patient data integrity and patient data matching in healthcare

Achieving Higher Patient Data Integrity Requires a Multi-Layered Approach

patient data integrity and patient data matching in healthcare

Improving patient data integrity in healthcare requires a multi-layered approach that addresses both data matching and more accurate patient identification.

The following guest post was written by David Cuberos, Enterprise Sales Consultant with RightPatient®

Patient Data Integrity and Duplicate Medical Records

It is a well known fact that inaccurate or incomplete data within a patient’s medical record can be a catastrophic risk to patient safety, not to mention a serious hospital liability. As a result, many hospitals and healthcare organizations across the industry are closely examining the integrity of their health data and taking steps to clean it, most by using third party probabalistic and deterministic de-duplication matching algorithms (often directly from their EHR providers) that search and identify possible duplicates for an automatic or manual merge.

Several key players in the healthcare industry including CHIME, AHIMA, HIMSS, and major EHR providers are beating the drum to improve patient identification and patient data matching, all important catalysts for the push to improve patient data integrity.

If you are a hospital or healthcare organization that is knee deep in the middle of a health IT initiative to help increase patient data integrity (especially in the context of prepping for participation in a local or regional health information exchange), you may want to stop and reassess your strategy.  The rush to cleanse “dirty data” from EHR and EMPI databases is often addressed by relying on an EHR vendor’s de-duplication algorithm which is supposed to search and identify these duplicate medical records and either automatically merge them if similarity thresholds are high, or pass them along to the HIM department for further follow up if they are low. 

This could be a very effective strategy to cleanse an EMPI to ensure patient data accuracy moving forward, but is it enough? Is relying on an EHR vendor’s de-duplication algorithm sufficient to achieve high levels of patient data integrity to confidently administer care?  It actually isn’t. A more effective strategy combines elements of a strong de-duplication algorithm with strong patient identification technology to ensure that patient data maintains its integrity.

Duplicate Medical Record Rates are Often Understated

The industry push for system-wide interoperability to advance the quality and effectiveness of healthcare for both individuals and the general population has been one of the main catalysts motivating healthcare organizations to clean and resolve duplicates but it also has revealed some kinks in the data integrity armor of many different medical record databases. Most hospitals we speak with either underestimate their actual duplicate medical rate, or do not understand how to properly calculate it based on the actual data they can access.  An AHIMA report entitled “Ensuring Data Integrity in Health Information Exchange” stated that:

“…on average an 8% duplicate rate existed in the master patient index (MPI) databases studied. The average duplicate record rate increased to 9.4% in the MPI databases with more than 1 million records. Additionally, the report identified that the duplicate record rates of the EMPI databases studied were as high as 39.1%.”

“High duplicate record rates within EMPI databases are commonly the result of loading unresolved duplicate records from contributing MPI files. EMPI systems that leverage advanced matching algorithms are designed to automatically link records from multiple systems if there is only one existing viable matching record. If the EMPI system identifies two or more viable matching records when loading a patient record, as is the case when an EMPI contains unresolved duplicate record sets, it must create a new patient record and flag it as an unresolved duplicate record set to be manually reviewed and resolved. Therefore, if care is not taken to resolve the existing EMPI duplicate records, the duplicate rate in an EMPI can grow significantly as additional MPI files are added.”

(AHIMA report, “Ensuring Data Integrity in Health Information Exchange”  http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_049675.pdf)

Clearly, the importance of cleansing duplicate medical records from a database cannot be understated in the broader scope of improving patient data integrity but relying on an EHR vendor’s probabilistic matching algorithm as the only tool to clean and subsequently maintain accurate records may not always be the most effective strategy. Instead, healthcare organizations should consider a multi-layered approach to improving patient data integrity beyond relying exclusively on an EHR vendor’s de-duplication algorithm. Here’s why.

Why Patient Data Integrity is a Multi-Layered Approach

Often not clearly explained to healthcare organizations, EHR de-duplication algorithms allow end users to set matching thresholds to be more or less strict, which comes with trade-offs. The more strict the threshold is set, the less chance of a false match but the higher chance of a false reject. The less strict the algorithm is set, the lower the chance of a false reject but the higher the chance of false acceptance.

Translation: Often times hospitals who say they have a low duplicate medical record rate might have a strict false acceptance rate (FAR) threshold setting in their de-duplication algorithm. That may mean that there could be a significant amount of unknown duplicate medical records that are being falsely rejected. Obviously, this is a concern because these databases must be able to identify virtually every single duplicate medical record that may exist in order to achieve the highest level of patient data integrity.

