A Sensitive Data Access Model in Support of Learning Health Systems

Friedman and colleagues1 outline a vision of the learning health system founded on the sharing of data and achieved through alignment of information technology, advanced analytics and clinical expertise. The...

BackgroundThe Learning Health System (LHS) framework is designed to enhance healthcare by systematically integrating internal data and external evidence to promote quality, safety, and efficiency, aligning science, informatics, incentives, and culture for continuous improvement, innovation, and equity.MethodsThe Agency for Healthcare Research and Quality (AHRQ) also outlined key LHS learning goals structured around four interconnected approaches: (1) evidence generation to create new knowledge, (2) evidence adoption to translate findings into practice, (3) evidence dissemination to share best practices across systems, and (4) evidence management to integrate internal and external insights using technology and informatics. We propose this model can also enhance workforce well‐being (also termed “flourishing” or eudaimonia by Aristotle) through system‐level changes informed by rigorously collected local data.FindingsHealthcare workers who flourish realize their purpose, improve patient health, and align themselves through daily decisions and actions toward this end. However, excessive workload, documentation burden, and unsupported caregiving responsibilities can detract from this goal. A wide range of LHS methods can be applied to address healthcare worker well‐being and result in LHS cycles of learning and improvement. We present four examples demonstrating how LHS‐concordant research methods align with AHRQ's learning goals to transition from mitigating burnout to actively promoting flourishing.ContributionTogether, the application of the AHRQ learning goals forms a continuous feedback loop that facilitates mutual enhancement between healthcare delivery and research, advancing clinician well‐being and system‐wide improvement. This change in focus offers a new method for the design and evaluation of workforce well‐being interventions, can restore excellence in patient care, and contributes to creating sustainable, human‐centered healthcare systems.

This tutorial will focus on Learning Health Systems (LHS) as a data-driven approach to quality improvement in complex healthcare organisations. The tutorial will present the concept of the learning health system and give participants an opportunity to consider a range of typical problems involved in managing healthcare organisations and to reflect on the different forms of data and analytical methods that can be used to identify improvements. These will be discussed in the context of recent LHS projects from the UK such as Connected Health Cities. The tutorial leads will present examples of approaches to identify outliers, to predicting demand, to measuring flow, and to stratifying populations. They will illustrate how initiatives to collect, share and link electronic health data support can support these aims. Finally the tutorial will explore the practical challenges involves in trying to move from analysis to impact. Intended Audience The tutorial is aimed at a those interested in learning about the challenges faced in implementing learning health systems, with a focus on practical problems associated with access to data, data quality, choice of analytical approach and implementing change. Short Biography - Georgina Moulton is a Professor of Bio-Health Informatics Education at The University of Manchester, UK. She is an Associate Director of Training at the national institute, Health Data Research in the UK (HDR UK) as well as being Director of Workforce Development and Training for the Department of Health funded programme, Connected Health Cities. She leads the education and training in health informatics and health data science at her host institution. Paul Taylor is a Professor of Health Informatics at University College London, author of ‘From Patient Data to Medical Knowledge’ and the Deputy Director of the Institute of Health Informatics at UCL. He works closely with partners in local NHS Foundation Trusts and with industry on projects aimed at improving clinical processes.

The connection of clinical care with clinical research is the main purpose of the Learning Health System (LHS) integrating scientific information, informatics, and patient care. The LHS generates new medical knowledge as a by-product of the care process. For this purpose, the aggregation of data from Electronic Health Records (EHR), Case Report Forms (CRF), web questionnaires with other data sources like primary care databases and genetic data repositories is necessary for research purposes. This joining of healthcare and research processes results in challenges for the privacy protection framework of the LHS. Based on an exploration of EU legal requirements for data protection and privacy, different data access policies of data provider organizations as well as existing privacy frameworks of research projects, basic privacy principles and privacy requirements were extracted. Based on privacy principles and legal requirements a graphical model to display privacy protection requirements was created. This graphical model is based on concepts of requirements engineering and can be used like a model kit to create new privacy frameworks and to ease knowledge exchange with stakeholders of the LHS. Our model is built upon the concept of three privacy zones (Care Zone, Non-Care Zone and Research Zone) representing areas where similar legal requirements and rules apply. These zones contain databases, data transformation operators, such as data linkers and privacy filters and graphs to indicate the data flow necessary for research processes. The aim of the model is to help arrange its components in a way that creates a risk gradient for the data flow from a zone of high risk for patient identification to a zone of low risk. The model is applied to the analysis of several general clinical research usage scenarios and two research use cases from the TRANSFoRm project (finding patients for clinical research and linkage of databases). Both use cases represent different data collection aspects of the LHS. The model was used during discussions with data managers from the NIVEL Primary Care Database in the Netherlands and validated by representing an approved research case of using primary care data employing NIVEL services. Experiences with the graphic privacy model used to improve the privacy framework of TRANSFoRm and with the presentation of the model to LHS stakeholders and the research community are discussed.

