Healthcare Industry Research Articles

The psychological empowerment and quality of work life among Jordanian primary care nurses and midwives

IntroductionThe healthcare industry, particularly in the context of primary care, presents various challenges to nurses and midwives, influencing their psychological empowerment (PE) and quality of work life (QWL).ObjectiveThis study’s objective is to assess the levels of PE and QWL among Jordanian primary care nurses and midwives and explore the relationship between PE and QWL.MethodsUtilizing a descriptive correlational design, the study included 273 Jordanian primary care nurses and midwives through convenience sampling. The Psychological Empowerment Instrument and Brook’s Quality of Nursing Work Life Survey were used to measure PE and QWL. Data was analyzed using descriptive statistics to describe the study participant’s characteristics and inferential analysis such as Pearson correlation, and multiple regression to examine relationships and identify predictors of study variables.ResultsThe study found that the QWL scale had high reliability (Cronbach’s alpha = 0.954), and similarly, the PE scale demonstrated strong reliability (Cronbach’s alpha = 0.948). Moving on to the core findings, significant positive correlations were identified between PE and QWL, with a correlation coefficient of r = 0.568 (p < 0.01), indicating that higher levels of psychological empowerment were associated with better quality of work life. Furthermore, the regression analysis revealed that PE accounted for approximately 32.3% of the variability in QWL scores. Interestingly, participants holding diplomas reported the highest QWL scores (mean = 136.14), while those contemplating leaving the nursing profession displayed significantly lower QWL scores (mean = 114.14). No significant correlations were found between PE and sociodemographic variables such as age, income, and years of experience.ConclusionThis study reveals a crucial need to enhance PE and QWL. Key findings show moderate PE and QWL levels, with variations based on educational background and workplace. The positive correlation between PE and QWL identifies the benefits of fostering empowerment through professional development, job autonomy, and decision-making. These insights are vital for improving nursing practice and policy and enhancing nurse satisfaction and patient care.

Hospital Customer Competition Issues are being addressed through Confirmation Factor Analysis on Experiential Marketing, Brand Trust, and Loyalty

Introduction The post-COVID-19 pandemic has caused an epidemiological transition that has pushed the pace of development of the health service system even further. Issues and revolutions in the health service industry have made all health service industry providers compete with each other to get service users with competitive advantages in the potential global health market. The hospital has improved revenue sustainability while promoting quality assurance practices. These healthcare industry managers have become adept at introducing business practices into business operations to increase the market for hospital customers. Exploratory factor analysis on important marketing dimensions such as experiential marketing, brand trust, and patient loyalty is very much needed in the issue of competition in the health service industry in hospitals. Materials and Methods This research was quantitative research with a survey approach to outpatients with a sample size of n=255 patients according to Lemeshow (1997). Identification of loading factor analysis includes experiential marketing variables, brand trust, and loyalty. Data analysis used IBM SPSS Statistics 26 Results The research results showed 1) an important loading factor on the experiential marketing variable is the Think dimension indicator (0.845), 2) an important loading factor on the brand trust variable is the brand characteristic dimension (0.945) and 3) an important loading factor on variable loyalty is Repeat Purchase (0.925) Conclusion Dimension of brand trust is the most important dimension in the issue of competition in the health service industry in hospitals. Strengthening and expanding hospital branding as a promotion strategy to add positive value to the reputation of patient and community services at the hospital.

A Bird’s Eye View on the Integration of Artificial Intelligence (AI) In Ayurveda

The integration of Artificial Intelligence (AI) into Ayurveda marks a significant advancement in traditional medical practices. This review article explores AI's multifaceted uses and relevance in the conventional Healthcare industry and Ayurveda. It also highlights the transformative potential of AI in Ayurveda while acknowledging the importance of maintaining the holistic essence of traditional practices. By embracing AI, Ayurveda can evolve to meet contemporary healthcare demands, offering a synergistic approach that combines ancient wisdom with cutting-edge technology. The article concludes with a discussion about the future of Ayurveda in the era of Artificial Intelligence.

