Over the past couple months, HIMB has had audits on the brain. We covered the progress of the 2-midnight audits and walked you through the pass-fail meaningful use audits in detail. Now it's time to get a bird's-eye view of the 2016 audit landscape to ensure you're prepared for whatever comes your way this year.
Recovery Auditors
With 2-midnight rule audits shifting to the BFCC-QIOs, Crump predicts the Recovery Auditors will likely spend 2016 focusing on diagnosis-related group (DRG) audits and medical necessity reviews. These audits will likely focus on reviewing medical necessity for procedures, tests, and treatments in relation to what the Payment Integrity Manual states should be captured in the health information. Records that do not capture information related to local and national coverage determinations will likely be the low-hanging fruit if the Recovery Auditors are approved to focus on these reviews, says Dawn Crump, MA, SSBB, CHC, vice president of audit management solutions for CIOX Health in Alpharetta, Georgia.
To prepare for the Recovery Auditors, HIM professionals should focus on analyzing the risk at their facility. In addition, they should ensure there is a continuous feedback loop not only within the department but outside of it as well. Coding, compliance, and medical staff should be in the loop too, Crump says. Solid communication and education can go a long way in ensuring everyone is well prepared for an audit.
Establishing good quality checks, especially with EMRs, can also help a hospital bolster its audit preparation. HIM should be involved in checking that the information in the record tells the patient's complete story, Crump says.
"Records are evolving and EMRs are evolving, so I think status quo needs to be checked on a regular basis," she says.
For example, EMRs don't always capture all of the needed information. As local and national coverage determinations change for high-risk procedures and admissions, HIM and coding should be involved in the process of ensuring the EMR captures the latest changes and meets the new requirements; this way, the hospital will be ready to present information in the event of an audit, Crump says.
Do you recall the recent humorous television commercial for phone services that featured children who wanted more and tried to explain why? The core message was that more isn't always better. I believe there are many applications of this principle in healthcare. To understand why this is the case, since large evolves from small, you might have to engage your sense of recall to visualize the past compared to the present. We'll look at some examples below.
Big (bad?) data
For all the talk about population health and big data, there is less discussion about data integrity, a key principle in data usage. Anyone who has worked with the most basic of databases, the master patient index, knows how many errors occur in collecting up-front patient access data. Errors still abound in duplicate medical record and account data. How can any of the data associated with these accounts be considered valid and worthy of basing conclusions upon? How confident are we, really, in our interpretation of this data?
For example, comparative MedPAR data will not display ICD-10-CM/PCS data until at least 18 months after ICD-10 implementation. There is no way to measure if we are undercoding, overcoding, erroneously coding, or problematically grouping any cases until we have enough data to make some judgments. Even then, the only true audit is one that compares the collected data with the source documents (in this case, the medical record). Organizations must conduct multiple rounds of these audits before findings can even be discussed.
The best approach is to begin your own audit of small segments (e.g., most common, most at risk) of diagnoses and procedures rather than waiting until the MedPAR data arrives. Be aware that if you are looking at any comparison databases, there is likely a crudely mapped comparison going on between ICD-9 databases (and ICD-10). As we all know, comparisons are not possible in all cases, and the more cross-mapping we do, the less granularly correct the comparison outcome data is, which decreases the validity of the universe of data.
In HIM, there are other data quality issues that have an unknown impact on integrity comparisons. For example, are we comparing apples to apples for sites that are using computer-assisted coding applications versus those that are not? Is it fair to compare outsourced coding with in-house coding? In a recent study conducted for a client, I observed that the time for coding of outsourced cases was dropping in a direct ratio to the case mix. Are we gaining productivity but sacrificing quality and reimbursement potential?
Last year, as ICD-10 implementation approached, organizations throughout the U.S. reported varying levels of comfort with regard to readiness and understanding of the impact of ICD-10 on physician workflow. For some, it was business as usual. For other physicians, ICD-10 became one more check box on the list of reasons to leave practice.
Our readers have been asking for an updated medical record documentation guide, and here it is?new and improved! The guide provides references to the associated CMS Conditions of Participation and new and revised standards and elements of performance (EP). A recent Joint Commission column discussed ongoing record reviews and the continued focus of Joint Commission surveyors related to documentation in the medical record. The guide takes the Record of Care, Treatment, and Services chapter and breaks it down into an easy-to-use tool for comprehensive record reviews by topic.
In February 2016, just four months after ICD-10 go-live, HIM Briefings asked a range of healthcare professionals to weigh in on their productivity in ICD-9 versus ICD-10.
In general, the time spent coding records has increased since ICD-10 implementation for most record types. In fact, one respondent said his or her facility noticed a 40%?50% decline in productivity. However, another respondent noted that coder productivity often varies based on the physician who documented in the record, as some physicians are more in tune with the language of ICD-10 than others. One-third (33%) of respondents were coders, whereas 21% identified as coding directors, managers, or supervisors. Approximately 16% identified as HIM directors, managers, or assistant directors or managers, while 12% of respondents were clinical documentation improvement (CDI) specialists. A small percentage of quality/performance improvement directors, vendors, consultants, IT directors/managers, billers, and auditors weighed in as well. More than half (53%) of respondents work in acute care hospitals.
One respondent said that his or her facility expects the same productivity in ICD-10 as it had in ICD-9, a nearly impossible feat in some cases. "The productivity requirements have not changed from ICD-9 to ICD-10. The current requirement for our facility is 18 charts per day (minimum 14). Very challenging and almost unobtainable."
The HCPro survey questions asked for the average minutes to code a record type. Some respondents wrote in the daily number of records coded, while others indicated the number of records averaged per hour.
One of the last major shifts in coding before the transition to ICD-10 was the introduction of diagnosis-related groups (DRG) in 1982. Now that the struggle to implement ICD-10 has passed, how do the two transitions measure up? Both Wendy Coplan-Gould, RHIA, president of HRS Coding in Baltimore, and Sallee Silverman, RHIA, an AHIMA-approved ICD-10 trainer and a manager with HRS Coding, were actively working in healthcare through both shifts and shared their thoughts with HIM Briefings shortly after ICD-10 went live in October 2015. When DRGs were introduced, Coplan-Gould was working as the associate director of the Maryland Resource Center in Maryland.
Over-querying is a common concern in CDI. It can influence productivity and workflow. It can cause delays in documentation and coding processes. It can also overwhelm and frustrate physicians, who in turn may be less likely to support or engage with CDI program efforts.