BackgroundIn the context of expanding digital health tools, the health system is ready for Learning Health System (LHS) models. These models, with proper governance and stakeholder engagement, enable the integration of digital infrastructure to provide feedback to all relevant parties including clinicians and consumers on performance against best practice standards, as well as fostering innovation and aligning healthcare with patient needs. The LHS literature primarily includes opinion or consensus-based frameworks and lacks validation or evidence of benefit. Our aim was to outline a rigorously codesigned, evidence-based LHS framework and present a national case study of an LHS-aligned national stroke program that has delivered clinical benefit.Main textCurrent core components of a LHS involve capturing evidence from communities and stakeholders (quadrant 1), integrating evidence from research findings (quadrant 2), leveraging evidence from data and practice (quadrant 3), and generating evidence from implementation (quadrant 4) for iterative system-level improvement. The Australian Stroke program was selected as the case study as it provides an exemplar of how an iterative LHS works in practice at a national level encompassing and integrating evidence from all four LHS quadrants. Using this case study, we demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare improvement. We emphasize the transition from research as an endpoint, to research as an enabler and a solution for impact in healthcare improvement.ConclusionsThe Australian Stroke program has nationally improved stroke care since 2007, showcasing the value of integrated LHS-aligned approaches for tangible impact on outcomes. This LHS case study is a practical example for other health conditions and settings to follow suit.

BackgroundThe transition to electronic health records offers the potential for big data to drive the next frontier in healthcare improvement. Yet there are multiple barriers to harnessing the power of data. The Learning Health System (LHS) has emerged as a model to overcome these barriers, yet there remains limited evidence of impact on delivery or outcomes of healthcare.ObjectiveTo gather evidence on the effects of LHS data hubs or aligned models that use data to deliver healthcare improvement and impact. Any reported impact on the process, delivery or outcomes of healthcare was captured.MethodsSystematic review from CINAHL, EMBASE, MEDLINE, Medline in-process and Web of Science PubMed databases, using learning health system, data hub, data-driven, ehealth, informatics, collaborations, partnerships, and translation terms. English-language, peer-reviewed literature published between January 2014 and Sept 2019 was captured, supplemented by a grey literature search. Eligibility criteria included studies of LHS data hubs that reported research translation leading to health impact.ResultsOverall, 1076 titles were identified, with 43 eligible studies, across 23 LHS environments. Most LHS environments were in the United States (n = 18) with others in Canada, UK, Sweden and Australia/NZ. Five (21.7%) produced medium-high level of evidence, which were peer-reviewed publications.ConclusionsLHS environments are producing impact across multiple continents and settings.

Leadership is vital to a well-functioning and effective health system. This importance was underscored during the COVID-19 pandemic. As disparities in infection and mortality rates became pronounced, greater calls for equity-informed healthcare emerged. These calls led some leaders to use the Learning Health System (LHS) approach to quickly transform research into healthcare practice to mitigate inequities causing these rates. The LHS is a relatively new framework informed by many within and outside health systems, supported by decision-makers and financial arrangements and encouraged by a culture that fosters quick learning and improvements. Although studies indicate the LHS can enhance patients' health outcomes, scarce literature exists on health leaders' use and incorporation of equity into the LHS. This article begins addressing this gap by examining how equity can be incorporated into LHS activities and discussing ways leaders can ensure equity is considered and achieved in rapid learning cycles.

