Pulmonary function testing (PFT) labs face a persistent data problem: results generated by spirometers, body plethysmographs, and diffusion testing equipment must somehow get into reporting systems accurately and efficiently. Two competing approaches dominate the conversation: Optical Character Recognition (OCR), which reads and converts printed or image-based test output into digital data, and native device integration, which pulls structured data directly from the device. The difference between these two approaches is not just technical - it determines whether a lab can truly eliminate manual data entry or simply reduce it.

TL;DR

• OCR converts scanned or image-based text into machine-readable data, but introduces accuracy risks in clinical workflows.

• Native device integration transfers structured data directly, removing transcription error at the source.

• For PFT labs, the stakes of data errors include misdiagnosis and clinical risk.

• Most labs still rely on manual or semi-manual workflows, creating inefficiencies that compound over time.

• Platforms built specifically for respiratory workflows, like Rezibase, offer native-style direct import that bypasses OCR limitations entirely.

What Is OCR and How Does It Work in Clinical Settings?

Optical Character Recognition (OCR) is the technological process of transforming images of typed, handwritten, scanned, or printed text into machine-readable data. According to the New Jersey State Policy Lab at Rutgers University, OCR enables information locked in static documents to become searchable, editable, and processable by software systems.

In clinical settings, OCR is typically applied when a device produces a printed report or PDF output that a software system then "reads" to extract values. The appeal is obvious: no manual retyping. But the execution is more complicated.

A survey of modern OCR techniques published on arXiv notes that document recognition accuracy depends heavily on image quality, font consistency, and document structure. PFT reports, which often include graphical elements like flow-volume loops alongside numerical values, present exactly the kind of mixed-content challenge that degrades OCR reliability.

Research published via IEEE Xplore in 2025 (Khan et al.) found that OCR accuracy using machine vision has significantly improved with AI enhancements, but variability remains a concern, particularly with non-standard document layouts. That variability is a real risk when the data being extracted informs clinical decisions.

Where Does OCR Fall Short in PFT Workflows?

OCR works best when documents are clean, standardised, and consistently formatted. PFT reports are rarely all three simultaneously.

Key limitations of OCR in pulmonary function testing:

• Format inconsistency: Different device manufacturers produce reports in different layouts. A Jaeger report looks nothing like a Vyntus or ndd report.

• Graphical data loss: Flow-volume loops are images, not text. OCR cannot extract the underlying numerical data from a curve.

• Confidence thresholds: As Kili Technology's 2026 OCR annotation guide notes, OCR systems require labelled training data and ongoing validation to maintain accuracy. That overhead is rarely built into clinical lab operations.

• Post-extraction validation burden: Even high-confidence OCR outputs require human review in clinical settings, which reintroduces manual effort.

• Error propagation: A misread FEV1 value or FVC ratio, even by a small margin, can shift interpretation from normal to obstructed.

Tungsten Automation's research into straight-through processing (STP) found that advanced OCR can reduce processing time by 82% and lower operational costs by 80% in document-heavy workflows. Those numbers are compelling, but they apply to high-volume, standardised document environments, not the heterogeneous output landscape of a mixed-device PFT lab.

What Is Native Device Integration and Why Does It Matter?

Native device integration means the software connects directly to the testing device or its data output format, pulling structured, discrete data without any image interpretation step.

Instead of reading a PDF of results, the system receives the actual data values in a structured format: FEV1, FVC, TLC, DLCO, and associated predicted values, as discrete fields. No image, no interpretation, no margin for character misreading.

The practical advantages in PFT workflows:

For labs trying to genuinely eliminate manual data entry, native integration is the more reliable path. OCR reduces it. Native integration removes it at the source.

How Do Real PFT Labs Handle This Today?

The honest answer is that many labs are still using workflows that are more manual than they should be. Respiratory scientists print reports, re-enter values into reporting systems, and then interpret results, sometimes checking their own transcription work.

According to Athento, text recognition technology is most effective when it is embedded within a broader document management and workflow system rather than applied as a standalone extraction step. That principle applies directly to respiratory labs: the tool is only as good as the workflow it sits within.

