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Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator.

ABSTRACT: Background: Frequent simulation-based education is recommended to improve health outcomes during neonatal resuscitation but is often inaccessible due to time, resource, and personnel requirements. Digital simulation presents a potential alternative; however, its effectiveness and reception by healthcare professionals (HCPs) remains largely unexplored. Objectives: This study explores HCPs' attitudes toward a digital simulator, technology, and mindset to elucidate their effects on neonatal resuscitation performance in simulation-based assessments. Methods: The study was conducted from April to August 2019 with 2-month (June-October 2019) and 5-month (September 2019-January 2020) follow-up at a tertiary perinatal center in Edmonton, Canada. Of 300 available neonatal HCPs, 50 participated. Participants completed a demographic survey, a pretest, two practice scenarios using the RETAIN neonatal resuscitation digital simulation, a posttest, and an attitudinal survey (100% response rate). Participants repeated the posttest scenario in 2 months (86% response rate) and completed another posttest scenario using a low-fidelity, tabletop simulator (80% response rate) 5 months after the initial study intervention. Participants' survey responses were collected to measure attitudes toward digital simulation and technology. Knowledge was assessed at baseline (pretest), acquisition (posttest), retention (2-month posttest), and transfer (5-month posttest). Results: Fifty neonatal HCPs participated in this study (44 females and 6 males; 27 nurses, 3 nurse practitioners, 14 respiratory therapists, and 6 doctors). Most participants reported technology in medical education as useful and beneficial. Three attitudinal clusters were identified by a hierarchical clustering algorithm based on survey responses. Although participants exhibited diverse attitudinal paths, they all improved neonatal resuscitation performance after using the digital simulator and successfully transferred their knowledge to a new medium. Conclusions: Digital simulation improved HCPs' neonatal resuscitation performance. Medical education may benefit by incorporating technology during simulation training.

SUBMITTER: Lu C

PROVIDER: S-EPMC7518390 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator.

Lu Chang C Ghoman Simran K SK Cutumisu Maria M Schmölzer Georg M GM

Frontiers in pediatrics 20200911

<b>Background:</b> Frequent simulation-based education is recommended to improve health outcomes during neonatal resuscitation but is often inaccessible due to time, resource, and personnel requirements. Digital simulation presents a potential alternative; however, its effectiveness and reception by healthcare professionals (HCPs) remains largely unexplored. <b>Objectives:</b> This study explores HCPs' attitudes toward a digital simulator, technology, and mindset to elucidate their effects on ne ...[more]

PMID: 33042905

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Project description:Background: Simulation education can benefit healthcare providers (HCPs) by providing opportunities to practice complex neonatal-resuscitation tasks in low-stake environments. To our knowledge, no study investigated the role of growth mindset on longitudinal performance on neonatal resuscitation before and after simulation-based training. Objectives: This study examines whether 1) the RETAIN digital/table-top simulators facilitate HCPs' neonatal resuscitation knowledge gain, retention, and transfer and 2) growth mindset moderates HCPs' longitudinal performance in neonatal resuscitation. Methods: Participants were n = 50 HCPs in a tertiary perinatal center in Edmonton, Canada. This longitudinal study was conducted in three stages including 1) a pretest and a mindset survey, immediately followed by a posttest using the RETAIN digital simulator from April to August 2019; 2) a 2-month delayed posttest using the same RETAIN neonatal resuscitation digital simulator from June to October 2019; and 3) a 5-month delayed posttest using the low-fidelity table-top neonatal resuscitation digital simulator from September 2019 to January 2020. Three General Linear Mixed Model (GLMM) repeated-measure analyses investigated HCPs' performance on neonatal resuscitation over time and the moderating effect of growth mindset on the association between test time points and task performance. Results: Compared with their pretest performance, HCPs effectively improved their neonatal resuscitation knowledge after the RETAIN digital simulation-based training on the immediate posttest (Est = 1.88, p < 0.05), retained their knowledge on the 2-month delayed posttest (Est = 1.36, p < 0.05), and transferred their knowledge to the table-top simulator after 5 months (Est = 2.01, p < 0.05). Although growth mindset did not moderate the performance gain from the pretest to the immediate posttest, it moderated the relationship between HCPs' pretest and long-term knowledge retention (i.e., the interaction effect of mindset and the 2-month posttest was significant: Est = 0.97, p < 0.05). The more they endorsed a growth mindset, the better the HCPs performed on the posttest, but only when they were tested after 2 months. Conclusions: Digital simulators for neonatal resuscitation training can effectively facilitate HCPs' knowledge gain, maintenance, and transfer. Besides, growth mindset shows a positive moderating effect on the longitudinal performance improvement in simulation-based training. Future research can be conducted to implement growth-mindset interventions promoting more effective delivery of technology-enhanced, simulation-based training and assessment.

