Article type
Abstract
Background:
The increasing availability of targeted therapies that focus on molecular subgroups has broadened patient treatment options and introduced molecular diagnostics as an important element of healthcare. However, the absence of a comprehensive overview detailing the frequency, costs, and outcomes of molecular diagnostics within the context of expensive drugs poses challenges for informed policy decisions. Current obstacles encompass variations in the testing landscape, funding dilemmas, and implementation issues in healthcare. The anticipation of limited research possibilities due to a growing number of diverse subgroups and a subsequently reduced patient pool per group further reduces the likelihood of conducting (large) randomized trials in Molecular Testing.
Objectives:
This project aims to create a generic design for Evidence Ecosystems (cyclic Learning System) in the Netherlands; a system to continuously update guidelines by monitoring guideline implementation and effects of diagnostics/interventions, in combination with published evidence. Goals include developing a Proof-of-Principle and providing recommendations for establishing a management structure.
Methods:
The project focuses on developing a Learning System for Non-Small Cell Lung Cancer and Endometrial Cancer. Analyses will include identifying guideline-conform molecular tests and assessing non-conform instances, specifying type and timing in patient pathways. The study investigates treatment and patient outcomes associated with molecular diagnostics. The components provided comprise a digitized guideline, a merged dataset, and templates for standardized, structured clinical reporting. We will remodel guideline knowledge, compile a dataset from four nationwide real-world data sources, and jointly develop clinical reporting templates. These instruments are interconnected through data collection, data processing, and decision support processes. To ensure interoperability across components and processes, adherence to information standards is applied.
Results:
The results will include lessons learned, conditions for sustaining Learning Systems, visualizations of monitoring results, agreements with relevant parties, and a proposed management structure for oncology Learning Systems. A blueprint for expansion to other indications will be provided.
Conclusion:
The initiative integrates (real-world) data, (guideline) knowledge, and (clinical) practice into a Learning Healthcare System, harmonized through an information standard and ‘systems thinking’. It establishes a robust framework for monitoring molecular diagnostics in dynamic healthcare. Finally, this framework can be applied to other conditions.
The increasing availability of targeted therapies that focus on molecular subgroups has broadened patient treatment options and introduced molecular diagnostics as an important element of healthcare. However, the absence of a comprehensive overview detailing the frequency, costs, and outcomes of molecular diagnostics within the context of expensive drugs poses challenges for informed policy decisions. Current obstacles encompass variations in the testing landscape, funding dilemmas, and implementation issues in healthcare. The anticipation of limited research possibilities due to a growing number of diverse subgroups and a subsequently reduced patient pool per group further reduces the likelihood of conducting (large) randomized trials in Molecular Testing.
Objectives:
This project aims to create a generic design for Evidence Ecosystems (cyclic Learning System) in the Netherlands; a system to continuously update guidelines by monitoring guideline implementation and effects of diagnostics/interventions, in combination with published evidence. Goals include developing a Proof-of-Principle and providing recommendations for establishing a management structure.
Methods:
The project focuses on developing a Learning System for Non-Small Cell Lung Cancer and Endometrial Cancer. Analyses will include identifying guideline-conform molecular tests and assessing non-conform instances, specifying type and timing in patient pathways. The study investigates treatment and patient outcomes associated with molecular diagnostics. The components provided comprise a digitized guideline, a merged dataset, and templates for standardized, structured clinical reporting. We will remodel guideline knowledge, compile a dataset from four nationwide real-world data sources, and jointly develop clinical reporting templates. These instruments are interconnected through data collection, data processing, and decision support processes. To ensure interoperability across components and processes, adherence to information standards is applied.
Results:
The results will include lessons learned, conditions for sustaining Learning Systems, visualizations of monitoring results, agreements with relevant parties, and a proposed management structure for oncology Learning Systems. A blueprint for expansion to other indications will be provided.
Conclusion:
The initiative integrates (real-world) data, (guideline) knowledge, and (clinical) practice into a Learning Healthcare System, harmonized through an information standard and ‘systems thinking’. It establishes a robust framework for monitoring molecular diagnostics in dynamic healthcare. Finally, this framework can be applied to other conditions.