Transform Health Care Research Articles

Digital health competencies and affecting factors among healthcare professionals: additional findings from a systematic review.

In recent decades, nurses' and healthcare professionals' digital health competencies have emerged as key factors in enabling the digital healthcare transformation. Therefore, this review aims to describe the level of digital health competencies among nurses and healthcare professionals and the factors affecting them. We performed an additional analysis of a database populated in our original systematic review by identifying the data set, evaluating its appropriateness to the research questions, and performing the data analysis. Four areas of digital health competencies among nurses and healthcare professionals emerged: 'self-rated competencies', 'psychological and emotional aspects toward the use of digital technologies', 'knowledge about digital technologies' and 'use of digital technologies'. Item scores and affecting factors have been extracted and classified into poor, moderate and good levels of digital health competencies; factors have been categorised and then summarised. 'Use of digital technologies' was the area with the highest prevalence of poor-level scores, whereas the 'knowledge about digital technologies' had a good level of competence. Across categories, most of the items' scores reflected a moderate level of competence. The main factors affecting digital health competencies in nursing were being younger, more educated, having positive collegial nurse-physician relationships and participating in hospital business. Digital health competence might be further improved. Educators, policymakers and nurse managers should develop educational nursing programmes and opportunities by acting on the modifiable factors to develop digital health competencies.

Advancing Rheumatology Care Through Machine Learning.

Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring.Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity.Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms.In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Patient Victimhood and the Risks of Using Artificial Intelligence Technology in Healthcare

Artificial intelligence technologies are of increasing interest in the field of medicine and are one of the key areas for the digital transformation of healthcare . According to a number of experts, medical professionals and digital technology developers, the use of medical devices equipped with artificial intelligence technologies will raise healthcare to a high level, which will lead to improved clinical decision-making, high-quality analysis of digital images, prediction and control of the correctness of the prescribed treatment .However, failures associated with the use of medical devices equipped with artificial intelligence systems can have serious consequences for both clinical outcomes and patients . These consequences could undermine public confidence in artificial intelligence technologies and health care institutions in general . Given the certain novelty of technological solutions, data on the clinical efficacy and safety of products equipped with artificial intelligence are currently considered insufficient .This publication raises two important issues . The first part of the study describes the main physical, social and mental characteristics (properties) of patients that increase the likelihood that they will be victimized in the event of a crime situation in innovative health care services . The second part of the study identifies the risks of using AI technologies in health care that are of greatest concern to both patients and those exploiting innovative technologies .

Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning.

Clinical text and documents contain very rich information and knowledge in healthcare, and their processing using state-of-the-art language technology becomes very important for building intelligent systems for supporting healthcare and social good. This processing includes creating language understanding models and translating resources into other natural languages to share domain-specific cross-lingual knowledge. In this work, we conduct investigations on clinical text machine translation by examining multilingual neural network models using deep learning such as Transformer based structures. Furthermore, to address the language resource imbalance issue, we also carry out experiments using a transfer learning methodology based on massive multilingual pre-trained language models (MMPLMs). The experimental results on three sub-tasks including (1) clinical case (CC), (2) clinical terminology (CT), and (3) ontological concept (OC) show that our models achieved top-level performances in the ClinSpEn-2022 shared task on English-Spanish clinical domain data. Furthermore, our expert-based human evaluations demonstrate that the small-sized pre-trained language model (PLM) outperformed the other two extra-large language models by a large margin in the clinical domain fine-tuning, which finding was never reported in the field. Finally, the transfer learning method works well in our experimental setting using the WMT21fb model to accommodate a new language space Spanish that was not seen at the pre-training stage within WMT21fb itself, which deserves more exploitation for clinical knowledge transformation, e.g. to investigate into more languages. These research findings can shed some light on domain-specific machine translation development, especially in clinical and healthcare fields. Further research projects can be carried out based on our work to improve healthcare text analytics and knowledge transformation. Our data is openly available for research purposes at: https://github.com/HECTA-UoM/ClinicalNMT.

Reflections on 3 Years of Innovation: Recognizing the Need for Innovation Beyond the Clinical Care Pathway.

The Susan and Richard Levy Healthcare Delivery Incubator is designed to bring about rapid, sustainable, scalable, and transformational health care redesign. All 10 projects in the initial 3 cohorts of teams embraced the Incubator process-forming diverse teams and following a design-thinking informed curriculum-and each successfully implemented improvements or innovations by the end of their project. The purpose of this article is to identify the key features of teams' work that may help account for projects' success. For the 10 projects completed, findings from debrief interviews and staff observations were examined to identify processes key to project's success. Analysis highlighted cross-project learnings that indicate nonclinical aspects of care delivery that play a critical role in project innovation success. Innovating health care delivery requires considering social and political determinants of health. The Incubator's process and structures enable teams to identify and respond to a broad range of health determinants.

