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.

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