Healthcare Scenario Research Articles

Artificial intelligence probabilities scheme for disease prevention data set construction in intelligent smart healthcare scenario

In the face of an aging population, smart healthcare services are now within reach, thanks to the proliferation of high-speed internet and other forms of digital technology. Data problems in smart healthcare, unfortunately, put artificial intelligence in this area to serious limitations. There are several issues, including a lack of standard samples, noisy data interference, and actual data that is missing. A three-stage AI-based data generating strategy is suggested to handle missing datasets, using a small sample dataset obtained from a smart healthcare program community in a specific city: Step one involves generating the dataset's basic attributes using a tree-based generation strategy that takes the original data distribution into account. Step two involves using the Naive Bayes algorithm to create basic indicators of behavioural capability assessment for the samples. Step three builds on stage two and uses a multivariate linear regression method to create evaluation criteria and indicators of high-level behavioural capability. Six problems involving multiple classifications and two tasks using multiple labels are implemented using various neural network-based training strategies on the obtained data to assess the usefulness of the dataset for downstream tasks. To ensure that the data collected is genuine and useful, the experimental data must be analysed and expert knowledge must be included.

HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks.

While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or simple data pre-processing can greatly limit the performance or even applicability of downstream tasks using EHRs. To address this challenge, we present HealthGAT, a novel graph attention network framework that utilizes a hierarchical approach to generate embeddings from EHR, surpassing traditional graph-based methods. Our model iteratively refines the embeddings for medical codes, resulting in improved EHR data analysis. We also introduce customized EHR-centric auxiliary pre-training tasks to leverage the rich medical knowledge embedded within the data. This approach provides a comprehensive analysis of complex medical relationships and offers significant advancement over standard data representation techniques. HealthGAT has demonstrated its effectiveness in various healthcare scenarios through comprehensive evaluations against established methodologies. Specifically, our model shows outstanding performance in node classification and downstream tasks such as predicting readmissions and diagnosis classifications.

Integration of Machine Learning with Fog Computing for Health Care Systems Challenges and Issues: A Review

Fog computing, a distributed cloud computing model, extends the traditional cloud paradigm to the network's edge, reducing latency and alleviating congestion. It addresses challenges in classical cloud architectures exacerbated by real-time IoT applications, which produce massive amounts of data that traditional cloud computing struggles to process due to limited bandwidth and high propagation delays. Fog computing is crucial in latency-sensitive applications like health monitoring and surveillance, where it processes vast volumes of data, minimizing delays and boosting performance. This technology brings computation, storage, monitoring, and services closer to the end-user, enhancing real-time decision-making capabilities. This paper presents the challenges of IoT applications. It also demonstrates the role of two emerging technologies fog computing and machine learning in health care scenarios.

Transdisciplinary Skills for AI Ecosystems: Using Future Visioning to Collaboratively Unpack Skills in UK Health and Emergency Response Scenarios

Transdisciplinary practices in artificial intelligence (AI) requires good methodological techniques to bring researchers together from diverse disciplines. Different fields and traditions each may have an impact on the greater AI ecosystem, however, to properly gauge this impact, and include diverse voices, research methodologies must evolve to adopt new methods and techniques. To understand the shifting demands for new skills, we conducted a transdisciplinary workshop, bringing together stakeholders from industry, government and academia, by engaging with scenarios in healthcare and emergency response, using future visioning to imagine new skill requirements across job roles. We found that future visioning was a useful methodology to help frame discussions in a manner conducive to eliciting qualitative evidence of different policy approaches. Beyond individual skills, we propose the Human-AI Synergy Matrix as a collaborative competency framework, which integrates technical, professional, and strategic skills with levels of engagement such as awareness, communication, synergy, governance, and transformation. The matrix aims to facilitate evaluation and understanding of skills and critical issues in human-AI collaboration within AI ecosystems, emphasizing the need to address broader challenges in an interconnected manner.

Anthropomorphic motion planning for multi-degree-of-freedom arms.

