Walk into Ultrahuman’s ‘Performance Lab’ in Bengaluru’s Indiranagar and it is easy to forget that you are there for a health check-up. There are no crowded waiting rooms, numbered tokens or long lines outside a doctor’s cabin. The space feels closer to a high-end wellness studio. But before a doctor sees you, a different kind of examination begins: you are asked to fill out a detailed questionnaire about your sleep, diet, medications, health history, daily routine and personal goals, and give the company consent to use your health data.
You change into a T-shirt and joggers and move through a battery of tests. There are blood and genetic tests, gut analysis, cardiopulmonary assessments, a DEXA scan and other measurements designed to capture what is happening inside your body. Ultrahuman says its system can capture up to 3,500 signals spanning genetics, gut, blood, cardiopulmonary, metabolic, cognitive and bio-mechanical health.
Then comes the part that would have seemed almost futuristic a decade ago: the data is pulled together into a single picture of you. Ultrahuman’s Biointelligence AI reads the markers in context, cross-references them and looks for relationships between signals to generate a personalised health plan. A doctor interprets the findings, followed by nutritionists and performance coaches who help translate them into changes in diet, exercise and recovery. In the days and weeks that follow, many therapies, such as cryotherapy, could be recommended.
A few minutes away, another new-age clinic is pursuing a similar idea. Diagnostics, analytics and medical expertise are brought together to build a preventive health strategy, with AI (artificial intelligence) helping doctors make sense of complex health signals and recommend targeted interventions.
“One of the biggest gaps in conventional healthcare is that the data you have today can be extremely predictive and form the basis for personalization, but we largely miss both. Hospitals do a very good job of treating the problem once it has manifested. But a preventive approach is about being predictive,” said Rishi Pardal, co-founder and CEO at Biopeak Health, a preventive healthcare company.
These clinics offer a glimpse of a larger shift underway in healthcare: the patient is no longer simply a person who walks into a hospital when they fall sick, but a constantly evolving stream of health data.
For decades, healthcare has largely been episodic, wherein you consult a doctor, undergo a test, receive treatment and leave. The information generated in that encounter can remain fragmented across hospitals, laboratories, apps and paper records. The new model is built around continuity: regular testing, monitoring, coaching and intervention, creating what is effectively a living health record that becomes richer with every interaction.
This is why the race is no longer simply about building another hospital, diagnostic chain or wellness brand. It is about owning the patient relationship, the data generated through it and the intelligence built on top of that data.
But it raises a more difficult question: does collecting more data and more interventions actually make people healthier? And as companies build increasingly detailed digital portraits of our bodies, who controls that information, how is consent obtained, and what safeguards stand between a living health record and its commercial use?
Mint dives into the race to build the healthcare platform of the future, and the business, technological and regulatory questions that come with it.
The full-stack shift
India’s healthcare system is still largely organised around individual problems: a physician treats an illness, a physiotherapist works on an injury, while nutrition, fitness and rehabilitation often sit outside the medical system. Biopeak co-founder and CTO Shiva Subramanian believes this fragmented approach misses the bigger picture.
“If you actually look at somebody having an injury on their leg, first you don’t even know why that injury happened,” he said. A physiotherapist may fix the injury, he argues, without necessarily understanding whether it was caused by a genetic predisposition, a nutritional deficiency, stress or some other underlying factor.
Biopeak is trying to bring these pieces together. Its model combines genetics, pharmacogenomics, microbiome and blood data with clinical diagnostics, and puts clinicians, geneticists, nutritionists, physiotherapists and health coaches around the same patient. “It’s not like I give you a report and then I don’t see you. We have to work with you to change that,” Subramanian said.
Ultimately, he says, “you’re treating the story of the human being and not just trying to treat the liver.”
The company’s physical centre reflects that full-stack approach. Rehabilitation and therapies such as infrared treatment and cold plunges occupy the ground floor; diagnostics are on the first floor, while a café offering nutrition-focused meals is being set up on the second.
