Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated n = 890/1,010) especially those with over 20 years' experience , as compared to clinical decision support tools  or diagnostic tools . A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced , and felt COVID-19 catalyzed willingness to adopt AI . Common barriers to implementation include medical liability from errors  whereas enablers include improving access . Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83.One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology. Aging populations are fueling an exponential growth in the demand for eye care and insufficient capacity of eye care services in many health systems –3. This Currently, AI for automated classification in ophthalmic imaging has been validated with clinically acceptable performance and evaluated in many studies –15, inclEarlier studies have surveyed general perceptions of AI among different users  –28, althThis was an expert survey investigation of eye care practitioners regarding their perspectives for clinical artificial intelligence (AI) solutions in Ophthalmology. Responses from ophthalmologists to this anonymous web-based electronic survey are investigated in partnership with professional associations through convenient selection to reflect the spectrum of geographical regions and subspecialties across the Ophthalmology medical field. The temporal proximity of the study period (1 March 2020–1 March 2021) to the COVID-19 outbreak  also enabled collection of data regarding its impact on provider perspectives of AI applications. This research adhered to the tenets of the declaration of Helsinki, and Singhealth Institutional Review Board (IRB) approval was obtained with waiver of the need for informed consent (CIRB Ref 2020/2219).The study survey was iteratively refined through literature review to develop semi-structured dichotomous and Likert questions . This waFirst, professional acceptance of various clinical AI applications for eye care services was evaluated based on the regulatory guidance outlined in the SAMD document prepared by the International Medical Device Regulators Forum (IMDRF) working group chaired by the US FDA . A risk-Intended users included ophthalmologists, primary eye care providers  and primary care providers (PCPs) with eye care services. Clinical contexts evaluated include the detection of common eye diseases DR, glaucoma, AMD and/or cataract.Significance of information provided to the healthcare decision were classified based on the SaMD framework for intended uses to inform clinical management, drive clinical management, or diagnose eye diseases, as assistive tool, clinical decision support (CDS) tool or diagnostic tool, respectively . ApplicaNext, Ophthalmologists' views on factors contributing to AI acceptance were evaluated considering all levels of the healthcare ecosystem from the microsystem to the macrosystem. First, the factors contributing to technology acceptance at the level of the healthcare microsystem including professional acceptance of clinical AI applications, acceptable level of error, perceived impact on professional roles, and potential barriers/enablers for adoption were explored . Second,via their official established channels with all actively enrolled members. Recruitment was led by a study team member that was a member in good standing in each participating professional association.The web-based survey was disseminated through snow-ball sampling of professional Ophthalmology associations. Collaborating associations were selected to represent participants from a breadth of clinical Ophthalmology and imaging subspecialties as well as geographical regions of practice (Acknowledgment). Study recruitment was conducted using standardized invitations sent by the associations The initial invitation to participate was sent to all actively enrolled members within each association. All invitations were sent by email and supplemented by regional practices based on the societies established channels with their members, such as WeChat in China. Invitations included the unique uniform resource locator (URL) of the web-based survey, that was programmed to restrict entries to one per participant-device to avoid duplication of entries from providers enrolled in multiple associations and/or receiving invites from multiple channels. Invitations were followed by 3 reminders at ~2-week intervals, coordinated by the study team members.Responses were described with valid percentages for categorical variables as well as mean and standard deviation (SD) for continuous variables, with response rate tabulated for each question. The geographical origin of participants is classified based on the World Bank (WB) classification for 7 global regions . The ecop-value of 0.05. Analysis was performed using SPSS .Quantitative analysis of any associations between provider acceptance and demographic information are reported. Multivariable logistic regression was performed to investigate any linear associations between provider acceptance of AI application in Ophthalmology and demographic information including age, gender, country (region of practice), economic background, experience, and self-rated understanding of AI for participants. To obtain a 95% confidence interval with 5% for the margin of error and 50% response distribution, a minimum sample size of 385 was calculated for the outcome of willingness to adopt AI in the next 5 years. Statistical significance was set at a In addition, machine learning (ML) analysis of survey responses was conducted using six selected input variables , to predict a total of 15 outcomes (output variables). An independent random forest model was trained to predict each outcome from the input variables in an exploratory analysis to assess for any non-linear associations between provider acceptance of AI and demographic information. The training dataset was randomly divided into 1,000 subjects for training, and 176 subjects for validation. To train each random forest model, five-fold cross-validation was first performed on the training dataset, to optimize four hyperparameters: the entropy criterion, the maximum depth of the random forest trees, the maximum number of features, and the number of tree estimators. The optimal hyperparameters thus found were then used on train the final model on the full training dataset, and subsequently applied to the validation dataset to evaluate the area under the receiver operating curve (AUC).The Breiman-Cutler permutation importance measure was used to determine the most important input variable(s) in predicting each outcome . The perA total of 1,176 ophthalmologists from 70 countries responded to the survey with representation from all 7 world