The Impact and Potential of Quick Response (QR) Codes in Healthcare: A Comprehensive Review

In today’s rapidly evolving healthcare landscape, the integration of innovative technologies is becoming increasingly essential to enhance patient care, medication and overall healthcare outcomes. Quick Response (QR) codes have emerged as a versatile tool with significant potential to revolutionize such aspects of the healthcare industry. Through a review of 30 studies, we uncover the significant impact of integrating QR codes into healthcare systems. Across various domains, QR codes prove instrumental in enhancing patient care, streamlining workflows, and improving communication between healthcare professionals and patients. Notable findings include the role of QR in ensuring medication safety and advancing education as well as training within healthcare settings. Despite some challenges, user satisfaction remains high, underscoring QR codes’ effectiveness in optimizing healthcare delivery and outcomes from a human-centred perspective. This includes considering usability, cognitive load, and ergonomic design, all of which are crucial for maximizing the benefits of QR code technology in healthcare.

Using patient-reported experiences to inform the use of foam dressings for hard-to-heal wounds: perspectives from a wound care expert panel.

Caring for patients with hard-to-heal (chronic) wounds requires a multifaceted approach that addresses their diverse needs, which can contribute to the complexity of care. Wound care providers must have a comprehensive understanding of the patient's comorbid conditions and psychosocial issues to provide personalised and effective treatment. Key quality indicators for effective wound care involves not only selecting appropriate local wound care products, such as foam dressings, but also addressing individual patient experiences of wound-related pain, odour, itch, excessive wound drainage, and self-care needs. The purpose of this review is to inculcate the wound care practice community, research scientists and healthcare industry with a sense of accountability in order to work collaboratively in addressing these unmet care needs.

Synthesis and Characterization of Carbonaceous Materials for Medical Applications: A Comprehensive Review

Carbonaceous materials have gained significant attention in recent years for their various applications in the field of medicine and biotechnology. This comprehensive review explores the synthesis and characterization of carbon-based materials and their potentials in various medical applications. The paper delves into the methods of fabrication of carbon-based nanoparticles, such as carbon nanotubes, biochar, and graphene, while highlighting their unique properties. Characterization techniques, such as microscopy, spectroscopy, and surface analysis, are discussed to provide insights into the chemical and structural properties of these materials. Furthermore, the review examined their wide-ranging medical applications, encompassing tissue engineering, drug delivery, biosensing, and imaging, showcasing the versatility and promising contributions of carbonaceous materials in the healthcare industry. The review outlines the current challenges and prospects in the field, emphasizing the growing significance of carbon-based materials as valuable tools in advancing medical science and technology, as well as public health.

Mechanistic Insight into the Role of Peptides Secreted from Bacillus clausii and Future Opportunities.

Bacillus clausii is a commercial spore probiotic known to treat multiple diseases. An increased interest in exploring the nutraceutical and probiotic properties of various microorganisms has made researchers explore more about these bacteria. The current trends in the healthcare industry are majorly focused on devising new therapies to avoid drug and pathogen resistance in patients. Antimicrobial peptides have been considered a source of antibiotics for a long time. Still, getting new therapies into the market is a big challenge. Members of the genus Bacillus have been reported to have a broad spectrum of antimicrobial peptides. One of the least explored species under this genus is Bacillus clausii, concerning peptide drug therapy. The applications of Bacillus clausii in treating or preventing gut dysbiosis and respiratory infections have been largely supported in the past two decades. Yet research is lacking in explaining the pathways at molecular levels in targeting pathogens. In this mini-review, we are going to summarise the research that has been reported so far about peptide extraction from Bacillus clausii, their mode of action and advantages to mankind, and the challenges lying in the isolation of peptides.

A method and validation for auditing e-Health applications based on reusable software security requirements specifications

ObjectiveThis article deals with the complex process of obtaining security requirements for e-Health applications. It introduces a tailored audit and validation methodology particularly designed for e-Health applications. Additionally, it presents a comprehensive security catalog derived from primary sources such as law, guides, standards, best practices, and a systematic literature review. This catalog is characterized by its continuous improvement, clarity, completeness, consistency, verifiability, modifiability, and traceability. MethodsThe authors reviewed electronic health security literature and gathered primary sources of law, guides, standards, and best practices. They organized the catalog according to the ISO/IEC/IEEE 29148:2018 standard and proposed a methodology to ensure its reusability. Moreover, the authors proposed SEC-AM as an audit method. The applicability of the catalog was validated through the audit method, which was conducted on a prominent medical application, OpenEMR. ResultsThe proposed method and validation for auditing e-Health Applications through the catalog provided a comprehensive framework for developing or evaluating new applications. Through the audit of OpenEMR, several security vulnerabilities were identified, such as DDOs, XSS, JSONi, and CMDi, resulting in a “Secure” classification of OpenEMR with a compliance rate of 66.97%. ConclusionThe study demonstrates the proposed catalog’s feasibility and effectiveness in enhancing health software security. The authors suggest continuous improvement by incorporating new regulations, knowledge from additional sources, and addressing emerging zero-day vulnerabilities. This approach is crucial for providing practical, safe, and quality medical care amidst increasing cyber threats in the healthcare industry.

