Use Of Secondary Data Research Articles

Improving Clinical Documentation of Rare Neuromuscular Diseases: Development of a Standardised Information Model.

Rare neuromuscular diseases (NMDs) encompass various disorders of the nervous system and skeletal muscles, and present intricate challenges in diagnosis, treatment, and research due to their low prevalence and often diverse multisystemic manifestations. Leveraging collected patient data for secondary use and analysis holds promise for advancing medical understanding in this field. However, a certain level of data quality is a prerequisite for the methods that can be used to analyze data. The heterogeneous nature of NMDs poses a significant obstacle to the creation of standardized documentation, as there are still many challenges to accurate diagnosis and many discrepancies in the diagnostic process between different countries. This paper proposes the development of an information model tailored to NMDs, aiming to augment visibility, address deficiencies in documentation, and facilitate comprehensive analysis and research endeavors. By providing a structured framework, this model seeks to propel advancements in understanding and managing NMD, ultimately benefiting patients and healthcare providers worldwide.

Quality Assessment Framework of Clinical Routine Data for Secondary Use.

To systematically and comprehensively identify data issues in large clinical datasets, we adopted a harmonized data quality assessment framework with Python scripts before integrating the data into FHIR® for secondary use. We also added a preliminary step of categorizing data fields within the database scheme to facilitate the implementation of the data quality framework. As a result, we demonstrated the efficiency and comprehensiveness of detecting data issues using the framework. In future steps, we plan to continually utilize the framework to identify data issues and develop strategies for improving our data quality.

Smart FOX - Enabling Citizen-Based Donation of EHR-Standardised Data for Clinical Research in Austria.

Access to healthcare data for secondary use in clinical research is often restricted due to privacy concerns or business interests, hindering comprehensive analysis across patient pathways. The Smart FOX project seeks to address this challenge by developing concepts, methods, and tools to facilitate citizen/patient-driven donations of health data for clinical research. Leveraging the groundwork, laid by the national Electronic Health Record implementation in Austria (called ELGA), Smart FOX aims to harness structured datasets from ELGA for research purposes through an opt-in approach. With funding secured from the Austrian Research Promotion Agency, the project embarks on innovative solutions encompassing governance frameworks, community engagement, and technical infrastructure. The Smart FOX consortium, comprising key stakeholders across various healthcare-associated domains, will evaluate these efforts through demonstrators focusing on clinical registries, patient-generated data, and recruitment services. The project targets to accompany the development of future data donation infrastructure while ultimately advancing clinical research efficiency and bolstering Austria's preparedness for the European Health Data Space. This paper presents the first systematic evaluation of the technical concept and proposal for the federated system architecture of the Austrian Health Data Donation Space, which is the socio-technical goal of Smart FOX.

NCI SEER-Linked Virtual Tissue Repository Pilot.

The Surveillance, Epidemiology, and End Results (SEER) Program with the National Cancer Institute tested whether population-based cancer registries can serve as honest brokers to acquire tissue and data in the SEER-Linked Virtual Tissue Repository (VTR) Pilot. We collected formalin-fixed, paraffin-embedded tissue and clinical data from patients with pancreatic ductal adenocarcinoma (PDAC) and breast cancer (BC) for two studies comparing cancer cases with highly unusual survival (≥5 years for PDAC and ≤30 months for BC) to pair-matched controls with usual survival (≤2 years for PDAC and ≥5 years for BC). Success was defined as the ability for registries to acquire tissue and data on cancer cases with highly unusual outcomes. Of 98 PDAC and 103 BC matched cases eligible for tissue collection, sources of attrition for tissue collection were tissue being unavailable, control paired with failed case, second control that was not requested, tumor necrosis ≥20%, and low tumor cellularity. In total, tissue meeting the study criteria was obtained for 70 (71%) PDAC and 74 (72%) BC matched cases. For patients with tissue received, clinical data completeness ranged from 59% for CA-19-9 after treatment to >95% for margin status, whether radiation therapy and chemotherapy were administered, and comorbidities. The VTR Pilot demonstrated the feasibility of using SEER cancer registries as honest brokers to provide tissue and clinical data for secondary use in research. Studies using this program should oversample by 45% to 50% to obtain sufficient sample size and targeted population representation and involve subspecialty matter expert pathologists for tissue selection.

