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Feasibility Study for Improving the Relevance of Diagnostic Proposals for an Artificial Intelligence Software in the Elderly Population. (Intel@med-F)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details. Identifier: NCT04242043
Recruitment Status : Recruiting
First Posted : January 27, 2020
Last Update Posted : February 24, 2020
Information provided by (Responsible Party):
University Hospital, Limoges

Brief Summary:

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

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Study Type : Observational
Estimated Enrollment : 50 participants
Observational Model: Case-Only
Time Perspective: Prospective
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

Group/Cohort Intervention/treatment
AI 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.

Primary Outcome Measures :
  1. AI diagnostic proposals [ Time Frame: 1 month ]
    Number of AI diagnostic proposals in adequacy with the medical diagnosis in the month of the study

Secondary Outcome Measures :
  1. 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

  2. satisfaction survey [ Time Frame: 1 month ]
    Analysis of the satisfaction survey filled in by the users (NHs nurses, participants, NHs Directors and geriatricians)

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

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Ages Eligible for Study:   65 Years and older   (Older Adult)
Sexes Eligible for Study:   All
Sampling Method:   Non-Probability Sample
Study Population
Resident in nursing home presenting a health problem that requires the call of his/her attending physician.

Inclusion Criteria:

  • Patient aged 65 or over
  • Patient living in one of the two NHs tests
  • Patient with a functional complaint or abnormal symptoms involving the call of a physician
  • Patient or his legal representative who has not expressed his opposition to the collection of his medical and personal data
  • Patient affiliated to social security

Exclusion Criteria:

  • End-of-life patient
  • Patient with a clear vital emergency according to the physician
  • Chronic aphasic patient

Information from the National Library of Medicine

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 identifier (NCT number): NCT04242043

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Contact: TCHALLA Achille, MD/PhD 555058626 ext +33

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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         
Sponsors and Collaborators
University Hospital, Limoges

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Responsible Party: University Hospital, Limoges Identifier: NCT04242043    
Other Study ID Numbers: 87RI19_0026 (Intel@med-Faisa)
First Posted: January 27, 2020    Key Record Dates
Last Update Posted: February 24, 2020
Last Verified: February 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by University Hospital, Limoges:
Artificial Intelligence
diagnostic proposals elderly