What Does Big Data Mean for Your Practice?
If you want to find out how Big Data is helping businesses, there’s no better example than healthcare. Beyond improving profits and cutting out waste, Big Data in healthcare is predicting epidemics, curing disease, improving quality of life and avoiding preventable death. But what does Big Data mean for your practice? The biggest benefit is that it identifies industry trends. But note that trends are just that – trends. So, although they may not be immediately applicable to your practice, being aware of them could give you valuable insights into your business.
What is Big Data? This term refers to extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.
Spot the Trend!
Using Healthbridge data, we compared the percentage of ‘not covered’ patient Benefit Check responses received in 2014, 2015 and 2016.
At first glance, the graph indicates that as each year progresses, so does the number of ‘not covered’ patient Benefit Check responses. This makes perfect sense, and for most privately practising medical professionals, it comes as no surprise either – after all, medical-aid savings tend to be depleted towards the end of the year. However, on closer inspection, what is interesting is the speed at which this starts to happen. In 2016, the first major spike of ‘not covered’ patient Benefit Check responses occurs as early as April, at the 8% mark. By comparison in 2015, the 8% mark was only reached in May; in 2014, it was reached closer to June.
Possible Reasons for this Trend?
• Patients “Buying Down”
As medical-aid rates increase and economic conditions worsen, patients start moving from fully comprehensive plans down to mid-range plans and even saver plans1. This means they have lower benefit levels at the start of each year.
• Annual Changes in Medical-scheme Cover
Every year, medical-aid schemes change how their benefits are structured, especially for out-of-hospital cover.
• Changing Medical Aid Patient Profile
The rate at which older members are replaced with younger members is slowing. As the average member age rises, the average claims per member are seen increasing too.
So, What’s the Takeaway?
In this example, Big Data allowed us to analyse the responses received for patient Benefit Checks across hundreds of different medical practices in South Africa, and to see how these varied over a three-year time period: 2014, 2015 and 2016.
What’s the lesson for your practice? You could, for example, review the robustness of your collection processes. Many practices choose to run a different process at different times of the year, depending on the monies they need to collect. However, as the above example has shown, you might want to tweak your approach, depending on what the trend reveals to you. You could chat to your business partners who may have some best practice examples to share with you. In any case, try to check patient benefits all year around, and not just from the halfway mark.
Medical Schemes: http://www.medicalschemes.com/files/Research%20Briefs/RBPrevCD20150128.pdf
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