Right now, an infinite number of people are typing out an infinite number of predictions on what will happen next year. Rather than join the monkey band, I thought I’d make a list of what I hope health IT leaders do in 2019. I’d like to see them:
- Find the courage to take a stand on interoperability: Over the last several years I’ve written perhaps 43.72 gazillion words on obstacles to health data interoperability, but even with my colorful imagination I could not have anticipated how little progress we would have made by this point. I’d love to see more health IT leaders get practical and launch a small interoperability project they’re sure they can manage. My instinct is that limited local efforts can still generate a lot of value.
- Take a chance with blockchain: I know, blockchain currently seems to sit in the uncanny valley between mature no-brainer technology and hype-driven mirageware. It doesn’t need to be this way, and probably shouldn’t, as there’s no better technology on the horizon which stands to solve so many problems at once. More enterprises should pilot blockchain solutions and see if the tantalizing promise of fine-grained data security controls actually materializes.
- Define AI use cases: I spend a lot of time on AI of late, and to my eye it clearly has the capacity to change the healthcare business for the better. Before it can do that, though, industry thought leaders (you know, those folks in the easily-recognized thought leader hats) need to point the way to AI uses that have already proven to make sense. For example, we’ve published some nifty case studies describing how hospitals have predicted and tackled sepsis. Time to find more of those suckers.
- Approve bigger cybersecurity budgets: Over the last few years, the number of high-profile health data breaches has risen steadily, perhaps because we’re simply trying to deploy more data to more people In more places. Unfortunately, providers have still been underspending on cybersecurity. Let’s all hope that things are different in 2019. Such budgets should include a hefty dose of employee training, as research shows that staff members are responsible for many serious breaches.
- Create better consumer health interfaces: Over time I’ve read many a complaint about feckless consumers who won’t use a provider’s online portal, kiosk or mobile app. I understand why health IT leaders struggle with this, as making it easy to navigate through data is no joke. Regardless, it’s long past time to take responsibility for the problem. Yes, it’s true that a fair number of patients are resistant to engaging with their data, but as Apple has demonstrated so resoundingly over the years, it’s possible to create interfaces anyone can (and will) use.
- Develop approaches for populating longitudinal patient records: It’s becoming clear that there is value to collecting data that never made it into patient charts in previous eras. The question is, which of these data points matters? Has it become important what the patient’s temperature was in November 3, 1972, and if not, why? Do we resolve once and for all that wearables data is legit medical data and if so, are some wearable tech vendors better than others? Are reports on social determinants of health become part of the medical record, and if so, why? Simply setting forth to create a longitudinal record isn’t enough. Let’s establish what we’ll do with new data once we have it.
- Establish goals for wearables data quality: Speaking of wearables, it’s high time that the health IT industry develops some standards for sorting out usable from unusable wearables results. Historically, it’s been hard to tell whether a given, say, pulse reading was reliable or valid. Now, we’ll be seeing an increasing number of device makers seeking FDA approval for medical-grade wearable devices and sensors. As FDA-approved devices become available, does that mean we should discard data from consumer-grade wearables? If not all consumer-grade devices are fatally flawed, which of them produce data that should be integrated into larger health datasets? We need to answer questions like these by establishing industry-wide standards.
- Support the training of more data scientists: Without data science experts, much of what we hope to accomplish with big data analytics just isn’t going to happen. Despite this, I see no signs that the health IT industry is taking steps to foster the training of additional data scientists, which strikes me as short-sighted at best. Not all providers are going to be in the position to endow a chair at a university or even pay for their employees to take data science courses, but it’d be smart for healthcare organizations to do whatever they can.
In my mind’s eye, I can see readers saying, “Stop – my job is hard enough already!” And that’s certainly fair. Still, it never hurts to see if there are ways to seize opportunities outside of their day-to-day comfort zone, particularly if the projected payoff is big enough.