This monthly blog highlights and discuss emerging trends and challenges related to healthcare data and its ever changing life cycle.
By Shawn Wells, RHIT, CHDA
Data: we love it, we hate it. We can’t get enough of it.
We create and consume data 24/7, from fitness trackers and online shopping to social media and music streaming services. It is estimated that more than 2.5 million Terabytes of data is created globally every single day.1 Data is addictive. I don’t know how many times I have sworn off Twitter just to log in the next day to see the latest post from David Duchovny’s dog (yes, that’s a thing*). Data seems to rule our lives—increasingly so in the healthcare arena.
When we talk about healthcare data, the focus is primarily on data analytics: usage, analysis, and predictive modeling to drive decisions and actions. What is the true cost of a procedure? What is the likelihood of a patient being readmitted within the next 30 days? How does physician A compare to physician B when it comes to lab utilization? The insights gleaned from this data is invaluable, but what if the data is incomplete or incorrect? We’ve all heard the phrase “garbage in, garbage out.” Your analytics are only as good as the data that feeds them. There needs to be a greater emphasis on data integrity, the assurance of the accuracy and consistency of data over its life cycle. The question becomes how to make that happen.
Sell the Benefits of Good Data
In order to improve data integrity you need resources—specifically, people. Asking for more staff in the current healthcare environment is tricky business, so you need justification.
Increase in reimbursement: If your Medicare base rate is $5,000 and you increase your average Case Mix Index just 0.1000, you increase reimbursement $500 per case. If you have 2,000 monthly discharges, that’s a positive financial impact of $1 million/month or $12 million annually. If the salary and benefits of a clinical documentation improvement (CDI) specialist and concurrent coding specialist is $150,000/year, you could hire five CDI specialists and five coding professionals and still have a net gain of $10.5 million.
Better Public Reporting: Facility data is publicly available in the form of clinical outcomes, surgery complications, preventable readmissions, patient satisfaction, and other metrics. The more in-depth the documentation review and the more complete and accurate the coding, the better this data looks. Being able to say that your facility is #1 in quality is something that the public recognizes.
Better Patient Care: This one is simple—if the data is good then the analytics will be good. If the analytics are good, clinicians can make sound, informed decisions that will benefit the patient.
Identify the Risks of Bad Data
Some of the risks around bad data are simply the opposite of the benefits of good data. Other risks are more obscure.
Incorrect Problem Lists: If you have ever seen a hospital problem list, you know that the accuracy is suspect at best. You are lucky if any of the problems are even current. If you are using a computer-assisted coding (CAC) solution, it will likely auto-suggest every diagnosis on that list, increasing the likelihood of incorrect coding. The workaround is easy: you just region the problem list out of the auto-suggest, but that doesn’t fix the problem. Others may be extracting and analyzing that data, making decisions with faulty information.
Patient Privacy: Many electronic health records (EHRs) have the ability to send records to providers automatically via fax. The provider table that the EHR uses requires maintenance and updating, and if that is not being done regularly, the chance of a HIPAA violation is high.
Incorrect Visit Number: When a document is created in an EHR, it is created at the visit level and linked to an account number. If the provider chooses the wrong visit number, that document will not interface into the CAC solution to be auto-suggested and viewable to the coding professional. If the coder doesn’t double check the EHR, valuable documentation could be missed and the coded data will be inaccurate.
Moving Forward with Data Integrity
What are you doing at your organization to address data integrity? What challenges are you facing?
*For more information on David Duchovny’s dog, follow @brick_duchovny on Twitter.
- “What will we make of this moment? 2013 IBM Annual Report.” https://www.ibm.com/annualreport/2013/bin/assets/2013_ibm_annual.pdf.
Shawn Wells is associate director of health information at University of Utah Health.