| 1. |
- Abovarda, Adam, et al.
(författare)
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E-learning as Part of Residency Education
- 2023
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Ingår i: Caring is Sharing. - : IOS Press. - 9781643683881 - 9781643683898 ; , s. 496-497
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Konferensbidrag (refereegranskat)abstract
- Through a literature review in combination with qualitative analysis of course evaluations, this study examines aspects that contribute to enhancing elearning for physicians in a residency education program. The literature review and the qualitative analysis outline three main factors (pedagogical, technological, and organizational), highlighting the importance of a holistic approach that includes learning and technology in context when integrating e-learning strategies in adult learning programs. The findings contribute insights and practical guidance for education organizers on how to conduct e-learning during and after the pandemic.
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| 2. |
- Amirahmadi, Ali, 1994-, et al.
(författare)
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A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning
- 2023
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Ingår i: Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. - Amsterdam : IOS Press. - 0926-9630 .- 1879-8365. - 9781643683881 ; 302, s. 609-610, s. 609-610
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Konferensbidrag (refereegranskat)abstract
- Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes).
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| 3. |
- Björkquist, Anna, et al.
(författare)
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Towards Implementation of a Home-Based Phantom Limb Pain Treatment Facilitated by Textile-Electrode System - A Case Study
- 2023
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Ingår i: Caring is Sharing — Exploiting the Value in Data for Health and Innovation. - : IOS Press. - 9781643683881 - 9781643683898 ; , s. 682-683
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Konferensbidrag (refereegranskat)abstract
- This case study reports the use of a new textile-electrode system for self-administered Phantom Motor Execution (PME) treatment at home in one patient with Phantom Limb Pain (PLP). In follow-up interviews, the patient reported reduced pain, increased mobility, and improved mental health, and aspects such as motivation, usability, support, and treatment outcome, could be recognized from an earlier study as crucial for successful implementation and adoption of the home-based long-term treatment. The findings are of interest to developers, providers, users, and researchers planning home-based clinical studies and/or scenarios based on technology-assisted treatment.
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| 4. |
- Blusi, Madeleine, 1968-, et al.
(författare)
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Factors to consider when introducing digital social activities to older persons with home care
- 2023
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Ingår i: Caring is sharing. - : IOS Press. - 9781643683881 - 9781643683898 ; , s. 453-457
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Konferensbidrag (refereegranskat)abstract
- Social isolation and loneliness have become everyday concerns forpopulations all over the world as these factors are affecting both physical and mentalhealth in a negative way. Feelings of isolation and loneliness are increasinglyacknowledged as a health risk among older persons. ICTs have been recognized aseffective tools to combat social isolation among older people. The aim of this studywas to explore factors of significance when introducing a tablet-based systemproviding digital social activities for older persons with home care. Participants were17 persons, age 70 and older, who lived alone and had assistance from home care.This exploratory study used cross-sectional qualitative data analyzed throughthematic analysis. Three themes were generated: 1) lacking vocabulary related tothe context, 2) intuitive user interface may replace extensive instructions and 3)unwillingness to commit to a pre-defined measure of performance.
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| 5. |
- Budu, Emmanuella, 1995-, et al.
(författare)
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A Framework for Evaluating Synthetic Electronic Health Records
- 2023
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Ingår i: Caring is Sharing – Exploiting the Value in Data for Health and Innovation. - Amsterdam : IOS Press. - 9781643683881 - 9781643683898 ; , s. 378-379
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Konferensbidrag (refereegranskat)abstract
- Synthetic data generation can be applied to Electronic Health Records (EHRs) to obtain synthetic versions that do not compromise patients' privacy. However, the proliferation of synthetic data generation techniques has led to the introduction of a wide variety of methods for evaluating the quality of generated data. This makes the task of evaluating generated data from different models challenging as there is no consensus on the methods used. Hence the need for standard ways of evaluating the generated data. In addition, the available methods do not assess whether dependencies between different variables are maintained in the synthetic data. Furthermore, synthetic time series EHRs (patient encounters) are not well investigated, as the available methods do not consider the temporality of patient encounters. In this work, we present an overview of evaluation methods and propose an evaluation framework to guide the evaluation of synthetic EHRs. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.