bank geographical regions , althougn = 639/1,176). Participant demographics are detailed in Participants reported a spectrum of clinical experience mostly between 10 and 30 years, whereby 323 participants had 10–20 years (27.5%) and 337 participants 20–30 years (28.7%) of experience. When asked to rate their understanding about machine learning (ML), deep learning (DL), and artificial intelligence (AI), a majority self-rated their understanding as average  receiving higher acceptance than those intended for use by Primary Eye Care Providers . Professional acceptance of AI applications as CDS tools to drive clinical management received lower acceptance. Diagnostic tools intended for use by ophthalmologists received the lowest  acceptance among the 6 categories .Participants were asked about their acceptance of various applications of AI for eye care services based on the solutions' intended user and clinical application in accordance with the SaMD regulatory framework. Assistive tools to inform clinical management were the most acceptable form of clinical AI application in ophthalmology, with applications designed for use by ophthalmologists  than those practicing in East Asia and the Pacific. However, acceptance of AI applications for PECPs as diagnostic tools was relatively higher among participants that self-rated their understanding of AI as average  or above average .Multivariate analysis was also conducted for the professional acceptance of clinical AI applications based on their intended users with demographic factors included for adjustment . In thisp = 0.035). Similarly, acceptance of AI applications for ophthalmologists as diagnostic tools was relatively higher among participants with increasing age , male gender , and participants practicing in the Latin America and the Caribbean region , although it was lower among resource-abundant practice settings . No demographic variables had statistically significant associations with professional acceptance of AI applications as CDS tools for either group of intended users.On the other hand, the odds of professional acceptance of AI applications for ophthalmologists as assistive tools was relatively higher among those with clinical experience of 20 or more years . Participants were divided about the acceptable level of error for diagnostic tool applications of AI intended to be used by ophthalmologists, with equivalent to a general ophthalmologist  or subspecialty-trained ophthalmologist  being the most frequent responses.Next, participants reported the acceptable level of error for the various applications of AI for eye care services based on the intended user and application, when level of error was benchmarked against various practitioners . Overall, participants had greater expectations for the performance of clinical AI applications intended for use by ophthalmologists as opposed to that for use by PECPs . Specifin = 632/927), although those of others may be partially replaced including allied primary eye care service (PECS) providers  and primary care providers (PCP) with eye care services . Detailed responses are included in Next, participants were surveyed about the potential impact of clinical AI on their professional roles and responsibilities at the level of the healthcare microsystem. The majority of participants that responded indicated that the eye care roles of ophthalmologists are not likely to be replaced , more targeted referrals to specialist care , and reduced time spent by specialists on monotonous tasks . The disadvantages of clinical AI for Ophthalmology that were most frequently reported include concerns over medical liability due to machine error , data security & privacy concerns , and concerns over the divestment of healthcare to large technology and data companies . Further detailed responses are depicted in Finally, participants were surveyed about their perceptions of potential advantages and disadvantages of clinical AI for Ophthalmology to identify potential barriers and enablers for clinical AI adoption. Overall, the perceived advantages of clinical AI for Ophthalmology that were most frequently reported include improved patient access to disease screening  in their organizations. Six hundred four participants (51.4%) reported that their organizations were willing to adopt AI for clinical practice in general within the next 5 years. A multivariate logistic regression model was applied to evaluate associations with participant demographics.p = 0.006) compared to the East Asia and Pacific region. However, the odds of organizational willingness to adopt AI was lower among participants from resource-abundant regions . There were no significant associations between the odds of organizational willingness to adopt AI and the remaining demographic factors, including age, clinical experience, and self-rated understanding of AI  and those practicing in the North American region  compared to East Asia and the Pacific. On the other hand, that for glaucoma screening reported by ophthalmologists in the Middle east and North Africa was relatively lower for glaucoma screening  compared to East Asia and the Pacific. Finally, the odds of organizational willingness to adopt AI for cataract screening being reported by ophthalmologists of older age was higher , while that by ophthalmologists of male gender was lower . That for cataract screening was similarly lower among those practicing in the Europe and Central Asia region  and North American region .In this model, the odds of organizational willingness to adopt AI being reported by ophthalmologists practicing in South Asia was relatively higher for DR screening . That for DR diagnosis was also higher among participants practicing in the Latin America & the Caribbean region  as well as the South Asian region  relative to the East Asia and Pacific region. Similarly, the odds of organizational willingness to adopt AI being reported for Glaucoma diagnosis was higher for participants of older age  and male gender . However, that for Glaucoma diagnosis was lower for participants in the Middle east and North African region .In this model, the odds of organizational willingness to adopt AI being reported for DR diagnosis was higher for participants of older age  and male gender . In addition, that for AMD diagnosis was also higher for participants practicing in the Latin America and the Caribbean region  compared to the East Asia and the Pacific. On the other hand, the odds of organizational willingness to adopt AI being reported for Cataract screening were lower for participants practicing in the Europe and Central Asia region  compared to the East Asia and the Pacific region.Furthermore, the odds of organizational willingness to adopt AI being