I recently sat down with Advisory Board's resident technology expert (and my former manager), John League, to discuss one of our favorite topics: data.
We focused our conversation on what has and hasn't changed about using patient data in population health by comparing today's status quo to a past Advisory Board study. Although some progress has been made in data analytics, there are plenty of lingering issues.
In the spirit of prioritizing, there are two main advantages and one lingering problem in population health data analytics today. And this is important because if the industry is ever going to succeed under value-based care, leaders must use data effectively to target care interventions.
2 advantages and 1 problem in data analytics today
Advantage #1: "Data analytics have improved over the last eight years—and at a better price point. If you took the same amount of investment from 2014 and invested it now, you could get a much better, more scalable analytics software."
It is easier than ever to collect and move data. Further, data analytic and machine learning capabilities are less expensive than in the past due to the proliferation of vendors and the rise of compute power.
It's no surprise that 90% of providers use data analytics in clinical areas, according to one study. But only about 22% report using data analytics for population health. Population health leaders would benefit from plugging into even simple data analytics rather than pass them by.
Problem: "But data analytics can't do everything. How do we get analytics into our workflow correctly? We still don't know how to put data in front of physicians in a way that is both clear and engaging but nudges them to take action."
Data analytics are not the actual lynchpin of population health but rather a red herring. Instead, the real culprits are staffing design and workflow processes.
Many care teams still work below top-of-license. At one large health system, RN- care managers spend over 60% of their time mining patient records for patient interventions as opposed to treating patients. This is a problem that's only grown over time as data needs grow—and as physicians shift "less engaging" tasks to other care team members.
Workflow constraints also prevent data analytics from being used effectively in population health management. In a survey of 100 providers, physicians said the main reason they don't trust their population health management (PHM) solutions is having to leave the workflow.
This is why you see so many vendors trying to sell directly to clinicians. The industry recognizes there's a workflow problem when it comes to data; their value propositions focus on workflow integration and reducing burnout.
Advantage #2: "The industry is experiencing a clear-eyed realism about what technology is, what technology is capable of, and learning where technology has been overhyped. This is something we didn't have eight years ago."
The constant fanfare around data, machine learning, and automation is beginning to meet realistic expectations. For example, in 2018, 37% of providers said "we believe AI will become a transformative, essential part of our health system" while today only 19% say the same thing, according to a recent Advisory Board survey.
There's a reason we're categorizing realistic expectations as an advantage. The realization that technology isn't going to solve all problems in health care means we can start to use technology as what it is: a tool in our toolbox.