Patient-reported disability progression outcomes among patients with multiple sclerosis: Results of an outcomes-based agreement.

Outcomes-based agreements (OBAs) are agreements between payers and manufacturers in which payment for medications is tied to patient outcomes. These contracts aim to measure the value of prescription medications on predefined clinical indicators in real-world patient populations. OBAs are gaining traction in the United States as the health care industry shifts from volume-based to value-based care. Multiple sclerosis (MS) is an appealing therapeutic area for OBAs because of its prevalence, high cost of medications, and multiple effective therapeutic options. To describe findings from an OBA that was prospectively conducted in a large regional health system for patients with MS taking interferon β-1a or dimethyl fumarate. In this prospective real-world analysis, commercial or health insurance exchange members were included based on the parameters of the OBA. Disability progression was assessed using a patient-reported outcome, patient-determined disease steps (PDDS). In the OBA, members aged 18 years or older with an MS diagnosis were included in the contract. A baseline score was collected for eligible members, with follow-up scores occurring between a 90-day and 180-day postbaseline score. If a follow-up score was greater than the baseline score, a subsequent PDDS score was collected between 90-days and 120-days to determine if the PDDS score remained elevated, indicating that the member had disability progression. During the contract period, 410 patients were eligible for PDDS collection, with 241 and 169 patients in the dimethyl fumarate and interferon β-1a cohorts, respectively. There were 162 patients who were lost to follow-up, and 64 patients who were ineligible per contract parameters. Of the remaining 184 eligible patients (107 on dimethyl fumarate and 77 on interferon β-1a), 21 (11%) patients had confirmed disability progression (6 on dimethyl fumarate [5.6%] and 15 on interferon β-1a [19.5%]). Our findings suggest that meaningful patient-reported outcomes, such as disability progression, can be operationalized in an innovative OBA.

THE EFFECT OF AI-DRIVEN INVENTORY MANAGEMENT SYSTEMS ON HEALTHCARE OUTCOMES AND SUPPLY CHAIN PERFORMANCE: A DATA-DRIVEN ANALYSIS

There has been much focus toward increasing integration of AI in healthcare generally, with a specific focus on inventory management systems in particular. As more hospitals in the United States public and private side face increasing costs and concerns over cost and length of operation all systems supporting inventory must be made most efficient and AI system offers the US hospitals the opportunity to maintain accurate inventory and hence improve the quality of patient care. But the literature review presents a small number of studies that focus on the effects of these systems as the means to improve healthcare results and organization. In this study, it is proposed that improving inventory management by actioning artificial intelligence in healthcare organizations will result in enhanced patient care and improved inventory accuracy, cost reduction and supply chain efficiency in the United States of America’s healthcare institutions. A cross-sectional, quantitative survey was administrated to 200 healthcare personnel and supply chain managers from different healthcare organizations based on hospitals, clinics and specialty medical centers of the USA. Sample design: Participants were purposefully chosen, who had prior exposure to the AI-induced systems or are aware about workflow of such systems. Respondents’ data was gathered via an online questionnaire and analyzed using SPSS: descriptive analysis, chi-square test, one-way ANOVA, correlation analysis, multiple regression analysis was used to establish the relationships of the study variables: AI systems, healthcare outcomes and supply chain performance. The results showed that the use of AI in inventory management systems enhances its accuracy in ordering order itself and overall patient care. Separate studies showed that where the management of a facility has deeper insight of the AI system then there will be more operations worked on to improve efficiency or cut costs. Adoption of these pathways among smaller providers including clinics and long-term care facilities was difficult, especially due to cost constraints. An analysis of the multiple regression equation showed that knowledge of AI improved the projection of potential healthcare outcome, creating greater worth and also increases the performance of supply chain. The outcomes of using AI based inventory management systems in the United States seem to have a lot of advantages that may help improve the efficacy of operations in the healthcare system. Issues such as funding and skills needed for implementation, especially in small healthcare organization, continue to slow the uptake of the technology. These existing problems may be solved if more focused investments in the development of comprehensive AI training or systematic solutions are given to enable the healthcare industry to reach the full potential of the application of artificial intelligence.

