Posts Tagged: data cleaning

Legacy Databases: Should You Purge Your Records?

Posted by & filed under Healthcare, Pharmacy.

legacy databases

Legacy databases are both a necessity and a headache. On the one hand, due to medical record retention requirements, you have to keep some patient records for at least seven years. On the other hand, keeping your legacy system running is costly, risky, and cumbersome. So, if you’re finally making the choice to archive your data, what should you and can you purge?

The Rules of Purging 

What are the rules of purging? Are there any industry standards? While there are no definitive purging guidelines, you can look to these three areas to provide insight.

First, you need to be clear on compliance. Determine based on HIPAA and other regulations what patient records you must retain. These will differ by state and the type of provider you are—hospital, healthcare system, pharmacy, etc. 

For example, in pharmacy, you need to retain patient signatures they provide at pickup much of the time. However, you may not have to keep POS data. 

Second, purging should really focus on data and records that are stale or inaccurate. That data could be things like old addresses or insurance information for patients. It could also include inactive or unlinked data not attached to a prescription in the case of a pharmacy. 

Third, there could be unnecessary file extensions in your legacy database. These may be files created by the legacy system, which aren’t patient-related. They only relate to the software, so you don’t need those when you archive.

How Can You Identify the “Right” Files to Purge?

Well, we have described the three buckets for purging. You can work with your data archiving partner to create rules around what to purge. That could include records older than a specific date that no longer fall into mandatory retention. The archiver can also do a data cleanse of your records before archive. The cleanse will do two things: repair issues around structure and formatting and discover stale data. 

The parameters of your “stale” data are for you to define. They could include unlinked data that has no record home. They may also outline the specific types of files to keep versus delete. Not everything in a patient record requires retention; some of it could just be junk that would never be part of an audit request. (FYI: audit requests are the leading reason to produce archival data in healthcare.)

Archive Legacy Databases and Purge!

There are numerous reasons why archiving is beneficial for healthcare. You’ll save money and time and provide a more secure environment. However, don’t just move everything. Purging old records that have no value is a must when you archive. 

Have questions about archiving and purging? Visit our information page on our archive platform, ViewMaster, and request a quick five-minute demo today. 

Healthcare Data Management: Interoperability, Portability, and Accessibility Challenges

Posted by & filed under Healthcare.

healthcare data management

Healthcare data management is complex, and not something most organizations have the expertise or bandwidth to handle on their own. Most stakeholders understand the opportunities of leveraging such an asset but aren’t sure how. They struggle with interoperability, portability, and accessibility. The healthcare system is ripe with challenges that leave many entities unable to use their data for the greater good optimally. 

So, what are the biggest challenges and possibilities? As a healthcare data management company with over two decades of experience, we’re happy to share our ideas.

Interoperability Remains at an Impasse

The healthcare community at large, and its regulators, are certainly trying to make headway here. The new interoperability and information blocking rule from HHS tackles this head-on. The focus of the new rules is to allow patients more access to their medical records. The rules also express the need to eliminate information blocking. However, the enforceability of this is still up in the air. 

We’ve seen lots of interoperability issues in our years of working on over 27,000 data management projects. Moving PHI isn’t a straightforward process. There have been many efforts to drive standardization in EHRs, mostly through FHIR (Fast Healthcare Interoperability Resources), which should “simplify implementation without sacrificing information integrity). 

However, there are different versions of FHIR so that systems could be technically “standardizing,” but there’s still incompatibility. Additional inconsistencies include EHRs not using all available FHIR APIs. The interoperability rule does address the need for FHIR APIs to improve data sharing. Most EHRs have adopted FHIR, but the new interoperability rule moves to make FHIR Release 4 the standard. 

What’s the biggest roadblock? Health information systems aren’t “open.” They are designed to integrate, like many SaaS platforms. Healthcare data, it’s just different. 

Portability Isn’t Easy

In many cases, a business can quickly move databases around. For example, if you move to new CRM software, you’ll migrate your customer data to a new platform. It’s usually not a heavy lift—maybe a little light data cleaning or purging.

Unfortunately, in healthcare data management, portability is a huge pain point. You can’t just move to a new EHR or decision support system by copying and pasting. It requires a data conversion to make sure the information is securely sent, and that field matching is as accurate as possible.

Beyond moving from one software to another, many healthcare organizations also need the capability to data share across their different systems. Again, not an easy road, even for IT experts. What typically occurs is that systems don’t sync, and implementations that don’t launch on time. 

Access to Data Shouldn’t Be a Roadblock 

The third concern that you may be dealing with in healthcare data management is accessibility. Can you access your data when and how you need it? While you may have no issues with reviewing and analyzing data in your current EHR or pharmacy system, what about legacy records? 