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| 6. |
- Bärkås, Annika, et al.
(författare)
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Patients' Experiences of Unwanted Access to Their Online Health Records
- 2023
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Ingår i: Studies in Health Technology and Informatics. - : IOS Press. - 0926-9630 .- 1879-8365. ; 302, s. 356-357, s. 356-357
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Tidskriftsartikel (refereegranskat)abstract
- Patient-Accessible Electronic Health Records (PAEHR) are particularly disputed in mental healthcare. We aim to explore if there is any association between patients having a mental health condition and someone unwanted seeing their PAEHR. A chi-square test showed a statistically significant association between group belonging and experiences of someone unwanted seeing their PAEHR.
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| 7. |
- Gharehbaghi, Arash, 1972-, et al.
(författare)
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Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification
- 2023
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Ingår i: CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023. - : IOS PRESS. - 9781643683898 ; , s. 526-530
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Konferensbidrag (refereegranskat)abstract
- This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.
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| 8. |
- Hagström, Josefin, et al.
(författare)
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Adolescents Identifying Errors and Omissions in Their Electronic Health Records : A National Survey
- 2023
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Ingår i: Caring is Sharing. - : IOS Press. - 9781643683881 - 9781643683898 ; 302, s. 242-246
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Konferensbidrag (refereegranskat)abstract
- Patient accessible electronic health records (PAEHRs) have been proposed as a means to improve patient safety and documentation quality, as patients become an additional source to detect mistakes in the records. In pediatric care, healthcare professionals (HCP) have noted a benefit of parent proxy users correcting errors in their child's records. However, the potential of adolescents has so far been overlooked, despite reports of reading records to ensure accuracy. The present study examines errors and omissions identified by adolescents, and whether patients reported following up with HCPs. Survey data was collected during three weeks in January and February 2022 via the Swedish national PAEHR. Of 218 adolescent respondents, 60 reported having found an error (27.5%) and 44 (20.2%) had found missing information. Most adolescents did not take any action upon identifying an error or an omission (64.0%). Omissions were more often perceived as serious than errors. These findings call for development of policy and PAEHR design that facilitates reports of errors and omissions for adolescents, which could both improve trust and support the individual's transition into an involved and engaged adult patient.
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| 9. |
- Hamed, Omar, 1979-, et al.
(författare)
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Temporal Context Matters : An Explainable Model for Medical Resource Utilization in Chronic Kidney Disease
- 2023
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Ingår i: Caring is Sharing – Exploiting the Value in Data for Health and Innovation. - Amsterdam : IOS Press. - 9781643683881 - 9781643683898 ; , s. 613-614
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Konferensbidrag (refereegranskat)abstract
- The prediction of medical resource utilization is beneficial for effective healthcare resource planning and allocation. Previous work in resource utilization prediction can be categorized into two main classes, count-based and trajectory-based. Both of these classes have some challenges, in this work we propose a hybrid approach to overcome these challenges. Our initial results promote the value of temporal context in resource utilization prediction and highlight the importance of model explainability in understanding the main important variables. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.
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| 10. |
- Hashemi, Atiye Sadat, 1991-, et al.
(författare)
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Domain Knowledge-Driven Generation of Synthetic Healthcare Data
- 2023
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Ingår i: Caring is Sharing – Exploiting the Value in Data for Health and Innovation. - Amsterdam : IOS Press. - 9781643683898 ; , s. 352-353
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Konferensbidrag (refereegranskat)abstract
- Healthcare longitudinal data collected around patients' life cycles, today offer a multitude of opportunities for healthcare transformation utilizing artificial intelligence algorithms. However, access to "real" healthcare data is a big challenge due to ethical and legal reasons. There is also a need to deal with challenges around electronic health records (EHRs) including biased, heterogeneity, imbalanced data, and small sample sizes. In this study, we introduce a domain knowledge-driven framework for generating synthetic EHRs, as an alternative to methods only using EHR data or expert knowledge. By leveraging external medical knowledge sources in the training algorithm, the suggested framework is designed to maintain data utility, fidelity, and clinical validity while preserving patient privacy. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.
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