Ultrahuman, whose core business is wearables such as the Ultrahuman Ring, opened its Performance Lab this year. The company has a product or retail experience centre on one floor, diagnostics—with multiple rooms for eye scans, oral scans, audiometry, ECG etc.—on another. Recovery therapies occupy a different floor.
While a test gives you a number, Biointelligence AI, Ultrahuman’s proprietary intelligence layer, puts that number in context, showing how your cardiopulmonary capacity relates to your metabolic health, how your gut markers connect to your recovery, how your genetics shape your response to training or supplementation, according to Hisham Syed, business head, Ultrahuman Performance Lab.
“If you wear an Ultrahuman Ring, your continuous data flows into the same intelligence layer: sleep, heart rate variability, recovery trends, glucose response. The lab gives depth, the ecosystem gives continuity, and Biointelligence AI reads both together,” Syed noted.
Similarly, ayurveda-focused direct-to-consumer (D2C) company Kapiva has recently launched clinics combining consultations, diagnostics, therapies and personalised treatment plans for chronic conditions such as diabetes, heart health, pain and women’s health. Senior-care company Praan Health has also recently opened an integrated clinic in Bengaluru.
Industry experts say there is a common thread running through these models: the attempt to move beyond a one-time consultation and build a continuous picture of the patient. Kapiva, for instance, tracks patients every week to understand how they are responding to a treatment protocol. The services business may bring higher retention and another revenue stream, but the bigger prize seems to be the data.
A living health record
Data is at the heart of it, according to Pardal of Biopeak, who says using that data to be both predictive and personalized is the micro manifestation, and the macro opportunity is using that same data to identify and predict broader health trends across the population.
“If someone comes to us at 26, when their body is at its peak, and stays with us through their 40s, we can see how their biology changes over time and understand what corrections may be needed. The ability to synthesise all that information and compare those changes with the person’s physical characteristics is very powerful,” Pardal said.
At Kapiva, R. Govindarajan, chief innovation officer explains that the company’s clinics use an AI-powered pulse diagnostic device that records pulse-wave patterns through sensors placed on the radial artery.
Machine-learning algorithms analyse the readings and score more than 20 ayurvedic health parameters, including tridosha balance (maintaining harmony among the three doshas: Vata, Pitta and Kapha, which govern the body’s physical and mental health), digestive health and stress levels. The data gives doctors a baseline to combine with the patient’s history and lifestyle when designing a personalized plan.
The ambition is to make that record increasingly useful as it grows. “As we build our longitudinal patient data (data gathered over a period of time) set across thousands of individuals over time, AI becomes the tool that converts that data into actionable clinical intelligence,” Govindarajan said.
Praan Health is approaching the problem from another direction. Founder and CEO Navneeth Ramprasad says its earlier digital model captured extensive behavioural data: how people moved, trained, ate and slept, but lacked the clinical information needed to anchor it.
“Hospitals have depth without continuity. Digital health has continuity without depth,” he said.
Its clinics are intended to bring the two together: clinical information such as diagnoses, imaging, medication and blood work alongside functional assessments and the behavioural data collected between visits. Strength, gait, range of motion and balance are measured repeatedly so that changes can be tracked over months.
Can hospitals keep up?
Hospitals have a head start as they already sit on years of clinical data, but much of it remains difficult for machines to interpret.
“A lot of the data hospitals have right now is scattered, event-based, and captured only at specific touch points,” said Vamsi Karatam, founder and CEO of remote patient monitoring platform ProRithm. That makes it difficult for AI to identify patterns. “AI becomes far more powerful when it can look at trends over time, not just a single snapshot.”
The shift towards continuous, data-driven healthcare, therefore, is not lost on hospitals.