IntroductionCritical for advancing a Learning Health System (LHS) in the U.S., a regulatory safe harbor for deidentified data reduces barriers to learning from care at scale while minimizing privacy risks. We examine deidentified data policy as a mechanism for synthesizing the ethical obligations underlying clinical care and human subjects research for an LHS which conceptually and practically integrates care and research, blurring the roles of patient and subject.MethodsFirst, we discuss respect for persons vis‐a‐vis the systemic secondary use of data and tissue collected in the fiduciary context of clinical care. We argue that, without traditional informed consent or duty to benefit the individual, deidentification may allow secondary use to supersede the primary purpose of care. Next, we consider the effectiveness of deidentification for minimizing harms via privacy protection and maximizing benefits via promoting learning and translational care. We find that deidentification is unable to fully protect privacy given the vastness of health data and current technology, yet it imposes limitations to learning and barriers for efficient translation. After that, we evaluate the impact of deidentification on distributive justice within an LHS ethical framework in which patients are obligated to contribute to learning and the system has a duty to translate knowledge into better care. Such a system may permit exacerbation of health disparities as it accelerates learning without mechanisms to ensure that individuals' contributions and benefits are fair and balanced.ResultsWe find that, despite its established advantages, system‐wide use of deidentification may be suboptimal for signaling respect, protecting privacy or promoting learning, and satisfying requirements of justice for patients and subjects.ConclusionsFinally, we highlight ethical, socioeconomic, technological and legal challenges and next steps, including a critical appreciation for novel approaches to realize an LHS that maximizes efficient, effective learning and just translation without the compromises of deidentification.

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

Reg-ent within the Learning Health System.

In this paper, we present a framework for developing a Learning Health System (LHS) to provide means to a computerized clinical decision support system for allied healthcare and/or nursing professionals. LHSs are well suited to transform healthcare systems in a mission-oriented approach, and is being adopted by an increasing number of countries. Our theoretical framework provides a blueprint for organizing such a transformation with help of evidence based state of the art methodologies and techniques to eventually optimize personalized health and healthcare. Learning via health information technologies using LHS enables users to learn both individually and collectively, and independent of their location. These developments demand healthcare innovations beyond a disease focused orientation since clinical decision making in allied healthcare and nursing is mainly based on aspects of individuals’ functioning, wellbeing and (dis)abilities. Developing LHSs depends heavily on intertwined social and technological innovation, and research and development. Crucial factors may be the transformation of the Internet of Things into the Internet of FAIR data & services. However, Electronic Health Record (EHR) data is in up to 80% unstructured including free text narratives and stored in various inaccessible data warehouses. Enabling the use of data as a driver for learning is challenged by interoperability and reusability.To address technical needs, key enabling technologies are suitable to convert relevant health data into machine actionable data and to develop algorithms for computerized decision support. To enable data conversions, existing classification and terminology systems serve as definition providers for natural language processing through (un)supervised learning.To facilitate clinical reasoning and personalized healthcare using LHSs, the development of personomics and functionomics are useful in allied healthcare and nursing. Developing these omics will be determined via text and data mining. This will focus on the relationships between social, psychological, cultural, behavioral and economic determinants, and human functioning.Furthermore, multiparty collaboration is crucial to develop LHSs, and man-machine interaction studies are required to develop a functional design and prototype. During development, validation and maintenance of the LHS continuous attention for challenges like data-drift, ethical, technical and practical implementation difficulties is required.

Given the wide distribution of software and its current growth, security is inevitable, and software systems have become more complex. To avoid the complexity and protect valuable assets of software systems, researchers, and practitioners in the fields of security requirement engineering (SRE) and security assurance advise incorporating security requirements early in software development life cycle (SDLC). Nowadays, security requirements specification and identification are an active research area for ensuring the effectiveness of software systems. However, security requirement identification is a challenging task due to sheer numbers of threats and attacks to a software system. Previous researchers have identified different SRE techniques, each looking at the same problem from a different perspective. Therefore, a literature review conducted on SRE techniques. We compared and analyzed different SRE techniques to find the most suitable SRE techniques for researchers and practitioners. Selected SRE techniques compared based on various parameters, such as different attributes, requirement elicitation, requirement analysis, project size, and integration of standards. The literature also indicates that among SRE approaches, security quality requirements engineering (SQAURE), secure tropos, security requirements engineering process (SREP), and comprehensive lightweight application security process (CLASP) are the most used SRE approaches that focus on activities such as threats, security requirements identification, and security objectives identification. Unfortunately, several SRE approaches fail to explicitly integrate the security standard and security assurance perspective. Three questions developed for this study: Question 1 is based on comparison of existing SRE approaches, Question 2 is based on SRE with security requirements elicitation and analysis, and Question 3 highlights the incorporation of security requirements assurance by SRE approaches implicitly or explicitly. The researcher must work with the developer to easily adopt SRE approaches to elicit and ensure security requirements for the software system.

