Healthcare Context Research Articles

Adaptation of the Nursing Activities Score in Latvia.

This study focuses on the adaptation and validation of the Nursing Activities Score (NAS) for use in Latvian intensive care units (ICUs) to measure nursing workload. The NAS, widely used internationally, was selected for its comprehensive ability to reflect 81% of ICU nursing activities, making it a suitable tool for assessing nursing workload in the Latvian healthcare context. The study followed a two-phase methodology: (1) expert validation using the Content Validity Index (CVI) and (2) a pilot study to assess the psychometric properties of the adapted tool. In the first phase, 10 ICU nursing experts assessed the translated NAS items, resulting in revisions to three specific paragraphs (4a, 14, and 20) based on low CVI scores. After refinement, CVIs improved from 0.6 to 0.8 for paragraphs 4a and 14, and from 0.5 to 0.9 for paragraph 20. The final CVI for all items reached 0.909. In the second phase, a pilot study was conducted in a Latvian ICU with 42 patients and 226 NAS assessments. The psychometric evaluation showed strong reliability and validity, confirming the NAS's suitability for measuring nursing workload in this context. Cronbach's alpha for the scale was 0.973. The adapted NAS provides a standardised method for workload assessment in Latvian ICUs, offering potential improvements in nurse resource management and patient care.

The role of chief medical officers in the Italian context: managers or clinicians?

PurposeDespite the advent of New Public Management theories over three decades ago, doubts persist regarding the practical implementation of these principles in the public health-care context. Challenges arise particularly from the type of system where this phenomenon is analyzed. In the Italian context, for instance, it can be arduous to define universally applicable organizational behaviors, given the interregional disparity that characterizes such a system. Furthermore, the professional identity of clinician-managers influences the perception of what “being a manager” means. This paper, thus, using the Italian context as a reference, aims to delineate what is the perception of Italian public hospitals clinician-middle-managers in terms of their responsibilities and tasks.Design/methodology/approachA survey-questionnaire was distributed to 6,011 Chief Medical Officers (CMOs) in Italian public hospitals, representing 100% of such role holders in the country. With a response rate of 16.7%, 1,005 responses were obtained. The questionnaire assessed CMOs' attitudes toward specific activities relevant to clinician-management, ranked from most to least important. Activities were derived from literature and categorized to discern management styles. Subgroups based on geographical location and professional orientation were also identified to isolate regional effects and professional identity influences.FindingsResults suggested that activities associated with a collaborative approach are perceived as most important. Furthermore, it clearly emerges the difference based on professional orientation of CMOs. However, it could not be appreciated the same level of difference basing the analysis on regional disparities.Originality/valueThe interest in the role of middle management in healthcare organizations has increased over the years. Nevertheless, currently the authors believe that not many studies are focused on defining what “being a manager” means for clinician-managers themselves, rather than explaining what clinician-managers shall do.

Sources of trust in the healthcare context. A multilevel relationship with work engagement and organisational outcomes.

The present study analysed the mediating role of interactional justice and horizontal trust between transformational leadership and organisational outcomes (i.e., job performance and service quality) at the work team level and the cross-level relationship of team horizontal trust with job performance at the individual level, controlling for work engagement based on the HERO Model. Through structural equations and hierarchical linear models, the proposed hypotheses were addressed. The sample corresponds to 1,638 workers grouped into 109 work teams belonging to 28 hospitals in Spain. First, Structural Equation Modelling analysis revealed that, as expected, at the team level, interactional justice and horizontal trust mediate positive and significantly the relationship between transformational leadership and organisational outcomes (i.e., job performance and service quality). Secondly, the results of the Linear Hierarchical Models showed a positive relationship between work engagement and individual level performance. Finally, the multilevel analysis revealed that horizontal trust at the team level is positively related to work engagement at individual level; however, there is no cross-level relationship between horizontal trust at the team level and individual performance controlled by work engagement. Horizontal trust, at the team level, is positioned as a mediating variable between resources and organisational outcomes. Furthermore, it proves to be a key cross-level element for generating work engagement and job performance. The theoretical and practical implications of the study based on the HERO Model are discussed.