With the development of technology, the humanoid robot is no longer a concept, but a practical partner with the potential to assist people in industry, healthcare and other daily scenarios. The basis for the success of humanoid robots is not only their appearance, but more importantly their anthropomorphic behaviors, which is crucial for the human-robot interaction. Conventionally, robots are designed to follow meticulously calculated and planned trajectories, which typically rely on predefined algorithms and models, resulting in the inadaptability to unknown environments. Especially when faced with the increasing demand for personalized and customized services, predefined motion planning cannot be adapted in time to adapt to personal behavior. To solve this problem, anthropomorphic motion planning has become the focus of recent research with advances in biomechanics, neurophysiology, and exercise physiology which deepened the understanding of the body for generating and controlling movement. However, there is still no consensus on the criteria by which anthropomorphic motion is accurately generated and how to generate anthropomorphic motion. Although there are articles that provide an overview of anthropomorphic motion planning such as sampling-based, optimization-based, mimicry-based, and other methods, these methods differ only in the nature of the planning algorithms and have not yet been systematically discussed in terms of the basis for extracting upper limb motion characteristics. To better address the problem of anthropomorphic motion planning, the key milestones and most recent literature have been collated and summarized, and three crucial topics are proposed to achieve anthropomorphic motion, which are motion redundancy, motion variation, and motion coordination. The three characteristics are interrelated and interdependent, posing the challenge for anthropomorphic motion planning system. To provide some insights for the research on anthropomorphic motion planning, and improve the anthropomorphic motion ability, this article proposes a new taxonomy based on physiology, and a more complete system of anthropomorphic motion planning by providing a detailed overview of the existing methods and their contributions.

Integrating blockchain and ZK-ROLLUP for efficient healthcare data privacy protection system via IPFS

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.

An immersive virtual reality communication skills training for dietitians: A feasibility study

ObjectiveThe training of dietitians emphasizes the development of strong communication skills. Immersive virtual reality (IVR) has been successfully employed for various healthcare scenarios; however, it is yet understudied in dietetics education. Therefore, we conducted a feasibility study to investigate the usefulness of IVR for the purpose of communication skills training. MethodsWe designed a multi-user virtual hospital environment that enabled the training of professional conversations between dietitians and patients. Divided into groups of three, 30 dietetics students participated in a role-play task. Taking a qualitative approach to inquiry, we evaluated how participants assessed the benefits and limitations of the IVR training. ResultsParticipants appreciated the authenticity of the training environment and mentioned various advantages of IVR (e.g., sense of privacy, better focus on conversation) over traditional modes of instruction. On the other side, participants frequently mentioned that the lack of avatars' facial expressions might present an obstacle for effective communication skills training. ConclusionIVR enables authentic communication skills trainings for dietitians. Special consideration should be given to providing ample social cues during training. InnovationThe study demonstrates that the promising results from other healthcare professions regarding the usefulness of IVR training also apply to dietetics.

Enhancing AI interpretation and decision-making: Integrating cognitive computational models with deep learning for advanced uncertain reasoning systems

Advancements in uncertain reasoning systems within healthcare are crucial for navigating the complexities of patient data, requiring innovative methodologies that integrate AI interpretation capabilities and robust handling of inherent ambiguity. Healthcare systems face the challenge of handling uncertainty inherent in patient data, necessitating sophisticated decision-making tools like Uncertain Reasoning Systems (URS) for effective ambiguity navigation. Recognizing the complexity of healthcare scenarios, advancements in AI interpretation within URS are crucial beyond traditional methods. Conventional techniques like statistical approaches and rule-based systems often prove inadequate due to their rigid frameworks and limited ability to manage inherent ambiguity. This paper proposes an innovative methodology that integrates Min-Max normalization and robust missing data handling techniques with Hybrid Fuzzy Rule-Based Systems and Neural Networks, supplemented by Game Theory for model refinement. Through the integration of Game Theory, it can dynamically adjust its strategies to healthcare data uncertainties, thereby enhancing its resilience and efficacy. Implemented using Python tools, the proposed system achieves an exceptional 99.4 % accuracy, surpassing baseline methods such as FNN (88.1 %) and Naïve Bayes (90 %), highlighting its superior performance in healthcare decision-making. These findings represent significant strides in AI interpretation and decision-making within Uncertain Reasoning Systems, underscoring the practical relevance of the proposed approach.

