Systems In Healthcare Research Articles

A systematic review on fall detection systems for elderly healthcare

A systematic review on fall detection systems for elderly healthcare

Challenges of Health Informatics Systems in Primary Health Care

Health informatics systems are indispensable implements for cultivating the eminence and effectiveness of primary health care. Health informatics systems are formed of databases and figures regarding all clinical facilities. The utilization of health informatics systems helps both healthcare providers and patients in the management of disorders and disease risks and in developing well-being. However, there are a number of challenges to the application and practice of health informatics systems in the settings of primary health care. These challenges embrace; the absence of capital and money, the absence of technical skill, the absence of user recognition and approval, information quality problems, safety and confidentiality concerns, incorporation with other systems, and interoperability deficiency. Human, cultural, and financial barriers with deficiency of technical proficiency, deficiency of a relation between clinicians and patients, worries about information privacy, absence of administration guidelines, bureaucracy, lack of training, English language barrier, the degree of workload due to the time shortness during clinical settings, infrastructure concerns e.g. availability of the electricity, computers, and internet, also high turnover in IT among healthcare are also some challenges that should be addressed. These challenges can hold back the capacity of health informatics systems to comprehend their complete perspective in primary health care. To address these challenges, it is essential to provide sufficient capital and funds, offer practice and maintenance for users, overcome information quality concerns, apply safety and confidentiality procedures, incorporate health informatics systems with other systems, and endorse interoperability. By overcoming these challenges, health informatics systems can play a major role in developing the quality and efficiency of primary health care.

State of Newborn Care in Armed Forces Hospitals

Organized perinatal and newborn care in armed forces hospitals includes preconception and antenatal care, care during labor and childbirth, immediate newborn care, care of small and sick newborn including screening for hypoglycemia, care of healthy newborns in the first week including newborn screening for thyroid disorders, Glucose 6 phosphate dehydrogenase deficiency, universal hearing screening, and universal pulse oximetry. Neonatal mortality was 24.3% in less than 26 weeks (n = 37), 32.6% in neonates born between 26 weeks to 27+6 weeks (n = 95), 6.2% in neonates born between 28 weeks and 31+6 weeks (n = 242), 0.8% in 32 weeks to 36+6 weeks (n = 1890), and 0.1% in term neonates (n = 21,292). The comprehensive health care model in armed forces can be a model for other institutes and health systems for more effective neonatal health care.

Application of IT Systems in Health Care

Application of IT Systems in Health Care

A vendor-neutral business analytics and quality measurement system for radiation dose index monitoring

Radiation Dose Index (RDI) analytics tools are main components of the dose management systems in healthcare. Business analytics for radiation data can go from simple statistics to dynamic data analysis. It treats data visualization, analytics, and quality measurements. The complexity behind RDI analytics is not only about the technology used to create dashboards and compute quality measurements, but it is especially related to the complexity of collecting the data needed for the analytics tool. Most Dose Management Systems (DMS) are complex applications with many modules that provide multiple functionalities, like data collection, enhancement, and access. The central DMS module is the dose index tracker, which contains the data structure containing the radiation data collected from the different irradiating modalities from the various connected healthcare facilities. Most DMS modules are proprietary and connected to the dose index tracker without being independent of the vendor-specific dose database. Business analytics components in dose management systems have the same interoperability issue. Most of the radiation analytics tools and components have proprietary connectivity with a specific dose management system provider and cannot connect agnostically to different DMS providers. We design, test, and document multiple methods for radiation analytics to be agnostic and independent from the radiation dose index monitoring providers based on standardized Fast Healthcare Interoperability Resources (FHIR®) Application Programming Interfaces (API) connectivity.

