AI-Driven Personalization of End-of-Life Care for the Elderly
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-based Software for Personalized End-of-Life Care.
- Who it may be relevant to
- Registry conditions: Artificial Intelligence (AI), Dementia, Palliative Care. Basic parameters: from 60 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
"Evaluating the Effectiveness of an AI-Based Software in Personalizing High-Quality End-of-Life Care for the Elderly: A Randomized Controlled Clinical Trial"
Overview
This study aims to evaluate the effectiveness of an artificial intelligence (AI)-based software in personalizing high-quality end-of-life care for elderly patients. As the elderly population grows, providing tailored and quality care during the final stages of life becomes increasingly important. This AI software continuously monitors vital signs and behaviors through wearable sensors, offers smart medication reminders, alerts the care team to potential risks, and provides personalized care plans along with psychological and social support. The study is designed as a randomized controlled trial comparing two groups: one receiving standard end-of-life care and the other using the AI software. Key outcomes include improving quality of life, reducing adverse events like falls and emergency hospitalizations, increasing patient and family satisfaction, improving medication management, and reducing caregiver burden. Data will be collected over six months to assess these effects. The results will help determine whether AI technology can enhance end-of-life care for seniors and support families and healthcare providers.
Detailed description
This section provides a comprehensive overview of the study design, objectives, population, interventions, and methods without repeating information already included in other sections of the record. It elaborates on the rationale for using AI-based software to personalize end-of-life care for elderly patients, details the randomized controlled trial setup, explains inclusion and exclusion criteria, intervention specifics, outcome measures, data collection methods, and planned statistical analyses. This description ensures a clear understanding of the study's scope and methodology.
Interventions
- Behavioral AI-based Software for Personalized End-of-Life Care
This intervention involves the use of an AI-powered software system designed to personalize end-of-life care for elderly patients. The software continuously monitors vital signs and behavior through wearable sensors, sends smart medication reminders, issues preventive alerts to care providers, delivers customized care plans, and provides psychological and social support through communication features.
Primary outcome measures
- Change in Quality of Life Score [Time frame: Up to 3 months after intervention start]
Eligibility criteria
Inclusion criteria
Age ≥ 60 years
Clinical diagnosis of being in the end-of-life stage, based on criteria such as the Karnofsky Performance Scale or Palliative Performance Scale
Informed consent obtained from the participant or legal representative
Ability to use technology independently or with support provided by the research team
Access to necessary equipment for the intervention (e.g., wearable sensors, smartphone/tablet)
Exclusion criteria
Presence of severe cognitive impairment preventing software use
Voluntary withdrawal from the study at any stage
Critical medical deterioration or death during the study
Poor adherence or insufficient engagement with the intervention software in the intervention group
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Prevention
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07027618 · BUMS