Healthcare Information Exchange Research Articles

Toward a New Era of Smart and Secure Healthcare Information Exchange Systems: Combining Blockchain and Artificial Intelligence

Healthcare Information Exchange (HIE) is becoming a fundamental operation in current healthcare systems. In such systems, electronic health records (EHRs) are digitally stored inside each medical centers and, sometimes, are required to be shared between various healthcare facilities (HCFs). Indeed, sharing patient information is crucial and might be vulnerable to power outages, data misuse, privacy or security violations, and an audit trail. Hence, researchers have focused recently on cutting-edge technologies to develop secure HIE systems and ensure data privacy during transactions. Among such technologies, blockchain and artificial intelligence (AI) occupy a vital role in researchers’ focuses and efforts to detect risky transactions in HIE systems, thus enhancing their security and privacy. While the blockchain allows HCFs to link to each other without requiring a central authority, AI models offer an additional security layer when sharing patient data between HCFs. This paper presents a survey about HIE systems, and the aim is two-fold: we first present the architecture of HIE systems along with their challenges; then, we categorize and classify the current state-the-art-techniques that show the potential of using blockchain and AI technologies in such systems.

HEALTHSOLID 4.0: A novel solid-pod and blockchain-enabled framework for role-based access control and secure healthcare information exchange

HEALTHSOLID 4.0: A novel solid-pod and blockchain-enabled framework for role-based access control and secure healthcare information exchange

Blockchain-Inspired Lightweight Dynamic Encryption Schemes for a Secure Health Care Information Exchange System

Blockchain-Inspired Lightweight Dynamic Encryption Schemes for a Secure Health Care Information Exchange System

Secure System with Security Protocol for Interactions in Healthcare Internet of Things

The Internet of Things is a network of connected devices that can communicate and share data over the Internet. These devices often have sensors that collect data for various purposes, such as usage statistics, data processing, or performing specific actions based on the collected data. Also, medical Internet of Things devices are crucial in monitoring critical functions, measuring blood glucose levels, indicating when patients require medicine, and ensuring timely medication delivery. Communication in the Internet of Things is demanding, requiring diverse protocols that address communication security concerns. These protocols must be robust and secure, considering technical factors such as the network’s objective, energy requirements, and the nature of the communication because they can be exploited. This paper proposes an innovative system with a security protocol that supports and improves communication security in modern Internet of Things networks. The protocol aims to enhance communication safety between interconnected devices for information exchange in medicine or healthcare, ensuring the confidentiality and integrity of sent data and devices. The proposed protocol, tested through formal and automated verification, meets all security goals, including identity verification, anonymity protection, and access revokement. It also protects against Man-in-the-middle, modification, replay, and impersonation attacks.

Revolutionizing healthcare: IoMT-enabled digital enhancement via multimodal ADL data fusion

The present research develops a framework to refine the classification of an individual’s activities and recognize wellness associated with their routine. The framework improves the accuracy of the classification of routine activities of a person, the activation time data of sensors fixed on objects linked with the routine activities of the person, and the aptness of an incessant activity pattern with the routine activities. The existing techniques need continuous monitoring and are non-adaptive to a person’s persistent habitual variations or individualities. The research involves applying Internet of Medical Things (IoMT)-based sensor information fusion to the novel multimodel data analytics to develop Activities of Daily Living (ADL) pattern, behavioral pattern generation and anomaly recognition. The novel multimodel data analytics approach is named AiCareLiving. AicareLiving is an IoMT and artificial intelligence (AI) enabled approach. The research work describes activity data using an individual’s activities within a specified area before evaluating the activity data to detect the existence of an anomaly by identifying the deviation of the activity data from the activity profile, which indicates the anticipated behavior and activity of the person. This wellness information would be shared to the caregivers, related healthcare professionals, care providers and municipalities through the secured healthcare information exchange protocol and IoMT. AiCareLiving framework aims to least false positive in terms of anomaly detection and forecasting; the high precision is close to the confidence level of 95%.

