From Raw Spirometry Data to Clinician-Ready Reports: Anatomy of a Modern PFT Reporting Workflow
A modern pulmonary function testing (PFT) reporting workflow transforms raw spirometry signals into structured, clinician-ready reports through a sequence of defined steps: data capture, import and validation, normal value application, interpretation, and physician sign-off. When each step is well-designed, the result is faster turnaround, fewer errors, and better patient outcomes. When any step is broken, the entire chain suffers.
TL;DR
Raw spirometry data is only the starting point. The real clinical value is created during interpretation, normal value application, and structured reporting.
Most inefficiencies in PFT labs occur at the data handoff points, not during the test itself.
Both expiratory and inspiratory limb data carry diagnostic weight and should be captured and preserved throughout the workflow.
Automation and vendor-neutral import tools can significantly reduce manual data entry and the clinical risk that comes with it.
A well-configured reporting system should enforce ATS/ERS guideline compliance by design, not by memory.
What Actually Happens Between a Spirometry Test and a Final Report?
The gap between a completed spirometry maneuver and a signed clinical report is where most PFT labs lose time, introduce errors, and create clinical risk. Understanding this gap is the first step to closing it.
A typical PFT reporting workflow moves through these stages:
Test performance - The patient performs the maneuver; the device records raw flow and volume signals.
Quality assessment - The respiratory scientist reviews acceptability and reproducibility criteria per ATS/ERS standards.
Data export and import - Raw or processed data is transferred from the device to the reporting system.
Normal value application - Predicted values are applied based on the patient's demographics and the chosen reference equation.
Interpretation - The scientist applies pattern recognition (obstructive, restrictive, mixed) and grades severity.
Physician review - The referring or supervising physician reviews, adds clinical context, and signs off.
Report delivery - The final report is sent to the referrer, stored in the EMR, and filed for audit.
Each handoff between these stages is a potential failure point. Manual re-entry of data, mismatched normal value sets, and disconnected software systems are the most common culprits.
Why Does Raw Spirometry Data Require So Much Post-Processing?
Raw spirometry output is a time-series signal, not a clinical conclusion. The device captures airflow and volume over time, but converting that into meaningful indices like FEV1, FVC, and the FEV1/FVC ratio requires signal processing, quality filtering, and reference comparison.
A 2022 dataset study published in Frontiers in Physiology by Ibraheem et al. highlighted something often overlooked in clinical practice: most spirometry datasets focus exclusively on the expiratory limb. The authors noted the value of capturing raw data from both the expiratory and inspiratory limbs, pointing out that this more complete picture opens the door to richer clinical applications. It was an interesting reminder that the data collected during a test often contains more diagnostic information than standard reporting workflows are designed to surface.
This is relevant to workflow design because:
Systems that only capture expiratory indices may discard clinically useful inspiratory data.
Raw data preservation allows for retrospective analysis and quality audits.
Structured data (discrete fields) is far more useful than scanned PDFs for downstream analysis, AI interpretation, and research.
Where Do PFT Reporting Workflows Break Down?
The most common failure points in PFT workflows are predictable and largely avoidable:
Failure Point | Common Cause | Clinical Impact |
|---|---|---|
Manual data re-entry | No direct device integration | Transcription errors, delays |
Inconsistent normal values | Multiple reference sets in use | Misclassification of results |
Disconnected physician review | No structured sign-off queue | Delayed or lost reports |
PDF-only data storage | Legacy system limitations | No searchable, auditable data |
Non-compliance with ATS/ERS | Reliance on individual memory | Variable report quality |
The underlying problem in most labs is not the testing itself but the infrastructure around it. Devices have improved dramatically. The software connecting them to clinical decisions often has not.
What Does a Well-Designed PFT Reporting System Actually Look Like?
A modern PFT reporting system should do more than store results. It should actively support the scientist and physician at every stage of the workflow.
Key characteristics of a well-designed system:
Vendor-neutral data import - The ability to pull structured data from any device manufacturer without manual re-entry.
Automated normal value application - A regularly updated library of reference equations applied consistently based on patient demographics.
Guideline-enforced interpretation - ATS/ERS interpretation algorithms built into the workflow, not left to individual discretion.
Structured physician review queue - A dedicated interface for physicians to review, dictate, and sign off reports efficiently.
Full audit trail - Every action logged for accreditation and quality purposes.
EMR and PAS integration - Bidirectional data flow with hospital systems to eliminate duplicate records.
Rezibase was built around exactly this model. Its Magic Import feature extracts discrete data directly from device reports, including flow-volume loops, removing the manual transcription step entirely. Its Normal Values Library is pre-configured and kept current with evolving reference standards. And its physician-facing interface includes AI-assisted report writing structured around ATS guidelines, giving clinicians a starting point rather than a blank page.
How Should Normal Values Be Selected and Applied in a PFT Workflow?
Normal value selection is one of the most consequential decisions in a PFT workflow, and one of the least standardized in practice.
Best practice guidance:
Use reference equations validated for the patient population being tested (age, sex, height, ethnicity).
Apply a single, consistent reference set across the lab to ensure comparability between tests.
Document which reference equation was used in every report.
Update reference sets when new validated equations are published or when ATS/ERS guidelines change.
The risk of inconsistency here is real. A patient classified as having mild obstruction under one reference set may be classified as normal under another. This is not a minor administrative detail. It has direct implications for diagnosis, treatment, and insurance.
Frequently Asked Questions
What is a PFT reporting workflow?
It is the end-to-end process of capturing spirometry and other pulmonary function data, applying normal values, generating an interpretation, and delivering a signed clinical report.
Why is vendor-neutral data import important?
Labs often use devices from multiple manufacturers. A vendor-neutral system allows all device data to flow into a single reporting platform without manual re-entry, reducing errors and saving time.
What are ATS/ERS guidelines in the context of PFT reporting?
They are internationally recognized standards from the American Thoracic Society and European Respiratory Society that define how spirometry should be performed, quality-checked, and interpreted.
How does AI fit into PFT reporting?
AI can assist with structuring report language, flagging quality issues, and applying interpretation algorithms consistently. It supports the scientist and physician rather than replacing clinical judgment.
What data should be preserved from a spirometry test?
Ideally, both expiratory and inspiratory limb data should be preserved in structured form, along with raw flow-volume data, to support full clinical analysis and retrospective audit.
What is the risk of manual data entry in PFT workflows?
Manual transcription introduces transcription errors, slows turnaround, and creates audit gaps. It is the single most common source of avoidable error in PFT reporting.
How does cloud-based PFT software differ from on-premise systems?
Cloud-based systems are accessible from any location, require no local server management, and receive updates automatically. They also support multi-site deployments more easily than traditional on-premise installations.
About Rezibase
Rezibase is a cloud-based respiratory and sleep reporting platform designed by respiratory scientists, for respiratory scientists. Trusted by over 35 sites including NHS and NSW Health, Rezibase offers vendor-neutral data import, ATS-aligned reporting, and full accreditation support in a single integrated platform. Learn more at rezibase.com.
References
Ibraheem, D.L. et al. Beyond the Expiratory Limb: A Complete Raw Spirometry Dataset. Frontiers in Physiology, 2022. https://www.frontiersin.org/articles/10.3389/fphys.2022.898831/full