Dataset Information

Developing a framework to incorporate real-world evidence in cancer drug funding decisions: the Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration.

ABSTRACT: BACKGROUND:Oncology therapy is becoming increasingly more expensive and challenging the affordability and sustainability of drug programmes around the world. When new drugs are evaluated, health technology assessment organisations rely on clinical trials to inform funding decisions. However, clinical trials are not able to assess overall survival and generalises evidence in a real-world setting. As a result, policy makers have little information on whether drug funding decisions based on clinical trials ultimately yield the outcomes and value for money that might be expected. OBJECTIVE:The Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration, consisting of researchers, recommendation-makers, decision makers, payers, patients and caregivers, are developing and testing a framework for Canadian provinces to generate and use real-world evidence (RWE) for cancer drug funding in a consistent and integrated manner. STRATEGY:The CanREValue collaboration has established five formal working groups (WGs) to focus on specific processes in the generation and use of RWE for cancer drug funding decisions in Canada. The different RWE WGs are: (1) Planning and Drug Selection; (2) Methods; (3) Data; (4) Reassessment and Uptake; (5) Engagement. These WGs are acting collaboratively to develop a framework for RWE evaluation, validate the framework through the multiprovince RWE projects and help to integrate the final RWE framework into the Canadian healthcare system. OUTCOMES:The framework will enable the reassessment of cancer drugs, refinement of funding recommendations and use of novel funding mechanisms by decision-makers/payers across Canada to ensure the healthcare system is providing clinical benefits and value for money.

SUBMITTER: Chan K

PROVIDER: S-EPMC6955501 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Developing a framework to incorporate real-world evidence in cancer drug funding decisions: the Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration.

Chan Kelvin K Nam Seungree S Evans Bill B de Oliveira Claire C Chambers Alexandra A Gavura Scott S Hoch Jeffrey J Mercer Rebecca E RE Dai Wei Fang WF Beca Jaclyn J Tadrous Mina M Isaranuwatchai Wanrudee W

BMJ open 20200107 1

<h4>Background</h4>Oncology therapy is becoming increasingly more expensive and challenging the affordability and sustainability of drug programmes around the world. When new drugs are evaluated, health technology assessment organisations rely on clinical trials to inform funding decisions. However, clinical trials are not able to assess overall survival and generalises evidence in a real-world setting. As a result, policy makers have little information on whether drug funding decisions based on ...[more]

PMID: 31915169

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Project description:Introduction Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer. Methods Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported. Results Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%–100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%–94%; Manual accuracy: 78%–94%; concordance: 71%–82%). Concurrent medications (86%–100%) and comorbid conditions (96%–100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%–100%) and highly concordant (83%–99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%–98%), although detection of biomarker test results was more variable (accuracy 84%–100%, concordance 84%–99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%–99%; manual: 71%–100%; concordance: 70%–99%) with the exception of lung and lymph node metastases (NLP: 66%–71%; manual: 87%–92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition. Conclusions Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction

Dataset Information

Developing a framework to incorporate real-world evidence in cancer drug funding decisions: the Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration.

Publications

Developing a framework to incorporate real-world evidence in cancer drug funding decisions: the Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration.

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