Programpunkter

Despite years of work from both informaticians and IT-architects interoperability within healthcare is still low. This explorative case study performed on a well-staffed public health care provider shows that the involved roles were unclear, processes did not include each other, and that tooling was incompatible.However, interest in collaboration was high and technical advances and inhouse development were seen as incentives for increased collaboration.

The MIE conference has always been a hub for innovation and cutting-edge technology. This year was no exception several demonstrators showcase their unique solutions to healthcare problems. Use the opportunity to take a closer look at some of the most striking demonstrators present at the conference, highlighting their diversity and the potential impact they could have on various healthcare-related challenges

The MIE conference has always been a hub for innovation and cutting-edge technology. This year was no exception several demonstrators showcase their unique solutions to healthcare problems. Use the opportunity to take a closer look at some of the most striking demonstrators present at the conference, highlighting their diversity and the potential impact they could have on various healthcare-related challenges

It is an obvious paradox that we as citizens, present in all clinical health encounters, lack tools to take full advantage of ongoing digital transformation in health care. In this panel we will discuss user needs and design of digital tool to improve access to personally relevant health information from trusted sources. Of special interest is adherence to treatment. The attendees can learn about opportunities to “collect, curate and control” relevant, personal health information, and digital services for personal health data and access trustworthy, up-to-date information that better meet situated, everyday needs.

The workshop is cancelled. 

The aim of this work is to develop and evaluate a multi-stage procedure model for the identification of use problems and optimization of usability using biosignal data. The concept is divided into 5 steps: 1. static analysis of data to identify use problems; 2. conducting interviews within the context of use and requirements analysis to investigate problems in more detail; 3. developing new interface concepts to implement the requirements and a prototype of an interface including dynamic visualization of data; 4. formative evaluation using an unmoderated remote usability test; 5. usability test with realistic scenarios and influencing factors in the simulation room. The concept was evaluated in the ventilation setting as an example. The procedure allowed the identification of use problems in the ventilation of patients as well as the development of suitable concepts and their evaluation to counteract use problems. To relieve users, ongoing analyses of biosignals with respect to the use problem are to be carried out. To overcome technical barriers, further development is needed in this area.

The MIE conference has always been a hub for innovation and cutting-edge technology. This year was no exception several demonstrators showcase their unique solutions to healthcare problems. Use the opportunity to take a closer look at some of the most striking demonstrators present at the conference, highlighting their diversity and the potential impact they could have on various healthcare-related challenges

Autism spectrum disorder (ASD) is a developmental disability caused by differences in the brain regions. Analysis of differential expression (DE) of transcriptomic data allows for genome-wide analysis of gene expression changes related to ASD. De-novo mutations may play a vital role in ASD, but the list of genes involved is still far from complete. Differentially expressed genes (DEGs) are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches like machine learning and statistical analysis. In this study, we employed a machine learning-based approach to identify the differential gene expression between ASD and Typical Development (TD). The gene expression data of 15 ASD and 15 TD were obtained from NCBI GEO database. Initially, we extracted the data and used a standard pipeline to preprocess the data. Further, Random Forest (RF) was used to discriminate genes between ASD and TD. We identified the top 10 prominent differential genes and compared them with the statistical test results. Our results show that the proposed RF model yields 5-fold cross-validation accuracy, sensitivity and specificity of 96.67%. Further, we obtained precision and F-measure scores of 97.5% and 96.57%, respectively. Moreover, we found 34 unique DEG chromosomal locations having influential contributions in identifying ASD from TD. We have also identified chr3:113322718-113322659 as the most significant contributing chromosomal location in discriminating ASD and TD. Our machine learning-based method of refining DE analysis is promising for finding biomarkers from gene expression profiles and prioritizing DEGs. Moreover, our study reported top-10 gene signatures for ASD may facilitate the development of reliable diagnosis and prognosis biomarkers for screening ASD.Keywords: Gene expression data, NCBI, Autism Spectrum Disorder, Random Forest, Statistical test

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.