Profile and Implementation Guide for Advancing Collection of Standardized Symptoms Data (Jira PSS-2566)

Project Name
Profile and Implementation Guide for Advancing Collection of Standardized Symptoms Data (Jira PSS-2566)

Project Number
1929

Project Type

Product Family

Products
FHIR Implementation Guide; FHIR Profile

Project Intent
Create New Standard

Ballot Type

Project Need

The purpose and rationale for this project center around the absence of standardized definitions and formats for symptoms data that hinders effective sharing across providers, leading to data silos and interruptions in critical diagnostic thinking. Diagnostic errors in the United States, where 795,000 individuals annually experience death or permanent disability due to diagnostic error.[1],[2] This initiative aims to decrease missed, delayed, or inaccurate diagnoses by enhancing the documentation and exchange of symptoms, a crucial step in the diagnostic process. For example, Liberman and Newman-Toker's SPADE framework highlights how a lack of structured symptom-disease pairs in electronic health records has contributed to misdiagnosis, such as benign dizziness being incorrectly documented instead of early stroke, which resulted in delayed treatment and increased patient harm.[3] To overcome these barriers, the project will develop an HL7 Symptoms FHIR Profile and Implementation Guide (IG) that structures symptom data for seamless exchange across visits and health systems. This effort will enhance recognition of symptom patterns, reducing clinician burden by streamlining the diagnostic process, and ultimately support informed diagnostic decision-making, improving outcomes for patients. The project’s collaborative approach, which integrates input from medical professionals, healthcare IT experts, and standards subject matter experts, enhances its potential in advancing patient care.

There are many potential use cases for utilizing standardized symptoms data, for instance:

Allow for reaching an accurate diagnosis earlier in the patient journey

Allow for clustering of symptoms data using visualization at the point of care

Clinical decision support at the point of care

Provide enough granular data for advanced technologies like artificial intelligence and natural language processing to derive an accurate diagnosis early in the patient journey

Integration of patient symptoms from a personal health record to an electronic health record

Utilization in clinical research

Syndromic surveillance in public health

CodeX Data DIRECT – Real World Data: access real-world data for streamlined analysis and insights from diverse healthcare sources that reflect actual patient experiences

CodeX Prior authorization in oncology: augment prior authorization to include symptoms data for consideration in utilization management, i.e. know the symptom(s) before approving treatment plan

CardX mCARD - Minimum Core Cardiovascular Data: a set of common data elements for cardiovascular care that is standardized, computable, clinically applicable in every EHR for patients with a cardiovascular diagnosis

Quality improvement initiatives

Documents Templates and Rules (DTR) for prior authorization workflow use case to include more granular clinical criteria

Rather than create separate projects and IGs for each use case, the proposed project will act as an umbrella for profiling and writing an IG for symptoms data standardization that can be re-used for multiple use cases.

[1] Singh H, Meyer AN, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf. 2014;23(9):727-731. doi:10.1136/bmjqs-2013-002627

[2] Newman-Toker DE, Nassery N, Schaffer AC, et al. Burden of serious harms from diagnostic error in the USA. BMJ Qual Saf. 2024;33(2):109-120. doi:10.1136/bmjqs-2021-014130

[3] Liberman AL, Newman-Toker DE. Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): a conceptual framework and methodological approach for unearthing misdiagnosis-related harms using big data. BMJ Qual Saf. 2018;27(7):557-566. doi:10.1136/bmjqs-2017-007032

Objectives and Deliverables
FHIR IG R1 - STU Target May 2026

Project Document Repository
https://confluence.hl7.org/spaces/CIC/pages/324960988/Symptoms+Standardization+Project PSS: https://jira.hl7.org/browse/PSS-2566

Collaboration Efforts
National Quality Forum The Joint Commission Nictiz Zib-Centrum (HCIM-center) Cedars-Sinai Medical Center – Carl Berdahl, MD, MS, FACEP The American Medical Association, The American College of Emergency Physicians, Lehigh Valley Health Network - Jefferson Health, and Harrisburg University of Science and Technology – Steven Hardy, MD, MS American College of Physicians - Sharath Kharidi, MD, FACP, FAMIA UNC Health Alliance – Isha Mehta, MD, MPH, MMCi Elimu Informatics – David Lobach, MD, PhD, MS

Dependencies
External subject matter experts

Stakeholders
Regulatory Agency, Payors

Implementers
Nictiz Zib-Centrum (HCIM-center)

External Drivers
Clinical Decision Support Patient Satisfaction Patient Safety Expert Systems Electronic Health Records Claims and Payment Quality Assessment and Improvement Clinical Research Artificial Intelligence/Machine Learning

Common Names
Symptoms Data Standardization/Symptoms

Lineage

External Vocabularies
Yes

Backwards Compatibility
N/A

Joint Copyright
Yes