Digital-Health-Highlights 2020

Rafael Correo, de izquierdas, y Alvaro Noboa, magnate conservador, vencieron con un discurso clientelista en el pais mas inestable de America Latina.

Health Systems Need to Transform Data Collection to Advance Health Equity.

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. 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. 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. 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.

Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world's rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients' health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

Linking ‘data silos’ to investigate anaemia among Aboriginal and Torres Strait Islander mothers and children in Far North Queensland

Introduction: The Coronavirus Disease-2019 (COVID-19) pandemic has caused global disruption, putting health experts and healthcare systems at risk. However, the ultimate goal of medical systems to offer precise medical care in a holistic manner for the benefit of patients cannot be compromised. With the massive advancements in digital technology, it is now possible for healthcare systems and medical practitioners to handle and utilise enormous amounts of patient data to provide appropriate medical assistance with minimal error. Integrated with Electronic Health Records (EHR), Clinical Decision Support Systems (CDSS) are digital programs that analyse patient data and assist medical professionals in making decisions and recommendations, thereby enhancing patient care. CDSS have played a critical role in maximising care during the pandemic by helping clinicians offer evidence-based medical care using patient data, paving the way for a more accurate and personalised healthcare delivery. However, their extended usability to manage post-COVID-19 conditions remains unexplored. Aim: This scoping review seeks to outline the use of these CDSS in the management of COVID-19 and their potential usability in risk assessment, severity prediction, and treatment goals for patients experiencing long COVID-19 symptoms. Materials and Methods: A thorough literature search was conducted on Google Scholar and PubMed using key search terms to identify relevant articles that support the objective of this study. A total of 3,010 records were available, of which 13 articles were chosen for inclusion in this review after extensive screening in accordance with the eligibility standards set up. The supporting data were meticulously extracted and charted to provide a clear outline of the utility of CDSS. This scoping review also features a conceptual framework for the extended usability of CDSS in long COVID-19 management. Results: After the methodological search and selection process, data from 13 articles were analysed. Most of the included studies were conducted in the United States. The majority of the CDSS were designed to assess the severity of COVID-19. These CDSS predominantly analysed blood investigations, COVID-19 symptoms, and radiological findings of patients to make appropriate clinical decisions for managing the disease. There was a lack of scientific literature supporting the use of CDSS in long COVID-19 management. Conclusion: With healthcare systems dealing with massive amounts of patient data, especially during the pandemic and postpandemic crisis, appropriate methods to manage and handle this information are critical to delivering patient-centered medical care. CDSS have been widely utilised in this regard to enhance the health outcomes of patients by guiding health professionals to make the right treatment choices in the most evidence-based manner using patients’ health data. Thus, given the future of healthcare systems with Artificial Intelligence (AI), a greater emphasis on expanding the usability of these CDSS beyond the scope of COVID-19 is essential.

Improving Value in Healthcare

Chapter 10 - Security aspects and UAVs in Socialized Regions

The collection and use of patient health data are central to any kind of activity in the health care system. These data may be produced during routine clinical processes or obtained directly from the patient using patient-reported outcome (PRO) measures. Although efficiency and other reasons justify data availability for a range of potentially relevant uses, these data are nearly always collected for a single specific purpose. The health care literature reflects this narrow scope, and there is limited literature on the joint use of health data for daily clinical use, clinical research, surveillance, and administrative purposes. The aim of this paper is to provide a framework for discussing the efficient use of health data with a specific focus on the role of PRO measures. PRO data may be used at an individual patient level to inform patient care or shared decision making and to tailor care to individual needs or group-level needs as a complement to health record data, such as that on mortality and readmission, in order to inform service delivery and measure the real-world effectiveness of treatment. PRO measures may be used either for their own sake, to provide valuable information from the patient perspective, or as a proxy for clinical data that would otherwise not be feasible to collect. We introduce a framework to analyze any health care activity that involves health data. The framework consists of four data processes (patient identification, data collection, data aggregation and data use), further structured into two dichotomous dimensions in each data process (level: group vs patient; timeframe: ad hoc vs systematic). This framework is used to analyze various health activities with respect to joint use of data, considering the technical, legal, organizational, and logistical challenges that characterize each data process. Finally, we propose a model for joint use of health data with data collected during follow-up as a base. Demands for health data will continue to increase, which will further add to the need for the concerted use and reuse of PRO data for parallel purposes. Repeated and uncoordinated PRO data collection for the same patient for different purposes results in misuse of resources for the patient and the health care system as well as reduced response rates owing to questionnaire fatigue. PRO data can be routinely collected both at the hospital (from inpatients as well as outpatients) and outside of hospital settings; in primary or social care settings; or in the patient’s home, provided the health informatics infrastructure is in place. In the future, clinical settings are likely to be a prominent source of PRO data; however, we are also likely to see increased remote collection of PRO data by patients in their own home (telePRO). Data collection for research and quality surveillance will have to adapt to this circumstance and adopt complementary data capture methods that take advantage of the utility of PRO data collected during daily clinical practice. The European Union’s regulation with respect to the protection of personal data—General Data Protection Regulation—imposes severe restrictions on the use of health data for parallel purposes, and steps should be taken to alleviate the consequences while still protecting personal data against misuse.

