Patient Health Care Data Research Articles

A Framework of Digital Twins for Improving Respiratory Health and Healthcare Measures

The investigation describes an inventive use of digital twin technology and LSTM-based machine learning models for real-time patient lung disease monitoring and nutrition planning. The suggested application uses various patient healthcare data, treatment processes, dietary habits, and real-time sensor information to construct digital twins, which are virtual reproductions of specific patients. The LSTM model is trained on this large dataset to predict patient health improvements and dietary needs. For each patient’s digital twin, the program provides personalized treatment plans and nutritional advice, enabling proactive interventions and optimizing patient care. Using performance measures, the trained LSTM model achieves high scores for accuracy (92%), precision (89%), recall (93%), and F1 score (91%), proving its usefulness in generating credible health predictions. Patient feedback on the program shows that patients (98.8%) agree on the accuracy and importance of health feedback, as well as the convenience of access to health information (95.4%). The application’s response rate study reveals an average response rate of 85.87%, assuring prompt feedback. To secure patient information, the study emphasizes data privacy and security, adopting multilayered authentication and data encryption. The outcomes of this study demonstrate the application’s potential to revolutionize patient-centered healthcare by providing data-driven, personalized solutions to patients and healthcare professionals.

Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things

Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things

An Approach for Generating Realistic Australian Synthetic Healthcare Data.

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.

Encryption Gradient of Patients’ EMR using Forms of DICOM

Data protection is important. it is especially required for competent patient protection. If it is not done well, it can pose various problems. The problems of leakage can make patients vulnerable. This literally means that the healthcare environment can be threatened in this aspect. DICOM is a digitalized image compression technique using radiologic materials. It restores CT, PET-CT, and X-ray images in order to gain a specific image. It is then compiled to be sent as PACS. These imaging techniques use matrix as a data presenting method. However, it is sent in a linear form. It does not mean that it is not protected, but it means that there are various risks to be managed. In our report, we review that, in the future, a double layer of encryption with patient voice can dramatically evolve the probability of accessing patient healthcare data. This can also be applied in EMR or EHR in the future. The limitations of this study are that there are still risks of cyber-attack. The DDoS can totally make the system function down. However, we see this as a different problem as this is an electronic source matter rather than a hack of preserved data.

Questions to Ask When Considering the Use of Big Data

Savvy healthcare leaders must understand big data and how invaluable big data platforms are for improving patient care and generating potential sources of revenue. Leaders must acknowledge, however, the potential financial, legal, security, and ethical pitfalls when others work with their de-identified patient healthcare data. The authors provide a guide for healthcare leaders on how to use the sensitive and important patient data they house.

Antibacterial self-healing bilayer dressing for epidermal sensors and accelerate wound repair

This study aimed to investigate the effect of the bilayer hydrogel as a wound dressing on the wound-healing rate. We synthesized a self-healing hydrogel with optimized formulation by introducing natural polymer (chitosan) and arginine to the hydrogel composition. We then characterized the hydrogels using FT-IR, thermal analysis, mechanical testing, and in vitro and in vivo assay. The resulting bilayer wound dressing offers a lot of desirable characteristics, including good self-healing and repeatable adhesiveness. Likewise, the conductive bilayer wound dressing could be used to analyze the patient's healthcare data in real-time as epidermal sensors. Bilayer wound dressings remarkably have broad antibacterial efficacy against Gram-positive and Gram-negative bacteria. The potential applications of this bilayer wound dressing are illustrated by detectable body movement and conductivity. The wound-healing rate of bilayer wound dressings containing chitosan and arginine was higher, but those without the aforementioned ingredients had lower wound-healing efficacy. Additionally, promoting collagen synthesis and reducing wound infection has a considerable therapeutic impact on wounds. These results could have significant implications for the development of high-performance wound dressings.

Patient Perspectives on Artificial Intelligence in Healthcare Decision Making: A Multi-Center Comparative Study.

