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The product roadmap clarifies the why, what, and how behind what a tech startup is building. This week, YourStory shares the product journey of three healthtech startups — Practo, Nirmai, and MFine — how these startups viewed their products from the first principles, and how they have evolved since. 

From solving one problem to adding multiple elements to solve different healthcare issues, these startups have come a long way. Here’s a brief overview.

From a SaaS platform for doctors to a full-stack healthcare ecosystem

When NIT Suratkal engineering students Shashank ND and Abhinav Lal started healthtech platform Practo in 2007, the duo had envisioned three core principles — access to quality care, better and improved care with affordability, and safety and privacy of all information and exchange.

“These three core principles remain key to what we continue to do and build at Practo,” Abhinav says.  

The duo had decided to first start by making doctors and quality healthcare more accessible by making it easy for healthcare professionals and patients to connect. 

After 12 years of existence, by 2020, Practo had gained over 180 million users, five million patient stories, and enables 10 million hours of doctor consultation per month. The platform has over 76,000 clinics and hospital partners.

“The first step thus was to work with the doctor community. We decided to remove the administrative work to help manage their practice so they could focus on their expertise. That was the first product — Practo Ray — released in 2008. It was a software code Shashank and I wrote. We had focused on smaller, single-doctor clinics and built the product with both the doctors and patients in mind,” explains Abhinav. 

The idea was to use emerging technologies to revamp clinical functioning across India and enhance the patient experience by creating a software solution for doctors to efficiently manage their appointments and scheduling.

Shashank spent most of the days in clinics, interacting with doctors to understand the intricacies and challenges. It helped the team realise the needs of the doctors, and how the team could go about implementing them. 

SaaS platform Practo Ray managed the affairs of small practices in terms of appointment scheduling, patient record maintenance, billing, accounting, and analytics — catering to the SME segment. It formed the basis of all future products for Practo.

“We were selective in what we built, and this allowed us to build a product as per the customer needs. The next challenge was getting doctors to migrate as most had their systems on older technologies. We had built a lot of our initial tech on the web, where we would send reminders via SMSes to doctors and patients,” explains Abhinav. 

Another challenge, Abhinav adds, was to make the product reliable and more responsive. “If Ray stopped working, the business for the doctors would also stop. So, we primarily focused on solving for any redundancies and challenges,” he adds. 

Once the B2B product got the initial traction, the team focused on the consumer and began with the B2C platform — Practo Search — a doctor discovery and appointment booking platform for consumers. 

Around 2012, the team moved ahead to ensure that patients had 24×7 access to healthcare, which led to the launch of the Practo App. 

“We worked closely with our partners to understand their needs, building solutions to solve their current and future needs. We’re now looking forward to more standards in health data exchange,” says Abhinav. 

Simplifying breast cancer detection with Niramai 

Geetha Manjunath was working in AI R&D for multinational companies Xerox when she got the news that her close cousin had breast cancer. A few months later, her husband’s cousin also received the same diagnosis. Both women were under 45 years of age, and this came as a shock to Geetha.

In 2016, she joined hands with Nidhi Mathur to launch healthtech startup Niramai, which uses AI to detect cancer in the early stages. 

The startup’s patented product Thermalytix is a portable, non-invasive, radiation-free, and non-contact solution for early-stage detection of breast cancer. It works by measuring the temperature of the chest region and generating a report. 

Recently, the deep-tech Bengaluru-based healthcare startup received US FDA clearance for its first device, SMILE-100 System. 

The breast thermography device assists healthcare personnel to review, measuring and analysing thermally significant indications in the breast region. It can be used in the hospital, acute care settings, outpatient surgery, healthcare practitioner facilities, or an environment where patient care is provided by qualified healthcare personnel.

A few researchers in the US had mentioned thermography, but the founders realised that healthcare institutions did not use thermography due to the prevailing accuracy issues. But they felt the accuracy problems could be easily tackled using AI.

Geetha explains there are limited methods of detecting breast cancer. The most common is mammography, which tries to find malignant lumps in the breast using density differences. Geetha says it uses X-rays, and the lumps are seen as white. 

She says one cannot go for mammography more than once every two years as it can cause radiation problems. Also, women under the age of 40 have denser breasts. This means the entire breast appears white in mammography for a woman under 40, eliminating over 50 percent of women from getting a regular breast cancer test. Also, mammography can be extremely painful and uncomfortable. 

“Our technology works well on women of all age groups. We provide preventative breast health screening solutions in hospitals and diagnostic centres. Since our solution is portable, age-agnostic, and has zero radiation, we can also do the test outside hospital premises and help women in rural areas as well. These benefits effectively address the concerns and limitations for greater adoption of screening for women across all segments. Early detection saves lives,” Geetha says. 

The solution automatically generates detailed quantitative reports with clinical parameters and estimates the likelihood of cancer, which helps doctors make quick and accurate decisions. 

Leveraging AI to make virtual consults easier

When former Myntra co-founders Prasad Kompalli and Ashutosh Lawania started online telemedicine startup MFine in February 2017, they weren’t the first in the space. But they had found a major market gap. 

MFine is an AI-driven on-demand healthcare service that provides users access to virtual consultations and connected care programmes from different hospitals across India. 

The platform allows users to consult doctors from top healthcare institutions through video or chat. Unlike others in the space, MFine partners with leading hospitals rather than aggregating individual doctors on the platform. 

According to Crunchbase, MFine is one of the top 90 AI-based healthcare solutions across the world today. 

Ajit says, “We use mobile to make virtual care accessible to anybody who has a mobile and internet connection. But building something with deep medical interaction and traction value for a small mobile interface is complex. We needed to simplify it.”

He adds, “We realised if you manage to identify good doctors, you can mimic their capability in terms of treatment or evolve to looking at multiple doctors, seeing what works and replicating that.” 

MFine provides doctors with the best set of diagnoses, information, and treatment plans, based on the information the patient provides, who then choose the best course of action and treatment plan. All this is done with AI and machine learning. 

The idea is simple — “learn from the best and replicate best practices infinitely using AI.” 

He adds, if many people are looking at doctors through mobile, there will be little time. One may lose critical information if they are not attentive, and that is where AI helps.  

“In any consultation, close to 85 percent of the time is spent on the investigation. Doctors are focused on finding out root causes by way of questioning. The last 15 percent is earmarked for a conclusion and treatment plan,” Ajit explains. 

With MFine, the team wanted to find an automated way for that 85 percent time the doctor spends in collecting information from their patients. Every senior doctor has a virtual avatar in the system, which can talk to patients and summarise the prognosis.