scholarly journals 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

2020 ◽  
Vol 27 (5) ◽  
pp. 793-797 ◽  
Author(s):  
Jeffrey S Brown ◽  
Judith C Maro ◽  
Michael Nguyen ◽  
Robert Ball

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.

10.2196/16810 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e16810 ◽  
Author(s):  
Benjamin Scott Glicksberg ◽  
Shohei Burns ◽  
Rob Currie ◽  
Ann Griffin ◽  
Zhen Jane Wang ◽  
...  

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.


2019 ◽  
Author(s):  
Benjamin Scott Glicksberg ◽  
Shohei Burns ◽  
Rob Currie ◽  
Ann Griffin ◽  
Zhen Jane Wang ◽  
...  

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.


2021 ◽  
Author(s):  
Peter Klimek ◽  
Dejan Baltic ◽  
Martin Brunner ◽  
Alexander Degelsegger-Marquez ◽  
Gerhard Garhöfer ◽  
...  

UNSTRUCTURED Real-world data (RWD) collected in routine healthcare processes and transformed to real-world evidence (RWE) has become increasingly interesting within research and medical communities to enhance medical research and support regulatory decision making. Despite numerous European initiatives, there is still no cross-border consensus or guideline determining which quality RWD must meet in order to be acceptable for decision making within regulatory or routine clinical decision support. An Austrian expert group led by GPMed (Gesellschaft für Pharmazeutische Medizin, Austrian Society for Pharmaceutical Medicine) reviewed drafted guidelines, published recommendations or viewpoints to derive a consensus statement on quality criteria for RWD to be used more effectively for medical research purposes beyond registry-based studies discussed in the European Medicines Agency (EMA) guideline for registry-based studies


2018 ◽  
Vol 24 (3) ◽  
pp. 95-98 ◽  
Author(s):  
Daphne Guinn ◽  
Erin E Wilhelm ◽  
Grazyna Lieberman ◽  
Sean Khozin

Author(s):  
Alejandro Rodríguez-González ◽  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
José Emilio Labra Gayo ◽  
Juan Miguel Gómez-Berbís ◽  
...  

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.


2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Juan Jose Garcia Sanchez ◽  
Juan Jesus Carrero ◽  
Supriya Kumar ◽  
Roberto Pecoits-Filho ◽  
Glen James ◽  
...  

Abstract Background and Aims In 2012, the Kidney Disease Improving Global Outcomes (KDIGO) guidelines recommended categorising and prognosticating chronic kidney disease (CKD) based on estimated glomerular filtration rate (eGFR) and urine albumin to creatinine ratio (UACR). Contemporary studies describing the prevalence and characteristics of patients with CKD categorised according KDIGO 2012 and how studies of new pharmacotherapies relate to these categories are scarce. One such new therapy class of key interest are the sodium glucose co-transporter 2 inhibitors (SGLT-2i), shown to delay the progression to renal failure and prevent cardiovascular/renal death in patients with CKD. We aimed to describe patient characteristics and the prevalence of CKD according to the 2012 KDIGO categories in a large real-world US cohort of patients with CKD (part A). We also describe a subset of the population according to the DAPA-CKD trial inclusion criteria (eGFR [25-75ml/min/1.73m2] and UACR [200-5000mg/g]) (part B). Method DISCOVER-CKD is an international observational study in patients with CKD. The DISCOVER-CKD retrospective US cohort of patients was extracted using real-world data from the integrated Limited Claims and Electronic Health Record data (IBM Health, Armonk, NY) and HealthVerity. Patients were aged ≥18 years, with ≥1 UACR measure. For part A, required first diagnostic code of CKD (Stages 3A, 3B, 4, 5, or ESRD) or two eGFR of <75 mL/min/1.73 m2 recorded at least 90 days apart and for part B, two measures of eGFR 25-75 mL/min/1.73 m2 recorded at least 90 days apart between 1st January 2008 and September 2018. Index date was diagnostic code or 2nd eGFR. The first UACR, recorded +/-12 months of index, was used to categorise patients. Descriptive analyses were used to summarise prevalence and patient characteristics. Results Of the overall study cohort (N=4330, 49.1% women, mean age 65.3±10.64 years), by KDIGO categories (part A): 85.7% (n=3601) had normal to mildly increased albuminuria, 11.0% (n=463) had moderately increased albuminuria and 3.3% (n=137) had severely increased albuminuria (Figure 1). 4.6% (n=193) fulfilled DAPA-CKD trial inclusion criteria (part B). In both populations, the most common comorbidities were hypertension (HTN, 73.0% for both) and type 2 diabetes (T2D, 57.6% and 56.2%, respectively). Anti-hypertensive drugs were frequently used (76.4% and 76.9%, respectively). Conclusion This study, utilising real-world data, adds to the scarcity of knowledge reporting the characteristics of patients with CKD in different eGFR and UACR strata according to the KDIGO 2012 definitions. We observed a trend in higher UACR in the group of patients with lower eGFR and report a high prevalence of T2D and HTN in the study population, demonstrating the high co-morbidity burden in patients, for whom new therapies may be beneficial.


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