There are waves of data flowing within the healthcare industry, and providers that tame the swells and ensure the quality of health data can reap numerous benefits.
The importance of high-quality health data became even clearer after the HIMSS 2016 Conference, where many health IT companies noted they presuppose data from healthcare organizations is high-quality. Modern health IT solutions are built on the back of high-quality data, and if an organization’s data is not clean, the provider will likely miss out on many of these new tools. The reality is that the future of health IT initiatives is going to rest on the quality of the data involved.
No one person can make sure a healthcare organization’s data is clean. This task requires a team of people that spans across the organization. One way to ensure involvement from multiple stakeholders is to give formal titles and responsibilities to representatives in various departments so that data-cleaning tasks are an official part of their jobs. Data quality is not something that can be done casually — it takes long-term commitment from a deeply engaged team. However, once the team is engaged, the results will be significant.
Once a team has been identified, data quality efforts should start with patient identification and cleaning. Far too many organizations only clean up their master patient index. While this is a good first step, all the cleaning will be for naught if there are no procedures in place that prevent file duplication in the first place. The industry may never get to 100 percent patient matching, but all healthcare organizations can likely do better than they are currently doing.
The information that goes into a healthcare system directly affects the information that comes out. Organizations that want high-quality data must find ways to improve the data being entered into their electronic health records (EHRs). Start by making sure users are well-trained on the EHR system, then think about how people enter data into the electronic files. These two simple steps will go a long way toward solving EHR data quality issues.
Finally, while health data-sharing can improve healthcare, it also comes with the risk of propagating bad data. To avoid this issue, be sure all interfaces are sharing high-quality data and that the data being transferred does not lose its integrity in the process.
As organizations consider the quality of health data they collect, store and leverage, these efforts will help them secure a better future built on the back of high-quality health data.