Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence

BACKGROUND: Quantitative systematic reviews have identified clinical artificial intelligence (AI)-enabled tools with adequate performance for real-world implementation. To our knowledge, no published report or protocol synthesizes the full breadth of stakeholder perspectives. The absence of such a rigorous foundation perpetuates the "AI chasm," which continues to delay patient benefit. OBJECTIVE: The aim of this research is to synthesize stakeholder perspectives of computerized clinical decision support tools in any health care setting. Synthesized findings will inform future research and the implementation of AI into health care services. METHODS: The search strategy will use MEDLINE (Ovid), Scopus, CINAHL (EBSCO), ACM Digital Library, and Science Citation Index (Web of Science). Following deduplication, title, abstract, and full text screening will be performed by 2 independent reviewers with a third topic expert arbitrating. The quality of included studies will be appraised to support interpretation. Best-fit framework synthesis will be performed, with line-by-line coding completed by 2 independent reviewers. Where appropriate, these findings will be assigned to 1 of 22 a priori themes defined by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework. New domains will be inductively generated for outlying findings. The placement of findings within themes will be reviewed iteratively by a study advisory group including patient and lay representatives. RESULTS: Study registration was obtained from PROSPERO (CRD42021256005) in May 2021. Final searches were executed in April, and screening is ongoing at the time of writing. Full text data analysis is due to be completed in October 2021. We anticipate that the study will be submitted for open-access publication in late 2021. CONCLUSIONS: This paper describes the protocol for a qualitative evidence synthesis aiming to define barriers and facilitators to the implementation of computerized clinical decision support tools from all relevant stakeholders. The results of this study are intended to expedite the delivery of patient benefit from AI-enabled clinical tools. TRIAL REGISTRATION: PROSPERO CRD42021256005; https://tinyurl.com/r4x3thvp. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33145

Developing an Intervention for Fall-Related Injuries in Dementia (DIFRID): an integrated, mixed-methods approach

Background Falls in people with dementia can result in a number of physical and psychosocial consequences. However, there is limited evidence to inform how best to deliver services to people with dementia following a fall. The aim of the DIFRID study was to determine the feasibility of developing and implementing a new intervention to improve outcomes for people with dementia with fall-related injuries; this encompasses both short-term recovery and reducing the likelihood of future falls. This paper details the development of the DIFRID intervention. Methods The intervention was designed using an integrated, mixed-methods approach. This involved a realist synthesis of the literature and qualitative data gathered through interviews and focus groups with health and social care professionals (n=81). An effectiveness review and further interviews and observation were also conducted and are reported elsewhere. A modified Delphi panel approach with 24 experts was then used to establish a consensus on how the findings should translate into a new intervention. After feedback from key stakeholders (n=15) on the proposed model, the intervention was manualised and training developed. Results We identified key components of a new intervention covering three broad areas: • Ensuring that the circumstances of rehabilitation are optimised for people with dementia • Compensating for the reduced ability of people with dementia to self-manage • Equipping the workforce with the necessary skills and information to care for this patient group Consensus was achieved on 54 of 69 statements over two rounds of the Delphi surveys. The statements were used to model the intervention and finalise the accompanying manual and protocol for a feasibility study. Stakeholder feedback was generally positive and the majority of suggested intervention components were approved. The proposed outcome was a 12-week complex multidisciplinary intervention primarily based at the patient’s home. Conclusions A new intervention has been developed to improve outcomes for people with dementia following a fall requiring healthcare attention. The feasibility of this intervention is currently being tested. Trial registration ISRCTN41760734 (16/11/2015

Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence

Quantitative systematic reviews have identified clinical artificial intelligence (AI)-enabled tools with adequate performance for real-world implementation. To our knowledge, no published report or protocol synthesizes the full breadth of stakeholder perspectives. The absence of such a rigorous foundation perpetuates the "AI chasm," which continues to delay patient benefit. The aim of this research is to synthesize stakeholder perspectives of computerized clinical decision support tools in any health care setting. Synthesized findings will inform future research and the implementation of AI into health care services. The search strategy will use MEDLINE (Ovid), Scopus, CINAHL (EBSCO), ACM Digital Library, and Science Citation Index (Web of Science). Following deduplication, title, abstract, and full text screening will be performed by 2 independent reviewers with a third topic expert arbitrating. The quality of included studies will be appraised to support interpretation. Best-fit framework synthesis will be performed, with line-by-line coding completed by 2 independent reviewers. Where appropriate, these findings will be assigned to 1 of 22 a priori themes defined by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework. New domains will be inductively generated for outlying findings. The placement of findings within themes will be reviewed iteratively by a study advisory group including patient and lay representatives. Study registration was obtained from PROSPERO (CRD42021256005) in May 2021. Final searches were executed in April, and screening is ongoing at the time of writing. Full text data analysis is due to be completed in October 2021. We anticipate that the study will be submitted for open-access publication in late 2021. This paper describes the protocol for a qualitative evidence synthesis aiming to define barriers and facilitators to the implementation of computerized clinical decision support tools from all relevant stakeholders. The results of this study are intended to expedite the delivery of patient benefit from AI-enabled clinical tools. PROSPERO CRD42021256005; https://tinyurl.com/r4x3thvp. DERR1-10.2196/33145. [Abstract copyright: ©Mohaimen Al-Zubaidy, HD Jeffry Hogg, Gregory Maniatopoulos, James Talks, Marion Dawn Teare, Pearse A Keane, Fiona R Beyer. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 01.04.2022.