Next-Gen Medical Technology a Smart Approach to Syringe Injection Pump Automation

This presents the design, implementation, and successful validation of a cutting-edge IoT-based Syringe Injection Pump (SIP) for precision medication administration. In response to the increasing demand for enhanced healthcare automation, this system leverages technologies to address critical challenges in medication delivery. This SIP incorporates a robust Internet of Things (IoT) framework, enabling seamless communication between the device and a central healthcare management system. The automated syringe injection process is governed by a sophisticated algorithm that considers patient-specific parameters, ensuring precise dosage administration. Through extensive testing and real-world simulations, the results demonstrate a significant reduction in human error and improved medication adherence. The system’s architecture integrates secure communication protocols, ensuring data confidentiality and integrity during transmission. An innovative user interface provides healthcare professionals with real-time monitoring and control capabilities, enhancing situational awareness and responsiveness. Furthermore, the SIP’s adaptability to diverse medical environments underscores its versatility in meeting the evolving needs of healthcare institutions. To validate the system’s effectiveness, a comprehensive performance evaluations, including dosage accuracy, response time, and reliability under varying conditions was conducted. Results indicate an impressive dosage precision of 99.8%, showcasing the system’s reliability in critical healthcare scenarios. The IoT connectivity facilitates remote monitoring and control, allowing healthcare providers to intervene promptly in case of anomalies or emergencies. In addition to its technical prowess, the SIP aligns with current healthcare trends by promoting sustainability and cost-effectiveness. The automated system optimizes resource utilization, reduces medication waste, and minimizes the environmental impact associated with traditional manual injection methods. In conclusion, this IoT-based Syringe Injection Pump represents a paradigm shift in medical technology, offering a smart and results-oriented solution for precise medication administration. The successful implementation and validation of this system pave the way for improved patient outcomes, streamlined healthcare workflows, and a more sustainable approach to medication delivery. This research contributes to the ongoing discourse on advancing medical technology and underscores the potential of IoT in shaping the future of healthcare.

Organizational justice and organizational citizenship behavior: exploring the mediating role of psychological well-being at work

PurposeThe present research paper aims to examine the inter-relationship between organizational justice (Henceforth termed as OJ), psychological well-being at work (henceforth termed as PWBW) and organizational citizenship behavior (henceforth termed as OCB). More specifically, this paper attempts to critically analyze the mediating role of PWBW in the relationship between OJ and OCB. The study solely focuses on nurses working at private hospitals in Kerala, who are the largest group of healthcare personnel.Design/methodology/approachData collected from 308 nursing employees were analyzed by using statistical package for the social sciences (SPSS) software.FindingsThe outcomes of the analysis demonstrate that significant correlations exist between all the three key variables and their dimensions. Moreover, it has been found that the relationship between OJ and OCB is partially mediated by PWBW.Research limitations/implicationsIn the present healthcare scenario, just after the Covid-19 pandemic, there is a paramount need for the well-being of healthcare staff in order to improve the functioning of the healthcare system.Originality/valueThe study enabled us to develop and provide an explanation as to how social exchange relationship works between OJ and OCB.

Extended Review Concerning the Integration of Electrochemical Biosensors into Modern IoT and Wearable Devices.