Searchable Blockchain-Based Healthcare Information Exchange System to Enhance Privacy Preserving and Data Usability.

Ensuring the security and usability of electronic health records (EHRs) is important in health information exchange (HIE) systems that handle healthcare records. This study addressed the need to balance privacy preserving and data usability in blockchain-based HIE systems. We propose a searchable blockchain-based HIE system that enhances privacy preserving while improving data usability. The proposed methodology includes users collecting healthcare information (HI) from various Internet of Medical Things (IoMT) devices and compiling this information into EHR blocks for sharing on a blockchain network. This approach allows participants to search and utilize specific health data within the blockchain effectively. The results demonstrate that the proposed system mitigates the issues of traditional HIE systems by providing secure and user-friendly access to EHRs. The proposed searchable blockchain-based HIE system resolves the trade-off dilemma in HIE by achieving a balance between security and the data usability of EHRs.

Designing Medication-Related Profiles for Japanese ePrescriptions with HL7 FHIR.

HL7 FHIR is the standard for healthcare information exchange. In November 2022, our medication subgroup developed 8 profiles and 23 extensions for medication procedures in Japan, as part of the JP Core Implementation Guide 1.1. Our work demonstrates the ability of HL7 FHIR to describe Japanese prescription procedures while also addressing the requirements of other countries.

Empowering Electronic Health Record Systems (EHRs) with Cloud Storage Management

We present a novel method for building cloud based interoperable electronic health record (EHR) systems. All members of the healthcare ecosystem, including patients, providers, and payers, can benefit from cloud computing in a number of ways. The exchange of healthcare information among diverse stakeholders has been significantly hampered by the lack of standardised data interoperability. In order to solve this problem, semantic interoperability is used. With the use of a reference model that outlines a common set of data structures and an archetype model that details the characteristics of clinical data, we employ a general design technique. The loosely connected, asynchronously communicating components of the application are created utilising the cloud component model technique. This article discusses our methods for attaining semantic interoperability, data integration, and high-level architecture of our EHR system.

From randomised controlled trials to real-world data: Clinical evidence to guide management of diabetic macular oedema.

Randomised clinical trials (RCTs) are generally considered the gold-standard for providing scientific evidence for treatments' effectiveness and safety but their findings may not always be generalisable to the broader population treated in routine clinical practice. RCTs include highly selected patient populations that fit specific inclusion and exclusion criteria. Although they may have a lower level of certainty than RCTs on the evidence hierarchy, real-world data (RWD), such as observational studies, registries and databases, provide real-world evidence (RWE) that can complement RCTs. For example, RWE may help satisfy requirements for a new indication of an already approved drug and help us better understand long-term treatment effectiveness, safety and patterns of use in clinical practice. Many countries have set up registries, observational studies and databases containing information on patients with retinal diseases, such as diabetic macular oedema (DMO). These DMO RWD have produced significant clinical evidence in the past decade that has changed the management of DMO. RWD and medico-administrative databases are a useful resource to identify low frequency safety signals. They often have long-term follow-up with a large number of patients and minimal exclusion criteria. We will discuss improvements in healthcare information exchange technologies, such as blockchain technology and FHIR (Fast Healthcare Interoperability Resources), which will connect and extend databases already available. These registries can be linked with existing or emerging retinal imaging modalities using artificial intelligence to aid diagnosis, treatment decisions and provide prognostic information. The results of RCTs and RWE are combined to provide evidence-based guidelines.

Privacy and Security of Blockchain in Healthcare: Applications, Challenges, and Future Perspectives

Blockchain offers a cutting-edge solution for storing medical data, carrying out medical transactions, and establishing trust for medical data integration and exchange in a decentralized open healthcare network setting. While blockchain in healthcare has garnered considerable attention, privacy and security concerns remain at the center of the debate when adopting blockchain for information exchange in healthcare. This paper presents research on the subject of blockchain’s privacy and security in healthcare from 2017 to 2022. In light of the existing literature, this critical evaluation assesses the current state of affairs, with a particular emphasis on papers that deal with practical applications and difficulties. By providing a critical evaluation, this review provides insight into prospective future study directions and advances.

IoT‐based systemic lupus erythematosus prediction model using hybrid genetic algorithm integrated with ANN

AbstractInternet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine‐to‐machine networking, and data transmission. An SLE symptom‐predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state‐of‐the‐art prediction models at approximately 99.70%.

Blockchain-based framework for secure and efficient exchange of electronic medical records on mobile cloud environments

The maintenance of Electronic Medical Records (EMRs) on mobile cloud platforms, in which mobile devices are incorporated with cloud computing to ease healthcare information exchange between healthcare providers and patients, has become a paradigm shift in latest days. This cutting-edge strategy makes it possible to provide medical care at a low overhead expense, with a high level of flexibility, and with electronic health record access. Furthermore, this new approach also expresses concern over the confidentiality of patient data and the safety of the networks that support e-health systems. It is a tough problem to figure out a way to effectively share EMRs amongst mobile users while also maintaining maximum security measures in mobile cloud environments. In this research, a novel framework is presented for the sharing of EMRs that integrates blockchain technology and the decentralized Interplanetary File System (DIFS) on a mobile cloud. The empirical results suggest that the concept offers a useful method for ensuring the reliability of data transfers on mobile clouds, while also preserving important patient data from any potential dangers that may arise. Comparing the results of this performance assessment and security analysis to those of previously implemented data sharing models reveals significant performance enhancements in areas such as lightweight access control method, minimal network latency, maximum security levels, and data privacy protections.

Sustainable healthcare information exchanges network design: a scenario-based planning approach

Sustainable healthcare information exchanges network design: a scenario-based planning approach

Sustainable healthcare information exchanges network design: a scenario-based planning approach

Sustainable healthcare information exchanges network design: a scenario-based planning approach

Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study.

BackgroundVital signs have been widely adopted in in-hospital cardiac arrest (IHCA) assessment, which plays an important role in inpatient deterioration detection. As the number of early warning systems and artificial intelligence applications increases, health care information exchange and interoperability are becoming more complex and difficult. Although Health Level 7 Fast Healthcare Interoperability Resources (FHIR) have already developed a vital signs profile, it is not sufficient to support IHCA applications or machine learning–based models.ObjectiveIn this paper, for IHCA instances with vital signs, we define a new implementation guide that includes data mapping, a system architecture, a workflow, and FHIR applications.MethodsWe interviewed 10 experts regarding health care system integration and defined an implementation guide. We then developed the FHIR Extract Transform Load to map data to FHIR resources. We also integrated an early warning system and machine learning pipeline.ResultsThe study data set includes electronic health records of adult inpatients who visited the En-Chu-Kong hospital. Medical staff regularly measured these vital signs at least 2 to 3 times per day during the day, night, and early morning. We used pseudonymization to protect patient privacy. Then, we converted the vital signs to FHIR observations in the JSON format using the FHIR Extract Transform Load application. The measured vital signs include systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature. According to clinical requirements, we also extracted the electronic health record information to the FHIR server. Finally, we integrated an early warning system and machine learning pipeline using the FHIR RESTful application programming interface.ConclusionsWe successfully demonstrated a process that standardizes health care information for inpatient deterioration detection using vital signs. Based on the FHIR definition, we also provided an implementation guide that includes data mapping, an integration process, and IHCA assessment using vital signs. We also proposed a clarifying system architecture and possible workflows. Based on FHIR, we integrated the 3 different systems in 1 dashboard system, which can effectively solve the complexity of the system in the medical staff workflow.

