Encoding laboratory testing data: case studies of the national implementation of HHS requirements and related standards in five laboratories.

OBJECTIVE: Assess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions. MATERIALS AND METHODS: We compared gaps and similarities between diagnostic test manufacturers' recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests. RESULTS: Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P < .0001). DISCUSSION: The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability. CONCLUSION: The results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.

Establishing Trust in Pharmaceutical Data with an Independent Verification and Validation Methodology.

Our aim is to demonstrate a general-purpose data and knowledge validation approach that enables reproducible metrics for data and knowledge quality and safety. We researched widely accepted statistical process control methods from high-quality, high-safety industries and applied them to pharmacy prescription data being migrated between EHRs. Natural language medication instructions from prescriptions were independently categorized by two terminologists as a first step toward encoding those medication instructions using standardized terminology. Overall, the weighted average of medication instructions that were matched by reviewers was 43%, with strong agreement between reviewers for short instructions (K=0.82) and long instructions (K=0.85), and moderate agreement for medium instructions (K=0.61). Category definitions will be refined in future work to mitigate discrepancies. We recommend incorporating appropriate statistical tests, such as evaluating inter-rater and intra-rater reliability and bivariate comparison of reviewer agreement over an adequate statistical sample, when developing benchmarks for health data and knowledge quality and safety.

Automated Modeling of Clinical Narrative with High Definition Natural Language Processing Using Solor and Analysis Normal Form.

OBJECTIVE: One important concept in informatics is data which meets the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). Standards, such as terminologies (findability), assist with important tasks like interoperability, Natural Language Processing (NLP) (accessibility) and decision support (reusability). One terminology, Solor, integrates SNOMED CT, LOINC and RxNorm. We describe Solor, HL7 Analysis Normal Form (ANF), and their use with the high definition natural language processing (HD-NLP) program. METHODS: We used HD-NLP to process 694 clinical narratives prior modeled by human experts into Solor and ANF. We compared HD-NLP output to the expert gold standard for 20% of the sample. Each clinical statement was judged "correct" if HD-NLP output matched ANF structure and Solor concepts, or "incorrect" if any ANF structure or Solor concepts were missing or incorrect. Judgements were summed to give totals for "correct" and "incorrect". RESULTS: 113 (80.7%) correct, 26 (18.6%) incorrect, and 1 error. Inter-rater reliability was 97.5% with Cohen's kappa of 0.948. CONCLUSION: The HD-NLP software provides useable complex standards-based representations for important clinical statements designed to drive CDS.

Language barrier. Getting past the classifications and terminologies roadblock.

Health IT is bogged down in a quagmire of unaligned classification and terminology systems. New recommendations from AHIMA and AMIA help point to the way out.

Kaiser Permanente's Convergent Medical Terminology.

This paper describes Kaiser Permanente's (KP) enterprise-wide medical terminology solution, referred to as our Convergent Medical Terminology (CMT). Initially developed to serve the needs of a regional electronic health record, CMT has evolved into a core KP asset, serving as the common terminology across all applications. CMT serves as the definitive source of concept definitions for the organization, provides a consistent structure and access method to all codes used by the organization, and is KP's language of interoperability, with cross-mappings to regional ancillary systems and administrative billing codes. The core of CMT is comprised of SNOMED CT, laboratory LOINC, and First DataBank drug terminology. These are integrated into a single poly-hierarchically structured knowledge base. Cross map sets provide bi-directional translations between CMT and ancillary applications and administrative billing codes. Context sets provide subsets of CMT for use in specific contexts. Our experience with CMT has lead us to conclude that a successful terminology solution requires that: (1) usability considerations are an organizational priority; (2) "interface" terminology is differentiated from "reference" terminology; (3) it be easy for clinicians to find the concepts they need; (4) the immediate value of coded data be apparent to clinician user; (5) there be a well defined approach to terminology extensions. Over the past several years, there has been substantial progress made in the domain coverage and standardization of medical terminology. KP has learned to exploit that terminology in ways that are clinician-acceptable and that provide powerful options for data analysis and reporting.