aliasing modules. Empirical results indicate a consistent outperformance in performance metrics, including dice similarity coefficients (DSC) and intersection over union (IoU)
  arXiv  Detail & Related papers  (2023-11-14T15:10:05Z)
• Validating a virtual human and automated feedback system for training doctor-patient communication skills [3.0354760313198796]
  We present the development and validation of a scalable, easily accessible, digital tool known as the Standardized Online Patient for Health Interaction Education (SOPHIE) We found that participants who underwent SOPHIE performed significantly better than the control in overall communication, aggregate scores, empowering the patient, and showing empathy. One day, we hope that SOPHIE will help make communication training resources more accessible by providing a scalable option to supplement existing resources.
  arXiv  Detail & Related papers  (2023-06-27T05:23:08Z)
• 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)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)

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.