HEALTH CARE ELECTRONIC SYSTEM IMPLICATIONS TOWARD DECREASING DELAYS

: In the ever-evolving landscape of healthcare delivery, the adoption of electronic systems has emerged as a critical strategy to mitigate delays and enhance the efficiency of healthcare processes. This research paper explores the implications of health care electronic systems in the context of reducing delays within healthcare settings. Through a comprehensive review of the literature and an analysis of real-world case studies, this study examines the multifaceted impact of electronic systems on reducing patient waiting times, streamlining administrative tasks, and optimizing clinical workflows. The findings reveal that the integration of electronic health records (EHRs), appointment scheduling systems, and telemedicine platforms has the potential to significantly decrease delays in patient care, improve access to healthcare services, and enhance overall patient satisfaction. Additionally, the study highlights challenges and considerations associated with the implementation of these systems, including data security and interoperability issues. This research contributes to the growing body of knowledge on the role of electronic systems in healthcare and underscores their transformative potential in addressing delays and advancing the quality of patient care. The healthcare landscape is continually evolving, driven by technological advancements and a relentless pursuit of improving patient care. One of the persistent challenges in healthcare has been delays – delays in accessing care, delays in receiving diagnoses and treatment, and delays in the overall healthcare process. These delays not only affect the patient's experience but can also have significant implications for healthcare outcomes. In response to these challenges, the healthcare industry has turned to electronic systems as a powerful tool for reducing delays and enhancing the efficiency of healthcare delivery. Electronic systems encompass a wide array of technologies and tools, ranging from Electronic Health Records (EHRs) to appointment scheduling systems and telemedicine platforms. These systems have the potential to revolutionize the way healthcare is delivered, bringing about transformative changes across various facets of healthcare provision.

A pre-hospital mixed methods systematic review protocol.

Mixed methods research, a methodology entailing the integration of qualitative and quantitative data within a single study, offers researchers the ability to investigate complex processes and systems in health and healthcare. The collective strength gained through the data combination can provide an enhanced understanding of research problems, providing an ideal solution to understanding complex clinical issues in a range of settings. In pre-hospital practice, where often uncontrollable variables and environmental considerations increase healthcare complexity, mixed methods has emerged as a valuable approach to research. Given the exponential growth of pre-hospital mixed methods research since the publication of our first systematic review in 2014, we aim to provide an update. Our review will explore how mixed methods is utilised in pre-hospital research and identify what standards of reporting are achieved. This systematic review update will search MEDLINE, CINAHL Complete, Embase and Scopus bibliographic databases from 1 January 2012 to 15 March 2023, using an updated pre-hospital search strategy. Study screening will be performed in duplicate. Articles reported in English, explicitly stating the use of 'mixed methods' in the pre-hospital ambulance setting, including helicopter emergency medical services and community first-responder services, will be included. Data related to underpinning philosophy or theoretical framework, rationale for utilising mixed methods, background of the corresponding author, mode of data integration, model of publication and adherence to reporting standards, utilising the good reporting of a mixed methods study (GRAMMS) guidelines, will be extracted and analysed. All extracted data from study articles will be summarised in a table, allowing analysis of included studies against specified criteria.

Performance measurement systems in the health and care sector: Are targets and monitoring additional demands or resources for employees?

PurposeThis paper aims to improve our understanding of performance measurement systems in the health and care sector, by focussing on employee reactions to core performance measurement practices. Targets and monitoring are hypothesised to be associated with employee perceptions of job control, supportive management and job demands, which in turn, are expected to be linked to employee-wellbeing and organisational commitment.Design/methodology/approachMatched employee workplace data are extracted from a nationally representative and publicly available survey. Structural equation models are estimated.FindingsPerformance measurement systems are neither perceived as resources nor additional demands. Setting many targets and a focus on productivity can lead to negative employee outcomes, since these positively correlate with perceptions of job demands, which negatively correlate with employee wellbeing. However, monitoring financial performance and monitoring employee performance may be helpful to managers, as these are positively associated with employee perceptions of job control and supportive management, which positively correlate with job satisfaction and organisational commitment and, negatively, with anxiety. Overall, common criticisms of performance measurement systems in healthcare are questioned.Originality/valueGiven the lack of consensus on how performance measurement systems can influence employee experiences and outcomes, this study combines theories that argue for performance measurement systems in managing operations with models developed by psychologists to describe how perceptions of the work conditions can affect employee attitude and wellbeing. A conceptual model is therefore developed and tested, and potential direct and indirect effects of performance measurement systems in the health sector are inferred.

