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System Implementation Projects in the Time of Big Data Need to Change

Looking over medical imaging

Cynthia Centerbar, PMP

“Insanity is doing the same thing over and over again and expecting different results.” - Albert Einstein

Today, hospitals depend on key performance indicators (KPIs) to measure performance outcomes. Yet application implementation projects continue to follow tradition and focus solely on the installation of needed system features. Implementation projects rarely include scope and activity with a focus on the KPIs needed to inform and measure quality and financial outcomes. As the below hospital found out, the results of this neglect can be inefficient, risky and costly.

Implementing a Health Information System: A Case Study

Following the implementation of a new radiology information system (RIS), a hospital’s radiology director used work ques to manually count the number of exams with a prior authorization. He then manually calculated the percent of exams authorized vs. those that were unauthorized.

His organization performed thousands of these exams each week, creating an onerous task for him that he had to perform for months. Why? Because, leadership wanted to know if their new prior-authorization workflow, enabled by their new system, was working. Their anxiety for an answer was rooted in their knowledge that high cost imaging exams require prior authorization for payment by insurers.

Existing Process
“The goal is to turn data into information, and information into insight.” - Carly Fiorina

While inefficient, the radiology director could calculate a pre-authorization ratio, but it was just one data point. The other data point needed to sufficiently answer the question, “Was it working?” was the payment rate of authorized exams. The rate was available in the hospital’s billing data, but because they had used a traditional implementation, they had not even had a conversation on how to measure the outcome of the prior authorization process, let alone a deliverable that would turn these data points into meaningful information.

As it turned out, a technical issue in the new process resulted in a lower payment rate for authorized exams. Unfortunately, the root cause of this lower payment rate went undiscovered for a long time.

The lack of discovery was caused by a flawed comparison. It was too cumbersome for the radiology director to limit his sample to just insurers requiring prior authorization yet the payment ratio calculated by the billing system did. Thus, when the hospital manually compared the prior-authorization rate to the payment ratio they appeared in alignment but they were not. More exams were being authorized than required them, making the authorization rate look artificially high and leading them to explore other causes for the lower payment rate.

How We Got Here
“You may ask, how did this tradition get started? I’ll tell you.” - Fiddler on the Roof

The traditional approach is a result of the organic growth and evolution of applications and technology. In the beginning, applications focused on automating cumbersome tasks. For example, when I started in healthcare, the clinical-laboratory system was limited to automating the recording of lab results from lab machines capable of producing hundreds of them within minutes. It had no functionality for ordering, communicating results, or collecting payments. Year by year, software developers added these features and the implementation approach simply added them to the list of features installed during the implementation project.

6 Steps to Modernize Implementation
“You can’t save time. You can only spend it. But you can spend it wisely or foolishly.” - Benjamin Hoff

Today, we no longer have standalone applications. Lab applications along with all others have evolved to form a complex technology ecosystem and the traditional implementation is no longer applicable.

Today’s implementation needs to recognize that the data in a new system is a valuable organizational asset.

A modern implementation needs to be aware that data flows within the ecosystem are at risk of disruption every time a new system is installed with potentially adverse outcomes. To mitigate this risk, we must update the implementation approach and include scope and activity that:

  1. Identifies the business value chain(s) impacted by the new system, both upstream and downstream
  2. Defines the KPIs required to measure and monitor the value chain quality and financial outcomes
  3. Delivers these KPIs as easily understood information via snapshots, dashboards and other reports
  4. Establishes performance improvement processes to address adverse outcomes
  5. Establishes data governance standards, processes and tools for the new systems’ data
  6. Incorporates the new systems’ data into the organization’s business intelligence and analytics infrastructure

The resulting loss in revenue, caused by insufficiently being able to answer the question, “Was it working?” raised concerns about what other adverse outcomes were yet undiscovered. To address, the hospital spun up a secondary project. In this time of big data, the scope of the second project was representative of the resources, activity and deliverables that must be added to the traditional system implementation.

A lack of realization is one reason the implementation project hasn’t been modernized but another is the added effort, duration and costs resulting from the increase in scope. As the hospital in the case study discovered, the work must be done.

Until it is completed, answering the “Is it working?” question will remain inefficient if not impossible to ascertain. A hospital will remain at risk for undiscovered adverse quality and financial outcomes, and opportunities for performance improvement benefits are delayed. When considered within this context, the value of a modernized approach shouldn’t be disregarded or underestimated.

The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.

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