A scoping review of cloud computing in healthcare

Patient-centered radiology: a roadmap for outpatient imaging

Creating a patient-centered experience is becoming increasingly important for radiology departments around the world. The goal of patient-centered radiology is to ensure that radiology services are sensitive to patients' needs and desires. This article provides a framework for addressing the patient's experience by dividing their imaging journey into three distinct time periods: pre-exam, day of exam, and post-exam. Each time period has aspects that can contribute to patient anxiety. Although there are components of the patient journey that are common in all regions of the world, there are also unique features that vary by location. This paper highlights innovative solutions from different parts of the world that have been introduced in each of these time periods to create a more patient-centered experience. CLINICAL RELEVANCE STATEMENT: Adopting innovative solutions that help patients understand their imaging journey and decrease their anxiety about undergoing an imaging examination are important steps in creating a patient centered imaging experience. KEY POINTS: • Patients often experience anxiety during their imaging journey and decreasing this anxiety is an important component of patient centered imaging. • The patient imaging journey can be divided into three distinct time periods: pre-exam, day of exam, and post-exam. • Although components of the imaging journey are common, there are local differences in different regions of the world that need to be considered when constructing a patient centered experience.

Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities

The recent SARS-CoV-2 pandemic underscored the effectiveness and rapid deployment of digital public health interventions, notably the digital proximity tracing apps, leveraging Bluetooth capabilities to trace and notify users about potential infection exposures. Backed by renowned organizations such as the World Health Organization and the European Union, digital proximity tracings showcased the promise of digital public health. As the world pivots from pandemic responses, it becomes imperative to address noncommunicable diseases (NCDs) that account for a vast majority of health care expenses and premature disability-adjusted life years lost. The narrative of digital transformation in the realm of NCD public health is distinct from infectious diseases. Public health, with its multifaceted approach from disciplines such as medicine, epidemiology, and psychology, focuses on promoting healthy living and choices through functions categorized as "Assessment," "Policy Development," "Resource Allocation," "Assurance," and "Access." The power of artificial intelligence (AI) in this digital transformation is noteworthy. AI can automate repetitive tasks, facilitating health care providers to prioritize personal interactions, especially those that cannot be digitalized like emotional support. Moreover, AI presents tools for individuals to be proactive in their health management. However, the human touch remains irreplaceable; AI serves as a companion guiding through the health care landscape. Digital evolution, while revolutionary, poses its own set of challenges. Issues of equity and access are at the forefront. Vulnerable populations, whether due to economic constraints, geographical barriers, or digital illiteracy, face the threat of being marginalized further. This transformation mandates an inclusive strategy, focusing on not amplifying existing health disparities but eliminating them. Population-level digital interventions in NCD prevention demand societal agreement. Policies, like smoking bans or sugar taxes, though effective, might affect those not directly benefiting. Hence, all involved parties, from policy makers to the public, should have a balanced perspective on the advantages, risks, and expenses of these digital shifts. For a successful digital shift in public health, especially concerning NCDs, AI's potential to enhance efficiency, effectiveness, user experience, and equity-the "quadruple aim"-is undeniable. However, it is vital that AI-driven initiatives in public health domains remain purposeful, offering improvements without compromising other objectives. The broader success of digital public health hinges on transparent benchmarks and criteria, ensuring maximum benefits without sidelining minorities or vulnerable groups. Especially in population-centric decisions, like resource allocation, AI's ability to avoid bias is paramount. Therefore, the continuous involvement of stakeholders, including patients and minority groups, remains pivotal in the progression of AI-integrated digital public health.

Implementation of cloud computing in the German healthcare system

With the advent of artificial intelligence and Big Data - projects, the necessity for a transition from analog medicine to modern-day solutions such as cloud computing becomes unavoidable. Even though this need is now common knowledge, the process is not always easy to start. Legislative changes, for example at the level of the European Union, are helping the respective healthcare systems to take the necessary steps. This article provides an overview of how a German university hospital is dealing with European data protection laws on the integration of cloud computing into everyday clinical practice. By describing our model approach, we aim to identify opportunities and possible pitfalls to sustainably influence digitization in Germany.

Construction and evaluation of China older-adult care service smart supply chain system

Introduction

As the aging of the population continues to deepen, the pressure on social pensions is gradually increasing, and the issue of assistance has become a problem that must be solved. With the development of science and technology, people's living standard is constantly improving. The older-adult care services expected by the older-adult are wider than meeting the basic daily needs of individuals. The current industry should also consider combining modern science and technology with the older-adult care service industry to serve older-adult better and enable older-adult care service providers to move towards the service needs that make people happier and healthier. This research is about constructing and evaluating China's older-adult care services smart supply chain.

Methods

Based on the research results of previous scholars, this paper divides the Sun construction of the smart supply chain of China endowment service into four aspects: policy aspect, economic aspect, social aspect, and technical aspect; the four significant elements are divided into the first-level indicators, and 16 second-level indicators are divided under the first-level indicators. The importance and satisfaction of each evaluation index were obtained by distributing questionnaires to the managers who study the supply chain and the employees who are related to the old-age service.

Results and discussion

After the reliability analysis, the importance-performance analysis (IPA) quadrant analysis chart of the evaluation index was constructed using importance-performance analysis. The index of creating a smart supply chain system for China's old-age service is given priority, the supply chain system of China's old-age care service is further improved, and the social security of China's old-age service is enlarged.

Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study

BACKGROUND

This study aimed to evaluate the accuracy and clinical usability of implant system classification using automated machine learning on a Google Cloud platform.

METHODS

Four dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score.

RESULTS

The accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix.

CONCLUSION

Deep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.