Electrochemical biosensors include a recognition component and an electronic transducer, which detect the body fluids with a high degree of accuracy. More importantly, they generate timely readings of the related physiological parameters, and they are suitable for integration into portable, wearable and implantable devices that are significant relative to point-of-care diagnostics scenarios. As an example, the personal glucose meter fundamentally improves the management of diabetes in the comfort of the patients' homes. This review paper analyzes the principles of electrochemical biosensing and the structural features of electrochemical biosensors relative to the implementation of health monitoring and disease diagnostics strategies. The analysis particularly considers the integration of the biosensors into wearable, portable, and implantable systems. The fundamental aim of this paper is to present and critically evaluate the identified significant developments in the scope of electrochemical biosensing for preventive and customized point-of-care diagnostic devices. The paper also approaches the most important engineering challenges that should be addressed in order to improve the sensing accuracy, and enable multiplexing and one-step processes, which mediate the integration of electrochemical biosensing devices into digital healthcare scenarios.

SAFe‐Health: Guarding federated learning‐driven smart healthcare with federated defense averaging against data poisoning

AbstractFederated learning (FL) serves as a decentralized training framework for machine learning (ML) models, preserving data privacy in critical domains such as smart healthcare. However, it has been found that attackers can exploit this decentralized learning framework to perform data and model poisoning attacks, specifically in FL‐driven smart healthcare. This work delves into the realm of FL‐driven smart healthcare systems, consisting of multiple hospitals based architecture and focusing on heart disease detection using FL. We carry out data poisoning attacks, using two different attacking methods, label flipping attack and input data/feature manipulation attack to demonstrate that such FL‐driven smart healthcare systems are vulnerable to attacks. To guard the system against such attack, we propose a novel federated averaging defense mechanism to stop the identified poisoned clients in weight aggregation. This mechanism effectively detects and thwarts data poisoning attempts by identifying compromised clients during weight aggregation. The proposed mechanism is based on the idea of weighted averaging, where each client's contribution is weighted according to its trustworthiness. The proposed work addresses a critical gap in the literature by focusing on the often‐overlooked issue of poisoning attacks in tabular text datasets, which are crucial to the smart healthcare system. We conduct the testbed‐based experiment to demonstrate that the proposed mechanism is effectively detecting and mitigating data poisoning attacks in selected FL‐driven smart healthcare scenarios, while maintaining high accuracy and convergence rates.

Getting it right with discrete choice experiments: Are we hot or cold?

Discrete Choice Experiments (DCEs) are widely employed survey-based methods to assess preferences for healthcare services and products. While they offer an experimental way to represent health-related decisions, the stylized representation of scenarios in DCEs may overlook contextual factors that could influence decision-making. The aim of this paper was to evaluate the predictive validity of preferences elicited through a DCE in decisions likely influenced by a hot-cold empathy gap, and compare it to another commonly used method, a direct-elicitation question. We focused on preferences for pain-relief modalities, especially for an epidural during childbirth - a context where direct-elicitation questions have shown a preference for or intention to have a natural birth (representing the “cold” state), yet individuals often opt for an epidural during labor (representing the “hot” state). Leveraging a unique dataset collected from 248 individuals, we incorporated both the stated preferences collected through a survey administered upon hospital admission for childbirth and the actual pain-relief modality usage data documented in medical records. The DCE allowed for the evaluation of scenarios outside of those expected by respondents to simulate decision-making during childbirth. When we compared the predicted epidural use with the actual epidural use during labor, we observed a choice concordance of 71-60%, depending on the model specification. The concordance rate between the predicted and actual choices increased to 77-76% when accounting for the initial use of other ineffective modalities. In contrast, the direct-elicitation choices, relying solely on respondents’ baseline expectations, yielded a lower concordance rate of 58% with actual epidural use. These findings highlight the flexibility of the DCE method in simulating complex decision contexts, including those involving hot-cold empathy gaps. The DCE proves valuable in assessing nuanced preferences, providing a more accurate representation of the decision-making processes in healthcare scenarios.

Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS’s ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.

Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications.