Analysis of Determining Inpatient Room Rates Using the Activity-Based Costing Method in a Healthcare Setting

Background: Hospitalization rate determination is crucial for healthcare cost management, yet traditional methods often lead to cost distortions. Specific Background: Muhammadiyah Tuban Hospital has historically utilized conventional costing methods, which have resulted in higher than necessary charges for hospital services. Knowledge Gap: There is limited research comparing traditional costing methods with more accurate and modern approaches like Activity Based Costing (ABC) in the Indonesian healthcare context. Aim: This study aims to compare hospitalization rates at Muhammadiyah Tuban Hospital using traditional methods and the ABC method, to highlight discrepancies and propose a more accurate costing framework. Results: The ABC method yielded significantly lower hospitalization rates across various classes (VIP, Class 1, Class 2, and Class 3) compared to traditional methods. For instance, the ABC rate for the VIP class was IDR 270,619, compared to IDR 495,000 set by traditional methods. Novelty: This study introduces the application of ABC in an Indonesian hospital setting, providing a clear contrast in cost-efficiency against traditional costing. Implications: The findings suggest that adopting the ABC method can lead to more precise and lower hospitalization rates, potentially reducing healthcare costs for patients and improving financial management within hospitals.Highlights: Cost Accuracy: ABC method offers more precise hospitalization rates than traditional methods. Economic Impact: Adoption of ABC can lower patient costs and improve hospital financial management. Innovation: Introduces a modern costing method to Indonesian healthcare, suggesting a shift from outdated practices. Keywords: Activity Based Costing, Hospitalization Rates, Cost Management, Healthcare Economics

Development of a maturity model for demand and capacity management in healthcare

BackgroundThe aim of this paper is to develop a maturity model (MM) for demand and capacity management (DCM) processes in healthcare settings, which yields opportunities for organisations to diagnose their planning and production processes, identify gaps in their operations and evaluate improvements.MethodsInformed by existing DCM maturity frameworks, qualitative research methods were used to develop the MM, including major adaptations and additions in the healthcare context. The development phases for maturity assessment models proposed by de Bruin et al. were used as a structure for the research procedure: (1) determination of scope, (2) design of a conceptual MM, (3) adjustments and population of the MM to the specific context and (4) test of construct and validity. An embedded single-case study was conducted for the latter two - four units divided into two hospitals with specialised outpatient care introducing a structured DCM work process. Data was collected through interviews, observations, field notes and document studies. Thematic analyses were carried out using a systematic combination of deductive and inductive analyses - an abductive approach - with the MM progressing with incremental modifications.ResultsWe propose a five-stage MM with six categories for assessing healthcare DCM determined in relation to patient flows (vertical alignment) and organisational levels (horizontal alignment). Our application of this model to our specific case indicates its usefulness in evaluating DCM maturity. Specifically, it reveals that transitioning from service activities to a holistic focus on patient flows during the planning process is necessary to progress to more advanced stages.ConclusionIn this paper, a model for assessing healthcare DCM and for creating roadmaps for improvements towards more mature levels has been developed and tested. To refine and finalise the model, we propose further evaluations of its usefulness and validity by including more contextual differences in patient demand and supply prerequisites.

The promise of artificial intelligence in health: Portrayals of emerging healthcare technologies

AbstractEmerging technologies of artificial intelligence (AI) and automated decision‐making (ADM) promise to advance many industries. Healthcare is a key locus for new developments, where operational improvements are magnified by the bigger‐picture promise of improved care and outcomes for patients. Forming the zeitgeist of contemporary sociotechnical innovation in healthcare, media portrayals of these technologies can shape how they are implemented, experienced and understood across healthcare systems. This article identifies current applications of AI and ADM within Australian healthcare contexts and analyses how these technologies are being portrayed within news and industry media. It offers a categorisation of leading applications of AI and ADM: monitoring and tracking, data management and analysis, cloud computing, and robotics. Discussing how AI and ADM are depicted in relation to health and care practices, it examines the sense of promise that is enlivened in these representations. The article concludes by considering the implications of promissory discourses for how technologies are understood and integrated into practices and sites of healthcare.