Investigate the patient opinion on the use of Artificial Intelligence (AI) in Orthopaedics. 397 orthopaedic patients from a large urban academic center and a rural health system completed a 37-component survey querying patient demographics and perspectives on clinical scenarios involving AI. An average comfort score was calculated from thirteen Likert-scale questions (1, not comfortable; 10, very comfortable). Secondary outcomes requested a binary opinion on whether it is acceptable for patient healthcare data to be used to create AI (yes/no) and the impact of AI on: orthopaedic care (positive/negative); healthcare cost (increase/decrease); and their decision to refuse healthcare if cost increased (yes/no). Bivariate and multivariable analyses were employed to identify characteristics that impacted patient perspectives. The average comfort score across the population was 6.4, with significant bivariate differences between age (p = 0.0086), gender (p = 0.0001), education (p = 0.0029), experience with AI/ML (p < 0.0001), survey format (p < 0.0001), and four binary outcomes (p < 0.05). When controlling for age and education, multivariable regression identified significant relationships between comfort score and experience with AI/ML (p = 0.0018) and each of the four binary outcomes (p < 0.05). In the final multivariable model gender, survey format, perceived impact of AI on orthopaedic care, and the decision to refuse care if it were to increase cost remained significantly associated with the average AI comfort score (p < 0.05). Additionally, patients were not comfortable undergoing surgery entirely by a robot with distant physician supervision compared to close supervision. The orthopaedic patient appears comfortable with AI joining the care team.

CHARACTERIZING THE JOHNS HOPKINS INFLAMMATORY BOWEL DISEASE COHORT USING OMOP CDM

Abstract BACKGROUND Inflammatory Bowel Disease (IBD) is an increasingly prevalent condition that results in weight loss, bloody diarrhea, and poor quality of life. Traditional population-based research on patients with IBD has involved use of relatively shallow claims data that is deficient in precise demographics. IBD research using richly detailed electronic health record (EHR) data has been difficult due to the need for laborious manual chart review to extract information, but new methods and technology allow for rapid EHR data extraction. We present here the results of the extraction of IBD patient data from our EHR into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). METHODS This study is an observational cohort study using patient health care data from our EHR extracted into the OMOP CDM. Transforming data into the standard CDM format allows for information from different databases and coding schema to be compared and aggregated. We used a de-identified dataset of all patients seen at Johns Hopkins Medicine since 2016. Our cohort of IBD, Crohn’s disease (CD), and Ulcerative Colitis (UC) patients were stratified by age, sex, race, weight, and pregnancy status. Within the cohorts, we obtained demographics, comorbidities, symptoms, labs, medications, and other data. We validated the extraction by comparison with EHR queries using Slicer/Dicer (SD). RESULTS There were 11,357 IBD patients including 6,512 CD patients and 4,224 UC patients. There were more female patients for both CD and UC (56.4% v 57.5%) and more white patients for both CD and UC (55.4% v 56.7%) compared to black (23.5% v 22.6%) and Asian patients (5.6% v 4.9%). CD and UC patients were treated with systemic steroids (49.1% v 51.5%), 5-ASA (24.9% v 47.2%) anti-TNF inhibitors (24.0% v 12%), IL12/23 inhibitors (8.4% v 2.7%), and JAK inhibitors (0.3% v 1.8%). Using SD queries, the percent differences were determined between the OMOP and EHR data, revealing a 0.54% difference in the number of CD patients and 10.36% difference in the number of UC patients (Table 1). CONCLUSION Using OMOP CDM to analyze clinical characteristics and outcomes for patients with IBD is a valuable method for obtaining de-identified patient data, reducing patient risk, and facilitating rapid creation of on-demand cohorts for multi-institutional research studies. This advantage is balanced by the lack of access to row-level data to validate cohorts. Local validation of data extraction through SD queries can allow for rapid creation of cohorts for big data research. The most significant differences between SD queries and OMOP CDM was in the calculation of prevalence of medications and race, suggesting that differing data extraction coding methods could result in queries that underestimate rates of treatment due to proprietary local coding schema.

