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Quality Engineering & Testing for Digital Health Companies
Dec 2, 2022
Design Thinking

Why Digital Health Companies Fail to Gain Traction

Currently, there’s a huge gap between expectations and success for healthcare startups and businesses. Most digital health startups established in the last couple of years are now facing challenges as clients rationalize their product portfolios and VCs strengthen their investment terms and reassess their valuation thresholds. cited that nearly 20% of new U.S. startups fail within the first year of their establishment. Odds aren’t great after that, either. The article further stated that 2 out of 5 digital health startups will fail in the next 5 years, which is almost 40% of the healthcare companies in the U.S.

Some of the major reasons behind shutdowns could be attributed to poor market fit, choosing the wrong technology stack, bad hires, inequitable access, inconsistent quality, and one challenge we can all resonate with – not having enough funding or high costs relative to other nations. However, healthcare’s inconvenient truthis that “its super-sized gluttony has become the single greatest immediate threat to America.”

According to MarketsandMarkets, the global digital therapeutics (DTx) market will be valued at $17.7 billion by 2027 at a CAGR of 31.6%, up from $4.5 billion in 2022. Last year, the report found that the technology segment was the largest revenue generator and will grow exponentially over the coming years. Despite this progress, some of the largest health systems in the U.S. experienced significant losses in the first half of 2022, forcing many startups to reconsider their strategies. How did this come to be?

In this article, we will discuss some of the major differences in expectations between healthcare companies and health systems in the digital health opportunity landscape.

Choosing the Initial Indication & Wrong Technology Stack

The technology leveraged by most digital health companies has many likely indications. These startups select the primary indication majorly based on what the executives of that startup know about. Contrarily, this is not measured to be the right call at that time.

Put simply, the verification of your technology stack through initial indications will exhaust your resources before you have the time and energy to think and transform. Therefore, even while choosing the right technology stack, health startups need to properly articulate a value proposition analysis on the given set of indications and then choose the most profitable and successful one.

The value proposition is the new way to nail your elevator pitch. A lot of digital health startups represent themselves as “better, faster, and cost-effective” than what is already rolled out, and they lose the opportunity. But this isn’t enough. You must tell why your solution is better, how faster it is, and how much more cost-effective it is compared to the standard of care.

Apart from these factors, you can consider business scalability, competitive landscape, technology robustness, and user acceptance as possible indications.

Recent businesses fail to fully understand their user and technical requirements and opt for the latest technology stack such as AI, IoT, ML, or Beacon, and hence fail into production.

Complying with Rigorous Healthcare Regulations

Healthcare is not the industry to transform and revolutionize things quickly and hurry with innovations. It must precisely undergo several pivots such as VCs, guidelines, regulations, and interests.

The healthcare space is reluctant to change rapidly, and hence, acceptance and adoption is relatively slow, which poses a big challenge for most companies. In such cases, healthcare startups mustn’t sit and relax, rather work on these challenges and turn the digital health solutions environment into a wild west.

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Product Fails to Integrate Properly Into Existing Workflows

Is it possible to disrupt any industry without influencing people who have crucial roles to play? This is an important question. Most healthcare startups believe that their technology has the potential to revolutionize the industry because of improved patient care or lowered costs, but if it involves additional steps or changes an existing process, you’re basically interrupting their job – and most people do not agree to this change.

New solutions should fit as easily and seamlessly as possible into existing workflows, instead of interrupting people or slowing down the process. This is why it’s important to learn about each and every aspect of a client’s workflow and everyone who will be influenced by the changes. A healthcare practitioner might adapt to your healthtech solution easily, but if it adds a lot of pressure for nurses or technicians, it might be problematic. Organize focus groups, education campaigns, and user testing with everyone involved in the workflow and in as many clinical settings as possible.

Managing Unstructured Healthcare Data in the Right Way

The overall market size of big data in healthcare industry is expected to reach a whopping $81.3 billion by the end of 2030. In any case, the ever-increasing volume of both structured and unstructured healthcare data makes it extremely difficult to organize and classify.

Healthcare big data refers to huge volumes of data that is difficult to handle with traditional or legacy database management systems (DBMS) in a digitalized medical setting, including social media platforms, medical centers, and wearable devices. Medical big data is experiencing faster growth than traditional business data, and if leveraged in the right way, can work wonders.

