Healthcare 5.0: A secure and distributed network for system informatics in medical surgery.

INTRODUCTION: Health records serve not only as a database of a patient's health history and treatment process but also as a crucial tool for doctors to diagnose and treat patients. However, the storage and sharing of these records are sensitive issues as they involve maintaining patient privacy and ensuring data transparency, security, and interoperability between different parties. Challenges to achieving these goals in the current surgical process can impact the allocation of medical resources and surgical outcomes. METHODS: This article proposes a healthcare 5.0 framework for medical surgery that deploys a secure and distributed network using Blockchain to demonstrate transactions between different parties in the orthopedic surgery process. The proposed network uses the Hyperledger Composer platform for deployment, and a patient-doctor-supplier orthopedic surgery network is designed and implemented to enable the safe sharing of medical records. RESULTS: A benchmarking tool was implemented for analyzing different scenarios of applying blockchain technology to orthopedic surgery. The application of blockchain technology to orthopedic surgery presents a promising solution for data sharing and supply chain management in the field. The integration of blockchain with cloud storage and hybrid encryption ensures secure and efficient storage of Electronic Health Record (EHR) and Personal Health Record (PHR) data. By leveraging the tamper-proof nature of blockchain and addressing concerns regarding centralized data storage, this scenario demonstrates enhanced security, improved access efficiency, and privacy protection in medical data sharing. CONCLUSIONS: The article demonstrates the feasibility of using an IoT-based blockchain network in orthopedic surgery, which can reduce medical errors and improve data interoperability among different parties. This unique application of blockchain enables secure sharing of medical records, ensuring transparency, security, and interoperability. The network design may also be applicable to other surgeries and medical applications in the future.

Secure Messaging: Demonstration and Enrollment Patient Portal Program: Patient Portal Use in Vulnerable Populations.

Vulnerable populations face challenges gaining access to quality healthcare, which places them at a high risk for poor health outcomes. Using patient portals and secure messaging can improve patient activation, access to care, patient follow-up adherence, and health outcomes. Developing and testing quality improvement strategies to help reduce disparities is vital to ensure patient portals benefit all, especially vulnerable populations. This quality improvement initiative aimed to increase enrollment in a patient portal, use secure messages, and adhere to follow-up appointments. Before the project, no patients were enrolled in the portal at this practice site. Over 8 weeks, 61% of invited patients were enrolled in the patient portal. Eighty-five percent were Medicaid recipients, and the others were underinsured. Eight patients utilized the portal for secure messaging. The follow-up appointment attendance rate was better in the enrolled patients than in those who did not enroll. The majority of survey respondents reported satisfaction in using the patient portal. Patient portal utilization and adoption in vulnerable groups can improve when a one-on-one, hands-on demonstration and technical assistance are provided.

Managing cybersecurity risk in healthcare settings.

Cybersecurity attacks have been steadily increasing in the healthcare sector over the past decade. Health data is a valuable source of reliable and permanent personal information making it an attractive target. Institutions that have faced limited cybersecurity funding must now augment their approach to combat this threat. The Internet of Things (IoT) refers to the connection of physical operational devices to digital networks allowing for communication between devices. In the healthcare setting, this includes patient monitoring, diagnostics, and even robotic surgery devices. This increased connectivity increases the importance of agile and robust cybersecurity measures. A progressive approach must involve collaboration between information technology, clinical, and administrative leaders to be successful. Adequate protection of patient data and the integrity of digital infrastructure must be a priority mandate at the enterprise level.

Secret learning for lung cancer diagnosis-a study with homomorphic encryption, texture analysis and deep learning.

