Healthcare Analytics: Are You Using Data to Drive Decisions?
By Meghan Franklin
An electronic medical record company works with customers to identify providers who may be over-prescribing narcotics and patients who may be physician-hopping to feed an opioid addiction.
Physicians in Flint, MI discover the percentage of children under five-years-old in Flint with elevated blood lead levels nearly doubled after the city switched its water source to the Flint River.
Emergency Department (ED) nurses form a task force to identify factors that contribute to pediatric asthma patients being readmitted to their ED.
Hospital administrators launch a patient satisfaction survey to help assess how they can improve the patient experience, from hospital admittance to discharge.
What do all of these scenarios have in common? They all use healthcare analytics, the field that seeks to transform captured healthcare data – whether related to billing, clinical outcomes, patient satisfaction, etc. – into actionable insights. Healthcare analytics is increasingly important in a healthcare environment that continues to move away from a fee-for-service reimbursement model and towards value-based care/a pay-for-performance reimbursement model.
According to a 2018 article in Becker’s Hospital Review, organizations with data-driven decision making have output and productivity five to six percent higher than their peers. How is your organization currently capturing and using data to your advantage? Here are a few things you may want to consider as you dive deeper into healthcare analytics (or perhaps just start to get your feet wet).
- Do you have the right tools to capture the types of data that will help drive decisions that are important to your organization? The one with the most data doesn’t win; healthcare analytics is all about how data can provide insights that will contribute to your organization’s strategic goals. While some organizations may choose to implement a claims-based analytics system, others may choose an electronic medical record-based analytic module. Still others build enterprise-wide data warehouses that can help drive both clinical and business decisions. Ensuring your organization has the tools to capture meaningful data is an important first step in building an analytics program.
- Do you have people/roles identified who can use captured data in meaningful ways? As your organization’s analytics program matures, the team you have in place will likely grow and mature, too. No matter what stage you’re at, however, implementing any sort of data capture program should necessitate discussions about who should have access to what types of data and what the expectations are for that data usage. Is your patient experience team responsible for looking at how appointment wait times may affect patient satisfaction? Who is looking at how your organization’s new text messaging appointment reminders are reducing no-shows? Who is analyzing trends in readmission rates for certain patient populations? While some data analysis and decision support can be automated – an electronic medical record alerting a provider that it’s time for a patient to receive a certain immunization, for example – a lot of captured data requires deeper analysis to provide value to your organization. Do you have someone or a team of people who can derive that value from your data? Just as important, perhaps, do you have people who can communicate that data to those who need to act on it? In his article, “4 Ways Healthcare Data Analysts Can Provide Their Full Value,” health data analyst Russ Staheli, MPH says, “Presentation is critical. After the work spent getting data to this point, the analyst needs to tell a story with the data in a consumable, simple way that caters to the audience.”
- Have you made data an important part of your organization’s culture? There is a world of difference between setting an organizational goal and actually using data to track that goal and determine where you are succeeding or falling short. A hospital with which I worked launched an organization-wide initiative to eliminate preventable harm. It may have been a lofty goal, yes, but the program’s insistence on regularly capturing and reporting on preventable harm data led to an impressive reduction in preventable harm over a relatively short amount of time. Organizational leaders set clear, data-driven goals each quarter and encouraged complete transparency between units so that they could learn from each other. They even relied on data to launch a thrice-weekly email blast that talked about patient safety successes and near-misses. In short, this hospital’s data-driven patient safety program made data an integral part of the organization’s culture and a regular part of their patient care conversations.
Meghan Franklin is a freelance writer who has worked extensively in healthcare, both as a writer and as a project manager.