Healthcare Analytics, Population Health Management, Healthcare Big Data

Quality & Governance News

Four Ways to Boost EHR Data Integrity for Big Data Analytics

By Jennifer Bresnick

- At the center of every healthcare analytics project to support population health management or quality improvement lies a critical piece of technology: the electronic health record.   Most patient data enters the health IT ecosystem through the EHR, but any organization that engages in data analytics knows that the EHR is just the beginning of a long, complicated journey for vital signs, problem lists, diagnoses, and treatment records. 

EHR data integrity, data governance, and big data analytics

As patient data is moved, coded, translated, exchanged, analyzed, and reported, there are dozens of opportunities for errors to creep in and cause mistakes that may seriously impact patient care.  Providers must be sure to develop and maintain a high level of EHR data integrity in order to avoid potential adverse patient safety events, gaps in care coordination, and buggy, incomplete data sets that make big data analytics an impossibility.

How can healthcare providers make sure that EHR data integrity starts strong and stays high as data moves across the system as big data analytics becomes an increasingly important use of health IT infrastructure?

Start at the source with good data governance policies

“Garbage in, garbage out” is the mantra of all data analytics professionals, and for good reason.  A report can only be as good as the data it is built with, and healthcare organizations have been facing some major challenges when it comes to getting their input right.  Poor EHR usability is often to blame for the various workarounds developed by frustrated clinicians, from dangerous documentation cloning to post-it notes stuck onto laptop screens.

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These shortcuts and little cheats may seem harmless or even helpful when busy clinicians are in the midst of juggling patients and squeezing in consults, but they can hurt future efforts to use patient data down the line.  Healthcare organizations that wish to generate complete, detailed, and accurate documentation to support the impending transition to ICD-10, the shift to value-based reimbursement, and the increasing importance of population health management must make certain that physicians, nurses, and other professionals with direct access to EHR input are playing by the rules.

Ensuring that strong data governance and EHR data integrity policies are in place, understood, and actively enforced can help to prevent mishaps that reduce the usefulness of patient information.  Providers should take the following steps to make sure EHR data is created appropriately:

• Enlist a health information management team or healthcare informatics professional to develop EHR data integrity guidelines, educate users, and enforce policies.  HIM professionals can help healthcare organizations implement a strong governance framework that promotes data integrity, ensures compliance with patient privacy and security mandates, and prepares data to enter the analytics and reporting cycle.

• Reduce the practice of documentation cloning, or copying and pasting of data within a patient note, to prevent data duplication, accidental deletion, or misplacement of data within narrative text.  With more than three-quarters of providers admitting to using copy-and-paste to generate “significant” portions of a patient’s note, cloning has become a major EHR data integrity issue that should be addressed.

Optimize EHR interfaces in order to generate smooth and efficient workflows.  Ask clinical users about the EHR pain points that lead to unauthorized workarounds and try to adjust input options to create a balance between free text opportunities and standardized templates.

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Engage in continuous clinical documentation improvement

With ICD-10 just around the corner, most organizations are at least familiar with the need for a clinical documentation improvement (CDI) program, which can help to ensure that EHR data accurately reflects the detail and specificity needed to appropriately code and bill for services.

This isn’t just an ICD-10 compliance measure, but a major issue for revenue cycle management, especially as providers must work harder to prove that their patient outcomes justify their use of tests, procedures, and hospitalizations.  Improved documentation as the result of a CDI program can also help to pinpoint opportunities to decrease unnecessary service utilization, stratify patients by risk or condition, and provide a clearer view of a patient population for better long-term management.

A combination of query-based clinical documentation and real-time quality assurance can help providers generate the most comprehensive patient records that more accurately reflect the severity of conditions, the type and quantity of services provided, prevent claims denials or lower-than-expected reimbursements, and provide backup during a potential audit.

“By doing concurrent documentation reviews, we’re getting into those charts and really seeing how we can make improvements to work towards our goals,” Denise Stephens, Director of Documentation Integrity and Utilization Management at Southeastern Ohio Regional Medical Center in an interview with HealthITAnalytics.com.  “For example, we had a denial on a joint replacement procedure, so now we focus a little bit more on those.  Because if all the documentation is not there – specificity of the patient’s history; what medications they’ve taken; if they’ve done physical therapy before – if that’s all not documented properly, CMS will deny that right now.”

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“Because we’re in there concurrently reviewing the chart, we can put a query in and ask if there’s any additional information the clinician would like to include before we submit the claim,” she added.  “It’s about that transparency.  If we are audited, for whatever reason, we want that transparency there so CMS can see that.”

Ensure accurate patient record matching for care coordination

Even the most complete, up-to-date, analytics-friendly EHR data is useless if the file open on the computer doesn’t match the patient in the room.  As digital records proliferate and health information exchange expands the pool of possibilities for patients with common names, healthcare providers must make certain that they are viewing, editing, and acting upon the right information.

“Lack of a standard data set can lead to patient records not being linked to one another in the HIE, resulting in an incomplete health record being available to the provider for the patient being treated, thereby defeating the purpose of the HIE,” wrote Katherine G. Lusk, MHSM, RHIA and a team of AHIMA co-authors in January, discussing the need for better EHR data integrity and a more standardized approach to HIE data elements. “Even more concerning is the potential for different patients being identified as the same, resulting in the possibility of improper care rendered on the basis of inaccurate patient information.”

While privacy concerns over the use of a national patient identifier has prevented the healthcare system from adopting a single, comprehensive patient matching methodology, a number of different approaches to reliable patient matching have been attempting to overcome inconsistencies and conflicting policies that make health information exchange a headache.

Most providers have some version of a master patient index (MPI) that uses an internal identifier such as a patient ID number or an algorithm that uses demographic data to flag the best possible match.  However, these systems are prone to record duplication thanks to small human errors like typos or misheard spellings.

AHIMA suggests that EHR data integrity programs include periodic reviews of patient records to weed out duplicates or erroneous files, either through manual assessments conducted by HIM staff or an automated data-scraping package that pulls out potentially inaccurate results.  Providers should also be sure they are using robust patient matching algorithms to begin with in order to prevent the creation of incorrect records.

Alternatively, providers may wish to help along patient matching initiatives like the Virtual Clipboard project headed by WEDI, which hopes to develop a streamlined, universal, and easily adoptable patient intake experience that can eliminate many of these data integrity issues at the source.

Embrace interoperability and emerging data standards

Above all, providers concerned with EHR data integrity must look towards a future that significantly widens opportunities for health information exchange.  As the care continuum tightens and more providers are contributing data to a more universal patient record, healthcare organizations must ensure that their data creation and governance protocols do not limit data accessibility, exchange, or interoperability within the bounds of privacy regulations like HIPAA.

To do this, providers can implement technologies that not only allow for big data analytics that help improve internal organizational efficiencies, but also promote seamless communication among partners, peers, and the healthcare system as a whole.  The adoption of health IT infrastructure components that rely on industry-accepted data standards, like the Consolidated-Clinical Document Architecture (C-CDA) or the emerging popularity of FHIR, can make certain that patient data can be available and readable to other organizations for better care coordination and enhanced decision-making.

With a comprehensive EHR data integrity program that takes into account what happens to information once it leaves a single provider’s four, healthcare providers can avoid the consequences of “garbage in” and make sure they aren’t pushing any garbage out to their patients’ other providers.  The importance of data integrity for the development of an interoperable, data-driven, learning health system should not be underestimated as healthcare organizations increasingly rely on partnerships and health information exchange to help them provide meaningful, evidence-based care.

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