Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses

Introduction

The potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes.

Materials and methods

Our proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network.

Results

In a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria's national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested.

Discussion

The presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.

Recent Advances in Pathology: the 2023 Annual Review Issue of The Journal of Pathology

The 2023 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 12 invited reviews on topics of current interest in pathology. This year, our subjects include immuno-oncology and computational pathology approaches for diagnostic and research applications in human disease. Reviews on the tissue microenvironment include the effects of apoptotic cell-derived exosomes, how understanding the tumour microenvironment predicts prognosis, and the growing appreciation of the diverse functions of fibroblast subtypes in health and disease. We also include up-to-date reviews of modern aspects of the molecular basis of malignancies, and our final review covers new knowledge of vascular and lymphatic regeneration in cardiac disease. All of the reviews contained in this issue are written by expert groups of authors selected to discuss the recent progress in their particular fields and all articles are freely available online (https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896). © 2023 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The Relative Age Effect in Ice Hockey: Analysis of Its Presence, Its Fading and of a Reversal Effect among Junior and Professional Leagues

This study analyzes the relative age effect (RAE) among the world's best junior hockey leagues and in the NHL. Despite the prevalence of RAE in ice hockey, past research suggests its fading-reversal over time, which may occur at later stages of athletic development. The hypothesis of the RAE reversal was tested with two sources of raw data files from the 2021-2022 season: 15 of the best international junior and minor professional leagues (N = 7 399) and the NHL (N = 812). Birth quartile distributions were analyzed to verify the prevalence of RAE and quantile regression was used to test the reversal of RAE hypotheses. Advanced hockey metrics were aggregated from multiple data sources and used to compare early born with late born players using birth quartiles. Prevalence of the RAE was verified with crosstabs analyses and quantile regression was used to test the reversal effect. Results indicated that the RAE still prevailed in ice hockey, with higher magnitude in Canadian leagues. Regression analyses showed that late-born junior and minor pro players, despite getting less exposure in terms of games played, attained levels of offensive production similar to those of early born players. Late-born players able to emerge in the NHL performed similarly and sometimes displayed better performance (in some markers). Results suggest that stakeholders should find ways to pay special attention to late born players in talent identification processes and offer them opportunities to develop at the highest levels.

Estimating Prevalence and Characteristics of Statin Intolerance among High and Very High Cardiovascular Risk Patients in Germany (2017 to 2020)

Statin intolerance (SI) (partial and absolute) could lead to suboptimal lipid management. The lack of a widely accepted definition of SI results into poor understanding of patient profiles and characteristics. This study aims to estimate SI and better understand patient characteristics, as reflected in clinical practice in Germany using supervised machine learning (ML) techniques. This retrospective cohort study utilized patient records from an outpatient setting in Germany in the IQVIA™ Disease Analyzer. Patients with a high cardiovascular risk, atherosclerotic cardiovascular disease, or hypercholesterolemia, and those on lipid-lowering therapies between 2017 and 2020 were included, and categorized as having “absolute” or “partial” SI. ML techniques were applied to calibrate prevalence estimates, derived from different rules and levels of confidence (high and low). The study included 292,603 patients, 6.4% and 2.8% had with high confidence absolute and partial SI, respectively. After deploying ML, SI prevalence increased approximately by 27% and 57% (p < 0.00001) in absolute and partial SI, respectively, eliciting a maximum estimate of 12.5% SI with high confidence. The use of advanced analytics to provide a complementary perspective to current prevalence estimates may inform the identification, optimal treatment, and pragmatic, patient-centered management of SI in Germany.

How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review

Introduction:

Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is “How can big data analytics support people-centred and integrated health services?”

Methods:

A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review.

Results:

Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%).

Discussion:

The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics.

Conclusions:

Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.

COVID-19 Intelligence-Driven Operational Response Platform: Experience of a Large Tertiary Multihospital System in the Middle East

The COVID-19 pandemic has resulted in global disruptions within healthcare systems, leading to quick dynamic fluctuations in hospital operations and supply chain management. During the early months of the pandemic, tertiary multihospital systems were highly viewed as the go-to hospitals for handling these rapid healthcare challenges caused by the rapidly increasing number of COVID-19 cases. Yet, this pandemic has created an urgent need for coordinated mechanisms to alleviate increasing pressures on these large multihospital systems and ensure services remain high-quality, accessible, and sustainable. Digital health solutions have been identified as promising approaches to address these challenges. This case report describes results for developing multidisciplinary visualizations to support digital health operations in one of the largest tertiary multihospital systems in the Middle East. The report concludes with some lessons and insights learned from the rapid development and delivery of this user-centric COVID-19 multihospital operations intelligent platform.

Using Methods From Computational Decision-making to Predict Nonadherence to Fitness Goals: Protocol for an Observational Study

Background

Can methods from computational models of decision-making be used to build a predictive model to identify individuals most likely to be nonadherent to personal fitness goals? Such a model may have significant value in the global battle against obesity. Despite growing awareness of the impact of physical inactivity on human health, sedentary behavior is increasingly linked to premature death in the developed world. The annual impact of sedentary behavior is significant, causing an estimated 2 million deaths. From a global perspective, sedentary behavior is one of the 10 leading causes of mortality and morbidity. Annually, considerable funding and countless public health initiatives are applied to promote physical fitness, with little impact on sustained behavioral change. Predictive models developed from multimodal methodologies combining data from decision-making tasks with contextual insights and objective physical activity data could be used to identify those most likely to abandon their fitness goals. This has the potential to enable development of more targeted support to ensure that those who embark on fitness programs are successful.