Organizational and behavioral attributes' roles in adopting cloud services: An empirical study in the healthcare industry

The need for cloud services has been raised globally to provide a platform for healthcare providers to efficiently manage their citizens' health records and thus provide treatment remotely. In Iraq, the healthcare records of public hospitals are increasing progressively with poor digital management. While recent works indicate cloud computing as a platform for all sectors globally, a lack of empirical evidence demands a comprehensive investigation to identify the significant factors that influence the utilization of cloud health computing. Here we provide a cost-effective, modular, and computationally efficient model of utilizing cloud computing based on the organization theory and the theory of reasoned action perspectives. A total of 105 key informant data were further analyzed. The partial least square structural equation modeling was used for data analysis to explore the effect of organizational structure variables on healthcare information technicians' behaviors to utilize cloud services. Empirical results revealed that Internet networks, software modularity, hardware modularity, and training availability significantly influence information technicians' behavioral control and confirmation. Furthermore, these factors positively impacted their utilization of cloud systems, while behavioral control had no significant effect. The importance-performance map analysis further confirms that these factors exhibit high importance in shaping user utilization. Our findings can provide a comprehensive and unified guide to policymakers in the healthcare industry by focusing on the significant factors in organizational and behavioral contexts to engage health information technicians in the development and implementation phases.

Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field

OBJECTIVES

To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions.

DATA SOURCES

Peer-reviewed scientific publications and expert opinion.

CONCLUSION

The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services.

IMPLICATIONS FOR NURSING PRACTICE

As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.

Cross-disciplinary training of nursing informatics and nursing engineering at the postgraduate level: A feasibility analysis based on the qualitative method

BACKGROUND

The trend of interdisciplinary education is becoming increasingly prominent. Nursing informatics and nursing engineering have received much attention and development at different levels of nursing education in many Western countries. Meanwhile, in China, the cultivation of interdisciplinary nursing talents has either not been initiated or has only entered an initial stage.

OBJECTIVES

This study aims to explore experts' opinions from nursing, informatics and engineering on the feasibility of interdisciplinary education at graduate master's level in nursing through interview.

DESIGN

This was a descriptive qualitative study.

SETTING

Interviews were conducted online or face to face.

PARTICIPANTS

Experts in the fields of nursing, informatics, and engineering who met the study qualifications were enrolled.

METHODS

This study used a purposive sampling method and collected data via semi-structured interviews. A total of 14 experts were involved based on data saturation, which eight were interviewed face-to-face and six were interviewed online. A content analysis method was used to summarize and analyze the attitudes, opinions, and suggestions of experts.

RESULTS

A total of 579 min of interviews with 66,387 words were transcribed and analyzed after 30-50 min time range of each interview, and 4 themes were established. A consensus was obtained on the necessity and importance of interdisciplinary education. Policy guidance, financial support, and mutual recognition were the prerequisites for the cultivation. Moreover, feasibility of interdisciplinary education depends on multi-cooperation, including society, university, and hospital. Finally, a linkage mechanism among relevant stakeholders was required.

CONCLUSION

The necessity and feasibility of such integrated training was concluded. Learning from the experience of relevant countries, China should launch an interdisciplinary training model suitable for its national condition.

The Development and Concurrent Validity of a Multi-Sensor-Based Frailty Toolkit for In-Home Frailty Assessment

Early identification of frailty is crucial to prevent or reverse its progression but faces challenges due to frailty’s insidious onset. Monitoring behavioral changes in real life may offer opportunities for the early identification of frailty before clinical visits. This study presented a sensor-based system that used heterogeneous sensors and cloud technologies to monitor behavioral and physical signs of frailty from home settings. We aimed to validate the concurrent validity of the sensor measurements. The sensor system consisted of multiple types of ambient sensors, a smart speaker, and a smart weight scale. The selection of these sensors was based on behavioral and physical signs associated with frailty. Older adults’ perspectives were also included in the system design. The sensor system prototype was tested in a simulated home lab environment with nine young, healthy participants. Cohen’s Kappa and Bland−Altman Plot were used to evaluate the agreements between the sensor and ground truth measurements. Excellent concurrent validity was achieved for all sensors except for the smart weight scale. The bivariate correlation between the smart and traditional weight scales showed a strong, positive correlation between the two measurements (r = 0.942, n = 24, p < 0.001). Overall, this work showed that the Frailty Toolkit (FT) is reliable for monitoring physical and behavioral signs of frailty in home settings.

A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing

A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19.

Resolution-based distillation for efficient histology image classification

Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive strain on all parts of the computing pipeline. This paper proposes a novel deep learning-based methodology for improving the computational efficiency of histology image classification. The proposed approach is robust when used with images that have reduced input resolution, and it can be trained effectively with limited labeled data. Moreover, our approach operates at either the tissue- or slide-level, removing the need for laborious patch-level labeling. Our method uses knowledge distillation to transfer knowledge from a teacher model pre-trained at high resolution to a student model trained on the same images at a considerably lower resolution. Also, to address the lack of large-scale labeled histology image datasets, we perform the knowledge distillation in a self-supervised fashion. We evaluate our approach on three distinct histology image datasets associated with celiac disease, lung adenocarcinoma, and renal cell carcinoma. Our results on these datasets demonstrate that a combination of knowledge distillation and self-supervision allows the student model to approach and, in some cases, surpass the teacher model's classification accuracy while being much more computationally efficient. Additionally, we observe an increase in student classification performance as the size of the unlabeled dataset increases, indicating that there is potential for this method to scale further with additional unlabeled data. Our model outperforms the high-resolution teacher model for celiac disease in accuracy, F1-score, precision, and recall while requiring 4 times fewer computations. For lung adenocarcinoma, our results at 1.25× magnification are within 1.5% of the results for the teacher model at 10× magnification, with a reduction in computational cost by a factor of 64. Our model on renal cell carcinoma at 1.25× magnification performs within 1% of the teacher model at 5× magnification while requiring 16 times fewer computations. Furthermore, our celiac disease outcomes benefit from additional performance scaling with the use of more unlabeled data. In the case of 0.625× magnification, using unlabeled data improves accuracy by 4% over the tissue-level baseline. Therefore, our approach can improve the feasibility of deep learning solutions for digital pathology on standard computational hardware and infrastructures.