So, what can healthcare organizations do to ensure they are not only holistically addressing duplicate medical record rates, but also adopting technology that will maintain high patient data integrity levels moving forward? One answer is to implement a stronger de-duplication algorithm that has the ability to “key” and link medical records across multiple healthcare providers on the back end, and deploying a technology such as biometrics for patient identification on the front end to ensure that not only is care attribution documented to the accurate medical record, but a provider has the ability to view all patient medical data prior to treatment. 

For example, many credit bureaus offer big data analytics solutions that can dig deep into a medical record database to better determine what identities are associated with medical records. These agencies are experts in identity management with access to sophisticated and comprehensive databases containing the identification profiles for millions and millions of patients — databases that are reliable, highly accurate, and secure with current and historical demographic data.

Once data is analyzed by these agencies, they are able to assign a “key” to match multiple medical records for the same patient within a single healthcare organization and across unaffiliated healthcare organizations to create a comprehensive EHR for any patient. Offering a unique ability to augment master patient index (MPI) matching capabilities with 3rd party data facilitates more accurate matching of medical records across disparate health systems and circumvents the problem of MPIs assigning their own unique identifiers to individual patients that are different than unaffiliated healthcare organizations that have their own MPI identifiers.

Benefits of using a third party big data analytics solution that has the ability to “key” medical records for more accurate patient data matching at a micro level include:

  • More accurate identification of unique patient records resulting in a more complete medical record and improved outcomes
  • Prevention of duplicate medical records and overlays at registration reduces the cost of ongoing MPI cleanups
  • Medical malpractice risk mitigation 
  • Reduced patient registration times
  • The ability to more accurately link the most current insurance coverage patient information for more accurate billing

On the marco level, benefits include: 

  • Positive patient identification for eligibility verification, billing, coordination of benefits, and reimbursement
  • Improved care coordination
  • Information and record keeping organization 
  • Linkage of lifelong health records across disparate healthcare facilities
  • Aggregation of health data for analysis and research
  • Accurately aggregating patient federated data via a HIE

Conclusion

We have long championed the idea that improving patient data integrity can never be achieved in the absence of establishing patient identification accuracy or relying on EHR vendor de-duplication algorithms as the single resource to clean an MPI database. Hospitals and healthcare organizations that are truly committed to cleansing duplicate medical records from their databases and preventing them from reoccurring through more accurate patient identification must consider deploying stronger front and back end solutions that have the ability to more comprehensively identify and resolve these dangers to patient safety. Why not leverage the clout and reach of these big data analytics solutions to more effectively improve patient data integrity instead of putting all of your eggs in an EHR vendor’s de-duplication algorithm?

What other strategies have you seen as effective methods to increase patient data integrity in healthcare?

biometric patient identification prevents duplicate medical recordsDavid Cuberos is an Enterprise Sales Consultant with RightPatient® helping hospitals and healthcare organizations realize the benefits of implementing biometrics for patient identification to; increase patient safety, eliminate duplicate medical records and overlays, and prevent medical identity theft and healthcare fraud.

update revenue cycle management

5 Big Indicators You Should Replace Your Revenue Cycle Management Solution

update revenue cycle management

What are some obvious signs that you need to upgrade your revenue cycle management (RCM) system?

The following guest post was submitted by Eugenia Lin.

If you saw a friend using a computer still running on Windows XP, your immediate reaction would be to ask why they haven’t updated to a newer operating system. XP is now 15 years old and Microsoft no longer provides technical support or security updates for it. Unless they really enjoy playing Solitaire and putting their data at risk, then there’s no excuse for having outdated software. The same reasoning applies to your revenue cycle management (RCM) system. Having an updated RCM solution will not only empower your staff, but also benefit your financial bottom line. Here are a few indicators that your RCM software needs updating:

1. Lack of business intelligence (BI) reporting and analytics

BI reporting is an incredibly powerful tool that allows one to obtain insights and identify trends on both a macro and micro level. For example, through macro level reporting a practice’s overall profitability can be reviewed. Then through the same interface, the data can be segmented further down to the profitability of all offered procedures. Easily accessing such information in a self-service manner not only shortens the decision making process but also excludes the need for outside consulting parties.

2. Unable to manage a growing or a multi-clinic practice

If your RCM system isn’t scalable or doesn’t offer customization for medical specialties, then it is time to look for another solution. There are systems available that are able to manage a solo provider, a multi-clinic practice, and anything in between. The benefit of this, is that a business manager can oversee and administer at any level easily.

