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1.
BMJ Health Care Inform ; 31(1)2024 Aug 19.
Artigo em Inglês | MEDLINE | ID: mdl-39160082

RESUMO

OBJECTIVES: This project aims to determine the feasibility of predicting future critical care bed availability using data-driven computational forecast modelling and routinely collected hospital bed management data. METHODS: In this proof-of-concept, single-centre data informatics feasibility study, regression-based and classification data science techniques were applied retrospectively to prospectively collect routine hospital-wide bed management data to forecast critical care bed capacity. The availability of at least one critical care bed was forecasted using a forecast horizon of 1, 7 and 14 days in advance. RESULTS: We demonstrated for the first time the feasibility of forecasting critical care bed capacity without requiring detailed patient-level data using only routinely collected hospital bed management data and interpretable models. Predictive performance for bed availability 1 day in the future was better than 14 days (mean absolute error 1.33 vs 1.61 and area under the curve 0.78 vs 0.73, respectively). By analysing feature importance, we demonstrated that the models relied mainly on critical care and temporal data rather than data from other wards in the hospital. DISCUSSION: Our data-driven forecasting tool only required hospital bed management data to forecast critical care bed availability. This novel approach means no patient-sensitive data are required in the modelling and warrants further work to refine this approach in future bed availability forecast in other hospital wards. CONCLUSIONS: Data-driven critical care bed availability prediction was possible. Further investigations into its utility in multicentre critical care settings or in other clinical settings are warranted.


Assuntos
Cuidados Críticos , Estudos de Viabilidade , Previsões , Número de Leitos em Hospital , Humanos , Ocupação de Leitos/estatística & dados numéricos , Estudos Retrospectivos , Unidades de Terapia Intensiva
2.
Stud Health Technol Inform ; 316: 1510-1514, 2024 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-39176490

RESUMO

There is limited knowledge about early career researchers' challenges when studying the interdisciplinary field of Medical Informatics (MI). We conducted a qualitative content analysis through semi-structured interviews with early career researchers in MI, including individuals pursuing Master's, PhD, and postdoctoral research programmes, across two higher education institutions in the UK. We identified five challenges, including understanding biological jargon, interpreting biological data, interdisciplinary communication, understanding mathematical/statistical concepts, and programming difficulties. These insights and suggested actions to address those challenges can help to improve MI education.


Assuntos
Informática Médica , Pesquisadores , Informática Médica/educação , Humanos , Reino Unido , Entrevistas como Assunto
3.
Stud Health Technol Inform ; 316: 1540-1544, 2024 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-39176499

RESUMO

Despite the proliferation of educational programmes in Health Informatics (HI) worldwide, there is limited knowledge regarding students' preferences and learning strategies in HI courses. To address this gap, we conducted a study to gather and analyse data from three HI courses. Employing the Motivated Strategies for Learning Questionnaire (MSLQ) and theories of deep and surface learning, we designed a questionnaire to collect data. The analysis of students' responses indicates that machine learning emerges as one of the most interesting topics, while certain topics such as data wrangling of genomics data were more challenging for students. Students expressed a preference for sequential learning. They exhibited multimodal tendencies regarding the type of learning resources, with tendency to prefer learning resources that have more visual contents. In all three courses, learners reported using deep learning strategy rather than surface learning, yet they appear to struggle with employing organisation, elaboration, and peer learning tactics. This study provides valuable insights into HI education, offering recommendations for educators, learners, and researchers to enhance HI education.


Assuntos
Informática Médica , Informática Médica/educação , Humanos , Inquéritos e Questionários , Autorrelato , Aprendizagem , Currículo , Aprendizado de Máquina , Masculino
4.
JMIR Med Educ ; 10: e50667, 2024 Aug 12.
Artigo em Inglês | MEDLINE | ID: mdl-39133909

RESUMO

BACKGROUND: Learning and teaching interdisciplinary health data science (HDS) is highly challenging, and despite the growing interest in HDS education, little is known about the learning experiences and preferences of HDS students. OBJECTIVE: We conducted a systematic review to identify learning preferences and strategies in the HDS discipline. METHODS: We searched 10 bibliographic databases (PubMed, ACM Digital Library, Web of Science, Cochrane Library, Wiley Online Library, ScienceDirect, SpringerLink, EBSCOhost, ERIC, and IEEE Xplore) from the date of inception until June 2023. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and included primary studies written in English that investigated the learning preferences or strategies of students in HDS-related disciplines, such as bioinformatics, at any academic level. Risk of bias was independently assessed by 2 screeners using the Mixed Methods Appraisal Tool, and we used narrative data synthesis to present the study results. RESULTS: After abstract screening and full-text reviewing of the 849 papers retrieved from the databases, 8 (0.9%) studies, published between 2009 and 2021, were selected for narrative synthesis. The majority of these papers (7/8, 88%) investigated learning preferences, while only 1 (12%) paper studied learning strategies in HDS courses. The systematic review revealed that most HDS learners prefer visual presentations as their primary learning input. In terms of learning process and organization, they mostly tend to follow logical, linear, and sequential steps. Moreover, they focus more on abstract information, rather than detailed and concrete information. Regarding collaboration, HDS students sometimes prefer teamwork, and sometimes they prefer to work alone. CONCLUSIONS: The studies' quality, assessed using the Mixed Methods Appraisal Tool, ranged between 73% and 100%, indicating excellent quality overall. However, the number of studies in this area is small, and the results of all studies are based on self-reported data. Therefore, more research needs to be conducted to provide insight into HDS education. We provide some suggestions, such as using learning analytics and educational data mining methods, for conducting future research to address gaps in the literature. We also discuss implications for HDS educators, and we make recommendations for HDS course design; for example, we recommend including visual materials, such as diagrams and videos, and offering step-by-step instructions for students.