Security challenges and solutions using healthcare cloud computing

Cloud computing is among the most beneficial solutions to digital problems. Security is one of the focal issues in cloud computing technology, and this study aims at investigating security issues of cloud computing and their probable solutions. A systematic review was performed using Scopus, Pubmed, Science Direct, and Web of Science databases. Once the title and abstract were evaluated, the quality of studies was assessed in order to choose the most relevant according to exclusion and inclusion criteria. Then, the full texts of studies selected were read thoroughly to extract the necessary results. According to the review, data security, availability, and integrity, as well as information confidentiality and network security, were the major challenges in cloud security. Further, data encryption, authentication, and classification, besides application programming interfaces (API), were security solutions to cloud infrastructure. Data encryption could be applied to store and retrieve data from the cloud in order to provide secure communication. Besides, several central challenges, which make the cloud security engineering process problematic, have been considered in this study.

Security and Privacy in Cloud-Based E-Health System

Cloud based healthcare computing have changed the face of healthcare in many ways. The main advantages of cloud computing in healthcare are scalability of the required service and the provision to upscale or downsize the data storge, collaborating Artificial Intelligence (AI) and machine learning. The current paper examined various research studies to explore the utilization of intelligent techniques in health systems and mainly focused into the security and privacy issues in the current technologies. Despite the various benefits related to cloud-computing applications for healthcare, there are different types of management, technology handling, security measures, and legal issues to be considered and addressed. The key focus of this paper is to address the increased demand for cloud computing and its definition, technologies widely used in healthcare, their problems and possibilities, and the way protection mechanisms are organized and prepared when the company chooses to implement the latest evolving service model. In this paper, we focused on a thorough review of current and existing literature on different approaches and mechanisms used in e-Health to deal with security and privacy issues. Some of these approaches have strengths and weaknesses. After selecting original articles, the literature review was carried out, and we identified several models adopted in their solutions. We arrived at the reviewed articles after comparing the models used.

A telediagnosis assistance system for multiple-lead electrocardiography

The diffusion of telemedicine opens-up a new perspective for the development of technologies furthered by Biomedical Engineering. In particular, herein we deal with those related to telediagnosis through multiple-lead electrocardiographic signals. This study focuses on the proof-of-concept of an internet-based telemedicine system as a use case that attests to the feasibility for the development, within the university environment, of techniques for remote processing of biomedical signals for adjustable detection of myocardial ischemia episodes. At each signal lead, QRS complexes are detected and delimited with the J-point marking. The same procedure to detect the complex is used to identify the respective T wave, then the area over the ST segment is applied to detect ischemia-related elevations. The entire system is designed on web-based telemedicine services using multiuser, remote access technologies, and database. The measurements for sensitivity and precision had their respective averages calculated at 11.79 and 24.21% for the leads of lower noise. The evaluations regarding the aspects of user friendliness and the usefulness of the application, resulted in 88.57 and 89.28% of broad or total acceptance, respectively. They are robust enough to enable scalability and can be offered by cloud computing, besides enabling the development of new biomedical signal processing techniques within the concept of distance services, using a modular architecture with collaborative bias.

National health information technology priorities for research: A policy and development agenda

The growth of digitized health data presents exciting opportunities to leverage the health information technology (IT) infrastructure for advancing biomedical and health services research. However, challenges impede use of those resources effectively and at scale to improve outcomes. The Office of the National Coordinator for Health Information Technology (ONC) led a collaborative effort to identify challenges, priorities, and actions to leverage health IT and electronic health data for research. Specifically, ONC led a review of relevant literature and programs, key informant interviews, and a stakeholder workshop to identify electronic health data and health IT infrastructure gaps. This effort resulted in the National Health IT Priorities for Research: A Policy and Development Agenda, which articulates an optimized health information ecosystem for scientific discovery. This article outlines 9 priorities and recommended actions to be implemented in collaboration with the research and informatics communities for realizing this vision.

A survey of multiscale modeling: Foundations, historical milestones, current status, and future prospects

Research problems in the domains of physical, engineering, biological sciences often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.

COVID-19 Reducing the Risks: Telemedicine is the New Norm for Surgical Consultations and Communications

INTRODUCTION

COVID-19, a worldwide pandemic, has enforced a national lockdown in the UK which produced a paradigm shift about the way medical practitioners would perform consultations and communication with their patients. Senior authors realised that in lockdown there was only one option to see a patient: virtual consultation via telecommunication technologies. This paper will discuss the current benefits and considerations of Telemedicine, particularly in plastic surgery, to decipher the next route of action to further validate its use for future implementation.

METHOD

A detailed literature review was carried out comparing papers from 1992 to 2020. A survey of 122 consultant plastic surgeons found an encouraging result as 70% positively embraced the suggestion of Telemedicine in their current practice.

DISCUSSION

Telemedicine produced equal or improved patient satisfaction. Its utilisation reduced cost for patient, clinic and consultant. With accessibility to a large percentage of the population, Telemedicine enables infection control and adherence to social distancing during COVID-19. Considerations include dependability on internet access, legal aspects, cyber security and General Data Protection Regulation (GDPR), the inability to perform palpation or physical inspection and psychological impacts on the patient.

CONCLUSION

In modern times, Telemedicine has become more accessible and COVID-19 has made it more applicable than ever before. More in-depth research is needed for validation of this technique within plastic surgery. While maintaining quality of care and a vital role in social distancing, there is a strong need for standardisation of Telemedicine processes, platforms, encryption and data storage.

LEVEL OF EVIDENCE V

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Standard electronic health record (EHR) framework for Indian healthcare system

Digitization of health records in public health facility and its instant availability in the form of electronic records anywhere any time health service is yet to be implemented in developing nations like India and other countries. In India, patient care is mainly delivered through 3 levels namely Primary/Community Healthcare Centre (PHC/CHC), Secondary healthcare centre (District Hospital), and Tertiary Healthcare Centre (National level). The healthcare facilities face many challenges in collecting, processing, and storing these data and managing it without compromising security and privacy. Presently, some of the secondary and tertiary care facilities have started implementing healthcare IT application in terms of Hospital Information System, Hospital Management Information System, Electronic Medical Records (EMR) etc. to manage the patient data in electronic format. However, these systems are developed by different vendors by using different programming languages and databases. This approach makes the system unique but the patient details remains in the same hospital and cannot be shared with other hospitals when patient moves from one hospital to other for advanced or specialized treatment. This is because the data is not interoperable and semantic. In the proposed work, a standard secure Electronic Health Record(EHR) framework is developed using standard medical terminology and coding standards. Implementation of EHR framework for Indian health system will improve the work-flow of health services to the population. EHR at all levels of healthcare system enable efficient and continuous care to the patient.

Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models

Background

De-identifying personal information is critical when using personal health data for secondary research. The Observational Medical Outcomes Partnership Common Data Model (CDM), defined by the nonprofit organization Observational Health Data Sciences and Informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy.

Objective

This study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, l-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database.

Methods

The CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, l-diversity, and t-closeness privacy models.

Results

The CDM database, which was constructed according to the rules established by Observational Health Data Sciences and Informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one “highest risk” value of 100%, suitable anonymizing techniques are required; moreover, the CDM database preserves the “source values” (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, l-diversity, and t-closeness privacy models but also the overall possibility of re-identification.

Conclusions

Our proposed de-identification strategy effectively enhanced the privacy of the CDM database, thereby encouraging clinical research involving multiple centers.

How to Enhance Digital Support for Cross-Organisational Health Care Teams? A User-Based Explorative Study

Health care service provision of individualised treatment to an ageing population prone to chronic conditions and multimorbidities is threatened. There is a need for digitally supported care, that is, (1) person-centred, (2) integrated, and (3) proactive. The research project 3P, Patients and Professionals in Productive Teams, aimed to validate and verify the prerequisites for health care systems run with patient-centred service models. This paper presents an explorative study of the digital support of a cross-organisational health care team in Norway, providing services to elderly frail people with multimorbidities in hospital discharge transition. Qualitative research methods were employed, with interviews and observations to map and evaluate the information flow and the digital support of collaborative work across organisations. The evaluation showed a lacking interoperability between the digital systems and a limited support for cross-organisational teamwork, causing raised manual efforts to maintain the information flow. Tools for coordination and planning across organisations were lacking. To enhance the situation, principles for a cloud-based health portal are proposed with a shared workspace, teamwork functionality for cross-organisational health care teams, and automatic back-end synchronisation of stored information. The main implications of this paper lie in the proposed principles which are transferable to a multitude of clinical contexts, where ad-hoc based access to shared medical information is of importance for decision-making and life-saving treatment.

Impact of industry 4.0 to create advancements in orthopaedics

Scientists and health professional are focusing on improving the medical sciences for the betterment of patients. The fourth industrial revolution, which is commonly known as Industry 4.0, is a significant advancement in the field of engineering. Industry 4.0 is opening a new opportunity for digital manufacturing with greater flexibility and operational performance. This development is also going to have a positive impact in the field of orthopaedics. The purpose of this paper is to present various advancements in orthopaedics by the implementation of Industry 4.0. To undertake this study, we have studied the available literature extensively on Industry 4.0, technologies of Industry 4.0 and their role in orthopaedics. Paper briefly explains about Industry 4.0, identifies and discusses the major technologies of Industry 4.0, which will support development in orthopaedics. Finally, from the available literature, the paper identifies twelve significant advancements of Industry 4.0 in orthopaedics. Industry 4.0 uses various types of digital manufacturing and information technologies to create orthopaedics implants, patient-specific tools, devices and innovative way of treatment. This revolution is to be useful to perform better spinal surgery, knee and hip replacement, and invasive surgeries.

A Sociotechnical Framework for Safety-Related Electronic Health Record Research Reporting: The SAFER Reporting Framework

Electronic health record (EHR)-based interventions to improve patient safety are complex and sensitive to who, what, where, why, when, and how they are delivered. Success or failure depends not only on the characteristics and behaviors of individuals who are targeted by an intervention, but also on the technical characteristics of the intervention and the culture and environment of the health system that implements it. Current reporting guidelines do not capture the complexity of sociotechnical factors (technical and nontechnical factors, such as workflow and organizational issues) that confound or influence these interventions. This article proposes a methodological reporting framework for EHR interventions targeting patient safety and builds on an 8-dimension sociotechnical model previously developed by the authors for design, development, implementation, use, and evaluation of health information technology. The Safety-related EHR Research (SAFER) Reporting Framework enables reporting of patient safety-focused EHR-based interventions while accounting for the multifaceted, dynamic sociotechnical context affecting intervention implementation, effectiveness, and generalizability. As an example, an EHR-based intervention to improve communication and timely follow-up of subcritical abnormal test results to operationalize the framework is presented. For each dimension, reporting should include what sociotechnical changes were made to implement an EHR-related intervention to improve patient safety, why the intervention did or did not lead to safety improvements, and how this intervention can be applied or exported to other health care organizations. A foundational list of research and reporting recommendations to address implementation, effectiveness, and generalizability of EHR-based interventions needed to effectively reduce preventable patient harm is provided. The SAFER Reporting Framework is not meant to replace previous research reporting guidelines, but rather provides a sociotechnical adjunct that complements their use.