3. Fails to manage and facilitate the entire claims process

It is essential that your RCM system be capable of managing the claims process from start to finish. Starting with claims from scrubbing to prevent coding errors and typos, as human error is one of the most common causes for payment denial. Your RCM system should also provide snapshot reporting, allowing you to monitor outstanding claims and categorizing them by where in the process they are. It should also provide alerts on claims that need following up on, so that none fall through the cracks.

4. Doesn’t fit into current workflow

Another clue that your RCM solution needs replacing is when it’s impeding your practice’s efficiency. Current RCM systems contain tools and features that improve workflow and staff productivity. RCMs can instantly verify insurance eligibility directly through the user interface, allowing your staff to focus on other tasks and avoid wasting time navigating through an insurance provider’s automated phone system. They can also easily manage payment reminders and follow-ups through automated texts or emails.

5. Doesn’t provide important features or cannot integrate with other systems

Having an RCM system able to seamlessly cooperate with your practice’s other system can be greatly beneficial. For instance, when integrated with an EHR system, the claims process becomes even more automated and less prone to errors. As forms can be auto filled with data from a patient’s file, versus a staff member having to manually fill the forms while referring to a paper chart or a separate note taking system.how can you improve revenue cycle management?

Eugenia Lin avidly enjoys writing about a variety of topics and currently writes on behalf of the revenue cycle management experts at OmniMD. When not writing, she can be found spoiling her pet, Yeti, with treats or trying to be active outside on those typical Seattle rainy days. You can find her at LinkedIn.

biometric patient identification solutions prevent duplicate medical records and overlays

New Podcast: The Impact of Duplicates and Overlays on Health Information Management (HIM)

biometric patient identification solutions prevent duplicate medical records and overlays

Our latest podcast features HIM Director Erin Head discussing the impact of duplicate medical records and overlays on health information management (HIM).

Erin brings a wealth of experience to health information management (HIM) work flow and managing patient data integrity so naturally we were excited to tap into her knowledge base to better understand the HIM “front line” – a deeper discussion about the day to day activities in the trenches and a firsthand account of the negative impact of duplicate medical record and overlay identification and reconciliation. Our conversation with Erin covered the following topics:

— How duplicate medical record reconciliation impacts HIM workflow and other job responsibilities sacrificed due to duplicate/overlay reconciliation

— The average FTEs health information management spends reconciling duplicates and overlays and the financial impact on the hospital if FTE’s that are currently cleaning up duplicates and overlays could be reallocated to more revenue generating activities such as coding

— How the shift to quality vs. quantity based care impacts the responsibilities and sense of urgency for HIM

— Whether the ONC cost estimate of $60 per duplicate record is low or high compared to her own experience

— The impact on HIPAA violations that duplicates/overlays cause and the cost if a hospital releases information to wrong patient

— How the introduction of the patient portal complicates management of duplicates

— How the implementation of a biometric patient identification system helps to lower the burden of reconciling duplicates and overlays and allows health information management to focus on their core competencies

For a full version of the podcast, please visit the landing page for more information. 

Have an idea for a podcast or know a healthcare professional that would be a good candidate to interview? Email us at: info@rightpatient.com with your ideas!

patient ID in healthcare podcast

IntrepidNow Healthcare Podcast Highlights Patient Identification in Healthcare

patient identification in healthcare podcast

Joe Lavelle from IntrepidNow Healthcare interviewed RightPatient® President Michael Trader to discuss the current state of patient identification in healthcare. (photo courtesy of Joe Lavelle and IntrepidNow Healthcare)

Our thanks to Joe Lavelle and his staff for inviting our President Michael Trader to the IntrepidNow podcast to discuss patient identification in healthcare. Joe invited Michael to not only talk about the current state of patient ID in healthcare and some of the problems that misidentification of patients creates, he also provided the opportunity for Michael to discuss the RightPatient® biometric patient identification platform and what distinct advantages it provides compared to other solutions on the market.

Listen in to Joe’s podcast and learn:

  • The impact of biometric patient ID solutions to eliminate duplicate medical records/overlays and sustain patient data integrity
  • How modern patient identification solutions help prevent medical identity theft and fraud at the point of service
  • How the digitization of healthcare now makes accurate patient identification essential at every touchpoint along the care continuum 
  • The rising importance and ubiquity of photos for accurate patient ID in healthcare
  • The biometric patient identification solution competitive landscape
  • Updates on The College of Healthcare Information Management Executives (CHIME) national patient ID challenge
  • Looking ahead to what’s next for RightPatient® in 2016

Listen to the entire interview here:

Thanks again to Joe Lavelle from IntrepidNow for inviting us to be a guest on his podcast! For a complete list of all RightPatient® healthcare biometrics podcasts, please visit our podcast landing page.