Digitalisation of health and the use of health data in artificial intelligence, and machine learning (ML), including for applications that will then in turn be used in healthcare are major...

Emerging constraints across healthcare systems are driving the need for wearable digital health technologies capable of supporting patient monitoring in remote and extreme environments. 1 Advances in materials science and flexible electronics, including miniaturized sensors, low power systems, and edge AI, are enabling autonomous, real time physiological monitoring. 2 Yet, widespread clinical adoption is still limited by the lack of high-quality, real-world data validating system performance. 3 In defense applications, the demand for these innovations is intensified by the realities of modern conflict. Lessons from the Ukraine war highlight the prevalence of contaminated wounds, traumatic amputations, and delays in accessing advanced care. To meet the needs of prolonged field care and prehospital trauma management, modular and portable platforms must support diagnostics, haemorrhage control, resuscitation, endovascular stabilization, analgesia, organ support, blood substitution, and burn and wound care. Continuous physiological monitoring, beyond the ‘golden hour’, is critical for timely intervention and improved survival outcomes. Promising configurations using advanced materials and hybrid printed electronics (HPE) show potential for wound healing, 4 bacterial detection, 5 and continuous monitoring in extreme conditions. However, unresolved challenges remain, including motion artifacts, sensor drift, and integration issues affecting wearability and durability. 6–8 While academic developments demonstrate technological capabilities, many overlook real world deployment needs. There is an increasing need for lightweight, disposable, standalone wearable systems that can be rapidly deployed and resupplied. These technologies can enhance triage, casualty management, and wound care by delivering real time insights in constrained environments. 9 TNO has been conducting exploratory studies to integrate sensor systems for vital sign monitoring and smart wound care into hybrid printed electronic (HPE) designs. Preliminary findings demonstrate promising levels of accuracy, sensitivity, and long-term durability. The investigated sensing modalities include physical sensors, such as temperature and pressure, for monitoring inflammation, perfusion, and thermoregulation; optical sensors for assessing vascularity, tissue oxygenation, and microcirculatory status; and chemical and biological sensors for detecting pH shifts, bacterial load, and infection-related biomarkers (figure 1). In addition, these sensor systems show potential for evaluating broader physiological parameters such as hydration levels and stress responses. This could enable more comprehensive and adaptive health assessment in dynamic, resource-constrained environments. These integrated sensing platforms aim to support earlier and more targeted interventions. Further development of defense specific designs could accelerate real world data collection and advance adoption in both military and civilian healthcare. HPE technologies provide a scalable, adaptable platform for embedding sensor functionality into wearable patches and smart wound management. Customizable to mission needs, these systems can incorporate battery-less communication, sensor multiplexing, and optimized layouts, offering compact, deployable tools to improve frontline care and medical decision making in high risk, resource limited environments. References World Health Organisation, ‘Health Workforce’ https://www.who.int/health-topics/health-workforce#tab=tab_1. B. Srinivasaiah, 2022, Transformative wearables; how AI and ML are shaping healhtcare innovations. International Journal of Science and Research (IJSR) . 2319–7064 Zou KH, Berger ML. Real-world data and real-world evidence in healthcare in the United States and Europe union. Bioengineering (Basel) . 2024 Aug 2; 11 (8):784. doi: 10.3390/bioengineering11080784. PMID: 39199742; PMCID: PMC11351410. Freedman BR, Hwang C, Talbot S, Hibler B, Matoori S, Mooney DJ. Breakthrough treatments for accelerated wound healing . Sci Adv . 2023 May 19; 9 (20):eade7007. doi: 10.1126/sciadv.ade7007. Epub 2023 May 17. PMID: 37196080; PMCID: PMC10191440 Mohamed Salleh NAB, Tanaka Y, Sutarlie L, Su X. Detecting bacterial infections in wounds: a review of biosensors and wearable sensors in comparison with conventional laboratory methods. The Analyst . 2022; 147 (9):1756–1776. https://doi.org/10.1039/d2an00157h O. Such, ‘Motion tolerance in wearable sensors – The challenge of motion artifact,’ 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 2007, 1542–1545, doi: 10.1109/IEMBS.2007.4352597 Fruytier LA, Janssen DM, Campero Jurado I, van de Sande DA, Lorato I, Stuart S, Panditha P, de Kok M, Kemps HM. The utility of a novel electrocardiogram patch using dry electrodes technology for arrhythmia detection during exercise and prolonged monitoring: proof-of-concept study. JMIR Formative Research 2023; 7 :e49346. Stuart S, de Kok M, O’Searcoid B, Morrisroe H, Serban IB, Jagers F, Dulos R, Houben S, van de Peppel L, van den Brand J. Critical design considerations for longer-term wear and comfort of on-body medical devices. Bioengineering (Basel, Switzerland) . 2024; 11 (11):1058. https://doi.org/10.3390/bioengineering11111058 Hegarty-Craver M, Davis-Wilson H, Gaur P, et al . Wearable sensors for service members and first responders: considerations for using commercially available sensors in continuous monitoring. Research Triangle Park (NC): RTI Press. 2024. Abstract P08 Figure 1 TNO Smart wound care portfolio for current integration of sensors within dressings and conformability of electronics for wearable patches

