Clinical trial NCT05176054: AI Assessment and Prediction Models for Geriatric Trauma
Official title: Establishing AI Assessment and Prediction Models for Geriatric Trauma and Effects of AI Incorporated Into an Innovative Health Service Program in Geriatric Trauma Patients
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Trauma
Aging
Lead sponsor: Kaohsiung Medical University Chung-Ho Memorial Hospital
Sponsor class: OTHER
First posted: 2022-01-04
Enrollment: 418
Sex: ALL
Minimum age (years): 65
Maximum age (years): 74
Standard ages: OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: To fill up the research gaps and build up a healthcare service model for geriatric trauma patients, this two-stage study aims to (a) develop geriatric trauma Artificial Intelligence (AI) prediction models by comparing the outcomes from clinical assessment and AI joint images; and (b) examine the long-term effects of an innovative health service program in geriatric trauma patients.

Detailed description: Background: About 40% trauma patients will be over age 65 years by 2050, thus geriatric trauma constitutes an increasingly recognized problem that should be concerned. However, little is known about complicated causes of injury and long-term outcomes in older injured patients. Previous study strategies to promote health outcomes for geriatric trauma still are inconclusive. Due to the limited healthcare resources and workforce, health service for geriatric trauma care should be refined in the new era.

Expected outcomes/impacts: This study attempts to build up a modern healthcare model that may influence geriatric trauma care as well geriatric assessment. The AI prediction model can be used to quickly assess "degree of balance" for geriatric trauma patients. This study will involve a nursing student and healthcare professionals, and help them to learn how AI connects to geriatric trauma care. This would be an opportunity to cultivate potential experts for AI and healthcare. Last, the outcomes are highly associated with the government's policies related to smart health, smart healthcare, and long-term care workforce.

Eligibility criteria: Inclusion Criteria:

* admitted to the hospital due to injury first time
* able to communicate in Mandarin
* agree to participate in this study from hospital discharge to one month after hospital discharge
* patients and their primary caregivers have smart mobile phones and internet access available
* can move independently before trauma

Exclusion Criteria:

* not frequent user for smart phones
* severe cognitive impairment
* victims of hangings and poisonings

Primary outcomes:
measure: The Numeric Pain Rating Scale
timeFrame: at 1 day before hospital discharge
measure: The Numeric Pain Rating Scale
timeFrame: at 1 months after hospital discharge
measure: The Geriatric Depression Scale Short Form
timeFrame: at 1 months after hospital discharge
measure: The World Health Organization Quality of Life Questionnaire
timeFrame: at 1 months after hospital discharge
measure: The Berg Balance Scale
timeFrame: at 1 months after hospital discharge
measure: The key body points that match the items in the Berg Balance Scale
timeFrame: at 1 months after hospital discharge
Secondary outcomes:
Other outcomes:
Locations:
city: Kaohsiung City
country: Taiwan
facility: Kaohsiung Medical University
Study officials:
affiliation: Kaohsiung Medical University
name: Bih-O Lee, PHD
role: PRINCIPAL_INVESTIGATOR
References: