Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU

EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes

The adoption of electronic health records (EHRs) has created opportunities to analyse historical data for predicting clinical outcomes and improving patient care. However, non-standardised data representations and anomalies pose major challenges to the use of EHRs in digital health research. To address these challenges, we have developed EHR-QC, a tool comprising two modules: the data standardisation module and the preprocessing module. The data standardisation module migrates source EHR data to a standard format using advanced concept mapping techniques, surpassing expert curation in benchmarking analysis. The preprocessing module includes several functions designed specifically to handle healthcare data subtleties. We provide automated detection of data anomalies and solutions to handle those anomalies. We believe that the development and adoption of tools like EHR-QC is critical for advancing digital health. Our ultimate goal is to accelerate clinical research by enabling rapid experimentation with data-driven observational research to generate robust, generalisable biomedical knowledge.

Using big data and Population Health Management to assess care and costs for patients with severe mental disorders and move toward a value-based payment system

BACKGROUND

Mental health (MH) care often exhibits uneven quality and poor coordination of physical and MH needs, especially for patients with severe mental disorders. This study tests a Population Health Management (PHM) approach to identify patients with severe mental disorders using administrative health databases in Italy and evaluate, manage and monitor care pathways and costs. A second objective explores the feasibility of changing the payment system from fee-for-service to a value-based system (e.g., increased care integration, bundled payments) to introduce performance measures and guide improvement in outcomes.

METHODS

Since diagnosis alone may poorly predict condition severity and needs, we conducted a retrospective observational study on a 9,019-patient cohort assessed in 2018 (30.5% of 29,570 patients with SMDs from three Italian regions) using the Mental Health Clustering Tool (MHCT), developed in the United Kingdom, to stratify patients according to severity and needs, providing a basis for payment for episode of care. Patients were linked (blinded) with retrospective (2014-2017) physical and MH databases to map resource use, care pathways, and assess costs globally and by cluster. Two regions (3,525 patients) provided data for generalized linear model regression to explore determinants of cost variation among clusters and regions.

RESULTS

Substantial heterogeneity was observed in care organization, resource use and costs across and within 3 Italian regions and 20 clusters. Annual mean costs per patient across regions was €3,925, ranging from €3,101 to €6,501 in the three regions. Some 70% of total costs were for MH services and medications, 37% incurred in dedicated mental health facilities, 33% for MH services and medications noted in physical healthcare databases, and 30% for other conditions. Regression analysis showed comorbidities, resident psychiatric services, and consumption noted in physical health databases have considerable impact on total costs.

CONCLUSIONS

The current MH care system in Italy lacks evidence of coordination of physical and mental health and matching services to patient needs, with high variation between regions. Using available assessment tools and administrative data, implementation of an episodic approach to funding MH could account for differences in disease phase and physical health for patients with SMDs and introduce performance measurement to improve outcomes and provide oversight.

Big data-driven public health policy making: Potential for the healthcare industry

The use of healthcare data analytics is anticipated to play a significant role in future public health policy formulation. Therefore, this study examines how big data analytics (BDA) may be methodically incorporated into various phases of the health policy cycle for fact-based and precise health policy decision-making. So, this study explores the potential of BDA for accurate and rapid policy-making processes in the healthcare industry. A systematic review of literature spanning 22 years (from January 2001 to January 2023) has been conducted using the PRISMA approach to develop a conceptual framework. The study introduces the emerging topic of BDA in healthcare policy, goes over the advantages, presents a framework, advances instances from the literature, reveals difficulties and provides recommendations. This study argues that BDA has the ability to transform the conventional policy-making process into data-driven process, which helps to make accurate health policy decision. In addition, this study contends that BDA is applicable to the different stages of health policy cycle, namely policy identification, agenda setting as well as policy formulation, implementation and evaluation. Currently, descriptive, predictive and prescriptive analytics are used for public health policy decisions on data obtained from several common health-related big data sources like electronic health reports, public health records, patient and clinical data, and government and social networking sites. To effectively utilize all of the data, it is necessary to overcome the computational, algorithmic and technological obstacles that define today's extremely heterogeneous data landscape, as well as a variety of legal, normative, governance and policy limitations. Big data can only fulfill its full potential if data are made available and shared. This enables public health institutions and policymakers to evaluate the impact and risk of policy changes at the population level.

Mapping Factors That Affect the Uptake of Digital Therapeutics Within Health Systems: Scoping Review

BACKGROUND

Digital therapeutics are patient-facing digital health interventions that can significantly alter the health care landscape. Despite digital therapeutics being used to successfully treat a range of conditions, their uptake in health systems remains limited. Understanding the full spectrum of uptake factors is essential to identify ways in which policy makers and providers can facilitate the adoption of effective digital therapeutics within a health system, as well as the steps developers can take to assist in the deployment of products.

OBJECTIVE

In this review, we aimed to map the most frequently discussed factors that determine the integration of digital therapeutics into health systems and practical use of digital therapeutics by patients and professionals.

METHODS

A scoping review was conducted in MEDLINE, Web of Science, Cochrane Database of Systematic Reviews, and Google Scholar. Relevant data were extracted and synthesized using a thematic analysis.

RESULTS

We identified 35,541 academic and 221 gray literature reports, with 244 (0.69%) included in the review, covering 35 countries. Overall, 85 factors that can impact the uptake of digital therapeutics were extracted and pooled into 5 categories: policy and system, patient characteristics, properties of digital therapeutics, characteristics of health professionals, and outcomes. The need for a regulatory framework for digital therapeutics was the most stated factor at the policy level. Demographic characteristics formed the most iterated patient-related factor, whereas digital literacy was considered the most important factor for health professionals. Among the properties of digital therapeutics, their interoperability across the broader health system was most emphasized. Finally, the ability to expand access to health care was the most frequently stated outcome measure.

CONCLUSIONS

The map of factors developed in this review offers a multistakeholder approach to recognizing the uptake factors of digital therapeutics in the health care pathway and provides an analytical tool for policy makers to assess their health system's readiness for digital therapeutics.

Collecting and sharing self-generated health and lifestyle data: Understanding barriers for people living with long-term health conditions - a survey study

Background

The growing popularity of collecting self-generated health and lifestyle data presents a valuable opportunity to develop our understanding of long-term health conditions and improve care. Barriers remain to the effective sharing of health and lifestyle data by those living with long-term health conditions which include beliefs around concepts of Trust, Identity, Privacy and Security, experiences of stigma, perceptions of risk and information sensitivity.

Method

We surveyed 250 UK adults who reported living with a range of long-term health conditions. We recorded data to assess self-reported behaviours, experiences, attitudes and motivations relevant to sharing self-generated health and lifestyle data. We also asked participants about their beliefs about Trust, Identity, Privacy and Security, stigma, and perceptions of risk and information sensitivity regarding their health and lifestyle data.

Results

Three-quarters of our sample reported recording information about their health and lifestyle on a daily basis. However, two-thirds reported never or rarely sharing this information with others. Trust, Identity, Privacy and Security concerns were considered to be ‘very important’ by those with long-term health conditions when deciding whether or not to share self-generated health and lifestyle data with others, with security concerns considered most important. Of those living with a long-term health condition, 58% reported experiencing stigma associated with their condition. The greatest perceived risk from sharing with others was the potential for future harm to their social relationships.

Conclusions

Our findings suggest that, in order for health professionals and researchers to benefit from the increased prevalence of self-generated health and lifestyle data, more can be done to address security concerns and to understand perceived risks associated with data sharing. Digital platforms aimed at facilitating the sharing of self-generated health and lifestyle data may look to highlight security features, enable users to control the sharing of certain information types, and emphasise the practical benefits to users of sharing health and lifestyle data with others.

Concept and Proof of the Lifelog Bigdata Platform for Digital Healthcare and Precision Medicine on the Cloud

PURPOSE

We propose the Lifelog Bigdata Platform as a sustainable digital healthcare system based on individual-centric lifelog datasets and describe the standardization of lifelog and clinical data in its full-cycle management system.

MATERIALS AND METHODS

The Lifelog Bigdata Platform was developed by Yonsei Wonju Health System on the cloud to support digital healthcare and precision medicine. It consists of five core components: data acquisition system, de-identification of individual information, lifelog integration, analyzer, and service. We designed a gathering system into a dedicated virtual machine to save lifelog or clinical outcomes and established standard guidelines for maintaining the quality of gathering procedures. We used standard integration keys to integrate the lifelog and clinical data. Metadata were generated from the data warehouse after loading combined or fragmented data on it. We analyzed the de-identified lifelog and clinical data using the lifelog analyzer to prevent and manage acute and chronic diseases through providing results of statistics on analysis.

RESULTS

The big data centers were built in four hospitals and seven companies for integrating lifelog and clinical data to develop the Lifelog Bigdata Platform. We integrated and loaded lifelog big data and clinical data for 3 years. In the first year, we uploaded 94 types of data on the platform with a total capacity of 221 GB.

CONCLUSION

The Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem.

Drawing light from the pandemic: Rethinking strategies for health policy and beyond

The COVID-19 pandemic is a catastrophe. It was also preventable. The potential impacts of a novel pathogen were foreseen and for decades scientists and commentators around the world warned of the threat. Most governments and global institutions failed to heed the warnings or to pay enough attention to risks emerging at the interface of human, animal, and environmental health. We were not ready for COVID-19, and people, economies, and governments around the world have suffered as a result. We must learn from these experiences now and implement transformational changes so that we can prevent future crises, and if and when emergencies do emerge, we can respond in more timely, robust and equitable ways, and minimize immediate and longer-term impacts.