Pembangunan Strategi Pemasaran untuk Meningkatkan Daya Saing Rumah Sakit di Era Digital

Increased competition in the healthcare industry, especially in the digital era, requires hospitals to develop effective marketing strategies. This research aims to develop an appropriate marketing strategy to improve hospital competitiveness in the digital era. The research method used is descriptive qualitative with a literature study approach and SWOT analysis. Data were obtained from various sources, such as scientific journals, books, and articles related to hospital marketing in the digital era. The results showed that effective marketing strategies for hospitals in the digital era include building brand awareness through digital platforms, increasing engagement with patients through social media and websites, utilizing digital technology to improve patient services, building networks and collaborations with related parties, and building effective referral systems. This marketing strategy is expected to increase the hospital's competitiveness, attract more patients, and increase patient satisfaction.

Synergizing Ai and ERP for Predictive Supply Chain Management and Quality Assurance in Healthcare

This article explores the transformative potential of integrating Artificial Intelligence (AI) with Enterprise Resource Planning (ERP) systems in healthcare, focusing on applications in predictive supply chain management and quality assurance. The healthcare industry faces increasing pressure to improve efficiency while maintaining high-quality care, and AI-ERP integration offers a compelling solution. By combining ERP's comprehensive data management capabilities with AI's predictive and analytical power, healthcare organizations can enhance operational efficiency, improve decision-making, and ultimately deliver better patient outcomes. The article examines current implementations, case studies, and future projections to provide a comprehensive overview of how this technological synergy can help healthcare providers navigate the complex challenges of modern healthcare delivery, from optimizing inventory levels to ensuring regulatory compliance and proactive risk management.

Threat Modeling for Enhanced Security in the Healthcare Industry with a Focus on Mobile Health and IoT

The advancement of mobile health and the Internet of Things (IoT) promises to enhance healthcare quality while reducing costs, particularly with the transition from inpatient to home and ambulatory care. This shift, driven by an aging population, financial pressures, and a shortage of skilled healthcare professionals, presents significant opportunities and challenges. While mobile health improves access and encourages self-management, it also raises serious concerns regarding security and interoperability, especially with wearable devices equipped with sensors in a patient's Body Area Network (BAN). This paper critically analyzes the security and interoperability risks associated with these technologies, emphasizing the need for robust measures such as configuration and asset management. Utilizing recommendations from ENISA (2016) and conducting a risk and vulnerability assessment, this study develops a comprehensive security model tailored for healthcare architectures. Additionally, it applies the STRIDE threat modeling approach to identify and mitigate potential threats, providing valuable insights for securing healthcare systems and prioritizing critical assets vital to organizational operations.

AI security in different industries: A comprehensive review of vulnerabilities and mitigation strategies

Artificial intelligence has quickly and gradually spread across all sectors and fields, achieving exceptional performance in operations and decision-making. Yet, as industries process their tasks with the help of AI systems, new threats appear that require proper solutions. In the following section of this paper, the use of security solutions involving AI in various industries, which are finance, health, manufacturing, and government, as well as a discussion of risks involving artificial intelligence in particular fields and the corresponding measures to be taken against them will be discussed. AI is often applied to detect fraud and credit scoring in the financial sector but has potential risks, such as adversarial attacks and data manipulation. Similarly, the healthcare industry uses AI for predictive healthcare diagnostics and patient information protection; threats are data theft and adversarial examples attacking diagnostic models. In manufacturing, AI individuals contain predictive servicing and excellent control, though they are constantly exposed to industrial espionage and the sabotage of AI models. In retail, however, AI is used to improve marketing and predict consumer behavior. While these benefits exist, questions about using Algorithms that could be biased and privacy infringements continue to raise their ugly head. Defensive and governmental organizations that apply AI for surveillance or automatic operating systems are most vulnerable to adversarial intervention with key control systems and innate security systems. To counter such threats, this paper provides details of industry-specific measures, including adversarial training data sanitization, AI model audit, and privacy-preserving approaches. We illustrate examples linked to every one of these methods to show how they can be deployed in a variety of industries and to guard AI systems. To this end, the following review of the relationship between AI and cybersecurity demonstrates that security measures that rely on artificial intelligence must be regularly checked and updated. This reveals the call for proper, protective, and industry-specific measures to manage the risks and protect AI applications to ensure that the world’s modern, interconnected world is safe for AI.

