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With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this article to create a revolutionary solution for this problem. In addition to addressing these issues, the proposed framework, by focusing on the challenges associated with physician prescriptions, is a breakthrough in a new era of security and dependability for the healthcare sector. The Raft algorithm is a cornerstone that improves the diagnostic decision-making process, increases confidence in patients, and sets a new standard for robust healthcare systems. In the proposed consensus algorithm, a weighted sum of two influencing factors including the physician acceptability and inter-physicians\' reliability is used for selecting the participating physicians. An investigation is conducted to see how well the Raft algorithm performs in overcoming prescription-related roadblocks that support a compelling argument for improved patient care. Apart from its technological benefits, the proposed approach seeks to revolutionize the healthcare system by fostering trust between patients and providers. Raft\'s ability to communicate presents the proposed solution as an effective way to deal with healthcare issues and ensure security.

Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside.

Predictive modeling is becoming an essential tool for clinical decision support, but health systems with smaller sample sizes may construct suboptimal or overly specific models. Models become over-specific when beside true physiological effects, they also incorporate potentially volatile site-specific artifacts. These artifacts can change suddenly and can render the model unsafe. To obtain safer models, health systems with inadequate sample sizes may adopt one of the following options. First, they can use a generic model, such as one purchased from a vendor, but often such a model is not sufficiently specific to the patient population and is thus suboptimal. Second, they can participate in a research network. Paradoxically though, sites with smaller datasets contribute correspondingly less to the joint model, again rendering the final model suboptimal. Lastly, they can use transfer learning, starting from a model trained on a large data set and updating this model to the local population. This strategy can also result in a model that is over-specific. In this paper we present the consensus modeling paradigm, which uses the help of a large site (source) to reach a consensus model at the small site (target). We evaluate the approach on predicting postoperative complications at two health systems with 9,044 and 38,045 patients (rare outcomes at about 1% positive rate), and conduct a simulation study to understand the performance of consensus modeling relative to the other three approaches as a function of the available training sample size at the target site. We found that consensus modeling exhibited the least over-specificity at either the source or target site and achieved the highest combined predictive performance.

While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI\'s beneficial outputs and concerns about the challenges of human-computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a discourse analysis to understand how these guidelines construct, articulate, and frame ethics for AI in healthcare. We included eight guidelines and identified three prevalent and interwoven discourses: (1) AI is unavoidable and desirable; (2) AI needs to be guided with (some forms of) principles (3) trust in AI is instrumental and primary. These discourses signal an over-spillage of technical ideals to AI ethics, such as over-optimism and resulting hyper-criticism. This research provides insights into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare.

BACKGROUND: A growing body of evidence has demonstrated that a gender-sensitive approach to healthcare is needed in all areas of medicine. Although medical and nursing guidelines include gender-sensitive care (GSC+) recommendations, the level of implementation in health care practice is unknown. This study aims to examine the current level of implementation and acceptance of GSC+ among physicians and nurses and to identify potential gaps between guidelines and practice and barriers and facilitators of GSC+ implementation, taking the perceptions of all relevant stakeholders into account. The overarching aim is to develop holistic recommended actions to strengthen GSC+.
METHODS: This study has a mixed methods triangulation design. The preparation phase consisting of a literature review and a two-part (qualitative and quantitative) data analysis will be conducted in the cardiology department of 9 pilot hospitals in Berlin, North Rhine-Westphalia, Lower Saxony, Rhineland-Palatinate, Germany. 18 focus groups with clinicians and nurses as well as interviews with experts in other relevant fields will be performed. In the national roll-out phase, a questionnaire survey will be conducted with hospital clinicians (n = 382), nurses (n = 386) and patients (n = 388).
CONCLUSIONS: This study will provide comprehensive insights into the implementation and acceptance of GSC+ in cardiology from the perspective of doctors, nurses, patients, stakeholders and experts in relevant fields, such as policy and education. A focus will also be on the extent to which age or gender of health professionals, region and hospital type influence the implementation of GSC+. The identification of GSC+ implementation barriers and facilitators should help to improve the standard of care for cardiology patients of all genders. The outcomes from this study can be used to develop measures and recommended actions for the successful and sustainable implementation of gender-sensitive care.
BACKGROUND: The study is registered in the German Register of Clinical Studies (DRKS) under study number DRKS00031317.

BACKGROUND: Health workforce projection models are integral components of a robust healthcare system. This research aims to review recent advancements in methodology and approaches for health workforce projection models and proposes a set of good practice reporting guidelines.
METHODS: We conducted a systematic review by searching medical and social science databases, including PubMed, EMBASE, Scopus, and EconLit, covering the period from 2010 to 2023. The inclusion criteria encompassed studies projecting the demand for and supply of the health workforce. PROSPERO registration: CRD 42023407858.
RESULTS: Our review identified 40 relevant studies, including 39 single countries analysis (in Australia, Canada, Germany, Ghana, Guinea, Ireland, Jamaica, Japan, Kazakhstan, Korea, Lesotho, Malawi, New Zealand, Portugal, Saudi Arabia, Serbia, Singapore, Spain, Thailand, UK, United States), and one multiple country analysis (in 32 OECD countries). Recent studies have increasingly embraced a complex systems approach in health workforce modelling, incorporating demand, supply, and demand-supply gap analyses. The review identified at least eight distinct types of health workforce projection models commonly used in recent literature: population-to-provider ratio models (n = 7), utilization models (n = 10), needs-based models (n = 25), skill-mixed models (n = 5), stock-and-flow models (n = 40), agent-based simulation models (n = 3), system dynamic models (n = 7), and budgetary models (n = 5). Each model has unique assumptions, strengths, and limitations, with practitioners often combining these models. Furthermore, we found seven statistical approaches used in health workforce projection models: arithmetic calculation, optimization, time-series analysis, econometrics regression modelling, microsimulation, cohort-based simulation, and feedback causal loop analysis. Workforce projection often relies on imperfect data with limited granularity at the local level. Existing studies lack standardization in reporting their methods. In response, we propose a good practice reporting guideline for health workforce projection models designed to accommodate various model types, emerging methodologies, and increased utilization of advanced statistical techniques to address uncertainties and data requirements.
CONCLUSIONS: This study underscores the significance of dynamic, multi-professional, team-based, refined demand, supply, and budget impact analyses supported by robust health workforce data intelligence. The suggested best-practice reporting guidelines aim to assist researchers who publish health workforce studies in peer-reviewed journals. Nevertheless, it is expected that these reporting standards will prove valuable for analysts when designing their own analysis, encouraging a more comprehensive and transparent approach to health workforce projection modelling.

