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Opening the Door to Data Analytics in Medical Coding – HIM Scene

Posted on November 15, 2017 I Written By

The following is a HIM Scene guest blog post by Julia Hammerman, RHIA, CPHQ, is Director of Education and Compliance, himagine solutions.

Data analytics has moved from IT and finance to the majority of business functions—including clinical coding. However, most healthcare organizations admit they could do more with analytics. This month’s HIM Scene blog explores the importance of analyzing clinical coding data to improve quality, productivity, and compliance.

Coding Data in ICD-10: Where We Are Today

HIM leaders are implementing coding data analytics to continually monitor their coding teams and cost-justify ongoing educational investments. Coding data analytics isn’t a once-and-done endeavor. It is a long-term commitment to improving coding performance in two key areas: productivity and accuracy.

A Look at Productivity Data

Elements that impact coding productivity data include: the type of electronic health record (EHR) used, the number of systems accessed during the coding process, clinical documentation improvement (CDI) initiatives, turnaround time for physician queries, and the volume of non-coding tasks assigned to coding teams.

Once any coding delays caused by these issues are corrected, coding productivity is best managed with the help of data analytics. For optimal productivity monitoring, the following data must be tracked, entered, and analyzed:

  • Begin and end times for each record—by coder and chart type
  • Average number of charts coded per hour by coder
  • Percentage of charts that take more than the standard minutes to code—typically charts with long lengths of stay (LOS), high dollar or high case mix index (CMI)
  • Types of cases each coder is processing every day

A Look at Accuracy Data

Accuracy should never be compromised for productivity. Otherwise, the results include denied claims, payer scrutiny, reimbursement issues, and other negative financial impacts.

Instead, a careful balance between coding productivity and accuracy is considered best practice.

Both data sets must be assessed simultaneously. The most common way to collect coding accuracy data is through coding audits and a thorough analysis of coding denials.

  • Conduct routine coding accuracy audits
  • Analyze audit data to target training, education and other corrective action
  • Record data so that back-end analysis is supported
  • Assess results for individual coders and the collective team

Using Your Results

Results of data analysis are important to drive improvements at the individual level and across entire coding teams. For individuals, look for specific errors and provide coaching based on the results of every audit. Include tips, recommendations, and resources to improve. If the coding professional’s accuracy continues to trend downward, targeted instruction and refresher coursework are warranted with focused re-audits to assure improvement over time.

HIM and coding managers can analyze coding audit data across an entire team to identify patterns and trends in miscoding. Team data pinpoints where multiple coders may be struggling. Coding hotlines or question queues are particularly helpful for large coding teams working remotely and from different geographic areas. Common questions can be aggregated for knowledge sharing across the team.

Analytics Technology and Support: What’s Needed

While spreadsheets are still used as the primary tool for much data analysis in healthcare, this option will not suffice in the expanded world of ICD-10. Greater technology investments are necessary to equip HIM and coding leaders with the coding data analytics technology they need.

The following technology guidelines can help evaluate new coding systems and level-up data analytics staff:

  • Data analytics programs with drill-down capabilities are imperative. These systems are used to effectively manage and prevent denials.
  • Customized workflow management software allows HIM and coding leaders to assign coding queues based on skillset.
  • Discharged not final coded and discharged not final billed analytics tools are important to manage each piece of accounts receivables daily and provide continual reporting.
  • Systems should have the ability to build rules to automatically send cases to an audit queue based on specific factors, such as diagnosis, trend, problematic DRGs.
  • Capabilities to export and manipulate the data within other systems, such as Excel, while also trending data are critical.
  • Staff will need training on advanced manipulation of data, such as pivot charts.
  • Every HIM department should have a copy of the newly revised AHIMA Health Data Analysis Toolkit, free of charge for AHIMA members.

HIM directors already collect much of the coding data required for improved performance and better decision-making. By adding data analytics software, organizations ensure information is available for bottom-line survival and future growth.

If you’d like to receive future HIM posts in your inbox, you can subscribe to future HIM Scene posts here.

Healthcare’s Not Good At Mining Health Data

Posted on January 13, 2017 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I was really blown away by this quote from an interview with Rebecca Quammen.

The buzz around data analytics promotes the need for data scientists and data analysts as among the most sought-after roles, and that is problematic in and of itself. It’s creating a huge demand, but it’s also a demand that many healthcare organizations don’t know how to deal with right now. I see the buzz around data analytics increasing the pressure to “do something” with data, but many organizations across the nation, both large and small and in every setting of care, simply don’t have the foundational knowledge to manage the data to their benefit, and to know the database structure and how to get it the data out and what the data tells them when they get it. We are not an industry historically good at mining good, rich data out of products and doing something meaningful with it. We do traditional reporting and we may do a little bit of historical reporting, but we’re not good at looking at data to predict and promote and to work toward the future, or to see trends and do analysis across the organization.

Rebecca nailed this one on the head. I’ve seen a bunch of organizations go running towards healthcare informatics with no idea of what they wanted to accomplish or any sort of methodology for how they’re going to analyze the data to find useful insights. It kind of reminds me of the herd mentality that happens at conferences. If any sort of crowd starts to build at a conference, then the crowd quickly grows exponentially as people think that something interesting must be going on. The same seems to happen as healthcare organizations have run towards data analytics.

While I think there’s so much potential in health data analytics, I think that most organizations are afraid to fail. The culture in healthcare is “do no harm.” There are some very good reasons for this and some real fears when it comes to medical liability. There’s a lot more at stake when using data in healthcare than say Netflix trying to predict which shows you might be interested in watching. If Netflix gets it wrong, you just keep scrolling after some minor frustration which you quickly forget. In healthcare, if we get it wrong, people can die or be harmed in some major way.

I understand why this healthcare culture exists, but I also think that inactivity is killing as many or more people than would be damaged by our data mistakes. It’s a challenging balance. However, it’s a balance that we must figure out. We need to enable more innovation and thoughtful experimentation into how we can better use health data. Yes, I’m talking beyond the traditional reporting and historical reporting which doesn’t move the needle on care. I’m talking using data to really impact care. That’s a brave place to be, but I applaud all of those brave people who are exploring this new world.