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NDHM & What it means to be Integration Ready

5 minutes read

The healthcare industry in India has been steadily growing at a Compound Annual Growth Rate of around 22% since 2016 and is expected to reach USD 372 billion in 2022. 

NITI Aayog released a report titled ‘Investment Opportunities in India’s Healthcare Sector’ published by PIB which states that “The Indian Healthcare market is expected to reach $190 Bn by 2020; $372 Bn by 2022 at a CAGR of 39% The digital healthcare market in India was valued at INR 116.61 Bn in 2018, and is estimated to reach INR 485.43 Bn by 2024, expanding at a compound annual growth rate (CAGR) of ~27.41% during the 2019-2024 period.” 

The expansion of private hospitals to Tier-2 and Tier-3 cities is looking like an attractive investment opportunity in the hospital segment. With respect to the pharmaceutical industry, India is likely to boost domestic manufacturing, supported by recent Government schemes under the Aatmanirbhar Bharat initiative.

Wellness tourism, under the medical value travel diaspora, has given an impetus to the rise of alternative medicine and treatment prospects. Technology, by way of innovations in Artificial Intelligence (AI), wearable technologies, and the Internet of Things, also offer multiple avenues. 

The Indian healthcare system is fast-moving towards a wellness-driven model of care delivery from an otherwise historically siloed and episodic intervention approach. This streamlining of the healthcare system creates a wealth of new opportunities for healthcare enterprises and institutions. The hospital industry in India accounts for nearly 60% of the overall health ecosystem’s revenues. The addition of new frameworks for Health ID, PHR, telemedicine, and OPD insurance will create macro-level demand beyond local in-patient catchment zones.

Traditional modes of healthcare delivery are being phased out in favor of new and disruptive models. The COVID-19 pandemic and its subsequent waves have changed consumer demand and given a big push for the need for a digital healthcare ecosystem. 

Source: Mantra Labs Whitepaper, March 2021

The National Health Stack (NHS), a digital platform with the aim to create universal health records for all Indian citizens by 2022, aims to bring both central and state health verticals under the same umbrella. 

The action plan to fulfill the creation of the NHS is laid out in the National Digital Health

Blueprint (NDHB), which also outlines the vision for Universal Health Coverage, that’s been in the pipeline for India’s underprivileged. This is where the National Digital Health Mission (NDHM) comes into the picture, as the entity responsible for the successful implementation of the aforementioned Blueprint and subsequent Health Stack. 

The blueprint recommends two building blocks namely, Personal Health Identifier (PHI), and Health Master Directories & Registries, for handling the requirements of a unique identity (much akin to Aadhar) of persons, facilities, diseases, and devices. These building blocks that India is creating for its 1.4 billion citizens are said to be equipped with an interoperability option to seamlessly access digital records.

With rapid rates of digitalization and increasing demands from connected consumers, an integrated ecosystem will allow healthcare providers to deliver value-based care and outcomes in a real-world scenario. The NDHE can potentially create over US$200 billion in economic value for the health sector, over the next 10 years, according to BCG analysis. 

The National Digital Health Blueprint (NDHB) underlines key principles which include domain perspectives namely, Universal Health Coverage, Security & Privacy, Education & Empowerment, and Inclusiveness of citizens; and the technology perspective namely, Building Blocks, Interoperability, a set of Registries as single sources of truth, Open Standards and Open APIs.

Source: Mantra Labs Whitepaper, March 2021 

How integration-ready are we? 

Most hospitals in India continue to use paper-based medical records and verbal procedures to communicate among doctors and nurses for a patient’s treatment. This causes serious implications such as lack of transparency, lack of accountability, error-prone treatment, non-integrated patient health records, difficulty to understand the past medical history, poor collaboration within a team of doctors, a higher threat to infection, and a lack of progress towards adopting AI/ML-based technologies. As the consumer is being ushered into the ‘age of experiences‘, the onus is on digital healthcare enterprises to make them more relevant, emotional, and personalized.

Source: Mantra Labs Whitepaper, March 2021

An integration engine is not only an interface engine but also a healthcare integration platform that supports the day-to-day operations of a care delivery organization. From interfaces to workflow to operational decisions, integration engines assist in modernizing the healthcare system.

