Data analytics is a hot topic in the healthcare industry. However, people throw this term around with little understanding of what it really means. Similar to “Big Data” and “Affordable Healthcare”, it is difficult to know what people mean when the term can be applied to a wide variety of actual definitions that can be very different from each other.
For me, what differentiates analytics from other types of data reporting and analysis is how the topic or subject of the analysis is determined. In conventional data analysis, a report is developed to collect and format data in a manner pre-determined to address a specific issue or purpose. For example, you may develop a report that shows you the balance of your accounts receivables and to categorize these dollars into aging categories. This report allows you to get a current picture of how much money you are owed and by which health plans or patients. It allows you to focus on the money that is in danger of becoming uncollectable and to monitor your performance regarding this process over time. Similar reports are familiar to providers like census reports that show you admission and discharge activity and case mix reports that show you the clinical conditions associated with your patients.
These reports are normally run in conjunction with other management activities where tasks are involved that take action based on this data or the data is used to adjust the distribution of resources. They become a part of the operational management plan for the healthcare organization.
In analytics, you build a pool of data without a specific purpose in mind. At this stage, the objective is to get clean, reliable and timely information from whatever sources are available. These can be the clinical systems under the control of the healthcare provider that can provide detailed information about encounters with their own patients or data that is available to the industry that is collected outside of these systems, both of these sources are essential to building a database that reflects the performance of the provider and an environment for comparing or benchmarking this data against the rest of the industry.
Once you have access to this data, in analytics, instead of developing a report to provide you with specific information, you examine the data itself for clues and relationships that will guide you through a process of exploring these discoveries. True analytics is more like science than accounting.
One method of data analysis is to develop a hypothesis and then use the data to prove or disprove the hypothesis. This is done by taking the hypothesis in question and converting it into an actionable query of the data. For example, let’s say that Medicare is introducing a new value-based payment model for the services your facility provides. Your question might be “How will this new payment model affect my Medicare revenue?” You might guess that your revenue might decrease, since this is the normal result of these changes, but it is not always the case.