I’m increasingly interested in how we bridge the gap between research and practice in healthcare. No doubt my increased interest comes from the need to prove the value of data and technology in healthcare.
Remember that when we first started introducing EHR software into healthcare, the main goals were around billing and possibly efficiency. The former one has been a success in many aspects and the former has been a pretty big failure. However, the focus was never initially on how to improve care and the focus on billing has actually had a negative impact on care in ways that most people didn’t expect.
Now we’re seeing healthcare organizations trying to shift EHR models so that they do work to improve care. This has proven to be a challenge and it’s no doubt why many healthcare organizations are going beyond the EHR to make population health happen.
The other problem with moving into the clinical improvement space is that the bar is much higher. No one minds too much if you take risks in billing. That’s why most AI (Artificial Intelligence) is starting there as well. However, when you start dealing with the clinical aspects of healthcare, you have to take a much different approach and requires proper research of proposed ideas and methods.
Therein lies the challenge for much of the healthcare IT innovation. There’s a large gap between researchers and the bedside. This was highlighted really well by a researcher who described the challenge of translating research into medicine:
Speaker 3: The current models are not translational. We need more innovation and check out my cool data that does not address the topic.
The moderator was clearly the speaker’s past mentor as extra time was spent introducing this investigator’s novel interpretation of the topic. The introduction slide simply said NO in bold letters and the speaker launched into a TedX style talk on how these models are not translational and it is a waste of time for the Department of Defense or NIH to fund multi-team consortium to develop new relevant models. Remember, it was a panel discussion. This speaker left the panel and walked into the crowd spouting off about how translational research as it is defined would not prove useful and innovation was required to develop new therapies. In addition, replicative studies or lack of replication was moot because one can’t trust how other scientists conduct their science. As an example of innovation, studies demonstrating the effective integration of neuronal progenitor cells into the brain of a mouse model of epilepsy were shared. These studies were not done in a traumatic brain injury model, but a different model entirely. Innovative and published in a well-regarded journal, yes; translational, not likely and only time and additional studies will determine; relevant to the topic, no. Supporters of this young investigator probably called this display brave. There were no answers to be found here, only self-promotion. The presentation was not designed for discussion amongst peers, but was strategically delivered to help the investigator’s career trajectory. The song and dance number did not reflect a dedication to developing new therapies for people following a traumatic brain injury.
A successful Investigator’s Workshop speaker will address the topic using scientific data, but most importantly capture a story for the audience. Ideally, bullet points from learned experience or on which the speaker would like feedback will be shared and will foster discussion amongst the moderator, panelists, and audience members. It is an opportunity for the scientist to improve their approach as well as inform the audience.
This was an important insight to remember as we consider how to incorporate research into healthcare IT. The motivations of researchers are often not aligned with translating their research into practice. Researcher’s focus is often on career promotion, grant dollars, and publications. That’s a real disconnect between what most health IT vendors and healthcare organizations want to achieve.