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HomeUncategorizedWhy Point-of-Care Diagnostic Filtering, Interoperability Are Essential in the Age of TEFCA,...

Why Point-of-Care Diagnostic Filtering, Interoperability Are Essential in the Age of TEFCA, QHINs – MedCity News

David Lareau

interoperability, rope, braid

Health data interoperability recently took a major step forward when the U.S. Department of Health and Human Services announced the first six organizations as Qualified Health Information Networks (QHINs) under the Trusted Exchange Framework and Common Agreement (TEFCA).

Many predicted the coming “data tsunami” once the floodgates opened and information was shared more widely, and discussed how increased interoperability would create both opportunities and challenges. When the QHIN approvals were announced, Micky Tripathi, National Coordinator for Health IT, mentioned “operational friction in interoperability” and the challenges of moving information between enterprises — which is a primary issue that QHINs seek to address.

The challenge of finding diagnostically relevant data

As more information flows freely between systems, it will create an even bigger challenge for clinicians: finding diagnostically relevant information amidst the flood of incoming data. Healthcare information is currently organized using different terminologies and coding systems to support classification of information into separate domains such as diagnoses, labs, medications, orders, procedures, etc., primarily to support billing transactions and internal system workflows. The terminologies and codes are not organized to enable a clinician to quickly see how well a condition is being managed for a specific patient.

Clinicians are already frustrated with their EHRs, in part because of difficulties finding the information they need to determine how well a condition is being managed or if a patient is responding to treatment. Under value-based care, it is more critical than ever for clinical users to see longitudinal views of diagnostically relevant information for each of a patient’s conditions so they can take appropriate action and document accordingly.

This means clinicians need systems that do more than just support the coding of diagnoses and transactions; they also need their systems to diagnostically filter information at the point of care and present them with actionable views. In other words, clinicians require a new form of clinical decision support that presents the specific information needed to make decisions – regardless of the source. That new capability might be called “diagnostic interoperability.”

Time for new tools

The 21st Century Cures Act, TEFCA, and the imminent establishment of QHINs will, for the first time, make the long-awaited advent of interoperability a reality. Systems will be sending SNOMED, ICD-10, CPT, RxNorm, LOINC, HCPCS, and a host of other codes and narrative notes back-and-forth as part of the data tsunami, leaving it to the receiving systems to make sense of it for clinicians. The timing is perfect for the adoption of a new set of tools that make diagnostically relevant information discoverable and actionable by clinicians at the point of care.

A core requirement for these new tools is to enable a clinician to select any diagnosis, problem, or clinical issue for a patient and quickly view the hallmark indicators for that problem.

TEFCA, QHINs, FHIR and terminology standards will facilitate the transmission and receiving of information, but the critical task for clinicians will be finding the information needed to assess, evaluate, manage and treat a specific problem. Clinical users need to quickly view the symptoms, history, physical exam findings, test orders and results, therapies, comorbidities, sequalae and other data points related to any specific condition.

In the new world of interoperability, incoming information will be in a variety of terminologies and formats: ICD10-CM and SNOMED for problems and diagnoses, LOINC and CPT for lab orders and results, CPT, HCPCS, and ICD10-PCS for procedures and therapies, RxNorm and NDC for drugs, and a number of other specialized code sets. While these code sets and terminologies are useful for classifying information in a specific domain, they were not designed to work together to present a comprehensive view of a condition, nor for use by clinicians at the point of care.

Current EHRs typically organize this information into separate “tabs” or “buckets” in the medical record. To monitor the course of a disease, a user must navigate between sections and spend time hunting for the relevant details – which takes time that could be better-spent interacting with the patient and managing their condition. The EHR may contain all the relevant information a clinician needs for decision making, but finding the precise details they need is not always easy.

A better way

In the world of value-based care, the effective monitoring and management of chronic conditions requires that all relevant information for a diagnosis be instantly available to the clinician at the point of care, without requiring clinicians to waste precious time searching for details. A better way would be to empower clinicians with a clinical toolset that allows them to select any condition and immediately see a diagnostically organized view of all the relevant details. Such technology could replace manual searches by automatically filtering information for diagnostic relevancy based on the codified details and using natural language processing and mappings to organize the items.

In addition to diagnostic filtering and presentation, the ideal clinical toolset must also integrate with existing system workflows and provide point-of-care services to evaluate the patient’s medical record for adherence to clinical best practice guidelines and mandated quality measures, appropriateness of diagnostic coding, and sufficiency of documentation.

Without a new set of tools that clinicians can access at the point of care, the availability of information from QHINs will increase provider burdens because they will struggle to find the information needed to evaluate a patient, take action, complete documentation, and move to the next patient.

Basic interoperability is about to become real. The next step is diagnostic interoperability – which could very well be the impetus for value-base care success and for the transformation of EHRs from clinician burden to essential tool.

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