Using data analytics in healthcare can, and should, be more than just looking back at what happened in patient care. According to Michael Christiansen, Solutions Consultant at Perspectium, as an organization matures in its analytics’ efforts, it should move towards to a predictive model where data can be used to determine what will happen and leverage this intelligence to improve patient care.
The focus on evidence-based medicine is driving healthcare organizations to use data to predict what may happen in a large number of cases. Data can also be used to drive changes in clinical practices, such as what tests are run, and which procedures are recommended.
The two things that are imperative for a successful health analytics program, according to Christiansen, are that it comes from the top and it becomes a part of an organization’s culture.
“To focus on evidence-based medicine, you have to have protocols that are based on it,” he said. “You can’t get there without the data, but you also need the CEO level to push for it and insist that the organization is using analytics to get to the evidence that will allow it to get to the next level of patient care.”
With that support, it becomes a culture that it will continue to grow as teams invest in the processes and mindset needed to support it. For an organization to really excel in analytics, you must have open collaboration, if not close integration, between the data analysts that may be scattered throughout departments, such as network, procurement and business management, for example.
“When you have data analysts who were out in specific teams, as well as your clinical data analysts, it can be a struggle to keep them from becoming siloed,” Christiansen explained. “Collaboration is imperative.”
Healthcare organizations face challenges beyond potential silos, however. As Christiansen shared, the biggest obstacles surround information security and data privacy.
“There are numerous requirements for security, but there is probably a common thread of requirements for companies that want to go down that road,” he shared. “For example, they must understand where the data can and cannot go, as well as any mandates and regulations that surround where it is placed in terms of privacy and security.”
And, as an organization embarks on its data-driven journey to better patient care, they must make those determinations – and resulting decisions – up-front, based on industry best practices before they move forward.
“Another challenge is how quickly technology changes,” Christiansen said. “An organization may need more than three years to roll out a comprehensive EMR tool, for example, but technology changes during that time. They will need to decide: Are we going to stay cutting edge with the latest and greatest technology; or, are we going to stay with something that is three years old? It may no longer be considered ‘cutting edge,’ but it has proven to be reliable and valuable, so it is worth keeping.”
“Organizations need to move from spending money on analytics to investing money in analytics,” he continued. “There should be budget set aside for trying out new tools and methodologies and experimenting a little bit with new technology advancements happen – including new mandates and regulations – in the industry.”
“Just eight years ago, there was no way you would put healthcare data in the cloud, but today people ask, ‘Why do we need a server on site?’ or ‘Do we need to keep this data on site?’,” he explained.
Just as healthcare organizations have embraced cloud-based infrastructure because of its enhanced security and ability to contribute to patient care initiatives, they will also move towards investing in data analytics to better serve patients. The key to success will be to collaboratively analyze data with an holistic view to how it can be applied to improve care in the future and not just review what happened in the past.