With the increased use of digital health innovations in Canadian health care, educating health care users, professionals, and researchers on the ethical challenges and privacy implications of these tools is essential. The Datum project, funded by the Fondation Barreau du Quebec, was created to help these actors better understand legal and ethical issues regarding the collection, use, and disclosure of digital health data for the purposes of scientific research, thereby enhancing literacy around data privacy. The project consists of a multimedia website divided into legislation and policy documents and narrative-based video content. Users can access the core legislation and policies governing the collection and use of health care data geared toward researchers and health practitioners. Users can also view the narrative-based video content explaining key concepts related to digital health data. The Datum project makes an original contribution to the field of law and ethics in health science research by using novel approaches, such as learning health systems and data banks, to improve equity in health care delivery and by generating multimedia content aimed at encouraging health care users to become better consumers and supporting the collective use of their data. The Datum project also promotes digital literacy as a digital communication tool, which has the significant potential to improve health outcomes, bridge the digital divide, and reduce health inequities.

Describe the state of Electronic Health Records (EHRs) in 1992 and their evolution by 2015 and where EHRs are expected to be in 25 years. Further to discuss the expectations for EHRs in 1992 and explore which of them were realized and what events accelerated or disrupted/derailed how EHRs evolved. Literature search based on "Electronic Health Record", "Medical Record", and "Medical Chart" using Medline, Google, Wikipedia Medical, and Cochrane Libraries resulted in an initial review of 2,356 abstracts and other information in papers and books. Additional papers and books were identified through the review of references cited in the initial review. By 1992, hardware had become more affordable, powerful, and compact and the use of personal computers, local area networks, and the Internet provided faster and easier access to medical information. EHRs were initially developed and used at academic medical facilities but since most have been replaced by large vendor EHRs. While EHR use has increased and clinicians are being prepared to practice in an EHR-mediated world, technical issues have been overshadowed by procedural, professional, social, political, and especially ethical issues as well as the need for compliance with standards and information security. There have been enormous advancements that have taken place, but many of the early expectations for EHRs have not been realized and current EHRs still do not meet the needs of today's rapidly changing healthcare environment. The current use of EHRs initiated by new technology would have been hard to foresee. Current and new EHR technology will help to provide international standards for interoperable applications that use health, social, economic, behavioral, and environmental data to communicate, interpret, and act intelligently upon complex healthcare information to foster precision medicine and a learning health system.

BackgroundHealth data has become an abundant and rich source of information that can be leveraged for clinical trials. Health Level Seven (HL7) provides standards for health care data, while the Clinical Data Interchange Standards Consortium (CDISC) provides standards for research data. Bridging these worlds and leveraging health data has the potential to enhance knowledge and data quality, while reducing delays and cost in clinical trials.Objectives The aim was to provide an overview of the HL7 Fast Healthcare Interoperability Resources (FHIR®) to CDISC Joint Mapping Implementation Guide (IG) development. The ultimate aim is to increase awareness of this rich resource.MethodsA gap analysis was performed to assess the possibility and feasibility of mapping from FHIR to the CDISC’s Study Data Tabulation Model (SDTM) and Clinical Data Acquisition Standards Harmonization (CDASH) standards. The mapping of CDISC domains commenced using FHIRPath, a path-based navigation and extraction language. The domains mapped were Medical History (MH), Concomitant Medications (CM), Procedures (PR), Vital Signs (VS), Laboratory (LB), Adverse Events (AE), and Demographics (DM). The proposed mappings were evaluated through a formal review process.ResultsThe review process consisted of a CDISC Internal Review and a simultaneous CDISC Public Review and HL7 Ballot Review.  A total of 363 comments were obtained from the following reviews: 1) CDISC Internal Review (268), and 2) simultaneous Public Review (95) - CDISC (37), HL7 (58). The predominant gathered in the review included mapping updates, terminology, and requests for greater detail.ConclusionThe HL7 FHIR® to CDISC Joint Mapping Implementation Guide (IG) provides a rich resource for bridging health care and research standards that can be used with CDISC LB to LOINC mapping guide for leveraging real-world data for clinical trials. The guide assists stakeholders in utilizing health data through the HL7 FHIR® resources to CDISC, CDASH, and SDTM variables. As the community uses this resource, it will continue to mature and potentially expand into new domains.