Employees Want to Share Data for AI-Powered Healthcare, But They’re Nervous About It

Healthtech companies often struggle with fragmentation. A patient's blood reports sit with one diagnostic chain, prescriptions with another clinic, insurance documents with a third-party administrator, and wellness records across multiple apps. 

While digital health startups initially focused on bringing these disconnected systems online, a newer generation of enterprise healthtech companies is attempting something more ambitious: building continuous, AI-powered healthcare infrastructure around employees.

But as healthcare platforms become more predictive and deeply integrated into everyday wellness, they are also running into a harder question: how much personal health data are employees willing to share, and who gets access to it?

That tension is increasingly shaping the next phase of India's digital healthcare ecosystem, now valued at an estimated $14.5 billion in 2024 and projected to reach $107 billion by 2033, growing at a CAGR of over 25%, according to Grand View Research. 

India already has over 11,000 healthtech startups, spanning teleconsultation, medtech, and health informatics, and attracted $1.13 billion in investment in 2024 alone. 

Among them is ekincare, which began as an enterprise healthcare benefits platform and now uses AI to analyse medical reports, personalise health recommendations, monitor nutrition patterns, and automate insurance workflows. The company says its systems can extract structured information from unstructured PDFs, predict health risks based on longitudinal records, and eventually reduce insurance claims processing times from days to minutes.

Yet the more intelligent these systems become, the more dependent they are on access to deeply personal health information.

"Health data is deeply personal," says Kiran Kalakuntala, Co-founder and CEO of ekincare, in conversation with AIM, responding to enterprise privacy concerns, adding that its architecture is designed around user consent, encrypted records, and anonymised employer insights.

The concern is not hypothetical. Enterprise health platforms today sit at the intersection of healthcare, insurance, workplace productivity, and AI-driven analytics. In many cases, employees upload blood tests, prescriptions, fitness records, and mental wellness information into systems subsidised or distributed by their employers.

That creates a contradiction. The same data needed to build personalised healthcare experiences can also feel invasive if users are unclear about how it is being used.

A PwC India survey found that 20% of employees are not comfortable sharing their personal data with employers, and that's before accounting for the sensitivity of health records. A separate CII-Protiviti survey found that over 60% of Indian companies are engaged in practices that raise data privacy concerns, and 52% have experienced a data breach in the past five years. 

India's healthcare digitisation journey has already gone through one phase of trust-building. Early healthtech startups spent years convincing both doctors and patients to move away from offline systems. Practo, one of the country's most prominent digital healthcare platforms, encountered significant scepticism initially.

"We showed it to doctors, and they laughed in our faces," Practo co-founder Abhinav Lal told AIM in an earlier interaction, recalling the company's early attempts to digitise clinic workflows. "Nobody wanted to use the product."

At the time, the challenge was convincing healthcare providers to trust digital systems. Today, the challenge has evolved into determining what companies do with the data once those systems become intelligent.

Unlike hospital-focused healthcare, which is largely reactive, enterprise healthtech companies are increasingly building around preventive and everyday care—OPD consultations, mental health support, nutrition tracking, diagnostics, vaccinations, and chronic disease monitoring. 

The idea is to move healthcare from episodic treatment to continuous engagement. The business case is strong: companies report earning ₹3–6 back for every ₹1 spent on wellness programmes, driven by reduced insurance claims and improved productivity. According to Deloitte's 2024 Gen Z and Millennial Survey, 62% of young employees now expect personalised health and wellness benefits , creating direct demand pressure on employers to invest. 

ekincare has integrated with more than 2,000 diagnostic partners across 600 cities and connected with over 50,000 doctors. The company is also using AI models to analyse food images, structure medical histories, and generate personalised wellness recommendations based on behavioural patterns and historical records.

Startups are attempting to build longitudinal health intelligence systems. The approach, however, depends heavily on data continuity.

"The first five years were all about collecting data," Kalakuntala says while describing the company's AI journey, adding that the following years focused on refining and training models on top of those healthcare datasets.

Healthcare AI, unlike consumer chatbots, cannot function effectively without long-term patient histories, structured records, and behavioural context. Predictive systems need continuity—recurring blood reports, medication histories, consultation notes, and insurance records to identify risks accurately.

That dependency makes trust central to the business model. Yet a PwC India survey found that only 16% of Indian consumers understand the Digital Personal Data Protection (DPDP) Act, the framework that is supposed to govern how companies handle their information. Even more striking, 64% of organisations have not planned any initiatives to reassure employees about how their data is being used. PwC IndiaPwC India

The rise of India's DPDP framework and growing awareness around sensitive personal data are likely to place additional scrutiny on enterprise healthcare systems. Questions around consent, data portability, anonymisation, and employer visibility are becoming increasingly important as AI expands deeper into workplace wellness infrastructure.

At the same time, startups argue that intelligent healthcare systems could significantly improve preventive care and insurance efficiency in a country where access to healthcare remains uneven. ekincare is now exploring AI-led claims adjudication systems that could potentially automate parts of the insurance approval process. The ambition is not unusual Policybazaar's ClaimSetu, launched in mid-2025, claims to reduce group health insurance claims processing time by up to 50% through AI-powered insights and scoring

The opportunity is substantial. Corporate health insurance already accounts for 71% of India's health insurance market by revenue share, per Grand View Research, making the enterprise workforce one of the biggest distribution channels for preventive healthcare products. For healthtech startups, this creates access to recurring users, longitudinal datasets, and institutional partnerships, all critical ingredients for building scalable healthcare AI. 

But the same evolution also raises a fundamental question for the industry: can healthcare platforms become more predictive without becoming more intrusive?

Indian healthtech companies may have largely solved the problem of digitisation. The next challenge could be ensuring that increasingly intelligent healthcare systems remain trustworthy enough for users to continue sharing the data that powers them.