But how can you leverage data to benefit your patients and populations?
Health care leaders have never had more access to data than they do today. This data can potentially provide a better understanding of their organizations and empower well-informed decisions in this new environment. But health care data is complex. When you begin to map out all the potential interconnections, it reveals a very intricate decision-making environment.
Where are the problems and opportunities?
What drives a winning strategy?
(According to the Healthcare Executive Group’s 2018 “Top Ten” study.)
Why? Too many solutions look backward in an industry that needs to look forward.
A strong analytics approach is essential to draw needed insights from data. But for most organizations, today’s solutions offer more promise than performance. And since the data under analysis is retrospective, the solutions offer only a look backward, not forward.
But the promise of predictive, prescriptive data—data that tells you what actions to take before you need to take them—is a grand slam.
How well has your organization performed in the past?
How is your organization performing now at the point of care?
Who is likely to develop Disease A or progress to Disease B? Can the progression be slowed?
What action plan can produce real results?
(Most difficult and beneficial)
Analytic solutions must be clinically credible.
A strictly statistical approach to health care analytics risks identifying correlation without causation. Proven methodologies combined with clinical expertise are essential.
Pure machine-learning models are “black boxes.” The machine cannot explain how it comes to its recommendations. When pure machine models deal with human lives, mistakes may be made without explanation or intervention. Consider the self-driving car that struck and killed a pedestrian. Or the drivers of those cars who stop watching the road because they have too much faith in the vehicle’s algorithms.
A better model for health care begins with teaching the machine algorithms based on proven methodologies and then having subject matter experts review the machine output. With this approach, you can pursue opportunities consistent with your organization’s professional and ethical standards and knowledge.
While new vendors and technologies promise tremendous value, clinical expertise and experience are must-haves for creating meaningful insights.
With a strong foundation, advanced technology can apply clinical methodologies at scale.
Eighty percent of health information in medical records is unstructured notes, such as physician and nursing notes, and unusable. (Source) NLP is a technology that can read unstructured notes and turn them into structured information. NLP automates and augments the work of those spending time searching through medical records.
People trying to understand data spend considerable time thinking about and acting on hunches to determine the best way to slice and dice that data. With the advancements of information technologies, much more of this work can be automated and the information even augmented. Cutting edge technology, properly programmed and trained with clinical models, can reduce the time spent in manual search and analysis.
Example: Drilling down to the root cause of a length-of-stay (LOS) issue within a theoretical health system.
The example below clearly demonstrates how improvements in efficiency can be achieved through advanced technologies. In the past, a long LOS for a particular orthopedic procedure might mean working with all of the surgeons who perform that procedure. Instead, AI can provide more specificity more quickly and identify the areas to focus on. In this example, the analysis indicates there are only two surgeons whose performance needs review.
From broad issues (difficult to address) to specific, actionable issues
Hospital #1 accounts for 32% of total excess LOS
Of that 32%, excess LOS as a result of orthopedic surgery accounts for 23%
Within that 23%, potentially preventable complications are 46% higher than expected
Of those potentially preventable complications, 68% occurred in patients with preexisting conditions
Of those patients, 22% were treated by “Doctor A” and “Doctor B”
For each challenge, there is a potential solution.
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