reported for AMD diagnosis was higher for participants of older age . Furthermore, participants had mixed views regarding the impact of AI on ophthalmology clinical workload, with some anticipating reduced workload  and others instead anticipating increased workload . When asked if trainee numbers should be increased, decreased or kept the same, most participants indicated that ophthalmology trainee numbers should not be adjusted , although some indicated allied health eye care trainee numbers should be increased .Despite the current progress in validation and implementation of AI for eye care services, less than half of all participants in this survey felt that AI would be regularly used in clinical practice within the next 5 years , invest resources for implementation , and actively educate the public regarding the use of AI in Ophthalmology . Detailed responses are included in With respect to their organizational willingness to facilitate adoption of AI tools for eye care services, study participants were optimistic overall. Many participants indicated that their organizations were very likely or somewhat likely to specifically train healthcare workers in the use and understanding of AI , affordability , and quality  in eye care services. Detailed responses are depicted in n = 750/927). However, participants were closely divided regarding whether COVID-19 increased the likelihood of organizational AI adoption  as well as organizational facilitation. Participants remained divided when the likelihood of organizational facilitation was explored in greater detail in terms of investing resources to implement AI , training healthcare workers in AI , and educating the public regarding AI .Many participants indicated that they strongly agree or agree that clinical AI will facilitate improvements in accessibility  models, predictive AUCs of between 0.52 and 0.83 were obtained in predicting binary outcomes with corresponding permutation importance depicted in To our knowledge, this is the first study providing an in-depth evaluation of ophthalmologists acceptance of clinical AI for Ophthalmology that incorporates the relevant medical device regulatory framework. Provider perspectives on professional and organizational acceptance of clinical AI tools for eye care services are evaluated in this study involving participants from a spectrum of geographies and clinical subspecialties. A machine learning (ML) approach was applied to highlight the clustering of responses, illustrating the relevance of individual demographic and attitude variables on professional acceptance and likelihood of adoption. Overall, participants indicated high levels of professional and organizational acceptance of AI for eye care services. Potential important barriers and enablers for the implementation of these tools in clinical practice were also highlighted. Furthermore, the impact of COVID-19 on clinical AI adoption in Ophthalmology was assessed.The results of this study suggest several considerations for facilitating adoption of clinical AI at the level of healthcare microsystems. Participants were more accepting of clinical AI applications as assistive tools rather than CDS or diagnostic tools, based on the software as a medical device (SaMD) regulatory framework for clinical intended uses of AI technology to inform clinical management by highlighting areas of interest, drive clinical management by initiating referrals, or diagnose eye diseases to recommend management, respectively. The process of forming a clinical diagnosis is a fundamental role of healthcare practitioners. It is a complexed art based on probabilistic, causal or deterministic reasoning, often without the availability of complete information . The praThe perceived enablers of improved accessibility and optimized referrals from screening as well as acceptance of these tools when designed for used by allied PECPs also suggests avenues to optimize solution development and deployment. Applying design considerations to facilitate operationalising clinical AI used by PECPs within the community to filter out patients with advanced illness requiring tertiary care are more acceptable to stakeholders. This may require embedded systems to facilitate referrals to ophthalmologists using the sorting conveyor or pyramid operational models where required .Most ophthalmologists were not concerned about the threat of being replaced by AI . The vieTherefore, we find that participants are confident in their clinical roles and do not perceive AI to be a major threat to professional roles. This is consistent with the lower agreement reported for relevant potential disadvantages of AI , such asThe major disadvantages of clinical AI that participants agreed upon include potential medical liability from machine error, data security, privacy, and potential divestment of healthcare to corporate entities . Yet, deThese findings highlight the need for greater stakeholder engagement to emphasize advantages of AI in tandem with research to address disadvantages perceived by experts , 45. ForThe analysis of healthcare meso-system considerations for clinical AI implementation highlight several trends in receptiveness to the adoption of clinical AI at the level of specific eye care services and within healthcare organizations. Overall, participants reported greater likelihood of organizational willingness to adopt screening applications rather than diagnostic applications. There was greater acceptance of applications for detection of DR, particularly in South Asia, and lower acceptance of applications for detection of Cataract, particularly in Europe and Central Asia . This maInterestingly, the odds of reporting organizational willingness to adopt AI for certain applications were higher among participants with advanced age, including applications for detection of DR, glaucoma and AMD, as well as screening for cataracts . This isn = 527/1,176). In ophthalmology, confidence in professional responsibilities likely stems from the procedural and surgical roles of professionals that cannot be replaced by AI. This interventional workload will likely increase with enhanced detection of eye diseases through the use of clinical AI to scale-up screening services, as reflected in the advantages of clinical AI anticipated by participants , although they were less certain regarding improvements in affordability (61.9%) and quality (69.4%). Moreover, having experienced the macrosystem changes brought about by the coronavirus disease 2019 (COVID-19) pandemic, participants were optimistic about the potential value of clinical AI to minimize non-essential patient contact (80.9%). However, participants were divided regarding whether COVID-19 increased the likelihood of