Advanced lung cancer diagnoses from radiographic images include automated detection of lung cancer from CT-Scan images of the lungs. Deep learning is a popular method for decision making which can be used to classify cancerous and non-cancerous lungs from CT-Scan images. There are many experiments which show the uses of deep learning for performing such classifications but very few of them have preserved the privacy of users. Among existing methods, federated learning limits data sharing to a central server and differential privacy although increases anonymity the original data is still shared. Homomorphic encryption can resolve the limitations of both of these. Homomorphic encryption is a cryptographic technique that allows computations to be performed on encrypted data. In our experiment, we have proposed a series of textural information extraction with the implementation of homomorphic encryption of the CT-Scan images of normal, adenocarcinoma, large cell carcinoma and squamous cell carcinoma. We have further processed the encrypted data to make it classifiable and later we have classified it with deep learning. The results from the experiments have obtained a classification accuracy of 0.9347.

O sigilo médico e os dados sensíveis na telemedicina à luz da Lei Geral de Proteção de Dados / Medical confidentiality and sensitive data in telemedicine in light of the General Data Protection Law / Secret médical et données sensibles en télémédecine au regard de la Loi Générale sur la Protection des Données

A telemedicina, permitida em caráter emergencial durante a covid-19, foi autorizada e regulamentada pela Lei nº 14.510/2022. Reconhecida como serviço imprescindível para a garantia da equidade em saúde, na telemedicina veiculam-se dados considerados sensíveis pela Lei Geral de Proteção de Dados. Este ensaio apresenta uma discussão a respeito de tais dados, os quais detêm relação intrínseca com direitos da personalidade e que devem ser reconhecidos como sigilosos, a fim de garantir o direito à privacidade dos titulares, bem como o respeito ao sigilo médico. Conclui-se que eventual violação dos dados sensíveis pode ensejar sanções administrativas aos agentes de tratamento, mas há divergência doutrinária a respeito do regime de responsabilidade adotado pela Lei Geral de Proteção de Dados, com três possíveis interpretações: responsabilidade objetiva, responsabilidade subjetiva e responsabilidade ativa

Telemedicine, which had been allowed on an emergency basis during covid-19, was authorized and regulat-ed by Law nº 14.510/2022. Recognized as an essential service in guaranteeing equity in health, in telemedi-cine, data considered sensitive by the General Data Protection Law is transmitted. This essay elaborates on a discussion regarding such data, which are intrinsically related to personal rights and must be recognized as confidential in order to ensure the right to privacy of the data subjects, as well as respect for medical confidentiality. It is concluded that any violation of sensitive data may result in administrative sanctions for treatment agents. Still, doctrinal divergence exists regarding the liability regime adopted by the law, with three possible interpretations: strict liability, fault liability, and active liability

La télémédecine, qui avait été permise en urgence pendant le covid-19, a été autorisée et réglementée par la Loi nº 2022-14510. Reconnue comme un service essentiel pour garantir l'équité en santé, en télémédecine, des données considérées comme sensibles par la Loi Générale sur la Protection des Données sont transmises. Cet essai développe une discussion concernant de telles données, qui sont intrinsèquement liées aux droits personnels et doivent être reconnues comme confidentielles afin de garantir le droit à la vie privée des sujets de données, ainsi que le respect de la confidentialité médicale. On en conclut que la violation éventuelle de données sensibles peut entraîner des sanctions administratives pour les agents de traitement. Néanmoins, des divergences doctrinales existent quant au régime de responsabilité adopté par la loi, avec trois interprétations possibles: la responsabilité stricte, la responsabilité pour faute et la responsabilité active

Collaborative privacy-preserving analysis of oncological data using multiparty homomorphic encryption.

Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical datasets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their datasets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without intermediate decryptions, so the analytics results are obtained without revealing the raw data. This work presents a toolset for collaborative privacy-preserving analysis of oncological data using multiparty FHE. Our toolset supports survival analysis, logistic regression training, and several common descriptive statistics. We demonstrate using oncological datasets that the toolset achieves high accuracy and practical performance, which scales well to larger datasets. As part of this work, we propose a cryptographic protocol for interactive bootstrapping in multiparty FHE, which is of independent interest. The toolset we develop is general-purpose and can be applied to other collaborative medical and healthcare application domains.

