Why is robotic process automation disappointing healthcare organizations?

All healthcare organizations — on both the provider and payer side — struggle with three operational imperatives: maximizing revenues and operating margins, improving clinical outcomes, and optimizing administrative efficiency. The success or failure of these efforts directly determines the success or failure of the business.

As a result, leadership is always on the lookout for ideas, innovations, processes or anything else that can help. Operational leaders recognize that technology can be an enabler of virtually any operational improvement activity planned or considered. Yet most are intimidated by or even outright fearful of technology. It’s a foreign language to them and has often proven to be disappointing in terms of cost and deliverables.

In the arena of optimizing administrative efficiencies, there is no more promising technology than robotic process automation (RPA) and operational leaders know this. But they are often hesitant to implement RPA for one (or more) of these reasons:

  1. They generally distrust technology, based on decades of disappointment.
  2. They’ve already begun implementing RPA and have not seen desired or promised outcomes.
  3. They’ve heard from other colleagues in the industry or read articles about mixed results from RPA, which only supports their general paranoia about technology.

But it’s clear that RPA — when properly implemented — can create a dramatic positive change in the delivery of healthcare.

When I first moved into healthcare more than 30 years ago from an industrial engineering background, there were few defined processes or measurement tools and techniques in place. It was shocking, really. When I was chief executive officer of Humana, North Florida, working with the finest leadership team I’ve ever known, we had some rules:

  • If you can’t measure it, you can’t claim it. Subjective measures of success were meaningless.
  • If there is no clearly defined, written process, there is no process.

One of the things we learned was that even the most clearly written and defined processes rarely worked exactly as written and defined. We also learned that all processes, especially the complicated ones, experience scope creep.

And therein lies the core problem with RPA.

The typical methodology for implementing an RPA project is to work with leadership to first pick “an obvious” process — an important, complicated one that meets the basic criteria for effective RPA deployment. This seems simple enough. Leaders know which processes are critical. And they know which processes have repetitive elements with simple-to-define components that fit well into robotics’ sweet spots.

Once the process has been chosen, typically discussions take place with internal experts to define which process components offer the best opportunity for transformation and then to designate specific robotic solutions that can replace or augment human activity for the selected components.

This is a mistake.

First, this method of identifying a process ripe for RPA deployment is entirely subjective! As an engineer, I ask myself: Why in the world would a subjective process be at the center of the application for an objective solution like RPA?

What’s even worse is that the success of the project is hindered by the choice of people involved. By working with internal experts on the process, their own biases are brought to bear. These are the very people who designed the imprecise, nonoptimized process to begin with.

This is why it’s important to adopt an objective process when determining where to deploy RPA. One way this can be achieved is by leveraging dark (or hidden) data — in other words, the information assets the organization gathers during its regular business activities — to map processes, objectively defining them and their flaws and the areas ripe for improvement. This technique helps transform the choice of process and component from subjective to data-driven and objective. It establishes a methodology for measuring success and for creating a continuous process-improvement capability that is departmentally and functionally agnostic, applying to any area of operations — from finance, to operations, to clinical delivery.

This approach also benefits from using trained technicians who have implemented RPA at scale across multiple industries.

Simply put, the way to transform RPA from a disappointment to a success is to convert your process discovery and analysis from subjective to objective, use the right and best resources, and build into your processes the capability to continually evaluate, implement and improve.



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