Obligation for AI Systems in Healthcare: Prepare for Trouble and Make it Double?

AbstractThe Artificial Intelligence Act is the first EU proposal to impose binding provisions for AI Systems. As AI is now used in several different fields, those provisions setting AI legal standards might interact with other Regulations, especially in risky sectors, i.e. healthcare. This paper offers a brief analysis of what standards are to be considered if an AI system was intended to be used as a medical device. It shows the intersection between the Medical Device Regulation and the Artificial Intelligence Act, presenting which opportunities and challenges the new proposal might bring. Three case studies will test the intersection and report the practical outcomes in terms of classifications and obligations.

The possibility of AI-induced medical manslaughter: Unexplainable decisions, epistemic vices, and a new dimension of moral luck

The use of artificial intelligence (AI) systems in healthcare provides a compelling case for a re-examination of ‘gross negligence’ as the basis for criminal liability. AI is a smart agency, often using self-learning architectures, with the capacity to make autonomous decisions. Healthcare practitioners (HCPs) will remain responsible for validating AI recommendations but will have to contend with challenges such as automation bias, the unexplainable nature of AI decisions, and an epistemic dilemma when clinicians and systems disagree. AI decisions are the result of long chains of sociotechnical complexity with the capacity for undetectable errors to be baked into systems, which introduces a new dimension of moral luck. The ‘advisory’ nature of AI decisions constructs a legal fiction, which may leave HCPs unjustly exposed to the legal and moral consequences when systems fail. On balance, these novel challenges point towards a legal test of subjective recklessness as the better option: it is practically necessary; falls within the historic range of the offence; and offers clarity, coherence, and a welcome reconnection with ethics.

Assessing Available Open-Source PACS Options.

Medical imaging technology is producing a growing number of medical images types as well as patient-related information. The benefits of using modern medical imaging systems in healthcare are undeniable. Picture archiving and communication system (PACS) have revolutionized medical imaging practice. PACS have widely impacted the accessibility of medical images, reduced imaging costs, eliminated the physical storage of films, improved time management of radiologists, and allowed automated decision-making and diagnosis. Many health organizations and manufacturers have invested on developing commercial PACS. However, commercial PACS are not affordable for all hospitals while open-source PACS are increasingly becoming a viable option. Our research project is looking for an open-source PACS for the Donka University hospital of Guinea. Open-source PACS are currently available and are offering varying functionalities, documentation, and technical support from their developer communities. Selecting an open-source PACS is not an easy task and not only depends on the hospital requirements but also requires assessing each open-source PACS to find the best match. In this paper, the most popular open-source PACS are evaluated using a simple comparison approach based on four criteria. The result of this assessment shows that Orthanc, DCM4CHE, DCMTK, and Dicoogle are the most mature open-source PACS according to our criteria and the needs of Donka.

Operationalising AI ethics through the agile software development lifecycle: a case study of AI-enabled mobile health applications

AbstractAlthough numerous ethical principles and guidelines have been proposed to guide the development of artificial intelligence (AI) systems, it has proven difficult to translate these principles into actionable practices beyond mere adherence to ethical ideas. This is particularly challenging in the context of AI systems for healthcare, which requires balancing the potential benefits of the solution against the risks to patients and the wider community, including minorities and underserved populations. To address this challenge, we propose a shift from one-size-fits-all ethical principles to contextualized case-based ethical frameworks. This study uses an AI-enabled mHealth application as a case study. Our framework is built on existing ethical guidelines and principles, including the AI4People framework, the EU High-Level Expert Group on trustworthy AI, and wider human rights considerations. Additionally, we incorporate relational perspectives to address human value concerns and moral tensions between individual rights and public health. Our approach is based on ”ethics by design,” where ethical principles are integrated throughout the entire AI development pipeline, ensuring that ethical considerations are not an afterthought but implemented from the beginning. For our case study, we identified 7 ethical principles: fairness, agility, precision, safeguarding humanity, respect for others, trust and accountability, and robustness and reproducibility. We believe that the best way to mitigate and address ethical consequences is by implementing ethical principles in the software development processes that developers commonly use. Finally, we provide examples of how our case-based framework can be applied in practice, using examples of AI-driven mobile applications in healthcare.