Smart Wireless Sensor Technology for Healthcare Monitoring System Using Cognitive Radio Networks.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In a Wireless Body Area Network (WBAN), for example, patients' health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity, rendering the data transmission slower. Cognitive radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors, which renders the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

Clinical Informatics Approaches to Facilitate Cancer Data Sharing.

OBJECTIVES: Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data. METHODS: We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models. RESULTS: Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation. CONCLUSIONS: Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.

Perspectives on Cybersecurity and Plastic Surgery: A Survey of Plastic Surgeons and Scoping Review of the Literature.

BACKGROUND: Data breach costs in the United States are among the highest in the world, making robust cybersecurity an important bulwark of national defense. Healthcare is a popular target for cyber threats, and there is increasing emphasis on cybersecurity safeguards to protect sensitive patient data. OBJECTIVES: The objective of this national survey and scoping review is to (1) identify cybersecurity awareness, preparedness, and practices among plastic surgeons, and (2) to provide guidelines to mitigate the threat of cyberattacks. METHODS: A 16-question, anonymous online survey was developed and distributed to The Aesthetic Society registrants to ascertain plastic surgeons' cybersecurity practices. Utilizing PubMed, CINAHL, and Embase databases, eligible articles were identified as part of this scoping review. RESULTS: Of 89 individuals who began the survey, 69 completed it (77.5%). Sixty respondents agreed or strongly agreed that cybersecurity is an important issue in plastic surgery. The greatest perceived limitations for protection against cyberattacks were insufficient expertise (41.7%), followed by lack of funding and insufficient time to dedicate to this goal. Most respondents (78.7%) had cybersecurity policies incorporated into their practice. Those who agreed or strongly agreed they had technology to prevent data theft/breach were significantly more likely to be older than 54 years of age (P < .001). No articles identified in the literature specifically addressed cybersecurity in plastic surgery; however, 12 articles detailing cybersecurity in healthcare were identified and included. CONCLUSIONS: Despite possessing adequate technology and procedures in place to prevent cyberattacks, plastic surgeons perceive significant barriers to cybersecurity protection, including insufficient expertise and lack of dedicated funding. It is imperative that our field establishes standards and protocols to protect our patients.

Practical Steps to Mitigate Cybersecurity Attacks on Radiation Oncology Practices.

Radiation oncology, as a technologically intensive discipline that requires communication between multiple and diverse computer systems, is vulnerable to cyberattack. Given the enormous amount of the loss of time, energy, and money that results from a cyberattack, it behooves radiation oncologists and their teams to minimize cybersecurity threats to their practices. In this article, we present practical steps that radiation oncologists can take to prevent, prepare for, and respond to a cyberattack.

Blockchain, Information Security, Control, and Integrity: Who Is in Charge?

SUMMARY: Blockchain technology has attracted substantial interest in recent years, most notably for its effect on global economics through the advent of cryptocurrency. Within the health care domain, blockchain technology has been actively explored as a tool for improving personal health data management, medical device security, and clinical trial management. Despite a strong demand for innovation and cutting-edge technology in plastic surgery, integration of blockchain technologies within plastic surgery is in its infancy. Recent advances and mainstream adoption of blockchain are gaining momentum and have shown significant promise for improving patient care and information management. In this article, the authors explain what defines a blockchain and discuss its history and potential applications in plastic surgery. Existing evidence suggests that blockchain can enable patient-centered data management, improve privacy, and provide additional safeguards against human error. Integration of blockchain technology into clinical practice requires further research and development to demonstrate its safety and efficacy for patients and providers.

Implementation of Blockchain Technology Could Increase Equity and Transparency in Organ Transplantation: A Narrative Review of an Emergent Tool.