A System For Storing Anonymous Patient Healthcare Data Using Blockchain And Its Applications

Abstract In this paper, a system is proposed which uses blockchain technology in healthcare. In this system, patients can access their health records anytime from anywhere. Moreover, the patients’ health records are put into the blockchain anonymously. Whenever a patient visits a healthcare professional, the authorized entity filters patients’ medical report out by eliminating the patients’ sensitive information. Then, the filtered medical data are put into an off-chain database, while the address of the data is put into the blockchain with an assigned pseudo random identity of the patient. Thus, there are multi pseudo random identities for each patient. Unlike previous studies where the patients’ identities/reports were linkable, in the proposed protocol the patients’ identities are not linkable. The proposed system can also be used to show patients’ health status to some entities when a pandemic happens (e.g. COVID-19). During the COVID-19 pandemic, the patients are required to show their series of vaccinations before they travel internationally/nationally or participate in some social events. To travel or join some events, the patient needs to show only a partial medical history to the security guard without leaking any private information. Furthermore, once the anonymous medical data are put into the off-chain database, the data can be used for data mining and machine learning.

Artificial intelligence in healthcare: Setting new algo RHYTHM in medicine

Artificial intelligence (AI) is a rapidly expanding avenue in science and is often used to describe the use of computers, big data mining, modeling, and network that may simulate intelligent behavior and critical thinking similar to a human being but at an exceedingly fast pace. What lies at the heart of AI are intelligent algorithms that drive the whole process integratively, imparting the decisive ability to machines. The artificial intelligence market is expected to grow at a rate of 39.4% by 2022-2028. British mathematician Alan Turing’s 1950 paper “Computing Machinery and Intelligence " established AI's fundamental goal and vision. Fundamentally, AI is a branch of computer science that aims to answer Turing’s question of replicating human intelligence as machines. Since AI research aims to make smarter algorithms evolve and emulate human-like functioning, the degree to which an AI system can replicate human capabilities is used to determine the types of AI. The volume, magnitude, and complexity of data in healthcare mean that artificial intelligence (AI) will be increasingly applied and exploited, with several types of AI already being employed by researchers, clinicians, and data analysts. AI is a collective term, not just one technology, with an immediate relevance to healthcare. Few AI technologies of high importance to healthcare are: Machine learning, Neural Networks, and Deep Learning Natural Language Processing ( NLP) Physical Robots Rule-based expert systems The AI-associated healthcare market is growing at an exponential pace and is expected to reach USD 6.6 billion by 2021. Precision medicine initiatives can be divided into three types of clinical areas: complex algorithms, digital health applications, and “omics”-based tests. Large genomic datasets and informatics related to demographic data or diagnostic reports are exploited by machine learning algorithms for prediction and prognosis. This is coupled with patient healthcare data, such as treatment protocols, treatment response, response to therapy, and health monitoring, with data input from clinicians, researchers, and the public domain. High throughput big data from population-based studies are used with machine learning algorithms to establish correlations, build predictive models, and provide customized treatment protocols. In Omics based era, data from genomics, proteomics, and metabolomics are exceedingly used to address and identify health-related problems in large study cohorts . This will provide opportunities for translation and transition from bench to bedside through precision medicine.

CUNA: A privacy preserving medical records storage in cloud environment using deep encryption

Securing the medical images in cloud environment becomes mandatory because of massive image accumulation and information storage in cloud premises. Large accommodation of information in the cloud brings another risk of data security. Privacy preserving data mining techniques investigate privacy of data to be maintained before or either after the data mining technique. Lightweight technique to store the patient data securely in the cloud is focused. The proposed study is focused on designing a robust model in which medical records privacy is preserved using Customizable Unique node access (CUNA) architecture. The system design undergoes data preparation, encryption, cloud transfer, decryption and analysis modules. The input to the proposed system is patient health care data such as CT images, Medical records, Clinical treatment procedures etc. The CT images are encrypted with respect to their medical records, further transferred into the cloud by hiding the text based confidential data into the images. Inspite of normal cloud storage process, the proposed approach utilized deep cryptography technique to make authenticated storage mechanism adaptive in the cloud. CUNA is the customized commonly allocated key assigned at every node. The unique key word is encrypted, and utilized to transfer the privacy data of the patients. The data can be retrieved from the cloud, after the decryption of CUNA code. The correlation of the key is systematically achieved using Gaussian regression process algorithm (GRA). The proposed approach is compared with various state-of-art approaches and obtained the accuracy of 92% with image MSE of 0.0012%.

