TransFAIR study: a European multicentre experimental comparison of EHR2EDC technology to the usual manual method for eCRF data collection.

PURPOSE: Regulatory authorities including the Food and Drug Administration and the European Medicines Agency are encouraging to conduct clinical trials using routinely collected data. The aim of the TransFAIR experimental comparison was to evaluate, within real-life conditions, the ability of the Electronic Health Records to Electronic Data Capture (EHR2EDC) module to accurately transfer from EHRs to EDC systems patients' data of clinical studies in various therapeutic areas. METHODS: A prospective study including six clinical trials from three different sponsors running in three hospitals across Europe has been conducted. The same data from the six studies were collected using both traditional manual data entry and the EHR2EDC module. The outcome variable was the percentage of data accurately transferred using the EHR2EDC technology. This percentage was calculated considering all collected data and the data in four domains: demographics (DM), vital signs (VS), laboratories (LB) and concomitant medications (CM). RESULTS: Overall, 6143 data points (39.6% of the data in the scope of the TransFAIR study and 16.9% when considering all data) were accurately transferred using the platform. LB data represented 65.4% of the data transferred; VS data, 30.8%; DM data, 0.7% and CM data, 3.1%. CONCLUSIONS: The objective of accurately transferring at least 15% of the manually entered trial datapoints using the EHR2EDC module was achieved. Collaboration and codesign by hospitals, industry, technology company, supported by the Institute of Innovation through Health Data was a success factor in accomplishing these results. Further work should focus on the harmonisation of data standards and improved interoperability to extend the scope of transferable EHR data.

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.

CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research.

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.

Performances of a Solution to Semi-Automatically Fill eCRF with Data from the Electronic Health Record: Protocol for a Prospective Individual Participant Data Meta-Analysis.

Clinical trial data collection still relies on a manual entry from information available in the medical record. This process introduces delay and error risk. Automating data transfer from Electronic Health Record (EHR) to Electronic Data Capture (EDC) system, under investigators' supervision, would gracefully solve these issues. The present paper describes the design of the evaluation of a technology allowing EHR to act as eSource for clinical trials. As part of the EHR2EDC project, for 6 ongoing clinical trials, running at 3 hospitals, a parallel semi-automated data collection using such technology will be conducted focusing on a limited scope of data (demographic data, local laboratory results, concomitant medication and vital signs). The evaluation protocol consists in an individual participant data prospective meta-analysis comparing regular clinical trial data collection to the semi-automated one. The main outcome is the proportion of data correctly entered. Data quality and associated workload for hospital staff will be compared as secondary outcomes. Results should be available in 2020.

Federated electronic health records research technology to support clinical trial protocol optimization: Evidence from EHR4CR and the InSite platform.

OBJECTIVE: To determine if inclusion/exclusion (I/E) criteria of clinical trial protocols can be represented as structured queries and executed using a secure federated research platform (InSite) on hospital electronic health records (EHR) systems, to estimate the number of potentially eligible patients. METHODS: Twenty-three clinical trial protocols completed during 2011-2017 across diverse disease areas were analyzed to construct queries that were executed with InSite using EHR records from 24 European hospitals containing records of >14 million patients. The number of patients matching I/E criteria of each protocol was estimated. RESULTS: All protocols could be formalized to some extent into a medical coding system (e.g. ICD-10CM, ATC, LOINC, SNOMED) and mapped to local hospital coding systems. The median number of I/E criteria of protocols tested was 29 (range: 14-47). A median of 55% (range 38-89%) of I/E criteria in each protocol could be transformed into a computable format. The median number of eligible patients identified was 26 per hospital site (range: 1-134). CONCLUSION: Clinical trial I/E eligibility criteria can be structured computationally and executed as queries on EHR systems to estimate the patient recruitment pool at each site. The results further suggest that an increase in structured coded information in EHRs would increase the number of I/E criteria that could be evaluated. Additional work is needed on broader deployment of federated platforms such as InSite.

Business analysis for a sustainable, multi-stakeholder ecosystem for leveraging the Electronic Health Records for Clinical Research (EHR4CR) platform in Europe.