Assuntos
Aprendizagem , Humanos , Ciência de Dados/educação , Currículo
5.
BMC Med Educ ; 24(1): 564, 2024 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-38783229

RESUMO

BACKGROUND: Health Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students' learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs. METHODS: We applied artificial intelligence methods to uncover learning tactics and strategies employed by students in an HDS massive open online course with over 3,000 students enrolled. We also used statistical tests to explore students' engagement with different resources (such as reading materials and lecture videos) and their level of engagement with various HDS topics. RESULTS: We found that students in HDS employed four learning tactics, such as actively connecting new information to their prior knowledge, taking assessments and practising programming to evaluate their understanding, collaborating with their classmates, and repeating information to memorise. Based on the employed tactics, we also found three types of learning strategies, including low engagement (Surface learners), moderate engagement (Strategic learners), and high engagement (Deep learners), which are in line with well-known educational theories. The results indicate that successful students allocate more time to practical topics, such as projects and discussions, make connections among concepts, and employ peer learning. CONCLUSIONS: We applied artificial intelligence techniques to provide new insights into HDS education. Based on the findings, we provide pedagogical suggestions not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.


Assuntos
Inteligência Artificial , Ciência de Dados , Humanos , Ciência de Dados/educação , Currículo , Aprendizagem
6.
PLOS Glob Public Health ; 4(2): e0002709, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38363770

RESUMO

Antibacterial resistance (ABR) is a major public health threat. An important accelerating factor is treatment-seeking behaviour, including inappropriate antibiotic (AB) use. In many low- and middle-income countries (LMICs) this includes taking ABs with and without prescription sourced from various providers, including health facilities and community drug sellers. However, investigations of complex treatment-seeking, AB use and drug resistance in LMICs are scarce. The Holistic Approach to Unravel Antibacterial Resistance in East Africa (HATUA) Consortium collected questionnaire and microbiological data from adult outpatients with urinary tract infection (UTI)-like symptoms presenting at healthcare facilities in Kenya, Tanzania and Uganda. Using data from 6,388 patients, we analysed patterns of self-reported treatment seeking behaviours ('patient pathways') using process mining and single-channel sequence analysis. Among those with microbiologically confirmed UTI (n = 1,946), we used logistic regression to assess the relationship between treatment seeking behaviour, AB use, and the likelihood of having a multi-drug resistant (MDR) UTI. The most common treatment pathway for UTI-like symptoms in this sample involved attending health facilities, rather than other providers like drug sellers. Patients from sites in Tanzania and Uganda, where over 50% of patients had an MDR UTI, were more likely to report treatment failures, and have repeat visits to providers than those from Kenyan sites, where MDR UTI proportions were lower (33%). There was no strong or consistent relationship between individual AB use and likelihood of MDR UTI, after accounting for country context. The results highlight the hurdles East African patients face in accessing effective UTI care. These challenges are exacerbated by high rates of MDR UTI, suggesting a vicious cycle of failed treatment attempts and sustained selection for drug resistance. Whilst individual AB use may contribute to the risk of MDR UTI, our data show that factors related to context are stronger drivers of variations in ABR.

7.
IEEE Trans Vis Comput Graph ; 30(1): 649-660, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-37934634

RESUMO

This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.

8.
medRxiv ; 2023 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-36945627

RESUMO

Antibacterial resistance (ABR) is a major public health threat. An important accelerating factor is treatment-seeking behaviours, including inappropriate antibiotic (AB) use. In many low- and middle-income countries (LMICs) this includes taking ABs with and without prescription sourced from various providers, including health facilities and community drug sellers. However, investigations of complex treatment-seeking, AB use and drug resistance in LMICs are scarce. The Holistic Approach to Unravel Antibacterial Resistance in East Africa (HATUA) Consortium collected questionnaire and microbiological data from 6,827 adult outpatients with urinary tract infection (UTI)-like symptoms presenting at healthcare facilities in Kenya, Tanzania and Uganda. Among 6,388 patients we analysed patterns of self-reported treatment seeking behaviours ('patient pathways') using process mining and single-channel sequence analysis. Of those with microbiologically confirmed UTI (n=1,946), we used logistic regression to assessed the relationship between treatment seeking behaviour, AB use, and likelihood of having a multi-drug resistant (MDR) UTI. The most common treatment pathways for UTI-like symptoms included attending health facilities, rather than other providers (e.g. drug sellers). Patients from the sites sampled in Tanzania and Uganda, where prevalence of MDR UTI was over 50%, were more likely to report treatment failures, and have repeated visits to clinics/other providers, than those from Kenyan sites, where MDR UTI rates were lower (33%). There was no strong or consistent relationship between individual AB use and risk of MDR UTI, after accounting for country context. The results highlight challenges East African patients face in accessing effective UTI treatment. These challenges increase where rates of MDR UTI are higher, suggesting a reinforcing circle of failed treatment attempts and sustained selection for drug resistance. Whilst individual behaviours may contribute to the risk of MDR UTI, our data show that factors related to context are stronger drivers of ABR.

