Healthcare Data Research Articles

Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.

Interoperability Working Group: core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of adistributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for adistributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting afuture-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.

A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease. The objective is to design a big data approach for heart disease classification using the proposed Jellyfish Search Flow Regime Optimization (JSFRO)-based SpinalNet. The binary image conversion is applied on Electrocardiogram (ECG) images for converting the image to binary image. MapReduce model is adapted, in which the mappers execute feature extraction and the reducer performs heart disease classification. In the mapper phase, the features like statistical features, shape features and temporal features are extracted and in reducer, the SpinalNet with JSFRO is considered. Here, the training of SpinalNet is done with JSFRO, which is produced by the unification of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO). The JSFRO-based SpinalNet offered effectual performance with the finest accuracy of 90.8%, sensitivity of 95.2% and specificity of 93.6%.

Development of competencies in the Medical Informatics Initiative (MII)-educational offers for acompetent and secure handling of medical data

In order to achieve the goals of the Medical Informatics Initiative (MII), staff with skills in the field of medical informatics and data science are required. Each consortium has established training activities. Further, cross-consortium activities have emerged. This article describes the concepts, implemented programs, and experiences in the consortia. Fifty-onenew professorships have been established and 10new study programs have been created: 1bachelor's degree and 6consecutive and 3part-time master's degree programs. Further, learning and training opportunities can be used by all MII partners. Certification and recognition opportunities have been created.The educational offers are aimed at target groups with abackground in computer science, medicine, nursing, bioinformatics, biology, natural science, and data science. Additional qualifications for physicians in computer science and computer scientists in medicine seem to be particularly important. They can lead to higher quality in software development and better support for treatment processes by application systems.Digital learning methods were important in all consortia. They offer flexibility for cross-location and interprofessional training. This enables learning at an individual pace and an exchange between professional groups.The success of the MII depends largely on society's acceptance of the multiple use of medical data in both healthcare and research. The information required for this is provided by the MII's public relations work. There is also an enormous need in society for medical and digital literacy.

A general framework for developing computable clinical phenotype algorithms.

Present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data. Drawing on extensive prior phenotyping experiences and insights derived from three algorithm development projects conducted specifically for this purpose, our team with expertise in clinical medicine, statistics, informatics, pharmacoepidemiology, and healthcare data science methods conceptualized stages of development and corresponding sets of principles, strategies, and practical guidelines for improving the algorithm development process. We propose five stages of algorithm development and corresponding principles, strategies, and guidelines: 1) assessing fitness-for-purpose, 2) creating gold standard data, 3) feature engineering, 4) model development, and 5) model evaluation. This framework is intended to provide practical guidance and serve as a basis for future elaboration and extension.

Taipei Medical University Clinical Research Database: a collaborative hospital EHR database aligned with international common data standards

ObjectiveThe objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data...

Uneven primary healthcare supply of rural doctors and medical equipment in remote China: community impact and the moderating effect of policy intervention