INTRODUCTION: The Electronic Health Records for Clinical Research (EHR4CR) technological platform has been developed to enable the trustworthy reuse of hospital electronic health records data for clinical research. The EHR4CR platform can enhance and speed up clinical research scenarios: protocol feasibility assessment, patient identification for recruitment in clinical trials, and clinical data exchange, including for reporting serious adverse events. Our objective was to seed a multi-stakeholder ecosystem to enable the scalable exploitation of the EHR4CR platform in Europe, and to assess its economic sustainability. MATERIALS AND METHODS: Market analyses were conducted by a multidisciplinary task force to define an EHR4CR emerging ecosystem and multi-stakeholder value chain. This involved mapping stakeholder groups and defining their unmet needs, incentives, potential barriers for adopting innovative solutions, roles and interdependencies. A comprehensive business model, value propositions, and sustainability strategies were developed accordingly. Using simulation modelling (including Monte Carlo simulations) and a 5-year horizon, the potential financial outcomes of the business model were forecasted from the perspective of an EHR4CR service provider. RESULTS: A business ecosystem was defined to leverage the EHR4CR multi-stakeholder value chain. Value propositions were developed describing the expected benefits of EHR4CR solutions for all stakeholders. From an EHR4CR service provider's viewpoint, the business model simulation estimated that a profitability ratio of up to 1.8 could be achieved at year 1, with potential for growth in subsequent years depending on projected market uptake. CONCLUSIONS: By enhancing and speeding up existing processes, EHR4CR solutions promise to transform the clinical research landscape. The ecosystem defined provides the organisational framework for optimising the value and benefits for all stakeholders involved, in a sustainable manner. Our study suggests that the exploitation of EHR4CR solutions appears profitable and sustainable in Europe, with a growth potential depending on the rates of market and hospital adoption.

Evaluation of the use of Swedish integrated electronic health records and register health care data as support clinical trials in severe asthma: the PACEHR study.

BACKGROUND: In the development of new drugs for severe asthma, it is a challenge from an ethical point of view to randomize severe asthma patients to placebo, and to obtain long-term safety data due to discontinuations. The aim of this study was to evaluate the feasibility of using electronic health record (EHR) data to create a real-world reference population of uncontrolled asthmatic patients to supplement the concurrent control/placebo group in long-term studies of asthma. METHODS: EHR data from 36 primary care centres and a University hospital in Sweden were linked to Swedish mandatory health registers (2005-2013), creating a population covering 33 890 asthma patients, including data on co-morbidities, risk factors and laboratory/respiratory measurements. A severe asthma EHR reference cohort was established. We used logistic regression to estimate the propensity score (probability) of each RCT or EHR patient existing in the EHR cohort given their covariates. RESULTS: We created an EHR-derived reference cohort of 240 patients, matching the placebo group (N = 151) in an RCT of severe asthma. The exacerbation rate during follow-up in the EHR study population was 1.24 (weighted) compared to 0.9 in the RCT placebo group. Patients in the EHR cohort were of similar age as in the RCT placebo group, 50.6 years versus 50.1 years; had slightly higher body mass index 27.0 kg/m2 versus 27.3 kg/m2; and consisted of 40% versus 34% males. CONCLUSIONS: The results indicate that EHRs provide an opportunity to supplement the control group in RCTs of severe diseases.

Cost-benefit assessment of using electronic health records data for clinical research versus current practices: Contribution of the Electronic Health Records for Clinical Research (EHR4CR) European Project.

INTRODUCTION: The widespread adoption of electronic health records (EHR) provides a new opportunity to improve the efficiency of clinical research. The European EHR4CR (Electronic Health Records for Clinical Research) 4-year project has developed an innovative technological platform to enable the re-use of EHR data for clinical research. The objective of this cost-benefit assessment (CBA) is to assess the value of EHR4CR solutions compared to current practices, from the perspective of sponsors of clinical trials. MATERIALS AND METHODS: A CBA model was developed using an advanced modeling approach. The costs of performing three clinical research scenarios (S) applied to a hypothetical Phase II or III oncology clinical trial workflow (reference case) were estimated under current and EHR4CR conditions, namely protocol feasibility assessment (S1), patient identification for recruitment (S2), and clinical study execution (S3). The potential benefits were calculated considering that the estimated reduction in actual person-time and costs for performing EHR4CR S1, S2, and S3 would accelerate time to market (TTM). Probabilistic sensitivity analyses using Monte Carlo simulations were conducted to manage uncertainty. RESULTS: Should the estimated efficiency gains achieved with the EHR4CR platform translate into faster TTM, the expected benefits for the global pharmaceutical oncology sector were estimated at €161.5m (S1), €45.7m (S2), €204.5m (S1+S2), €1906m (S3), and up to €2121.8m (S1+S2+S3) when the scenarios were used sequentially. CONCLUSIONS: The results suggest that optimizing clinical trial design and execution with the EHR4CR platform would generate substantial added value for pharmaceutical industry, as main sponsors of clinical trials in Europe, and beyond.