Looking at Fog Computing for E-Health through the Lens of Deployment Challenges and Applications

Fog computing is a distributed infrastructure where specific resources are managed at the network border using cloud computing principles and technologies. In contrast to traditional cloud computing, fog computing supports latency-sensitive applications with less energy consumption and a reduced amount of data traffic. A fog device is placed at the network border, allowing data collection and processing to be physically close to their end-users. This characteristic is essential for applications that can benefit from improved latency and response time. In particular, in the e-Health field, many solutions rely on real-time data to monitor environments, patients, and/or medical staff, aiming at improving processes and safety. Therefore, fog computing can play an important role in such environments, providing a low latency infrastructure. The main goal of the current research is to present fog computing strategies focused on electronic-Health (e-Health) applications. To the best of our knowledge, this article is the first to propose a review in the scope of applications and challenges of e-Health fog computing. We introduce some of the available e-Health solutions in the literature that focus on latency, security, privacy, energy efficiency, and resource management techniques. Additionally, we discuss communication protocols and technologies, detailing both in an architectural overview from the edge devices up to the cloud. Differently from traditional cloud computing, the fog concept demonstrates better performance in terms of time-sensitive requirements and network data traffic. Finally, based on the evaluation of the current technologies for e-Health, open research issues and challenges are identified, and further research directions are proposed.

Laniakea: an open solution to provide Galaxy "on-demand" instances over heterogeneous cloud infrastructures

BACKGROUND

While the popular workflow manager Galaxy is currently made available through several publicly accessible servers, there are scenarios where users can be better served by full administrative control over a private Galaxy instance, including, but not limited to, concerns about data privacy, customisation needs, prioritisation of particular job types, tools development, and training activities. In such cases, a cloud-based Galaxy virtual instance represents an alternative that equips the user with complete control over the Galaxy instance itself without the burden of the hardware and software infrastructure involved in running and maintaining a Galaxy server.

RESULTS

We present Laniakea, a complete software solution to set up a "Galaxy on-demand" platform as a service. Building on the INDIGO-DataCloud software stack, Laniakea can be deployed over common cloud architectures usually supported both by public and private e-infrastructures. The user interacts with a Laniakea-based service through a simple front-end that allows a general setup of a Galaxy instance, and then Laniakea takes care of the automatic deployment of the virtual hardware and the software components. At the end of the process, the user gains access with full administrative privileges to a private, production-grade, fully customisable, Galaxy virtual instance and to the underlying virtual machine (VM). Laniakea features deployment of single-server or cluster-backed Galaxy instances, sharing of reference data across multiple instances, data volume encryption, and support for VM image-based, Docker-based, and Ansible recipe-based Galaxy deployments. A Laniakea-based Galaxy on-demand service, named Laniakea@ReCaS, is currently hosted at the ELIXIR-IT ReCaS cloud facility.

CONCLUSIONS

Laniakea offers to scientific e-infrastructures a complete and easy-to-use software solution to provide a Galaxy on-demand service to their users. Laniakea-based cloud services will help in making Galaxy more accessible to a broader user base by removing most of the burdens involved in deploying and running a Galaxy service. In turn, this will facilitate the adoption of Galaxy in scenarios where classic public instances do not represent an optimal solution. Finally, the implementation of Laniakea can be easily adapted and expanded to support different services and platforms beyond Galaxy.

Impact of the 12 January 2010 earthquake on HIV case reporting in Haiti

OBJECTIVE

To evaluate the impact of the 12 January 2010 earthquake on HIV cases from Haiti's national HIV surveillance system and assess the characteristics of people living with HIV 1-year before and after the earthquake.

DESIGN

An interrupted time-series design and cross-sectional analysis.

METHODS

We used autoregressive integrated moving average structures to model abrupt changes to the monthly, incident HIV case counts from HIV care clinics as reported to the Haitian Active Longitudinal Tracking of HIV System (French acronym SALVH) by clinical networks (n = 3) and earthquake instrumental intensity zones (n = 4). Preearthquake and postearthquake differences in patient-level characteristics including clinical values were examined using the χ test, t tests, Wilcoxon rank-sum test.

RESULTS

In the month immediately following the earthquake, all three clinical networks experienced statistically significant declines in cases reported: iSanté (-31.4%), Groupe Haïtien d'Etude du Sarcome de Kaposi et des Infections Opportunistes (-29.9%) and Zamni Lasante (-32.2%). Zone 8 (the most severe) was the only area with a statistically significant decline (-45.5%). Of the three clinical networks, only iSanté returned to preearthquake reporting levels by the end of our study period. Patient-level characteristics did not change dramatically after the earthquake.

CONCLUSION

Despite case reporting declines, especially in clinics near the earthquake epicenter, SALVH remained intact with less impact than expected. This national system is a critical component of Haiti's strategic health information system initiative and plays a central role to HIV monitoring and evaluation efforts.

Biosensors for Personal Mobile Health: A System Architecture Perspective

Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor-based sensors are enabling measurement of vital signs, and microfluidic-based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it will become critical for researchers to design biosensors - with appropriate functionalities and specifications - to work seamlessly with accompanying connected hardware and software. This article describes recent research in biosensors, as well as current mobile health devices in use, as classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. We also discuss the path forward for integrating biosensors into smartphone-based mobile health devices.

Usage of cloud storage facilities by medical students in a low-middle income country, Sri Lanka: a cross sectional study

Background

Cloud storage facilities (CSF) has become popular among the internet users. There is limited data on CSF usage among university students in low middle-income countries including Sri Lanka. In this study we present the CSF usage among medical students at the Faculty of Medicine, University of Kelaniya.

Methods

We undertook a cross sectional study at the Faculty of Medicine, University of Kelaniya, Sri Lanka. Stratified random sampling was used to recruit students representing all the batches. A self-administrated questionnaire was given.