Method "Monte Carlo" in healthcare.

In public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions. In simulation modeling, the Monte Carlo method emerges as a powerful yet underutilized tool. Although the Monte Carlo method has not yet gained widespread prominence in healthcare, its technological capabilities hold promise for substantial cost reduction and risk mitigation. In this review article, we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts. We underscore the significance of experiential insights derived from simulated experimentation, especially in resource-constrained scenarios where time, financial constraints, and limited resources necessitate innovative and efficient approaches. As public health faces increasing challenges, incorporating the Monte Carlo method presents an opportunity for enhanced system construction, analysis, and evaluation.

Exploring empathy in artificial intelligence: synthesis and paths for future research

Purpose The current research elucidates the role of empathy in design of artificial intelligence (AI) systems in healthcare context, through a structured literature review, analysis and synthesis of academic literature published between 1990 and 2024. Design/methodology/approach This study aims to advance the domain of empathy in AI by adopting theory constructs context method approach using the PRISMA 2020 framework. Findings The study presents a current state-of-the-art literature to review the connections between empathy and AI and identifying four clusters showing the emerging trajectories in the field of AI and empathy in healthcare setting. Originality/value Despite a rise in empirical research, the potential pathways enhancing AI accountability by incorporation of empathy is unclear. The research aims to contribute to the existing literature on AI and empathy in the healthcare sector by carving out four distinct clusters depicting the future research avenues.

Key stakeholders’ perspectives of illicit drug use and associated harms in the Northern Territory of Australia

BackgroundIn the Northern Territory (NT) of Australia, there are significant evidence gaps about illicit drug use and harms, despite having established monitoring and reporting systems. This paper reports on illicit drug use, associated harms, contributing factors, service needs and priorities in the NT from the perspective and experiences of key stakeholders engaged in providing services for, or advocating on behalf of, people who use illicit drugs in the NT.MethodsFace-to-face and online qualitative interviews were conducted with stakeholders across urban and remote locations in the NT. Key stakeholders were service providers, including acute and primary care clinicians, representatives of Aboriginal community controlled health organisations, lived experience advocates, peak body representatives and public health executives. Qualitative data were analysed thematically.ResultsFour researchers interviewed 21 participants across urban (62%), and remote areas (38%) of the NT. Themes identified were: (1) Illicit drug use and harms are diverse and distinct; (2) Client support needs are complex and influenced by co-morbidities, socio-demographic and cultural factors; (3) Priority population sub-groups need targeted strategies; (4) Local service strengths can be further developed and enhanced; (5) Local services need better resourcing; (6) Invest in progressive legislative and policy reforms; and (7) Improve routine monitoring and evaluation.ConclusionsKey stakeholders described illicit drug use, harms and contributing factors, which provided insights into the local challenges. Participants emphasised that clients have complex care needs, and further investment into targeted strategies are required to improve service engagement with priority groups. Service needs included greater understanding the role of dual diagnosis and its implementation and enhancing integrated and collaborative care in both primary health and acute care contexts. The voices of people with lived experience captured in this paper must inform local strategy and policy development relating to illicit drug use, in alignment with national strategy.

International students' perceived quality of university health centre services: an exploratory sequential mixed methods study.