organizational AI adoption (50.2%) and facilitation through investment (51.1%), training of healthcare workers (52.4%), and educating the public (54.2%). This suggests that ophthalmologists are unsure about the impact of the pandemic at the macrosystem level on influencing the willingness to implement clinical AI among service providers and organizations within the health system.Feature permutation importance estimates were obtained to estimate the contribution of each feature, within the random forest model trained to predict each question outcome. For example, for the question “Will your organization be willing to adopt AI in clinical practice in 5 years,” the features corresponding to one's understanding of DL/ML, and ICO2017 income, were significantly more important than the others. Feature permutation importance involves randomly shuffling the values for a particular feature, and observing the decrease in model performance due to this shuffling. If the performance decreases appreciably, the feature is regarded as relatively predictive.The AUCs of between 0.52 and 0.83 obtained in predicting binary outcomes with corresponding permutation importance suggest that eye care professionals' acceptance of AI and perceived likelihood of implementation can be anticipated from their demographics and attitudes toward AI to an extent . The outResponses to the APPRAISE survey indicated that participants were optimistic about the deployments of AI applications that reduce non-essential contact between patients and providers, thereby minimizing the risk of infectious disease transmission. This highlights the public health importance for further research and capacity building in this critical field to rapidly scale-up eye care services to meet the growing needs for eye screening in aging populations. Ophthalmologists will need to work closely with computer scientists to ensure that AI solutions for healthcare are appropriately designed to assimilate into clinical workflows and incorporate relevant considerations such as professional acceptance for specific AI applications based on the intended users for the given solution. Interestingly, participants from resource abundant settings reported potential barriers to adoption including lower odds of organizational willingness to adopt AI as well as lower acceptance of specific AI applications, including diagnostic tools for ophthalmologists that would need to be considered in developing specific applications. Although professional acceptance for AI solutions was relatively greater in resource-constrained settings, potential challenges with infrastructural availability such as intermittent electricity or internet access will need to be considered in the design of these solutions , 25.Limitations of this study include that a majority of participants originated from Asia Pacific, with less representation of participants from the West. Although the timing of survey dissemination facilitated evaluation of professional perspectives on AI adoption during an ongoing public health emergency, regions with higher official burden of COVID-19 at the time of survey dissemination had less representation in our results, as ophthalmologists may have been occupied with related public health initiatives at the time. Moreover, statistical assessment of the survey was not conducted and responses for all qualitative questions were not made compulsory, whereby non-response rates are indicated in the study tables. Therefore, description and analysis was conducted based on the valid responses with non-responses programmed as “missing.”In addition, limitations of the snowball sampling method used for the purpose of hypothesis-generation in this study include the exclusion of other stakeholders such as primary care providers (PCPs). Furthermore, specific response rate calculation for different regions and channels for recruitment were not possible given privacy restrictions of the professional associations and inability to deconflict participants with membership in multiple associations. Although the survey was programmed to restrict one response per participant to avoid duplicate responses, there is potential selection bias for stakeholders that are more actively engaged in professional associations. These limitations may limit the generalisability of findings from this study. Future studies can address these limitations through survey validation for reliability and reproducibility, multiple testing correction for future hypothesis-testing research, probability sampling methods with inclusion of PCPs, stratified response rate tabulation based on individual channels of recruitment, and increased representation of participants from the West.Finally, another limitation consistent with earlier survey investigations is the use of a logistic regression analytic approach to investigate associations between independent variables such as demographics and dependent variables such as acceptance, which assumes a linear relationship between them. However, unlike earlier investigations, one strength of this study is the use of a decision-tree based machine learning (ML) analytic approach called random forests to analyse responses in tandem with traditional logistic regression. The main distinction between the two analytic approaches rests in the transparency and underlying assumptions, whereby the flexibility of ML has allowed it to outperform the predictive accuracy of logistic regression in large empirical evaluations .For logistic regression, a linear regression model on the input variables is transformed using the logistic function. It is therefore readily interpretable in terms of these input variables, assuming linearity of the input variables and log odds. However, logistic regression therefore remains a linear classifier, and non-linear relationships are not well-accounted for by the model. In contrast, the random forests ML classifier is able to model non-linear relationships in the data, with the trade-off of being less interpretable. This allows evaluation of a broader variety of potential relationships between the variables.Additional strengths of this study include the consolidation of perspectives from a large and diverse spectrum of Ophthalmologists on the timely topic of clinical AI applications. The survey was also disseminated with close time-proximity to the COVID-19 outbreak, allowing assessment of the impact of a public health emergency on provider perspectives regarding clinical AI adoption. Finally, this study provides an in-depth investigation of professional acceptance of clinical AI solutions for automated classification of medical imaging in ophthalmology, incorporating a systematic approach to address factors affecting adoption at all levels from the micro-, meso-, and macrosystem. Furthermore, the intricacies of the latest regulatory guidance were applied in the evaluation of AI applications based on the intended user, significance of the information to the healthcare decision, and clinical context.Artificial Intelligence (AI) has been established as a tool for health systems to improve the right-siting of patients. This study outlines several key considerations that inform future research, communication and facilitation interventions to drive effective adoption and operationalization of these tools in clinical practice. Actionable insights to facilitate AI adoption are also highlighted, including engagement of relevant stakeholders and operationalization based on the enablers of AI adoption identified in this study, as well as addressing perceived barriers through development of the technology and guidelines in collaboration with ophthalmologists.mdcdvg@nus.edu.sg.The datasets presented in this article are not readily available because aggregated de-identified data for participant responses in this study are provided in the segment on The studies involving human participants were reviewed and approved by this research adhered to the tenets of the declaration of Helsinki, and Singhealth Institutional Review Board (IRB) approval was obtained with waiver of the need for informed consent (CIRB Ref 2020/2219). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.DG, FZ, GL, CCYC, SZ, and WN contributed to the data analysis and drafting the initial manuscript. All listed authors contributed to study conceptualization, design, recruitment, data interpretation, manuscript preparation, and meet the criteria for authorship as agreed upon by the International Committee of Medical Journal Editors and are in agreement with the content of the manuscript. All authors contributed to the article and approved the submitted version.DG reports appointment as Physician Leader (Telemedicine) for Raffles Medical Group (SGX:$BSL.SI) and investments in digital health start-ups AskDr, Doctorbell (acquired by MaNaDr), Shyfts, and VISRE. JC reports appointment as a consultant to Boston AI labs. AYL reports grants from Santen, personal fees from Genentech, US FDA, Johnson and Johnson, grants from Carl Zeiss Meditec, personal fees from Topcon, Gyroscope, non-financial support from Microsoft, grants from Regeneron, outside the submitted work; This article does not reflect the views of the US FDA. PK reports having acted as a consultant for DeepMind, Roche, Novartis, Apellis, and BitFount and is an equity owner in Big Picture Medical. He has received speaker fees from Heidelberg Engineering, Topcon, Allergan, and Bayer. AF reports honoraria, advisory board and grant funding from Alcon, Bayer, Novartis, Allergan, Roche, and Syneos Health. AL reports grants from Roche and Novartis, and appointment as a consultant to NotalVision, Allergan, Bayer, WebMD, and Beyeonics. AG reports appointment to provide lectures for Pfizer, Thea, and Polpharma. TS reports appointment as a consultant & advisory board for Bayer Yakuhin, Boehringer-Ingelheim, Novartis, Chugai, Senju, and Santen. DM reports funding support from the Singapore National Medical Research Council (NMRC-CIRG18Nov-0013), and the Duke-NUS Medical School, Singapore (ACP 05/FY2019/P2/06-A60). DM also reports appointment as consultant and Advisory Board Member of Optomed, Finland. TW reports appointment as the deputy group chief executive officer (research and education) of Singapore Health Services, a consultant & advisory board for Allergan, Bayer, Boehringer-Ingelheim, Genentech, Merck, Novartis, Oxurion (formerly ThromboGenics), Roche, and co-founder of Plano. DT reports funding from the following grants for research about AI in healthcare: National Medical Research Council, Singapore , National Health Innovation Center, Singapore (NHIC-COV19-2005017), SingHealth Fund Limited Foundation (SHF/HSR113/2017), Duke-NUS Medical School, Singapore , and Agency for Science, Technology and Research (ASTAR), Singapore (A20H4g2141 and A20H4g2141). DT, GL, and TW also report being the co-inventors of a deep learning system for retinal diseases and co-founders of related start-up Eyris; potential conflicts of interests are managed according to institutional policies of the Singapore Health System  and the National University of Singapore (NUS). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The incidence of adrenal crisis (AC) remains high, particularly for people with primary adrenal insufficiency, despite the introduction of behavioural interventions. The present study aimed to identify and evaluate available evidence of interventions aiming to prevent AC in primary adrenal insufficiency.This study is a systematic review of the literature and theoretical mapping.MEDLINE, MEDLINE in Process, EMBASE, ERIC, Cochrane CENTRAL, CINAHL, PsycINFO, the Health Management Information Consortium and trial registries were searched from inception to November 2021. Three reviewers independently selected studies and extracted data. Two reviewers appraised the studies for the risk of bias.Seven observational or mixed methods studies were identified where interventions were designed to prevent AC in adrenal insufficiency. Patient education was the focus of all interventions and utilised the same two behaviour change techniques, ‘instruction on how to perform a behaviour’ and ‘pharmacological support’. Barrier and facilitator themes aiding or hindering the intervention included knowledge, behaviour, emotions, skills, social influences and environmental context and resources. Most studies did not measure effectiveness, and assessment of knowledge varied across studies. The study quality was moderate.This is an emerging field with limited studies available. Further research is required in relation to the development and assessment of different behaviour change interventions to prevent AC. During acute illness or stress, the adrenal cortex produces higher amounts of the steroid hormone cortisol. Patients with adrenal insufficiency are unable to naturally produce enough cortisol and therefore are required to take daily steroid replacement therapy. These patients are advised to double or triple their dosage or to administer parental hydrocortisone during periods of acute stress, for example, during an illness, after a car accident, or before surgical intervention , 2. FailAC affects around 1 in 12 patients with primary adrenal insufficiency (PAI) each year . CompareAs managing one’s medication is behaviour based, interventions designed to change behaviour may assist patients with PAI to adopt the correct regime. Healthcare interventions that increase medication adherence tend to be education based and support the assumption that improving knowledge leads to optimal adherence. To date, such interventions have focused on increasing patients’ knowledge about their condition, how and when to take medication and the consequence of not managing their medications well , 9. HoweDeconstructing the interventions, to identify key components of maximum potential, is an essential step towards developing effective and acceptable future interventions . UtilisiThere is a need to reduce the frequency and consequence of AC in people with PAI. While much is known about interventions designed to enhance medication adherence , 10, thePrior to commencing the review, we performed a search of the Cochrane database of systematic reviews, Medline (using review filter) and Epistemonikos, which yielded no results for previous systematic reviews in this topic area.Our systematic review considered the following three research questions:What interventions have been developed and evaluated to prevent AC in adult patients with PAI?What is the effectiveness of the interventions?What are the barriers and facilitators targeted in the interventions?The systematic review was prospectively registered on PROSPERO (CRD 42019137412) and is reported based on the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement  (SupplemA broad search strategy was designed for MEDLINE (Supplementary Table 2) with no restrictions by publication type, study or language. The search strategy was adapted for use in different electronic bibliographical databases . The seaTitles and abstracts were independently screened by three reviewers  utilising Rayyan software . DisagreStudy inclusion criteria are listed in the PICOTTS  framework to identify key characteristics (Supplementary Table 3). Papers were excluded if they contained non-empirical data and/or were an expert opinion, editorial, narrative review or conference abstracts (where the author could not provide further data on request). Only papers published in English were included, as the research team did not have financial resources to translate non-English published papers.Data were extracted using a piloted data extraction form, adapted from the Cochrane expert group  that is The AXIS Appraisal Tool  was usedTo address the research questions, extracted data were arranged in tables and findings were reported narratively. No quantitative synthesis was possible due to the clinical and methodological heterogeneity of the studies, including interventions, outcome measures, study design and conduct.et al. hierarchy  utilising the AXIS and MMAT (where appropriate) critical appraisal tools is reported in Supplementary Tables 4 and 5. Due to the observational nature of these studies, confounding variables introduce potential bias that in turn limits confidence in the proposed interventions and their findings. However, two studies attempted to adjust for confounders in relation to knowledge , 27.Studies recruited between 26  and 423 n = 7) was to prevent AC  focused on education (n = 2) (n = 2)  , 27; gro (n = 2) , 25; pat (n = 2) ; and pat (n = 2) .Minimal information was provided about each intervention; for example, few studies included information about the frequency of intervention, supply of emergency injection kits and emergency injection training for carers or family members. The place and timing, frequency of intervention and personnel involved in delivering the intervention were not always available. Additionally, no study reported the use of theory in the development of the intervention, which is recommended when developing an effective intervention , or if tThe reporting of knowledge, frequency of AC, hospitalisation, death and quality of life were varied across studies . Two stuPatients’ knowledge of medication and dose adjustment was found to be insufficient to change behaviour when needed to avoid adrenal crises in all but one study . Two conFive studies , 25, 26 Preparation and administration of the emergency injection was a barrier for some participants and often relied on the support of others to perform the task . The numHaving the necessary equipment to perform the emergency injection is also necessary to self-administer. Two studies highlighted that participants did not have appropriate equipment, and, therefore, they would not be able to administer in times of need , 22. ConThe interventions included a narrow range of BCTs see  and 4. Tn = 7). Noticeably, this leaves seven domains  not yet applied in interventions to prevent AC and potential areas to be investigated. Four studies included more than two TDF domains  and developed using a systematic theoretical basis , 30. Henn = 8) showed improvement in knowledge at follow-up  and is funded by Health Education England (HEE)/National Institute for Health Research (NIHR) for this research project. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR (Partner Name), NHS or the UK Department of Health and Social Care. K A S is currently supported by the National Institute for Health Research (NIHR) Applied Research Centre (ARC) West Midlands (NIHR200165).All data generated or analysed during this study are included in this article.


The Centers for Medicare & Medicaid Services (CMS) is the largest payer of nursing home care in the US. CMS defines staffing levels as hours per resident day (HPRD) and staff turnover rates; it defines quality of care as nurse staffing levels, hospitalization rates, resident outcomes, and deficiencies (violations of regulations). However, these definitions have been inconsistently applied in the research literature, which has led to equivocal results in research on the association between staffing levels and the quality of care in nursing homes. We conducted a scoping review of how nursing home staffing levels and quality of care have been defined and measured in the research literature, guided by the PRISMA  framework. Out of the N=423 initially identified studies through PubMed, N=67 articles were selected, based upon the following inclusion criteria: 1) published after January 1, 2010; 2) published in the US; 3) focused on determinants of nursing home staffing levels or determinants of quality of care. Two independent reviewers conducted abstract screening and data extraction. The findings from our review showed that approximately 50% of studies partially adopted the definitions from CMS. For example, HPRD and deficiencies were utilized to measure staffing levels and quality of care in N=37 and N=12 studies, but none of them included all suggested measures. Future studies should carefully consider the approriate definitions as specific measures can yield contradictory associations between staffing level and quality of care, easily leading to confusion for policymakers.


