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Coding Challenges: Hospital Readmissions from a Health Information Exchange Perspective

A standard approach to managing population data often yields valuable results for clinical teams, as well as the patients they serve.

I discussed this topic in my previous blog. In this post, I reviewed the lack of a standard approach gathering and analyzing the readmission data from hospital to hospital for useful conclusions and better care.

Learn about the Nebraska Health Information Initiative - NeHII

The Nebraska Health Information Initiative (NeHII) serves as the statewide HIE and is the connection to care occurring in an acute setting versus care that happens across the community. Through collaborative efforts with our hospital partners, ACOs and provider practices, NeHII has developed a daily readmission report that enables our partners to drastically reduce, and, in some cases, eliminate the readmission penalty.

The readmission report facilitates the use of critical information needed to:

  • manage care transitions,
  • begin the process of preventing subsequent readmissions,
  • identify risk factors for admission or care transition risks.

Readmissions report brought renewed focus on data quality

We examined the success of our partners participating in the NeHII readmission project, specifically looking for implementation issues and obstacles for readmission reports. The lessons learned from this project has led NeHII to focus efforts on data quality and integrity.

Anecdotally, our partners in this effort have stated they are better managing risk for readmission and attribute workflow redesign and penalty avoidance to the information provided in the readmission report.

HIEs help manage populations and prevent readmissions, positioning HIEs as successful partners for health systems.

Through NeHII’s work to reduce readmissions with participating hospitals, a common theme surfaced: The full value of a readmission report depends on improving ICD-10 coding practices.

NeHII identified several opportunities for clinical documentation improvement and coding accuracy when the team analyzed the project issues in creating the readmission report. Because documentation and coding are basic elements for data integrity and data quality, data quality becomes foundational to success in value-based care models.

We concluded that NeHII must focus on data quality and understand the multiple factors for coding errors.

How to begin:

I recommend contacting the HIE in your region and signing up to receive event notification services.

When you sign up for event notification services offered by HIEs, you will be able to monitor high utilizers as they move across the continuum of care. Seek out Transforming Clinical Practice Initiative (TCPI) partners to begin the efforts around practice transformation for success in value-based contracts, and pay attention to clinical documentation and coding, as it is the first step in success with risk-based population management. You can expect surprises in what you discover!

Lessons learned from the readmission project:

  • Misinformation regarding “coverage risk”
    • The national discussion around healthcare reform and pre-existing conditions has many hospitals and clinics hesitating to code for chronic conditions.
    • The problem of down-coding or missed codes effects risk adjustments and impacts identification of risk for readmission.
       
  • Educating care providers on risk scoring and prevention
    • The current diagnosis and active treatment (as defined in ICD-10) need to be captured in order to accurately assess and analyze patient risks through a risk profile. This ensures providers receive credit for not only today’s care, but also for care they manage through Medicare and commercial plans.
    • Ensuring that the code used by the user interface accurately stores data into the system can help to overcome hurdles faced by labeling patients. Ensuring the quality of the data is important, as this allows for accurate risk profiling and avoids adverse events. System-identified care interventions should also be implemented, as these can prevent readmissions or complications, thus avoiding associated penalties.
  • Data quality drives the analytics engine.
    • Accurate coding is a foundation to analytic quality. The marketplace is flooded with population analytical tools for providers in their practices and health systems managing cost and quality efforts. Through NeHII’s efforts with readmission reports, we have identified that a heavy lift for health systems is ensuring clinical documentation quality and coding accuracy on the front end of the workflow. Data quality is what drives the analytics engine, risk profiling and identification not only of high utilizers of care, but also of those who are at risk for costly events.
    • When making technology-purchasing decisions, it is difficult to peer into the architecture of the analytics software. For example, when testing the sorting of data based upon proper diagnosis code and then making a count of the numerator/denominator for the accurate ratio: if the software isn’t recognizing nor sorting the data as it should, it leads to inaccurate conclusions and analysis, and thus misguided practice improvement at the facility or clinic. The root cause of data inaccuracies can take months to identify – this time can be costly, leading to penalties and less-than-anticipated performance in risk-based contracts.
    • Resources to review clinical documentation, analyze the data and manage populations are often scarce, but small steps to hardwire good practices on the front end can avoid resource intensive rework later on. Care managers dedicated to managing patients are the desired use of resources rather than a focus on addressing the root cause of the reimbursement shortcomings.

 

Hear more from Deb Bass at our upcoming HIMSS Joint Interoperability & HIE, Nursing Informatics and Connected Health Community Roundtable, on Wednesday, Sept. 27, when she will be joining us to discuss The Success Story of the Prescription Drug Monitoring Program in Nebraska.