In the last few years, innovative technology and health care digitalization played a major role in all medical fields and a great effort worldwide to manage this large amount of data, in terms of security and digital privacy has been made by different national health systems. Blockchain technology, a peer-to-peer distributed database without centralized authority, initially applied to Bitcoin protocol, soon gained popularity, thanks to its distributed immutable nature in several non-medical fields. Therefore, the aim of the present review (PROSPERO N° CRD42022316661) is to establish a putative future role of blockchain and distribution ledger technology (DLT) in the organ transplantation field and its role to overcome inequalities. Preoperative assessment of the deceased donor, supranational crossover programs with the international waitlist databases, and reduction of black-market donations and counterfeit drugs are some of the possible applications of DLT, thanks to its distributed, efficient, secure, trackable, and immutable nature to reduce inequalities and discrimination.

Pediatric biobanks to enhance clinical and translational research for children.

Including children in biomedical research is an argument for continual reflection and practice refinement from an ethical and legal standpoint. Indeed, as children reach adulthood, a reconsent method should be used, and data connected with samples should ideally be updated based on the children's growth and long-term results. Furthermore, because most pediatric disorders are uncommon, children's research initiatives should conform to standard operating procedures (SOPs) set by worldwide scientific organizations for successfully sharing data and samples. Here, we examine how pediatric biobanks can help address some challenges to improve biomedical research for children. Indeed, modern biobanks are evolving as complex research platforms with specialized employees, dedicated spaces, information technologies services (ITS), and ethical and legal expertise. In the case of research for children, biobanks can collaborate with scientific networks (i.e., BBMRI-ERIC) and provide the collection, storage, and distribution of biosamples in agreement with international standard procedures (ISO-20387). Close collaboration among biobanks provides shared avenues for maximizing scarce biological samples, which is required to promote the translation of scientific breakthroughs for developing clinical care and health policies tailored to the pediatric population. Moreover, biobanks, through their science communication and dissemination activities (i.e., European Biobank Week), may be helpful for children to understand what it means to be engaged in a research study, allowing them to see it as a pleasant, useful, and empowering experience. Additionally, biobanks can notify each participant about which projects have been accomplished (i.e., through their websites, social media networks, etc.); they can facilitate future reconsent procedures and update sample-associated data based on the children's growth. Finally, because of the increasing interest from public and commercial organizations in research efforts that include the sharing and reuse of health data, pediatric biobanks have a crucial role in this context. Consequently, they could benefit from funding opportunities for sustaining research activities even regarding rare pediatric disorders.  Conclusion: Pediatric biobanks are helpful for providing biological material for research purposes, addressing ethical and legal issues (i.e. data protection, consent, etc.), and providing control samples from healthy children of various ages and from different geographical regions and ethnicities. Therefore, it is vital to encourage and maintain children's engagement in medical research programs and biobanking activities, especially as children become adults, and reconsent procedures must be applied. What is Known: • Biobanks are critical research infrastructures for medical research, especially in the era of "omic" science. However, in light of their fragility and rights children's participation in biobanking and medical research programs is a complex argument of continuous debate in scientific literature. What is New: • We propose a review of the literature on pediatric biobanks with a particular focus on oncological biobanks. The main current limitations and challenges for pediatric biobanks are presented and possible solutions are discussed.

Privacy-preserving cancer type prediction with homomorphic encryption.

Cancer genomics tailors diagnosis and treatment based on an individual's genetic information and is the crux of precision medicine. However, analysis and maintenance of high volume of genetic mutation data to build a machine learning (ML) model to predict the cancer type is a computationally expensive task and is often outsourced to powerful cloud servers, raising critical privacy concerns for patients' data. Homomorphic encryption (HE) enables computation on encrypted data, thus, providing cryptographic guarantees to protect privacy. But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer type prediction using a dataset consisting of more than 2 million genetic mutations from 2713 patients for several cancer types by building a highly accurate ML model and then implementing its privacy preserving version in HE. Our solution for cancer type inference encodes somatic mutations based on their impact on the cancer genomes into the feature space and then uses statistical tests for feature selection. We propose a fast matrix multiplication algorithm for HE-based model. Our final model achieves 0.98 micro-average area under curve improving accuracy from 70.08 to 83.61% , being 550 times faster than the standard matrix multiplication-based privacy-preserving models. Our tool can be found at https://github.com/momalab/octal-candet .