A systematic study on integrating blockchain in healthcare for electronic health record management and tracking medical supplies

In the current landscape of swiftly evolving technology, security remains a paramount concern. Motivated by the challenges presented during pandemics such as COVID-19, this article aims to enhance healthcare transparency and expedite treatment processes. The existing centralized medical data management system scatters patient records across disparate hospitals and pharmacies, prompting the necessity for a patient-centric data repository. The integration of blockchain emerges as a viable solution, giving patients control over their information and facilitating seamless sharing with relevant institutions—be it hospitals, pharmacies, or insurance firms. This article outlines a strategic framework for securing medical data based on comprehensive literature review and proposes measures derived from previous studies. Furthermore, the study explores blockchain's application in tracing medicinal supplies, crucial for detecting counterfeit medications within the supply chain. Detailed elucidation of IBM Research's proposed model highlights the potential of blockchain in this domain. Additionally, an extensive comparative analysis of existing models underscores their strengths and limitations in these healthcare applications. Challenges persist in deploying blockchain in developing countries, primarily due to resource limitations, signalling the need for tailored solutions conducive to such environments. This study serves as an exploration, emphasising the need for continued research and innovation in harnessing blockchain's potential for healthcare transformation.

Primary Care as a Protective Factor: A Vision to Transform Health Care Delivery and Overcome Disparities in Health.

Primary Care as a Protective Factor: A Vision to Transform Health Care Delivery and Overcome Disparities in Health.

Readiness for green transformation process in healthcare organizations

PurposeGreen transformation is more than simply a trend; it is a way of life, a set of habits, a field of knowledge and a dedication to resource conservation. Going green is surely a creative and transformative process for both individuals and organizations. This paper aims to “identify,” “analyse” and “categorise” the readiness factors for green transformation process in health care using total interpretive structural modelling (TISM) and neutrosophic-MICMAC.Design/methodology/approachTo address the study objectives, the study used TISM and neutrosophic-MICMAC analysis. To identify the readiness factors, a literature study was conducted, and the factors were face-validated by the healthcare experts. The factors influence on one another were captured by using a scheduled interview with a closed ended questionnaire. The TISM addressed the identification and analysing of factors and the categorization and ranking the readiness factors is addressed by using neutrosophic-MICMAC analysis.FindingsThis study identified 11 green transformation process readiness factors for healthcare organizations. The study states that the key factors or driving factors are awareness of green governance principle, environment leadership and management, green gap analysis, information and communication technology and innovation dynamics.Research limitations/implicationsThe factor ranking is sensitive to the respondents’ ratings. The study relied on the past literature and experts’ opinion may result in the subjective biases. The complex nature of healthcare ecosystem challenges to capture all the factors. The study focussed on Indian hospitals.Practical implicationsStudy significantly impacts the healthcare practitioners, academicians and policymakers by providing critical insights into the readiness factors required for the healthcare green transformation process. The study offers a better understanding of the crucial or key or driving factors that aid in embracing green and sustainable practices.Originality/valueIdentifying a gap in conceptual and theoretical frameworks for green transformation readiness factors in healthcare organizations and in Indian context. The study addresses this gap by aiming to create a thorough theoretical framework and highlighted by its focus on Indian hospitals.

Scoping Review: Anesthesiologist Involvement in Alternative Payment Models, Value Measurement, and Nonclinical Capabilities for Success in the United States of America.

The US healthcare sector is undergoing significant payment reforms, leading to the emergence of Alternative Payment Models (APMs) aimed at improving clinical outcomes and patient experiences while reducing costs. This scoping review provides an overview of the involvement of anesthesiologists in APMs as found in published literature. It specifically aims to categorize and understand the breadth and depth of their participation, revolving around 3 main axes or "Aims": (1) shaping APMs through design and implementation, (2) gauging the value and quality of care provided by anesthesiologists within these models, and (3) enhancing nonclinical abilities of anesthesiologists for promoting more value in care. To map out the existing literature, a comprehensive search of relevant electronic databases was conducted, yielding a total of 2173 articles, of which 24 met the inclusion criteria, comprising 21 prospective or retrospective cohort studies, 2 surveys, and 1 case-control cohort study. Eleven publications (45%) discussed value-based, bundled, or episode-based payments, whereas the rest discussed non-payment-based models, such as Enhanced Recovery After Surgery (7 articles, 29%), Perioperative Surgical Home (4 articles, 17%), or other models (3 articles, 13%).The review identified key themes related to each aim. The most prominent themes for aim 1 included protocol standardization (16 articles, 67%), design and implementation leadership (8 articles, 33%), multidisciplinary collaboration (7 articles, 29%), and role expansion (5 articles, 21%). For aim 2, the common themes were Process-Based & Patient-Centric Metrics (1 article, 4%), Shared Accountability (3 articles, 13%), and Time-Driven Activity-Based Costing (TDABC) (3 articles, 13%). Furthermore, we identified a wide range of quality metrics, spanning 8 domains that were used in these studies to evaluate anesthesiologists' performance. For aim 3, the main extracted themes included Education on Healthcare Transformation and Policies (3 articles, 13%), Exploring Collaborative Leadership Skills (5 articles, 21%), and Embracing Advanced Analytics and Data Transparency (4 articles, 17%).Findings revealed the pivotal role of anesthesiologists in the design, implementation, and refinement of these emerging delivery and payment models. Our results highlight that while payment models are shifting toward value, patient-centered metrics have yet to be widely accepted for use in measuring quality and affecting payment for anesthesiologists. Gaps remain in understanding how anesthesiologists assess their direct impact and strategies for enhancing the sustainability of anesthesia practices. This review underscores the need for future research contributing to the successful adaptation of clinical practices in this new era of healthcare delivery.

