Nimith Agrawal, CEO, DoctCo explains that Investment in technologies like Artificial Intelligence (AI), the Internet of Medical Devices (IoMT), Machine Learning (ML), and a 360-degree approach that helps prevent, detect, treat and build can save lives from TB
Many strains resist drugs leading to chronic lung infection, the foremost cause of death among TB patients. Reports state that about 1.48 million[1] people died due to TB in 2020, and India accounted for 34 per cent of the deaths. The World Health Organization (WHO) data states that in 2020, the 30 high TB burden countries accounted for 86 per cent[2] of new TB cases. Eight countries accounted for two-thirds of this total, India leading the count.
TB has been one of the most infectious killers in the world, with thousands losing their lives each day. Global efforts have helped combat TB and save nearly 66 million[3] lives since 2000. However, the covid-19 pandemic jolted the fight against TB with the rise in death toll for the first time in over a decade.
As part of its continuing efforts and adherence to the theme, the Government of India is launched a Door-to-Door Tuberculosis Screening Initiative on World TB Day. Under this initiative, healthcare workers will visit vulnerable populations and test potentially exposed and symptomatic individuals over two to three weeks.
Since the COVID-19 pandemic, there has been a sharp drop in reporting active TB patients numbers. As per data on the Nikshay Dashboard – a government portal for TB-related information, there was a 60 per cent drop in TB reporting cases in 2020. The unique campaign targets to report TB notifications as of pre-COVID levels.
National Strategic Plan 2017-2025
The strategic pillars were integrated under National Strategic Plan 2017-2025 for moving towards TB elimination in India by 2025 – five years prior to United Nations Sustainable Developmental Goals (SDGs) of ending the TB epidemic.
Prevent: Prevention is better than cure – very crucial to fight a disease that is one of the top ten causes of death globally. BCG vaccines are one way to prevent severe forms of TB among children.
Since TB is an airborne disease, identifying high-risk areas or populations with Latent TB infections (LTBI) in asymptomatic patients can help control the spread of the infection. TB preventive treatment or TPT is prescribed to prevent TB bacteria from becoming active.
AI can help identify vulnerable locations and provide solutions by analyzing data to prevent potential outbreaks. New skin tests are researched and developed whose test results can be shared with a healthcare expert to decide the treatment of LTBI.
Detect: Early detection of the infection of LTBI and active TB is critical to control the transmission of the disease.
Chest X-rays, cough and voice-based screening are preliminary for symptomatic patients. A persistent cough that lasts for more than two weeks is the most prominent symptom of pulmonary TB; AI tools can study cough sound features indicative of the infection and classify them into healthy and TB-probable patients in real-time. A risk score indicates the infected individual’s high or low probability and helps doctors take prompt action. High probability patients can be directed to further confirmatory tests. Earlier the detection, the lesser the chances of the infection turning chronic.
Treat: TB programs are becoming more patient-centric globally through personalised healthcare. TB treatments are prolonged and require greater adherence in due course. The treatment for drug-susceptible TB lasts for a minimum of six months, and drug-resistant TB can last for over twenty-four months. Discontinuing the treatment mid-way can lead to TB recurrence and cause more extensive damage to the respiratory system. Hence, educating about the treatment and adherence is crucial among rural populations.
In 1962, The National TB Elimination Program (NTEP) was initiated as India’s response to eliminate TB in the country. As the most extensive TB control program globally, the program has matured significantly over the last six decades.
NTEP has introduced the TB Aarogya Sathi mobile app to create awareness among the regional public about the nearest diagnostic and treatment centers and screening tools in tier II and tier III cities. The app is bundled with features that help access and share digital health records, request a teleconsultation with a doctor, track benefits under various healthcare schemes like The Nikshay Poshan Yojana for TB patients.
Build: Healthtech startups are building a robust ecosystem and working in tandem with the government to provide quality healthcare in the most remote parts of the country. The healthcare sector has witnessed commendable results during the pandemic. AI-backed clinical applications helped improve the accuracy of results, pattern recognition, potential factors, thereby reducing the mortality rate.
Cognitive machine learning techniques can transform TB care by conducting GAP analysis, improving operational efficiencies, and managing the drug supply chain by providing real-time data.
Way forward
Investment in technologies like Artificial Intelligence (AI), the Internet of Medical Devices (IoMT), Machine Learning (ML), and a 360-degree approach that helps prevent, detect, treat and build can save lives.
Healthtech innovations such as telemedicine, wearable health monitors, automated radiology, and chatbots provide last-mile accessibility and personalised health care. NTEP can democratise by partnering with Healthtech startups to scale up and evaluate the progress of the public welfare programs. This is a crucial step in treating and eradicating tuberculosis from the country.