Augmented security for healthcare data using obfuscation and elliptic curve cryptography algorithm in health cloud environment

SummaryIn the past few years, the growing demands of cloud computing became high in the healthcare sector. The electronic health record (EHR) has turned out to be the chosen system to uphold patient's healthcare data. Due to the advancement of cloud computing, the users in the cloud are empowered to access their data with elasticity and offering huge storage capability at minimum costs. This motivates EHR maintainers to transfer their personal storage to the remote cloud servers as it significantly reduces the burden of storage and computation. Nevertheless, securing healthcare data file poses a gigantic challenge as data outsourced in the cloud are too sensitive. Several security features such as data confidentiality, data privacy, access control, data integrity, and data security necessities to be satisfied. This article proposed a secure sharing of healthcare data file by incorporating obfuscation technique and elliptic curve cryptography (ECC) algorithm to enforce better data confidentiality and integrity. Data obfuscation is a beneficial technique in preserving the data confidentiality on the cloud by minimizing the risk of data theft and data leakage. Moreover, ECC methodology aims to provide a high level of security, low communication and computation cost with smaller key size. Experimental analysis reveals better performance than other existing schemes in terms of computation time, communication time, key generation time, throughput rate, turnaround time, and uploading speed. Besides, it exposes that the proposed framework is highly secure and efficient in the field of healthcare system.

An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems.

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

Blockchain based context-aware CP-ABE schema for Internet of Medical Things security

Nowadays, the number of corona patients is increasing significantly. The relationship between the Internet of Medical Things (IoMT) and the Internet is struggling to keep up with this number of patients. The transmission of Patient Health Records (PHR) to the care of a patient through Internet plays an important role in the remote monitoring and fast detection of new contaminated patient with coronavirus. Moreover, it has generated significant security and privacy concerns for the global health care system due to tampering of control messages. This paper focuses on the application of blockchain and smart contract mechanisms to solve the shortcomings of the current health application and propose a new security schema based on context-aware CP-ABE. The proposed schema includes context-aware policies to achieve a robust authentication of identity and confidentiality of patient's healthcare data. Therefore, the proposed schema shows promising results in enhancing security and minimizing encryption time in Fog cloud environments based on proxy-fog and reinforcement of security policies.

Health technology assessments and real-world evidence: tell us what you want, what you really, really want.

Health technology assessments and real-world evidence: tell us what you want, what you really, really want.

An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network.

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.

An Effective Blockchain Based Secure Searchable Encryption System

Security of Patient health records (PHR) is the most important aspect of cryptography over the Internet due to its value and importance preferably in the medical Internet of Things (IoT). Search keywords access mechanism is one of the common approaches which is used to access PHR from database, but it is susceptible to various security vulnerabilities. Although Blockchain-enabled healthcare systems provide security, but it may lead to some loopholes in the existing schemes. However, these methods primarily focused on data storage, and blockchain is used as a database. In this paper, Blockchain as a distributed database is proposed with homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Moreover, the proposed approach provides a secure key revocation mechanism and update various policies accordingly. A secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Our proposed approach provides improved security, efficiency, and transparency with cost effectiveness. We performed our simulations based on the blockchain based tool termed as the Hyperledger fabric and Origionlab for analysis and evaluation. We have compared our proposed results with the benchmark models. Our comparative analysis justify that our proposed framework gives improvement in security and searchable mechanism for healthcare system.