In 2020–21 the Pan-European Commission on Health and Sustainable Development assessed the challenges posed by COVID-19 in the WHO European region and the lessons from the response. The Commissioners have addressed health in its entirety, analyzing the interactions between health and sustainable development and considering how other policy priorities can contribute to achieving both. The Commission's final report makes a series of policy recommendations that are evidence-informed and above all actionable. Adopting them would achieve seven key objectives and help build truly sustainable health systems and fairer societies.

Real-World Data for Healthcare Research in China: Call for Actions

OBJECTIVES

This study aimed to provide an overview of major data sources in China that can be potentially used for epidemiology, health economics, and outcomes research; compare them with similar data sources in other countries; and discuss future directions of healthcare data development in China.

METHODS

The study was conducted in 2 phases. First, various data sources were identified through a targeted literature review and recommendations by experts. Second, an in-depth assessment was conducted to evaluate the strengths and limitations of administrative claims and electronic health record data, which were further compared with similar data sources in developed countries.

RESULTS

Secondary databases, including administrative claims and electronic health records, are the major types of real-world data in China. There are substantial variations in available data elements even within the same type of databases. Compared with similar databases in developed countries, the secondary databases in China have some general limitations such as variations in data quality, unclear data usage mechanism, and lack of longitudinal follow-up data. In contrast, the large sample size and the potential to collect additional data based on research needs present opportunities to further improve real-world data in China.

CONCLUSIONS

Although healthcare data have expanded substantially in China, high-quality real-world evidence that can be used to facilitate decision making remains limited in China. To support the generation of real-world evidence, 2 fundamental issues in existing databases need to be addressed-data access/sharing and data quality.

Surveillance in Next-Generation Personalized Healthcare: Science and Ethics of Data Analytics in Healthcare

Advances in science and technology have allowed for incredible improvements in healthcare. Additionally, the digital revolution in healthcare provides new ways of collecting and storing large volumes of patient data, referred to as big healthcare data. As a result, healthcare providers are now able to use data to gain a deeper understanding of how to treat an individual in what is referred to as personalized healthcare. Regardless, there are several ethical challenges associated with big healthcare data that affect how personalized healthcare is delivered. To highlight these issues, this article will review the role of big data in personalized healthcare while also discussing the ethical challenges associated with it. The article will also discuss public health surveillance, its implications, and the challenges associated with collecting participants' information. The article will proceed by highlighting next generation technologies, including robotics and 3D printing. The article will conclude by providing recommendations on how patient privacy can be protected in next-generation personalized healthcare.

Application Research: Big Data in Food Industry

A huge amount of data is being produced in the food industry, but the application of big data—regulatory, food enterprise, and food-related media data—is still in its infancy. Each data source has the potential to develop the food industry, and big data has broad application prospects in areas like social co-governance, exploit of consumption markets, quantitative production, new dishes, take-out services, precise nutrition and health management. However, there are urgent problems in technology, health and sustainable development that need to be solved to enable the application of big data to the food industry.

Scoping review exploring the impact of digital systems on processes and outcomes in the care management of acute kidney injury and progress towards establishing learning healthcare systems

OBJECTIVES

Digital systems have long been used to improve the quality and safety of care when managing acute kidney injury (AKI). The availability of digitised clinical data can also turn organisations and their networks into learning healthcare systems (LHSs) if used across all levels of health and care. This review explores the impact of digital systems i.e. on patients with AKI care, to gauge progress towards establishing LHSs and to identify existing gaps in the research.

METHODS

Embase, PubMed, MEDLINE, Cochrane, Scopus and Web of Science databases were searched. Studies of real-time or near real-time digital AKI management systems which reported process and outcome measures were included.

RESULTS

Thematic analysis of 43 studies showed that most interventions used real-time serum creatinine levels to trigger responses to enable risk prediction, early recognition of AKI or harm prevention by individual clinicians (micro level) or specialist teams (meso level). Interventions at system (macro level) were rare. There was limited evidence of change in outcomes.

DISCUSSION

While the benefits of real-time digital clinical data at micro level for AKI management have been evident for some time, their application at meso and macro levels is emergent therefore limiting progress towards establishing LHSs. Lack of progress is due to digital maturity, system design, human factors and policy levers.

CONCLUSION

Future approaches need to harness the potential of interoperability, data analytical advances and include multiple stakeholder perspectives to develop effective digital LHSs in order to gain benefits across the system.

