Editor’s note: Datapipe was acquired by Rackspace in 2017.
Electronic health records are becoming the norm in the medical field. In addition to offering advantages for health care providers and patients in terms of information accessibility and fast, easy ways to share data with other members of patients’ care teams, the platform provides opportunities for deep analytics. By reporting on and assessing patient trends, treatment outcomes, demographic characteristics and more, medical centers can hone their policies and procedures to optimize patient care and promote health in their communities.
Big data analytics further enhance these possibilities, especially with the assistance of powerful cloud computing to store and process large amounts of information. However, health care organizations face significant challenges before they can turn digital resources into meaningful insights and effective programs. Especially since the medical sector is fairly new to the electronic record landscape, navigating the nuances and complications of the system can hinder data analytics efforts.
According to Health Affairs, one of the biggest obstacles for health care organizations to overcome is getting the right information for their analytics projects. This includes the type of information obtained as well as its quality. As the source emphasized, medical centers sometimes gather as much easy-to-obtain data as they can, without really honing in on the most appropriate and valuable resources. Consequently, their results have little real value.
Similarly, they might utilize data without knowing where it comes from and what’s already been done to it, the source added. This can cause skewed results and misleading conclusions – which is precisely what health care organizations must seek to avoid.
Furthermore, putting the right technical foundation in place is a prerequisite for successful data analysis. As Health IT Analytics highlighted, many medical entities struggle to eliminate data silos, which obstruct innovative analytical efforts. Particularly within a big data context, strong analytics are driven by integrating disparate sets of information, such as clinical, financial and operational data, the source added. Only by facilitating the flow of information from these components can health care organizations pursue insights about factors such as gaps in care, identify patients not adhering to instructions or reduce costs in resource utilization.
Another potential issue arises from poor documentation, whether it’s the result of human error or problems with EHR software. Ensuring data is clean and accurate before running analytics will naturally have a strong impact on the quality of the results. Reports and insights can only be as good as the quality of information that fuels them.
Finally, as they pursue these analytics and other digital initiatives, medical centers face strict requirements for upholding patient privacy and data security. In particular, they must ensure that their system abides by the Health Insurance Portability and Accountability Act at all points, including when data is in motion and at rest. Adhering to these stipulations helps organizations avoid legal consequences, while safeguarding their resources and their patients’ privacy.
Although some entities may find it challenging to create the IT infrastructure they need to pursue big data analytics and other projects while also implementing measures to comply with HIPAA, this basic foundation is a critical element to long-term success with analytical projects in the health field.