Clinical Online Recommendation with Subgroup Rank Feedback

Many real applications in experimental design need to make decisions online. Each decision leads to a stochastic reward with initially unknown distribution. New decisions are made based on the observations of previous rewards. To maximize the total reward, one needs to solve the tradeoff between exploring different strategies and exploiting currently optimal strategies. This kind of tradeoff problems can be formalized as Multi-armed bandit problem. We recommend strategies in series and generate new recommendations based on noisy rewards of previous strategies. When the reward for a strategy is difficult to quantify, classical bandit algorithms are no longer optimal. This paper, studies the Multi-armed bandit problem with feedback given as a stochastic rank list instead of quantified reward values. We propose an algorithm for this new problem and show its optimality. A real application of this algorithm on clinical treatment is helping paralyzed patient to regain the ability to stand on their own feet

Are clinical research professionals more inclined to participate in clinical trials?

The objective of this study was to identify the impact of professional knowledge and education on a willingness to participate in clinical trials. It hypothesized that there is no statistical difference in the median rank score between clinical research professionals and other post-graduate educated participants. The research question asked whether the clinical research program graduates were more inclined to participate in clinical trials than other groups with a post-graduate education. A cross-sectional quantitative study of 83 clinical research professionals was conducted. All participants were invited to complete a shortened version of the Center for Information and Study on Clinical Research Participation (CISCRP) survey assessing their willingness to participate in clinical trials. This study showed that there is a significant difference between the two groups. Although some factors must be considered when determining their actual participation rate, these findings should not discourage recruiting clinical research professionals into clinical trials

Evaluating preferences for online psychological interventions to decrease cannabis use in young adults with psychosis : an observational study

Innovative technology-based solutions have the potential to improve access to clinically proven interventions for cannabis use disorder (CUD) in individuals with first episode psychosis (FEP). High patient engagement with app-based interventions is critical for achieving optimal outcomes. 104 individuals 18 to 35 years old with FEP and CUD from three Canadian provinces completed an electronic survey to evaluate preferences for online psychological intervention intensity, participation autonomy, feedback related to cannabis use, and technology platforms and app functionalities. The development of the questionnaire was informed by a qualitative study that included patients and clinicians. We used Best-Worst Scaling (BWS) and item ranking methodologies to measure preferences. Conditional logistic regression models for BWS data revealed high preferences for moderate intervention intensity (e.g., modules with a length of 15 min) and treatment autonomy that included preferences for using technology-based interventions and receiving feedback related to cannabis use once a week. Luce regression models for rank items revealed high preferences for smartphone-based apps, video intervention components, and having access to synchronous communications with clinicians and gamification elements. Results informed the development of iCanChange (iCC), a smartphone-based intervention for the treatment of CUD in individuals with FEP that is undergoing clinical testing

A machine learning approach for mapping and accelerating multiple sclerosis research

The medical field, as many others, is overwhelmed with the amount of research-related information available, such as journal papers, conference proceedings and clinical trials. The task of parsing through all this information to keep up to date with the most recent research findings on their area of expertise is especially difficult for practitioners who must also focus on their clinical duties. Recommender systems can help make decisions and provide relevant information on specific matters, such as for these clinical practitioners looking into which research to prioritize. In this paper, we describe the early work on a machine learning approach, which through an intelligent reinforcement learning approach, maps and recommends research information (papers and clinical trials) specifically for multiple sclerosis research. We tested and evaluated several different machine learning algorithms and present which one is the most promising in developing a complete and efficient model for recommending relevant multiple sclerosis research.info:eu-repo/semantics/publishedVersio

Engaging residents to choose wisely: Resident Doctors of Canada resource stewardship recommendations

Background: Resident doctors are integral to healthcare delivery in Canada. Engaging residents in resource stewardship is important for professional development, but also as they are drivers of healthcare resource use. To date, no national resident-specific resource stewardship guideline has been developed. Resident Doctors of Canada (RDoC) in collaboration with Choosing Wisely Canada (CWC) sought to develop an evidence-informed, consensus-based list of five recommendations to promote resource stewardship.                  Methods: RDoC convened a taskforce with diverse geographic and specialty representation to develop candidate recommendations targeting resident resource stewardship behaviours using a consensus-based process, supported by a literature review. Residents across the country provided feedback on the candidate recommendations via an online questionnaire. The taskforce used this feedback to finalize the list.Results: The taskforce prepared 28 candidate recommendations for consideration. A detailed literature review and consensus process narrowed this list to 12 candidate recommendations for consultation. A total of 754 residents (754/10,068 residents = 7.5%) representing all provinces and levels of residency training reviewed and ranked the candidate recommendations. The highest-ranked recommendations comprised the final list.Conclusion: Resident doctors are willing and able to demonstrate leadership in advancing resource stewardship by the development of a national resident-specific list of Choosing Wisely Canada recommendations

