Blockchain-Authenticated Sharing of Genomic and Clinical Outcomes Data of Patients With Cancer: A Prospective Cohort Study

Background Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments, but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of patients with cancer to share their treatment experiences to fuel research, despite potential risks to privacy. Objective The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for the dissemination of deidentified clinical and genomic data with a focus on late-stage cancer. Methods We created and piloted a blockchain-authenticated system to enable secure sharing of deidentified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHRs), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (N=18), which we uploaded to the CGT. EHR data were extracted from both a hospital cancer registry and a common data model (CDM) format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold standard source documentation for patients with available data (n=17). Results Although the total completeness scores were greater for the registry reports than those for the CDM, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting CDM. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world data of patients with cancer in a more clinically useful time frame. We also developed an open-source Web application to allow users to seamlessly search, browse, explore, and download CGT data. Conclusions Our pilot demonstrates the willingness of patients with cancer to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third-party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

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Blockchain-Authenticated Sharing of Genomic and Clinical Outcomes Data of Patients With Cancer: A Prospective Cohort Study (Preprint)

10.2196/preprints.16810 ◽

2019 ◽

Author(s):

Benjamin Scott Glicksberg ◽

Shohei Burns ◽

Rob Currie ◽

Ann Griffin ◽

Zhen Jane Wang ◽

...

Keyword(s):

Real World ◽

Web Application ◽

Data Extraction ◽

Data Representation ◽

Health Data ◽

Common Data Model ◽

Patient Privacy ◽

Real World Data ◽

World Data ◽

Patients With Cancer

BACKGROUND Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments, but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of patients with cancer to share their treatment experiences to fuel research, despite potential risks to privacy. OBJECTIVE The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for the dissemination of deidentified clinical and genomic data with a focus on late-stage cancer. METHODS We created and piloted a blockchain-authenticated system to enable secure sharing of deidentified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHRs), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (N=18), which we uploaded to the CGT. EHR data were extracted from both a hospital cancer registry and a common data model (CDM) format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold standard source documentation for patients with available data (n=17). RESULTS Although the total completeness scores were greater for the registry reports than those for the CDM, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting CDM. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world data of patients with cancer in a more clinically useful time frame. We also developed an open-source Web application to allow users to seamlessly search, browse, explore, and download CGT data. CONCLUSIONS Our pilot demonstrates the willingness of patients with cancer to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third-party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

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Blockchain-authenticated sharing of cancer patient genomic and clinical outcomes data.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e19358 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e19358-e19358

Author(s):

Shohei Burns ◽

Eric Andrew Collisson

Keyword(s):

Cancer Patient ◽

Cancer Patients ◽

Real World ◽

Data Model ◽

Data Extraction ◽

Data Representation ◽

Health Data ◽

Patient Data ◽

Common Data Model ◽

Patient Privacy

e19358 Background: Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of cancer patients to share their treatment experiences to fuel research, despite potential risks to privacy. The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for dissemination of de-identified clinical and genomic data with a focus on late stage cancer. Methods: We created and piloted a blockchain-authenticated system to enable securely sharing de-identified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHR), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (n = 18), which we uploaded to CGT. EHR data were extracted from both a hospital cancer registry and a common data model format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold-standard source documentation for patients with available data (n = 17). Results: While the total completeness scores were greater for the registry reports than the common data model, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting common data model. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world cancer patient data in a more clinically useful timeframe. We also developed an open-source web application to allow users to seamlessly search, browse, explore, and download CGT data. Conclusions: Our pilot demonstrates the willingness of cancer patients to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

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Using and improving distributed data networks to generate actionable evidence: the case of real-world outcomes in the Food and Drug Administration’s Sentinel system

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa028 ◽

2020 ◽

Vol 27 (5) ◽

pp. 793-797 ◽

Cited By ~ 1

Author(s):

Jeffrey S Brown ◽

Judith C Maro ◽

Michael Nguyen ◽

Robert Ball

Keyword(s):

Decision Making ◽

Real World ◽

Common Data Model ◽

Distributed Data ◽

Patient Privacy ◽

Real World Data ◽

Electronic Health Record Data ◽

Critical System ◽

World Data ◽

Electronic Health

Abstract The US Food and Drug Administration (FDA) Sentinel System uses a distributed data network, a common data model, curated real-world data, and distributed analytic tools to generate evidence for FDA decision-making. Sentinel system needs include analytic flexibility, transparency, and reproducibility while protecting patient privacy. Based on over a decade of experience, a critical system limitation is the inability to identify enough medical conditions of interest in observational data to a satisfactory level of accuracy. Improving the system’s ability to use computable phenotypes will require an “all of the above” approach that improves use of electronic health data while incorporating the growing array of complementary electronic health record data sources. FDA recently funded a Sentinel System Innovation Center and a Community Building and Outreach Center that will provide a platform for collaboration across disciplines to promote better use of real-world data for decision-making.