Objectives: This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical information data management. The research seeks to propose a solution in the form of a medical metadata governance framework that is efficient and suitable for clinical research and transformation. Methods: The article begins by outlining the background of medical information data management and reviews the advancements in artificial intelligence (AI) technology relevant to the field. It then introduces the "Service, Patient, Regression, base/Away, Yeast" (SPRAY)-type AI application as a case study to illustrate the potential of AI in EMR data management. Results: The research identifies the scarcity of scientific research on the transformation of EMR data in Chinese hospitals and proposes a medical metadata governance framework as a solution. This framework is designed to achieve scientific governance of clinical data by integrating metadata management and master data management, grounded in clinical practices, medical disciplines, and scientific exploration. Furthermore, it incorporates an information privacy security architecture to ensure data protection. Conclusion: The proposed medical metadata governance framework, supported by AI technology, offers a structured approach to managing and transforming EMR data into valuable scientific research outcomes. This framework provides guidance for the identification, cleaning, mining, and deep application of EMR data, thereby addressing the bottlenecks currently faced in the healthcare scenario and paving the way for more effective clinical research and data-driven decision-making.

A Review on Real Time Patient Health Monitoring System Using Cloud Technology

Abstract: The rising demand for advanced healthcare solutions has driven the development of innovative technologies aimed at improving patient care. This project introduces a Real-Time Patient Health Monitoring System, which utilizes cloud technology to its full potential. By incorporating cloud infrastructure, data science, and real-time analytics, the system offers continuous monitoring and analysis of crucial health metrics such as SpO2, temperature, and pulse rate. Designed with wearability in mind, the system's architecture ensures uninterrupted monitoring and instant feedback for both patients and healthcare professionals. Cloud technology enables seamless data storage, retrieval, and real-time analysis, thereby providing comprehensive insights into a patient's health status. The project's scalability guarantees its suitability for various healthcare environments, rendering it a versatile tool for hospitals, clinics, and remote healthcare scenarios.

Advance Care Planning in the Context of Dementia: Defining Concordance.

Individuals with dementia may require a surrogate decision maker as their disease progresses. To prepare for this potential role, dementia care partners need to develop a thorough understanding of their care recipient's end-of-life values and preferences, or care dyad advance care planning (ACP) concordance. As part of our pilot study implementing the LEAD intervention with dementia care dyads, we conducted a multimethod investigation to define care dyad ACP concordance. We conducted a scoping review of peer-reviewed studies published after 1991 in English focusing on care dyad ACP concordance in dementia care and included 34 articles. Concurrently, we used descriptive qualitative analysis to analyze 7 dyadic ACP conversations from a pilot study about dyadic dementia ACP. The scoping review demonstrated (a) no definition of care dyad ACP concordance was reported; (b) surrogate accuracy in end-of-life decisions varies widely; and (c) best practices for ACP in dementia may aid in achieving ACP concordance, but do not prioritize it as an outcome. Qualitative analysis identified 7 elements for achieving concordance: Respect/Regard; use of Clarifying Processes; Conveying Health Care Scenarios; Affirmation of Understanding; Recognizing Uncertainty; Expression of Positive Emotions; and Trust. Care dyad ACP concordance occurs when care recipients and care partners both understand a care recipient's end-of-life values, understand the end-of-life preferences informed by those values, and the care partner expresses a willingness to accomplish the care recipient's wishes to the best of their ability. ACP concordance can be further operationalized for research and clinical care.

Critical incidents and post-traumatic stress symptoms among experienced registered nurses during the COVID-19 pandemic: A cross-sectional study