A reference architecture for personal health data spaces using decentralized content-addressable storage networks.

This paper addresses the dilemmas of accessibility, comprehensiveness, and ownership related to health data. To resolve these dilemmas, we propose and justify a novel, globally scalable reference architecture for a Personal Health Data Space (PHDS). This architecture leverages decentralized content-addressable storage (DCAS) networks, ensuring that the data subject retains complete control and ownership of their personal health data. In today's globalized world, where people are increasingly mobile for work and leisure, healthcare is transitioning from episodic symptom-based treatment toward continuity of care. The main aims of this are patient engagement, illness prevention, and active and healthy longevity. This shift, along with the secondary use of health data for societal benefit, has intensified the challenges associated with health data accessibility, comprehensiveness, and ownership. The study is structured around four health data use case scenarios from the Estonian National Health Information System (EHIS): primary medical use, medical emergency use, secondary use, and personal use. We analyze these use cases from the perspectives of accessibility, comprehensiveness, and ownership. Additionally, we examine the security, privacy, and interoperability aspects of health data. The proposed architectural solution allows individuals to consolidate all their health data into a unified Personal Health Record (PHR). This data can come from various healthcare institutions, mobile applications, medical devices for home use, and personal health notes. The comprehensive PHR can then be shared with healthcare providers in a semantically interoperable manner, regardless of their location or the information systems they use. Furthermore, individuals maintain the autonomy to share, sell, or donate their anonymous or pseudonymous health data for secondary use with different systems worldwide. The proposed reference architecture aligns with the principles of the European Health Data Space (EHDS) initiative, enhancing health data management by providing a secure, cost-effective, and sustainable solution.

Artificial intelligence based data curation: enabling a patient-centric European health data space.

The emerging European Health Data Space (EHDS) Regulation opens new prospects for large-scale sharing and re-use of health data. Yet, the proposed regulation suffers from two important limitations: it is designed to benefit the whole population with limited consideration for individuals, and the generation of secondary datasets from heterogeneous, unlinked patient data will remain burdensome. AIDAVA, a Horizon Europe project that started in September 2022, proposes to address both shortcomings by providing patients with an AI-based virtual assistant that maximises automation in the integration and transformation of their health data into an interoperable, longitudinal health record. This personal record can then be used to inform patient-related decisions at the point of care, whether this is the usual point of care or a possible cross-border point of care. The personal record can also be used to generate population datasets for research and policymaking. The proposed solution will enable a much-needed paradigm shift in health data management, implementing a 'curate once at patient level, use many times' approach, primarily for the benefit of patients and their care providers, but also for more efficient generation of high-quality secondary datasets. After 15 months, the project shows promising preliminary results in achieving automation in the integration and transformation of heterogeneous data of each individual patient, once the content of the data sources managed by the data holders has been formally described. Additionally, the conceptualization phase of the project identified a set of recommendations for the development of a patient-centric EHDS, significantly facilitating the generation of data for secondary use.

Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews.

Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data. This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework. A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English. A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions. The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study.

In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status. Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data. In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies. The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.

Developing Consent Tools for the Research Community at the German Human Genome-Phenome Archive (GHGA)

The German Human Genome-Phenome Archive (GHGA) aims to enable the responsible sharing of human omics data for secondary research use across Germany and Europe. Informed consent is the most commonly used legal and ethical basis for processing omics data for secondary use. However, obtaining informed consent from Data Subjects can be challenging when data is to be widely shared and reused beyond the initial purpose of collection. To address these challenges, the ELSI (Ethical, Legal, and Social Implications) Group of GHGA has developed consent tools for the research community. First, we have developed a toolkit for prospective data collection, which consists of consent modules and complementary advice on how to update or create new consent forms. Second, we have created a legacy consent toolkit that can be used by researchers to assess whether the consent under which data was originally collected covers further data processing for secondary research purposes.

6.G. Workshop: The European Health Data Space 2: is Europe ready to maximise the re-use of health data?

