Wound and episode level readmission risk or weeks to readmit: Why do
patients get readmitted? How long does it take for a patient to get
readmitted?
- URL: http://arxiv.org/abs/2010.02742v1
- Date: Mon, 5 Oct 2020 12:49:42 GMT
- Title: Wound and episode level readmission risk or weeks to readmit: Why do
patients get readmitted? How long does it take for a patient to get
readmitted?
- Authors: Subba Reddy Oota, Nafisur Rahman, Shahid Saleem Mohammed, Jeffrey
Galitz, Ming Liu
- Abstract summary: The Affordable care Act of 2010 had introduced Readmission reduction program in 2012 to reduce avoidable re-admissions to control rising healthcare costs.
Wound care impacts 15 of medicare beneficiaries making it one of the major contributors of medicare health care cost.
Our work focuses on identifying patients who are at high risk of re-admission & determining the time period with in which a patient might get re-admitted.
- Score: 4.59975324961346
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The Affordable care Act of 2010 had introduced Readmission reduction program
in 2012 to reduce avoidable re-admissions to control rising healthcare costs.
Wound care impacts 15 of medicare beneficiaries making it one of the major
contributors of medicare health care cost. Health plans have been exploring
proactive health care services that can focus on preventing wound recurrences
and re-admissions to control the wound care costs. With rising costs of Wound
care industry, it has become of paramount importance to reduce wound
recurrences & patient re-admissions. What factors are responsible for a Wound
to recur which ultimately lead to hospitalization or re-admission? Is there a
way to identify the patients at risk of re-admission before the occurrence
using data driven analysis? Patient re-admission risk management has become
critical for patients suffering from chronic wounds such as diabetic ulcers,
pressure ulcers, and vascular ulcers. Understanding the risk & the factors that
cause patient readmission can help care providers and patients avoid wound
recurrences. Our work focuses on identifying patients who are at high risk of
re-admission & determining the time period with in which a patient might get
re-admitted. Frequent re-admissions add financial stress to the patient &
Health plan and deteriorate the quality of life of the patient. Having this
information can allow a provider to set up preventive measures that can delay,
if not prevent, patients' re-admission. On a combined wound & episode-level
data set of patient's wound care information, our extended autoprognosis
achieves a recall of 92 and a precision of 92 for the predicting a patient's
re-admission risk. For new patient class, precision and recall are as high as
91 and 98, respectively. We are also able to predict the patient's discharge
event for a re-admission event to occur through our model with a MAE of 2.3
weeks.
Related papers
- Introducing the Large Medical Model: State of the art healthcare cost and risk prediction with transformers trained on patient event sequences [0.47901560316389713]
The Large Medical Model (LMM) is a generative pre-trained transformer (GPT) designed to guide and predict the broad facets of patient care and healthcare administration.
The model is trained on medical event sequences from over 140M longitudinal patient claims records with a specialized vocabulary built from medical terminology systems.
The LMM is able to improve both cost prediction by 14.1% over the best commercial models and chronic conditions prediction by 1.9% over the best transformer models in research predicting a broad set of conditions.
arXiv Detail & Related papers (2024-09-19T15:38:21Z) - Diagnosis Uncertain Models For Medical Risk Prediction [80.07192791931533]
We consider a patient risk model which has access to vital signs, lab values, and prior history but does not have access to a patient's diagnosis.
We show that such all-cause' risk models have good generalization across diagnoses but have a predictable failure mode.
We propose a fix for this problem by explicitly modeling the uncertainty in risk prediction coming from uncertainty in patient diagnoses.
arXiv Detail & Related papers (2023-06-29T23:36:04Z) - Management and Detection System for Medical Surgical Equipment [68.8204255655161]
Retained surgical bodies (RSB) are any foreign bodies left inside the patient after a medical procedure.
This paper describes the engineering process we have done to explore the design space, define a feasible solution, and simulate, verify, and validate a state-of-the-art Cyber-Physical System.
arXiv Detail & Related papers (2022-11-04T10:19:41Z) - Advances in Prediction of Readmission Rates Using Long Term Short Term
Memory Networks on Healthcare Insurance Data [1.454498931674109]
30-day hospital readmission is a long standing medical problem that affects patients' morbidity and mortality and costs billions of dollars annually.
We developed a bi-directional Long Short Term Memory (LSTM) Network that is able to use readily available insurance data.
Our results demonstrate that a machine learning model is able to predict risk of inpatient readmission with reasonable accuracy for all patients.
arXiv Detail & Related papers (2022-06-30T19:07:10Z) - Machine Learning for Deferral of Care Prediction [4.436632973105494]
Continual care deferral in populations may lead to a decline in population health and compound health issues leading to higher social and financial costs in the long term.
Minority and vulnerable populations are at a greater risk of care deferral due to socioeconomic factors.
Many health systems currently use rules-based techniques to retroactively identify patients who previously deferred care.
The objective of this model is to identify patients at risk of deferring care and allow the health system to prevent care deferrals through direct outreach or social mediation.
arXiv Detail & Related papers (2022-06-09T01:21:13Z) - 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) - 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) - COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for
COVID-19 Patients via Explainability and Trust Quantification [71.80459780697956]
We introduce COVID-Net Clinical ICU, a neural network for ICU admission prediction based on patient clinical data.
The proposed COVID-Net Clinical ICU was built using a clinical dataset from Hospital Sirio-Libanes comprising of 1,925 COVID-19 patients.
We conducted system-level insight discovery using a quantitative explainability strategy to study the decision-making impact of different clinical features.
arXiv Detail & Related papers (2021-09-14T14:16:32Z) - Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration [55.88616573143478]
Outcome prediction from clinical text can prevent doctors from overlooking possible risks.
Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets.
We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
arXiv Detail & Related papers (2021-02-08T10:26:44Z) - Improving healthcare access management by predicting patient no-show
behaviour [0.0]
This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance.
We assess the effectiveness of different machine learning approaches to improve the accuracy of regression models.
In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities.
arXiv Detail & Related papers (2020-12-10T14:57:25Z)
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.