Defining relevant indicators over time to measure the impact of big data technologies in healthcare

BIGMedilytics-Sandra-Sulz-KPI-Information-Road

Defining relevant indicators over time to measure the impact of big data technologies in healthcare

30 June, 2021

In terms of project outcomes, there is a clear expectation of what we intend to achieve that is frequently translated into key performance indicators, or KPIs, to measure and visualize in dashboards. And there is also a clear expectation regarding the process, defining milestones and achievements.

However, in big data projects, conditions change rapidly. In this case, we are facing an adaptive process of identifying, testing, and redefining.

In the third video released by the Erasmus University with the lessons learned in the BigMedilytics project, Dr. Sandra Sülz, assistant professor at Erasmus School of Health Policy & Management, presents a causal model that explains how to measure the productivity of big data.

Measuring the impact of big data technology requires different kind of KPIs. One needs KPIs that stimulate dialog on the sequence of cause and effect mechanisms. And one needs KPIs that remain relevant over time. In this video, the sequence of cause and effect mechanism is conceptualized with the notion of first, second, third, and fourth order of change.

Several lessons can be learned:

  • Measuring impact is an iterative, recursive process of moving back-and-forth between finding out which indicators are feasible, acceptable, measurable, and informative.
  • The impact of big data technologies can be measured with four types of KPIs that capture:
    • how big data innovations change the information provided.
    • how big data innovations change the decision-making process.
    • how decision-makers attribute value to the information.
    • how big data technologies might affect patient experience, population health, costs, and professional satisfaction.
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