Hospitals, which have traditionally been built around treating patients when they fall ill, are beginning to rethink their role too. Dr Pawan Kumar, CEO (India II), Aster DM Quality Care, says hospitals of the future want to become “lifelong companions of patients on their health journey.”
That means stitching together systems that have historically operated in silos. Aster is strengthening digital workflows across electronic medical records, diagnostics, imaging and clinical systems, while exploring AI for clinical decision-making, operational efficiency and patient experience.
The next challenge, Kumar said, is making these systems work together so that the right information is available to doctors when they need it. Wearables and remote monitoring could add another layer, particularly for chronic disease management, post-discharge care and prevention.
Apollo Hospitals is taking a similar approach. “The starting point is connecting what already exists,” a spokesperson said, so that laboratory results, imaging, health checks and hospital visits form one continuing picture rather than isolated events.
“Once connected, that data could help determine which tests a patient needs, support doctors at the point of decision-making and flag changes that require follow-up. Aggregated and de-identified, it could also reveal broader population-level health trends. We have that foundation in place and are building on it steadily. This is a fast-evolving field and we are evolving with it,” the spokesperson said.
The data dilemma
In all of this, there’s a difficult question: what happens to all that data once companies have it?
As healthcare becomes continuous, patient data can become more than a record of care—it can feed the AI, research and product development of the platform collecting it.
Biopeak’s privacy policy, for instance, allows de-identified or pseudonymised health data to be used to train and improve its AI systems, as well as for research, analytics and developing new health products and risk models. It also says that data already used to train an AI model cannot be individually removed if a user later withdraws consent.
However, the company says that the data belongs to the client, not Biopeak, and that patients can access their diagnostic reports and longitudinal health data through its app and take them to another healthcare provider.
Biopeak’s Pardal says the company has built safeguards around consent and de-identification, but acknowledges that patients have to be comfortable with the arrangement. “We are very open with clients about what they are signing up to,” he said. “If clients have an apprehension about sharing their de-identified data with the system, they can’t be a client of Biopeak. The whole thing is about harnessing the power of the Indian data set to provide a solution.”
Ultrahuman says users can delete their data directly through its app, including data powering its in-house AI systems, and that it does not share user data with external AI providers. Users can also share their data with any physician through an access code provided on the app or download raw reports and share them with anyone they choose.
Srikanth Mahadevan, director, Deloitte India, explains that electronic health record standards take the position that the information in a health record belongs to the patient, while the healthcare provider holds the record in trust. The Digital Personal Data Protection (DPDP) framework takes a different approach, focusing less on ownership and more on the rights of individuals and the responsibilities of organizations processing their data.
For continuous-care platforms, that distinction matters. A company may need a patient's data to monitor treatment or generate a health alert, but that does not automatically mean it should be able to use the same information for AI training, product development, research or commercial purposes.
Experts note that consent for care, in other words, cannot simply become a blanket permission for everything else.
There is another, more fundamental question: does more data actually mean better healthcare?
Doctors say longitudinal monitoring can be valuable for conditions such as diabetes, hypertension and cardiac disease, where changes over time can reveal more than a single reading. But continuous measurement should not be confused with continuous clinical benefit. A technology may be excellent at measuring a biomarker without proving that acting on that measurement improves outcomes.
Dr Praveen Gupta, senior neurologist and chairman at MAIINS, Marengo Asia Hospitals, warns that the pursuit of data can itself create problems.
“Healthcare should empower patients, not make them perpetual observers of their own physiology,” he said. Constant monitoring can turn normal fluctuations in sleep, heart rate or glucose into perceived health problems, leading to anxiety and unnecessary consultations.
And AI can make that problem harder to spot. “A score can seem very exact, but exactness of measurement is not the same as certainty of diagnosis,” Gupta said.
“Collecting thousands of data points does not necessarily provide a clinician with thousands of useful insights. If there is no validated clinical pathway after an abnormal result, then ongoing monitoring may result in additional investigations, rather than better health.”