Smart Program Design Through a Common Information Model.

Although much information is already available publically from information-sharing initiatives such as ClinicalTrials.gov, information about clinical programs is unstructured, inconsistent, and incomplete. Clinical research within bioscience companies, health care, academia, and governmental agencies could benefit from easier access to best practices, historical information, and improved information sharing. Facilitating information sharing requires a standardized information model. Information standards today focus on individual clinical trials and the representation of clinical trial data. Although work is ongoing to expand standards to cover the protocol, these are insufficient to capture the objectives, rationale, and design thinking behind clinical programs. An information model is proposed to cover the rationalization and decision-making aspects of designing a clinical program and its associated trials. This paper is the output of a newly formed multicompany working group that examines the merits of a clinical program-level information standard. An example information model is presented to explain the concept.

Using electronic health records for clinical research: the case of the EHR4CR project.

OBJECTIVES: To describe the IMI EHR4CR project which is designing and developing, and aims to demonstrate, a scalable, widely acceptable and efficient approach to interoperability between EHR systems and clinical research systems. METHODS: The IMI EHR4CR project is combining and extending several previously isolated state-of-the-art technical components through a new approach to develop a platform for reusing EHR data to support medical research. This will be achieved through multiple but unified initiatives across different major disease areas (e.g. cardiovascular, cancer) and clinical research use cases (protocol feasibility, patient identification and recruitment, clinical trial execution and serious adverse event reporting), with various local and national stakeholders across several countries and therefore under various legal frameworks. RESULTS: An initial instance of the platform has been built, providing communication, security and terminology services to the eleven participating hospitals and ten pharmaceutical companies located in seven European countries. Proof-of-concept demonstrators have been built and evaluated for the protocol feasibility and patient recruitment scenarios. The specifications of the clinical trial execution and the adverse event reporting scenarios have been documented and reviewed. CONCLUSIONS: Through a combination of a consortium that brings collectively many years of experience from previous relevant EU projects and of the global conduct of clinical trials, of an approach to ethics that engages many important stakeholders across Europe to ensure acceptability, of a robust iterative design methodology for the platform services that is anchored on requirements of an underlying Service Oriented Architecture that has been designed to be scalable and adaptable, EHR4CR could be well placed to deliver a sound, useful and well accepted pan-European solution for the reuse of hospital EHR data to support clinical research studies.

Multiple fiber-optic dual-beam UV/Vis system with application to dissolution testing.

A system for fiber-optic probing in dissolution testing of solid pharmaceutical formulations has been constructed. The system is based on an imaging spectrometer and a charged coupled device (CCD) detector and includes 12 fiber-optic probes with a novel dual-path design. UV light was produced by a small arc deuterium lamp illuminating an optical fiber bundle. Twelve fiber-optic dipping probes were constructed with a reflection geometry. A 5 mm diameter lens was used to achieve a parallel light beam. The light passed back and forth through the flow-through cuvette defined by a sapphire window and a coated aluminium mirror. The mirror was cut in half and each segment was tilted and set at different distances from the window to obtain two separate paths with different lengths. Two receiver fibers were used for each probe to collect the transmitted light. The 24 receiver fibers from the 12 probes were bunched to a linear bundle and fed to an imaging spectrometer and the corresponding spectra were detected with a 512 x 512 pixel cooled CCD detector. The sampling interval was typically a few seconds for all probes. A software package was developed for data recording and on-line analysis. The program includes tools for multi-component analysis. The system was tested for different tablet formulations. Prednisone 50 mg tablets, normally used for control tests of dissolution baths, were followed for 3 h. Secondly, an extended release low dosage tablet was followed for 7 h resulting in a linear dissolution profile. Finally, a combination tablet containing two active drugs was tested for 60 min profiles. In the latter case, separate dissolution curves for the two active components were obtained. Future work will mainly focus on further development of the multi-component capability of the system.