The Importance of a Nursing Data Framework To Achieve Consistent Quality In Health Information Exchange

By Dr. Luann Whittenburg, PhD, RN, FAAN.

Dr. Luann Whittenburg

With more than 4 million nurses, the largest segment of the U.S. healthcare sector, nurses have indisputably demonstrated an ability to improve healthcare outcomes. We are just beginning to utilize Healthcare IT data and AI to improve patient outcomes. One of the key benefits of AI will be the ability to leverage the data from nursing care plans and nursing diagnoses to perform work load balancing for nursing staff. This is a key solution to future management of the problem of the shortage of nurses.

Another problem that needs attention is the possible disconnects which can result from nurse to nurse hand offs with the use of virtual nurses who remotely monitor patients. They enter data into their own EHR system – not the same one in use by the hospital where the patient is located. We will discuss here the nature of the data, technologies and frameworks, the nursing information model and the structure of the data elements needed to provide care needed to implement solutions for staffing, interoperability and workflow improvements.

The National Academy of Medicine’s committee background report on the Future of Nursing 2020-2030, Activating Nursing to Address Unmet Needs in the 21st Century, found the worsening health profile in the United States requires “more than a traditional medical response.” As professionals in the care team, nursing documentation requires a standardized framework to achieve consistent data quality in healthcare communications about the work of nurses. This standardized framework recognized for professional nursing documentation is the American Nurses Association (ANA) Nursing Process. This ANA framework is essential to nurses for managing and improving healthcare outcomes, safety and reimbursement as proposed by the Institute for Healthcare Improvement (IHI).

In most electronic health information record systems, the standard nursing data implemented (sometimes called the system terminology, data dictionary, or nomenclature) is proprietary with a pre-existing data structure/framework. The proprietary framework acts as a barrier to nursing documentation by constraining the available concepts for nursing documentation and the nursing care plan fields.

Without interoperable electronic data concepts available for documentation, nursing care notes become unstructured free-text and are not included in coded health information exchanges. Due to the highly structured design of EHR systems, nursing practice is determined by the system’s terminology and ontology framework configuration. If nurses do not select the ANA framework; nursing care data takes on the sedentary shape of the local proprietary data structures, rather than nesting in a flexible, portable and universal tool to enable nurses and other episodic care providers to improve future nursing interventions, practice and care outcomes.

The American Nurses Association (ANA) describes the common nursing framework of the documentation of professional nursing practice as the Nursing Process. The Nursing Process is the foundation for the documentation of nursing care. Yet, in the EHR, nursing documentation is reused during the patient’s stay, over and over, with the documentation being done from the nursing assessment as if the documentation was a template. The Nursing Process is the framework and essential core of practice for the registered nurse to deliver holistic, patient-focused care.”

Producing effective EHR systems for nursing requires a deep understanding of how nurses create and conduct cognitive documentation as well as task-oriented documentation. Most EHR systems dictate rather than adapt to nursing workflows and nursing information is not organized to fit the ANA model of care. The EHRs often assume a nursing care delivery model that is represented as algorithmic sequences of choices, yet nursing care is iterative with reformation of patient goals, revising interventions and actions and updating care sequences with individual patients based on encountered condition changes and constraints. In the dictated workflow of EHRs, nursing data is collected as care assessments with nursing diagnoses, interventions and actions in formats used to create single patient encounters.

Yet, the electronic health record system (EHR), the communication tool for health information exchange (HIE), does not collect standardized nursing data. Data quality in healthcare communication is essential to promote the necessary changes in performance delivery among the nation’s healthcare provider of care. In order to obtain transparent and complete clinical communications and data quality, EHRs must collect and document the variations in data elements following the American Nurses Association (ANA) Nursing Process framework.

A standard, coded, structured nursing documentation standard is needed to exchange nursing communication to bring transparency to the contributions of nursing to patient care continuity and the quality of health outcomes. The Clinical Care Classification (CCC) System allows nurses to communicate the nursing diagnoses of patients, the nursing interventions performed, and resulting care outcomes. The CCC system is a “research-based, coded terminology standard that identifies the discrete data elements of nursing practice—the essence of care.

The CCC system also includes a holistic framework and coding structure of diagnoses, interventions, and outcomes for assessing, documenting and classifying care in all healthcare settings.” The CCC research project was conducted under a federal grant to develop a methodology for classifying patients and measuring outcomes.

The research project represented every state in the United States, including Puerto Rico and the District of Columbia. The CCC system uses the six steps of the nursing process to describe nursing practice in a coding structure designed for retrieving data from computer information systems. See www.clinicalcareclassification.com.

With the implementation of electronic nursing documentation systems using coded, standardized nursing language and the nursing process, nurse managers are able to query the electronic nursing documentation application about nursing workload, the actions required to provide care, and evaluate the outcomes of nursing care — the results of care — and examine what nursing actions improved care.

The EHR documentation applications of the future that use standard nursing terminology and follow the nursing process will be sufficiently flexible to meet the professional documentation needs of nursing and allied health professionals in all clinical settings. The 2001 Institute of Medicine’s (IOM’s) Committee on the Quality of Health Care in America report, “Crossing the Quality Chasm: A New Health System for the 21st Century” concluded only real-time access to appropriate healthcare knowledge will provide clinicians with the information required to make well-informed decisions.

Nursing documentation completed in a structured text must incorporate a nursing terminology standard following the nursing process for a useful, common representation of all pertinent nursing documentation. The retrieval of nursing documentation can then be exchanged among HIEs for patient care continuity and outcomes analysis. Standardized nursing data allows information from disparate locations to deliver data for evidence-based practice to improve the nation’s health profile and the data quality of exchanged healthcare information.

In summary, the usability of EHR systems, alignment to the nursing workflow and the meeting of nursing information needs are key for successfully designing and implementing EHRs to improve the nation’s healthcare profile, achieve consistent HIE data quality and retrieve and mine the comprehensive work flow data needed using the nursing process framework to improve care coordination and demonstrate nursing’s impact on outcomes. The money saving potential of AI is only possible if the right standardized and coded data structures are there to mine. Standardized frameworks, the use of data exchange standards and coded reference terminologies set the data free from proprietary and sedentary non-coded text. Only then can AI reasoners reach useful conclusions and calculate reliable results.

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