Results

Of 261 (90.9%) respondents, 181 (69.3%) were females. CSF awareness was 56.5% (95%CI: 50.3–62.6%) and CSF usage was 50.8% (95%CI: 44.4–57.2%). Awareness was higher in males (P = 0.003) and was low in senior students. Of CSF aware students, 85% knew about Google Drive and 70.6% used it. 73.6 and 42.1% knew about Dropbox and OneDrive. 50.0 and 22.0% used them respectively. There was no association between CSF awareness and pre-university entrance or undergraduate examination performance. Inadequate knowledge, time, accessibility, security and privacy concerns limited CSF usage. 69.8% indicated that they would like to undergo training on CSF as an effective tool for education.

Conclusion

CSF awareness and usage among the students were 56.5 and 50.8%. Google drive is the most popular CSF. Lack of knowledge, accessibility, concerns on security and privacy limited CSF usage among students. Majority were interested to undergo training on CSF and undergraduate Information Communication Technology (ICT) curricula should introduce CSF as effective educational tools.

Towards a maturity model for health-care cloud security (M2HCS)

Purpose

The purpose of this paper is to propose a novel maturity model for health-care cloud security (M2HCS), which focuses on assessing cyber security in cloud-based health-care environments by incorporating the sub-domains of health-care cyber security practices and introducing health-care-specific cyber security metrics. This study aims to expand the domain of health-care cyber security maturity model by including cloud-specific aspects than is usually seen in the literature.

Design/methodology/approach

The intended use of the proposed model was demonstrated using the evaluation method – “construct validity test” as the paper’s aim was to assess the final model and the output of the valuation. The study involved a literature-based case study of a national health-care foundation trust with an overall view because the model is assessed for the entire organisation. The data were complemented by examination of hospitals’ cyber security internal processes through web-accessible documents, and identified relevant literature.

Findings

The paper provides awareness about how organisational-related challenges have been identified as a main inhibiting factor for the adoption of cloud computing in health care. Regardless of the remunerations of cloud computing, its security maturity and levels of adoption varies, especially in health care. Maturity models provide a structure towards improving an organisation’s capabilities. It suggests that although several cyber security maturity models and standards resolving specific threats exist, there is a lack of maturity models for cloud-based health-care security.

Research limitations/implications

Due to the selected research method, the research results may lack generalizability. Therefore, future research studies can investigate the propositions further. Another is that the current thresholds were determined empirically, although it worked for the case study assessment. However, to establish more realistic threshold levels, there is a need for more validation of the model using more case studies.

Practical implications

The paper includes maturity model for the assessment management and improvement of the security posture of a health-care organisation actively using cloud. For executives, it provides a detailed security assessment of the eHealth cloud to aid in decision making. For security experts, its quantitative metrics support proactive and reactive processes.

Originality/value

The paper fulfils a recognised requirement for security maturity model focussed on health-care cloud. It could be extended to resolve evolving cyber settings.

Evaluating the factors that influence cloud technology adoption-comparative case analysis of health and non-health sectors: A systematic review

Cloud technology has brought great benefits to the health industry, including enabling improvement in the quality of services. The objective of this review study is to investigate the reported factors affecting the adoption of cloud in the health sector by comparing studies in the health and non-health sectors. This article is a systematized review of studies conducted in 2018. From 541 articles, 47 final articles were selected and classified into two categories: health and non-health studies; conclusions were drawn from the two sectors by comparing their effective factors. Based on the results of this review, the factors were categorized as technological, organizational, environmental, and individual. The results of this review study could be a beneficial guide to the health empirical research on cloud adoption. Individual domains have not been examined in health sector studies. Since the process of adoption of new technologies in organizations is time-consuming, due to the lack of managerial knowledge about the efficient factors, recognition of these factors by decision-makers while planning for cloud adoption becomes of great importance. The findings of this review study aim to help health decision-makers by increasing their awareness of the cloud and of the factors that impact decisions at both the organizational and individual levels.

Transformation of health professional/patient caring relationships through information and communication technologies used in telemedicine: a scoping review protocol

REVIEW OBJECTIVES/QUESTIONS

The objectives of this scoping review are to examine and map how telemedicine via information and communication technology (ICT) transforms caring relationships between health professionals and patients and how this transformation is conceptualized.The questions of this review are.

Causal Modeling in Environmental Health

The field of environmental health has been dominated by modeling associations, especially by regressing an observed outcome on a linear or nonlinear function of observed covariates. Readers interested in advances in policies for improving environmental health are, however, expecting to be informed about health effects resulting from, or more explicitly caused by, environmental exposures. The quantification of health impacts resulting from the removal of environmental exposures involves causal statements. Therefore, when possible, causal inference frameworks should be considered for analyzing the effects of environmental exposures on health outcomes.

Applications of Blockchain Technology in Medicine and Healthcare: Challenges and Future Perspectives

Blockchain technology has gained considerable attention, with an escalating interest in a plethora of numerous applications, ranging from data management, financial services, cyber security, IoT, and food science to healthcare industry and brain research. There has been a remarkable interest witnessed in utilizing applications of blockchain for the delivery of safe and secure healthcare data management. Also, blockchain is reforming the traditional healthcare practices to a more reliable means, in terms of effective diagnosis and treatment through safe and secure data sharing. In the future, blockchain could be a technology that may potentially help in personalized, authentic, and secure healthcare by merging the entire real-time clinical data of a patient’s health and presenting it in an up-to-date secure healthcare setup. In this paper, we review both the existing and latest developments in the field of healthcare by implementing blockchain as a model. We also discuss the applications of blockchain, along with the challenges faced and future perspectives.

Artificial intelligence-based decision-making for age-related macular degeneration

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces.