Abstract The EU's Heads of State called in October 2020 to set up a European Health Data Space (EHDS): “The European Council welcomes the creation of common European data spaces in strategic sectors, and in particular invites the Commission to give priority to the health data space”. As a response, multiple initiatives were set-up to engage the views and expectations from EU member states in the development of the upcoming legislative proposal on the EHDS for primary (EHDS1) and secondary use of health data (EHDS2). One of these was the launch of the Joint Action (JA) Towards the European Health Data Space (TEHDAS) in February 2021. The aim of TEHDAS is to support the European Commission (EC) and Member States (MS), identifying the challenges and providing concepts and options for the EHDS2. It is built on the needs and expectations of national and international stakeholders, citizens’ engagement and existing infrastructure. The major expected outcome of this JA is a sustainable roadmap towards the EHDS2 implementation promoting the cross-border sharing of health data for secondary use. The EHDS2 will benefit population health by facilitating the timely exchange of and access to health data, improving not only the care of patients but also facilitating research, epidemiology, disease prevention and data-based policy decisions. This workshop aims to provide an overview of the preparedness of MS to join the EHDS for secondary use of health data, covering current state-of-play of health data management systems in the EU, barriers on the secondary use of health data, the ongoing work on the development and deployment of the EHDS2 infrastructure and finally, how population health research can benefit from the EHDS2. The workshop will begin with a short introduction on the TEHDAS JA, followed by a presentation on the outcomes of the TEHDAS country visits. In these country visits, the state-of-play of the health data management system and the preparedness to join the EHDS2 were mapped in 12 different EU MS. The next presentation will continue with the results from a study that collected more evidence on the barriers to cross-border sharing of health data for secondary use and options on how to overcome these. It will focus on the legal preparedness and barriers and will discuss how the options proposed by experts in the field align with the legislative proposal for the EHDS2. The audience will be able to give their opinion on the different options through an interactive voting poll during the session. Some of the options on how to overcome technical barriers will be taken further in the development of a pilot infrastructure for the EHDS2. Therefore, the last two presentations will focus on the development and deployment of this pilot infrastructure for the EHDS2 and the role and benefits that population health research will have in it. Throughout the workshop there will be discussion sessions between presentations to allow exchange of knowledge, experiences and opinions with the audience. Key messages • The main outputs will support EU countries to prepare their health data management system for the EHDS2 based on the best practices and actions presented. • The presentations will inform and align participants on the current work on the technical EHDS infrastructure that is being developed for secondary use of health data, the EHDS2.

Barriers on cross-border sharing of health data for secondary use and options to overcome these

Abstract Cross-border health data sharing is essential to facilitate vital health research into new treatments and to improve patient care and safety across Europe. The flagship European Health Data Space (EHDS) aims to promote better exchange and access to health data through the creation of a European-wide infrastructure. However, significant barriers to cross-border data sharing must first be resolved in order to fully ensure the successful functioning of the Data Space. To this end, the European Commission established a Joint Action (TEHDAS), as the policy development tool for the development of the EHDS. This presentation will be based around the findings of the recent TEHDAS report ‘Barriers to cross-border sharing of health data for secondary use and options to overcome them.’ The report draws on the results of a dedicated literature review, 133 case studies from across 23 countries and expert interviews to identify the primary barriers to cross-border data sharing and options to overcome these from the perspective of European data users (research and policymakers). The session will start with a concise presentation of the report methodology. The research team will then deliver an interactive presentation, in which they succinctly outline each barrier and policy option and ask participants to vote for their preferred policy option to overcome each barrier identified. EUPHA participants votes will inform the final TEHDAS recommendations on mitigations and solutions for barriers to cross-border data sharing which will be submitted to the European Commission for incorporation in the European Health Data Space legislative proposal.

Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities.

Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital's data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one's own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls.

Australian general practitioner perceptions to sharing clinical data for secondary use: a mixed method approach