Hierarchically Plied Mechano-Electrochemical Energy Harvesting Using a Scalable Kinematic Sensing Textile Woven from a Graphene-Coated Commercial Cotton Yarn.

Wearable sensing systems are suitable for monitoring human motion. To realize a cost-effective and self-powered strain-sensing fiber, we developed a mechano-electrochemical harvesting yarn and textile using hierarchically arranged plied yarns composed of meter-long graphene-coated cotton yarns. Such a fiber relies on the principle of electrochemical capacity change to convert mechanical energy to electric energy. Further, this harvester can be used as a self-powered strain sensor because its output depends on mechanical stimuli. Additionally, the yarn can be woven into a kinematic sensing textile that measures the strength and direction of the applied force. The textile-type harvester can successfully detect various human movements such as pressing, bending, and stretching. The proposed sensing fiber will pave the way for the development of advanced wearable systems for ubiquitous healthcare in the future.

Nursing Informatics' Contribution to One Health.

To summarise contemporary knowledge in nursing informatics related to education, practice, governance and research in advancing One Health. This descriptive study combined a theoretical and an empirical approach. Published literature on recent advancements and areas of interest in nursing informatics was explored. In addition, empirical data from International Medical Informatics Association (IMIA) Nursing Informatics (NI) society reports were extracted and categorised into key areas regarding needs, established activities, issues under development and items not current. A total of 1,772 references were identified through bibliographic database searches. After screening and assessment for eligibility, 146 articles were included in the review. Three topics were identified for each key area: 1) education: "building basic nursing informatics competence", "interdisciplinary and interprofessional competence" and "supporting educators competence"; 2) practice: "digital nursing and patient care", "evidence for timely issues in practice" and "patient-centred safe care"; 3) governance: "information systems in healthcare", "standardised documentation in clinical context" and "concepts and interoperability", and 4) research: "informatics literacy and competence", "leadership and management", and "electronic documentation of care". 17 reports from society members were included. The data showed overlap with the literature, but also highlighted needs for further work, including more strategies, methods and competence in nursing informatics to support One Health. Considering the results of this study, from the literature nursing informatics would appear to have a significant contribution to make to One Health across settings. Future work is needed for international guidelines on roles and policies as well as knowledge sharing.

Surmounting Barriers to Healthcare Data and Information: International Case Studies

Objective: In this article, the authors review the progress in healthcare interoperability from 2010 to 2023 from an international perspective. Countries covered here include the Republic of China (Taiwan), the Republic of Croatia, the Republic of Estonia, and the Commonwealth of Australia. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations,” as defined by the Institute of Electrical and Electronic Engineers (IEEE). This is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting. Methodology: A literature review was conducted on established interoperability standards and systems in healthcare based on information obtained from journal publications, government, academy reports, published materials, and publicly available websites. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: 1) inter-facility (macro-tier) interoperability, 2) intra-facility (meso-tier) interoperability, and 3) Point-of-Care (micro-tier) interoperability. The authors assessed the adoption levels of each standard by looking at factors that support or limit its systemic adoption. Estimations on the number of users—medical professionals and patients—for each system were made in instances where verifiable data are available. Results: Presented here is an evaluation of four interoperability parameters: 1) device/equipment interoperability, 2) compatibility issues, 3) involved organizations, and 4) migration and conversion issues. Conclusions: Despite many parallel ongoing efforts to improve the standardization of healthcare information in the mobile devices, Internet of Things (IoT), and electronic health records (HER) sectors, there remains space for improvement. The recent development of the Trusted Exchange Framework and Common Agreement (TEFCA) framework has significantly reduced the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, may also be an effective strategy for consolidating healthcare data and improving information exchange.

