Analytics and data science are important tools in healthcare. Not just because they might be able to predict when someone will have a heart attack. Good data science and analytics are important tools because they can help make better decisions when it comes to spending and reducing inefficiencies in healthcare. Being able to reduce the cost of healthcare for the providers would in turn allow them to allow more people access to healthcare.
The temptation healthcare has when looking at all the other industries that have been using analytics and algorithms for a long time like finance/banking and ecommerce is to try to take on all the data problems in healthcare in their entirety. This in turn leads to large investments in data teams, data lakes, and other data centric initiatives that don’t always succeed. Similar to one of the reason the DC movies are failing to capture audiences attention like Marvel. They rushed into trying to create something similar to what Marvel created in a decade in a third of the time. Similarly, many healthcare companies try to set up data science shops and attempt to replicate what Google, Amazon and some of the other big tech companies have been doing for more than a decade in a few years.
It takes time to set up analytical teams and data systems at that proficiency level (let’s not even get started on data labeling). Instead of trying to create an entire universe of data analytics and algorithms in 1 year. We have had success by picking one or two problems to start with (like selling books online but in healthcare), pick a point of view to approach the problem and just tell a simple story. If your data teams are able to do this and succeed, then you will be that much closer to success and have started to gain supporters and buy in from managers. Healthcare is complex, so start by focusing on a few problems, and a limited perspective before investing millions of dollars in a large team.
Pick One Problem To Take One
When it comes to healthcare analytics(or any analytics), it is important to pick a specific problem and then break that problem into further bite sized pieces. For example, if you are trying to develop an algorithm that detects fraud. It can be very difficult to design a model, algorithm or system to abstract the concept of fraud as a whole into a single mathematical expression. Especially when the healthcare interactions are rarely classified fraud or not fraud.
That doesn’t stop you from doing analytics, or predictive modeling. It just changes your approach. Instead of developing complex data science models, focus on what has a good ROI first. A basic system that can help first reduce the amount of claims analysts have to wade through for fraud or point out larger systemic problems at a higher level can help know where to first focus your efforts. In addition, they can be labeling and noting patterns through the interactions they get through.
After spending some time looking at those cases, your analytics teams will have a further understanding of the problem as well as a lot of great best practices and labeled data. In the end, this might seem slow, but slow is better than failing.
Focus On One Perspective
When developing analytics of any kind there are lots of different angles and points of view that you can take. For instance, in healthcare you could focus on the healthcare providers (hospitals, ERs, etc), patients or types of procedures. Your team might not have the resources to develop a tool, dashboard or algorithm to approach all these different angles right away. Thus, when taking on a new project like “predicting patient readmissions”, pick a category that will benefit your team the most. Often times focusing on the provider first is one of the better options. This is because it is much easier to change provider behavior compared to patient behavior. When you alter one providers behavior the impact it has is much larger compared to changing one patients behavior. Both of these are two reasons why often focusing on analytics on the provider level first can be helpful. In addition, like was discussed above, it could help focus and pinpoint some of the more specific problems at the patient level.
Your Final Product Should Have A Clear Story
Once you have developed a final data points of the product, that product needs to be able to tell a story. Honestly, this is still a difficult part for me. However, it is a key concept that makes a huge difference. Even if you’re an amazing programmer, data engineer, or algorithm developer, if your product doesn’t portray a clear story to your end-users, then it could get ignored or misinterpreted. Usually, the flow I have seen that is successful for reports is have a high level of what the problem is, maybe include the cost of the problem if that is applicable. This gets everyone on the same page and also catches their attention. If you can state how big the problem is and how much it is costing them, it will draw the end users in.
Then you can either further go into the problem and break it down into subsets and then go into the next steps.
The next steps don’t have to be a direct solution. It might be a list of procedures that are problematic and causing a high amount of readmissions or fraud. This leads to the next possible project which is analyzing the why. Once you have figured out the possible why and solution, then you can refer back to the report and see if things changed.
Caption: Taking a quick detour, lets look at a few dashboards from actual companies. The dashboard above is from Health Catalyst. The top portion is attempting to state whether or not the cohort is currently meeting the target for various readmissions. This kind of states if there is a problem or not. It is pretty typical for medical analytics to do a comparison approach. The middle section is a little bit of a disconnect for me. It doesn't really flow with the story they are trying to tell. It talks about interventions, but this doesn't really connect with the first set of graphs. However, the last section that is a bar graph melded with a line chart makes sense because it tells you the history. So maybe you're not hitting your target readmission numbers, but you are improving!
Overall, the way you approach healthcare analytics or developing algorithms to predict events like patient readmission is no different than any other industry. It can be tempting to try to take on entire problems like fraud, or patient readmission as a whole. Healthcare’s massive amount of data and complexity of transaction does not make this easy. In order to manage this complexity breaking down the problem and deciding the point of view you plan to take will help increase your success and the rate of results.
Does your team need help developing healthcare analytics? Perhaps you are looking to measure provider quality or analyze patient patterns. If so, then contact our team today! We have a team of healthcare analytical specialist who can help create the tools you need.
If you want to read more about data science or analytics check out the articles below:
How Men's Wearhouse Could Use Data Science
How To Use R To Develop Predictive Models
Web scraping With Google Sheets
What is A Decision Tree
How Algorithms Can Become Unethical and Biased
How To Develop Robust Algorithms
4 Must Have Skills For Data Scientists
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