Methods: In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related macular degeneration (AMD) patients and used them to train three types of CNNs to perform AMD diagnosis.

Results: Here, we present an AI- and cloud-based telemedicine interaction tool for diagnosis and proposed treatment of AMD. Through deep learning process based on the analysis of preprocessed optical coherence tomography (OCT) imaging data, our AI-based system achieved the same image discrimination rate as that of retinal specialists in our hospital. The AI platform's detection accuracy was generally higher than 90% and was significantly superior (p < 0.001) to that of medical students (69.4% and 68.9%) and equal (p = 0.99) to that of retinal specialists (92.73% and 91.90%). Furthermore, it provided appropriate treatment recommendations comparable to those of retinal specialists.

Conclusions: We therefore developed a website for realistic cloud computing based on this AI platform, available at https://www.ym.edu.tw/~AI-OCT/. Patients can upload their OCT images to the website to verify whether they have AMD and require treatment. Using an AI-based cloud service represents a real solution for medical imaging diagnostics and telemedicine.

Biosurveillance and Dentistry

Events of public health concern continue to present a challenge for the general population. A key element to address appropriate health responses is the establishment of modern public health surveillance mechanisms. In this chapter we explore possible scenarios/use cases where dentists can use electronic dental record technology to increase the accuracy, coverage, and timeliness of existing public health surveillance efforts. We identify organizational, technical, and regulatory elements that influence the adoption of such approaches and possible benefits when integrated to the public health system at large.

The Design of a Cloud-based Clinical Decision Support System Prototype

The authors developed a mobile cloud-based clinical decision support system for drug poisoning in children. The system has a Client/Server architecture and provides a mobile application and a web service to be deployed on the Amazon Cloud infrastructure. Physicians benefit from a user interface to input patient data and to receive diagnosis and treatment protocol. The objective of this article is to help doctors, and particularly the beginners to manage and to treat children suffering from drug poisoning when toxicity is known or unknown. To do this, an intelligent system is developed. It is composed of an expert system, used when the toxic drug is known, and a case-based reasoning system applied when the toxic being ingested is unknown

Paving the way for precision medicine v2.0 in intensive care by profiling necroinflammation in biofluids

Current clinical diagnosis is typically based on a combination of approaches including clinical examination of the patient, clinical experience, physiologic and/or genetic parameters, high-tech diagnostic medical imaging, and an extended list of laboratory values mostly determined in biofluids such as blood and urine. One could consider this as precision medicine v1.0. However, recent advances in technology and better understanding of molecular mechanisms underlying disease will allow us to better characterize patients in the future. These improvements will enable us to distinguish patients who have similar clinical presentations but different cellular and molecular responses. Treatments will be able to be chosen more “precisely”, resulting in more appropriate therapy, precision medicine v2.0. In this review, we will reflect on the potential added value of recent advances in technology and a better molecular understanding of necrosis and inflammation for improving diagnosis and treatment of critically ill patients. We give a brief overview on the mutual interplay between necrosis and inflammation, which are two crucial detrimental factors in organ and/or systemic dysfunction. One of the challenges for the future will thus be the cellular and molecular profiling of necroinflammation in biofluids. The huge amount of data generated by profiling biomolecules and single cells through, for example, different omic-approaches is needed for data mining methods to allow patient-clustering and identify novel biomarkers. The real-time monitoring of biomarkers will allow continuous (re)evaluation of treatment strategies using machine learning models. Ultimately, we may be able to offer precision therapies specifically designed to target the molecular set-up of an individual patient, as has begun to be done in cancer therapeutics.

Critical care mostly implies life-threatening situations involving systemic infection, inflammation and necrosis. Biofluids are an easily accessible source of liquid biopsies that can be used to monitor the evolution of the patient’s critical illness. The cellular and molecular profiling of necrosis and inflammation in biofluids using cutting-edge technologies such as realtime immunodiagnostics, next-generation sequencing and mass spectrometry will pave the way for precision medicine v2.0 in critical care. This is needed for data mining approaches to allow patientclustering, identify novel biomarkers and develop novel intervention strategies controlling necrosis and inflammation. The real-time monitoring of biomarkers will allow continued (re)evaluation of treatment strategies using machine learning models.

Rethinking the Meaning of Cloud Computing for Health Care: A Taxonomic Perspective and Future Research Directions

BACKGROUND

Cloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications. Researchers claim that information technology (IT) services delivered via the cloud computing paradigm (ie, cloud computing services) provide major benefits for health care. However, due to a mismatch between our conceptual understanding of cloud computing for health care and the actual phenomenon in practice, the meaningful use of it for the health care industry cannot always be ensured. Although some studies have tried to conceptualize cloud computing or interpret this phenomenon for health care settings, they have mainly relied on its interpretation in a common context or have been heavily based on a general understanding of traditional health IT artifacts, leading to an insufficient or unspecific conceptual understanding of cloud computing for health care.

OBJECTIVE

We aim to generate insights into the concept of cloud computing for health IT research. We propose a taxonomy that can serve as a fundamental mechanism for organizing knowledge about cloud computing services in health care organizations to gain a deepened, specific understanding of cloud computing in health care. With the taxonomy, we focus on conceptualizing the relevant properties of cloud computing for service delivery to health care organizations and highlighting their specific meanings for health care.

METHODS

We employed a 2-stage approach in developing a taxonomy of cloud computing services for health care organizations. We conducted a structured literature review and 24 semistructured expert interviews in stage 1, drawing on data from theory and practice. In stage 2, we applied a systematic approach and relied on data from stage 1 to develop and evaluate the taxonomy using 14 iterations.

