The Portuguese NHS 2024 reform: transformation through vertical integration.

Vertical integration models aim for the integration of services from different levels of care (e.g., primary, and secondary care) with the objective of increasing coordination and continuity of care as well as improving efficiency, quality, and access outcomes. This paper provides a view of the Portuguese National Health Service (NHS) healthcare providers' vertical integration, operationalized by the Portuguese NHS Executive Board during 2023 and 2024. This paper also aims to contribute to the discussion regarding the opportunities and constraints posed by public healthcare organizations vertical integration reforms. The Portuguese NHS operationalized the development and generalization of Local Health Units management model throughout the country. The same institutions are now responsible for both the primary care and the hospital care provided by public services in each geographic area, in an integrated manner. This 2024 reform also changed the NHS organic and organizational structures, opening paths to streamline the continuum of care. However, it will be important to ensure adequate monitoring and support, with the participation of healthcare services as well as community structures and other stakeholders, to promote an effective integration of care.

Objective Assessment of Cognitive Workload in Surgery: A Systematic Review.

OBJECTIVE: To systematically review technologies that objectively measure CWL in surgery, assessing their psychometric and methodological characteristics. SUMMARY BACKGROUND DATA: Surgical tasks involving concurrent clinical decision-making and the safe application of technical and non-technical skills require a substantial cognitive demand and resource utilization. Cognitive overload leads to impaired clinical decision-making and performance decline. Assessing cognitive workload (CWL) could enable interventions to alleviate burden and improve patient safety. METHODS: Ovid MEDLINE, OVID Embase, the Cochrane Library and IEEE Xplore databases were searched from inception to August 2023. Full-text, peer-reviewed original studies in a population of surgeons, anesthesiologists or interventional radiologists were considered, with no publication date constraints. Study population, task paradigm, stressor, Cognitive Load Theory (CLT) domain, objective and subjective parameters, statistical analysis and results were extracted. Studies were assessed for a) definition of CWL, b) details of the clinical task paradigm, and c) objective CWL assessment tool. Assessment tools were evaluated using psychometric and methodological characteristics. RESULTS: 10790 studies were identified; 9004 were screened; 269 full studies were assessed for eligibility, of which 67 met inclusion criteria. The most widely used assessment modalities were autonomic (32 eye studies and 24 cardiac). Intrinsic workload (e.g. task complexity) and germane workload (effect of training or expertize) were the most prevalent designs investigated. CWL was not defined in 30 of 67 studies (44.8%). Sensitivity was greatest for neurophysiological instruments (100% EEG, 80% fNIRS); and across modalities accuracy increased with multi-sensor recordings. Specificity was limited to cardiac and ocular metrics, and was found to be sub-optimal (50% and 66.67%). Cardiac sensors were the least intrusive, with 54.2% of studies conducted in naturalistic clinical environments (higher ecological validity). CONCLUSION: Physiological metrics provide an accessible, objective assessment of CWL, but dependence on autonomic function negates selectivity and diagnosticity. Neurophysiological measures demonstrate favorable sensitivity, directly measuring brain activation as a correlate of cognitive state. Lacking an objective gold standard at present, we recommend the concurrent use of multimodal objective sensors and subjective tools for cross-validation. A theoretical and technical framework for objective assessment of CWL is required to overcome the heterogeneity of methodological reporting, data processing, and analysis.

Identifying barriers to outpatient appointment attendance in patient groups at risk of inequity: a mixed methods study in a London NHS trust.