Often, organizations will keep a legacy system running to store their old records. Storage is necessary to comply with medical record retention requirements, but using an old system is not the best user experience. It’s hard to find what you need, and you’re paying every month to retain access. 

The easiest answer to universal, compliant, and easy accessibility is data archiving. Choosing a web-based system that allows you to store documents, data, and images ensures regulatory adherence while boosting ease of use and reducing costs. 

Healthcare Data Management Made Easy with InfoWerks

Our goal is to promote interoperability, portability, and accessibility for every area of the healthcare ecosystem. Our experience and healthcare-centric approach to data management have made us the choice for thousands of organizations. Get in touch today to see how we can help.

Dirty Data Is Useless—Learn Why Healthcare Data Cleaning Matters

Posted by & filed under Healthcare.

healthcare data cleaning

Is dirty data impacting your operations? Or making it impossible to launch new applications? Healthcare systems collect, analyze, and share protected healthcare information (PHI) every day, but it’s not always accurate or properly structured. To ensure the portability, accessibility, and interoperability of such information, healthcare data cleaning is often a necessity.

But how can you do it efficiently and cost-effectively?

What is Healthcare Data Cleaning?

Typically, most organizations store data in databases. These could be associated with your EHR, decision support system, revenue cycle management, and many more applications designed to enable the healthcare ecosystem to work more cohesively. The value of healthcare big data is immense, helping improve care, boost revenue, and drive better decision-making. Dirty data makes that virtually impossible.

Dirty data describes information that is inaccurate, outdated, redundant, incomplete, or formatted incorrectly. Using healthcare data cleaning, you can bring consistency to your data. This consistency is necessary when integrating disparate streams of data. If you merge dirty data, then its ability to be actionable is lost. 

Where Hospitals and Healthcare Systems Stumble

In an ideal world, all healthcare information systems (HIS) would work together in harmony. Field matching wouldn’t be a roadblock, nor would duplicates or other inconsistencies. Unfortunately, that’s just not the case. There is currently no standardized practice for healthcare data interoperability. There are best practices, and the new HHS Interoperability Rule is the most significant step the country has made to improve on this. 

However, it’s still not as easy as moving data from one system to another or quickly aggregating different data sets and automatically have a working process. As healthcare data management experts, we see on a daily basis how difficult it is to map data from one system to another, even when they are in the same category. So, if you can adeptly move from one EHR to another, then it gets really tricky when combining data outputs or moving information into a completely different type of platform.

Key Causes of Healthcare Dirty Data

dirty data

Dirty data is not the result of one thing; it’s a culmination of lots of factors, some more significant than others. One of the biggest concerns is duplication. According to research, duplicate records make up 5-10% of a hospital’s EHR. That number expands to rates of 20% for healthcare entities that have multiple locations.

Duplications happen for many reasons, including errors in spelling or other patient data. Depending on the parameters of the system, it may be unable to search for duplicates as new patients are added.

Another symptom of dirty data is that it’s incomplete. Without all the appropriate fields, records may be useless. If a patient record list omits things like preexisting conditions or allergies, it’s not only incomplete but could impact care. Incomplete information can be attributed to user error or system limitations.

The third significant cause of dirty data is inaccuracies. Errors might have occurred in the original set-up (i.e., misspelled names, transposed numbers), or the data may not have been updated correctly. If you don’t have accurate information about your patients, from contact information to insurance codes, then it’s harder to communicate with them and leverage your information for better outcomes and insights. 

The Cost of Dirty Data

healthcare dirty data costs

The consequences of dirty data can be numerous. First, there are the monetary losses. Gartner researchers revealed that the cost of poor data equates to $9.7 to $14.2 million for businesses every year. Those numbers reflect all types of companies, but it’s still an important figure to know. 

Where do these losses come from? For healthcare, it could be from several things, such as opportunity costs associated with being able to launch new applications to the higher hard costs of unpaid reimbursements from payers and additional labor needed to strip out the bad data. 

The costs are more than fiscal. You’ll lose time because you can’t seamlessly convert data into new platforms. You’ll miss out on insights that could help you find ways to cut costs and work more efficiently. Worst of all, it could impact patient care. 

Feel Confident in Your Data

If you don’t feel confident about the health of your data, then you know it’s holding you back. You may also like the bandwidth or expertise to clean your data. Rely on InfoWerks to be your data liaison. We’ve been cleaning and purging healthcare data for years, enabling easy, compliant data sharing and data conversions for any system. 

Make your data work for you again. Learn more about how we can help by getting in touch.