BackgroundRegistered nurses working on the frontline during the COVID-19 pandemic encountered significant challenges, including exposure to critical incidents. Critical incidents refer to sudden unexpected clinical events that surpass an individual's ability to cope, leading to considerable psychological distress, which could potentially result in the development of post-traumatic stress disorder symptoms. Research has shown a high prevalence of post-traumatic stress disorder symptoms among healthcare workers, particularly those in close contact with COVID-19 patients. ObjectiveTo assess the levels of post-traumatic stress symptoms among registered nurses in relation to exposure to working conditions during the COVID-19 pandemic, such as how much their work was affected by the pandemic, re-deployment, working hours hindering sufficient recovery between shifts and critical incidents. DesignCross sectional study. Setting(s)The registered nurses working in multiple health care services covering all 21 geographic regions in Sweden. ParticipantsA total of 1,923 registered nurses, who are part of a Swedish national cohort and have been followed since their nursing education, were invited to participate in a survey in late September 2021 (15 to 19 years post graduation). MethodsThe data were analyzed using descriptive statistics, unpaired t-tests, and one-way analysis of variance. Cohen's d was employed to quantify differences in mean levels between subgroups. ResultsThe response rate were 56.5 %. Over 50 % of experienced registered nurses reported significant disruptions to their work environments. In total, 85 % of registered nurses were exposed to at least one critical incident in their work during the pandemic, with 60 % facing organisational changes and nearly 50 % experiencing emotionally distressing situations. The exposure to work situations involving critical incidents consistently demonstrated strong associations with higher levels of post-traumatic stress disorder symptoms compared to those not exposed, with effect sizes ranging from moderate to high. ConclusionsThis study underscores the profound impact that working conditions, such as redeployment and exposure to critical incidents, have on the mental health of registered nurses. We offer valuable insights into registered nurses’ pandemic-related challenges, highlighting the need for support and interventions to prevent and manage critical incidents, ultimately promoting their well-being. We also highlight the significance of thorough workforce readiness planning for future pandemics and other challenging health care scenarios, such as staff shortage.

Role of Artificial Intelligence in Health care System

The Indian healthcare scenario presents a spectrum of contrasting landscapes. In all, 400 million individuals had no access to any form of basic healthcare while two billion people do not have access to required medications. Therefore, more than one-fourth of the world population has unmet health needs. This leaves the global community with the challenge of how to support a significant number of the world’s populace still lacking in access to basic healthcare facilities. AI has been a disruptive healthcare innovation. With its sophisticated algorithms and several applications, AI has assisted doctors and medical professionals in the domains of health information systems, geocoding health data, epidemic and syndromic surveillance, predictive modelling and decision support, and medical imaging. Artificial intelligence (AI) rapidly dominates the health service system. It removes the manual health system into automatic, in which humans conduct the routine works/tasks in medical practice to the management of patients and medical resources. Drug development is a famously costly procedure. Machine Learning can improve the efficiency of many of the analytical techniques used in drug development. In terms of practical implications, this paper aims to create a fruitful discussion with healthcare professionals and administrative staff and the role of artificial intelligence in healthcare system. Furthermore, this investigation offers a broad comprehension of bibliometric variables of AI techniques in healthcare.

Scaling up and implementing the digital Survivorship Passport tool in routine clinical care – The European multidisciplinary PanCareSurPass project

BackgroundChildhood cancer survivors (CCS), of whom there are about 500,000 living in Europe, are at an increased risk of developing health problems [1–6] and require lifelong Survivorship Care. There are information and knowledge gaps among CCS and healthcare providers (HCPs) about requirements for Survivorship Care [7–9] that can be addressed by the Survivorship Passport (SurPass), a digital tool providing CCS and HCPs with a comprehensive summary of past treatment and tailored recommendations for Survivorship Care. The potential of the SurPass to improve person-centred Survivorship Care has been demonstrated previously [10,11]. MethodsThe EU-funded PanCareSurPass project will develop an updated version (v2.0) of the SurPass allowing for semi-automated data entry and implement it in six European countries (Austria, Belgium, Germany, Italy, Lithuania and Spain), representative of three infrastructure healthcare scenarios typically found in Europe. The implementation study will investigate the impact on person-centred care, as well as costs and processes of scaling up the SurPass. Interoperability between electronic health record systems and SurPass v2.0 will be addressed using the Health Level Seven (HL7) International interoperability standards. ResultsPanCareSurPass will deliver an interoperable digital SurPass with comprehensive evidence on person-centred outcomes, technical feasibility and health economics impacts. An Implementation Toolkit will be developed and freely shared to promote and support the future implementation of SurPass across Europe. ConclusionsPanCareSurPass is a novel European collaboration that will improve person-centred Survivorship Care for CCS across Europe through a robust assessment of the implementation of SurPass v2.0 in different healthcare settings.