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Related Concept Videos

Interdisciplinary Care: The Health Care Team-I01:21

Interdisciplinary Care: The Health Care Team-I

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An interdisciplinary team includes many healthcare professionals working together and utilizing their skills, knowledge, and expertise to provide holistic and quality patient care.
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Interdisciplinary Care: The Health Care Team-II01:18

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An interdisciplinary team includes many healthcare professionals working together and utilizing their skills, knowledge, and expertise to provide holistic and quality patient care. Here are a few more healthcare professionals.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Continuing Care01:25

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Continuing care describes the variety of health, personal, and social services provided over a prolonged period. The need for continuing care is increasing because people are living longer. Many people do not have families or others to care for them. Continuing care is mainly for patients who are disabled, functionally dependent, or suffering from a terminal disease. It is available within institutional settings or in homes. Examples include nursing centers or facilities, assisted living,...
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Federal statutes profoundly impact nursing practice, providing critical guidelines to ensure patient care is equitable, accessible, and of the highest quality. The following laws address distinct aspects of healthcare provision and patient rights:
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Ostomy Care01:24

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Introduction
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Improving palliative care with deep learning.

Anand Avati1, Kenneth Jung2, Stephanie Harman3

  • 1Department of Computer Science, Stanford University, Stanford, CA, USA. avati@cs.stanford.edu.

BMC Medical Informatics and Decision Making
|December 13, 2018
PubMed
Summary
This summary is machine-generated.

Machine learning models predict patient mortality using electronic health records (EHR) to identify those needing palliative care. This improves access and proactive care for patients nearing end-of-life.

Keywords:
Deep learningElectronic health recordsInterpretationPalliative care

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Area of Science:

  • Healthcare quality improvement
  • Machine learning in medicine
  • Palliative care research

Background:

  • Access to palliative care is crucial but limited by physician prognosis overestimation and staff shortages.
  • Treatment inertia can lead to care misalignments with patient end-of-life wishes.
  • Improving palliative care access is a key healthcare quality metric.

Purpose of the Study:

  • To leverage machine learning and EHR data to proactively identify patients eligible for palliative care.
  • To overcome challenges in palliative care access and improve end-of-life care alignment.

Main Methods:

  • A Deep Neural Network model was trained on historical Electronic Health Record (EHR) data.
  • The model predicts patient mortality within a 3-12 month timeframe.
  • This prediction serves as a proxy for identifying patients who would benefit from palliative care.

Main Results:

  • The algorithm automatically screens daily EHR data for patients predicted to have increased mortality risk.
  • The palliative care team receives automatic notifications for patients identified by the model.
  • A novel decision interpretation technique provides explanations for the model's predictions.

Conclusions:

  • Automated screening via machine learning reduces manual chart review burden for palliative care teams.
  • This enables a proactive approach to patient outreach, moving beyond traditional referral systems.
  • The system facilitates timely palliative care interventions, improving patient outcomes.