Strengthening National Digital Health Missions with Data and Technology-BusinessLine

With the wave of a pandemic revival, our belief in the benefits and positive externalities of a robust medical infrastructure was reaffirmed. The existing health policy framework is multifaceted and focuses on capacity building, strengthening primary health care, reducing the burden of non-communicable diseases, health insurance, research and investment.

A new initiative in this area, the National Digital Health Mission aims to address medical challenges using data and technology and is the first step in the right direction. The program provides a unique health ID created for all citizens and integrates it seamlessly into the health infrastructure. Several data-centric and technology-centric solutions further enhance the scope and strategy of this mission, enabling us to achieve the health outcomes we desire.

Electronic health record (EHR) creation uses a unique patient ID (health ID) to generate large amounts of data, including history of comorbidities, medical records, doctor consultations, diagnostic and procedure profiles, and prescribed medications. Collecting is a good starting point. This helps to create a 360 degree patient profile.

Machine learning algorithm

The database is anonymized and encrypted to provide analytical insights for different population groups and geographic regions. Non-supervised machine learning algorithms available in healthcare using unsupervised machine learning algorithms such as image recognition (such as recognition patterns and medical history from CT scans and X-rays) and natural language processing (such as extracting key phrases from prescriptions) Structured pool data (eg prescriptions, pharmacy bills, scans, etc.).

Metadata allows you to create a characteristic profile of a patient cohort and view longitudinal sections across different treatment lines (diabetes, cardiovascular disease, cancer, etc.). Specific characteristics of the population (characteristics such as age, region, comorbidities, etc.) can be enriched with advanced machine learning prediction algorithms to identify the exact points of care management.

Coordinated interventions lead to improved quality of care and improved health. For example, historical data from images from CT scans shows tumor progression in a particular population cohort and can be input to deep learning algorithms (such as convolutional neural networks) to predict survival between different treatment lines.

Taking this exercise to the next level includes geoprofiling to develop evidence-based policies for local health authorities. At the district level, you can unleash the potential of EHR by analyzing health / comorbidity profiles and disease burdens and providing coordinated infrastructure / capacity support at the primary health care level.

An example of data-driven community health care from the current pandemic wave is vaccine inventory management. Input metrics such as population density, age profile, and distance into spatial algorithms to optimize resources, uninterrupted vaccine supply chain, select optimal vaccination centers, and front-line healthcare engagement in different parts of the district. You can secure the adjustment of the person.

Clustering algorithms can also be used to determine the distribution and intensity of disease burden across different populations in the district to improve primary health care according to local needs. In addition, real-time dashboards (hosted in web and mobile versions) can provide community health managers with easy-to-interpret and quick visualizations to facilitate customized care.

Digital footprint

Creating a digital footprint of a citizen’s health profile avoids duplicate diagnostics and tests and allows records to be seamlessly exchanged between different care points. It also helps reduce costs and provide continuous care to patients. Healthcare can be integrated into the trusted Arogya Setu app and use its growing foundation to extend existing capabilities, facilitating wellness management and patient medical compliance.

You can integrate a variety of services into your app, including active aftercare, health news, lifestyle advocacy, searching for nearby primary / community health centers, bookings, and telemedicine. These applications are some of the solutions that real-time data and technology can bring to healthcare. Add the latest data / technology to your existing healthcare strategy as countries around the world use cutting-edge innovations to find new solutions for long-tail events like the Covid-19 pandemic. We need to stay ahead of the curve.

Data-driven analytics can help meet diverse state / county health needs and provide customized solutions. The results will help improve the health and productivity of our population, reduce the burden of their illness and provide efficient medical care, to name a few.

The author is an assistant director who is a trainee of an Indian business service provider.The view is personal

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