Shaping a Future Healthcare Ecosystem with AI and Analytics

What will it take to create a truly connected, data-driven, and intelligent healthcare system? Building a future healthcare ecosystem requires more than technology; it demands reimagining how care is delivered, how data is harnessed, and how insights are acted upon. In this blog, we share key insights from a recent session at the Indian Institute of Foreign Trade, where Indegene’s Vice President of Analytics, Jitesh Shah explained how digital adoption is changing the way healthcare works . From stronger use of data to the rise of AI-powered healthcare, the session reveals how these advances are shaping better experiences for patients and providers alike.

Current state of digital adoption in the healthcare industry

Automation and AI are opening new possibilities across industries. In healthcare, adoption has been more gradual and there are still hurdles to overcome. The chart below shows where the industry stands as it works toward building a future healthcare ecosystem¹. (illustrated in Figure 1).

Multiple factors contribute to this slower pace, including legacy systems, complex regulations, fragmented data, and the scale of change required as illustrated in Figure 2.

While the pace of digital adoption has been slow over the years, things are picking up. Healthcare organizations are ramping up their AI investments² (Figure 3), and a lot of this is driven by the demand for modern healthcare systems and online engagements by customers.

Investments in healthcare are moving beyond telehealth platforms to areas such as virtual patient monitoring platforms, digital therapeutics, surgical robots, AI-based drug discovery, patient identification, recruitment, and monitoring of clinical trials – all of which play an important role in shaping a more connected, AI-powered healthcare model. These investments contribute to:

However, at the very core of this transformation lies a foundational piece - data. Data is the bedrock of intelligence upon which healthcare business processes and analytical layers are built. Without a solid data foundation, AI machines will struggle to deliver intelligent and trustworthy insights and recommendations.

The volume of healthcare data trickling in every day is rising exponentially. A single patient generates nearly 80 megabytes of data each year in imaging and electronic medical records ³. This massive trove of data has obvious clinical, financial, and operational value as it allows organizations to unearth golden insights hidden deep beneath the data surface.

"To let all this data paint a larger picture, healthcare organizations are leveraging modern data platforms, such as a data lake house, to connect large volumes of patient or consumer data from disparate sources, unlocking a comprehensive, 360° view of their key customers," Jitesh said, adding that these platforms are fuelled by powerful data discovery, management, quality assurance, governance, and security processes. This helps organizations strengthen their data pipeline orchestrations, Data Operations (DataOps), and Machine Learning Operations (MLOps) at scale.

Healthcare organizations need a holistic data management platform that spans their entire enterprise and serves a number of critical functions, including ingestion of all data streams and sources such as electronic health records, standardization of data through industry code-based classification, standardization of frameworks to send patient data to upstream and downstream workflows, and more.

"By serving these critical functions, healthcare data platforms help you maintain the consistency in data management processes. It allows you to effectively take control of your entire patient and consumer data landscape and gives you access to actionable insights in real-time – all through a single gateway." Jitesh said.

Modern data platforms are central to enabling AI-powered future healthcare ecosystem. They ensure interoperability, standardization, and quality across diverse healthcare data sources. The illustration below highlights the core building blocks of robust healthcare data platforms.

The following examples show how data, analytics, and AI are being applied to solve real challenges and create value across healthcare.

ML and advanced analytics, combined with data, are helping organizations run targeted clinical trials, transform drug discovery, and control treatment outcomes effectively.

"Take chronic diseases as an example. They do not follow a linear progression. They may get worse, better, worse again, better again, and so on. Understanding what influences the progression, predicting when the progression is going to occur, and deciding what interventions can be made to impede the progression are essential to determining effective treatment strategies. The use of data, ML, and advanced analytics models here are crucial as they assist organizations through the faster diagnosis of diseases, early detection of adverse events, prevention of medical errors, and more," Jitesh said.

Here are a few more examples of how AI/ML and analytics are driving positive healthcare outcomes:

In addition, NLP is also being used to extract information on patient treatment patterns to identify drug switching or discontinuation. This is done by extracting numerical data such as lab values, dosage information, and patient-specific details such as disease history, demographics, social factors, and lifestyle. NLP is also used in Automatic Speech Recognition (ASR) medical systems where virtual and in-person patient/clinician interactions are captured and injected directly into medical charts, enabling real-time medical transcription.