Artificial intelligence and health information: A bibliometric analysis of three decades of research.

Information on the application of artificial intelligence (AI) in healthcare is needed to align healthcare transformation efforts. This bibliometric analysis aims to establish the patterns of publication activities on the application of AI in health. A total of 1083 scholarly papers published between 1993 and 2023 were retrieved from the Web of Science and Scopus databases. R Studio and VOSviewer were applied to quantify and illustrate publication patterns and citation rates. Publication rates grew by an average rate of 13% yearly, with each document being cited averagely 12 times. The articles had a mean of five co-authors, with a global co-authorship rate of 10%. COVID-19, artificial intelligence, and machine learning dominated the publications. The US, China, UK, Canada, and India coordinated most of the collaborative research. AI-based health information research is growing steadily. International collaborations can be leveraged to ensure the spread and interoperability of AI-based healthcare innovations globally.

The Impact of Artificial Intelligence in Critical Care: Transforming Health Care

The Impact of Artificial Intelligence in Critical Care: Transforming Health Care

A Compendium of Clinical Nurse Specialist Roles in a Canadian Health Authority.

Interior Health in British Columbia advocates for and supports advanced practice nurses, inclusive of regional clinical nurse specialists (CNSs). CNSs develop evidence-informed programs, education, policies and practice standards. The article uses an advanced practice nursing framework to anchor two case studies: (1) the operationalization and implementation of a regional CNS role in primary care and (2) the use of a knowledge translation strategy to build clinical capacity in serious illness conversations. These case studies embody the CNS role and provide a voice for CNSs to communicate the ways in which their advanced practice nursing role is pivotal in healthcare system transformation and for providing quality patient care.

Relational Work Is the Work: Virtual Healthcare Transformation for Rural, Remote and First Nations Communities in British Columbia.

The healthcare crisis across unceded First Nations' territories in rural, remote and Indigenous communities in British Columbia (BC) is marked by persistent barriers to accessing care and support close to home. This commentary describes an exceptional story of how technology, trusted partnerships and relationships came together to create an innovative suite of virtual care programs called "Real-Time Virtual Support" (RTVS). We describe key approaches, learnings and future considerations to improve the equity of healthcare delivery for rural, remote and First Nations communities. The key lessons include the following: (1) moving beyond a biomedical model - the collaboration framework for health service design incorporated First Nations' perspective on health and wellness; (2) relational work is the work - the RTVS collaboration was grounded in building connections and relationships to prioritize cultivating trust in the partnership over specific outputs; and (3) aligning to the core values of co-creation - working from a commitment to do things differently and applying an inclusive approach of engagement to integrate perspectives across different sectors and interest groups.

Healthcare Through AI: Integrating Deep Learning, Federated Learning, and XAI for Disease Management

The applications of Artificial Intelligence (AI) have been resonating across various fields for the past three decades, with the healthcare domain being a primary beneficiary of these innovations and advancements. Recently, AI techniques such as deep learning, machine learning, and federated learning have been frequently employed to address challenges in disease management. However, these techniques often face issues related to transparency, interpretability, and explainability. This is where explainable AI (XAI) plays a crucial role in ensuring the explainability of AI models. There is a need to explore the current role of XAI in healthcare, along with the challenges and applications of XAI in the domain of healthcare and disease management. This paper presents a systematic literature review-based study to investigate the integration of XAI with deep learning and federated learning in the digital transformation of healthcare and disease management. The results of this study indicate that XAI is increasingly gaining the attention of researchers, practitioners, and policymakers in the healthcare domain.