Predictive accuracy of risk prediction models for recurrence, metastasis and survival for early-stage cutaneous melanoma : a systematic review

OBJECTIVES: To identify prognostic models for melanoma survival, recurrence and metastasis among American Joint Committee on Cancer stage I and II patients postsurgery; and evaluate model performance, including overall survival (OS) prediction. DESIGN: Systematic review and narrative synthesis. DATA SOURCES: Searched MEDLINE, Embase, CINAHL, Cochrane Library, Science Citation Index and grey literature sources including cancer and guideline websites from 2000 to September 2021. ELIGIBILITY CRITERIA: Included studies on risk prediction models for stage I and II melanoma in adults ≥18 years. Outcomes included OS, recurrence, metastases and model performance. No language or country of publication restrictions were applied. DATA EXTRACTION AND SYNTHESIS: Two pairs of reviewers independently screened studies, extracted data and assessed the risk of bias using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist and the Prediction study Risk of Bias Assessment Tool. Heterogeneous predictors prevented statistical synthesis. RESULTS: From 28 967 records, 15 studies reporting 20 models were included; 8 (stage I), 2 (stage II), 7 (stages I-II) and 7 (stages not reported), but were clearly applicable to early stages. Clinicopathological predictors per model ranged from 3-10. The most common were: ulceration, Breslow thickness/depth, sociodemographic status and site. Where reported, discriminatory values were ≥0.7. Calibration measures showed good matches between predicted and observed rates. None of the studies assessed clinical usefulness of the models. Risk of bias was high in eight models, unclear in nine and low in three. Seven models were internally and externally cross-validated, six models were externally validated and eight models were internally validated. CONCLUSIONS: All models are effective in their predictive performance, however the low quality of the evidence raises concern as to whether current follow-up recommendations following surgical treatment is adequate. Future models should incorporate biomarkers for improved accuracy. PROSPERO REGISTRATION NUMBER: CRD42018086784

Do self-reported intentions predict clinicians behaviour: a systematic review.

Background: Implementation research is the scientific study of methods to promote the systematic uptake of clinical research findings into routine clinical practice. Several interventions have been shown to be effective in changing health care professionals' behaviour, but heterogeneity within interventions, targeted behaviours, and study settings make generalisation difficult. Therefore, it is necessary to identify the 'active ingredients' in professional behaviour change strategies. Theories of human behaviour that feature an individual's "intention" to do something as the most immediate predictor of their behaviour have proved to be useful in non-clinical populations. As clinical practice is a form of human behaviour such theories may offer a basis for developing a scientific rationale for the choice of intervention to use in the implementation of new practice. The aim of this review was to explore the relationship between intention and behaviour in clinicians and how this compares to the intention-behaviour relationship in studies of non-clinicians. Methods: We searched: PsycINFO, MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, Science/Social science citation index, Current contents (social & behavioural med/clinical med), ISI conference proceedings, and Index to Theses. The reference lists of all included papers were checked manually. Studies were eligible for inclusion if they had: examined a clinical behaviour within a clinical context, included measures of both intention and behaviour, measured behaviour after intention, and explored this relationship quantitatively. All titles and abstracts retrieved by electronic searching were screened independently by two reviewers, with disagreements resolved by discussion. Discussion: Ten studies were found that examined the relationship between intention and clinical behaviours in 1623 health professionals. The proportion of variance in behaviour explained by intention was of a similar magnitude to that found in the literature relating to non-health professionals. This was more consistently the case for studies in which intention-behaviour correspondence was good and behaviour was self-reported. Though firm conclusions are limited by a smaller literature, our findings are consistent with that of the non-health professional literature. This review, viewed in the context of the larger populations of studies, provides encouragement for the contention that there is a predictable relationship between the intentions of a health professional and their subsequent behaviour. However, there remain significant methodological challenges