SHARIF: Solid Pod-Based Secured Healthcare Information Storage and Exchange Solution in Internet of Things

The recent development has enlightened health informatics on the Internet of medical Things (IoT) 5.0. Healthcare services have seen greater acceptance of information and communications technology (ICT) in recent years; in light of the increasing volume of patient data, the traditional way of storing data in physical files has eventually moved to a digital alternative such as electronic health record (EHR). However, conventional healthcare data systems are plagued with a single point of failure, security issues, mutable logging, and inefficient methods to retrieve healthcare records. Social linked data (Solid) has been developed as a decentralized technology to alter digital data sharing and ownership for its users radically. However, Solid alone cannot address all the security issues posed to data exchange and storage. Present research combines two decentralized technologies, Solid ecosystem and blockchain technology, to tackle all potential security issues using solidity-based smart contracts, thereby providing a secure patient-centric design for the complex under developing EHR data exchange.

Intrauterine device-related uterine perforation incidence and risk (APEX-IUD): a large multisite cohort study

Reports of perforation risk related to intrauterine devices (IUDs) inserted immediately post partum and among non-post-partum individuals are scarce, and previous studies with only 12-month follow-ups underestimate the risk. Breastfeeding at IUD insertion and insertion within 36 weeks post partum have been associated with increased risk of uterine perforation. The aim of these analyses was to compare the incidence and risks of IUD-related uterine perforations by non-post-partum and post-partum intervals at IUD insertion, and among post-partum individuals, to assess the impact of breastfeeding on these outcomes. We did a multisite cohort study in the USA, using electronic health records (EHR). Study sites were three health-care systems and a site that used data from a health-care information exchange. The study population included individuals who were aged 50 years or younger and had an IUD insertion between Jan 1, 2001, and April 30, 2018. Individuals were excluded if they had not been in the health-care system for at least 12 months before IUD insertion. The primary outcome for this analysis was any IUD-related uterine perforation diagnosis for the first IUD insertion in this time period. Both complete and partial IUD-related perforations were identified. Chart abstraction was done to validate EHR-based algorithms or confirm perforations. The crude rate and cumulative incidence of uterine perforation were evaluated by non-post-partum and post-partum intervals at IUD insertion in the full cohort, and by breastfeeding status in a subcohort of post-partum individuals. Cox models estimated crude and adjusted hazard ratios (aHRs). Data from 326 658 individuals in the full cohort and 94 817 individuals in the post-partum subcohort were analysed. In the full cohort, we identified 1008 uterine perforations (51·2% complete), with the 5-year cumulative incidence being the lowest in the non-post-partum group (0·29%, 95% CI 0·26-0·34). The aHR for the post-partum interval relative to non-post partum ranged from 2·73 (95% CI 1·33-5·63; 0 to 3 days post partum) to 6·71 (4·80-9·38; 4 days to ≤6 weeks post partum). The post-partum subcohort of individuals with breastfeeding information had 673 uterine perforations (62% complete), with a 5-year cumulative incidence of 1·37% (95% CI 1·24-1·52) and an increased risk with breastfeeding (aHR 1·37, 95% CI 1·12-1·66). Although the risk for uterine perforation with IUD insertion 4 days to 6 weeks or less post partum is nearly seven times that of insertion non-post partum, perforation remains an incredibly rare event for all clinical time points. Despite a slight increased risk of perforation with breastfeeding at IUD insertion, the benefits of breastfeeding and effective contraception generally outweigh risks and should have little clinical impact. Therefore, IUD insertion timing should be based on individual desire for IUD contraception and patient convenience to assure an IUD insertion can occur. Careful follow-up of individuals at higher risk of uterine perforation is warranted. Bayer AG.

Association between intrauterine device type and risk of perforation and device expulsion: results from the Association of Perforation and Expulsion of Intrauterine Device study

