Improving Dementia Screening in India with Telemedicine and Artificial Intelligence: A Scalable Solution to Address Clinical Gaps in Care

Abstract

AbstractBackgroundOver 4.4 million people live with Alzheimer’s and related dementia (ADRD) in India. There is significant clinical service gap of 90% and only 1 in 10 patients affected with ADRD in India receives screening, diagnosis, treatment, and care. Combining telemedicine with an “Expert system,” can help evaluate, screen, and triage patients with ADRD and reduce the time to diagnosis. We demonstrate early results from a feasibility study that uses state‐of‐the‐art Artificial Intelligence (AI), data analytics, and a HIPAA‐compliant Telehealth platform to screen patients over 65 years in India for ADRD.MethodWe integrated AI‐based platform and a standardized cognitive test into the telemedicine‐based primary‐care workflow. A primary care provider (PCP) screened every consenting patient aged 65 years and older on a telemedicine platform in India. The screening used an algorithm‐based intake tool, baseline Mini‐Mental Status Exam (MMSE), and clinical judgment of the PCP. We compared the final screening assessment generated by AI with the MMSE evaluation and clinical judgment of PCP for nine patients.ResultsNine patients (4 females, 5 males) in age group of 65‐90 years consented and enrolled in this initial study. Using the intake data as inputs, the AI model predicted MCI in 5 out of 9 patients (55%) and mild ADRD in 4 out of 9 patients (44%). The clinical severity from the tele‐encounter was rated as mild ADRD in 3 out of 9 patients (33%) and no ADRD/Mild Cognitive Impairment in 6 out of 9 patients (66%). Neuropsychological testing with the MMSE demonstrated scores from 24‐30 in 7 out of 9 patients (77%) and <24 in 2 out of 9 patients (22%). The AI model accurately predicted the severity in 6 out of 9 patients (66.6%) of cases.ConclusionIt is feasible to combine AI and telemedicine to improve ADRD screening and clinical service gaps for healthcare delivery in India. AI severity models also demonstrated adequate predictability to triage patients who have cognitive impairment using digital intake data. The accuracy of the AI prediction models may be further improved with supplemental electronic medical data and translating the intake to a local language.