Feasibility Study for Improving the Relevance of Diagnostic Proposals for an Artificial Intelligence Software in the Elderly Population. (Intel@med-F)
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|ClinicalTrials.gov Identifier: NCT04242043|
Recruitment Status : Recruiting
First Posted : January 27, 2020
Last Update Posted : February 24, 2020
The ageing of the population is accompanied by the problem of chronic pathologies and sometimes heavy dependence, requiring admission to Nursing Homes (NHs). Approximately 660,000 people currently live in NHs in France. One out of 3 NHs does not have a coordinating doctor, even though the law requires it, and access to care in these NHs is very unequal nationally and especially in the Limousin and Dordogne regions. Some Hospices may find themselves in a situation where there is no coordinating doctor and difficult access to General Practitioners (GPs) visiting a large area.
This inequality of access to care results in a difference in care that can go as far as a loss of opportunity for residents who are immediately transferred to the emergency department (ED) with a risk of iatrogeny or delirium once in the ED or a risk of inappropriate hospitalization.
Residents are hospitalized:
- when the latter could have been avoided because the health care team, not knowing what attitude to adopt, prioritized hospitalization
- Late because the resident waited for the attending physician to come, which resulted in a worsening of symptoms.
The arrival of Artificial Intelligence (AI) is an opportunity to find new models of care organization that can mitigate medical desertification but also develop advanced practices in gerontology. For example, nurses will be able to intervene at a first level for early detection, better triage and early management of certain pathologies.
The "MEDVIR society" AI, developed by a French company, is a medical decision support system with Artificial Intelligence and offers pre-diagnosis based on the information collected (medical and surgical history, concomitant treatments and symptoms). MEDVIR is a diagnostic aid tool and does not replace the doctor who remains at the end of the chain, the final decision-maker.
Before research is conducted to integrate this technology into routine care, it is important to validate the diagnostic relevance of AI in the elderly, as it has been validated in the general population.
This pilot feasibility study will then enable us to methodologically dimension a future project to evaluate the efficiency of this new care system in the management of elderly patients in medical deserts in France.
|Condition or disease||Intervention/treatment|
|Artificial Intelligence Geriatrics Diagnostic Proposals Elderly||Diagnostic Test: intel@med-feasibility|
|Study Type :||Observational|
|Estimated Enrollment :||50 participants|
|Official Title:||Feasibility Study for Improving the Relevance of Diagnostic Proposals for an Artificial Intelligence Software in the Elderly Population.|
|Actual Study Start Date :||December 21, 2019|
|Estimated Primary Completion Date :||March 21, 2020|
|Estimated Study Completion Date :||March 21, 2020|
Diagnostic Test: intel@med-feasibility
Initial evaluation by the Nurse using the AI tool which enters into MEDVIR the patient's symptoms or functional complaints and comorbidities and is complemented by a telemedicine solution for data transmission to the remote tele-expert physician located in a regulatory center or health center. The remote doctor (a geriatrician from "Prevention Care for Elderly Unit (UPSAV) platform" of the Clinical Gerontology Division of the Limoges University Hospital) analyses the data collected by the Nurse and establishes a symptom severity criterion and a diagnosis with the help of the AI technology.
Both the geriatrician and the NHs nurse are not aware of the proposals made by AI.
The geriatrician can nevertheless initiate a visit to the resident's place of residence in order to verify the information necessary to establish the diagnosis and thus improve the relevance of the algorithm. For the resident, this study doesn't interfere with the usual care.
- AI diagnostic proposals [ Time Frame: 1 month ]Number of AI diagnostic proposals in adequacy with the medical diagnosis in the month of the study
- severity diagnoses [ Time Frame: 1 month ]Number of severity diagnoses proposed by the AI solution versus medical diagnosis of the remote geriatrician over the month of the study
- satisfaction survey [ Time Frame: 1 month ]Analysis of the satisfaction survey filled in by the users (NHs nurses, participants, NHs Directors and geriatricians)
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04242043
|Contact: TCHALLA Achille, MD/PhD||555058626 ext +email@example.com|
|Ehpad Des Bayles||Recruiting|
|Isle, France, 87170|
|Contact: VIDAL Maxime, MD|
|Principal Investigator: VIDAL Maxime, MD|
|Ehpad Le Roussillon||Recruiting|
|Limoges, France, 87000|
|Contact: CHABUT Eric, MD|
|Principal Investigator: CHABUT Eric, MD|