How Better Data Management Improves Healthcare Practices

By Kayla Matthews, HealthIT writer and technology enthusiast, Tech Blog
Twitter: @ProductiBytes

Clutter, general disorganization and fragmented systems are never good. But in the medical industry — which happens to be wrought with these issues — it’s more than just bad. It’s damaging and downright dangerous.

The displacement of a patient’s records can mean serious issues for future treatments. Then there’s the matter of security and privacy, as these records often contain highly sensitive or personal information.

In the wrong hands, the info can be used to harm or blackmail someone. And that’s just patient data and records. Imagine the treasure trove of dangerous and sensitive information pertaining to medical procedures, equipment, policies and much more.

Healthcare and clinical data are incredibly important in the modern medical industry, yet so is the proper management and organization of said content. How do you organize digital content and data? Through the proper data management and metrics protocols.

For example, medical data is collected in vast quantities, but most of it is being stored away instead of being processed and converted to usable insights. McKinsey Global Institute says that data can contribute to more than $300 billion annually in reduced costs across the entire United States healthcare industry. That is, if it is processed and organized properly and then tapped into.

Here are several ways improved data management is helping in healthcare.

1. Making Data Accessible
C-suite executives, decision makers and even various personnel need real-time access to information that is in an easily recognizable format. The problem with raw data, however, is that such a thing is not possible — at least not without converting it into a more readable form. It would take data scientists months, maybe even years to collate and organize every piece or segment of data flowing into — or out of — a healthcare IT system.

Naturally, the solution is to deploy and maintain AI and analytics tools that can and will filter this information. More importantly, these systems can be trained to identify and alert the necessary parties when actionable content is pulled. This move is a strong step toward making data more accessible — not just to scientists and IT personnel, but to everyone else, too.

2. Improving Quality
When raw data is flowing in continuously from several sources, it’s nearly impossible to predict or understand how reliable the information at hand is. And since the quality of data is what drives the quality of predictive results and insights, there’s a huge chance you’re taking in having more unusable data than you can handle. Even worse, you’ll need to sift through all of it if you want to find the good stuff.

Did you know, for instance, that 80 percent of medical bills contain errors? That disconcerting, especially when you consider how strict and trigger-happy insurance companies are in regards to proper billing and coding.

Your bill will likely be rejected, and it may take weeks — sometimes longer — to handle the resubmission and receive payment. All the while, data systems are collecting actual, usable information, and it’s all being dumped right into the same pile as the unusable data.

With modern data management and metrics tools, this problem can be waylaid. The data is interrogated and assessed, then filtered for accuracy, relevancy, consistency and even timeliness. Algorithms are deployed and relied on to detect poor or irrelevant data.

There’s another aspect to data quality, however, which relates to staff and medical personnel. Both the providers and the staff must be trained and given the necessary tools to ensure accurate data is being collected and fed into the system on the front end.

3. Establishing Centralized Repositories
In following through with data management standards, you’ll adopt and establish a centralized repository that houses all data in a single location. This process is one of the best ways to break down traditional data silos.

Even so, it’s about more than simply having a data warehouse or central location where all your information and data can be stored. You must have a relational database that can pull information and organize it, no matter the source. It will be the brains of the operation, collecting data flowing in and sending it all where it needs to be in the warehouse or storage system.

The benefit of an advanced repository is that the data isn’t just being dumped. It’s still managed, sorted and integrated for quick access later.

4. Allowing Data Governance
What information do you need? Why is it necessary, and how will it shape your existing processes? Finally, where is it coming from, and does that have a bearing on accuracy? These are crucial questions that all healthcare organizations must ask and know the answers to.

Adopting a governance strategy early on is the way to achieve such information. Data governance is the oversight of data integrity, availability, reliability and security. It involves coming up with and honoring a clear set of procedures or policies for the handling of data — sensitive or otherwise. Think of it as an overarching plan that unites all your data strategies toward a common goal.

Final Thoughts
The healthcare marketplace — and related data — is increasingly complex. Security, privacy and integrity are of the utmost importance, but so is the effective management and use of the data involved.

You see, you can collect all the information you can handle, but it won’t do you or your organization any good if you don’t understand it. You have to know what it is that’s coming in, why you have it and what you can do to make it useful for your team.

Really, data management in healthcare is no different than any other industry. The information may be different, sure, but the way it needs to be handled, organized and distributed is very much the same.