BACKGROUND: There is significant health inequity in the United Kingdom (U.K.), with different populations facing challenges accessing health services, which can impact health outcomes. At one London National Health Service (NHS) Trust, data showed that patients from deprived areas and minority ethnic groups had a higher likelihood of missing their first outpatient appointment. This study's objectives were to understand barriers to specific patient populations attending first outpatient appointments, explore systemic factors and assess appointment awareness. METHODS: Five high-volume specialties identified as having inequitable access based on ethnicity and deprivation were selected as the study setting. Mixed methods were employed to understand barriers to outpatient attendance, including qualitative semi-structured interviews with patients and staff, observations of staff workflows and interrogation of quantitative data on appointment communication. To identify barriers, semi-structured interviews were conducted with patients who missed their appointment and were from a minority ethnic group or deprived area. Staff interviews and observations were carried out to further understand attendance barriers. Patient interview data were analysed using inductive thematic analysis to create a thematic framework and triangulated with staff data. Subthemes were mapped onto a behavioural science framework highlighting behaviours that could be targeted. Quantitative data from patient interviews were analysed to assess appointment awareness and communication. RESULTS: Twenty-six patients and 11 staff were interviewed, with four staff observed. Seven themes were identified as barriers - communication factors, communication methods, healthcare system, system errors, transport, appointment, and personal factors. Knowledge about appointments was an important identified behaviour, supported by eight out of 26 patients answering that they were unaware of their missed appointment. Environmental context and resources were other strongly represented behavioural factors, highlighting systemic barriers that prevent attendance. CONCLUSION: This study showed the barriers preventing patients from minority ethnic groups or living in deprived areas from attending their outpatient appointment. These barriers included communication factors, communication methods, healthcare the system, system errors, transport, appointment, and personal factors. Healthcare services should acknowledge this and work with public members from these communities to co-design solutions supporting attendance. Our work provides a basis for future intervention design, informed by behavioural science and community involvement.

Impact of the cost-of-living crisis on patient preferences towards virtual consultations.

INTRODUCTION: Since 2021, the world has been facing a cost-of-living crisis which has negatively affected population health. Meanwhile, little is known about its impact on patients' preferences to access care. We aimed to analyse public preference for the modality of consultation (virtual vs face-to-face) before and after the onset of crisis and factors associated with these preferences. METHODS: An online cross-sectional survey was administered to the public in the United Kingdom, Germany, Italy and Sweden. McNemar tests were conducted to analyse pre- and post-crisis differences in preferences; logistic regression was used to examine the demographic factors associated with public preferences. RESULTS: Since the onset of crisis, the number of people choosing virtual consultations has increased in the United Kingdom (7.0% vs 9.5% P < 0.001), Germany (6.6% vs 8.6%, P < 0.008) and Italy (6.0% vs 9.8%, P < 0.001). Before the crisis, a stronger preference for virtual consultations was observed in people from urban areas (OR 1.28, 95% CI 1.05-1.56), while increasing age was associated with a lower preference for virtual care (OR 0.966, 95% CI 0.961-0.972). Younger people were more likely to switch to virtual care, while change to face-to-face was associated with younger age and lower income (OR 1.34, 95% CI 1.12-1.62). Older adults were less likely to change preference. CONCLUSIONS: Since the onset of the cost-of-living crisis, public preference for virtual consultations has increased, particularly in younger population. This contrasts with older adults and people with lower-than-average incomes. The rationale behind patients' preferences should be investigated to ensure patients can access their preferred modality of care.

ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines.

Introduction: Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans. Methods: We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. Results: Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise. Conclusion: An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.

Usability and Feasibility Evaluation of a Web-Based and Offline Cybersecurity Resource for Health Care Organizations (The Essentials of Cybersecurity in Health Care Organizations Framework Resource): Mixed Methods Study.

