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BLOG. 5 min read

What Payers Should Know About Convergence of Administrative and Clinical Data

Technology for the sharing of health data between providers and payers is significantly changing, bringing the convergence of administrative and clinical data and new opportunities to improve care and value. With the breadth of administrative information and the depth of clinical information, blending this data can improve analytics and opportunities for care delivery, quality improvement and operational efficiencies.

In May 2020, CMS finalized a milestone regulation requiring that government-sponsored and exchange health plans release their enrollees’ claims and clinical data, when requested by members, through open application programming interfaces (APIs), including any regularly maintained clinical data they may have. This Patient Access API interoperability mandate is the first step towards a system-wide exchange of administrative and clinical health information. 

The complexities that exist with this data convergence are not minimal. We will discuss the basics of the two sides of the data coin to show how the joining of these data types can lead to more holistic value in healthcare, and the role this initial step in health plan interoperability plays in the longer-term health value equation. Let’s begin by defining the two types of data.

Administrative Data: A Powerful Source for Analytics

Administrative data is fundamental information used by health plans to oversee payment for care, monitor utilization, manage populations, and better understand resource use and needs of specific populations of patients. Administrative data includes enrollment information, eligibility data, claims and managed care encounters.

Overall, analysis of administrative data allows multiple functions to:

  • Offer insights about diagnoses, treatments, and billed and paid amounts.
  • Interpret costs.
  • Track utilization and treatment.
  • Track medication refill patterns and changes in medications.

While health plans have used claims information extensively to manage and stratify risk, conduct predictive modeling and other aims, one of the biggest cultural barriers for providers is thinking of the claim as more than just a mechanism for payment. Providers paid on a productivity basis view claims as a vehicle to revenue—the more claims that go out, the more revenue that comes in. However, claims are actually a means for providers to communicate the nuances of their patients to the health plans that pay them using specificity in coding, such as diagnoses (ICD-10) and procedures (CPTs). 

Numerous systems capture administrative data, including provider systems generating the claim, clearinghouses that route the claims and the payer systems adjudicating the claims. Housed in each system’s database, structured data is codified, machine-readable and organized into fields as part of a schema with each field having an express purpose. 

Clinical Data: A Valuable Source for Patient Care

Clinical data is gathered and stored in relation to a specific patient’s care. Broadly, clinical data includes all information and results related to an individual’s care including medical records, vital signs, medications, imaging data, scans, questionnaires and results of biomedical or genetic analysis. Electronic clinical data consists of health-related information gathered during patient care such as results, orders, medications and immunizations, and encounter notes. There are many sources of electronic clinical data such as lab information systems, registries, medical devices, Electronic Medical Records (EMRs) and patient Electronic Health Records (EHRs).

While both EMRs and EHRs contain non-structured information, they can be treasure-troves of rich structured data using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for EHR coded clinical diagnosis data and Logical Observation Identifiers Names and Codes (LOINC), which is primarily used for lab codes, medical observations and vitals. In addition, RxNorm[1] provides normalized names for clinical drugs and links those to many of the drug vocabularies commonly used in pharmacy management and drug interaction software to facilitate messages between systems that don’t use the same vocabulary or software.

These classification identifiers systematically organize and encode medical terms in a machine-readable fashion, allowing the medical chart to be shared across multiple platforms on a real-time basis by authorized users.

  • Medical record review by treating physicians, unfamiliar with the patient.
  • Precise Hierarchical Condition Category (HCC) coding.
  • Research.
  • Public health surveillance.
  • Greater population health management.

Government Regulations Could Accelerate the Timeline

There are two pending regulations that could impact the timing for convergence of administrative and clinical data.

  1. In December 2020, the Department of Health and Human Services issued proposed regulations, modifying HIPAA to guarantee patients the right to request their providers to share data with plans, and vice versa.  
  2. In January 2021, CMS finalized additional patient data interoperability regulations to require plans to share enrollee claims and clinical data with requesting providers via APIs. The regulation would also create a new API to exchange plan Prior Authorization transactions between providers and plans.  

The Biden Administration is currently reviewing these proposals from the final months of the Trump Administration.

What Does Success Look Like?

Our healthcare industry is in its infancy with merging administrative and claims data, but these recent regulations will cause provider and plan data exchanges to mature quickly. With the adoption of electronic claims at 96%[2] and the adoption of certified EHR technology (CEHRT) between 86-96%,[3] convergence of this data is possible.  However, remember that data interoperability is bi-directional. While plans have focused on meeting the July 1, 2021, effective date to push claims, directory, preferred drug lists and regularly maintained clinical data to their members, they now need to plan for the receipt of clinical information, which has previously been unavailable.

The first step is always the hardest. Data sharing not only requires new technologies, but also leadership strategy to consider new ways to make real progress. The payoff is tremendous value awaiting patients, providers and payers as administrative and clinical data mix it up. 

  • Patients can expect easy access to information about out-of-pocket costs, in-network providers, and less delays in care. 
  • Providers will see improvements in administrative activities such as prior authorization. 
  • Payers can incorporate clinical information into such activities as identifying fraud and abuse, risk management, quality measures and population health management. 

As sharing of electronic health information (EHI) matures, health plans have an opportunity to realize new value from data. Imagine if providers know in advance what documentation a plan needs for prior authorization. Start to visualize a world where there is no fax machine, where providers and payers collaborate as a single care team about case management, and where there is no more chasing of paper charts for audit and chart review.

What’s Your Strategy?

Armed with a strategy that seeks an increase in business value through enhanced and new potential processes, a decrease in administrative burden associated with clinical data acquisition and interpretation, and meeting related regulatory requirements, payers will reap the benefits promised by this new era.

We are at the inflection point. Modernization will lead to improvements in patient care and health value. If you are ready to incorporate data-sharing opportunities into your strategy, we are here to guide you along the way. 

 

[1] https://www.nlm.nih.gov/research/umls/rxnorm/index.html

[2] CAQH, 2020 CAQH Index, Closing the Gap: The Industry Continues to Improve, But Opportunities for Automation Remain, accessed Mar 23 2021, https://www.caqh.org/sites/default/files/explorations/index/2020-caqh-index.pdf

[3] Office of National Coordinator for Health Information Technology, ibid.

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