Governing Personalized Health: A Scoping Review

Genetic research is advancing rapidly. One important area for the application of the results from this work is personalized health. These are treatments and preventive interventions tailored to the genetic profile of specific groups or individuals. The inclusion of personalized health in existing health systems is a challenge for policymakers. In this article, we present the results of a thematic scoping review of the literature dealing with governance and policy of personalized health. Our analysis points to four governance challenges that decisionmakers face against the background of personalized health. First, researchers have highlighted the need to further extend and harmonize existing research infrastructures in order to combine different types of genetic data. Second, decisionmakers face the challenge to create trust in personalized health applications, such as genetic tests. Third, scholars have pointed to the importance of the regulation of data production and sharing to avoid discrimination of disadvantaged groups and to facilitate collaboration. Fourth, researchers have discussed the challenge to integrate personalized health into regulatory-, financing-, and service provision structures of existing health systems. Our findings summarize existing research and help to guide further policymaking and research in the field of personalized health governance.

Towards Equitable AI Interventions for People Who Use Drugs: Key Areas That Require Ethical Investment

There has been growing investment in artificial intelligence (AI) interventions to combat the opioid-driven overdose epidemic plaguing North America. Although the evidence for the use of technology and AI in medicine is mounting, there are a number of ethical, social, and political implications that need to be considered when designing AI interventions. In this commentary, we describe 2 key areas that will require ethical deliberation in order to ensure that AI is being applied ethically with socially vulnerable populations such as people who use drugs: (1) perpetuation of biases in data and (2) consent. We offer ways forward to guide and provide opportunities for interventionists to develop substance use-related AI technologies that account for the inherent biases embedded within conventional data systems. This includes a discussion of how other data generation techniques (eg, qualitative and community-based approaches) can be integrated within AI intervention development efforts to mitigate the limitations of relying on electronic health record data. Finally, we emphasize the need to involve people who use drugs as stakeholders in all phases of AI intervention development.

Concurrence of big data analytics and healthcare: A systematic review

BACKGROUND

The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care.

PURPOSE

This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges.

DATA SOURCES

A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered.

STUDY SELECTION

Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected.

DATA EXTRACTION

Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare.

RESULTS

A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare.

CONCLUSION

This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.

Health systems reforms in Singapore: A qualitative study of key stakeholders

In response to a growing chronic disease burden and ageing population, Singapore implemented Regional Health Systems (RHS) in 2008. In January 2017, the MOH announced that the six RHS clusters would be reorganised into three in 2018. This qualitative study sought to identify the health system challenges, opportunities, and ways forward for the implementation of the RHS. We conducted semi-structured interviews with 35 key informants from RHS clusters, government, academia, and private and voluntary sectors. Integration, innovation, and people-centeredness were identified as the key principles of the RHS. The RHS was described as an opportunity to holistically care for a person across the care continuum, address social determinants of health, develop new models of care, and work with social and community partners. Challenges to RHS implementation included difficulties aligning the goals, values, and priorities of multiple actors, the need for better integration across clusters, differing care capabilities and capacities across partners, healthcare financing structures that may not reflect RHS goals, scalability and evaluation of pilot programmes, and disease-centricity, provider-centricity, and medicalisation in health and healthcare. Suggested ways forward included building relationships between actors to facilitate integration; exploring innovative new models of care; clear long-term/scale-up plans for successful pilots; healthcare financing reforms to meet changing patient and population needs; and developing evaluation systems reflective of RHS principles and priorities.

Identifying critical steps towards improved access to innovation in cancer care: a European CanCer Organisation position paper

In recent decades cancer care has seen improvements in the speed and accuracy of diagnostic procedures; the effectiveness of surgery, radiation therapy and medical treatments; the power of information technology; and the development of multidisciplinary, specialist-led approaches to care. Such innovations are essential if we are to continue improving the lives of cancer patients across Europe despite financial pressures on our healthcare systems. Investment in innovation must be balanced with the need to ensure the sustainability of healthcare budgets, and all health professionals have a responsibility to help achieve this balance. It requires scrutiny of the way care is delivered; we must be ready to discontinue practices or interventions that are inefficient, and prioritise innovations that may deliver the best outcomes possible for patients within the limits of available resources. Decisions on innovations should take into account their long-term impact on patient outcomes and costs, not just their immediate costs. Adopting a culture of innovation requires a multidisciplinary team approach, with the patient at the centre and an integral part of the team. It must take a whole-system and whole-patient perspective on cancer care and be guided by high-quality real-world data, including outcomes relevant to the patient and actual costs of care; this accurately reflects the impact of any innovation in clinical practice. The European CanCer Organisation is committed to working with its member societies, patient organisations and the cancer community at large to find sustainable ways to identify and integrate the most meaningful innovations into all aspects of cancer care.