Walking the Workflow

Making clinical workflow work

The use of artificial intelligence and machine learning solutions for early detection for increased risk of high-burden disease is an increasingly hot topic.

However, even the greatest algorithms are of little value unless they can quickly become actionable and lead you to intervention strategies.

In our initial testing and validation, we quickly realized that flagging high-risk patients is only impactful if it results in a clear task or action, and if it brings those patients in for further evaluation and possible intervention. Otherwise it’s just another report.

Maximizing workflow requires data about patients flagged as high risk being prioritized for follow up evaluation and intervention by physicians, care managers and case managers.

Machine learning solutions are part of the solution

Strong workflows require timely intervention aimed to help clinicians detect, prevent, or delay onset of specific diseases.
Numerous challenges must be overcome, including:

Alert fatigue

Doctors with a panel of 2,500 patients need 21.7 hours per day to provide recommended care. Given the current average panel size,  primary care physicians are nearing their breaking point with burnout being a growing problem.

Scheduling

Understaffed hospitals require ways to stratify patients needing evaluation to determine their priority among previously scheduled patients.

Manual administration

Updating flowsheets, taking notes, notification follow-up, checking insurance, and other labor-intensive chores have traditionally been done manually. They remain critical to having follow-up appointments and evaluations approved.

Actionable data

Overblown hype about machine learning and cognitive initiatives has led to unrealistic wish lists.  Analytical issues such as needing to create longitudinal records of patients, doing quality scores, and ensuring regulatory reporting compliance don’t get fully discussed, even as facilities integrate incomplete big data solutions.

Not surprisingly, these “solutions” largely fail to produce the insights that quality healthcare providers need. 
Facilitating intervention strategies that can reduce risk or prevent onset entirely don’t fare much better.

Key to success

At EarlySign, we’ve found that implementation only succeeds within a culture combining an embrace of innovation with a realistic assessment of how machine learning technology can be applied to patient care.
By embracing that commitment, organizations can streamline their care management process while demonstrating to their stakeholders the competitive advantages new approaches will give them.

Investing time and resources into a solution that is both disease-specific and agile will drive the processes…and the success…needed to quickly show value and justify the decision to implement.

Are you looking to solve high-burden disease challenges?