Healthcare innovation isn’t rocket science, but you still need to work the experience

There are enough challenges in healthcare where a good digitization of a process can increase engagement, improve connections to services and content, and provide a foundation to scale and deliver better care. Innovation doesn’t have to require AI, chatbots, complex analytics, or the latest emerging tech. Here are four companies who took existing paper-based processes and worked hard on a great digital experience to help the customer and patient and deliver an incredible impact on care

Highlights from RockHealth’s report on “Streamlining Enterprise Sales in Digital Health”

I always value studies or feedback that validate the insights I have developed over the many years of selling to healthcare. Indeed, when I advise companies, many who are trying to wrap their head with the eccentricities of healthcare, having examples that go beyond my own experience is important. The study report is short, so I suggest you to read it. Nonetheless, I’ve highlighted a few of items for you.

How do we counter the inertia of fee-for-service?

Docs are still stuck a fee-for-service world. We need to help providers build systems of engagement that measure individual caregiver and patient behavior and show how that behavior ties to revenues. Not only will this help boost the value of these soft services, but also give providers visibility into the whole of their business, not just how many patients came in and how much money was made.

Don’t forget the voice of the patient when designing systems of engagement

The future of healthcare is high-touch, through properly designed software, data, and devices that allow caregivers in these systems of engagement to provide high-value interactions, with optimal cost and outcomes. User-centric design, understanding the care pathway, and not being fixated on technological wonders are all key to provide something useful to caregivers and patients.

Components of a successful data analytics program

Data is a big barrier in adopting analytics across an organization. But it is the lack of an organizational structure that will lead to failure, no matter how sophisticated the technology or algorithm. A successful data analytics program takes into account organizational awareness, an iterative strategy, a stepwise approach to adopting applications, and the development of data fluency.