This study aims to investigate international students' perspectives on service quality and analyse factors contributing to the perceived service quality of the university health centre. International students are at increased risk of experiencing poor mental health, isolation from families and cultures, language barriers, financial stress and academic pressures. It is important that universities support international students to enable them to complete their degrees and reach their desired level of achievement and performance. One of the supports required by international students is the availability of healthcare services. Therefore, improving the quality of services to meet international students' requirements, including healthcare services, is essential. A three-phase exploratory sequential mixed methods design was used. Phase 1 aims to explore international students' perceptions of primary healthcare quality by conducting in-depth interviews and focus group discussions. Data were analysed using thematic analysis. Phase 2 is to form questionnaire items based on the results of the qualitative study. The questionnaire is subject to pilot testing to measure validity and reliability. Phase 3 analyses factors influencing international students' perceived primary healthcare service quality. Multiple regression was used to analyse factors contributing to the perceived service quality of international students. The qualitative strand revealed five major themes representing the study participants' thoughts about the quality of services in the university healthcare context. Perceived quality attributes identified in this study were primarily empathy, equity, effectiveness, efficiency and safety. The quantitative strand found that 35.57% of participants consider the perceived quality of the centre as good. The highest and lowest service quality attributes were related to safety and efficiency, with a score of 21.12 ± 3.58 and 19.57 ± 4.34, respectively. The multiple linear regression analyses showed that PhD students from Health Faculty and Scholarship awardees were significantly associated with the perceived quality of healthcare services. Thus, the university management needs to improve service quality considering the diversity of international students' socio-demographic characteristics.

Assessing the Capability of Advanced AI Models in Cardiovascular Symptom Recognition: A Comparative Study

The field of medical informatics has been significantly transformed in recent years with the emergence of Natural Language Understanding (NLU) and Large Language Models (LLM), providing new opportunities for innovative patient care solutions. This study aims to evaluate the effectiveness of publicly available LLMs as symptom checkers for cardiological diseases by comparing their diagnostic capabilities in real disease cases. We employed a set of 9 models, including ChatGPT-4, OpenSource models, Google PaLM 2, and Meta’s LLaMA, to assess their diagnostic accuracy, reliability, and safety across various clinical scenarios. Our methodology involved presenting these LLMs with symptom descriptions and test results in Spanish, requiring them to provide specialist diagnoses and recommendations in English. This approach allowed us to compare the performance of each model, highlighting their respective strengths and limitations in a healthcare context. The results revealed varying levels of accuracy, precision, and sensitivity among the models, demonstrating the potential of LLMs to enhance medical education and patient care. By analysing the capabilities of each model, our study contributes to a deeper understanding of artificial intelligence’s role in medical diagnosis. We argue for the strategic implementation of LLMs in healthcare, emphasizing the importance of balancing sensitivity and realism to optimize patient outcomes.

Comprehensive needs assessment tool for informal cancer caregivers (CNAT-ICs): Instrument development and cross-sectional validation study

BackgroundGrowing cancer incidence and its subsequent burden is a worldwide concern. Needs assessment for caregivers has recently received growing attention, as it identifies specific unmet needs. The remaining tools have been established within the healthcare context of Western countries and have been studied only in some Asian populations; it seems appropriate to develop needs assessment tools that apply to a wider ethnic and socio-cultural context. ObjectiveThis study planned to adapt and examine the psychometric properties of the CNAT-C for the Sri Lankan informal caregivers for wider applicability. DesignAn instrument development and cross-sectional validation study was conducted. SettingApeksha Hospital Maharagama, Sri Lanka (National Cancer Institute). ParticipantsA sample of 226 informal caregivers (ICs) providing palliative care for patients with advanced cancer was selected. MethodsA CNAT-C (41 items; seven factors) was incorporated and used after a cross-cultural adaptation following WHO guidelines after the permission and pilot test. ICs completed the socio-demographic and clinical details along with the validated Centre for Epidemiological Studies-Depression (CES-D), and the World Health Organization-Quality of Life-Brief (WHOQOL-BREF). Internal consistency and test-retest were used to check the reliability. Convergent and divergent validity of the Sinhala version of CNAT (S-CNAT) was confirmed using the CES-D scale and WHOQOL-BREF. Construct validity was evaluated using the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). ResultsMost of the participants were female (60 %) and married (72 %), and the mean age was 41.78 (SD+14.54). Face and content validity were established during the cross-cultural adaptation. Cronbach's alpha was 0.903 for the overall S-CNAT and the test-retest reliability was 0.965. The S-CNAT was associated positively with the CES-D while negatively with the WHOQOL-BREF. Both EFA and CFA discovered a structure contained seven factors (35 items); domain named as healthcare staff/nurses’ support and information, physical/practical needs, medical officers’ support, psychological needs, social/family support, spiritual/religious support, and hospital facilities/service. ConclusionsThe Sinhala version of CNAT is shown to have adequate validity and reliability in assessing the comprehensive and multidimensional/unmet needs of informal caregivers of patients with advanced cancer (S-CNAT-ICs). It would be a helpful tool to determine the unmet needs of ICs and guide future interventions to meet those needs and enhance or maintain the quality of life for patients and their informal caregivers.

Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.

The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitudes of health professionals, but far fewer studies have explored the perspectives of patients or the general public. Studies investigating patient perspectives have focused on somatic issues, including those related to radiology, perinatal health, and general applications. Patient feedback has been elicited in the development of specific mental health care solutions, but broader perspectives toward AI for mental health care have been underexplored. This study aims to understand public perceptions regarding potential benefits of AI, concerns about AI, comfort with AI accomplishing various tasks, and values related to AI, all pertaining to mental health care. We conducted a 1-time cross-sectional survey with a nationally representative sample of 500 US-based adults. Participants provided structured responses on their perceived benefits, concerns, comfort, and values regarding AI for mental health care. They could also add free-text responses to elaborate on their concerns and values. A plurality of participants (245/497, 49.3%) believed AI may be beneficial for mental health care, but this perspective differed based on sociodemographic variables (all P<.05). Specifically, Black participants (odds ratio [OR] 1.76, 95% CI 1.03-3.05) and those with lower health literacy (OR 2.16, 95% CI 1.29-3.78) perceived AI to be more beneficial, and women (OR 0.68, 95% CI 0.46-0.99) perceived AI to be less beneficial. Participants endorsed concerns about accuracy, possible unintended consequences such as misdiagnosis, the confidentiality of their information, and the loss of connection with their health professional when AI is used for mental health care. A majority of participants (80.4%, 402/500) valued being able to understand individual factors driving their risk, confidentiality, and autonomy as it pertained to the use of AI for their mental health. When asked who was responsible for the misdiagnosis of mental health conditions using AI, 81.6% (408/500) of participants found the health professional to be responsible. Qualitative results revealed similar concerns related to the accuracy of AI and how its use may impact the confidentiality of patients' information. Future work involving the use of AI for mental health care should investigate strategies for conveying the level of AI's accuracy, factors that drive patients' mental health risks, and how data are used confidentially so that patients can determine with their health professionals when AI may be beneficial. It will also be important in a mental health care context to ensure the patient-health professional relationship is preserved when AI is used.

External validation of the parsimonious EuroLung risk models: analysis of the Brazilian Lung Cancer Registry.

The purpose of this study was to assess performance in the Brazilian Lung Cancer Registry Database by using the parsimonious EuroLung risk models for morbidity and mortality. The EuroLung1 and EuroLung2 models were tested and evaluated through calibration (calibration plot, Brier score, and the Hosmer-Lemeshow test) and discrimination (ROC AUCs), in a national multicenter registry of 1,031 patients undergoing anatomic lung resection. The evaluation of performance in Brazilian health care facilities utilizing risk-adjustment models, specifically EuroLung1 and EuroLung2, revealed substantial miscalibration, as evidenced by calibration plots and Hosmer-Lemeshow tests in both models. In terms of calibration, EuroLung1 exhibited a calibration plot with overlapping points, characterized by a slope of 1.11 and a Brier score of 0.15; the Hosmer-Lemeshow test yielded a statistically significant p-value of 0.015; and the corresponding ROC AUC was 0.678 (95% CI: 0.636-0.721). The EuroLung2 model displayed better calibration, featuring fewer overlapping points in the calibration plot, with a slope of 1.22, with acceptable discrimination, as indicated by a ROC AUC of 0.756 (95% CI: 0.670-0.842). Both models failed to accurately predict morbidity and mortality outcomes in this specific health care context. Discrepancies between the EuroLung model predictions and outcomes in Brazil underscore the need for model refinement and for a probe into inefficiencies in the Brazilian health care system.