Hospitality in Healthcare: Leveraging Employee e-WOM

ABSTRACT Providing hospitality services to patients to enhance their overall healing process has gained importance in the healthcare industry. It is not only just about getting healed but also the experience involved. This entails quality hospitality services in addition to medical services. Given that a happy employee provides better services, the role of job satisfaction of employees providing these services thus becomes important. Drawing from service quality frameworks, this article aims to identify enablers and deterrents of job satisfaction and validate with practitioners pulling out latent aspects that need to be focused on integrating hospitality in healthcare. We explored these aspects using a three-pronged approach. Initially, we identified the key themes influencing job satisfaction by applying data mining techniques to 13,000 employee reviews from companies recognized as Great Place to Work, sourced from Glassdoor. 1 Second, we validated the identified theme from experts through 24 focused group discussions comprising HR practitioners, thereby gaining interesting insights about certain important overlooked aspects. Third, we include literature support for each of the identified themes, stating their importance to the healthcare sector. The study provides a rubric of enablers and deterrents to job satisfaction, which can help the sector enhance employee satisfaction.

Impact of nurse turnover on patient care in a private healthcare facility in UAE

Aim: The aim of this study was to determine the pattern and impact of nurse turnover in a private healthcare facility in United Arab Emirates (UAE) and also to recommend the management strategies that could be adopted to overcome the problem of nurse turnover. Back ground: Global healthcare organizations are in a precarious situation due to the increasing attrition among healthcare professionals. Nurse turnover is an issue of international concern causing widening of supply - demand gaps in the healthcare industry. Despite the acknowledged importance of managing nurse turnover in hospitals, the pattern, causes and consequences of nurse turnover in UAE are poorly understood. Method and analysis: This is an empirical, descriptive, quantitative survey based research conducted in a private healthcare facility in UAE. Results: Hospital witnessed significant nurse shortage secondary to higher nurse turnover. More than two thirds (69.9%) nurses showed intention to leave the organization. Higher nurse turnover intentions were significantly associated with job satisfaction (p<0.01) and workload (p<0.001). Patient fall and medication errors were not significantly associated with nurse turnover however there was significant reduction in patient satisfaction score for nurse services in the hospital. Conclusion: Higher work load, lack of conducive work environment, lack of managerial support mainly contributed to job dissatisfaction and are the key drivers for nurse turnover. Decline in patient satisfaction implied compromised quality of care. However larger trials are needed to effectively implement administrative strategies to overcome the nurse turnover problem.

Application of artificial intelligence (AI) as a process analytical technology (PAT) tool in healthcare industry

Process Analytical Technology (PAT) has revolutionized manufacturing by providing real-time insights into process parameters. This guide delves into the synergistic potential of AI and PAT, offering a comprehensive overview of their integration. It explores the way AI algorithms can enhance data analysis, prediction, and control complex manufacturing processes. The document provides a fundamental understanding of AI concepts relevant to PAT, including machine learning, deep learning, and computer vision. Real-world case studies help to illustrate the practical application of AI in diverse industries, such as pharmaceuticals, chemicals, and food processing. By highlighting the benefits of AI-driven PAT, such as improved product quality, increased efficiency, and reduced costs, this guide empowers readers to harness the power of AI for process optimization and innovation. The objective of this review is to provide a base for establishing the regulatory guidelines and adaptation of PAT-controlled applications in the regulations.

