scholarly journals Nudging healthcare professionals in clinical settings: a scoping review of the literature

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Anita Sant’Anna ◽  
Andreas Vilhelmsson ◽  
Axel Wolf
Keyword(s):  
Scoping Review ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Clinical Settings ◽  
Feedback Information ◽  
Information Transparency ◽  
Clinical Procedures ◽  
Healthcare Settings ◽  
Active Choice

Abstract Background Healthcare organisations are in constant need of improvement and change. Nudging has been proposed as a strategy to affect people’s choices and has been used to affect patients’ behaviour in healthcare settings. However, little is known about how nudging is being interpreted and applied to change the behaviour of healthcare professionals (HCPs). The objective of this review is to identify interventions using nudge theory to affect the behaviour of HCPs in clinical settings. Methods A scoping review. We searched PubMed and PsycINFO for articles published from 2010 to September 2019, including terms related to “nudging” in the title or abstract. Two reviewers screened articles for inclusion based on whether the articles described an intervention to change the behaviour of HCPs. Two reviewers extracted key information and categorized included articles. Descriptive analyses were performed on the data. Results Search results yielded 997 unique articles, of which 25 articles satisfied the inclusion criteria. Five additional articles were selected from the reference lists of the included articles. We identified 11 nudging strategies: accountable justification, goal setting, suggested alternatives, feedback, information transparency, peer comparison, active choice, alerts and reminders, environmental cueing/priming, defaults/pre-orders, and education. These strategies were employed to affect the following 4 target behaviours: vaccination of staff, hand hygiene, clinical procedures, prescriptions and orders. To compare approaches across so many areas, we introduced two independent dimensions to describe nudging strategies: synchronous/asynchronous, and active/passive. Conclusion There are relatively few studies published referring to nudge theory aimed at changing HCP behaviour in clinical settings. These studies reflect a diverse set of objectives and implement nudging strategies in a variety of ways. We suggest distinguishing active from passive nudging strategies. Passive nudging strategies may achieve the desired outcome but go unnoticed by the clinician thereby not really changing a behaviour and raising ethical concerns. Our review indicates that there are successful active strategies that engage with clinicians in a more deliberate way. However, more research is needed on how different nudging strategies impact HCP behaviour in the short and long term to improve clinical decision making.

scholarly journals AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

2021 ◽  
Vol 10 (4) ◽  
pp. 766 ◽  
Author(s):  
Ljiljana Trtica Majnarić ◽  
František Babič ◽  
Shane O’Sullivan ◽  
Andreas Holzinger
Keyword(s):  
Big Data ◽  
Clinical Decision Making ◽  
Big Data Analytics ◽  
Clinical Decision ◽  
Radical Change ◽  
Research Approach ◽  
Classification Problems ◽  
Care Needs ◽  
Healthcare Settings ◽  
Interaction Factors

Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

Ethical and culturally competent care of transgender patients: A scoping review

2021 ◽  
pp. 096973302098830
Author(s):  
Amara Sundus ◽  
Sharoon Shahzad ◽  
Ahtisham Younas
Keyword(s):  
Scoping Review ◽  
Healthcare Professionals ◽  
Culturally Sensitive ◽  
Culturally Competent ◽  
Culturally Competent Care ◽  
Individual Level ◽  
Culturally Sensitive Care ◽  
Healthcare Settings ◽  
Ethical Approval ◽  
Sensitive Issues

Background: Transgender individuals experience discrimination, stigmatization, and unethical and insensitive attitudes in healthcare settings. Therefore, healthcare professionals must be knowledgeable about the ways to deliver ethical and culturally competent care. Ethical considerations: No formal ethical approval was required. Aim: To synthesize the literature and identify gaps about approaches to the provision of ethical and culturally competent care to transgender populations. Design: A Scoping Review Literature Search: Literature was searched within CINAHL, Science Direct, PubMed, Google Scholar, EMBASE, and Scopus databases using indexed keywords such as “transgender,” “gender non-conforming,” “ethically sensitive care,” and “culturally sensitive care.” In total, 30 articles, which included transgender patients and their families and nurses, doctors, and health professionals who provided care to transgender patients, were selected for review. Data were extracted and synthesized using tabular and narrative summaries and thematic synthesis. Findings: Of 30 articles, 23 were discussion papers, 5 research articles, and 1 each case study and an integrative review. This indicates an apparent dearth of literature about ethical and culturally sensitive care of transgender individuals. The review identified that healthcare professionals should educate themselves about sensitive issues, become more self-aware, put transgender individual in charge during care interactions, and adhere to the principles of advocacy, confidentiality, autonomy, respect, and disclosure. Conclusions: The review identified broad approaches for the provision of ethical and culturally competent care. The identified approaches could be used as the baseline, and further research is warranted to develop and assess organizational and individual-level approaches.

scholarly journals Exploring the role of the Care and Health Information Exchange (CHIE) in clinical decision-making: a realist evaluation

