Volume – Data from Thousands of patients is not enough. Hundreds of Thousands is better. Millions is best. Once we are talking Millions of patients and Millions of surgeries, algorithms can tease out mind-blowing predictions and analytics.
Access – Today, a given device manufacturer cannot access their competitor’s data. For instance, it will be hard for ZB to obtain data from surgeries with Stryker or DePuy, so likely an independent 3rd party company will have the competitive advantage to capture all data.
Regs – You must navigate the hurdles in privacy, HIPPA and regulatory compliance in the US and other markets. How does a company get a patient to opt-in to “giving away” pre-op and post-op data for the greater good of medicine? How do you encrypt the data to protect the patient ID, surgeon, hospital? Who “owns” the data?
Technology – How many data capturing devices are needed in the continuum of care, and where are data capture devices located? How do you get the data during the surgical procedure and also from a stroll in the park a week later uploaded into the cloud?
This is not “winner take all”. One company doesn’t need ALL of the data. There will be many data subcategories where smaller software companies can carve out niches and create large SaaS wins.
Let’s review some of the subset data opportunities.
Pre-op Clinical data – activity, pain, bone quality, ROM, baselines before surgery.
Patient demographics – zip code, sex, age, wt, pain score, activity, occupation, etc.
Surgical procedure data – devices implanted, surgery images in 3D space with references, frame by frame, time of surgery, materials used.
Device usage data – type of device, tissue implanted, manufacturer, cost, date of surgery.
Surgeon performance – compared the data set baselines.
Hospital/ASC performance – device survivorship from time from primary surgery to revision at a given primary Hospital/ASC by a given surgeon.
Post-op Clinical data – ROM, post-op x-rays, steps, activity, pain score, life quality.
Once the winning company has Millions of data across patients, hospitals, surgeons, PTs, the company can slice and dice the data into endless SaaS products. This is gold.
The future buyers of these SaaS products will be insurance companies, national healthcare systems, clinical registries, the FDA, wealthy patients, and even device companies themselves.
Insurance companies will want to know… Which device and which hospital has the best outcomes for the lowest cost?
National healthcare systems will want to know… For a given procedure, which device and which hospital has the lowest complication rates?
The FDA will want to know… What are the real outcomes per device? Per device, what is the adverse events rate and how does it compare with the self-reported rates in the MAUDE database? This will be the first time in history that the FDA has a true feedback loop.
Patients will want to know… Who is the best surgeon for my upcoming procedure? Which hospital has the best outcomes record from my upcoming procedure?
Device companies will want to know… How does my device outcome compare with competitors’? What surgeons (potential KOLs) will have the best clinical outcomes using our new device? What procedures are the best design opportunities for us (ranked by worst outcomes by procedure)?