Healthcare Data Management is a Rocky Road

Commercials and TV shows have shown a silly scene 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. This is why data management in the healthcare industry is very important and it is necessary that every healthcare organization masters it.

At moments there are millions of bytes of data is being taken in and stored for later use by those organizations. Data of this magnitude is called Big Data and as the name implies it is a big deal. What makes it even more complicated is that the data isn’t all in simple forms, 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. The paperwork can be quite overwhelming All of this information provides a bigger picture when it comes to treating other patients, but not unless it can be tapped and analyzed and an accurate and timely manner.

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
  • Timekeeping 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.

This is why it is important to know the clear definition of who a data analyst is and what they do.

Healthcare Data Analyst Definition

A healthcare data analyst is a person who collects, manages, and analyzes clinical to improve the quality of care for patients. This definition is nearly a mirror-image of a health information technician, as explained by the Bureau of Labor Statistics. Healthcare data analysts, who may actually have a degree in health information management, are responsible for the following activities:

  • Reviewing patient records for accuracy, completeness, appropriate documentation, and timeliness
  • Maintaining organization and management of healthcare data
  • Assigning clinical codes to clinical data for use in healthcare data analysis. This role is not as code-extensive as a medical billing and coding specialist
  • Record data into the electronic health record for collection, storage, and analysis
  • Maintain security and privacy of protected health information
  • Analyzing Healthcare Data

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 judgments movements and it was all because of data management.