Parkinson\'s disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

In the digital era, the seamless integration of electronic medical records (EMR) stands as a pivotal milestone in transforming healthcare delivery, with Saudi Arabia at the forefront of this revolution in the Middle East. This literature review comprehensively explores the challenges and opportunities associated with adopting EMR in the Kingdom of Saudi Arabia (KSA) in alignment with the nation\'s Vision 2030 healthcare objectives. The review synthesizes research from various scholarly sources, utilizing databases such as PubMed, Scopus, Google Scholar, and regional databases, and focuses on literature published between 2010 and 2023. Our methodology included a strategic combination of keywords and a stringent selection criterion to ensure a focus on relevant EMR adoption studies within KSA. The review addresses key aspects of EMR adoption, including technical challenges, financial constraints, human factors, cultural and organizational barriers, privacy and security concerns, and policy and regulatory challenges. It also explores the integration of EMR with other digital health initiatives like telehealth, personal health records, and community pharmacy services. The findings reveal a complex interplay of factors influencing EMR adoption, highlighting the need for comprehensive strategies that address technical, financial, cultural, and policy-related barriers. The review concludes that while significant challenges exist, strategic approaches and solutions tailored to the specific context of Saudi Arabia can effectively facilitate EMR integration, thereby enhancing healthcare quality and efficiency in line with the nation\'s Vision 2030 goals.

Perceptions of errors associated with healthcare information technology (HIT) often depend on the context and position of the viewer. HIT vendors posit very different causes of errors than clinicians, implementation teams, or IT staff. Even within the same hospital, members of departments and services often implicate other departments. Organizations may attribute errors to external care partners that refer patients, such as nursing homes or outside clinics. Also, the various clinical roles within an organization (e.g., physicians, nurses, pharmacists) can conceptualize errors and their root causes differently. Overarching all these perceptual factors, the definitions, mechanisms, and incidence of HIT-related errors are remarkably conflictual. There is neither a universal standard for defining or counting these errors. This paper attempts to enumerate and clarify the issues related to differential perceptions of medical errors associated with HIT. It then suggests solutions.

UNASSIGNED: As part of the FDA\'s DSCSA Pilot Project Program, UCLA and its solution partner, LedgerDomain (collectively referred to as the team hereafter), focused on building a complete, working blockchain-based system, BRUINchain, which would meet all the key objectives of the Drug Supply Chain Security Act (DSCSA) for a dispenser operating solely on commercial off-the-shelf (COTS) technology.
UNASSIGNED: The BRUINchain system requirements include scanning the drug package for a correctly formatted 2D barcode, flagging expired products, verifying the product with the manufacturer, and quarantining suspect and illegitimate products at the last mile: pharmacist to patient, the most complex area of the drug supply chain.The authors demonstrate a successful implementation where product-tracing notifications are sent automatically to key stakeholders, resulting in enhanced timeliness and reduction in paperwork burden. At the core of this effort was a blockchain-based solution to track and trace changes in custody of drug. As an immutable, time-stamped, near-real-time (50-millisecond latency), auditable record of transactions, BRUINchain makes it possible for supply chain communities to arrive at a single version of the truth. BRUINchain was tested using real data on real caregivers administering life-saving medications to real patients at one of the busiest pharmacies in the United States.
UNASSIGNED: In addition to communicating with the manufacturer directly for verification, BRUINchain also initiated suspect product notifications. During the study, a 100% success rate was observed for scanning, expiration detection, and counterfeit detection; and paperwork reduction from approximately 1 hour to less than a minute.
UNASSIGNED: By automatically interrogating the manufacturer\'s relational database with our blockchain-based system, our results indicate a projected DSCSA compliance cost of 17 cents per unit, and potentially much more depending on regulatory interpretation and speed of verification. We project that this cost could be reduced with manufacturers\' adoption of a highly performant, fully automated end-to-end system based on digital ledger technology (DLT). During an examination of the interoperability of such a system, we elaborate on its capacity to enable verification in real time without keeping humans in the loop, the key feature driving lower compliance cost. With 4.2 billion prescriptions being dispensed each year in the United States, DLT would not only reduce the projected per-unit cost to 13 cents per unit (saving $183 million in annual labor costs), but also serve as a major bulwark against bad or fraudulent transactions, reduce the need for safety stock, and enhance the detection and removal of potentially dangerous drugs from the drug supply chain to protect US consumers.