2020 ◽  
Author(s):  
Philip Scott ◽  
Elisavet Andrikopoulou ◽  
Haythem Nakkas ◽  
Paul Roderick
Keyword(s):  
Decision Making ◽  
Health Information ◽  
Information Exchange ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Social Care ◽  
Health Information Exchange ◽  
Clinical Decision ◽  
Health And Social Care ◽  
The Impact

Background: The overall evidence for the impact of electronic information systems on cost, quality and safety of healthcare remains contested. Whilst it seems intuitively obvious that having more data about a patient will improve care, the mechanisms by which information availability is translated into better decision-making are not well understood. Furthermore, there is the risk of data overload creating a negative outcome. There are situations where a key information summary can be more useful than a rich record. The Care and Health Information Exchange (CHIE) is a shared electronic health record for Hampshire and the Isle of Wight that combines key information from hospital, general practice, community care and social services. Its purpose is to provide clinical and care professionals with complete, accurate and up-to-date information when caring for patients. CHIE is used by GP out-of-hours services, acute hospital doctors, ambulance service, GPs and others in caring for patients. Research questions: The fundamental question was How does awareness of CHIE or usage of CHIE affect clinical decision-making? The secondary questions were What are the latent benefits of CHIE in frontline NHS operations? and What is the potential of CHIE to have an impact on major NHS cost pressures? The NHS funders decided to focus on acute medical inpatient admissions as the initial scope, given the high costs associated with hospital stays and the patient complexities (and therefore information requirements) often associated with unscheduled admissions. Methods: Semi-structured interviews with healthcare professionals to explore their experience about the utility of CHIE in their clinical scenario, whether and how it has affected their decision-making practices and the barriers and facilitators for their use of CHIE. The Framework Method was used for qualitative analysis, supported by the software tool Atlas.ti. Results: 21 healthcare professionals were interviewed. Three main functions were identified as useful: extensive medication prescribing history, information sharing between primary, secondary and social care and access to laboratory test results. We inferred two positive cognitive mechanisms: knowledge confidence and collaboration assurance, and three negative ones: consent anxiety, search anxiety and data mistrust. Conclusions: CHIE gives clinicians the bigger picture to understand the patient's health and social care history and circumstances so as to make confident and informed decisions. CHIE is very beneficial for medicines reconciliation on admission, especially for patients that are unable to speak or act for themselves or who cannot remember their precise medication or allergies. We found no clear evidence that CHIE has a significant impact on admission or discharge decisions. We propose the use of recommender systems to help clinicians navigate such large volumes of patient data, which will only grow as additional data is collected.

scholarly journals P071: Artificial intelligence in emergency medicine: A scoping review

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S90-S90
Author(s):  
A. Kirubarajan ◽  
A. Taher ◽  
S. Khan ◽  
S. Masood
Keyword(s):  
Artificial Intelligence ◽  
Emergency Medicine ◽  
Language Processing ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
A Priori ◽  
Clinical Decision ◽  
Hospital Environment ◽  
Clinical Implementation

Introduction: The study of artificial intelligence (AI) in medicine has become increasingly popular over the last decade. The emergency department (ED) is uniquely situated to benefit from AI due to its power of diagnostic prediction, and its ability to continuously improve with time. However, there is a lack of understanding of the breadth and scope of AI applications in emergency medicine, and evidence supporting its use. Methods: Our scoping review was completed according to PRISMA-ScR guidelines and was published a priori on Open Science Forum. We systematically searched databases (Medline-OVID, EMBASE, CINAHL, and IEEE) for AI interventions relevant to the ED. Study selection and data extraction was performed independently by two investigators. We categorized studies based on type of AI model used, location of intervention, clinical focus, intervention sub-type, and type of comparator. Results: Of the 1483 original database citations, a total of 181 studies were included in the scoping review. Inter-rater reliability for study screening for titles and abstracts was 89.1%, and for full-text review was 77.8%. Overall, we found that 44 (24.3%) studies utilized supervised learning, 63 (34.8%) studies evaluated unsupervised learning, and 13 (7.2%) studies utilized natural language processing. 17 (9.4%) studies were conducted in the pre-hospital environment, with the remainder occurring either in the ED or the trauma bay. The majority of interventions centered around prediction (n = 73, 40.3%). 48 studies (25.5%) analyzed AI interventions for diagnosis. 23 (12.7%) interventions focused on diagnostic imaging. 89 (49.2%) studies did not have a comparator to their AI intervention. 63 (34.8%) studies used statistical models as a comparator, 19 (10.5%) of which were clinical decision making tools. 15 (8.3%) studies used humans as comparators, with 12 of the 15 (80%) studies showing superiority in favour of the AI intervention when compared to a human. Conclusion: AI-related research is rapidly increasing in emergency medicine. AI interventions are heterogeneous in both purpose and design, but primarily focus on predictive modeling. Most studies do not involve a human comparator and lack information on patient-oriented outcomes. While some studies show promising results for AI-based interventions, there remains uncertainty regarding their superiority over standard practice, and further research is needed prior to clinical implementation.