Intrauterine devices, including levonorgestrel-releasing and copper devices, are highly effective long-acting reversible contraceptives. The potential risks associated with intrauterine devices are low and include uterine perforation and device expulsion. This study aimed to evaluate the risk of perforation and expulsion associated with levonorgestrel-releasing devices vs copper devices in clinical practice in the United States. The Association of Perforation and Expulsion of Intrauterine Device study was a retrospective cohort study of women aged ≤50 years with an intrauterine device insertion during 2001 to 2018 and information on intrauterine device type and patient and medical characteristics. Of note, 4 research sites with access to electronic health records contributed data for the study: 3 Kaiser Permanente-integrated healthcare systems (Northern California, Southern California, and Washington) and 1 healthcare system using data from a healthcare information exchange in Indiana (Regenstrief Institute). Perforation was classified as any extension of the device into or through the myometrium. Expulsion was classified as complete (not visible in the uterus or abdomen or patient reported) or partial (any portion in the cervix or malpositioned). We estimated the crude incidence rates and crude cumulative incidence by intrauterine device type. The risks of perforation and expulsion associated with levonorgestrel-releasing intrauterine devices vs copper intrauterine devices were estimated using Cox proportional-hazards regression with propensity score overlap weighting to adjust for confounders. Among 322,898 women included in this analysis, the incidence rates of perforation per 1000 person-years were 1.64 (95% confidence interval, 1.53-1.76) for levonorgestrel-releasing intrauterine devices and 1.27 (95% confidence interval, 1.08-1.48) for copper intrauterine devices; 1-year and 5-year crude cumulative incidence was 0.22% (95% confidence interval, 0.20-0.24) and 0.63% (95% confidence interval, 0.57-0.68) for levonorgestrel-releasing intrauterine devices and 0.16% (95% confidence interval, 0.13-0.20) and 0.55% (95% confidence interval, 0.44-0.68) for copper intrauterine devices, respectively. The incidence rates of expulsion per 1000 person-years were 13.95 (95% confidence interval, 13.63-14.28) for levonorgestrel-releasing intrauterine devices and 14.08 (95% confidence interval, 13.44-14.75) for copper intrauterine devices; 1-year and 5-year crude cumulative incidence was 2.30% (95% confidence interval, 2.24-2.36) and 4.52% (95% confidence interval, 4.40-4.65) for levonorgestrel-releasing intrauterine devices and 2.30% (95% confidence interval, 2.18-2.44) and 4.82 (95% confidence interval, 4.56-5.10) for copper intrauterine devices, respectively. Comparing levonorgestrel-releasing intrauterine devices with copper intrauterine devices, the adjusted hazard ratios were 1.49 (95% confidence intervals, 1.25-1.78) for perforation and 0.69 (95% confidence intervals, 0.65-0.73) for expulsion. After adjusting for potential confounders, levonorgestrel-releasing intrauterine devices were associated with an increased risk of uterine perforation and a decreased risk of expulsion relative to copper intrauterine devices. Given that the absolute numbers of these events are low in both groups, these differences may not be clinically meaningful.

How Do Dental Clinicians Obtain Up-To-Date Patient Medical Histories? Modeling Strengths, Drawbacks, and Proposals for Improvements.

BackgroundAccess to up-to-date patient medical history is essential for dental clinicians (DCs) to avoid potential harm to patients and to improve dental treatment outcomes. The predominant approach for dental clinicians (DCs) to gather patients' medical history is through patient-reported medical histories and medical consults. However, studies reported varied concordance and reliability of patient-reported medical conditions and medication histories compared to the patient medical records and this process also places a significant burden on patients. Information technology tools/platforms such as an integrated electronic health record containing an electronic dental record module may address these issues. However, these integrated systems are expensive and technically complex and may not be easily adopted by DCs in solo and small group practice who provide the most dental care. The recent expansion of regional healthcare information exchange (HIE) provides another approach, but to date, studies on connecting DCs with HIE are very limited. Our study objectives were to model different aspects of the current approaches to identify the strengths and weaknesses, and then model the HIE approach that addresses the weaknesses and retain the strengths of current approaches. The models of current approaches identified the people, resources, organizational aspects, workflow, and areas for improvement; while models of the HIE approach identified system requirements, functions, and processes that may be shared with software developers and other stakeholders for future development.MethodsThere are three phases in this study. In Phase 1, we retrieved peer-reviewed PubMed indexed manuscripts published between January 2013 and November 2020 and extracted modeling related data from selected manuscripts. In Phase 2, we built models for the current approaches by using the Integrated DEFinition Method 0 function modeling method (IDEF0), the Unified Modeling Language (UML) Use Case Diagram, and Business Process Model and Notation (BPMN) methods. In Phase 3, we created three conceptual models for the HIE approach.ResultsFrom the 47 manuscripts identified, three themes emerged: 1) medical consult process following patient-reported medical history, 2) integrated electronic dental record-electronic health record (EDR-EHR), and 3) HIE. Three models were built for each of the three themes. The use case diagrams described the actions of the dental patients, DCs, medical providers and the use of information systems (EDR-EHR/HIE). The IDEF0 models presented the major functions involved. The BPMN models depicted the detailed steps of the process and showed how the patient's medical history information flowed through different steps. The strengths and weaknesses revealed by the models of the three approaches were also compared.ConclusionsWe successfully modeled the DCs' current approaches of accessing patient medical history and designed an HIE approach that addressed the current approaches' weaknesses as well as leveraged their strengths. Organizational management and end-users can use this information to decide the optimum approach to integrate dental and medical care. The illustrated models are comprehensive and can also be adopted by EHR and EDR vendors to develop a connection between dental systems and HIEs.