BackgroundUnequal access to primary healthcare (PHC) has become a critical issue in global health inequalities, requiring governments to implement policies tailored to communities’ needs and abilities. However, the place-based facility dimension of PHCs is oversimplified in current healthcare literature, and formulating the equity-oriented PHC spatial planning remains challenging without understanding the multiple impacts of community socio-spatial dynamics, particularly in remote areas. This study aims to push the boundary of PHC studies one step further by presenting a nuanced and dynamic understanding of the impact of community environments on the uneven primary healthcare supply.MethodsFocusing on Shuicheng, a remote rural area in southwestern China, multiple data are included in this village-based study, i.e., the facility-level healthcare statistics data (2016–2019), the statistical yearbooks, WorldPop, and Chinese GDP’s spatial distribution data. We evaluate villages’ PHC service capacity using the number of doctors and essential equipment per capita, which are the major components of China’s PHC delivery. The indicators describing community environments are selected based on extant literature and China’s planning paradigms, including town- and village-level factors. Gini coefficients and local spatial autocorrelation analysis are used to present the divergences of PHC capacity, and multilevel regression model and (heterogeneous) difference in difference model are used to examine the driving role of community environments and the dynamics under the policy intervention.ResultsDespite the general improvement, PHC inequalities remain significant in remote rural areas. The village’s location, aging, topography, ethnic autonomy, and economic conditions significantly influence village-level PHC capacity, while demographic characteristics and healthcare delivery at the town level are also important. Although it may improve the hardware setting in village clinics (coef. = 0.350), the recent equity-oriented policy attempts may accelerate the loss of rural doctors (coef. = − 0.517). Notably, the associations between PHC and community environments are affected inconsistently by this round of policy intervention. The town healthcare centers with higher inpatient service capacity (coef. = − 0.514) and more licensed doctors (coef. = − 0.587) and nurses (coef. = − 0.344) may indicate more detrimental policy effects that reduced the number of rural doctors, while the centers with more professional equipment (coef. = 0.504) and nurses (coef. = 0.184) are beneficial for the improvement of hardware setting in clinics.ConclusionsThe findings suggest that the PHC inequalities are increasingly a result of joint social, economic, and institutional forces in recent years, underlining the increased complexity of the PHC resource allocation mechanism. Therefore, we claim the necessity to incorporate a broader understanding of community orientation in PHC delivery, particularly the interdisciplinary knowledge of the spatial lens of community, to support its sustainable development. Our findings also provide timely policy insights for ongoing primary healthcare reform in China.

Hospitalization of patients with nutritional anemia in the United States in 2020

BackgroundNutritional anemia is highly prevalent and has triggered a globally recognized public health concern worldwide.ObjectiveTo better understand the prevalence of anemia and the state of nutritional health in developed countries to inform global nutritional health and better manage the disease.MethodWe employed the Healthcare Cost and Utilization Project (HCUP)-2020 National Inpatient Health Care Data (NIS), administered by The Agency for Healthcare Research and Quality. Nutritional anemia was diagnosed according to the International Classification of Diseases, 10th Revision (ICD-10). Matching analysis and multivariate regression were used to adjust for patient and hospital characteristics. Controls were obtained by stratifying and matching for age and sex.ResultsThe 2020 HCUP-NIS database encompassed a survey over 6.4 million hospitalized patients, among which 1,745,350 patients diagnosed with anemia, representing approximately 26.97% of the hospitalized population, over 310,000 were diagnosed with nutritional anemia, and 13,150 patients were hospitalized for nutritional anemia as primary diagnosis. Hospitalization rate for nutritional anemia exhibited an increased age-dependent increase nationwide, especially among females, who displayed 1.87 times higher than males. Notably, in comparison to the control group, individuals of the Black race exhibit a higher prevalence of nutritional anemia (case group: 21.7%, control group: 13.0%, p < 0.001). In addition, hospitalization rates were higher among low-income populations, with lower rates of private insurance (case group: 18.7%, control group: 23.5%, p < 0.001) and higher rates of Medicaid insurance (case group: 15.4%, control group: 13.9%, p < 0.001). In areas characterized by larger urban centers and advanced economic conditions within the urban–rural distribution, there was an observed increase in the frequency of patient hospitalizations. Iron deficiency anemia emerged as the predominant subtype of nutritional anemia, accounting for 12,214 (92.88%). Secondary diagnosis among patients hospitalized for nutritional anemia revealed that a significant number faced concurrent major conditions like hypertension and renal failure.ConclusionIn economically prosperous areas, greater attention should be given to the health of low-income individuals and the older adult. Our findings hold valuable insights for shaping targeted public health policies to effectively address the prevalence and consequences of nutritional anemia based on a overall population health.