Objective

The aim of this study is to determine whether it is possible to use decision-making tasks such as the Iowa Gambling Task to help determine those most likely to abandon their fitness goals. Predictive models built using methods from computational models of decision-making, combining objective data from a fitness tracker with personality traits and modeling from decision-making games delivered via a mobile app, will be used to ascertain whether a predictive algorithm can identify digital personae most likely to be nonadherent to self-determined exercise goals. If it is possible to phenotype these individuals, it may be possible to tailor initiatives to support these individuals to continue exercising.

Methods

This is a siteless study design based on a bring your own device model. A total of 200 healthy adults who are novice exercisers and own a Fitbit (Fitbit Inc) physical activity tracker will be recruited via social media for this study. Participants will provide consent via the study app, which they will download from the Google Play store (Alphabet Inc) or Apple App Store (Apple Inc). They will also provide consent to share their Fitbit data. Necessary demographic information concerning age and sex will be collected as part of the recruitment process. Over 12 months, the scheduled study assessments will be pushed to the subjects to complete. The Iowa Gambling Task will be administered via a web app shared via a URL.

Results

Ethics approval was received from Dublin City University in December 2020. At manuscript submission, study recruitment was pending. The expected results will be published in 2022.

Conclusions

It is hoped that the study results will support the development of a predictive model and the study design will inform future research approaches.

Trial Registration

ClinicalTrials.gov NCT04783298; https://clinicaltrials.gov/ct2/show/NCT04783298

Digital transformation in healthcare - architectures of present and future information technologies

Healthcare providers all over the world are faced with a single challenge: the need to improve patient outcomes while containing costs. Drivers include an increasing demand for chronic disease management for an aging population, technological advancements and empowered patients taking control of their health experience. The digital transformation in healthcare, through the creation of a rich health data foundation and integration of technologies like the Internet of Things (IoT), advanced analytics, Machine Learning (ML) and Artificial Intelligence (AI), is recognized as a key component to tackle these challenges. It can lead to improvements in diagnostics, prevention and patient therapy, ultimately empowering care givers to use an evidence-based approach to improve clinical decisions. Real-time interactions allow a physician to monitor a patient 'live', instead of interactions once every few weeks. Operational intelligence ensures efficient utilization of healthcare resources and services provided, thereby optimizing costs. However, procedure-based payments, legacy systems, disparate data sources with the limited adoption of data standards, technical debt, data security and privacy concerns impede the efficient usage of health information to maximize value creation for all healthcare stakeholders. This has led to a highly-regulated, constrained industry. Ultimately, the goal is to improve quality of life and saving people's lives through the creation of the intelligent healthcare provider, fully enabled to deliver value-based healthcare and a seamless patient experience. Information technologies that enable this goal must be extensible, safe, reliable and affordable, and tailored to the digitalization maturity-level of the individual organization.

In silico approaches for enhancing retrieval analysis as a source for discovery of implant reactivity-related mechanisms and biomarkers

The ability to characterize implant debris in conjunction with corresponding immune and tissue-destructive responses renders retrieval analysis as an important tool for evaluating orthopedic devices. We applied advanced analytics and in silico approaches to illustrate the retrieval-based potential to elucidate host responses and enable discovery of corresponding biomarkers indicative of in vivo implant performance. Hip retrieval analysis was performed using variables based on immunostaining, polarized microscopy, and fretting-corrosion and oxidation analyses. Statistical analyses were performed in R. Hierarchical/k-means clustering and principal component analysis were used for data analysis and visualization. Correlation Engine (CE) and Ingenuity Pathway Analysis (IPA) were employed for in silico corroboration of putative biomarkers. Higher giant cell and histiocyte scores and positivity for CD68 and CD3 indicating infiltration with macrophages and T-cells, respectively, were detected mainly among older generation hips with higher ultra-high-molecular-weight-polyethylene loads. Our in silico analysis using pre-existing data on wear particle-induced loosening substantiated the role of CD68 in implant-induced innate responses and identified the CD68-related molecular signature that can be indicative of development of aseptic loosening and can be further corroborated for diagnostic/prognostic testing in clinical setting. Thus, this study confirmed the great potential of advanced analytics and in silico approaches for enhancing retrieval analysis applications to discovery of new biomarkers for optimizing implant-related preclinical testing and clinical management. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 108B:263-271, 2020.

Simulating a Virtual Machining Model in an Agent-Based Model for Advanced Analytics

Monitoring the performance of manufacturing equipment is critical to ensure the efficiency of manufacturing processes. Machine-monitoring data allows measuring manufacturing equipment efficiency. However, acquiring real and useful machine-monitoring data is expensive and time consuming. An alternative method of getting data is to generate machine-monitoring data using simulation. The simulation data mimic operations and operational failure. In addition, the data can also be used to fill in real data sets with missing values from real-time data collection. The mimicking of real manufacturing systems in computer-based systems is called "virtual manufacturing". The computer-based systems execute the manufacturing system models that represent real manufacturing systems. In this paper, we introduce a virtual machining model of milling operations. We developed a prototype virtual machining model that represents 3-axis milling operations. This model is a digital mock-up of a real milling machine; it can generate machine-monitoring data from a process plan. The prototype model provides energy consumption data based on physics-based equations. The model uses the standard interfaces of Step-compliant data interface for Numeric Controls (STEP-NC) and MTConnect to represent process plan and machine-monitoring data, respectively. With machine-monitoring data for a given process plan, manufacturing engineers can anticipate the impact of a modification in their actual manufacturing systems. This paper describes also how the virtual machining model is integrated into an agent-based model in a simulation environment. While facilitating the use of the virtual machining model, the agent-based model also contributes to the generation of more complex manufacturing system models, such as a virtual shop-floor model. The paper describes initial building steps towards a shop-floor model. Aggregating the data generated during the execution of a virtual shop-floor model allows one to take advantage of data analytics techniques to predict performance at the shop-floor level.