AI-enhanced OCR can modernise inspection planning but requires integration with robust backend systems to deliver reliable outcomes. A standalone OCR tool bolted onto a legacy reporting system does not solve the underlying workflow problem.

What Should a PFT Lab Look for in a Data Integration Solution?

Whether a lab is evaluating OCR tools or native integration platforms, the evaluation criteria should be clinically grounded:

• Device agnosticism: Can the solution handle data from multiple manufacturers without requiring separate configurations for each?

• Discrete data extraction: Are individual test parameters captured as structured fields, not just images or PDFs?

• Graphical data support: Are flow-volume loops and other visual outputs captured in a usable form?

• Validation transparency: How does the system flag uncertain or incomplete data?

• Workflow fit: Does the solution reduce steps for the scientist, or does it create new ones?

This is where platforms purpose-built for respiratory science have a structural advantage. Rezibase's Magic Import feature, for example, allows labs to directly import device reports and automatically extract discrete data, including flow-volume loops, without relying on OCR interpretation. Because Rezibase is manufacturer-agnostic, it handles output from any device type, removing the format-inconsistency problem that undermines OCR accuracy in mixed-device environments.

Frequently Asked Questions

Can OCR fully replace manual data entry in PFT labs?
OCR can significantly reduce manual entry, but it rarely eliminates it entirely in PFT environments due to format variability and the presence of graphical data like flow-volume loops that OCR cannot interpret.

What is native device integration in the context of PFT software?
It is a direct data connection between a testing device and a reporting system that transfers structured, discrete result values without an image interpretation step.

Is OCR accurate enough for clinical use?
OCR accuracy has improved significantly with AI, but variability remains, particularly with non-standard layouts. Clinical use requires validation processes that add operational overhead.

What happens to flow-volume loops if only OCR is used?
Flow-volume loops are graphical outputs. OCR reads text, not curve data. Without native integration, loop data is typically lost or stored only as an image, not as discrete values.

How difficult is it to switch to a platform like Rezibase?
Rezibase is designed to make data migration straightforward. The platform's cloud-based architecture and import tools are built to bring existing data across without disruption to day-to-day lab operations.

Does vendor-neutral software mean lower accuracy?
No. Vendor-neutral platforms that use structured import rather than OCR can match or exceed the accuracy of proprietary systems, with the added benefit of flexibility across device brands.

What is the clinical risk of manual data entry errors in PFT reporting?
Transcription errors in key parameters like FEV1/FVC ratios can shift diagnostic interpretation, potentially leading to incorrect classifications of respiratory disease severity.

About Rezibase

Rezibase is a cloud-based respiratory and sleep reporting platform built by and for respiratory scientists. Trusted by over 35 sites including NHS and NSW Health, it offers vendor-neutral, manufacturer-agnostic data integration, AI-assisted reporting, and a full accreditation module, all without lock-in contracts. Learn more at rezibase.com.

If your lab is still relying on manual transcription or OCR workarounds to move PFT data into your reporting system, there is a better path. Visit rezibase.com to explore how native device integration can simplify your workflow and reduce clinical risk.

References

• New Jersey State Policy Lab. Smart OCR - Advancing the Use of Artificial Intelligence with Open Data. https://policylab.rutgers.edu/publication/smart-ocr-advancing-the-use-of-artificial-intelligence-with-open-data/

• arXiv / ar5iv. A Survey of Modern Optical Character Recognition Techniques. https://ar5iv.labs.arxiv.org/html/1412.4183

• IEEE Xplore. OCR for Text Recognition Using Machine Vision (Khan et al., 2025). https://ieeexplore.ieee.org/iel8/6287639/6514899/11193825.pdf

• Kili Technology. The Complete Guide to OCR Data Labeling: 2026 Update. https://kili-technology.com/blog/ocr-annotation

• Tungsten Automation. Tungsten OCR Enables Accurate Straight-Through Processing. https://www.tungstenautomation.com/learn/blog/how-advanced-ocr-powers-straight-through-processing-stp

• Athento. Text Recognition: What It Is, Benefits, and Best Implementation Practices. https://www.athento.com/text-recognition-what-it-is-benefits-and-best-implementation-practices/