From Network Governance to Real-World-Time Learning: A High-Reliability Operating Model for Rare Cancers
Bruno Fuchs1,2,3,4, Anna L Falkowski2, Ruben Jaeger2,3
1Faculty of Health Sciences & Medicine, University of Lucerne, Frohburgstrasse 3, 6002 Luzern, Switzerland.
Cancers
|February 27, 2026
Summary
This study presents a rare-cancer Learning Health System (LHS) blueprint for continuous learning and improved care quality. It establishes a framework for auditable improvement science in rare cancers, ensuring valid benchmarking and reducing harm.
Area of Science:
- Healthcare Systems Science
- Oncology
- Health Services Research
Background:
- Rare cancers present unique challenges due to low incidence, high heterogeneity, and fragmented multi-institutional care.
- Pathway fragmentation in rare cancer care leads to preventable harm, variability, and waste.
- Quality of care is best assessed by pathway integrity across the entire patient journey.
Purpose of the Study:
- To define a pragmatic and transferable operating blueprint for a rare-cancer Learning Health System (LHS).
- To enable continuous, auditable learning from routine care under explicit governance.
- To maintain claims discipline and protect measurement validity within the LHS.
Main Methods:
- Synthesized an implementation-oriented operating model using the Swiss Sarcoma Network (SSN) as an exemplar.
- Coupled clinical governance (Integrated Practice Unit logic, hub-and-spoke routing, auditable multidisciplinary team decisions) with an interoperable data backbone.
- Implemented a closed-loop control cycle: capture → harmonize → benchmark → learn → implement → re-measure, with defined owners and failure modes.
Main Results:
- Specified minimal data primitives including time-stamped decisions, characteristics, treatments, outcomes, and PROMs/PREMs.
- Developed a Value-Based Health Care (VBHC)-ready measurement backbone for outcomes, harms, timeliness, process fidelity, and resource stewardship.
- Instituted validity guardrails: explicit applicability rules and mandatory case-mix/complexity stratification.
Conclusions:
- The blueprint provides an operating model, not a platform, enabling credible improvement science and causal learning for rare cancers.
- It distinguishes enabling infrastructure from the governed clinical system, supporting scalable excellence while preventing gaming and inequity.
- Crucial validity gates (applicability rules, denominator integrity, anti-gaming safeguards, escalation governance) are specified for rare-cancer benchmarking, mitigating artifacts and unsafe inferences.
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