OBJECTIVE: This study investigates the influence of socioeconomic status, health literacy, and numeracy on treatment decisions and the occurrence of adverse events in patients with breast, lung, and prostate cancer within a Nordic healthcare setting.
METHODS: A follow-up to a cross-sectional, mixed-methods, single-centre study.
METHODS: A Nordic, tertiary cancer clinic.
METHODS: A total of 244 participants with breast, lung and prostate cancer were initially identified, of which 138 first-line treatment participants were eligible for this study. First-line treatment participants (n=138) surpassed the expected cases (n=108).
METHODS: Not applicable as this was an observational study.
METHODS: The study\'s primary endpoint was the rate of guideline adherence. The secondary endpoint involved assessing treatment toxicity in the form of adverse events.
RESULTS: Guideline-adherent treatment was observed in 114 (82.6%) cases. First-line treatment selection appeared uninfluenced by participants\' education, occupation, income or self-reported health literacy. A minority (3.6%) experienced difficulties following treatment instructions, primarily with oral cancer medications.
CONCLUSIONS: The findings indicated lesser cancer health disparities regarding guideline adherence and treatment toxicity within the Nordic healthcare framework. A causal connection may not be established; however, the findings contribute to discourse on equitable cancer health provision.

BACKGROUND: Despite being a global public health concern, there is a research gap in analyzing implementation strategies for managing off-label drug use in children. This study aims to understand professional health managers\' perspectives on implementing the Guideline in hospitals and determine the Guideline\'s implementation facilitators and barriers.
METHODS: Pediatric directors, pharmacy directors, and medical department directors from secondary and tertiary hospitals across the country were recruited for online interviews. The interviews were performed between June 27 and August 25, 2022. The Consolidated Framework for Implementation Research (CFIR) was adopted for data collection, data analysis, and findings interpretation to implement interventions across healthcare settings.
RESULTS: Individual interviews were conducted with 28 healthcare professionals from all over the Chinese mainland. Key stakeholders in implementing the Guideline for the Management of Pediatric Off-Label Use of Drugs in China (2021) were interviewed to identify 57 influencing factors, including 27 facilitators, 29 barriers, and one neutral factor, based on the CFIR framework. The study revealed the complexity of the factors influencing managing children\'s off-label medication use. A lack of policy incentives was the key obstacle in external settings. The communication barrier between pharmacists and physicians was the most critical internal barrier.
CONCLUSIONS: To our knowledge, this study significantly reduces the implementation gap in managing children\'s off-label drug use. We provided a reference for the standardized management of children\'s off-label use of drugs.

OBJECTIVE: A shared consensus on the safety about physical agent modalities (PAMs) practice in physiotherapy and rehabilitation is lacking. We aimed to develop evidence-informed and consensus-based statements about the safety of PAMs.
METHODS: A RAND-modified Delphi Rounds\' survey was used to reach a consensus. We established a steering committee of the Italian Association of Physiotherapy (Associazione Italiana di Fisioterapia) to identify areas and questions for developing statements about the safety of the most commonly used PAMs in physiotherapy and rehabilitation. We invited 28 National Scientific and Technical Societies, including forensics and lay members, as a multidisciplinary and multiprofessional panel of experts to evaluate the nine proposed statements and formulate additional inputs. The level of agreement was measured using a 9-point Likert scale, with consensus in the Delphi Rounds assessed using the rating proportion with a threshold of 75%.
RESULTS: Overall, 17 (61%) out of 28 scientific and technical societies participated, involving their most representative members. The panel of experts mainly consisted of clinicians (88%) with expertise in musculoskeletal (47%), pelvic floor (24%), neurological (18%) and lymphatic (6%) disorders with a median experience of 30 years (IQR=17-36). Two Delphi rounds were necessary to reach a consensus. The final approved criteria list comprised nine statements about the safety of nine PAMs (ie, electrical stimulation neuromodulation, extracorporeal shock wave therapy, laser therapy, electromagnetic therapy, diathermy, hot thermal agents, cryotherapy and therapeutic ultrasound) in adult patients with a general note about populations subgroups.
CONCLUSIONS: The resulting consensus-based statements inform patients, healthcare professionals and policy-makers regarding the safe application of PAMs in physiotherapy and rehabilitation practice. Future research is needed to extend this consensus on paediatric and frail populations, such as immunocompromised patients.