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The Full Spectrum of Information Governance – HIM Scene

Posted on February 7, 2018 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.

Information governance is such an important topic across so many areas of healthcare. It impacts almost every organization and quite frankly takes the full organization to buy in to ensure proper information governance. Doing it right is going to be essential for any healthcare organization to work efficiently and effectively in the future.

While information governance impacts everyone in healthcare, I have to give credit to AHIMA and their HIM professional community for leading the way on the topic of information governance. A great illustration of this leadership is in the AHIMA Information Governance Adoption Model Competencies (IGAM):


*Thanks to HIM professional, Katherine Downing for sharing it on Twitter.

I think a lot of people that work in a hospital and healthcare system don’t recognize a lot of these areas of information governance. At least they don’t look at them from that lens.

My favorite part of this model is that it starts with creating the right information governance structure and the strategic alignment. If you don’t get the right people assigned as part of their job to work on information governance, it will never happen. Plus, if you don’t realize how information governance aligns with the organizations priorities, then you’ll fall short as well.

How far along are you in your information governance efforts? Have you incorporated all of the above elements into your information governance strategy? We’d love to hear your experiences, insights, and perspectives in the comments.

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

An HIM Twitter Roundup – HIM Scene

Posted on December 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.

For those that aren’t participating on Twitter, you’re missing out. The amount of knowledge and information that’s shared on Twitter is astounding. The problem is that many people think that Twitter is where you go to talk about yourself. Certainly, that’s an option if you want to do that, but I find that consuming information that people share on Twitter is extremely valuable.

If you’ve never done Twitter before, sign up (it’s free) and then you need to go in and follow about 50 HIM professionals and other healthcare influencers. You can start by following @healthcarescene. HIM professionals are easy to find. Just search for the term AHIMA or ICD-10 and you’ll find a lot of them to follow.

Ok, enough of the Twitter lesson. Just to show you some of the value of Twitter, here’s a quick roundup of HIM related tweets. Plus, I’ll add a little commentary of my own after each tweet.


This is becoming such an important role for HIM professionals in a healthcare organization. HIM staff can do an amazing work ensuring that the data that’s stored in an EHR or other clinical system is accurate. If the data’s wrong, then all these new data based decisions are going to be wrong.


I think upcoding stories are like an accident on the freeway. When you see one you just have to look.


I’m still chewing on this one. Looks like a lot of deep thoughts at the AHIMA Data Summit in Orlando.


The opioid epidemic is such an issue. We need everyone involved to solve it. So, it’s great to see HIM can help with the problem as well. I agree that proper documentation and EHR interoperability is a major problem that could help the opioid epidemic. It won’t solve everything, but proper EHR documentation is one important part.


This is an illustration of where healthcare is heading. So far we’ve mostly focused on data collection. Time to turn the corner and start using that data in decision making.

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.