Video Feedback and Video Modeling in Teaching Laparoscopic Surgery: A Visionary Concept from Kiel

Learning curves for endoscopic surgery are long and flat. Various techniques and methods are now available for surgical endoscopic training, such as pelvitrainers, virtual trainers, and body donor surgery. Video modeling and video feedback are commonly used in professional training. We report, for the first time, the application of video modeling and video feedback for endoscopic training in gynecology. The purpose is to present an innovative method of training. Attendees (residents and specialists) of minimally invasive surgery courses were asked to perform specific tasks, which were video recorded in a multimodular concept. Feedback was given later by an expert at a joint meeting. The attendees were asked to fill a questionnaire in order to assess video feedback given by the expert. The advantages of video feedback and video modeling for the development of surgical skills were given a high rating (median 84%, interquartile ranges (IQR) 72.5-97.5%, n = 37). The question as to whether the attendees would recommend such training was also answered very positively (median 100%, IQR 89.5-100%, n = 37). We noted a clear difference between subjective perception and objective feedback (58%, IQR 40.5-76%, n = 37). Video feedback and video modeling are easy to implement in surgical training setups, and help trainees at all levels of education

Usability of an app-based clinical decision support system to monitor psychotropic drug prescribing appropriateness in dementia

BACKGROUND: Guidelines recommend reluctant psychotropic drug (PD) prescribing in nursing home residents with dementia and neuropsychiatric symptoms (NPS), as efficacy of PDs is limited, and side effects are common. Nevertheless, PDs are commonly prescribed to reduce NPS. A smartphone application that evaluates appropriateness of PD prescriptions and provides recommendations from the revised Dutch guideline on problem behaviour in dementia may promote guideline adherence and increase appropriate prescribing.OBJECTIVE: This study aimed to assess user experiences, barriers and facilitators of the Dutch 'Psychotropic Drug Tool' smartphone application (PDT) in the context of appropriate prescribing of PDs to nursing home residents with dementia and NPS.METHODS/DESIGN: The PDT was developed according to the recommendations of the Dutch guideline for treatment of NPS in people with dementia. Feedback provided during usability testing with two end-users was applied to improve the PDT before implementation in day-to-day practice. Sixty-three prescribers were asked to use the PDT at their own convenience for four months. User expectations and experiences were assessed at baseline and after four months with the System Usability Scale and the Assessment of Barriers and Facilitators for Implementation.RESULTS: Expected usability (M = 72.59; SD = 11.84) was similar to experienced usability after four months (M = 69.13; SD = 16.48). Appreciation of the PDTs user-friendliness (on average 6.7 out of 10) and design (7.3) were moderately positive, in contrast to the global rating of the PDT (5.7). Perceived barriers for PDT use were time consumption and lack of integration with existing electronic systems. Perceived facilitators were ease of use and attractive lay out. For broader implementation, physicians suggested a change in direction of the PDT: start assessment of appropriateness based on the list of NPS instead of PD as primary input.CONCLUSIONS: In this pragmatic prospective cohort study we found that the PDT was used by elderly care physicians, with mediocre user satisfaction. The PDT will be optimized based on user feedback regarding experienced usability, barriers and facilitators, after which broader implementation can be initialized. The Medical Ethics Review Board of the University Medical Center Groningen declared this is a non-WMO study (UMCG RR Number: 201800284).</p

Monitoring and evaluation of breast cancer screening programmes : Selecting candidate performance indicators

In the scope of the European Commission Initiative on Breast Cancer (ECIBC) the Monitoring and Evaluation (M&E) subgroup was tasked to identify breast cancer screening programme (BCSP) performance indicators, including their acceptable and desirable levels, which are associated with breast cancer (BC) mortality. This paper documents the methodology used for the indicator selection. The indicators were identified through a multi-stage process. First, a scoping review was conducted to identify existing performance indicators. Second, building on existing frameworks for making well-informed health care choices, a specific conceptual framework was developed to guide the indicator selection. Third, two group exercises including a rating and ranking survey were conducted for indicator selection using pre-determined criteria, such as: relevance, measurability, accurateness, ethics and understandability. The selected indicators were mapped onto a BC screening pathway developed by the M&E subgroup to illustrate the steps of BC screening common to all EU countries. A total of 96 indicators were identified from an initial list of 1325 indicators. After removing redundant and irrelevant indicators and adding those missing, 39 candidate indicators underwent the rating and ranking exercise. Based on the results, the M&E subgroup selected 13 indicators: screening coverage, participation rate, recall rate, breast cancer detection rate, invasive breast cancer detection rate, cancers > 20 mm, cancers ≤10 mm, lymph node status, interval cancer rate, episode sensitivity, time interval between screening and first treatment, benign open surgical biopsy rate, and mastectomy rate. This systematic approach led to the identification of 13 BCSP candidate performance indicators to be further evaluated for their association with BC mortality