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Protocol for data extraction: how real-world data have been used in the National Institute for Health and Care Excellence appraisals of cancer therapy

BMJ Open ◽

10.1136/bmjopen-2021-055985 ◽

2022 ◽

Vol 12 (1) ◽

pp. e055985

Author(s):

Jiyeon Kang ◽

John Cairns

Keyword(s):

Real World ◽

Data Extraction ◽

Systematic Research ◽

London School ◽

Real World Data ◽

World Data ◽

Data Extraction Tool ◽

Potential Benefits ◽

Critical Components ◽

Randomised Controlled

IntroductionDue to the limitations of relying on randomised controlled trials, the potential benefits of real-world data (RWD) in enriching evidence for health technology assessment (HTA) are highlighted. Despite increased interest in RWD, there is limited systematic research investigating how RWD have been used in HTA. The main purpose of this protocol is to extract relevant data from National Institute for Health and Care Excellence (NICE) appraisals in a transparent and reproducible manner in order to determine how NICE has incorporated a broader range of evidence in the appraisal of oncology medicines.Methods and analysisThe appraisals issued between January 2011 and May 2021 are included following inclusion criteria. The data extraction tool newly developed for this research includes the critical components of economic evaluation. The information is extracted from identified appraisals in accordance with extraction rules. The data extraction tool will be validated by a second researcher independently. The extracted data will be analysed quantitatively to investigate to what extent RWD have been used in appraisals. This is the first protocol to enable data to be extracted comprehensively and systematically in order to review the use of RWD.Ethics and disseminationThis study is approved by the Ethics Committee of the London School of Hygiene and Tropical Medicine on 14 November 2019 (17315). Results will be published in peer-reviewed journals.

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Use of Social Media in the Assessment of Relative Effectiveness: Explorative Review With Examples From Oncology (Preprint)

10.2196/preprints.7952 ◽

2017 ◽

Author(s):

Rachel R.J. Kalf ◽

Amr Makady ◽

Renske M.T. ten Ham ◽

Kim Meijboom ◽

Wim G. Goettsch ◽

...

Keyword(s):

Quality Of Life ◽

Social Media ◽

Adverse Events ◽

Real World ◽

Relative Effectiveness ◽

Health Data ◽

Real World Data ◽

World Data ◽

Effectiveness Assessment

BACKGROUND An element of health technology assessment constitutes assessing the clinical effectiveness of drugs, generally called relative effectiveness assessment. Little real-world evidence is available directly after market access, therefore randomized controlled trials are used to obtain information for relative effectiveness assessment. However, there is growing interest in using real-world data for relative effectiveness assessment. Social media may provide a source of real-world data. OBJECTIVE We assessed the extent to which social media-generated health data has provided insights for relative effectiveness assessment. METHODS An explorative literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify examples in oncology where health data were collected using social media. Scientific and grey literature published between January 2010 and June 2016 was identified by four reviewers, who independently screened studies for eligibility and extracted data. A descriptive qualitative analysis was performed. RESULTS Of 1032 articles identified, eight were included: four articles identified adverse events in response to cancer treatment, three articles disseminated quality of life surveys, and one study assessed the occurrence of disease-specific symptoms. Several strengths of social media-generated health data were highlighted in the articles, such as efficient collection of patient experiences and recruiting patients with rare diseases. Conversely, limitations included validation of authenticity and presence of information and selection bias. CONCLUSIONS Social media may provide a potential source of real-world data for relative effectiveness assessment, particularly on aspects such as adverse events, symptom occurrence, quality of life, and adherence behavior. This potential has not yet been fully realized and the degree of usefulness for relative effectiveness assessment should be further explored.

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Honeur (Heamatology Outcomes Network in Europe): A Federated Model to Support Real World Data Research in Hematology

Blood ◽

10.1182/blood-2018-99-111093 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 4839-4839

Author(s):

Kristina Bardenheuer ◽

Alun Passey ◽

Maria d'Errico ◽

Barbara Millier ◽

Carine Guinard-Azadian ◽

...

Keyword(s):