Blockchain with optimal deep learning assisted secure data sharing and classification on future healthcare systems

In the future of healthcare, Blockchain (BC) technology holds immense potential for improving the security and privacy of data. By allowing the secure and immutable storage of medical files and healthcare-related transactions, BC ensured that sensitive medical data remains tamper-proof and open only to authorized parties. Patients have greater control over their data's development, revoking or granting access as required, but healthcare workers can streamline data sharing and ensure the integrity of important data. The decentralized nature of BC networks decreases the risk of centralized data breaches, eventually fostering trust and transparency in the healthcare ecosystems. Conversely, deep learning (DL) has great to revolutionize healthcare diagnostics in the future, offering quick and extremely accurate estimates of medical conditions. This technology has greatly enhanced patient solutions, decreased medical expenses, and improved the burden on medical staff by providing appreciated insights into an extensive range of conditions, from cancer to neurological disorders. With this stimulus, this study presents a novel BC with optimal DL-based secure data sharing and classification (BCODL-SDSC) technique in the future healthcare system. The goal of the BCODL-SDSC technique is to secure and thoroughly examine healthcare data using BC and DL techniques. Primarily, the BCODL-SDSC technique enables BC technology to store and maintain the patient’s data from the procedure of several transactions and enable access control to the various stakeholders. For the security of the medical images, the BCODL-SDSC technique applies the Fractional Order Lorenz system (FOLS) based encryption technique with tuna swarm optimization (TSO) algorithm based optimal key generation process. Finally, a multi-stage process performs the classification of the medical images: MobileNetv1 feature extractor, artificial rabbit’s optimization (ARO) based hyperparameter tuning, and stacked recurrent neural network (SRNN) based classification. The experimental outcome of the BCODL-SDSC technique was examined on a benchmark medical image database. An extensive comparative study reported that the BCODL-SDSC technique reaches an effective performance with other models with a maximum accuracy of 99.11%.

Introducing a digital emergency obstetric and newborn care register for indoor obstetric patient management: An implementation research in selected public health care facilities of Bangladesh.

Digital health records have emerged as vital tools for improving health care delivery and patient data management. Acknowledging the gaps in data recording by a paper-based register, the emergency obstetric and newborn care (EmONC) register used in the labour ward was digitised. In this study, we aimed to assess the implementation outcome of the digital register in selected public health care facilities in Bangladesh. Extensive collaboration with stakeholders facilitated the development of an android-based electronic register from the paper-based register in the labour rooms of the selected district and sub-district level public health facilities of Bangladesh. We conducted a study to assess the implementation outcome of introducing the digital EmONC register in the labour ward. The digital register demonstrated high usability with a score of 83.7 according to the system usability scale, and health care providers found it highly acceptable, with an average score exceeding 95% using the technology acceptance model. The adoption rate reached an impressive 98% (95% confidence interval (CI) = 98-99), and fidelity stood at 90% (95% CI = 88-91) in the digital register, encompassing more than 80% of data elements. Notably, fidelity increased significantly over the implementation period of six months. The digital system proved a high utility rate of 89% (95% CI = 88-91), and all outcome variables exceeded the predefined benchmark. The implementation outcome assessment underscores the potential of the digital register to enhance maternal and newborn health care in Bangladesh. Its user-friendliness, improved data completeness, and high adoption rates indicate its capacity to streamline health care data management and improve the quality of care.

Analysis of healthcare data security with DWT-HD-SVD based-algorithm invisible watermarking against multi-size watermarks

In the modern day, multimedia and digital resources play a crucial role in demystifying complex topics and improving communication. Additionally, images, videos, and documents speed data administration, fostering both individual and organizational efficiency. Healthcare providers use tools like X-rays, MRIs, and CT scans to improve diagnostic and therapeutic capacities, highlighting the importance of these tools in contemporary communication, data processing, and healthcare. Protecting medical data becomes essential for maintaining patient confidentiality and service dependability in a time when digital assets are crucial to the healthcare industry. In order to overcome this issue, this study analyses the DWT-HD-SVD algorithm-based invisible watermarking in medical data. The main goal is to verify medical data by looking at a DWT-based hybrid technique used on X-ray images with various watermark sizes (256*256, 128*128, 64*64). The algorithm’s imperceptibility and robustness are examined using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) and are analyzed using Normalized Connection (NC), Bit Error Rate (BER), and Bit Error Rate (BCR) in order to evaluate its resistance to various attacks. The results show that the method works better with smaller watermark sizes than it does with larger ones.

