A participatory learning model and person-centered healthcare: moving away from “one hand clapping”

2015 ◽  
Vol 3 (3) ◽  
pp. 279 ◽  
Author(s):  
John Stephens
Keyword(s):  
Professional Education ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Learning Model ◽  
Training Needs ◽  
Service Users ◽  
Close Collaboration ◽  
Participatory Learning ◽  
Healthcare Practice

The level of complexity within healthcare practice makes close collaboration with ‘service users’ a necessity in the education of pre-registration healthcare professionals to promote the development of optimal clinical decision making skills in the delivery of safe, effective, and efficient person-centred care.  This article reports on the development of a series of facilitated workshops within two pre-registration physiotherapy programmes framed by an adapted participatory learning model, underpinned by the concepts of complexity science, in an attempt gain some understanding of the facilitation of learning that is person-centred and collaborative in nature.The participatory learning model offers a structure to organise a learning process in a manner that is not only conceptually appealing but also of practical use. The model would seem to have the potential for transfer to broader areas of professional education. In embracing participation and complexity ‘don’t be afraid to start’, and ‘learn from doing’ are key messages of encouragement. However, it is important to think about any training needs across organisations and individuals, to find and nurture relationships, and to be creative and also have clarity of purpose.    

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 Chiropractic conservatism among chiropractic students in Denmark: prevalence and consequences

2020 ◽  
Vol 28 (1) ◽  
Author(s):  
Casper Glissmann Nim ◽  
Henrik Hein Lauridsen ◽  
Søren O’Neill ◽  
Guillaume Goncalves ◽  
Rikke K. Jensen ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Educational Institutions ◽  
Close Collaboration ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Chiropractic Education ◽  
The University ◽  
Chiropractic Students ◽  
Academic Year

Abstract Background The chiropractic profession is split between those practicing evidence-based and those whose practice is honed by vitalism. The latter has been coined ‘chiropractic conservatism’. In Denmark, the chiropractic education program is university-based in close collaboration with a medical faculty. We wanted to investigate if such conservative attitudes were present in this environment. Our objectives were to i) determine the level of chiropractic conservatism, ii) investigate if this was linked to academic year of study, iii) determine the level of clinical appropriateness, and iv) to investigate if this was affected by the level of conservatism among students in a chiropractic program, where the students are taught alongside medical students at the University of Southern Denmark (SDU). Methods A cross-sectional survey of 146 (response-rate 76%) 3rd to 5th year pre-graduate students and 1st year postgraduate clinical interns from the chiropractic degree course at the University of Southern Denmark was conducted during autumn of 2019. The students’ levels of conservatism were dichotomized into appropriate/inappropriate, summed up, and used in a linear regression model to determine the association with academic year of study. Thereafter, the conservatism score was categorized into four groups (from low -1- to high -4-). Conservatism groups were cross-tabulated with the ability to answer appropriately on nine cases concerning i) contra-indications, ii) non-indications, and iii) indications for spinal manipulation and analyzed using logistic regression. Results Generally, the Danish chiropractic students had low conservatism scores, decreasing with increasing academic year of study. Seventy percent of the students were placed in the two lowest conservative groups. The level of conservatism (categories 1–3) was moderately (but not statistically significantly) associated with an inability to recognize non-indications to treatment. Three outliers (category 4), however, revealed a highly inappropriate handling of the clinical cases. Conclusions Chiropractic students enrolled at a university-based course closely integrated with a medical teaching environment are not immune to chiropractic conservatism. However, the course appears to attenuate it and limit its effect on clinical decision-making compared to other educational institutions.

scholarly journals Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 76
Author(s):  
Fernando Ribeiro ◽  
Filipe Fidalgo ◽  
Arlindo Silva ◽  
José Metrôlho ◽  
Osvaldo Santos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pressure Ulcer ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Pressure Ulcers ◽  
Learning Algorithms ◽  
Clinical Decision ◽  
Predisposing Factors ◽  
Machine Learning Algorithms ◽  
Challenges And Opportunities

Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities.

scholarly journals Exploring healthcare professionals’ understanding and experiences of artificial intelligence technology use in the delivery of healthcare: An integrative review