ObjectiveThe potential for data collected in general practice to be linked and used to address health system challenges of maintaining quality care, accessibility and safety, including pandemic support, has led to an increased interest in public acceptability of data sharing, however practitioners have rarely been asked to share their opinions on the topic. This paper attempts to gain an understanding of general practitioner’s perceptions on sharing routinely collected data for the purposes of healthcare planning and research. It also compares findings with data sharing perceptions in an international context. Materials and methodsA mixed methods approach combining an initial online survey followed by face-to-face interviews (before and during COVID-19), designed to identify the barriers and facilitators to sharing data, were conducted on a cross sectional convenience sample of general practitioners across Western Australia (WA).ResultsEighty online surveys and ten face-to-face interviews with general practitioners were conducted from November 2020 – May 2021. Although respondents overwhelmingly identified the importance of population health research, their willingness to participate in data sharing programs was determined by a perception of trust associated with the organisation collecting and analysing shared data; a clearly defined purpose and process of collected data; including a governance structure providing confidence in the data sharing initiative simultaneously enabling a process of data sovereignty and autonomy.DiscussionResults indicate strong agreement around the importance of sharing patient’s medical data for population and health research and planning. Concerns pertaining to lack of trust, governance and secondary use of data continue to be a setback to data sharing with implications for primary care business models being raised.ConclusionTo further increase general practitioner’s confidence in sharing their clinical data, efforts should be directed towards implementing a robust data governance structure with an emphasis on transparency and representative stakeholder inclusion as well as identifying the role of government and government funded organisations, as well as building trust with the entities collecting and analysing the data.

474 Innovative solutions to streamline data collection, exchange, and utilization in translational research

OBJECTIVES/GOALS: To determine the utility of any standard, one must first evaluate whether or not the standard meets the needs of the use case it is meant to support. We aim to quantify data availability of the HL7 FHIR standard to support data collection for three state-based registries in a rural state and measure the potential effects on data quality and collection time. METHODS/STUDY POPULATION: FHIR mapping will be performed to assess the level of HL7 FHIR standard completeness (or data element coverage) in supporting data collection for the three registries. A systematic approach, previously developed and used by Garza and Zozus, will be used to map registry data elements to corresponding HL7 FHIR standard resources. FHIR coverage will be calculated as a percentage (total data elements “Available in FHIR” vs. total data elements overall) and will be observed across different domain areas (i.e., demographics vs. medications vs. vital signs, etc.) in order to identify the domains with the least and most coverage. RESULTS/ANTICIPATED RESULTS: Although there have been informatics solutions that relied on data exchange standards to improve data collection, none have actually evaluated the coverage of the standard for supporting the needs of clinical data registries. To address this gap, we aim to evaluate the availability of the HL7 FHIR standard to support data collection for three state-based registries. These results will provide insight into the generalizability of a FHIR-based solution to support data acquisition and processing across multiple registries and demonstrate the potential for seamless exchange of that data for secondary use in clinical and translational research. Quantifying the coverage will also be used to further advance its development in order to meet the data collection needs of state and national clinical data registries. DISCUSSION/SIGNIFICANCE: Registries often rely on manual abstraction of EHR data. These manual approaches have had a negative impact on data quality and cost, often due to the complexities associated with collection and mapping of the data to fit the registry model. HL7 FHIR has the potential to address these issues by automating part or all of the data collection process.

The Secondary Use of Data to Support Medication Safety in the Hospital Setting: A Systematic Review and Narrative Synthesis.

Objectives: To conduct a systematic review and narrative synthesis of interventions based on secondary use of data (SUD) from electronic prescribing (EP) and electronic hospital pharmacy (EHP) systems and their effectiveness in secondary care, and to identify factors influencing SUD. Method: The search strategy had four facets: 1. Electronic databases, 2. Medication safety, 3. Hospitals and quality/safety, and 4. SUD. Searches were conducted within EMBASE, Medline, CINAHL, and International Pharmaceutical s. Empirical SUD intervention studies that aimed to improve medication safety and/or quality, and any studies providing insight into factors affecting SUD were included. Results: We identified nine quantitative studies of SUD interventions and five qualitative studies. SUD interventions were complex and fell into four categories, with ‘provision of feedback’ the most common. While heterogeneous, the majority of quantitative studies reported positive findings in improving medication safety but little detail was provided on the interventions implemented. The five qualitative studies collectively provide an overview of the SUD process, which typically comprised nine steps from data identification to analysis. Factors influencing the SUD process were electronic systems implementation and level of functionality, knowledge and skills of SUD users, organisational context, and policies around data reuse and security. Discussion and Conclusion: The majority of the SUD interventions were successful in improving medication safety, however, what contributes to this success needs further exploration. From synthesis of research evidence in this review, an integrative framework was developed to describe the processes, mechanisms, and barriers for effective SUD.