Project description:BackgroundNeonatal resuscitation involves a complex sequence of actions to establish an infant's cardiorespiratory function at birth. Many of these responses, which identify the best action sequence in each situation, are taught as part of the recurrent Neonatal Resuscitation Program training, but they have a low incidence in practice, which leaves health care providers (HCPs) less prepared to respond appropriately and efficiently when they do occur. Computer-based simulators are increasingly used to complement traditional training in medical education, especially in the COVID-19 pandemic era of mass transition to digital education. However, it is not known how learners' attitudes toward computer-based learning and assessment environments influence their performance.ObjectiveThis study explores the relation between HCPs' attitudes toward a computer-based simulator and their performance in the computer-based simulator, RETAIN (REsuscitation TrAINing), to uncover the predictors of performance in computer-based simulation environments for neonatal resuscitation.MethodsParticipants were 50 neonatal HCPs (45 females, 4 males, 1 not reported; 16 respiratory therapists, 33 registered nurses and nurse practitioners, and 1 physician) affiliated with a large university hospital. Participants completed a demographic presurvey before playing the game and an attitudinal postsurvey after completing the RETAIN game. Participants' survey responses were collected to measure attitudes toward the computer-based simulator, among other factors. Knowledge on neonatal resuscitation was assessed in each round of the game through increasingly difficult neonatal resuscitation scenarios. This study investigated the moderating role of mindset on the association between the perceived benefits of understanding the terminology used in the computer-based simulator, RETAIN, and their performance on the neonatal resuscitation tasks covered by RETAIN.ResultsThe results revealed that mindset moderated the relation between participants' perceived terminology used in RETAIN and their actual performance in the game (F3,44=4.56, R2=0.24, adjusted R2=0.19; P=.007; estimate=-1.19, SE=0.38, t44=-3.12, 95% CI -1.96 to -0.42; P=.003). Specifically, participants who perceived the terminology useful also performed better but only when endorsing more of a growth mindset; they also performed worse when endorsing more of a fixed mindset. Most participants reported that they enjoyed playing the game. The more the HCPs agreed that the terminology in the tutorial and in the game was accessible, the better they performed in the game, but only when they reported endorsing a growth mindset exceeding the average mindset of all the participants (F3,44=6.31, R2=0.30, adjusted R2=0.25; P=.001; estimate=-1.21, SE=0.38, t44=-3.16, 95% CI -1.99 to -0.44; P=.003).ConclusionsMindset moderates the strength of the relationship between HCPs' perception of the role that the terminology employed in a game simulator has on their performance and their actual performance in a computer-based simulator designed for neonatal resuscitation training. Implications of this research include the design and development of interactive learning environments that can support HCPs in performing better on neonatal resuscitation tasks.

Project description:Background: Frequent and objective summative assessment of neonatal healthcare providers is important to ensure high-quality care to patients during neonatal resuscitation. Currently, neonatal resuscitation providers are only individually assessed using an at-home online multiple-choice questionnaire. While simulation-based assessment is preferred, resource constraints limit its widespread uptake. An alternative approach to simulation-based summative assessment is needed. Simulation-based serious games may provide a solution. Objective: The aim of this study was to examine if individual performance on the RETAIN (REsuscitation TrAINing for healthcare professionals) tabletop simulator can be used as a summative assessment of neonatal resuscitation providers, regardless of their prior board game experience. Method: Neonatal healthcare providers were recruited from a tertiary perinatal center to complete a (1) demographic pre-survey, (2) neonatal resuscitation scenario using an open-answer written pre-test, (3) neonatal resuscitation scenario using the RETAIN tabletop simulator, and (4) post-survey measuring usage and attitudes toward board games. Multiple linear regression analyses using the Johnson-Neyman technique were conducted in R to probe the moderation effect of years of board game on the relationship between pre-test and game performance. Results: Twenty Neonatal Resuscitation Program-trained healthcare providers (nurses, nurse practitioners, respiratory therapists, and fellows) were recruited for this study (n = 19 females). Participants' mean (standard deviation) pre-test score was 8.35 (1.81) out of a total 16 possible points (52%) and a score of 18 (4.41) out of a total of 40 possible points (45%) using RETAIN. Overall board game experience was 22.5 (12.6) years. Finally, years of board game moderated significantly the relation between the pre-test and game performance (B = -0.13, SE = 0.05, beta = -0.48, t = -2.77, p < 0.05; 95% CI [-0.24, -0.03]). Thus, participants' performance on the two tests (written and simulator) was significantly positively associated, but only for those who reported fewer than 21.5 years of board game experience. Conclusion: This study reports the preliminary results of a pilot study, indicating that the RETAIN tabletop simulator could be used as a simulation-based summative assessment, an enjoyable, low-cost alternative to traditional assessment approaches. RETAIN offers a solution to the need for more frequent and continued assessment of neonatal resuscitation providers.