2019 ◽  
Vol 26 (2) ◽  
pp. 1225-1236 ◽  
Author(s):  
Lucy Shinners ◽  
Christina Aggar ◽  
Sandra Grace ◽  
Stuart Smith
Keyword(s):  
Artificial Intelligence ◽  
Technology Use ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Healthcare Setting ◽  
Clinical Decision ◽  
Integrative Review ◽  
Ageing Population ◽  
Delivery Of Healthcare ◽  
Delivery Of Care

Background: The integration of artificial intelligence (AI) into our digital healthcare system is seen as a significant strategy to contain Australia’s rising healthcare costs, support clinical decision making, manage chronic disease burden and support our ageing population. With the increasing roll-out of ‘digital hospitals’, electronic medical records, new data capture and analysis technologies, as well as a digitally enabled health consumer, the Australian healthcare workforce is required to become digitally literate to manage the significant changes in the healthcare landscape. To ensure that new innovations such as AI are inclusive of clinicians, an understanding of how the technology will impact the healthcare professions is imperative. Method: In order to explore the complex phenomenon of healthcare professionals’ understanding and experiences of AI use in the delivery of healthcare, an integrative review inclusive of quantitative and qualitative studies was undertaken in June 2018. Results: One study met all inclusion criteria. This study was an observational study which used a questionnaire to measure healthcare professional’s intrinsic motivation in adoption behaviour when using an artificially intelligent medical diagnosis support system (AIMDSS). Discussion: The study found that healthcare professionals were less likely to use AI in the delivery of healthcare if they did not trust the technology or understand how it was used to improve patient outcomes or the delivery of care which is specific to the healthcare setting. The perception that AI would replace them in the healthcare setting was not evident. This may be due to the fact that AI is not yet at the forefront of technology use in healthcare setting. More research is needed to examine the experiences and perceptions of healthcare professionals using AI in the delivery of healthcare.

Digital Healthcare: Moving beyond the data input/out problem toward enhancing clinical judgment

2019 ◽  
Vol 8 (1) ◽  
pp. 57-61
Author(s):  
John Flach ◽  
Peter Reynolds ◽  
Libby Duryee ◽  
Bryan Young ◽  
Jeff Graley
Keyword(s):  
Systems Engineering ◽  
Clinical Judgment ◽  
Nuclear Power ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Digital Information ◽  
Cognitive Systems Engineering ◽  
The Right ◽  
Digital Healthcare

The design of digital information management systems for healthcare presents developers with several formidable engineering challenges. These systems must manage huge amounts of data and support communications across disparate platforms and divisions within a healthcare organization. They must ensure that data is kept private, secure, and available to the right people at the right time. However, as shown in other complex systems (e.g., nuclear power), simply making data available may be insufficient. The goals in designing digital healthcare as a ‘cognitive system’ are to present patient information in more meaningful ways, to help healthcare professionals become more productive, and to support healthcare professionals with clinical decision making. This paper describes how principles of cognitive systems engineering (CSE) and user experience (UX) design were applied to Cardiac Consultant, an interactive cardiovascular risk calculator, with those goals in mind.

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.

Multidisciplinary Approach to Critical Care Design

2016 ◽  
Vol 5 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Yuval Bitan ◽  
Roy Ilan ◽  
Steven D. Harris ◽  
Keith S. Karn
Keyword(s):  
Decision Making ◽  
Information Exchange ◽  
Multidisciplinary Approach ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Relevant Information ◽  
Optimal Decisions ◽  
Source Of Information

The goal of this project is to improve clinical decision-making in the intensive care unit (ICU) environment. Making the optimal decisions depends on the quality and timeliness of the information available to the clinician. We believe that healthcare professionals will make better clinical decisions when the relevant information is collected and organized in a manner appropriate to support in situ decision-making. This is especially important in complex situations such those commonly encountered in the ICU environment. Currently there is no single integrated source of information that presents relevant information to clinicians. This project is developing methods to identify the core information required to engineer the information exchange among medical devices, and the information presentation layer, to support clinical decision-making in the ICU.

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