BACKGROUND: Cybersecurity is a growing challenge for health systems worldwide as the rapid adoption of digital technologies has led to increased cyber vulnerabilities with implications for patients and health providers. It is critical to develop workforce awareness and training as part of a safety culture and continuous improvement within health care organizations. However, there are limited open-access, health care-specific resources to help organizations at different levels of maturity develop their cybersecurity practices. OBJECTIVE: This study aims to assess the usability and feasibility of the Essentials of Cybersecurity in Health Care Organizations (ECHO) framework resource and evaluate the strengths, weaknesses, opportunities, and threats associated with implementing the resource at the organizational level. METHODS: A mixed methods, cross-sectional study of the acceptability and usability of the ECHO framework resource was undertaken. The research model was developed based on the technology acceptance model. Members of the Imperial College Leading Health Systems Network and other health care organizations identified through the research teams' networks were invited to participate. Study data were collected through web-based surveys 1 month and 3 months from the date the ECHO framework resource was received by the participants. Quantitative data were analyzed using R software (version 4.2.1). Descriptive statistics were calculated using the mean and 95% CIs. To determine significant differences between the distribution of answers by comparing results from the 2 survey time points, 2-tailed t tests were used. Qualitative data were analyzed using Microsoft Excel. Thematic analysis used deductive and inductive approaches to capture themes and concepts. RESULTS: A total of 16 health care organizations participated in the study. The ECHO framework resource was well accepted and useful for health care organizations, improving their understanding of cybersecurity as a priority area, reducing threats, and enabling organizational planning. Although not all participants were able to implement the resource as part of information computing technology (ICT) cybersecurity activities, those who did were positive about the process of change. Learnings from the implementation process included the usefulness of the resource for raising awareness and ease of use based on familiarity with other standards, guidelines, and tools. Participants noted that several sections of the framework were difficult to operationalize due to costs or budget constraints, human resource limitations, leadership support, stakeholder engagement, and limited time. CONCLUSIONS: The research identified the acceptability and usability of the ECHO framework resource as a health-focused cybersecurity resource for health care organizations. As cybersecurity in health care organizations is everyone's responsibility, there is potential for the framework resource to be used by staff with varied job roles. Future research needs to explore how it can be updated for ICT staff and implemented in practice and how educational materials on different aspects of the framework could be developed.

Patients' subjective well-being: Determinants and its usage as a metric of healthcare service quality.

It is commonly suggested that patients' subjective well-being (SWB) can be affected by pre-treatment conditions and treatment experiences, and hence SWB can be used to measure and improve healthcare quality. With data collected in a hospital in the UK (N = 446), we investigated the determinants of patients' SWB and evaluated its use in healthcare research. Our findings showed strong relationships between pre-treatment conditions and patients' SWB: anxiety and depression negatively predicted SWB across all three domains, mobility positively predicted the life satisfaction and happiness domains, while the ability to self care and pain and discomfort also predicted SWB in some domains. In contrast, patients' satisfaction with the treatment only played minor roles in determining SWB, much less so the characteristics of their nurses. The general lack of associations between treatment experiences and patient's SWB highlighted the challenges of using SWB to measure healthcare quality and inform policy making.

Testing the impact of differing behavioural science informed text message content in COVID-19 vaccination invitations on vaccine uptake: A randomised clinical trial.

Behavioural science constructs can be incorporated into messaging strategies to enhance the effectiveness of public health campaigns by increasing the occurrence of desired behaviours. This study investigated the impact of behavioural science-informed text message strategies on COVID-19 vaccination rates in 18-39-year-olds in an area of low uptake in London during the first vaccination offer round in the United Kingdom. This three-armed randomised trial recruited unvaccinated residents of an urban Central London suburb being offered their first vaccination between May and June 2021. Participants were randomised to receive the control (current practice) text message or one of two different behavioural science-informed COVID-19 vaccine invitation strategies. Both intervention strategies contained the phrase "your vaccine is ready and waiting for you", aiming to evoke a sense of ownership, with one strategy also including a pre-alert message. The main outcome measures were vaccination rates at 3 and 8 weeks after message delivery. A total of 88,820 residents were randomly assigned to one of the three trial arms. Each arm had a vaccine uptake rate of 27.2 %, 27.4 % and 27.3 % respectively. The mean age of participants was 28.2 years (SD ± 5.7), the mean index of multiple deprivation was 4.3 (SD ± 2.0) and 50.4 % were women. Vaccine uptake varied by demographics, however there was no significant difference between trial arms (p = 0.872). Delivery was successful for 53.6 % of text messages. Our choice of behavioural science informed messaging strategies did not improve vaccination rates above the rate seen for the current practice message. This likely reflects the wide exposure to public health campaigns during the pandemic, as such text messages nudges were unlikely to alter existing informed decision-making processes. Text message delivery was relatively low, indicating a need for accurate mobile phone number records and multi-modal approaches to reach eligible patients for vaccination. The protocol was registered at clinicaltrials.gov (NCT04895683) on 20/05/2021.

Transcutaneous fluorescence spectroscopy: development and characterization of a compact, portable, and fiber-optic sensor.