Source: Mantra Labs Whitepaper, March 2021 

By preparing for integration readiness, healthcare providers can access new patient demand pools from Tier-2 and Tier-3 cities, identify insights about the health consumer’s lifecycle needs, and leverage new technologies to draw in more value from these interactions than ever before.

As a result, hospitals will be able to drive improved margins from reduced administrative costs and gain higher utilization through increased demand. 

Healthcare experiences future will include insights harnessed from data and human expertise to bring sensory value to each interaction, in other words, the integration of IX or Intelligent Experiences.

Read our detailed Digital Health whitepaper to get more insights into NDHM and what it means to be integration-ready. 

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When Data Meets the Heart: A Tale of Sentiments and Science

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Do you think technology will advance to a point where people rely on it for deeper emotional connections, perhaps even finding companionship? Just like in the movie Her, where a man falls for an AI, we all thought it was science fiction. But it seems we’re closer to that reality than we might have imagined. Now, it’s not just about crunching numbers. Technology is evolving every day, becoming so advanced that it’s learning to interpret human emotions and reactions. This is the core of sentiment analysis, where data meets emotions, and technology helps us make sense of human feelings in ways that were once only imaginable.

Is Data Science the Key to Unlocking Sentiment Analysis?

Sentiment analysis is more than just gauging emotions in text; it’s a powerful application of data science that transforms chaotic data into actionable insights. Data science deciphers human feelings hidden in reviews, tweets, and comments, enabling AI to capture not just whether sentiments are positive or negative but also the nuances of emotional expression. With the ongoing evolution in data science, sentiment analysis is moving beyond basic detection to uncover deeper emotional insights, allowing businesses to truly understand their customer’s sentiments. This capability empowers organizations to anticipate customer behavior and make informed decisions in a data-driven world.

According to Forbes, 80% of the world’s data is unstructured, like blog posts, reviews, and customer feedback. Sentiment analysis helps companies make sense of this unorganized heap using data analytics, turning it into actionable insights. Tools like Python libraries for sentiment analysis and AI models help refine this process further, offering businesses more profound insights into customer behavior.

How Does Sentiment Analysis Work?

Imagine you’ve just posted a review online: “This phone has a great camera, but the battery life is terrible.” While a human can quickly spot that you love the camera but hate the battery, AI needs to go a step further by:

  1. Text Preprocessing: Breaking the sentence down into words (tokens), removing stop words (like “the” and “has”), and normalizing the text.
  2. Natural Language Processing (NLP): This is where the AI engine learns the context of each word. It identifies if the sentiment is positive (great camera) or negative (terrible battery life).
  1. Machine Learning Models: These models classify the sentiment of the text. With more data science applications, the models become better at predicting human emotions.

Why Does Sentiment Analysis Matter?

In a world where emotions drive decisions, sentiment analysis helps businesses, governments, and even individuals make better decisions. Whether it’s reading reviews, understanding customer feedback, or gauging public opinion on social media, sentiment analysis tells us how people feel.

Beyond the Text: How Data Science Decodes Emotional Intelligence

What if Data science could detect more than just positive or negative feelings? What if it could understand sarcasm, context, and complex emotions like nostalgia or regret? The future of sentiment analysis is heading towards these intricate feelings, making it possible to “read between the lines”. With advancements in data science and machine learning, sentiment analysis is set to dive deeper into human emotions, potentially offering an unprecedented understanding of how we feel.

Real-World Applications

  • Customer Service: Have you ever left a review or complaint on a company’s Twitter? Chances are AI detected your dissatisfaction before a human even read it.
  • Healthcare: Doctors and mental health professionals are using sentiment analysis to detect early signs of depression or anxiety based on patient communication.
  • Politics: Predicting election outcomes? Analyzing public sentiment towards political candidates can be more accurate than traditional polls.

The Road Ahead: Can Data Science Fully Understand Us?

While today’s data science techniques are great at reading general sentiment from text, we’re not yet at the stage where machines can truly “understand” emotions. However, advancements in data science continue to refine how we interpret human sentiment. Techniques like sentiment mining and sentiment classifier are being used to recognize the subtle emotional cues. Perhaps one day, the power of data science will allow us to decode human emotions with such precision that it fundamentally changes the way we interact with data, bringing emotional insights into our daily lives.

Feeling curious? Explore how Mantra Labs is shaping the future with cutting-edge data science techniques and solutions that can read the world’s emotions—literally.

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