Multiple Myeloma ◽

Overall Survival ◽

Real World ◽

Data Model ◽

Quality Analysis ◽

Common Data Model ◽

Real World Data ◽

World Data ◽

New Concepts ◽

The Common

Abstract Introduction: The Haematology Outcomes Network in EURope (HONEUR) is an interdisciplinary initiative aimed at improving patient outcomes by analyzing real world data across hematological centers in Europe. Its overarching goal is to create a secure network which facilitates the development of a collaborative research community and allows access to big data tools for analysis of the data. The central paradigm in the HONEUR network is a federated model whereby the data stays at the respective sites and the analysis is executed at the local data sources. To allow for a uniform data analysis, the common data model 'OMOP' (Observational Medical Outcomes Partnership) was selected and extended to accommodate specific hematology data elements. Objective: To demonstrate feasibility of the OMOP common data model for the HONEUR network. Methods: In order to validate the architecture of the HONEUR network and the applicability of the OMOP common data model, data from the EMMOS registry (NCT01241396) have been used. This registry is a prospective, non-interventional study that was designed to capture real world data regarding treatments and outcomes for multiple myeloma at different stages of the disease. Data was collected between Oct 2010 and Nov 2014 on more than 2,400 patients across 266 sites in 22 countries. Data was mapped to the OMOP common data model version 5.3. Additional new concepts to the standard OMOP were provided to preserve the semantic mapping quality and reduce the potential loss of granularity. Following the mapping process, a quality analysis was performed to assess the completeness and accuracy of the mapping to the common data model. Specific critical concepts in multiple myeloma needed to be represented in OMOP. This applies in particular for concepts like treatment lines, cytogenetic observations, disease progression, risk scales (in particular ISS and R-ISS). To accommodate these concepts, existing OMOP structures were used with the definition of new concepts and concept-relationships. Results: Several elements of mapping data from the EMMOS registry to the OMOP common data model (CDM) were evaluated via integrity checks. Core entities from the OMOP CDM were reconciled against the source data. This was applied for the following entities: person (profile of year of birth and gender), drug exposure (profile of number of drug exposures per drug, at ATC code level), conditions (profile of number of occurrences of conditions per condition code, converted to SNOMED), measurement (profile of number of measurements and value distribution per (lab) measurement, converted to LOINC) and observation (profile of number of observations per observation concept). Figure 1 shows the histogram of year of birth distribution between the EMMOS registry and the OMOP CDM. No discernible differences exist, except for subjects which have not been included in the mapping to the OMOP CDM due to lacking confirmation of a diagnosis of multiple myeloma. As additional part of the architecture validation, the occurrence of the top 20 medications in the EMMOS registry and the OMOP CDM were compared, with a 100% concordance for the drug codes, which is shown in Figure 2. In addition to the reconciliation against the different OMOP entities, a comparison was also made against 'derived' data, in particular 'time to event' analysis. Overall survival was plotted from calculated variables in the analysis level data from the EMMOS registry and derived variables in the OMOP CDM. Probability of overall survival over time was virtually identical with only one day difference in median survival and 95% confidence intervals identically overlapping over the period of measurement (Figure 3). Conclusions: The concordance of year of birth, drug code mapping and overall survival between the EMMOS registry and the OMOP common data model indicates the reliability of mapping potential in HONEUR, especially where auxiliary methods have been developed to handle outcomes and treatment data in a way that can be harmonized across platform datasets. Disclosures No relevant conflicts of interest to declare.

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Abstract S10-02: Using real-world data (RWD) from an integrated platform for rapid analysis of patients with cancer with and without COVID-19 across distinct health systems

10.1158/1557-3265.covid-19-s10-02 ◽

2020 ◽

Author(s):

Shirish M. Gadgeel ◽

Michael A. Thompson ◽

Monika A. Izano ◽

Clara Hwang ◽

Tom Mikkelsen ◽

...

Keyword(s):

Health Systems ◽

Real World ◽

Rapid Analysis ◽

Real World Data ◽

World Data ◽

Patients With Cancer ◽

Integrated Platform

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Aggregating multiple real-world data sources using a patient-centered health-data-sharing platform

npj Digital Medicine ◽

10.1038/s41746-020-0265-z ◽

2020 ◽

Vol 3 (1) ◽

Cited By ~ 2

Author(s):

Sanket S. Dhruva ◽

Joseph S. Ross ◽

Joseph G. Akar ◽

Brittany Caldwell ◽

Karla Childers ◽

...

Keyword(s):

Data Sharing ◽

Real World ◽

Health Data ◽

Data Sources ◽

Real World Data ◽

Patient Centered ◽

World Data

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Clinical Research Informatics

Yearbook of Medical Informatics ◽

10.1055/s-0040-1702007 ◽

2020 ◽

Vol 29 (01) ◽

pp. 203-207

Author(s):

Christel Daniel ◽

Dipak Kalra ◽

Keyword(s):

Clinical Research ◽

Real World ◽

Reporting System ◽

Quality Data ◽

Editorial Team ◽

Common Data Model ◽

Free Text ◽

Real World Data ◽

World Data ◽

Clinical Research Informatics

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019. Method: A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers. Results: Among the 517 papers, published in 2019, returned by the search, that were in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes the use of a homomorphic encryption technique to enable federated analysis of real-world data while complying more easily with data protection requirements. The authors of the second best paper demonstrate the evidence value of federated data networks reporting a large real world data study related to the first line treatment for hypertension. The third best paper reports the migration of the US Food and Drug Administration (FDA) adverse event reporting system database to the OMOP common data model. This work opens the combined analysis of both spontaneous reporting system and electronic health record (EHR) data for pharmacovigilance. Conclusions: The most significant research efforts in the CRI field are currently focusing on real world evidence generation and especially the reuse of EHR data. With the progress achieved this year in the areas of phenotyping, data integration, semantic interoperability, and data quality assessment, real world data is becoming more accessible and reusable. High quality data sets are key assets not only for large scale observational studies or for changing the way clinical trials are conducted but also for developing or evaluating artificial intelligence algorithms guiding clinical decision for more personalized care. And lastly, security and confidentiality, ethical and regulatory issues, and more generally speaking data governance are still active research areas this year.

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