A Public Key Authentication and Privacy Preserving Model for Securing Healthcare System

Over the last few years, the medical Internet of Things (IoMT) has experienced numerous security challenges related to the monitoring of patient healthcare data. In IoT centric healthcare framework, connected clinical devices are more prone to severe security menaces and attacks against other network devices. Current solutions can ensure the patient’s information during data transfer to some extent but cannot avoid some of the advanced cyber-attacks like data leakage, collision attacks, and data integrity. Thus, this paper proposes an efficient privacy-preserving and public-key authentication model for securing IoT-based healthcare systems. An efficient public-key authentication system with access policy-based confidentiality is used to preserve privacy. In this proposed model, the patients stay in their homes, and their health assistance will send the sensor data using a mobile healthcare application. First, the patient must be registered with the corresponding hospital using patient credentials on the hospital website or mobile application. After getting the unique login id and successful login, sensed data is encrypted using the proposed method and securely uploaded to the hospital cloud server by generating an access policy. Then, in the hospital, the corresponding doctor can download the secured patient data with high confidentiality using the same access policy generated while uploading the health data from the hospital cloud server. The results gained from the experiments demonstrate a superior performance of the proposed mechanism over the existing methodologies, and it is evidenced to be more secure and efficient.

Iris based cancelable biometric cryptosystem for secure healthcare smart card

Health related information of an individual is very sensitive and demands a high level of security and privacy. Healthcare providers have the responsibility to ensure that patient information is secure and accessible only to authorized users. Healthcare systems are using biometrics since long for authentication and/or access control purposes. Biometrics can also be used for healthcare data security and privacy. This paper proposes an iris based cancelable biometric cryptosystem to securely store the healthcare data of patients on the smart card. It employs symmetric key cryptography to encrypt the healthcare data and store it on the smart card in encrypted form. We use the fuzzy commitment scheme to bind the secret encryption key with the cancelable iris template of the patient. Our proposed scheme provides user authentication as well as the decryption of healthcare data when needed by using the iris template of the owner of the healthcare smart card. The implementation results show that our proposed scheme provides better performance as compared to other schemes. It can generate an encryption key of a maximum of 252 bits from the input iris template with a false acceptance rate (FAR) of 0 and a false rejection rate (FRR) of 0.07. The generated key can be used for encrypting the health care data of patients using a symmetric encryption algorithm, e.g. Advance Encryption Standard (AES), International Data Encryption Algorithm (IDEA), Blowfish, etc. As compared to a conventional encryption system where the security of the system depends on keeping the key secret, our proposed scheme binds the encryption key with the iris - template of the patient impeccably without the need to store it securely. The security analysis demonstrates that it is not possible for an attacker to retrieve the secret key or healthcare data of the patient from the stolen healthcare card.

Cryptanalysis and Security Improvement of Two Authentication Schemes for Healthcare Systems Using Wireless Medical Sensor Networks

Wireless medical sensor networks (WMSNs) play an important role in collecting healthcare data of the remote patient and transmitting them to the medical professional for proper diagnosis via wireless channel. To protect the patient's healthcare data which is private-related and sensitive, some authentication schemes for healthcare systems using WMSN have been proposed to ensure the secure communication between the medical sensors and the medical professional. Since cryptanalyzing the security defects of authenticated protocols is crucial to put forward solutions and propose truly robust protocols, we scrutinize two state-of-the-art authentication protocols using WMSN for healthcare systems. Firstly, we examine Ali et al.’s enhanced three-factor based authentication protocol and show that although it provides a formal proof and a security verification, it still fails to resist offline dictionary guessing attack, desynchronization attack, and privileged insider attack and contains a serious flaw in the password change phase. Secondly, we investigate Shuai et al.’s lightweight and three-factor based authentication protocol and point out that it cannot achieve high security level as they claimed; it is actually subject to offline dictionary guessing attack and privileged insider attack, and it also has a design flaw in the password change phase. In addition, we suggest several countermeasures to thwart these security weaknesses in these two schemes for WMSN and the similar kinds.