The major challenge in healthcare fields is that more than 80% of this data remains unstructured and unorganized after it is generated. Since it is difficult to handle this kind of information for electronic medical record (EMR) or most hospital information systems, it mostly gets ignored, deleted or abandoned for a long time.

Even though massive amounts of data is still generated in many medical institutions, it is hard to be connected with medical big data research, machine learning or AI industry in healthcare. This is why, health tech startups need to manage this data in healthcare systems before they build medical AI systems, which are currently powered by machine learning technology.

For amplifying the use of these unstructured healthcare big data, healthtech startups need to establish the data collection, anonymization, and quality engineering processes. Additionally, data sets need to be defined, regulated, extracted, and visualized automatically. Finally, healthcare startups need to build a platform for integrating and leveraging this unstructured clinical data while applying these concepts in their technologies.

Failing to Deliver as Expected

When it comes to healthcare, the percentage of people needing support is quite high. Not everyone has adequate health insurance, access to HSPs, active gym membership, and a personal support system or internet access to communicate with HSPs.

Digital health solutions are well poised to fill this gap.

However, oftentimes, these digital health solutions seem designed for individuals in the same socio-economic landscape as the designers themselves. A recent study by the California Health Care Foundation indicated that modern digital health solutions are not generally developed for vulnerable and underserved populations – the group of people majorly needing these technologies. That’s the terrible news.

In perhaps one of the most infamous controversies ever to take the healthcare startup industry by storm, Theranos, a $10 billion-value startup, failed to produce accurate and adequate results for certain blood tests. Furthermore, reports also claimed that the tests performed on Theranos technology failed to provide reliable, precise, and fast results. These “over-the-top” promises are not uncommon in the startup world, however, in healthcare industry, they are unique.

The healthcare industry is serious when it comes to their solutions and ground-breaking innovations, so mistakes of all kinds are practically unforgivable from a client’s perspective. Therefore, healthcare startups must avoid this at all costs.

If a medical institution commits a similar mistake, the entire industry along with the patients who are subjected to the tech solutions suffer. Unreliable and inadequate products, failing to meet end results, improper doctors, and other challenges are heartbreaking to everyone involved in the industry. Hence, all companies specializing in providing healthcare solutions must consider all these factors when leading business and focusing on growth.

What Digital Health Startups Can Do About It

Notwithstanding these digital health challenges for startups, the sector is alive and well for healthcare companies. If anything, enterprises are elevating their digital health programs. They are building the groundwork for understanding and mapping patient journeys, evaluating client and patient data to understand their needs better, and reinforcing their technology platforms for a seamless data flow and integration.

Digital health startups and businesses continue to invest in new healthcare solutions from innovative tech companies. In any case, they are examining the impact on patient experiences and financial returns more comprehensively.

Design thinking is one such tool; it is a creative, patient-centered problem-solving approach that leverages rapid prototyping, repetitive testing, collective ideation, and empathy to solve complex problems.  It develops best-fit solutions that are rapidly prototyped and excessively refined so they can be leveraged quickly and at low costs. Unlike legacy approaches to problem solving, design thinkers guide the early stages of innovation through deep empathy for users and a thorough understanding of the problems facing them.

Building empathy is the most important factor in design thinking but is also the most challenging. It is difficult to put yourself in someone else’s shoes and actually understand their experience. For example, if the person is facing a grave challenge such as managing a chronic illness, fighting against cancer or caring for a paralyzed family member.

Without patient centricity, design thinking, and building empathy for the healthcare net, digital health solutions for their care will be under par.

Wrapping Up

Remember that patients are the main drivers of seismic change within the healthcare industry. Your digital health startup will be doomed to fail if you make solutions that are inconvenient for patients to use.

So, what does it take to offer the right care to the patient at the right time? Design thinking; it is one of the most promising approaches for getting an in-depth understanding of patients’ experiences.  

And if you’ve already integrated design thinking into your workflows, look for new outlets to explore and improve.

If you wish to achieve this level of digitization and patient centricity, join our webinar on Patient-Centric Care Through Design Thinking on November 17th to learn how to gain a competitive edge with your next wearable or biosensor project.