Patients and caregivers as ‘regulatory actors’ in healthcare systems: Assisted dying as a case study

This article examines the role of patients and caregivers as ‘regulatory actors’ in healthcare. It uses and extends ‘regulatory space theory’ to argue that failing to consider the role of patients and caregivers as ‘regulatory actors’ in healthcare regulatory spaces misses a key piece of the regulatory puzzle. We argue that in assisted dying and other healthcare contexts, evidence shows that some patients and caregivers do behave as regulatory actors, engaging in sustained and focused attempts to alter behaviour. We then suggest a framework which explicitly incorporates notions of why patients and caregivers act in regulatory ways is helpful in understanding what the appropriate response to such actions should be. While the contribution of patients and caregivers to regulation is valuable and should be supported where desired by the person, if patients and caregivers are acting out of necessity to address system issues or ‘regulatory failure’, other regulatory actors (e.g. governments) should proactively respond and address these issues to reduce the burden on patients and caregivers of having to take regulatory action.

Designing for social relatedness between stroke survivors and eHealth: ‘Edo’ an embodied coach for stroke rehabilitation in the home context

eHealth solutions at home are gaining interest and relevance in healthcare; however, they face challenges in sustaining motivation for therapy due to difficulties in creating meaningful connections between technology and people receiving care. In this article, we explore how embodied agents in eHealth interventions could be designed to establish a motivating relationship with people in the context of home-based stroke rehabilitation. We studied this potential by referring to the need for social relatedness in Self-Determination-Theory (SDT) and how this translates to the design of embodied agents’ characters, the collaborations they afford and the partnerships they establish over time. In co-creative ideation sessions with stroke survivors, this potential was explored, resulting in an interactive prototype of ‘Edo the Coach’, i.e. an embodied agent for home-based upper extremity stroke rehabilitation that was evaluated by stroke survivors in their home contexts. Based on the insights gained, we reflect on the potential of using SDT for designing embodied agents that foster motivational relationships between patient and agent as an answer to the growing demand for eHealth-supported self-care within an increasingly digitized healthcare context.

'I Think the First Priority is Physically Safe First, Before You Can Actually Get Psychologically Safe': Staff Perspectives on Psychological Safety in Inpatient Mental Health Settings.

While the concept of psychological safety has been gaining momentum, research concerning psychological safety in inpatient mental health wards is lacking. To investigate how psychological safety is conceptualised by healthcare staff in inpatient mental health units, and what barriers and facilitators exist. Reflexive Thematic Analysis was used to analyse 12 interviews. Participants conceptualised psychological safety as feeling safe from physical harm, being able to develop meaningful relationships and feeling valued at work. Participants often did not feel physically safe at work, which led them to feel psychologically unsafe. Barriers to psychological safety were reliance on agency workers, punitive management approaches and the inherent risk in working with mental health inpatients. Facilitators included appropriate staffing ratios and skill mix, being able to form meaningful relationships and having access to support. The emphasis on the physical safety element within psychological safety means that existing definitions of psychological safety require extension for the mental healthcare context. However, large-scale research is needed to further understand experiences of psychological safety in this group. A better understanding of the dimensions of psychological safety in inpatient mental health settings could support the development of tools to investigate psychological safety interventions. Organisations could support psychological safety through regular staff supervision and improved staffing ratios and skill mix.