Improving Transitions of Care: Designing a Blockchain Application for Patient Identity Management

Background: The current healthcare ecosystem in the United States is plagued by inefficiencies in transitions of patient care between healthcare providers due in large part to a lack of interoperability among the many electronic medical record (EMR) systems that exist today. Both providers and patients experience significant frustration due to the negative effects of increased costs, unnecessary administrative burden, and duplication of services that occur because of data fragmentation in the system. Blockchain technology provides a solution to mitigate or eliminate these gaps by allowing for healthcare information exchange that is distributed, auditable, immutable, and respectful of patient autonomy. Our multidisciplinary team identified key tasks required for a transition of care to design and develop a blockchain application, MediLinker, that served as a patient-centric identity management system to address the issues of data fragmentation ultimately allowing for the delivery of high value care.   Methods: The MediLinker application was evaluated for its ability to accomplish various key tasks needed for a successful transition of patient care in an outpatient setting. Our team created twenty unique patient use cases covering a diversity of medical needs and social circumstances that were played out by participants who were asked to perform various tasks as they received case across a simulated healthcare ecosystem composed of four clinics, a research institution, and other ancillary public services. Tasks included, but were not limited to, clinic enrollment, verification of identity, medication reconciliation, sharing insurance and billing information, and updating demographic information. With this iteration of MediLinker, we specifically focused on the functionality of Guardianship and patient revocation of healthcare information. Additionally, throughout the simulation we surveyed participant perceptions regarding the use of MediLinker and blockchain technology to better ascertain comfortability and usability of the application.   Results: Quantitative evaluation of simulation results revealed that MediLinker was able to successfully accomplish all seven clinical scenarios tested across the twenty patient use cases. MediLinker successfully achieved its goal of patient-centered interoperability as participants transitioned their simulated healthcare data, including COVID-19 vaccination status and current medications, across the four clinic sites and research institution. In addition to completing all key tasks designated, all eligible participants were able to enroll with and subsequently revoke data access with our simulated research site. A high degree of data accuracy was noted with most errors occurring due to inaccurate data entry from user input. Our qualitative analysis of user perceptions indicated that comfortability and trust with blockchain technology, such as MediLinker, grew with increased education and exposure to such technology.   Conclusion: The ubiquitous problem of data fragmentation in our current healthcare ecosystem has placed considerable strain on providers and patients alike. Blockchain applications for health identity management, such as MediLinker, provide a viable solution to stem the inefficiencies that exist today. The interoperability that MediLinker provided across our simulated healthcare system has the potential to improve transitions of care by sharing key aspects of healthcare information in a timely, secure, and patent-centric fashion allowing for the delivery of consistent personalized high-value care. Blockchain technologies appear to face similar challenges to widespread adoption as other novel interventions, namely recognition, trust, and usability. Further development and scaling are required for such technology to realize its full potential in the real-world and transform the practice of modern healthcare.