Why are cancer guidelines stuck in PDFs?

Original Article ↗ Hacker News Discussion ↗

Why guidelines stay in PDFs

  • PDFs are seen as durable, portable, universally viewable, and stable across devices and decades; they “just work” and are easy to email and print.
  • Many clinicians prefer a single, shared PDF over practice‑specific tools that may be brittle, locked behind logins, or go down.
  • Critics note PDFs are poorly machine-readable, often lack semantic structure, and make downstream parsing expensive and error‑prone, yet are used as systems of record anyway.
  • Some point out PDF can embed structured data (XML/JSON, structure trees), but authoring tools and workflows rarely exploit this.

Push for structured / computable guidelines

  • Several argue guidelines are fundamentally decision trees/DAGs, and should be published in machine-interpretable formats alongside PDFs.
  • Suggested benefits: EMRs could offer context-aware prompts, automatic test suggestions, consistency checks, and generate PDFs from a single source of truth.
  • Existing standards and efforts mentioned: HL7 FHIR (PlanDefinition, CQL), CDS Connect, WHO SMART Guidelines, FHIR clinical reasoning specs, and earlier “computable clinical guidelines” and expert systems.

Complexity, skepticism, and limits

  • Implementers report that encoding real guidelines is hard: ambiguous clinical concepts, varying local semantics, incomplete evidence, and frequent guideline changes.
  • There is concern about guidelines becoming constrained by whatever data model or spaghetti code exists, drifting away from cutting‑edge clinical knowledge.
  • Some argue guidelines are not true decision trees; many branches rest on weak or non‑differentiating evidence and require human judgment or patient‑specific tradeoffs.
  • Standards like FHIR/CQL are powerful but perceived as complex and intimidating for small teams.

Incentives, industry, and access

  • Commenters highlight business incentives: organizations charge for structured “template” data or EMR integration while offering only PDFs freely.
  • Lab and EMR vendors are criticized for poor implementations and profit‑driven underinvestment in quality and interoperability; others attribute failures partly to inherent difficulty and lack of clear ROI.
  • Licensing barriers (e.g., for structured cancer protocols or value sets) are seen as gatekeeping that hinders open tooling.

AI/ML and decision support

  • Some envision AI/LLMs as key to extracting structure or even generating treatment plans; others warn about hallucinations, bias, and lack of proven outcome benefits.
  • Debate over complex opaque models vs interpretable decision trees: advanced models may be more accurate in principle but are hard to explain, regulate, and defend in court.

Clinician behavior and role of guidelines

  • There is disagreement over how diligently doctors, especially oncologists, keep up with rapidly evolving literature.
  • Several emphasize guidelines as frameworks for informed teams, not strict algorithms for identical patients; structured rules should remain secondary to clinical expertise and ongoing research.