Project description:BackgroundTreatment adherence is a vital aspect in the management of chronic diseases like leprosy; however, most studies on treatment adherence focus on patients. This study aims to examine the perceptions of healthcare providers on factors that can promote or prevent patients from adhering to treatment.MethodsWe conducted three focus group discussions (FGDs) with healthcare providers who have at least one year experience in managing leprosy in three large leprosy case-holding hospitals in Metro Manila, Philippines. We audio-recorded, transcribed, translated the FGD proceedings, and analyzed the transcripts thematically to identify patient-intrinsic and patient-extrinsic enablers and barriers to treatment adherence of leprosy patients.ResultsPatient-intrinsic motivators to complete treatment include innate desire to be cured, fear of infecting family and friends, fear of disability, good knowledge about the disease, need for medical clearance to be considered fit to work, and experiencing leprosy reactions. Patient-extrinsic motivators to complete treatment include free treatment, immediate and sufficient counselling, flexibility in treatment, follow-up and motivation of healthcare workers, and presence of Hansen's Club and support groups. Patient-intrinsic barriers to good treatment adherence include distance between residence and hospital, financial and opportunity costs, adverse drug reactions, misconceptions about being cured, disabilities and presence of leprosy reactions, stubbornness and/or laziness, and undergoing clinical depression. Patient-extrinsic barriers to good treatment adherence include poor availability of MDT, transfer to other leprosy treatment facilities without informing current facility, and stigma.ConclusionHealthcare providers perceive that patient-intrinsic and patient-extrinsic factors influence the treatment adherence of leprosy patients through different mechanisms. We highlight the role of healthcare provider attitudes, stigma, and support groups in promoting treatment adherence.

Project description:Adherence to prescribed medication is suboptimal in 50% of the chronic population, resulting in negative medical and economic outcomes. With the widespread use of mobile phones worldwide, medication adherence apps for mobile phones become promising medication adherence aids thanks to simplicity, user-friendliness, and accessibility for the public. Yet, until today, there is insufficient evidence in favor of using mobile health (mHealth) apps to increase medication adherence. This study aims to develop a methodology for scientific and end-user (patient) mHealth evaluation (a) to identify medication adherence apps search terms, (b) to evaluate identified apps based on scientific criteria, and (c) to report best smartphone apps evaluated by patients. Search terms were identified via literature review and expertise. Firstly, an online questionnaire was developed to identify frequently used search terms by recruited patients. Related medication adherence apps were identified and selected using predefined inclusion criteria. Secondly, identified apps were evaluated thanks to a scientific evaluation method and a created online questionnaire for patient feedback. Recruited patients were invited to test and evaluate the selected apps. Out of 1,833 free-of-charge and 307 paid apps identified, only four free-of-charge and three paid apps remained included in the study after eligibility criteria. None of the selected app reached a high score. Looking at the overall scores, Medisafe (59%), MyTherapy (56%), and Meds on time (44%) received the highest scores in the scientific app evaluation. In the patient evaluation, Dosecast (3.83 out of five points), Medisafe (3.62), and SwissMeds (3.50) received the highest scores. None of the apps in this research has undergone a process for certification, for example, CE marking, through a notified body. Security and data protection aspects of existing apps highly contribute to these low evaluation scores through little information on patient's data processing and storage. This might be corrected through the introduction of General Data Protection Regulation (GDPR) in the European Economic Area (EEA) and more scrutiny through regulatory bodies in the EU/EEA and the USA. None of the applications should be recommended by healthcare providers. In addition, clinical studies with chronic patients are necessary to measure long-term app impacts.

Dataset Information

Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator.

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Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator.

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