With health care spending in the United States now exceeding $3 trillion, and a recent government report stating that Medicare could become insolvent by 2026, it’s critical health care providers have a clinical analytics program to measure and evaluate outcomes that leads to improvement.
Yet creating, sustaining and deriving value from a program that can connect financial, operational, quality, safety and satisfaction data for insights poses many challenges if not done correctly. The American health care system is world-renowned for developing advanced treatments and technologies, but often suffers from an inability to use data to improve in meaningful ways.
In my role at Vizient, I help health care leaders across the country define and implement data-driven improvement programs. One thing is certain: the American health care system doesn’t suffer from lack of data or information. Instead, the biggest gap we have in achieving improvement is the lack of insights.
In fact, data by itself can’t do much of anything without some help. The data must be transparent, deep and allow for meaningful comparisons. And the people around the data, from engaged leaders to skilled data scientists, play equally important roles.
The three C’s—centralization, cultivation and change—are solid characteristics to emulate for any health care organization trying to use their analytics program to drive improvement.
Centralization. The concept here is that organizations in health care (and probably other industries, too) tend to keep their data and analytic resources in silos. Every larger hospital has disparate databases and analysts attached to each. By its very nature, this discrete approach to data can only ever result in change that is equally isolated. A centralized data approach, often called Big Data, delivers a more robust and cohesive way to reveal big insights and opportunities, often through a simple dashboard.
The alternative is an untenable situation where organizational leaders have to make broad decisions based on assumptions, leading to changing things that don’t need to be changed and missing the actual things that should be addressed.
Cultivation. Having the right people for a clinical analytics program means using data scientists. These specialists go a step beyond data analysis, delivering the context that decision-makers truly need to guide improvement. We have too many analysts whose role is to respond to requests and not enough data scientists who are proactive and have the ear of leadership working in health care today; and yet those data scientists are critical to understanding and achieving improvement.
Cultivating data scientists from your bench of data analysts and making sure they are being properly leveraged is essential to the ongoing success of any data-centric improvement program. Data scientists are key to unlocking more engaged clinicians and delivering far better improvement results.
Change. Data is not the end of the improvement journey, it’s just the beginning. Data transformed into insights is what informs change, but it takes an informed team—an improvement community consisting of leadership, data scientists and clinicians—to truly make performance improvement happen. I have found that however you define it, improvement is always a team sport.
That also means it’s OK to get outside help. There are many organizations like Vizient able to provide the necessary support, networking and guidance along the journey to a successful health care analytics program.
