The healthcare industry is focused on the triple aim: reducing healthcare costs, improving patient experience, and improving the health outcomes of populations. Healthcare organizations will no longer be paid based on the volume of services provided but rather on the value of care delivery.
Q: The emergency department (ED) at the hospital where I work often becomes so busy that we do not have enough rooms for all of our patients. This occurred last weekend, which meant that several patients were brought into the ED on stretchers to be evaluated but could not be placed in a room. I witnessed a nurse perform a physical/abdominal examination on a patient who was on a stretcher in the ED hallway and discuss medical history and current treatment options with the patient in this open space where plenty of patients and staff members could see/hear the encounter. Is this a HIPAA violation?
A: What you are describing is an incidental disclosure, not necessarily a HIPAA violation. Organizations must take steps to limit incidental disclosures and mitigate the risks to the patient’s privacy and the security of information. In the case you describe, for instance, could a screen have been erected to protect the patient’s privacy even if circumstances led to no choice but to perform the exam in the hallway? Could a white noise machine have been brought over to reduce the chance of being overheard? Could the gurney have been moved to a private area (or even a slightly more private one) when the exam had to take place? Could the exam have been postponed until a more private space was available, or was it necessary to do it right then? These are the questions staff should ask themselves in these situations.
Editor's note: Simons is the director of health information and privacy officer of Maine General Medical Center in Augusta. She is also an HIMB advisory board member. This information does not constitute legal advice. Consult legal counsel for answers to specific privacy and security questions. Send your questions related to HIPAA compliance to Editor Jaclyn Fitzgerald atjfitzgerald@hcpro.com.
Accurate patient matching within the EMR should not be a concern limited to HIM professionals. Ensuring that medical record data is correct and complete and that duplicate records are not created is key to various healthcare initiatives, including population health management, analytics, information governance, patient-centric care, health information exchanges, and finance. It all starts with the patient's record. Reducing the number of duplicate records at a hospital and being able to effectively match records is critical to ensuring that these healthcare initiatives are successful, says Lesley Kadlec, MA, RHIA, CHDA, director of HIM practice excellence for AHIMA.
"Patient matching is really the underpinning of all the strategic initiatives that are going on in healthcare," Kadlec says. "You have to have accurate patient information to have accurate patient care. Ensuring that you have the right patient and the right information at the right time is really what drives the physicians' and clinicians' ability to actually provide that patient with care."
More than half of HIM professionals work with mitigating duplicate patient records, and of that group, 72% do so on a weekly basis, according to a recent survey of AHIMA members. Unfortunately, less than half of all respondents have quality assurance in place for their registration or post-registration processes. (A summary of the data is available in the Journal of AHIMA.)
"The challenge is having the staff to be able to dedicate to making the corrections, doing the matching, and ensuring that everything is getting put back together," Kadlec says.
Patient matching and duplicate records are a major issue right now because hospitals are using so many different systems and there is often a lack of information governance across those systems, says Megan Munns, RHIA, identity manager at Just Associates, Inc., based in Denver.
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.
In our last article, I provided an overview of the Comprehensive Care for Joint Replacement (CJR) model, described in a recent Healthcare Financial Management Association webinar as one of the biggest Medicare changes since the implementation of DRGs.
Under the CJR, which began April 1, acute care hospitals in selected geographic areas assume quality and payment accountability for retrospectively calculated bundled payments for lower extremity joint replacement (LEJR) episodes.
The impact of CDI on CJR patient selection
A Medicare fee-for-service beneficiary is included in the CJR model when a claim is submitted for an inpatient encounter assigned MS-DRGs 469 or 470. These surgical MS-DRGs include total hip and knee replacements, ankle arthroplasties, partial hip replacements, lower leg, ankle and thigh reattachments, and hip resurfacing procedures. In the CJR final rule, CMS noted that the majority of the procedures in these MS-DRGs are total and partial hip replacements, and total knee replacements (see Figure 1 on p. 5).
The key CDI vulnerability associated with CJR patient selection is inaccurate MS-DRG assignment. The included MS-DRGs are replacement—not revision—procedures. Joint revision procedures are more complex, have higher costs, and are therefore assigned to different MS-DRGs (466-468, revision of hip or knee replacement with or without MCC).
If the coder omits assignment of the ICD-10-PCS code for the removal of the original device and only codes the replacement procedure, a patient with a revision—who should be assigned to MS-DRGs 466-468—will instead be misclassified into MS-DRGs 469 or 470, and will skew CJR clinical and cost outcomes.
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.
