Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit

Developing a consensus-based motivational care pathway for individuals with lower limb fractures: a Delphi protocol

Older adults with lower limb fractures often harbor concerns about losing their mobility, fearing a loss of independence. It is vital to develop strategies that foster their active engagement in the rehabilitation process. The present protocol aims to create a care pathway tailored to motivate individuals with lower limb fractures to adhere to rehabilitation. We will develop an observational, cross-sectional, and descriptive study using the Delphi data-gathering approach. Purposive sampling will recruit a panel of healthcare professionals and experts who care for patients with lower limb fractures. Aligned with the Delphi method, a series of iterative rounds will be developed to gather consensus around the motivational strategies used by health professionals in the rehabilitation of people with lower limb fractures. We will employ the Qualtrics platform for data collection and analysis, and a consensus target of 75% has been predetermined. For quantitative data analysis, we will use descriptive statistics encompassing a range of measures, including count, mean, standard deviation, median, minimum, maximum, and range. An inductive thematic analysis procedure will be employed to extract meaningful themes and patterns from qualitative data. The study results are expected to significantly impact clinical practice by creating a specialized care pathway to motivate individuals with lower limb fractures to adhere to rehabilitation. Adopting these explicit standards by professionals will ensure uniform and high-quality care.

Consensus statements on the utility of defining ARDS and the utility of past and current definitions of ARDS-protocol for a Delphi study

INTRODUCTION

Acute respiratory distress syndrome (ARDS), marked by acute hypoxemia and bilateral pulmonary infiltrates, has been defined in multiple ways since its first description. This Delphi study aims to collect global opinions on the conceptual framework of ARDS, assess the usefulness of components within current and past definitions and investigate the role of subphenotyping. The varied expertise of the panel will provide valuable insights for refining future ARDS definitions and improving clinical management.

METHODS

A diverse panel of 35-40 experts will be selected based on predefined criteria. Multiple choice questions (MCQs) or 7-point Likert-scale statements will be used in the iterative Delphi rounds to achieve consensus on key aspects related to the utility of definitions and subphenotyping. The Delphi rounds will be continued until a stable agreement or disagreement is achieved for all statements.

ANALYSIS

Consensus will be considered as reached when a choice in MCQs or Likert-scale statement achieved ≥80% of votes for agreement or disagreement. The stability will be checked by non-parametric χ2 tests or Kruskal Wallis test starting from the second round of Delphi process. A p-value ≥0.05 will be used to define stability.

ETHICS AND DISSEMINATION

The study will be conducted in full concordance with the principles of the Declaration of Helsinki and will be reported according to CREDES guidance. This study has been granted an ethical approval waiver by the NMC Healthcare Regional Research Ethics Committee, Dubai (NMCHC/CR/DXB/REC/APP/002), owing to the nature of the research. Informed consent will be obtained from all panellists before the start of the Delphi process. The study will be published in a peer-review journal with the authorship agreed as per ICMJE requirements.

TRIAL REGISTRATION NUMBER

NCT06159465.

[Digital twin hospitals: transforming the future of healthcare]

Since the concept of digital twin technology has been put forward, after decades of rapid development and wide application, it has not only made great achievements in many fields, but also brought broader prospects for the development of the medical field. As an important trend in the medical industry, digital twin hospitals play multiple roles by connecting physical hospitals and virtual hospitals and benefit the "patient-medical staff-hospital administrators", highlighting the immeasurable promising application of digital twin technology in smart hospitals. This review takes digital twin technology as an entry point, briefly introduces the progress of its application in various fields, focuses on the characteristics of digital twin technology, practical application cases in hospitals and their limitations, and also looks forward to its future development prospects, aiming to provide certain useful insights and guidance for the future of digital twin hospitals, and also expecting it to play an important role in changing the future of healthcare to a certain extent.

Digital twins in medicine

Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.

Development and usability testing of a patient digital twin for critical care education: a mixed methods study

Background

Digital twins are computerized patient replicas that allow clinical interventions testing in silico to minimize preventable patient harm. Our group has developed a novel application software utilizing a digital twin patient model based on electronic health record (EHR) variables to simulate clinical trajectories during the initial 6 h of critical illness. This study aimed to assess the usability, workload, and acceptance of the digital twin application as an educational tool in critical care.

Methods

A mixed methods study was conducted during seven user testing sessions of the digital twin application with thirty-five first-year internal medicine residents. Qualitative data were collected using a think-aloud and semi-structured interview format, while quantitative measurements included the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and a short survey.

Results

Median SUS scores and NASA-TLX were 70 (IQR 62.5-82.5) and 29.2 (IQR 22.5-34.2), consistent with good software usability and low to moderate workload, respectively. Residents expressed interest in using the digital twin application for ICU rotations and identified five themes for software improvement: clinical fidelity, interface organization, learning experience, serious gaming, and implementation strategies.

Conclusion

A digital twin application based on EHR clinical variables showed good usability and high acceptance for critical care education.

Gaining consensus on expert rule statements for acute respiratory failure digital twin patient model in intensive care unit using a Delphi method

Digital twin technology is a virtual depiction of a physical product and has been utilized in many fields. Digital twin patient model in healthcare is a virtual patient that provides opportunities to test the outcomes of various interventions virtually without subjecting an actual patient to possible harm. This can serve as a decision aid in the complex environment of the intensive care unit (ICU). Our objective is to develop consensus among a multidisciplinary expert panel on statements regarding respiratory pathophysiology contributing to respiratory failure in the medical ICU. We convened a panel of 34 international critical care experts. Our group modeled elements of respiratory failure pathophysiology using directed acyclic graphs (DAGs) and derived expert statements describing associated ICU clinical practices. The experts participated in three rounds of modified Delphi to gauge agreement on 78 final questions (13 statements with 6 substatements for each) using a Likert scale. A modified Delphi process achieved agreement for 62 of the final expert rule statements. Statements with the highest degree of agreement included the physiology, and management of airway obstruction decreasing alveolar ventilation and ventilation-perfusion matching. The lowest agreement statements involved the relationship between shock and hypoxemic respiratory failure due to heightened oxygen consumption and dead space. Our study proves the utility of a modified Delphi method to generate consensus to create expert rule statements for further development of a digital twin-patient model with acute respiratory failure. A substantial majority of expert rule statements used in the digital twin design align with expert knowledge of respiratory failure in critically ill patients.

Intelligent Digital Twins for Personalized Migraine Care

Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.