Opportunities for nurses to address employee voice in health care providers: a scoping review

BackgroundEmployees’ decision to speak up or to stay silent can have implications for health care providers, employees and people who need care. As a result, a shift is needed from blindly following guidelines to implementing a sustainable proactive organizational culture in which employees, especially nurses, can evaluate their work environment and take advantage of growth opportunities. The aim of this review is to analyse the characteristics of employee voice opportunities in the health care context, particularly for nurses.MethodsThe search was conducted in April 2023 in the following databases: MEDLINE via PubMed, CINHAL via EBSCO, Scopus via Elsevier, Wiley/Web of Science and Cochrane Library. The search results were imported into the COVIDENCE program and screened by two researchers separately. We used the following search components: health care organization, opportunities, and employee voice. The review followed the PRISMA-ScR guidelines. We identified 951 studies in five databases and via citation tracking. After we removed 102 duplicates and screening 839 titles and abstracts, 23 full texts were assessed. According to our inclusion and exclusion criteria, we included 9 studies.ResultsThree main characteristics of employee voice opportunities that need to be considered to enable nurses to have a voice in the organization were identified. These main categories are individual factors, organizational culture, and available voice channels. It is not possible to rank them in order of importance; they are interrelated.ConclusionsTo conclude, employee voice is a process. In order for utilize employee voice opportunities, individual employee factors, organizational culture and its embedded context must be considered. Individual internal and external motivation, which is influenced by socio-cultural aspects and work hierarchies, must also be considered for successful use of opportunities.

Ecg signal watermarking using QR decomposition.

This study introduces a novel watermarking technique for electrocardiogram (ECG) signals. Watermarking embeds critical information within the ECG signal, enabling data origin authentication, ownership verification, and ensuring the integrity of research data in domains like telemedicine, medical databases, insurance, and legal proceedings. Drawing inspiration from image watermarking, the proposed method transforms the ECG signal into a two-dimensional format for QR decomposition. The watermark is then embedded within the first row of the resulting R matrix. Three implementation scenarios are proposed: one in the spatial domain and two in the transform domain utilizing discrete wavelet transform (DWT) for improved watermark imperceptibility. Evaluation on real ECG signals from MIT-BIH Arrhythmia database and comparison to existing methods demonstrate that the proposed method achieves: (1) higher Peak Signal-to-Noise Ratio (PSNR) indicating minimal alterations to the watermarked signal, (2) lower bit error rates (BER) in robustness tests against external modifications such as AWGN noise (additive white Gaussian noise), line noise and down-sampling, and (3) lower computational complexity. These findings emphasize the effectiveness of the proposed QR decomposition-based watermarking method, achieving a balance between robustness and imperceptibility. The proposed approach has the potential to improve the security and authenticity of ECG data in healthcare and legal contexts, while its lower computational complexity enhances its practical applicability.

Evidence based management of medical devices: A follow-up experiment

Ensuring the optimal operation and longevity of medical devices is essential for maintaining high safety standards in healthcare. This study presents a significant advancement in the field by enhancing an existing evidence-based maintenance (EBM) framework, which is crucial for the effective management of medical equipment. Building upon previous methodologies, this research introduces a novel, comprehensive taxonomy for categorizing maintenance reports by revising and updating existing failure codes. This refinement addresses gaps in previous models, enabling more precise and effective maintenance strategies.A key innovation of this study is the development of a standard XML protocol, which addresses the current lack of standardization in the field, providing a consistent and unified approach to data management. This standardization is critical for improving communication and data exchange across different healthcare systems and technologies.To validate the enhanced EBM framework, the study applies it to corrective maintenance work orders related to Digital Angiography Systems, Magnetic Resonance Imaging, and Computed Tomography, using data from the Pisa University Hospital’s Computerized Maintenance Management System from 2021 to 2022. This validation demonstrates the reproducibility and adaptability of the framework across different medical equipment, healthcare contexts, and timeframes.The findings represent a significant advancement in healthcare technology management, offering a robust, standardized approach that optimizes the safety, functionality, and longevity of medical devices. This research not only expands maintenance standards in the healthcare sector but also provides a critical tool for addressing the current challenges in medical device maintenance, paving the way for more reliable and effective healthcare technologies.