Automating a little can help a lot

David Mitzel, Senior Developer, X by 2

Written by: David Mitzel

Hospitals are nothing if not busy places. Doctors, nurses, patients, families, and support staff make up the human fabric of the daily life at any hospital system. And undergirding all of these stakeholders and activities is another sort of lifeblood for the hospital – data and information. The reliance on information has become the way that hospitals operate, and creating, organizing, and optimizing information on a real time basis is an operational imperative for any hospital system. One of the problems, however, is that hospitals need and use a fair amount of information that they do not create. This article focuses on one of those information streams – the data sent to hospitals from insurance companies – and suggests some best practice approaches for quickly automating the file load process as part of any hospital’s overall data quality improvement efforts.

The initial process is likely familiar to anybody in a hospital working with data from insurance companies. There are multiple file loads – from dozens to hundreds to thousands – on a daily and monthly basis, sent from a multitude of insurance companies. Somebody, often from the IT group, is tasked with reviewing the files, determining what should and shouldn’t be loaded, firing off the necessary load processes, validating the results, and following up on any files that weren’t received this month or didn’t load successfully. The entire process is laborious and time-consuming, and can easily result in errors. It screams out for an automated solution.

That said, there are challenges that any automated solution needs to address. Chief among the challenges is deciding what can and should be automated, and what can and should be left for operator intervention and decision-making. While automating the entire process is the goal, in some cases, experience and intuition cannot be automated, so a process should be created that allows for an operator to make a judgment on something where appropriate. Another challenge is determining how to handle the multitude of file format and data errors that inevitably occur on a daily basis. Some of the errors are often due to developer mistakes, while others are transmission problems, and still, others are from files that should not be loaded at all, either because they are duplicates, or because they aren’t relevant.

The first step in the automation process is to identify what human operators already know and codify that through enhanced metadata. Operator decisions such as file action (load, archive, ignore, etc.), load process, load frequency, identifying file formats, and many more can all be easily expressed in metadata. From there, a standard and automatic way of responding to data and load errors and exceptions should be determined. This is not just a technical requirement, but also requires the input of those members of the team responsible for communication back to the insurance companies. All of that becomes input for the requirements for developing an automated system that handles incoming files from any and all insurance companies.

Automating file handling in this way will significantly reduce the time required of operators as a part of the process. The whole process orientation is flipped from being manual labor dependent – complete with such human delays as conflicting priorities, run on meetings, and sick days – to a process where operator intervention is entirely exception based. Going forward, rather than manual monitoring of a file load system, operators are instead notified via an electronic ticket from any number of collaborative software tools available. This kind of automation approach also has the important side benefit of allowing valuable business and IT resources to be repurposed for much more important tasks – tasks that have more of a direct impact on patient and employee well being.

From a technical perspective, automating the file load process can drastically reduce the number of file processing errors by reducing operator intervention. Also, the creation of more robust metadata to define the files took what was information that was housed in people’s heads and placed it in a systems structure, thus reducing organizational risk. Finally, one of the most important technical benefits is that the automation structure provides a place to perform data validation on each and every file, thereby beginning the process of improving any hospital’s data quality quotient over time. That is a non-trivial benefit that pays short and long term dividends for any hospital hoping to improve their data quality for the purpose of implementing an analytics approach to information.

There is one additional benefit worth mentioning. Many hospitals struggle with breaking the cultural barriers of doing things differently. It can be difficult to abandon something that works – even if it is cumbersome – for something that requires less human intervention. Some people will view the automation of formerly manual tasks as a diminishment in the value of the job they perform for the hospital. That, of course, is not true, and the key is to allow people to do things that are more important for, and valued by the hospital. This relatively simple automation process is a great step in that direction and has the potential to significantly improve the validity and integrity of data received from insurance companies. It can also be used as a good teaching moment for hospitals working toward modernization and better data usage and insights: a little automation can go a long way.

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