Significance: The integrity of the intestinal barrier is gaining recognition as a significant contributor to various pathophysiological conditions, including inflammatory bowel disease, celiac disease, environmental enteric dysfunction (EED), and malnutrition. EED, for example, manifests as complex structural and functional changes in the small intestine leading to increased intestinal permeability, inflammation, and reduced absorption of nutrients. Despite the importance of gut function, current techniques to assess intestinal permeability (such as endoscopic biopsies or dual sugar assays) are either highly invasive, unreliable, and/or difficult to perform in certain patient populations (e.g., infants). Aim: We present a portable, optical sensor based on transcutaneous fluorescence spectroscopy to assess gut function (in particular, intestinal permeability) in a fast and noninvasive manner. Approach: Participants receive an oral dose of a fluorescent contrast agent, and a wearable fiber-optic probe detects the permeation of the contrast agent from the gut into the blood stream by measuring the fluorescence intensity noninvasively at the fingertip. We characterized the performance of our compact optical sensor by comparing it against an existing benchtop spectroscopic system. In addition, we report results from a human study in healthy volunteers investigating the impact of skin tone and contrast agent dose on transcutaneous fluorescence signals. Results: The first study with eight healthy participants showed good correlation between our compact sensor and the existing benchtop spectroscopic system [correlation coefficient (r)>0.919, p<0.001]. Further experiments in 14 healthy participants revealed an approximately linear relationship between the ingested contrast agent dose and the collected signal intensity. Finally, a parallel study on the impact of different skin tones showed no significant differences in signal levels between participants with different skin tones (p>0.05). Conclusions: In this paper, we demonstrate the potential of our compact transcutaneous fluorescence sensor for noninvasive monitoring of intestinal health.

Evaluating the Impact of the National Health Service Digital Academy on Participants' Perceptions of Their Identity as Leaders of Digital Health Change: Mixed Methods Study.

BACKGROUND: The key to the digital leveling-up strategy of the National Health Service is the development of a digitally proficient leadership. The National Health Service Digital Academy (NHSDA) Digital Health Leadership program was designed to support emerging digital leaders to acquire the necessary skills to facilitate transformation. This study examined the influence of the program on professional identity formation as a means of creating a more proficient digital health leadership. OBJECTIVE: This study aims to examine the impact of the NHSDA program on participants' perceptions of themselves as digital health leaders. METHODS: We recruited 41 participants from 2 cohorts of the 2-year NHSDA program in this mixed methods study, all of whom had completed it >6 months before the study. The participants were initially invited to complete a web-based scoping questionnaire. This involved both quantitative and qualitative responses to prompts. Frequencies of responses were aggregated, while free-text comments from the questionnaire were analyzed inductively. The content of the 30 highest-scoring dissertations was also reviewed by 2 independent authors. A total of 14 semistructured interviews were then conducted with a subset of the cohort. These focused on individuals' perceptions of digital leadership and the influence of the course on the attainment of skills. In total, 3 in-depth focus groups were then conducted with participants to examine shared perceptions of professional identity as digital health leaders. The transcripts from the interviews and focus groups were aligned with a previously published examination of leadership as a framework. RESULTS: Of the 41 participants, 42% (17/41) were in clinical roles, 34% (14/41) were in program delivery or management roles, 20% (8/41) were in data science roles, and 5% (2/41) were in "other" roles. Interviews and focus groups highlighted that the course influenced 8 domains of professional identity: commitment to the profession, critical thinking, goal orientation, mentoring, perception of the profession, socialization, reflection, and self-efficacy. The dissertation of the practice model, in which candidates undertake digital projects within their organizations supported by faculty, largely impacted metacognitive skill acquisition and goal orientation. However, the program also affected participants' values and direction within the wider digital health community. According to the questionnaire, after graduation, 59% (24/41) of the participants changed roles in search of more prominence within digital leadership, with 46% (11/24) reporting that the course was a strong determinant of this change. CONCLUSIONS: A digital leadership course aimed at providing attendees with the necessary attributes to guide transformation can have a significant impact on professional identity formation. This can create a sense of belonging to a wider health leadership structure and facilitate the attainment of organizational and national digital targets. This effect is diminished by a lack of locoregional support for professional development.

Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment.