scholarly journals 23. An Internal Audit of a Virtual Learning Space to Facilitate Clinical Decision-Making in Nursing

2012 ◽  
Vol 5 ◽  
pp. 132 ◽  
Author(s):  
Beryl McEwan ◽  
Gylo Hercelinskyj
Keyword(s):  
Nursing Students ◽  
Situated Learning ◽  
Teaching And Learning ◽  
Clinical Decision Making ◽  
Charles Darwin ◽  
Internal Audit ◽  
Clinical Decision ◽  
Nursing Program ◽  
Clinical Settings ◽  
Learning Space

In any nursing program, it is a challenge to foster an awareness of, and engagement with, the complexity and reality of nursing practice.  During their studies, nursing students have to learn the relevant underpinning theoretical knowledge for practice as well as develop their understanding of the role and responsibilities of the registered nurse in clinical settings. At a regional Australian university the Bachelor of Nursing is offered externally with the student cohort predominantly off-campus. There are significant challenges in providing opportunities to enhance learning (Henderson, Twentyman, Heel, & Lloyd, 2006) and to foster early professional engagement with the nursing community of practice (Andrew, McGuiness, Reid, & Corcoran, 2009; Elliot, Efron, Wright, & Martinelli, 2003; Morales-Mann & Kaitell, 2001) in a context for learning nursing knowledge and inter-professional collaborative practice. This paper presents the results of a series of internal audits of students’ feedback of the Charles Darwin Hospital (CDU) vHospital™ undertaken from 2008 to 2010, following integration into theory and clinical nursing subjects in the Bachelor of Nursing program.  The feedback from students demonstrates the value students place on teaching and learning activities that provide realistic situated learning opportunities (Hercelinskyj & McEwan, 2011).

scholarly journals A realist review of brief interventions for alcohol misuse delivered in emergency departments

2021 ◽  
Author(s):  
Caitlin J Davey ◽  
Meredith SH Landy ◽  
Amanda Pecora ◽  
David Quintero ◽  
Kelly E McShane
Keyword(s):  
Alcohol Use ◽  
Emergency Departments ◽  
Clinical Decision Making ◽  
Alcohol Misuse ◽  
Realist Review ◽  
Clinical Decision ◽  
Brief Interventions ◽  
Future Research ◽  
Healthcare Settings ◽  
Favorable Conditions

Background: Brief interventions (BIs) involve screening for alcohol misuse and providing feedback to patients about their use, with the aim of reducing alcohol consumption and related consequences. BIs have been implemented in various healthcare settings, including emergency departments (ED), where they have been found to contribute mixed results in their ability to address alcohol misuse among adults. Mechanisms through which BIs work and contextual factors impacting BI effectiveness are not clear. The purpose of this review was to understand how, for whom, and under what circumstances BIs work for adults misusing alcohol and who have been admitted to an ED. A realist review was chosen to answer these questions as realist reviews create context-mechanism-outcome configurations, leading to the development of comprehensive and detailed theories; in this case explaining how and for whom BIs work. Methods: Databases including PsycINFO, Healthstar, CINAHL, Medline, and Nursing and Allied Health were searched for articles published until December 2013. The search strategy focused on studies examining BIs that targeted alcohol misuse among adults admitted into the ED. The search identified 145 relevant abstracts, of which 36 were included in the review. The literature was synthesized qualitatively (immersion/crystallization). Results: Four mechanisms were found within reviewed studies, including engagement in/retention of BI materials, resolving ambivalence, increased awareness/insight into consequences of drinking, and increased self-efficacy/empowerment to use skills for change. The following contexts were found to impact mechanisms: emotional state, injury attributed to alcohol use, severity of alcohol use, and baseline stage of change. Conclusions: This realist review provides advances in theories regarding which mechanisms to target during a BI and which contexts create the most favorable conditions for these mechanisms to occur, ultimately leading to optimal BI outcomes. These results can inform future clinical decision-making when delivering BIs in ED settings. Future research should conduct quantitative examination to confirm these findings. Systematic review registration: PROSPERO CRD42013006549

scholarly journals Economic evaluations of big data analytics for clinical decision-making: a scoping review

2020 ◽  
Vol 27 (9) ◽  
pp. 1466-1475
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
William Redekop
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Economic Impact ◽  
Scoping Review ◽  
Data Analytics ◽  
Clinical Decision Making ◽  
Big Data Analytics ◽  
Clinical Decision ◽  
Economic Evaluations ◽  
Definition Of

Abstract Objective Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making. Materials and Methods We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed “big data analytics” based on a broad definition of this term. Results The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined “big data analytics” and only 7 reported both cost-savings and better outcomes. Discussion The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of “big data” limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

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