Reflecting their commitment to advancing health equity, the Cancer Prevention and Control Research Network (CPCRN) established a Health Equity Workgroup to identify and distill guiding principles rooted in health equity, community-engaged participatory research (CBPR), social determinants of health, and racial equity frameworks to guide its collective work. The Health Equity Workgroup utilized a multi-phase, participatory consensus-building approach to: (1) identify recurrent themes in health and racial equity frameworks; (2) capture perspectives on and experiences with health equity research among CPCRN members through an online survey; (3) engage in activities to discuss and refine the guiding principles; and (4) collect case examples of operationalizing equity principles in cancer research. Representatives from all CPCRN centers endorsed nine core principles to guide the Network’s strategic plan: (1) Engage in power-sharing and capacity building with partners; (2) Address community priorities through community engagement and co-creation of research; (3) Explore and address the systems and structural root causes of cancer disparities; (4) Build a system of accountability between research and community partners; (5) Establish transparent relationships with community partners; (6) Prioritize the sustainability of research benefits for community partners; (7) Center racial equity in cancer prevention and control research; (8) Engage in equitable data collection, analysis, interpretation, and dissemination practices; and (9) Integrate knowledge translation, implementation, and dissemination into research plans. Dissemination products, such as toolkits and technical assistance workshops, reflecting these principles will foster knowledge transfer to intentionally integrate health and racial equity principles in cancer prevention and control research. The year 2020 marked a social, political, and public health reckoning as communities across the United States (US) bore witness to the impact of the COVID-19 pandemic and the disproportionate burden and severity of outcomes among communities already experiencing health and economic disparities. The intersection of the COVID-19 pandemic with the Black Lives Matter Movement also motivated deep personal self-reflection and raised critical public consciousness of the need to address and reduce systemic racism. From social justice and population health perspectives, increasing awareness of, and taking action against systemic racism must occur across all spheres and disciplines of public health and health research. Achieving the vision of population health equity requires a more integrated approach than has previously been attempted in the US—one that leverages authentic community engagement and meaningful partnerships, accounts for the roles of social determinants of health (SDH), and demonstrates a deep commitment to tackling systemic racism at multiple levels of influence within the healthcare system and broader socio-political environment. In this paper, we describe a set of principles that can help achieve these objectives, derived both from the existing literature and from our experience in cancer prevention and control.Cancer remains a significant public health problem, with 1.8 million Americans diagnosed in 2021 and more than 600,000 expected to die from cancer in 2022 , 4. ThesThe CPCRN convenes a wide array of perspectives and expertise for the common goal of reducing cancer burden . The CPCIn many ways, these past several years concretized the historic inequities in SDH. The COVID-19 pandemic highlighted that racial and ethnic minoritized and  medically underserved populations experience a disproportionate burden of morbidity and mortality due to unequal access to and distribution of social resources –11. SimuDecades of research have documented how systemic racism is reflected in highly segregated and densely populated neighborhoods whose residents experience substantial levels of poverty and near-poverty –17. ThesBy mid-summer 2020, the growing momentum to name racism as a root cause of inequities culminated in the CDC declaring racism a serious threat to the public’s health . The NatAlthough multiple health and racial equity frameworks exist in the literature, there is less guidance on how to operationalize these frameworks in practice, particularly for implementation science and cancer prevention and control, as they focus on equity-oriented research , 35. ThiThe development of principles reflecting SDH, health and racial equity, and participatory approaches included a high-level environmental scan of the academic and gray literature, a cross-center survey of CPCRN members, guided discussions in CPCRN Steering and Workgroup meetings, and consensus-building activities at two Annual CPCRN Meetings. This process was iterative and participatory, with built in feedback loops to ensure the rich expertise, perspectives, and experiences of the CPCRN centers were reflected in the equity principles and their operational definitions.Initial discussions within the Health Equity Workgroup revolved around frameworks currently used by CPCRN centers to ground their cancer prevention and control research in equity. The most commonly cited frameworks included community-based participatory research (CBPR) , SDH 3737, and tNext, the Health Equity Workgroup disseminated an online survey to current and former members of the CPCRN, federal agency partners, and affiliate members, to assess whether they currently apply the principle in their center’s work  and to rate the relevance of each principle for a framework common across the CPCRN . Respondents were also asked to share case examples from their respective centers, suggest evaluation metrics, propose alternative principles informed by their work or existing frameworks, and provide general feedback. Ratings were analyzed and open-ended responses informed operational definitions of the equity principles. The Health Equity Workgroup presented survey results, the revised set of principles, and their definitions at the CPCRN Annual Meeting in 2021 and engaged in consensus building with attendees to refine and approve each guiding principle. Overall, the 28 survey respondents and 79 Annual Meeting participants endorsed nine health and racial equity principles with minor suggestions for modifications. After the meeting, the recommended changes were incorporated into a revised set of principles.We calculated the average relevance for each principle (score range = 1 [low relevance]–5 [high relevance]) as well as their average application in CPCRN centers. All but one principle (“Practice and Policy Translation” (P9)) were rated 4.11 or above  Engage in power-sharing and capacity building with partners; (2) Address community priorities through community engagement and co-creation of research; 3) Explore and address the systems and structural root causes of cancer disparities; (4) Build a system of accountability between research and community partners; (5) Establish transparent relationships with community partners; (6) Prioritize the sustainability of research benefits for community partners; (7) Center racial equity in cancer prevention and control research; (8) Engage in equitable data collection, analysis, interpretation, and