BACKGROUND: Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and poorer survival. The authors investigated diffuse reflectance spectroscopy (DRS) to distinguish tumour and non-tumour tissue in ex-vivo colorectal specimens, to aid margin assessment and provide augmented visual maps to the surgeon in real-time. METHODS: Patients undergoing elective colorectal cancer resection surgery at a London-based hospital were prospectively recruited. A hand-held DRS probe was used on the surface of freshly resected ex-vivo colorectal tissue. Spectral data were acquired for tumour and non-tumour tissue. Binary classification was achieved using conventional machine learning classifiers and a convolutional neural network (CNN), which were evaluated in terms of sensitivity, specificity, accuracy and the area under the curve. RESULTS: A total of 7692 mean spectra were obtained for tumour and non-tumour colorectal tissue. The CNN-based classifier was the best performing machine learning algorithm, when compared to contrastive approaches, for differentiating tumour and non-tumour colorectal tissue, with an overall diagnostic accuracy of 90.8% and area under the curve of 96.8%. Live on-screen classification of tissue type was achieved using a graduated colourmap. CONCLUSION: A high diagnostic accuracy for a DRS probe and tracking system to differentiate ex-vivo tumour and non-tumour colorectal tissue in real-time with on-screen visual feedback was highlighted by this study. Further in-vivo studies are needed to ensure integration into a surgical workflow.

Human robotic surgery with intraoperative tissue identification using rapid evaporation ionisation mass spectrometry.

Instantaneous, continuous, and reliable information on the molecular biology of surgical target tissue could significantly contribute to the precision, safety, and speed of the intervention. In this work, we introduced a methodology for chemical tissue identification in robotic surgery using rapid evaporative ionisation mass spectrometry. We developed a surgical aerosol evacuation system that is compatible with a robotic platform enabling consistent intraoperative sample collection and assessed the feasibility of this platform during head and neck surgical cases, using two different surgical energy devices. Our data showed specific, characteristic lipid profiles associated with the tissue type including various ceramides, glycerophospholipids, and glycerolipids, as well as different ion formation mechanisms based on the energy device used. This platform allows continuous and accurate intraoperative mass spectrometry-based identification of ablated/resected tissue and in combination with robotic registration of images, time, and anatomical positions can improve the current robot-assisted surgical platforms and guide surgical strategy.

An introduction to digital determinants of health.

In recent years, technology has been increasingly incorporated within healthcare for the provision of safe and efficient delivery of services. Although this can be attributed to the benefits that can be harnessed, digital technology has the potential to exacerbate and reinforce preexisting health disparities. Previous work has highlighted how sociodemographic, economic, and political factors affect individuals' interactions with digital health systems and are termed social determinants of health [SDOH]. But, there is a paucity of literature addressing how the intrinsic design, implementation, and use of technology interact with SDOH to influence health outcomes. Such interactions are termed digital determinants of health [DDOH]. This paper will, for the first time, propose a definition of DDOH and provide a conceptual model characterizing its influence on healthcare outcomes. Specifically, DDOH is implicit in the design of artificial intelligence systems, mobile phone applications, telemedicine, digital health literacy [DHL], and other forms of digital technology. A better appreciation of DDOH by the various stakeholders at the individual and societal levels can be channeled towards policies that are more digitally inclusive. In tandem with ongoing work to minimize the digital divide caused by existing SDOH, further work is necessary to recognize digital determinants as an important and distinct entity.

A YOLOv5-based network for the detection of a diffuse reflectance spectroscopy probe to aid surgical guidance in gastrointestinal cancer surgery.

PURPOSE: A positive circumferential resection margin (CRM) for oesophageal and gastric carcinoma is associated with local recurrence and poorer long-term survival. Diffuse reflectance spectroscopy (DRS) is a non-invasive technology able to distinguish tissue type based on spectral data. The aim of this study was to develop a deep learning-based method for DRS probe detection and tracking to aid classification of tumour and non-tumour gastrointestinal (GI) tissue in real time. METHODS: Data collected from both ex vivo human tissue specimen and sold tissue phantoms were used for the training and retrospective validation of the developed neural network framework. Specifically, a neural network based on the You Only Look Once (YOLO) v5 network was developed to accurately detect and track the tip of the DRS probe on video data acquired during an ex vivo clinical study. RESULTS: Different metrics were used to analyse the performance of the proposed probe detection and tracking framework, such as precision, recall, mAP 0.5, and Euclidean distance. Overall, the developed framework achieved a 93% precision at 23 FPS for probe detection, while the average Euclidean distance error was 4.90 pixels. CONCLUSION: The use of a deep learning approach for markerless DRS probe detection and tracking system could pave the way for real-time classification of GI tissue to aid margin assessment in cancer resection surgery and has potential to be applied in routine surgical practice.