Background Hospital length of stay (LOS) is an important indicator of healthcare systems’ performance. Several factors are closely related to LOS, which is characterized by high heterogeneity among countries. This study aims to investigate the 20-year time-trends of LOS and their association with different healthcare systems’ and reimbursement models among 25 European countries. Methods We conducted a 20-year time-trend analysis (2000-2019) using a Weighted Least Squares model for panel data to investigate the association between LOS, healthcare systems’ models (National Health Service, NHS, National Health Insurance, NHI, Social Health Insurance, SHI, and Etatist Social Health Insurance, ESH), reimbursement schemes (Prospective Global Budget, PGB, Diagnosis Related Groups, DRG, and Procedure Service Payment, PSP), and several control variables. Data were extracted from the Eurostat and GBD study databases. Results The analysis showed a generalized reduction of LOS across the 25 countries, ranging from 9.20 days in 2000 to 7.24 in 2019. SHI was associated with a lower LOS compared to the reference category (NHS) (b=−0.6327, p < 0.05). Both DRG (b = 1.2399, p < 0.05) and PSP (b = 1.1677, p < 0.05) reimbursement models were positively associated with LOS when compared to PGB. Conclusions Our results confirmed the downward trend of LOS in the last 20 years among European countries, highlighting the strong influence of the healthcare system model. The results of this study offer insight into the factors influencing healthcare utilization and can inform the design of more efficient and sustainable healthcare systems. Key messages • This study investigated the relationship between HS models, financing, regulation, service provision, reimbursement schemes, and LOS, across 25 European countries in the last 20 years. • This study found a dominant influence of the HS models on LOS. SHI models were associated with shorter LOS compared to National Health Systems, after accounting for several factors.

Routinely collected health data (or real-world data) from hospitals is becoming increasingly important to advance medical progress. Anonymisation of these data facilitates data sharing processes. This allows stakeholders of the healthcare system to compliantly access this anonymised data to address epidemiological questions, advance precision medicine, support drug development or address other medical needs. As the willingness of the general Swiss population to share anonymised health data has been uncertain, a survey was conducted to better understand their perception of sharing such data for research purposes. The present survey focused on the re-use (secondary use) of hospital-derived health data in anonymised form. A cross-sectional survey was conducted in a representative random sample (n = 1006) of the general Swiss population. The general population was contacted by phone between 14 September and 3 October 2020. The survey was also conducted in an additional population with chronic disease (n = 225) via an online panel. An independent research organisation (gfs-zürich) was commissioned to conduct the survey. The survey participation was anonymous and voluntary. The demographic composition of the interviewed participants from the general population was specifically constructed to be representative of the 18+-year-old French- and German-speaking population of Switzerland, according to the quota features gender, age and language region. Representativeness of the chronic disease population is unclear. 71% of the general population and 81% of the chronic disease group reported that they would share their anonymised health data for medical research. The drivers were mainly of an altruistic nature. Hurdles concern mainly data protection issues, potential misuse or disadvantages, e.g., by health insurers. About 56% of the general population would like to be better informed about the use of their personal health data, and 69% spontaneously reported health authorities as the stakeholder responsible for providing such information. The survey showed that the Swiss population is willing to share anonymised health data given that some key concerns are addressed. Our findings underline that a better understanding of the standards and processes around health data privacy and transparent data usage is important to build trust in the public eye. An open dialogue is required to develop a common consent on data governance for Switzerland, which would allow health data sharing with third parties. This open dialogue should involve all stakeholders of the healthcare system, so as to strive towards both a more personalised and a more sustainable Swiss healthcare system.