RESULTS

Our taxonomy is composed of 8 dimensions and 28 characteristics that are relevant for cloud computing services in health care organizations. By applying the taxonomy to classify existing cloud computing services identified from the literature and expert interviews, which also serves as a part of the taxonomy, we identified 7 specificities of cloud computing in health care. These specificities challenge what we have learned about cloud computing in general contexts or in traditional health IT from the previous literature. The summarized specificities suggest research opportunities and exemplary research questions for future health IT research on cloud computing.

CONCLUSIONS

By relying on perspectives from a taxonomy for cloud computing services for health care organizations, this study provides a solid conceptual cornerstone for cloud computing in health care. Moreover, the identified specificities of cloud computing and the related future research opportunities will serve as a valuable roadmap to facilitate more research into cloud computing in health care.

Cloud computing applications for biomedical science: A perspective

Biomedical research has become a digital data–intensive endeavor, relying on secure and scalable computing, storage, and network infrastructure, which has traditionally been purchased, supported, and maintained locally. For certain types of biomedical applications, cloud computing has emerged as an alternative to locally maintained traditional computing approaches. Cloud computing offers users pay-as-you-go access to services such as hardware infrastructure, platforms, and software for solving common biomedical computational problems. Cloud computing services offer secure on-demand storage and analysis and are differentiated from traditional high-performance computing by their rapid availability and scalability of services. As such, cloud services are engineered to address big data problems and enhance the likelihood of data and analytics sharing, reproducibility, and reuse. Here, we provide an introductory perspective on cloud computing to help the reader determine its value to their own research.

Managing public health data: mobile applications and mass vaccination campaigns

In response to data collection challenges during mass immunization events, Denver Public Health developed a mobile application to support efficient public health immunization and prophylaxis activities. The Handheld Automated Notification for Drugs and Immunizations (HANDI) system has been used since 2012 to capture influenza vaccination data during Denver Health's annual employee influenza campaign. HANDI has supported timely and efficient administration and reporting of influenza vaccinations through standardized data capture and database entry. HANDI's mobility allows employee work locations and schedules to be accommodated without the need for a paper-based data collection system and subsequent manual data entry after vaccination. HANDI offers a readily extensible model for mobile data collection to streamline vaccination documentation and reporting, while improving data quality and completeness.

The Use of Technology in Identifying Hospital Malnutrition: Scoping Review

Background

Malnutrition is a condition most commonly arising from the inadequate consumption of nutrients necessary to maintain physiological health and is associated with the development of cardiovascular disease, osteoporosis, and sarcopenia. Malnutrition occurring in the hospital setting is caused by insufficient monitoring, identification, and assessment efforts. Furthermore, the ability of health care workers to identify and recognize malnourished patients is suboptimal. Therefore, interventions focusing on the identification and treatment of malnutrition are valuable, as they reduce the risks and rates of malnutrition within hospitals. Technology may be a particularly useful ally in identifying malnutrition due to scalability, timeliness, and effectiveness. In an effort to explore the issue, this scoping review synthesized the availability of technological tools to detect and identify hospital malnutrition.

Objective

Our objective was to conduct a scoping review of the different forms of technology used in addressing malnutrition among adults admitted to hospital to (1) identify the extent of the published literature on this topic, (2) describe key findings, and (3) identify outcomes.

Methods

We designed and implemented a search strategy in 3 databases (PubMed, Scopus, and CINAHL). We completed a descriptive numerical summary and analyzed study characteristics. One reviewer independently extracted data from the databases.

Results

We retrieved and reviewed a total of 21 articles. We categorized articles by the computerized tool or app type: malnutrition assessment (n=15), food intake monitoring (n=5), or both (n=1). Within those categories, we subcategorized the different technologies as either hardware (n=4), software (n=13), or both (n=4). An additional subcategory under software was cloud-based apps (n=1). Malnutrition in the acute hospital setting was largely an unrecognized problem, owing to insufficient monitoring, identification, and initial assessments of identifying both patients who are already malnourished and those who are at risk of malnourishment. Studies went on to examine the effectiveness of health care workers (nurses and doctors) with a knowledge base focused on clinical care and their ability to accurately and consistently identify malnourished geriatric patients within that setting.

Conclusions

Most articles reported effectiveness in accurately increasing malnutrition detection and awareness. Computerized tools and apps may also help reduce health care workers’ workload and time spent assessing patients for malnutrition. Hospitals may also benefit from implementing malnutrition technology through observing decreased length of stay, along with decreased foregone costs related to missing malnutrition diagnoses. It is beneficial to study the impact of these technologies to examine possible areas of improvement. A future systematic review would further contribute to the evidence and effectiveness of the use of technologies in assessing and monitoring hospital malnutrition.

Real-time evaluation of an image analysis system for monitoring surgical hemoglobin loss

Monitoring blood loss is important for management of surgical patients. This study reviews a device (Triton) that uses computer analysis of a photograph to estimate hemoglobin (Hb) mass present on surgical sponges. The device essentially does what a clinician does when trying to make a visual estimation of blood loss by looking at a sponge, albeit with less subjective variation. The performance of the Triton system is reported upon in during real-time use in surgical procedures. The cumulative Hb losses estimated using the Triton system for 50 enrolled patients were compared with reference Hb measurements during the first quarter, half, three-quarters and full duration of the surgery. Additionally, the estimated blood loss (EBL) was calculated using the Triton measured Hb loss and compared with values obtained from both visual estimation and gravimetric measurements. Hb loss measured by Triton correlated with the reference method across the four measurement intervals. Bias remained low and increased from 0.1 g in the first quarter to 3.7 g at case completion. The limits of agreement remained narrow and increased proportionally from the beginning to the end of the cases, reaching a maximum range of -15.3 to 22.7 g. The median (IQR) difference of EBL derived from the Triton system, gravimetric method and visual estimation versus the reference value were 13 (74), 389 (287), and 4 (230) mL, respectively. Use of the Triton system to measure Hb loss in real-time during surgery is feasible and accurate.