Data-driven decision making has completely transformed the way that today’s businesses operate.
For healthcare, using data to drive better decisions forward has quickly become the new catalyst for improving patient outcomes, the quality of care and organizational efficiencies across the entire healthcare setting.
Take, for example, the shift to value-based care. Under this new care model, organizations are under pressure to reduce overall spending while dramatically improving the quality – and safety – of care being delivered. Doing this successfully requires access to reliable and timely data to make more informed clinical and financial decisions, as well as to help monitor the impact of those decisions on outcomes over time.
While it’s clear that data analytics plays a major role in this equation, many healthcare organizations are still struggling to effectively use data to drive informed decision making. One of the biggest hurdles standing in the way concerns data integrity since what may appear to be the same thing could be defined – and interpreted – differently even within the same organization.
There is a light at the end of the tunnel for healthcare, however, where the key to overcoming these challenges lies in ensuring that the data being used to make decisions is reliable. After all, having trust in data minimizes the time and energy spent on debating who is, and is not, correct. Having trust in, and an agreed upon understanding of, data also helps bridge communication barriers, and provides an objective basis for tackling difficult issues. The end result is a single version of truth, which is agreed upon across the organization.
Arriving at that single version of the truth, however, is easier said than done. It requires collaboration, consensus and communication across the entire organization. From understanding the reasoning behind the agreed upon logic to coming to a general consensus on the interdependencies of measures that are defining the data, it’s unusual for different departments to easily see eye to eye. After all, adequately capturing the resulting knowledge that’s produced through this process is complicated, to say the least.
Thankfully, data governance strategies can make this a practical process - and one that saves time while avoiding unproductive arguments. With a data governance strategy in place, organizations have the basis for ensuring that data is reliable, which is a prerequisite to making the best decisions possible for the entire organization. This type of strategy also creates an opportunity to assemble repositories of knowledge that document the reasoning, debate and overall collective wisdom that go into effective decision making.
The next step is being able to translate decisions made during the governance process into useable and meaningful information. To do this, organizations need to have discipline across all entities, as well as the right technology in place to support and streamline the process. Even the most seemingly straightforward measures may require expressing and computing complex business logic. If done manually, however, defining, documenting and maintaining that logic can be an overwhelming task. Because of this, automating the process through the use of modern analytics technologies holds the promise of higher quality and more reliable data with the added bonus of more optimal use of valuable staff resources.
Today’s modern analytics platforms have emerged to provide comprehensive solutions that combine critical capabilities needed - such as data integration, operational control and automated rules processing - that tie together functions across the realms of governance, information administration and data analysis. This data foundation lays the groundwork for the use of advanced analytics, which is quickly becoming an important reality with the evolution of machine learning and artificial intelligence (AI). Without a foundation, organizations will be left to deal with the age old problem of “garbage in, garbage out,” which threatens to compromise the promise of data-driven decision making.
Data-driven decisions, fueled by comprehensive, timely and reliable data sets and powerful analytics, hold the potential to significantly transform the healthcare industry as we know it. For that to happen, though, good information governance practices need to be seamlessly integrated into analytical solutions. With the foundation of strong governance and trusted data in place, virtually everyone who plays a role in the healthcare system can be an analyst, equipped with the information necessary to make ever better decisions.
By George Dealy, Vice President of Healthcare Solutions, Dimensional Insight
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