dissemination practices; and (9) Integrate knowledge translation, implementation, and dissemination into research plans  thehow these guiding principles were applied in CPCRN research with diverse populations in diverse contexts [The nine equity principles are informed by seminal and contemporary literature and frameworks on health equity, SDH, racial equity, CBPR, and implementation science , 47–52. contexts , 54. Opeactions. A detailed description of a toolkit outlining steps towards health and racial equity in cancer prevention and control research can be found in Adsul et al. in this supplement [First, by acknowledging and measuring inequities and structural racism as fundamental causes of cancer disparities, our work must be followed with deliberate and proactive pplement . BrieflySecond, the CPCRN equity principles will formalize the integration of health equity into the mission and projects of the CPCRN. This commitment to equity is aligned with the priority of the CDC and NCI—CPCRN’s current and previous funding agencies—to address SDH related to cancer disparities. Drawing from the equity principles, the CPCRN proposes several recommendations to diversify the cancer research workforce and improve health equity: (1) increase the diversity of the NIH and biomedical workforce ; (2) involve representatives from minoritized communities on the decision making and scientific review committees ; (3) direct priority funding towards studies that consider the impact of more than one social/structural determinant on health outcomes ; and (4) support long-term community-academic-clinical partnerships for meaningful and impactful health equity research . In addition, the equity principles are also informing the development of study protocols that prioritize health equity. For example, the Cancer Survivorship Workgroup engaged in a collective reflective process to ensure their planned study on the experiences of racism and patients’ engagement with survivorship care is built around the equity principles from the outset .metrics and evaluation tools for the CPCRN to ensure quality research and accountability to our stated commitment to equity. Importantly, members of the CPCRN centers reported that articulating their engagement with each equity principle allowed them to self-reflect, assess their performance, and identify opportunities to further advance equity in their own work. Recognizing that measurement is integral to accountability, the Health Equity Workgroup is developing and validating measures and tools that explicitly assess and evaluate research efforts across CPCRN centers.Third, heeding the warnings against “health equity tourism” , 58, we


Hollow organs are visceral organs that are hollow tubes or pouches  or that include a cavity (such as the heart) and which subserve a vital function. Hollow organs show a similar histo-anatomical organization, consisting of an epithelium surrounded by a collagen-rich connective stratum and one or multiple muscular layers . These cThe Special Issue aims to provide an overview of bioengineering activities in the field of hollow organ functionality, pathophysiology, and surgery. More in detail, the bioengineering approach frequently relies on developing models of biological structures using a coupled experimental and computational approach . First, Both experimental and computational approaches toward hollow organ functionality are reported within the Special Issue. Particular emphasis is placed on in silico methods because of their versatility and reliability in the accurate prediction of biological structure functionality, accounting for many different subject conditions and clinical and surgical situations.Experimental activities provide the basis for the comprehension of the behavior and the functionality of a biological structure. Regarding biomechanics, both histo-morphometric and mechanical experimentations are usually performed ,15,16. TIn the case of hollow organs, general experimental activities pertain to the mechanical and structural characterization of the specific anatomical district: mechanical tests at the tissues level ,32,33,34Experimental activities provide data for the development of computational models. In detail, results from mechanical tests at the tissue level, together with preliminary hypotheses from the histological analysis, determine the constitutive formulation that actually interprets tissue mechanics ,18. RegaComputational models allow the investigation of anatomical districts in both healthy and pathologic conditions, providing information about the biomechanical functionality and the effects of pathologies ,49,50. FThe papers within this Special Issue report an almost comprehensive description of the application of biomechanical methods in the framework of hollow organ functionality in health and disease. Both experimental and computational activities are reported, also considering coupled approaches.In the paper “Variation of Passive Biomechanical Properties of the Small Intestine along Its Length: Microstructure-Based Characterization” by Sokolis (2020) , the autThe paper “The Macro- and Micro-Mechanics of the Colon and Rectum I: Experimental Evidence” by Siri et al. (2020)  providesIn the paper “The Macro- and Micro-Mechanics of the Colon and Rectum II: Theoretical and Computational Methods” by Zhao et al. (2020) , a revieThe paper “Biomechanical Force Prediction for Lengthening of Small Intestine during Distraction Enterogenesis” by Hosseini and Dunn (2020)  reports In the paper “Biomechanical Investigation of the Stomach Following Different Bariatric Surgery Approaches” by Toniolo et al. (2020) , a biomeIn the paper “A Preliminary Validation of a New Surgical Procedure for the Treatment of Primary Bladder Neck Obstruction Using a Computational Modeling Approach” by Serpilli et al. (2020) , a novelThe paper “Numerical Models Can Assist Choice of an Aortic Phantom for In Vitro Testing” by Comunale et al. (2020)  reports In the paper “Novel Bionics Assessment of Anorectal Mechanosensory Physiology” by Gregersen (2020) , a novelFinally, the paper “Biomechanics Assist Measurement, Modeling, Engineering Applications, and Clinical Decision Making in Medicine” by Chi et al. (2023)  reports Currently, the application of biomechanical analysis has been broadened to many different fields: the evaluation of the anatomical district functionality in health and disease, the definition of diagnostic techniques and devices, the design of surgical instrumentations and prostheses, the pre-surgical planning of interventions, etc. Regarding specific biomedical fields, biomechanical methods have found wide applications in orthopedics or cardio-vascular biomechanics. Nowadays, biomechanical interest is also focusing on other biomedical fields, with regard to the gastrointestinal region and the urinary tract.Hollow organs include a variety of biological structures from many different anatomical regions. A similar approach can be adopted for the experimental and computational investigation of functionality in health and disease. On the other side, specific techniques must be adopted for the computational and/or experimental investigation of diagnostic and surgical procedures. The Special Issue aims to provide a general overview of the mentioned topics.