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Published on: May 17, 2020
3 minute read
By: Dr Rajan Choudhary

Contact Tracing During Covid Times

Testing is quickly becoming a statistic of national pride. Countries are clamoring to test more and more people and increase accessibility for the population to receive testing. We will cover testing, its aims, and its future in a separate blog. Testing people is only half the story. It gives us information on who is infected, but to minimize the spread of infection other measures must be introduced alongside it. Contact tracing is one of these measures.

WHAT IS CONTACT TRACING

Most people who fall ill or test positive for COVID can spread the virus 2-3 days before getting the first symptoms. They can unintentionally infect the people they meet in public or work with. If infected, these people can also spread the infection without knowing it, and this leads to a rapid spread of COVID in the community, as we saw at the start of this pandemic.

Lockdown has been very good at reducing R0 (rate of infection spread), but this is done crudely by physically keeping people separated in their own homes. As R0 reduces and lockdown restrictions ease, this will no longer be feasible. By aggressively testing the population we can identify people who may be infected and instruct them and their household to remain in lockdown.

Contact tracing aims to identify the people who have been in close proximity to a person who has tested positive. This is traditionally done by questioning the infected person about their recent travel, people they met, where they work, and so on. These contacts are then instructed to isolate as well and can be tested to confirm infection. By preventing these potentially infected patients from spreading the infection, the R0 decreases and the second peak in infections is prevented.

This is not a new concept and is used often for communicable diseases such as tuberculosis or sexually transmitted infections. It has also been used in previous pandemics including 2003 SARS outbreak. In 2014 Liberia experienced one of the largest contact tracing efforts in history, with 25,000 people identified annually. Similarly in the US 29,000 people were monitored by state and local health departments after returning from West Africa, and this laid the groundwork for future COVID-19 contact tracing efforts.

The WHO has laid out guidelines for identifying potential contacts, including:

Being within 1 metre of a COVID-19 case for >15 minutes;
Direct physical contact with a COVID-19 case;
Providing direct care for patients with COVID-19 disease without using proper personal protective equipment (PPE);

COVID TRACING

Today countries have updated their methodology for contact tracing, utilizing technology and smartphones to increase the accuracy and volume of data available to public health officials.

South Korea had contact tracing plans in place due to the MERS epidemic in 2015, and this was built upon for COVID. Contact tracing utilizes smartphone GPS data, credit card transaction records, and surveillance camera footage. At Seoul’s Incheon International Airport, there are walkthrough facilities to test people with symptoms of COVID and follow up those without symptoms in 3 days. New arrivals also have to download a government smartphone app to track their location and provide info on symptoms.

Singapore’s mobile app also utilizes Bluetooth data to determine devices that have been in close proximity to the infected persons’ phone and tracking these devices can identify potential contacts. It has over 1.1 million users, just under one-fifth of the country’s population.

Apple and Google together own almost the entire mobile operating system market with their respective iOS and Android platforms. They have worked together to create a framework that can allow governments to efficiently create and utilize contact tracing apps. Their efforts use Bluetooth Low Energy beacons. Nearby devices that wirelessly “shake hands” create randomly generated codes without any user-identifiable details (name, location etc). Based off Bluetooth data it can provide an estimate on distance and length of contact. If one of the devices is identified as belonging to an infected person, all devices that have been in close proximity are alerted.

LIMITATIONS

There are limitations present, both with the methodology used and with contact tracing itself. For one, it is quite a laborious and expensive process, and works well when there are low levels of infection in the community. During pandemics, it can quickly overwhelm the contact tracing departments if they are not adequately prepared and provide excessive information that cannot be utilized effectively. It is also not useful during a lockdown, as the lockdown itself artificially lowers infectivity. Instead, it must be implemented once the peak has passed.

Effective contact tracing is also expensive and labor-intensive. The state of Massachusetts has budgeted $44 million for its contact tracing program with 2,000 tracers. If implemented nationally it would cost the US an estimated $3.6 billion and require as many as 300,000 tracers. For app-based contact tracing to work around 80% of the population needs to have the app installed, and we have seen even small countries like Singapore struggle to push past 20%.

Finally, a major issue is a privacy. Poorly coded apps with little transparency can fail to anonymize vital personally identifiable data. This may be accessed by third parties or sold on to others, putting the privacy of millions of people at risk. There are also concerns by privacy watchdogs on the unfettered access by governments to this data, and whether this can be used in an oppressive manor.

If implemented correctly contact tracing has the potential to have a significant impact on reducing infectivity and allowing states and countries to open up their economies quickly and safely. Of course, this depends on the widespread use of contact tracing, and people abiding by government suggestions. Sadly, in the news, it is now too common to see resistance to basic measures such as use of masks in public, so we will have to see whether contact tracing will have any better success.

Dr Rajan Choudhary, UK, Chief Product Officer, Second Medic Inc

www.secondmedic.com

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How Artificial Intelligence Is Transforming Patient Care in India

As a clinician working closely with patients across urban clinics and remote teleconsultation setups, I have seen firsthand how delayed diagnosis, fragmented follow-up, and specialist shortages affect outcomes in India. Artificial intelligence is not a futuristic concept in Indian healthcare anymore. It is actively reshaping how we diagnose diseases, monitor patients, and prevent complications.

AI, when used responsibly under clinical supervision, is becoming a critical support system for doctors and a powerful safety net for patients navigating a complex healthcare ecosystem.

