The Challenge
Tuberculosis is curable, but treatment requires adherence to a strict regimen—any lapse can substantially increase the risk of drug-resistant TB (DR-TB) and adverse outcomes.
Loss to Follow-Up (LFU)—when TB treatment is interrupted for two or more consecutive months—is an important treatment outcome. Among people diagnosed with drug-sensitive TB in India in 2021, 2.6% were lost to follow-up, while 4.2% died during treatment1. At the population level, India’s TB mortality rate was estimated at 22 deaths per 100,000 people in 2023, down from 28 per 100,000 in 20152. Together, these indicators highlight the importance of timely diagnosis, treatment continuity, and sustained patient support throughout the TB care journey.
Our AI Solution
Prediction of Adverse TB Outcomes (PATO) is an AI-powered risk stratification solution that identifies patients at higher risk of adverse outcomes (including LFU and mortality) at the onset of TB treatment, enabling prompt, targeted, and effective interventions that help lower mortality and prevent drug resistance over time.
The solution is being deployed pan-India in collaboration with the Central TB Division (CTD) under the National Tuberculosis Elimination Programme (NTEP).
How it works
01
Data collection:
Persons with TB (PwTB) are tracked from detection to completion of treatment, with detailed information — including medical history and socio-economic status — recorded on the Ni-kshay portal.
Data collection:
Persons with TB (PwTB) are tracked from detection to completion of treatment, with detailed information — including medical history and socio-economic status — recorded on the Ni-kshay portal.
02
Analysis and risk stratification
The model analyzes demographics, location, comorbidity status, and other factors to predict the likelihood of an adverse outcome such as LFU or mortality.
Analysis and risk stratification
The model analyzes demographics, location, comorbidity status, and other factors to predict the likelihood of an adverse outcome such as LFU or mortality.
03
Categorization
PATO helps healthcare providers detect and categorize individuals as high or low-risk for adverse treatment outcomes.
Categorization
PATO helps healthcare providers detect and categorize individuals as high or low-risk for adverse treatment outcomes.
04
Intervention
Healthcare workers use this information to conduct intensified follow-ups and provide additional support to identified high-risk patients.
Intervention
Healthcare workers use this information to conduct intensified follow-ups and provide additional support to identified high-risk patients.
Where It's Deployed
Deployed pan-India
Impact
PATO enables health systems to move from reactive to preventive TB care by identifying patients at higher risk of loss to follow-up or mortality early in treatment, helping focus outreach and support where they are most needed. The solution correctly identifies 69% of adverse outcomes by targeting the top 35% highest-scoring patients at Pan India level. By enabling targeted interventions and more efficient use of limited healthcare resources, PATO can support treatment continuity and contribute to India’s TB elimination goals.
Our Other Solutions
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