Improving Patient Flows With Data Science And Analytics

Our team was recently asked how data analytics and data science can be used to improve bottlenecks and patient flows in hospitals. Healthcare providers and hospitals can have very complex patient flows. Many steps can intertwine, resources have to shift in between tasks all the time, and severity of patients and new patients push the order of who needs to be seen all the time. This does not make it easy to approach process improvement in a hospital. This problem is a process problem. Something industrial engineers and six sigma practitioners love. They love looking at healthcare process problems in Excel sheets with thousands of tabs and thousands of rows of data. However, now we no longer are limited to doing our analysis and model development in Excel spreadsheets on thousands of rows of data. We now access to more complete data sets that in our experience can be in upwards of the billions of rows and more powerful computational systems we can analyze patient flows and bottlenecks much accurately and effectively.

Now with these tools like SQL, R and python we can analyze these data sets quickly.
It’s not just about the tools. In fact, with such powerful tools it can be tempting to try to make models and algorithms that can solve all the problems in one go. One of the big issues with this approach when looking at patient flows and bottlenecks in hospitals ( or really any problem) is it is far too general of an angle. It makes it very difficult to assess when an analysis is finished and often keeps the data scientists and analysts spinning for weeks without getting a real answer.

The problem here is the scope of looking at everything is very difficult to manage and pinpoint issues. Instead of trying to attack all the processes and procedures a hospital has. It is a better idea to break down several general categories of procedures/patient flows/processes that you believe are likely to have bottle necks. This is because hospitals have so many different possible paths and processes (I am going to use the word process to describe the patient flow below) that blindly looking for some sort of bottleneck will take forever (it is like looking for fraud in healthcare, if you try to do it too generally, then it will be near impossible to find).
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The first step is to find out the problem areas. Without knowing what you want to target it can be very difficult to know what the solution is. In a perfect world your hospital has a database that tracks all the processes and procedures that are done. This will make it easy to develop a query or Jupyter notebook that can point out the main choke points. This will further help your team limit the amount of unnecessary work that is required. Once your team knows where the problems are, then there are low hanging fruit your teams can use to look for issues.

Abnormalities

Abnormalities, like inconsistent times for patient flows, whether that is specific doctor or in general can state that there is a problem. Finding these specific outliers can be quite simple.

For instance, let’s say you look for outliers in times for patient flow x and you hypothesize that specific days of the week, or times of the day are more likely to have longer times for certain steps. Then you pull out those steps and analyze time at a time granularity to flag each process individually. You might find that summer’s see decrease in productivity or that during the 4th of July your ERs overflow or perhaps some much less obvious data points.

One key point here, is that you come up with a theory first. Because having a clear question and hypothesis makes it much easier to look for evidence. With a clear question you know where to target further analysis. You can use a query to clean up your data and break it down to the granularity required. This might be on a hospital level, doctor level or maybe even down to the procedure level.
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From there you could apply a basic algorithm that is good at highlighting outliers(like a basic IQR calculation or something more complex). Once you have found outliers then it can lead to further analysis into why there were longer times or inconsistent times in specific processes. There are many plausible causes, but now you have decided on a category of procedures, hypothesized and found a plausible weak point. Having these basic steps makes it much easier to move forward.
Following these steps you can repeat a similar process. Theorize why you are seeing the outliers, what might be causing it, and further research the data. This could be caused by bad processes, having too few staff during times of day when you need more people on hand (think queue theory). Once you know which steps to look at, you can start putting next steps in place such as process improvement teams who are now pinpointed towards the exact problem rather than simply sending in a team of analysts to follow around doctors and guess where the problem area is.

Chokepoints

Besides abnormalities, another common issue is that some processes might need the same resource. Now one way to locate these bottlenecks is based on the first point of abnormalities. As a bottleneck might be one of the problems causing the the abnormality. However, choke points might also hide themselves in the fact that the step in question always runs long and thus there is no abnormality. Instead, this analysis will require asking a simple question. Are there steps in patient flows that overlap and seem to take a long time or at least longer than expected. Certain steps might take a long time, like certain labs that take a while to run. There are others that shouldn’t. Analyzing these steps could lead to hospitals putting in new suites or hiring new specialists to deal with the heavy load in certain areas.

​Conclusion

​Improving patient flows is an important step to reducing patient costs and improving their satisfaction. By reducing the time they spend in the hospital and healthcare system you reduce the amount of hours required for staff to take care of them. This should help reduce the overall costs. Our team always looks as it as a reduction to the patient even though it should also in turn be a reduction in cost to the healthcare system. From our perspective, anything hospitals, insurance providers and consultants can do to help reduce healthcare costs in our current system need to be done.
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