Surmounting Barriers to Healthcare Data and Information: Cases in Point, the U.S. Experience

Objective: The authors review the progress in healthcare interoperability from 2010 to 2023 in the United States. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations”, as defined by the IEEE (Institute of Electrical and Electronic Engineers). This is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting. Methodology: A literature review was conducted on established interoperability standards and systems in healthcare based on information obtained from journal publications, government, academy reports, published materials, as well as publicly available websites. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: 1) inter-facility (macro-tier) interoperability, 2) intra-facility (meso-tier) interoperability, and 3) Point-of-Care (micro-tier) interoperability. An evaluation of four interoperability parameters: 1) device/equipment interoperability, 2) compatibility issues, 3) involved organizations, and 4) migration and conversion issues are presented. The evaluation assessed the adoption levels of each standard by looking at factors that support or limit its systemic adoption. Estimations on the number of users—medical professionals and patients—for each system were made in instances where verifiable data were available. Results: This review reveals that… Conclusions: Despite many parallel ongoing efforts to improve the standardization of healthcare information, in the mobile devices, Internet of Things (IoT), and electronic health record (HER) sectors, there remains space for improvement. The recent development of the Trusted Exchange Framework and Common Agreement (TEFCA) greatly reduces the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, might also be an effective strategy for consolidating healthcare data and improving information exchange.

Pragmatic Approaches to Interoperability – Surmounting Barriers to Healthcare Data and Information Across Organizations and Political Boundaries

Objective This paper provides a review of the landscape of interoperability efforts in healthcare from 2010 to 2023, in the US and abroad. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations”, as defined by the IEEE (Institute of Electrical and Electronic Engineers). This review is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting. Methodology and Results A literature review was conducted on established interoperability standards and systems in healthcare, based on information obtained from journal publications, government, academy reports, published materials, and publicly available websites. The review emphasizes four interoperability parameters: device/equipment interoperability, compatibility issues, involved organizations, and migration and conversion issues. It evaluates adoption levels for each standard, evaluating factors supporting and/or limiting systemic adoption. Estimations on the number of users – both medical professionals and patients – for each system were made in instances where verifiable data were available. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: Inter-facility (macro-tier) interoperability, Intra-facility (meso-tier) interoperability, and Point-of-care (micro-tier) interoperability. Conclusions Despite many parallel ongoing efforts to improve the standardization of healthcare information, in the mobile devices, IoT, and EHR sectors, there is still more space for improvement. The recent development of the TEFCA framework has greatly reduced the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, may also be an effective strategy for consolidating healthcare data and improving information exchange.

A TOGAF based interoperable health information system needs assessment for practitioner–patient interaction

AbstracteHealth systems have been created in most developing countries to facilitate the functioning of healthcare operations and as such various healthcare applications are existing as fragmented silo systems. Such autonomous standalone systems do not communicate through a network thereby making it a challenge to share healthcare data. Therefore, to re‐engineer health information systems (HISs) to develop interoperable adaptive enterprise architecture (EA) systems for healthcare, the study aimed to discover and capture healthcare interoperability functional imperatives through understanding the expectations of healthcare practitioners and patients on post‐development of an interoperable HIS. Therefore, the study's aim was to determine the HISs interoperability perceived functional expectations by both patients and practitioners in fulfilling their healthcare receipt and provision needs in an integrated healthcare environment, respectively. The Open Group Architecture Framework (TOGAF) was used as the main study framework to guide the classification of the expectations from the HIS by patients and practitioners deriving the needs from the four domains which are, business architecture (BA), application architecture (AA), data architecture (DA), and technical architecture. The study used interviews and questionnaire surveys to collect qualitative and quantitative data respectively. The study used purposive sampling to select interview participants. A total of 19 interviews were conducted with healthcare practitioners. Questionnaires were collected from 71 healthcare practitioners and 143 patients and analyzed quantitatively respectively to understand the most significant needs anticipated in an interoperable HIS. The research targeted age groups of at least 20 years and above. The study discovered that patients and practitioners expect the interoperable healthcare environment to support the acquisition of disease knowledge through healthcare surveillance synergies; create healthcare awareness through coordinated digital interactions; augmentation of healthcare intelligence for patient‐care through the healthcare knowledgebase; allow treatment collaboration by various healthcare practitioners in the healthcare ecosystem and mostly achieving a guaranteed healthcare system security and assurance environment.