The effectiveness of strategies to change organisational culture to improve healthcare performance: a systematic review

<p>Abstract</p> <p>Background</p> <p>Organisational culture is an anthropological metaphor used to inform research and consultancy and to explain organisational environments. In recent years, increasing emphasis has been placed on the need to change organisational culture in order to improve healthcare performance. However, the precise function of organisational culture in healthcare policy often remains underspecified and the desirability and feasibility of strategies to be adopted have been called into question. The objective of this review was to determine the effectiveness of strategies to change organisational culture in order to improve healthcare performance.</p> <p>Methods</p> <p>We searched the following electronic databases: The Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, CINAHL, Sociological Abstracts, Web of Knowledge, PsycINFO, Business and Management, EThOS, Index to Theses, Intute, HMIC, SIGLE, and Scopus until October 2009. The Database of Abstracts of Reviews of Effectiveness (DARE) was searched for related reviews. We also searched the reference lists of all papers and relevant reviews identified, and we contacted experts in the field for advice on further potential studies. We considered randomised controlled trials (RCTs) or well designed quasi-experimental studies (controlled clinical trials (CCTs), controlled before and after studies (CBAs), and interrupted time series (ITS) analyses). Studies could be set in any type of healthcare organisation in which strategies to change organisational culture in order to improve healthcare performance were applied. Our main outcomes were objective measures of professional performance and patient outcome.</p> <p>Results</p> <p>The search strategy yielded 4,239 records. After the full text assessment, two CBA studies were included in the review. They both assessed the impact of interventions aimed at changing organisational culture, but one evaluated the impact on work-related and personal outcomes while the other measured clinical outcomes. Both were at high risk of bias. Both reported positive results.</p> <p>Conclusions</p> <p>Current available evidence does not identify any effective, generalisable strategies to change organisational culture. Healthcare organisations considering implementing interventions aimed at changing culture should seriously consider conducting an evaluation (using a robust design, <it>e.g.</it>, ITS) to strengthen the evidence about this topic.</p

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Background: The rhetoric surrounding clinical artificial intelligence (AI) often exaggerates its effect on real-world care. Limited understanding of the factors that influence its implementation can perpetuate this. Objective: In this qualitative systematic review, we aimed to identify key stakeholders, consolidate their perspectives on clinical AI implementation, and characterize the evidence gaps that future qualitative research should target. Methods: Ovid-MEDLINE, EBSCO-CINAHL, ACM Digital Library, Science Citation Index-Web of Science, and Scopus were searched for primary qualitative studies on individuals’ perspectives on any application of clinical AI worldwide (January 2014-April 2021). The definition of clinical AI includes both rule-based and machine learning–enabled or non–rule-based decision support tools. The language of the reports was not an exclusion criterion. Two independent reviewers performed title, abstract, and full-text screening with a third arbiter of disagreement. Two reviewers assigned the Joanna Briggs Institute 10-point checklist for qualitative research scores for each study. A single reviewer extracted free-text data relevant to clinical AI implementation, noting the stakeholders contributing to each excerpt. The best-fit framework synthesis used the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. To validate the data and improve accessibility, coauthors representing each emergent stakeholder group codeveloped summaries of the factors most relevant to their respective groups. Results: The initial search yielded 4437 deduplicated articles, with 111 (2.5%) eligible for inclusion (median Joanna Briggs Institute 10-point checklist for qualitative research score, 8/10). Five distinct stakeholder groups emerged from the data: health care professionals (HCPs), patients, carers and other members of the public, developers, health care managers and leaders, and regulators or policy makers, contributing 1204 (70%), 196 (11.4%), 133 (7.7%), 129 (7.5%), and 59 (3.4%) of 1721 eligible excerpts, respectively. All stakeholder groups independently identified a breadth of implementation factors, with each producing data that were mapped between 17 and 24 of the 27 adapted Nonadoption, Abandonment, Scale-up, Spread, and Sustainability subdomains. Most of the factors that stakeholders found influential in the implementation of rule-based clinical AI also applied to non–rule-based clinical AI, with the exception of intellectual property, regulation, and sociocultural attitudes. Conclusions: Clinical AI implementation is influenced by many interdependent factors, which are in turn influenced by at least 5 distinct stakeholder groups. This implies that effective research and practice of clinical AI implementation should consider multiple stakeholder perspectives. The current underrepresentation of perspectives from stakeholders other than HCPs in the literature may limit the anticipation and management of the factors that influence successful clinical AI implementation. Future research should not only widen the representation of tools and contexts in qualitative research but also specifically investigate the perspectives of all stakeholder HCPs and emerging aspects of non–rule-based clinical AI implementation. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42021256005; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=25600

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic