Commercials and TV shows have shown a silly scenario where a hardworking administrator is almost finished with his or her “in” box and next thing you know, someone shows up with a brand new pile that is more than should be humanly possible to finish. It isn’t hard to imagine something on even a grander scale when it comes to managing data within the healthcare industry.
At moments there are millions of bytes of data is being taken in and stored for later use by those organizations. What makes it even more complicated is that the data isn’t all in simple form, such as intake paperwork where the information is basic letters, numbers and checked boxes. Think about all that goes into a medical file, things like x-rays, MRI, physician’s notes, copy of insurance card, lab tests, prescriptions, and so much more. All of this information provides a bigger picture when it comes to treating other patients, but not unless it can be tapped and analyzed. This is where the data management aspect of healthcare information plays a huge role.
Patterns found within data are hidden in the structured and unstructured data being collected and saved by organizations. It contains vital information to help:
- Workflow processes
- Electronic health record (EHR)
- Human resources information
- Time keeping and processing
- Areas with possible waste
One of the items above that might stand out from the others is the EHR system. This system has been structured by the government as a means to be more helpful. However, like much that comes down from the government it is also a costly mandate that if left as is doesn’t produce much of any insights; it must be paired with a healthcare data management system and heavily configured to produce data-driven decisions. In fact, there is a specific position that is trained to discern the patterns, a data analyst.
With many jobs and positions in the workplace, a data analyst has had to broaden their foundational knowledge and step partially into the role an IT specialist to work through the raw data stored and discover what data points will generate an understanding of what is going on under the surface of the day-to-day tasks and appointments. Analysts might as well call themselves hunter/gatherers because they seem to be doing more of that over the ideal responsibility of deciphering what the data is actually indicating.
Spending more time dedicated to hunting for and gathering up information is considered wasteful on the part of spending and time management, yet the software these organizations employ doesn’t aid in any way to eliminate this inefficient process. There is software designed to manage incoming data and remove the hunter/gatherer qualities in favor of interpreting and finding ways to implement and improve those conclusions.
Removing wasteful uses of time doesn’t have to mean that corners are cut and shortcuts are the only answers, in fact, establishing procedures that will prove to be fundamental in both the short- and long-term. To nick off better- or best-practices as a means of hopefully cutting costs and improving care isn’t sufficient.
Sometimes it goes without saying that too much of a good thing isn’t so good, and in data management reporting, this is definitely the case. There is a term that is growing by leaps and bounds in just about any industry, it’s called spreadsheet mania. This is when organizations want nitpicky details about every aspect happening within, and so report after report is generated to try and support or oppose a general thought. The routine of an overabundance of spreadsheets usually adds up to no real gain insight and no added value to company procedures.
Healthcare data analytics and data management are not topics that can be glossed over in any good organization. Dedicating the time and effort to establishing a culture of analytics and data-driven decisions may take a lot of cultivating and bite-sized pieces of change. Overall, the knowledge that can be gleaned from what was once raw data turns an organization from gut reactionary to info-based judgements movements and it was all because of data management.