Q: Rural health clinics have to start to bill all services on individual lines with HCPCS codes and charges. Is there a way to report these services on a separate line without the appearance of inflating our charges?
Research shows that ethnic and racial minorities may wind up back in the hospital after discharge more often than their white counterparts for certain conditions, such as pneumonia and heart failure. This increased rate of readmissions is due to many factors, including:
A higher incidence of some chronic diseases
Social, economic, cultural, and linguistic barriers to care
CMS is hoping to change that with a new publication, "Guide to Preventing Readmissions Among Racially and Ethnically Diverse Beneficiaries." Its authors said that the guide aims to accomplish three main goals:
Providing an overview of the issues that can lead to higher readmission rates among this group
Outlining actions hospital leaders can take to reduce these avoidable readmissions
Providing case studies and examples of initiatives that have worked to reduce readmissions among racial and ethnically diverse Medicare beneficiaries
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.
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?
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.
