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Documentation of Nursing Diagnosis01:10

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Recognising dying: Will artificial intelligence (AI) help improve clinical accuracy?

Eleni Lester1, Simon Tavabie1, Nicola White2

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The UK faces a rising need for palliative care, highlighting the importance of early end-of-life recognition. Artificial intelligence (AI) may aid prognosis prediction but requires careful integration to maintain compassionate, person-centred care.

Keywords:
Artificial intelligenceDyingHuman-AI collaborationPalliative carePrognosisUncertainty

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Area of Science:

  • Palliative Care
  • Medical Informatics
  • Health Services Research

Background:

  • The UK anticipates a significant increase in palliative care demand, necessitating improved methods for identifying patients nearing the end of life.
  • Existing clinical prognostic tools exhibit limitations in accuracy and are susceptible to various biases.
  • Artificial intelligence (AI) presents a potential solution for enhancing the prediction of patient deterioration and mortality.

Purpose of the Study:

  • To explore the potential of AI in improving the prediction of end-of-life trajectories within palliative care.
  • To identify key considerations for the ethical and effective integration of AI into palliative care services.
  • To emphasize the irreplaceable human element in recognizing and responding to the needs of dying patients.

Main Methods:

  • Review of current literature on AI applications in palliative care prognosis.
  • Analysis of challenges and opportunities in implementing AI for end-of-life prediction.
  • Discussion of ethical frameworks for human-AI collaboration in clinical settings.

Main Results:

  • AI demonstrates promise in enhancing the accuracy of predicting patient deterioration and mortality.
  • Early AI integration may support timely interventions and advance care planning.
  • Critical factors for AI implementation include data integrity, accountability, and mitigating health inequities.

Conclusions:

  • AI can augment clinical judgment in palliative care but cannot replace the human aspects of care.
  • Successful AI adoption hinges on a collaborative approach, ensuring technology supports rather than supplants compassionate, person-centred care.
  • Ethical considerations and a focus on equity are paramount for responsible AI implementation in end-of-life care.