Hybrid cryptosystem based healthcare data sharing with access control policy in cloud environment

Hybrid cryptosystem based healthcare data sharing with access control policy in cloud environment

Implementation of the electronic health record in the German healthcare system: an assessment of the current status and future development perspectives considering the potentials of health data utilisation by representatives of different stakeholder groups

IntroductionThe digitalisation of the German healthcare system enables a wide range of opportunities to utilize healthcare data. The implementation of the EHR in January 2021 was a significant step, but compared to other European countries, the implementation of the EHR in the German healthcare system is still at an early stage. The aim of this paper is to characterise the structural factors relating to the adoption of the EHR in more detail from the perspective of representatives of stakeholders working in the German healthcare system and to identify existing barriers to implementation and the need for change.MethodsQualitative expert interviews were conducted with one representative from each of the stakeholder groups health insurance, pharmacies, healthcare research, EHR development and panel doctors.ResultsThe interviews with the various stakeholders revealed that the implementation process of the EHR is being delayed by a lack of a viable basis for decision-making, existing conflicts of interest and insufficient consideration of the needs of patients and service providers, among other things.DiscussionThe current status of EHR implementation is due to deficiency in legal regulations as well as structural problems and the timing of the introduction. For instance, the access rights of various stakeholders to the EHR data and the procedure in the event of a technical failure of the telematics infrastructure are remain unclear. In addition, insufficient information and communication measures have not led to the desired acceptance of EHR use among patients and service providers.

DNA Steganography: Embedding the Secret Messages Using Glass Stack Method and Detecting Errors Made by Different Attacks

Cyber threats pose a significant challenge to protecting cloud-based health data, including DNA sequences and patient information. Steganography is a well-known solution to this problem. Recent studies have limitations, such as not reconstructing the cover or enlarging the original sequence, increasing personal data security risks. In this study, we provide a novel data-hiding technique that uses a secret key, bit padding, and exclusive OR (XOR) operation to insert secrets into a pre-processed DNA sequence. The message encryption and pre-processing of the cover are performed using a new glass stack pattern technique. The message encryption and pre-processing processes using the new glass stack pattern method increase the security and robustness of the system. The method embeds 3 bits per nucleotide which is greater than the existing schemes. It has a relatively small DNA expansion which is 50% and has a low risk of breaking security. The method also preserves the biological properties of DNA sequences. Additionally, a parity check approach further improves security by thwarting attacks like man-in-the-middle, chosen stego, and modification attacks that alter the stego DNA. These advances improve the system's overall security capabilities. The experimental findings demonstrate that the suggested method reconstructs the cover, healthcare data, and DNA sequence properly and error-free, and outperforms competing algorithms on all performance metric dimensions. Highlights The scheme implants a maximum of 3 bits per nucleotide with a small expansion. Its cover pre-processing and message encryption using a glass stack pattern makes strong security. The scheme can detect and fix changed bits caused by attacks. A very low cracking probability shows strong security and robustness. The biological properties of the stego DNA sequence are preserved by this scheme.

Health Care Utilization During the First 5½ Years of Authorized Service by Transgender U.S. Service Members (2016-2021).