A Systematic Approach to Reconciling Data Quality Failures: Investigation Using Spinal Cord Injury Data

Abstract Background Secondary use of electronic health record's (EHR) data requires evaluation of data quality (DQ) for fitness of use. While multiple frameworks exist for quantifying DQ, there are no guidelines for the evaluation of DQ failures identified through such frameworks. Objectives This study proposes a systematic approach to evaluate DQ failures through the understanding of data provenance to support exploratory modeling in machine learning. Methods Our study is based on the EHR of spinal cord injury inpatients in a state spinal care center in Australia, admitted between 2011 and 2018 (inclusive), and aged over 17 years. DQ was measured in our prerequisite step of applying a DQ framework on the EHR data through rules that quantified DQ dimensions. DQ was measured as the percentage of values per field that meet the criteria or Krippendorff's α for agreement between variables. These failures were then assessed using semistructured interviews with purposively sampled domain experts. Results The DQ of the fields in our dataset was measured to be from 0% adherent up to 100%. Understanding the data provenance of fields with DQ failures enabled us to ascertain if each DQ failure was fatal, recoverable, or not relevant to the field's inclusion in our study. We also identify the themes of data provenance from a DQ perspective as systems, processes, and actors. Conclusion A systematic approach to understanding data provenance through the context of data generation helps in the reconciliation or repair of DQ failures and is a necessary step in the preparation of data for secondary use.

Role of next-generation genomic sequencing in targeted agents repositioning for pancreaticoduodenal cancer patients

BackgroundPancreaticoduodenal cancer (PDC) is a group of malignant tumors arising in the ampullary region, which lack approved targeted therapies for their treatment. MethodsThis retrospective, observational study is based on Secondary Data Use (SDU) previously collected during a multicenter collaboration, which were subsequently entered into a predefined database and analyzed. FoundationOne CDx or Liquid, a next-generation DNA sequencing (NGS) service, was used to identify genomic alterations of patients who failed standard treatments. Detected alterations were described according to ESMO Scale of Clinical Actionability for molecular Targets (ESCAT). ResultsNGS analysis was performed in 68 patients affected by PDC. At least one alteration ranking tier I, II, III, or IV according to ESCAT classification was detected in 8, 1, 9, and 12 patients respectively (44.1%). Ten of them (33.3%) received a matched therapy. Patients with ESCAT tier I to IV were generally younger than the overall population (median = 54, range = 26–71 years), had an EGOG performance status score = 0 (83.3%), and an uncommon histological or clinical presentation. The most common mutations with clinical evidence of actionability (ESCAT tier I-III) involved genes of the RAF (10.3%), BRCA (5.9%) or FGFR pathways (5.9%). We present the activity of the RAF kinases inhibitor sorafenib in patients with RAF-mutated advanced PDC. ConclusionsIn advanced PDC, NGS is a feasible and valuable method for enabling precision oncology. This genomic profiling method might be considered after standard treatments failure, especially in young patients maintaining a good performance status, in order to detect potentially actionable mutations and offer molecularly targeted therapeutic approaches.

Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models

BackgroundCOVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedical research. However, it is usual for each data analysis initiative to define its own information model in line with its requirements. These specifications share clinical concepts, but differ in format and recording criteria, something that creates data entry redundancy in multiple electronic data capture systems (EDCs) with the consequent investment of effort and time by the organization. ObjectiveThis study sought to design and implement a flexible methodology based on detailed clinical models (DCM), which would enable EHRs generated in a tertiary hospital to be effectively reused without loss of meaning and within a short time. Material and methodsThe proposed methodology comprises four stages: (1) specification of an initial set of relevant variables for COVID-19; (2) modeling and formalization of clinical concepts using ISO 13606 standard and SNOMED CT and LOINC terminologies; (3) definition of transformation rules to generate secondary use models from standardized EHRs and development of them using R language; and (4) implementation and validation of the methodology through the generation of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC-WHO) COVID-19 case report form. This process has been implemented into a 1300-bed tertiary Hospital for a cohort of 4489 patients hospitalized from 25 February 2020 to 10 September 2020. ResultsAn initial and expandable set of relevant concepts for COVID-19 was identified, modeled and formalized using ISO-13606 standard and SNOMED CT and LOINC terminologies. Similarly, an algorithm was designed and implemented with R and then applied to process EHRs in accordance with standardized concepts, transforming them into secondary use models. Lastly, these resources were applied to obtain a data extract conforming to the ISARIC-WHO COVID-19 case report form, without requiring manual data collection. The methodology allowed obtaining the observation domain of this model with a coverage of over 85% of patients in the majority of concepts. ConclusionThis study has furnished a solution to the difficulty of rapidly and efficiently obtaining EHR-derived data for secondary use in COVID-19, capable of adapting to changes in data specifications and applicable to other organizations and other health conditions. The conclusion to be drawn from this initial validation is that this DCM-based methodology allows the effective reuse of EHRs generated in a tertiary Hospital during COVID-19 pandemic, with no additional effort or time for the organization and with a greater data scope than that yielded by conventional manual data collection process in ad-hoc EDCs.

