By Alex Mason
Precision beats vague promises in medicine. Still, many areas of healthcare run on pure subjectivity. The typical scenario: A patient reports feeling unwell, a doctor makes an assessment and then the treatment regimen begins. But what if that initial assessment was based on incomplete information or was simply incorrect? What if guesswork could be removed from the equation?
Technology is ushering in a new era of objectivity in healthcare. Advanced analytics and AI are bringing long-promised enhancements to fields including autism, oncology, ADHD and musculoskeletal health.
A May study found that AI models could identify rheumatoid arthritis using only referral letters, routing patients to specialists faster. Physicians are using more data and better diagnostic tools to detect conditions earlier, tailor treatment to patients more precisely and provide more effective care.
Meanwhile, with automation helping clinical staff cut down on tedious tasks, practitioners have more time to focus on patients.
One barrier: healthcare IT’s ancient infrastructure
One of the biggest barriers to reaping real benefits from all this tech-fueled objectivity: legacy IT systems. In 2024, the healthcare industry sent more than 9 billion faxes, per Retarus. While electronic medical records, or EMRs, are now used in most states (often living alongside faxes), healthcare consistently lags other sectors when it comes to adopting technology.
Healthcare transformation must start small
The industry’s caution is warranted — not all digital health investments pan out. A decade ago, tech companies raised more than $4 billion to develop apps for everything from telehealth to hyperactivity. The promise was big, but the results were sometimes dismal.
What went wrong? Early startups set goals that were simply too lofty. In healthcare, the secret to creating sustained impact is to think small. Instead of revolutionizing blood testing or overhauling the concept of a clinic, the next wave of successful companies will use specialized datasets to address narrow problems that medical professionals face every day.
Musculoskeletal tech company Vald (one of my firm’s portfolio companies) has accumulated more than 54 million musculoskeletal health records over a decade, giving the company a first-of-its-kind dataset for benchmarking and AI.
The genomics company Foundation Medicine built its own massive dataset, FoundationCore, with more than 800,000 genetic profile samples from cancer patients. The Finnish company behind the Oura Ring collected data from 220,000 customers, revealing why some people sleep better than others in 35 countries around the world.
Harnessing the power of prediction
With such specialized information, practitioners can do more than cut out the diagnosis guesswork. They can move into the realm of predictive analytics and preventative care, where the upsides for patients could be huge.
For example, Foundation Medicine partnered with Flatiron Health to analyze 78,287 cancer patient records, identifying 776 gene alterations associated with survival outcomes across 20 cancer types. The company used AI to create risk scores for patient responses to treatments.
Data plus AI essentially turned cancer research into preventative oncology, as researchers could see which treatments worked and for whom.
The move from reactive to predictive has also changed how doctors treat autism. Early psychotherapy relied on subjective impressions, like whether a child appeared agitated or cooperative. But 1960s behaviorists started making more objective assessments, like whether a child avoided eye contact for 35 minutes in a 60-minute session.
Analyzing data on triggers, frequency and duration of certain behaviors evolved into modern Applied Behavior Analysis, where therapists now look at patterns to create personalized treatment plans. The results are overwhelming: Intensive, evidence-based ABA intervention improves autistic children’s social and communication skills. Worn devices only accelerate the effects, flagging patterns that humans might miss.
The next frontier: core clinical insights built on data and AI
Companies that take healthcare’s next big leap will live where data, devices and software converge. Consider how “quantified” we have all become. Most smartphones track step counts and sleep, and the most health-conscious among us are uploading photos and symptoms to ChatGPT.
Companies that use this data to paint a holistic picture of health — cross-referencing device outputs against lab results or population benchmarks — will create immense value.
Data from worn devices plus software analytics plus clinician advice should create one harmonious information loop, with everyone in the loop (including patients) using the information for meaningful improvement.
At companies like Vald, the convergence is underway. Currently Vald applies predictive AI to musculoskeletal data to tell a 20-year-old soccer star, for example, how long it will take her to get back onto the field after a sprained ankle.
In the near future, Vald’s process around an ankle injury might look like this: The athlete reports the injury to her physical therapist, who uses data from Vald devices (ForceDecks for balance and jumping, and DynaMo for strength and range-of-motion testing) in an initial assessment. The therapist compares that data against population benchmarks, noting that, say, 73% of similar cases had an eight-week recovery time. The therapist creates baselines for further assessments and a personalized treatment plan, continuously adjusting therapies based on data-driven protocols and Vald device feedback.
Here, data not only predicts outcomes, but also directs information flow inside the clinic, right down to the billing codes.
Musculoskeletal services account for a staggering 12% of annual U.S. healthcare expenditures. If a precise, predictive system like Vald’s can make even minor across-the-board improvements, the change could help many people. Hinge Health, another musculoskeletal company combining data and devices, had a successful IPO in May — suggesting the market may be ready to look past the previous decade’s digital-health stumbles.
Healthcare’s ultimate prize is not simply more accurate diagnoses. It is innovation that brings software, AI and devices together to transform existing clinical systems. AI is keeping costs down for providers and turning older systems of record into preventative systems of action. Moving from the anecdotal to the ultra-data-driven in the health sector means medicine turns more proactive and patients ultimately receive better, less-expensive care.
Alex Mason has been a growth equity investor at FTV Capital for more than six years and leads investments in enterprise technology and services, and healthcare technology and services. Prior to joining FTV, he was a managing director at Carrick Capital Partners, where he had a successful track record investing in high-growth enterprise technology, financial services and healthcare companies. Before that, he was a vice president at Accel-KKR and worked at TCV and Morgan Stanley.
Illustration: Dom Guzman
Stay up to date with recent funding rounds, acquisitions, and more with the
Crunchbase Daily.