Purpose: This study examined the utilization of gender-affirming health care by active-duty service members during the initial 5½ years that transgender and gender-diverse (TGD) individuals were authorized to serve in the U.S. military. The aim of this study was to inform policy discussions regarding inclusion of TGD individuals in the military. Methods: We conducted a retrospective cohort study using administrative health care data from the Military Data Repository (MDR). We assessed the association of demographic factors with the utilization of gender-affirming medical and surgical care. We calculated the incidence rate of initial TGD-related encounters and new prescriptions for gender-affirming hormones among Defense Department Service members between July 2016 and December 2021. Results: We identified 2481 service members with an initial health care encounter with an associated TGD-related diagnosis. More than half (53%) of these service members started gender-affirming hormones, and 14% underwent gender-affirming surgery. Mastectomies and hysterectomies accounted for more than 70% of surgeries. Service members made 3.22 initial encounters with a TGD-related diagnosis per 10,000 service members per year, with an overrepresentation of service members who were designated female in the MDR (8.62), junior enlisted (4.98), and young (4.64). Individuals designated female in the MRD comprised 17% of all service members but accounted for 46% of initial encounters, 51% of new prescriptions, and 73% of surgeries. Conclusion: The study revealed a higher-than-expected number of service members seeking gender-affirming care, particularly among service members designated female in the MDR. Military Health System clinicians provided most of this care, which may mitigate the cost of delivering this essential medical care.

A Standard Framework for Evaluating Large Health Care Data and Related Resources.

MMWR supplement presents a standard framework for evaluating large health care data and related resources, including constructs, criteria, and tools that investigators and evaluators can apply and adapt.

Students health physique information sharing in publicly collaborative services over edge-cloud networks

Data privacy is playing a vital role while facing the digital life aspects. Today, the world is being extensively inter-connected through the internet of things (IoT) technologies. This huge interconnectivity is bringing very wonderful capabilities for improving the quality of life (QoL) with itself, for instance, in distributed healthcare. On the other hand, there are new challenges in the interconnectivity per use. One of the most challenging issues of IoT use in social systems and digital life is secure, trustable, and reliable interactions over IoT networks such that safety, security, and privacy in both aspects of cyber and physical worlds for humankind should be planned and controlled.Due to the less physical activity of most people in the current world, fitness and aerobic sports are now an important need at any age to help them keep healthy in their cyber-physical life, specifically, for the younger student that are still in the growth ages. However, these sport activities need to be monitored seriously and closely to not put their life in danger. Herewith, healthcare services through IoT is becoming more applicable. Therefore, health information privacy for athletes is now a hot topic of investigation as we present the topic here. We propose an IoT-based physique healthcare system considering private information sharing for athletes based on data hiding at the edge of a collaborative system. The proposed system pays attention to the key factors of healthcare IoT infrastructure but it is bringing its new suggestions for more safety. Moreover, many evaluations based on different kinds of healthcare data are provided.

Shielding Healthcare Data Strategies for Preventing Breaches

As the healthcare sector embraces digital transformation, the risk of cyberattacks on sensitive patient data escalates. This paper presents a comprehensive analysis of contemporary strategies and technologies employed to mitigate data breaches in healthcare. Through an extensive review of 70 research papers, we identify a strong focus on securing electronic health records, data storage systems, access controls, and personal health information. Noteworthy trends include the integration of advanced technologies such as Blockchain and Artificial Intelligence, coupled with robust encryption methods, to fortify data security. Our analysis underscores the urgent need for innovative approaches to combat prevalent cybersecurity threats, particularly targeting hacking incidents and unauthorized access. Furthermore, we emphasize the ongoing evolution of mitigation strategies and the imperative of continual technological advancements to safeguard healthcare data effectively. This paper also outlines future research directions, highlighting areas for further technological innovation and addressing existing gaps in data breach prevention measures.

Safeguarding Research Using Federal Health Insurance Data

This Viewpoint discusses the importance of researcher access to federal health care data following a CMS decision to limit the use of physical data and proposes solutions to maintain access and security.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder

BackgroundMajor Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies.MethodsThis prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source “Pothos” air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels.ResultsFollowing initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness.ConclusionThis study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.