9.
Int J Med Inform ; 159: 104668, 2022 03.
Artigo em Inglês | MEDLINE | ID: mdl-35033982

RESUMO

BACKGROUND: Despite the growing interest in health data science education, it is not embedded in undergraduate medical curricula and little is known about best teaching practices. This paper presents a highly innovative course in a UK university that introduces undergraduate medical students to data science. It also discusses a study on student perspectives on the learning and teaching of health data science. METHODS: The pedagogical design elements of the Data Science in Medicine course are discussed, along with its syllabus, assessment methodology and flipped classroom delivery. The course has been offered to approximately 630 students over three years. Student perspectives were investigated through three focus groups with the participation of 19 students across different study years in medicine. An experiment was conducted regarding instructor-led vs. video-based modalities of online programming labs, with the participation of 8 students. RESULTS: The course has led to improved data competency among medical students and to a positive change in their opinions about data science. Motivating the course and showing relevance to clinical practice was one of the biggest challenges. Statistics was perceived by focus group participants as an essential data skill. Including data science in the medical curriculum was perceived as important by Year 1 students, while opinions varied between Year 4/5 participants. Video-based online labs were preferred over instructor-led online labs, and they were found to be more useful and enjoyable, without leading to any significant difference in academic performance. CONCLUSIONS: Teaching data science to undergraduate medicine students is highly desirable and feasible. We recommend including statistics in the curriculum and practical skill development through simple and clinically-relevant data science tasks, supported through video-based online labs. Further reporting on similar courses is needed, as well as larger-scale studies on student perspectives.


Assuntos
Educação de Graduação em Medicina , Estudantes de Medicina , Currículo , Ciência de Dados , Humanos , Aprendizagem , Universidades
10.
AMIA Annu Symp Proc ; 2018: 498-507, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30815090

RESUMO

EHRs transform work practices in ways that enhance or impede the quality of care. There is a need for in-depth analysis of EHR workflows, particularly in complex clinical environments. We investigated EHR-basedpre-operative workflows by combining findings from 18 interviews, 7 days of observations, and process mining of EHR interactions from 31 personnel caring for 375 patients at one tertiary referral center. We provided high-definition descriptions of workflows and personnel roles. One third (32.2%) of the time with each patient was spent interacting with the EHR and 4.2% using paper-based artifacts. We also mined personnel social networks validating observed personnel's EHR-interactions. When comparing workflows between two similar pre-operative settings at different hospitals, we found significant differences in physical organization, patient workflow, roles, use of EHR, social networks and time efficiency. This study informs Mayo Clinic's enterprise-wide conversion to a single EHR and will guide before and after workflow comparisons.


Assuntos
Registros Eletrônicos de Saúde/organização & administração , Centro Cirúrgico Hospitalar/organização & administração , Análise e Desempenho de Tarefas , Fluxo de Trabalho , Humanos , Entrevistas como Assunto , Equipe de Assistência ao Paciente/organização & administração , Rede Social
11.
IEEE J Biomed Health Inform ; 21(4): 1156-1162, 2017 07.
Artigo em Inglês | MEDLINE | ID: mdl-27305690

RESUMO

Intrahospital transfers are a common but hazardous aspect of hospital care, with a large number of incidents posing a threat to patient safety. A growing body of work advocates the use of checklists for minimizing intrahospital transfer risk, but the majority of existing checklists are not guaranteed to be error-free and are difficult to adapt to different clinical settings or changing hospital policies. This paper details an approach that addresses these challenges through the employment of workflow technologies and formal methods for generating structured checklists. A three-phased methodology is proposed, where intrahospital transfer processes are first conceptualized, then rigorously composed into workflows that are mechanically verified, and finally, translated into a set of checklists that support hospital staff while maintaining the dependencies between different transfer tasks. A case study is presented, highlighting the feasibility of this approach, and the correctness and maintainability benefits brought by the logical underpinning of this methodology. A checklist evaluation is discussed, with promising results regarding their usefulness.


Assuntos
Lista de Checagem , Transferência de Pacientes , Fluxo de Trabalho , Estudos de Viabilidade , Humanos , Informática Médica , Modelos Teóricos , Segurança do Paciente , Transferência de Pacientes/métodos , Transferência de Pacientes/normas , Traqueostomia
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