Why India’s Healthcare System Needs AI

India’s healthcare challenges are deeply structural. A large population burdened by lifestyle diseases, combined with uneven access to medical expertise, creates gaps that traditional systems struggle to bridge.

In daily practice, we increasingly see patients presenting late with diabetes, hypertension, heart disease, or cancer. Many ask a simple but important question: why was this not detected earlier? The answer often lies in limited screening, overloaded clinicians, and lack of continuous monitoring.

Chronic conditions dominating Indian clinics today include:

Diabetes affecting over 100 million individuals.
Hypertension rising even among young adults.
Cardiovascular disease driven by late detection.
Increasing cancer incidence with delayed diagnosis.

AI matters here because it supports earlier identification of risk patterns, reduces diagnostic delays, and allows clinicians to focus on decision-making rather than data overload.

How AI Is Changing Medical Diagnosis

One common concern patients raise during consultations is whether AI can truly diagnose diseases accurately. In practice, AI does not replace a doctor. It acts as a high-speed analytical assistant.

AI in Imaging and Diagnostics

AI systems can rapidly analyse:

X-rays and CT scans.
MRI images.
Mammograms.
Pathology slides.
Cardiac and neurological imaging.

These tools flag abnormalities within seconds, allowing doctors to prioritise critical findings. Clinical studies published in peer-reviewed journals have shown that AI models can match specialist-level accuracy for specific imaging tasks when used correctly.

From a physician’s perspective, the real benefit is not speed alone. It is consistency. AI reduces the risk of missed findings during high-volume diagnostic workflows, especially in resource-constrained settings.

Can AI Monitor Patients Outside Hospitals

Patients managing chronic illness often ask whether technology can help them avoid repeated hospital visits. AI-enabled remote monitoring is one of the most meaningful advances in this area.

AI-Supported Remote Patient Monitoring

AI continuously evaluates trends in:

Blood pressure.
Heart rate variability.
Blood glucose patterns.
Oxygen saturation.
Physical activity and sleep quality.

Rather than reacting to a single abnormal value, AI identifies worsening trends over time. Clinically, this allows early intervention before complications escalate.

Evidence from global health system studies shows that continuous monitoring can significantly reduce avoidable hospital admissions, particularly for diabetes, heart disease, and elderly patients.

Using AI to Predict and Prevent Chronic Diseases

Preventive healthcare remains underdeveloped in India. Most patients seek care after symptoms appear. AI helps shift this model.

By analysing medical history, lifestyle habits, vitals, and environmental factors, predictive models can estimate:

Future heart attack risk.
Progression of diabetes.
Decline in kidney function.
Stroke probability.
Asthma exacerbation triggers.

Patients often ask if AI can really prevent disease. Prevention here means early warnings. When risk patterns are detected early, doctors can adjust treatment plans, recommend lifestyle changes, and prevent irreversible damage.

Personalised Treatment in a Diverse Indian Population

Indian patients differ widely in genetics, diet, stress patterns, and cultural habits. Standardised treatment protocols often fall short.

AI supports personalised care by analysing:

Medication responses.
Dietary intake.
Blood markers.
Sleep and stress trends.
Coexisting medical conditions.

For example:

In diabetes care, AI helps personalise carbohydrate distribution and medication timing.
In hypertension, it identifies sodium sensitivity and stress-related spikes.
In hormonal conditions like PCOS, it aligns nutrition and activity with cycle patterns.

From a clinical standpoint, personalised insights improve adherence and reduce relapse rates.

AI-Enabled Telemedicine and Smarter Consultations

Telemedicine has become an essential part of care delivery in India. Patients frequently ask whether online consultations are as effective as in-person visits.

AI enhances telemedicine by:

Structuring symptom inputs before consultations.
Routing patients to the appropriate specialist.
Generating concise medical summaries for doctors.
Supporting follow-up reminders and medication adherence checks.

When used correctly, AI reduces diagnostic delays and improves consultation efficiency without compromising safety.

Expanding Healthcare Access Beyond Cities

A major question in public health is whether AI can truly improve rural healthcare access. In practice, it already is.

AI enables:

Remote diagnostics supported by portable devices.
Virtual specialist consultations for rural clinics.
Smartphone-based imaging and screening tools.
AI-guided triage in underserved regions.

By reducing dependence on physical proximity to specialists, AI helps bridge longstanding geographical barriers in India’s healthcare system.

Safety, Ethics, and the Role of Doctors in AI Care

Patients rightly express concern about safety, privacy, and over-reliance on technology. These concerns are valid.

Responsible AI use in healthcare requires:

Transparent algorithms.
Explicit patient consent.
High-quality, verified medical datasets.
Strict data privacy safeguards.
Continuous clinical supervision.

In ethical practice, AI outputs never replace medical judgment. Doctors remain accountable for decisions. Human-in-the-loop verification is essential to ensure patient safety and trust.

What This Transformation Means for Indian Patients

Artificial intelligence is fundamentally changing patient care in India by making healthcare more proactive, more precise, and more accessible. From early diagnosis to personalised treatment and continuous monitoring, AI empowers both patients and clinicians with data-backed clarity.

SecondMedic’s patient-first approach integrates AI as a clinical support system, not a replacement for doctors. By combining medical expertise with digital intelligence, the goal remains simple: better outcomes, earlier intervention, and care that adapts to each patient’s real-world needs.

As clinicians, our responsibility is to ensure that technology serves patients ethically and effectively. When used with care and oversight, AI has the potential to redefine healthcare delivery across India in a way that is inclusive, preventive, and sustainable.

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