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Published on: Jul 10, 2021
1 minute read
By: Raj Dwivedi

The Hybrid Way - Telehealth !

Covid 19 has transformed our lives in so many ways. The reality is that most likely, the pandemic will not end suddenly, and we’ll be dealing with some version of it for years to come.

As we slowly adapt to our new normal, we’ll embrace some changes and reset our lives in many ways. We have noticed some of the shifts in our personal lives and the world around us.

Health care has gone through a complete digital revolution: As the pandemic intensified, more and more providers have switched from in-person visits to telehealth appointments over video chat. Globally in April 2020, telemedicine services increased by more than 35,00 percent, and nearly half of every consultation visit was delivered virtually. Of course, this was a welcome change. Virtual visits have prevented excess COVID-19 exposure and saved others—especially those in rural areas—from a long commute to the doctor’s office.

The increase in remote and telehealth was driven by general fear of infection. Lot of telehealth consultation took place in areas of substance abuse recovery and mental health as well.

What was meant to be a temporary solution worked surprisingly well, at least once they tackled tech barriers? Some clients relied on their own devices before the dedicated platforms and purpose-built suites were launched. For people without smartphones or data plans to support these services, virtual care was out of reach before the suites arrived.

Post-pandemic many clients will prefer to return to face-to-face services. Yet in most cases, virtual visits would make it easier for his clients to access health care. For instance, lack of transportation could stand in the way of someone making their appointment, but that concern goes away with virtual meetings.

Appointment cancellations will be reduced. More Hybrid models will replace the existing singular OPD model. OPD will have two elements and one will be remote and then followed by in person. What happens in an actual scenario that a typical non-life-threatening issue comes up at work or home which needs medical attention. The appointment with the doctor and time off at work combined defers the visit most of the time to next week. This delay has its own downside and risk. In some instances, the problem sorts out by home remedies. Telehealth solves this problem and takes care of issues that never need to be treated and looked at in-person saving everyone financially.

We have watched this play out during the early months of the pandemic. The pandemic has flipped the switch for patients who had grown accustomed to traditional doctors’ visits. But now that many people have tried out virtual care, some are reluctant to give it up. This will force providers to change the model and be ready to service the hybrid way.

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Machine Learning in Healthcare India: A New Era of Predictive and Personalized Care

Machine learning is driving one of the biggest transformations in Indian healthcare. Machine learning in healthcare India is improving diagnostics, predicting diseases early, and enabling personalized treatment plans based on large volumes of medical data. India’s enormous population, diverse health patterns, and rising burden of lifestyle diseases make ML an essential technology for improving care outcomes.

SecondMedic integrates machine learning across diagnostics, risk scoring, preventive care, and remote monitoring to create intelligent, data-driven healthcare experiences.

Why Machine Learning Is Crucial for India’s Healthcare

India faces major challenges: increasing chronic diseases, low doctor-to-patient ratio, and gaps in early diagnosis. Machine learning helps overcome these limitations through automated analysis and predictive insights.

ML supports:

Accurate disease prediction
Faster diagnosis
Personalized treatment
Proactive health management
Population-level insights

These benefits significantly improve care outcomes.

Machine Learning in Diagnostics

ML excels at interpreting complex medical data faster than traditional methods.

ML improves diagnostics by:

Identifying abnormal patterns
Analyzing imaging scans
Interpreting lab values
Comparing historical trends
Supporting clinical decisions

This reduces misdiagnosis and saves time.

Predictive Healthcare with Machine Learning

Predictive analytics is one of the most powerful ML applications.

ML predicts risks for:

Heart disease
Diabetes
Kidney disorders
Thyroid imbalances
Mental health issues
Respiratory disorders

SecondMedic provides predictive scoring for early detection.

Personalized Treatment Planning

Machine learning tailors treatment to individual needs.

ML personalizes care based on:

Age and genetics
Lifestyle patterns
Vitals and wearable data
Sleep and stress levels
Previous medical history

This ensures more accurate and effective treatment.

ML in Remote Patient Monitoring

With the rise of home healthcare, ML analyzes continuous vitals data.

ML monitors:

Heart rate
Blood oxygen
Blood sugar
Blood pressure
Sleep cycles

AI-generated alerts support timely intervention.

ML in Medical Imaging

ML enhances imaging interpretation by detecting subtle visual patterns.

Applications include:

Lung infections
Cancer markers
Cardiac abnormalities
Brain lesions
Kidney anomalies

This improves radiology accuracy and speed.

ML for Population Health in India

ML identifies health trends at a large scale, helping policymakers and hospitals plan resources.

ML provides:

Outbreak prediction
Disease burden patterns
Community health insights
Regional risk mapping

These tools help improve national healthcare planning.

Challenges in ML Healthcare Adoption

While ML is powerful, challenges include:

Data quality issues
Need for clinical validation
Privacy concerns
Infrastructure limitations
Need for skilled professionals

SecondMedic follows ethical ML standards and ensures secure data practices.

Future of Machine Learning in Indian Healthcare

Upcoming innovations include:

Deep learning diagnostics
Digital health twins
Fully AI-driven preventive dashboards
ML-based robotic treatments
Genomic ML predictions

SecondMedic is committed to building future-ready ML healthcare solutions.

Conclusion

Machine learning in healthcare India is transforming medical care through predictive analytics, personalized treatment, and early disease detection. SecondMedic uses machine learning across its digital ecosystem to deliver accurate, efficient, and patient-centered care.

To explore ML-powered healthcare tools, visit www.secondmedic.com

References

NITI Aayog – AI & ML in Indian Healthcare
WHO – Machine Learning in Clinical Practice
ICMR – India Chronic Disease Data
IMARC – AI & ML Healthcare India
FICCI – Emerging Health Technologies India

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