Ways to improve the efficiency of implementing artificial intelligence systems in medical practice

BACKGROUND: The medical market offers interesting solutions that use artificial intelligence (AI) technologies; however, such solutions often remain at the startup level or are used locally. The question is how to achieve the maximum when introducing AI systems into medical practice. AIM: To answer the question of why the large number of existing developments in AI and medical decision support systems are not used as widely as medical information systems, telemedical consultations, and other health IT solutions. Possible ways to improve the efficiency of implementing AI technologies and medical decision support systems in the work of physicians were presented. METHODS: Theoretical and general scientific (analysis of literature and Internet sources on the problem of research, synthesis, generalization, comparison, and systematization) and empirical (observation, interview, and testing) methods were used. RESUTLS: The main barriers to the effective implementation of AI systems in medical practice and possible options to solve the following problems were highlighted. Problem 1: incorrect data collection. Solution: care must be taken with the accumulation of materials used for the analysis and training by AI systems in medicine. Problem 2: incompetent developers. Solutions: involvement of effective third-party specialists or training ones own. Problem 3: medical workers and/or patients aversion to AI technologies. Solutions: education in the successful application of AI technologies and medical decision support systems in healthcare and involvement of practicing physicians as experts during the AI conceptualization. CONCLUSIONS: If the above criteria for the development and use of AI technologies and medical decision support systems are met, the effect of their introduction into medical practice will tend to be maximized.

The concept of responsible artificial intelligence as the future of artificial intelligence in medicine

Active deployment of artificial intelligence (AI) systems in medicine creates many challenges. Recently, the concept of responsible artificial intelligence (RAI) was widely discussed, which is aimed at solving the inevitable ethical, legal, and social problems. The scientific literature was analyzed and the possibility of applying the RAI concept to overcome the existing AI problems in medicine was considered. Studies of possible AI applications in medicine showed that current algorithms are unable to meet the basic enduring needs of society, particularly, fairness, transparency, and reliability. The RAI concept based on three principles accountability for AI activities and responsibility and transparency of findings (ART) was proposed to address ethical issues. Further evolution, without the development and application of the ART concept, turns dangerous and impossible the use of AI in such areas as medicine and public administration. The requirements for accountability and transparency of conclusions are based on the identified epistemological (erroneous, non-transparent, and incomplete conclusions) and regulatory (data confidentiality and discrimination of certain groups) problems of using AI in digital medicine [2]. Epistemological errors committed by AI are not limited to omissions related to the volume and representativeness of the original databases analyzed. In addition, these include the well-known black box problem, i.e. the inability to look into the process of forming AI outputs when processing input data. Along with epistemological errors, normative problems inevitably arise, including patient confidentiality and discrimination of some social groups due to the refusal of some patients to provide medical data for training algorithms and as part of the analyzed databases, which will lead to inaccurate AI conclusions in cases of certain gender, race, and age. Importantly, the methodology of the AI data analysis depends on the program code set by the programmer, whose epistemological and logical errors are projected onto the AI. Hence the problem of determining responsibility in the case of erroneous conclusions, i.e. its distribution between the program itself, the developer, and the executor. Numerous professional associations design ethical standards for developers and a statutory framework to regulate responsibility between the links described. However, the state must play the greatest role in the development and approval of such legislation. The use of AI in medicine, despite its advantages, is accompanied by many ethical, legal, and social challenges. The development of RAI has the potential both to solve these challenges and to further the active and secure deployment of AI systems in digital medicine and healthcare.