Do You Need My Health Data - Just Ask: Using Blockchain Technology for Collaborative Patient-Centric Health Care

IntroductionAcquiring healthcare data for secondary use should benefit from a transparent and highly auditable process when handling patient consent. In the current healthcare infrastructure, the healthcare providers hold the stewardship for the patient data, which includes an authority to determine the data access often without involving the respective patients. The current approach to obtaining patient consent as a one-off task is inadequate to facilitate continuous communication among the patients, healthcare providers and data requestors to manage more personalised data access.
 Objectives and ApproachThis study explores a novel dynamic patient consent mechanism based on blockchain technology and smart contracts. The aim is to enable patients to actively participate in this process by dynamically managing data consent preferences. Furthermore, it also explores the feasibility of implementing a transparent and auditable collaborative access control infrastructure for clinical data analytics, where all the stakeholders can be involved in determining access to health data.
 ResultsThe solution has been designed leveraging the blockchain and smart contracts to ensure auditability and transparency of the consent management process where a set of smart contracts controls access to data with minimum human-interferences. In addition, multiple design goals such as data security, privacy, interoperability, legal compliance and accessibility have also been considered when designing a prototype solution. An empirical evaluation is planned to obtain feedback from stakeholders involved in health data sharing for secondary use.
 Conclusion / ImplicationsThe contribution of this research study is to augment the existing data acquisition procedures for clinical data analytics using blockchain and smart contracts. The proposed novel approach aims to empower and enable patients to play a more active role in controlling access to their data for secondary use. The study will also illuminate the opportunities and challenges which blockchain-based technologies can address related to creating collaborative patient-centric healthcare.

Designing and piloting a generic research architecture and workflows to unlock German primary care data for secondary use

BackgroundMedical data from family doctors are of great importance to health care researchers but seem to be locked in German practices and, thus, are underused in research. The RADAR project (Routine Anonymized Data for Advanced Health Services Research) aims at designing, implementing and piloting a generic research architecture, technical software solutions as well as procedures and workflows to unlock data from family doctor’s practices. A long-term medical data repository for research taking legal requirements into account is established. Thereby, RADAR helps closing the gap between the European countries and to contribute data from primary care in Germany.MethodsThe RADAR project comprises three phases: (1) analysis phase, (2) design phase, and (3) pilot. First, interdisciplinary workshops were held to list prerequisites and requirements. Second, an architecture diagram with building blocks and functions, and an ordered list of process steps (workflow) for data capture and storage were designed. Third, technical components and workflows were piloted. The pilot was extended by a data integration workflow using patient-reported outcomes (paper-based questionnaires).ResultsThe analysis phase resulted in listing 17 essential prerequisites and guiding requirements for data management compliant with the General Data Protection Regulation (GDPR). Based on this list existing approaches to fulfil the RADAR tasks were evaluated—for example, re-using BDT interface for data exchange and Trusted Third Party-approach for consent management and record linkage. Consented data sets of 100 patients were successfully exported, separated into person-identifying and medical data, pseudonymised and saved. Record linkage and data integration workflows for patient-reported outcomes in the RADAR research database were successfully piloted for 63 responders.ConclusionThe RADAR project successfully developed a generic architecture together with a technical framework of tools, interfaces, and workflows for a complete infrastructure for practicable and secure processing of patient data from family doctors. All technical components and workflows can be reused for further research projects. Additionally, a Trusted Third Party-approach can be used as core element to implement data privacy protection in such heterogeneous family doctor’s settings. Optimisations identified comprise a fully-electronic consent recording using tablet computers, which is part of the project’s extension phase.