Patient safety incidents in endoscopy: a human factors analysis of nonprocedural significant harm incidents from the National Reporting and Learning System (NRLS).

BACKGROUND: Despite advances in understanding and reducing the risk of endoscopic procedures, there is little consideration of the safety of the wider endoscopy service. Patient safety incidents (PSIs) still occur. We sought to identify nonprocedural PSIs (nPSIs) and their causative factors from a human factors perspective and generate ideas for safety improvement. METHODS: Endoscopy-specific PSI reports were extracted from the National Reporting and Learning System (NRLS). A retrospective, cross-sectional human factors analysis of data was performed. Two independent researchers coded data using a hybrid thematic analysis approach. The Human Factors Analysis and Classification System (HFACS) was used to code contributory factors. Analysis informed creation of driver diagrams and key recommendations for safety improvement in endoscopy. RESULTS: From 2017 to 2019, 1181 endoscopy-specific PSIs of significant harm were reported across England and Wales, with 539 (45.6%) being nPSIs. Five categories accounted for over 80% of all incidents, with "follow-up and surveillance" being the largest (23.4% of all nPSIs). From the free-text incident reports, 487 human factors codes were identified. Decision-based errors were the most common act prior to PSI occurrence. Other frequent preconditions to incidents were focused on environmental factors, particularly overwhelmed resources, patient factors, and ineffective team communication. Lack of staffing, standard operating procedures, effective systems, and clinical pathways were also contributory. Seven key recommendations for improving safety have been made in response to our findings. CONCLUSIONS: This was the first national-level human factors analysis of endoscopy-specific PSIs. This work will inform safety improvement strategies and should empower individual services to review their approach to safety.

Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI / Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension / Diretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI

resumen está disponible en el texto completo

ABSTRACT The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

RESUMO A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

resumen está disponible en el texto completo

ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

The impact of collaborative organisational models and general practice size on patient safety and quality of care in the English National Health Service: A systematic review.

Collaborative primary care has become an increasingly popular strategy to manage existing pressures on general practice. In England, the recent changes taking place in the primary care sector have included the formation of collaborative organisational models and a steady increase in practice size. The aim of this review was to summarise the available evidence on the impact of collaborative models and general practice size on patient safety and quality of care in England. We searched for quantitative and qualitative studies on the topic published between January 2010 and July 2023. The quality of articles was assessed using the Newcastle-Ottawa Scale and the Critical Appraisal Skills Programme checklist. We screened 6533 abstracts, with full-text screening performed on 76 records. A total of 29 articles were included in the review. 19 met the inclusion criteria following full-text screening, with seven identified through reverse citation searching and three through expert consultation. All studies were found to be of moderate or high quality. A predominantly positive impact on service delivery measures and patient-level outcomes was identified. Meanwhile, the evidence on the effect on pay-for-performance outcomes and hospital admissions is mixed, with continuity of care and access identified as a concern. While this review is limited to evidence from England, the findings provide insights for all health systems undergoing a transition towards collaborative primary care.

Identifying benefits and concerns with using digital health services during COVID-19: Evidence from a hospital-based patient survey.

Despite large-scale adoption during COVID-19, patient perceptions on the benefits and potential risks with receiving care through digital technologies have remained largely unexplored. A quantitative content analysis of responses to a questionnaire (N = 6766) conducted at a multi-site acute trust in London (UK), was adopted to identify commonly reported benefits and concerns. Patients reported a range of promising benefits beyond immediate usage during COVID-19, including ease of access; support for disease and care management; improved timeliness of access and treatment; and better prioritisation of healthcare resources. However, in addition to known risks such as data security and inequity in access, our findings also illuminate some less studied concerns, including perceptions of compromised safety; negative impacts on patient-clinician relationships; and difficulties in interpreting health information provided through electronic health records and mHealth apps. Implications for future research and practice are discussed.