UNASSIGNED: Health organizations increasingly digitize. However, studies reveal contradictory findings regarding the impact of healthcare information technology on health professionals. Therefore, the aim of this study is to describe the prevalence of technostress among health professionals and elaborate on the influencing factors.
UNASSIGNED: A secondary analysis was conducted utilizing cross-sectional data from the study, \"Work-related stress among health professionals in Switzerland\", which included 8,112 health professionals from 163 health organizations in Switzerland.
UNASSIGNED: ANOVA for group comparisons followed by post-hoc analyses, along with a Multilevel Model to identify influencing factors for technostress ranging from \"0\" (never/almost never) to \"100\" (always), were conducted.
UNASSIGNED: Health professionals experienced moderate technostress (mean 39.06, SD 32.54). Technostress differed between settings (p <.001) and health professions (p < .001). The model explains 18.1% of the variance with fixed effects, or 24.7% of the variance with fixed and random effects. Being a physician (β = 12.96), a nurse (β = 6.49), or the presence of an effort-reward-imbalance, increased technostress most (β = 6.11). A professional with no professional qualification (β = -7.94) showed the most reduction.
UNASSIGNED: Health professionals experience moderate technostress. However, decision-makers should consider the cognitive and social aspects surrounding digitalization, to reach a beneficial and sustainable level of usage.

OBJECTIVE: Mobile health (m-Health) technologies may provide an appropriate follow-up support service for patient groups with post-treatment conditions. While previous studies have introduced m-Health methods for patient care, a smart system that may provide follow-up communication and decision support remains limited to the management of a few specific types of diseases. This paper introduces an m-Health solution in the current climate of increased demand for electronic information exchange.
METHODS: Adopting a novel design science research approach, we developed an innovative solution model for post-treatment follow-up decision support interaction for use by patients and physicians and then evaluated it by using convergent interviewing and focus group methods.
RESULTS: The cloud-based solution was positively evaluated as supporting physicians and service providers in providing post-treatment follow-up services. Our framework provides a model as an artifact for extending care service systems to inform better follow-up interaction and decision-making.
CONCLUSIONS: The study confirmed the perceived value and utility of the proposed Clinical Decision Support artifact indicating that it is promising and has potential to contribute and facilitate appropriate interactions and support for healthcare professionals for future follow-up operationalization. While the prototype was developed and tested in a developing country context, where the availability of doctors is limited for public healthcare, it was anticipated that the prototype would be user-friendly, easy to use, and suitable for post-treatment follow-up through mobility in remote locations.

The ability of nurses to adopt and successfully use EMR is expected to have a significant impact on achieving benefits such as reduction in healthcare costs and improvement in healthcare quality. A review of the current research literature reveals issues and concerns relating to the adoption and use of EMR by nurses in hospital environments. This article presents a literature review of such issues and concerns, and suggests a framework for enhancing the adoption and use of EMR by nurses and hospitals.

In order to improve the quality and performance of healthcare services, healthcare information technology is among the most important technology in healthcare supply chain management. This study sets out to apply and test the Unified Theory of Acceptance and Use of Technology (UTAUT), to examine the factors influencing healthcare Information Technology (IT) services. A structured questionnaire was developed and distributed to healthcare representatives in each province surveyed in Thailand. Data collected from 400 employees including physicians, nurses, and hospital staff members were tested the model using structural equation modeling technique. The results found that the factors with a significant effect are performance expectancy, effort expectancy and facilitating conditions. They were also found to have a significant impact on behavioral intention to use the acceptance healthcare technology. In addition, in Thai provincial areas, positive significance was found with two factors: social influence on behavioral intention and facilitating conditions to direct using behavior. Based on research findings, in order for healthcare information technology to be widely adopted and used by healthcare staffs in healthcare supply chain management, the healthcare organizational management should improve healthcare staffs\' behavioral intention and facilitating conditions.

Task and error analysis research was performed to identify: a) the process for healthcare organisations in managing healthcare for patients with mental illness or substance abuse; b) how the process can be enhanced and; c) if electronic medical records (EMRs) have a role in this process from a business and safety perspective. The research question is if EMRs have a role in enhancing the healthcare for patients with mental illness or substance abuse. A discussion on the business of EMRs is addressed to understand the balancing act between the safety and business aspects of an EMR.

The Secretary of Health and Human Services (HHS) acting through the Food and Drug Administration (FDA), and in collaboration with the Federal Communications Commission (FCC) and Office of the National Coordinator for Health IT (ONC) was tasked with delivering a report on an appropriate, risk-based regulatory framework for health information technology (IT). An expert stakeholder group was established under the auspices of the Health IT Policy Committee to help provide input into the development of this framework, including how healthcare IT systems could be stratified in terms of risk and recommendations about how the regulatory requirements currently in place should be adapted. In this paper, we summarize the public deliberations and final public report of the expert stakeholder group, and conclude with key suggestions intended to address the charge to recommend the features of a risk-based regulatory framework that promote innovation, protect patient safety, and avoid regulatory duplication.