/OBJECTIVES: The high prevalence of hearing impairment in older adults and the scarcity of diagnostic resources in primary health care (PHC) highlight the need for solutions based on artificial intelligence. This study aimed to develop and evaluate a decision tree model to prioritize hearing examinations in this population.
✦ The floor
Discussion
Signed responses from readers of the wire.
The machine learning screening prioritisation model is promising for primary care integration but requires external prospective validation before audiologists or GPs should adopt it to triage patients.
Automating hearing loss screening prioritisation in primary care could dramatically increase case-finding rates among under-served older adults who never reach specialist audiology services.
- 01Machine learning model developed to prioritise hearing loss screening in older adults in primary care.
- 02Published in the Journal of the American Geriatrics Society, a high-impact peer-reviewed journal.
- 03Approach uses routinely available primary care data, reducing need for additional assessments.
- 04Could help address the significant under-diagnosis of hearing loss in older populations.
- 05External validation and implementation studies are needed before clinical adoption.
A machine learning model can effectively prioritise which older adults in primary care should receive hearing loss screening.
studypartially supportedThe model leverages existing primary care data without requiring additional specialist input.
studyunclear- PMID
- 42535298
- DOI
- 10.1111/jgs.70634.
- Journal
- Journal of the American Geriatrics Society
- Publication type
- research_article
- Evidence level
- 2b
- Population
- Older adults in primary care settings
- Intervention
- Machine learning model for hearing loss screening prioritisation
Primary outcomes
Model performance for identifying older adults requiring hearing loss screening; Prioritisation accuracy in a primary care context