ABSTRACT
 ObjectivesStatistics New Zealand, the official statistics agency of the New Zealand (NZ) Government, has developed an Integrated Data Infrastructure (IDI). We aimed to review the value of the IDI to date from a research perspective and to consider the opportunities it provides.
 ApproachWe considered recent projects in which we have been involved: the New Zealand Census-Mortality and CancerTrends studies (NZCMS/CT), a Suicide Mortality Review Committee (SuMRC) feasibility trial, and a study on cardiovascular disease costs in NZ. We also considered IDI-related work published by other agencies and the views expressed at IDI meetings involving health and social sector personnel.
 ResultsSince 1993 in NZ, it has been possible to link all official health datasets using the National Health Index (NHI) number. However it was much more difficult to link health data with data from other sources. Studies often involved much ad hoc data linkage, with different versions of the same data being stored in multiple sites resulting in data integrity and security issues. The IDI provides one place to securely link the various data sources then make the anonymised data available for research for public good.
 The IDI provides the following advantages:
 
 Administrative data sources are available in one place nationally rather than many data silos.
 Health data can be linked with other administrative data eg, tax, education, crime, migration, welfare, housing, population census. 
 It facilitates collaboration between agencies and the sharing of code and associated documentation.
 Increased use of these data means there is more feedback to the source government agencies around data collection standards and data quality issues. 
 It allows for nationally-standardised processes for ensuring that only responsible and capable researchers with appropriate research questions have access to the relevant anonymised data.
 Perhaps the greatest long-term research potential is with the repeated measures data, allowing analyses with greater potential causal inference than previously available.
 All the above reduce the cost of doing research as well as assisting government departments with evaluation and costing studies.
 
 ConclusionsNZ is making good progress with integration of administrative data from the health and social sectors. The IDI is a valuable tool for advancing research that provides government departments and academic researchers with new insights into complex social issues such as crime and vulnerable children. It allows government to target limited resources to those areas where the greatest long-term benefit might be made.

The rapid proliferation of digital health records, wearable IoT devices, and continuous monitoring has created an unprecedented influx of heterogeneous clinical data. These include static information (demographics, medical history, lab values) and sequential time-series signals (ECG, continuous glucose monitoring, vital signs, etc.). Conventional models often handle each modality in isolation or simply concatenate features, failing to exploit the deep cross-modal relationships that can enhance predictive power. We present a holistic, system-level framework that deeply integrates static and sequential data through coordinated representation learning and fusion. Our design is guided by several principles: (1) Modality-Specific Representation Learning, which uses distinct encoder architectures (e.g. feedforward networks for tabular data vs. RNNs/Transformers for time-series) tailored to each modality’s statistical properties; (2) Representation Alignment and Compatibility, ensuring that learned embeddings from different branches are dimensionally and semantically harmonized for fusion; (3) Structured Fusion Strategy, where modality encoders operate in parallel and integrate their outputs at appropriate stages (early/intermediate/late or attention-based fusion); (4) Temporal–Contextual Interaction Modeling, whereby static context dynamically modulates the interpretation of sequential signals (e.g. via gating or attention conditioned on patient baseline); (5) Robustness and Missing Data Handling, employing mechanisms like time-aware interpolation, masking, and imputation to cope with irregular sampling, incomplete records, and noisy sensor data; and (6) Interpretability and Clinical Alignment, which incorporate feature attribution and attention visualization so that predictions can be explained in terms of medically meaningful factors. Together, these principles form a cohesive blueprint for constructing adaptive, personalized, and clinically trustworthy AI in healthcare. We discuss each principle in detail and illustrate how they interconnect to produce a unified multimodal architecture that respects the unique characteristics of static and dynamic health data.