Priority-Elastic net for binary disease outcome prediction based on multi-omics data

BackgroundHigh-dimensional omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential to improve predictive models. However, the data integration process faces several challenges, including data heterogeneity, priority sequence in which data blocks are prioritized for rendering predictive information contained in multiple blocks, assessing the flow of information from one omics level to the other and multicollinearity.MethodsWe propose the Priority-Elastic net algorithm, a hierarchical regression method extending Priority-Lasso for the binary logistic regression model by incorporating a priority order for blocks of variables while fitting Elastic-net models sequentially for each block. The fitted values from each step are then used as an offset in the subsequent step. Additionally, we considered the adaptive elastic-net penalty within our priority framework to compare the results.ResultsThe Priority-Elastic net and Priority-Adaptive Elastic net algorithms were evaluated on a brain tumor dataset available from The Cancer Genome Atlas (TCGA), accounting for transcriptomics, proteomics, and clinical information measured over two glioma types: Lower-grade glioma (LGG) and glioblastoma (GBM).ConclusionOur findings suggest that the Priority-Elastic net is a highly advantageous choice for a wide range of applications. It offers moderate computational complexity, flexibility in integrating prior knowledge while introducing a hierarchical modeling perspective, and, importantly, improved stability and accuracy in predictions, making it superior to the other methods discussed. This evolution marks a significant step forward in predictive modeling, offering a sophisticated tool for navigating the complexities of multi-omics datasets in pursuit of precision medicine’s ultimate goal: personalized treatment optimization based on a comprehensive array of patient-specific data. This framework can be generalized to time-to-event, Cox proportional hazards regression and multicategorical outcomes. A practical implementation of this method is available upon request in R script, complete with an example to facilitate its application.

An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers

Anaemia is a disorder marked by low blood levels of haemoglobin (HGB), affecting people of all ages and ethnicities and is a major global public health concern. Anaemia must be diagnosed as soon as possible to enable prompt treatment and intervention, which can reduce complications and enhance patient outcomes. With the ability to improve diagnostic precision and expedite patient care procedures, machine learning (ML) has become a potent instrument in the healthcare industry. Hence, we examine the use of ML approaches to predict haemoglobin-like anaemia in this research article. Based on a heterogeneous dataset of blood markars, we investigate the performance of many machine learning techniques such as Logistic Regression, CatBoost, XgBoost Decision Trees, KNN and others. The algorithms are further ensembled using a customized stacking approach. The ML models' judgments are interpreted using explainable artificial intelligence (XAI) methods. The xgboost and the stacking classifier obtained an accuracy, precision and recall of 99% each. Our research shows how ML models can help with the early diagnosis and treatment of anaemia, which will ultimately lead to better patient outcomes and healthcare results. Overall, the research shows how ML emphasizes the value of interdisciplinary cooperation in solving challenging medical problems.

Eco-friendly treatment: a win-win for patients, health systems, and the planet. Rapid scoping review

Abstract Background As the fifth-largest CO2 emitter globally, the healthcare industry must act to mitigate its environmental impact. This includes de-implementing low-value care and adopting eco-friendly innovative treatment alternatives. Our study aims to explore existing research on the environmental effects of these actions and the outcome measures utilized. Methods We conducted a rapid scoping review following a pre-published protocol, searching Ovid MEDLINE® and Scopus (Elsevier) from January 2015 to June 19, 2023. Initially, dual abstract and full-text screening was performed, followed by a single review. Data extraction was cross-checked by a second reviewer. Findings were narratively described using summary statistics. Results We analyzed 182 articles, primarily life cycle assessments and modeling studies (92 out of 182), with only one study being a randomized controlled trial. The research covered various healthcare practices, including consultation methods/telemedicine (32%), surgical and medical device usage (26%), medication (25%), surgical procedures (19%), as well as diagnostic tests, screening, and imaging (13%). The main sustainability strategies mentioned were ‘Rethink’ (99%) and ‘Reduce’ (81%), with ‘Reuse’ (32%), ‘Recycle’ (19%), and ‘Refuse’ (10%) being less utilized. Environmental outcomes reported included global warming (88%), resource consumption (27%), and chemical hazards (23%). Most studies were conducted in Europe (48%), followed by the USA (37%), with 75% published after 2020. Conclusions The environmental impact of healthcare is a pivotal area for public health action and fortunately, it is also emerging as a significant research field. Utilizing methodological approaches less common in evidence-based healthcare research underscores the necessity for innovative interdisciplinary collaboration and capacity building. The most pressing research gap lies in the impact of de-implementation strategies (“refuse”) targeting low-value care practices. Key messages • Measuring the environmental impact of healthcare demands the development of novel cross-disciplinary research methods and collaborations. • De-implementing low-value care represents a future win-win scenario for patients, the financial viability of healthcare systems, and global environmental sustainability.