Digital health literacy in primary care: a key driver of patient satisfaction in the era of digital transformation in healthcare

Aim. To analyze digital technologies in the primary health care system, as well as studying the impact of digital health literacy on patient satisfaction with the services provided.Material and methods. A study assessing patients' opinions on the digitalization of primary care in Russia was conducted from June to September 2023 among 1004 participants from 42 regions. A questionnaire with 33 questions was used, of which the 8 most relevant ones were selected for the article, aimed at assessing the satisfaction and personal experience of patients in using digital technologies. Data was collected through Yandex Forms. Data analysis included synthesis of results, classification and content analysis of literature.Results. The study examined the impact of digitalization on primary care patient satisfaction. Analysis of digital technologies in the primary health care system made it possible to establish that the target audience has a positive attitude towards the introduction of innovations. The patients also aimed at using them and are ready to seek new digital medical services. At the same time, it is important for patients to increase the effectiveness of personal interaction with the physicians. In order to improve digital innovation, development of practical guidelines for the updating and further implementation of digital tools is needed.Conclusion. The study confirmed that digital health literacy plays an important role in improving primary care patient satisfaction. Based on the data obtained, it is necessary to develop and implement educational programs for patients and medical professionals aimed at increasing the level of digital literacy and optimizing the use of digital tools in me dicine.

The Impact of Medical Coding on Healthcare Transformation and Bridging the Gap in Pakistan

In the field of health care, medical coding entails translating diagnoses, treatments, and medical procedures into standardized alphanumeric numbers. This process is critical in establishing reliable and precise health records among various healthcare providers.

Digital Healthcare: Requirements for a Successful Transformation

Digital transformation of healthcare is the dominating discussion topic for all healthcare stakeholders. Digital transformation encompasses all areas of healthcare and is far more than digital healthcare applications (DiGA), digital care applications (DiPA), telemedicine applications, telematics infrastructure activities, and applications from the fields of eHealth, mHealth, or Telehealth. All existing care processes and structures in the healthcare system are undergoing an inventory in order to transfer analog components of care into a digital context. The digital transformation is not taking place exclusively in economic sectors such as healthcare, but is a process of change throughout society in the collection, use, provision, linking and evaluation of information (=data). For the healthcare sector, it is clear that different technical concepts are used, while digital healthcare takes place in different places and at different times, may include different user (groups) and retains, expands or changes the healthcare context. Established healthcare functions such as diagnostics, therapy, documentation and the management of healthcare services are retained and transferred to a digital context. In addition, new application areas will emerge, such as the overarching access to health data by different actors, real-time-driven monitoring systems of holistic health data, (clinical) decision systems, or the provision of data for health services. Even if the majority of the digital transformation has not yet taken place, it is assumed that these functions and application areas of healthcare will serve to sustainably improve the quality of care and benefit the well-being of all (future) patients. For the transfer of existing processes into a digital context and the establishment of new application areas, there are prerequisites for healthcare institutions and the healthcare system itself.

ЦИФРОВА ТРАНСФОРМАЦІЯ В СИСТЕМІ ОХОРОНИ ЗДОРОВ'Я

This article examines the digital transformation in healthcare, a strategic imperative aimed at enhancing the quality of medical services, improving efficiency, and reducing costs. Digital transformation involves the integration of electronic health records, telemedicine, artificial intelligence (AI), and big data analytics to optimize clinical and administrative processes, thereby improving service coordination and reducing diagnostic and treatment errors. The research focuses on ensuring digital transformation complements rather than replaces human interaction in healthcare, addressing issues such as patient-provider communication and digital literacy disparities. The economic implications, including cost-benefit analysis and resource optimization, are explored, highlighting the potential for significant cost savings. Additionally, the article discusses the potential of digital tools to expand access to healthcare in underserved communities, promoting health equity. The study delves into how these technologies can support a patient-centered approach to care, tackling challenges like disrupted patient-provider relationships and disparities in digital skills. Emphasizing the enhancement rather than displacement of healthcare professionals' roles, the article compares traditional and digital healthcare methods, illustrating the advantages of personalized treatment pathways enabled by digital technologies. Understanding the economic impact of digital transformation is crucial, as the study examines the potential for cost savings through streamlined processes and optimized resource allocation. Furthermore, the research investigates how digital tools can increase access to medical care in underserved areas, ensuring that the benefits of digital transformation are inclusive. The article provides a comprehensive overview of the key components, impacts, challenges, and future trends of digital transformation in healthcare, offering valuable insights for shaping the future of medical services. The article also examines the potential of digital tools to expand access to medical care in underserved communities, promoting healthcare equity.

Balancing Health-care Privatization in Kerala: The Imperative of Public Sector Intervention.

This article addresses the escalating privatization of health-care services in Kerala, an Indian state. While Kerala's public health-care system has been consistently lauded for its commendable strategy of providing high-quality and affordable health care, recent developments have seen the emergence of large commercial multispecialty hospitals and the integration of private insurance into the public health sector, transforming health care into a profitable industry. This situation underscores the significance of equity, accessibility, and affordability in the health-care sector. Substantial government intervention becomes imperative to curb the increasing involvement of private entities in health-care services.