Sessions

Presentation från Socialstyrelsen och ehälsomyndigheten om det övergripande arbetet med EHDS

Svensk sjuksköterskeförening och Socialstyrelsen leder en workshop om användning och nytta med ICNP i Snomed CT för dokumentation och uppföljning. Workshopen är en fortsättning av samverkan gällande omvårdnadsterminologin ICNP och begreppssystem Snomed CT.Som deltagare kommer du att lära dig mer om införande av ICNP-urval i ett vårdinformationsystem och återanvändning av omvårdnadsdata utifrån perspektiven kvalitetsutveckling och patientsäkerhet.

Artificial intelligence (AI) has been used to support decision making in the mental health domain for both healthcare professionals and patients on different levels in the healthcare systems and self-care services. AI has been included in several health applications such as predicting mental health problems, diagnostics, treatment plans, workflows, and other duties that require decision support. Understanding the use of AI tools and how it is applied for support decision making in the mental health would contribute in improving the design, development and implementation in practice. Further knowledge will also help benefit shaping person-centered care approaches when using AI tools by understanding the dynamics used nowadays in support decision making and how to include patients in the development of future processes and tools,It is unclear what types of decisions that AI tools most commonly participate in supporting in mental health, how these tools are applied, and whether its implementation is engaging patients in the process of decision making or not, this uncertainty may contribute in shifting the decision- making process towards more technology polarization and far from patient centeredness. Therefore, a scoping review is conducted to map the characteristics of research done on AI tools for supporting decision making in the mental health field to contribute to the body of knowledge regarding how decisions are supported. In the proposed lecture we will present our findings regarding the use of AI in mental health and reflections on support decision making.

This presentation is about our work on a brain tumor detection using clustering methods. The ultimate goal is to use this process in a Digital Twin prototype

The smart data extractor is a semi-automatic data extraction software to assist clinician in the completion of case report forms.It is a pipeline build on top of Dr Warehouse, the Necker (ap-hp, France) data warehouse.It is based on thesauri and regular expression.It saves time and error and it is clinician friendly and easy to use.

A great pitch@vitalisAt A great pitch@vitalis, the most innovative Healthtech companies will showcase new digital solutions in healthcare. Attendees can listen to short pitches where approximately 10 companies present their innovations, followed by questions from a distinguished panel. The participating companies will be announced in early May. We welcome you to join us for this engaging event with high energy and excitement!When: Tuesday, May 23, from 11 am to 12:30 pm
Where: Vitalis Matchmaking and Event Area (in the exhibition)
 
A conference pass is not required as it is included in the exhibition ticket.

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

Erfarenheter från Ukraina gällande omhändertagande av skadade, har gett många värdefulla insikter som vi i Sverige bör vara medvetna om när vi nu bygger upp vår militära och civila beredskap inom hälso- och sjukvård. Att förberedelse, underhållssäkerhet samt förråd är helt avgörande för en fungerande vårdkedja har blivit tydligt. OneMed och Adcuris har som ambition att kunna hjälpa till med uppbyggnaden av krisberedskapen både vad gäller utbildningar och produkter/system som är anpassade för ambitionen som myndigheter/regioner/kommuner eftersträvar.

Vad är Ena – Sveriges digitala infrastruktur och hur kopplar Ena till den sektorsspecifika infrastruktur som finns inom hälsa, vård och omsorg? Daniel ger en övergripande presentation av Enas olika delar med fokus på de som har en närmare koppling till hälsa, vård och omsorg.

Presentation of the Regional digital health action plan for the WHO European Region 2023–2030.

Utredningen om infrastruktur för hälsodata som nationellt intresse (S 2022:10) har i uppdrag att bl.a. föreslå effektivare och säkrare tillgång till patienters hälsodata för såväl primär- som sekundäranvändning. Det förutsätter en digital infrastruktur som bygger på att vårdens olika aktörer tillämpar gemensamma tekniska och semantiska standarder. Gemensamma standarder är också nödvändigt för att minska dubbeldokumentationen och annan onödig efterfrågan som uppstår till följd av bristande interoperabilitet mellan systemen. 

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.

In this presentation I talk about a setup for using remote eye-tracking on a touchscreen tablet to evaluate user interaction for older adults interacting with a user-driven hearing test. By using video recordings to support the eye-tracking data, it was possible to evaluate quantitative usability metrics that could be compared to other research findings. The video recordings revealed useful information to distinguish between reasons for gaps in data and missing data and to inform future similar studies of human-computer interaction on a touch screen. Using only portable equipment allows researchers to move to the location of the user and investigate the user interaction of devices in real-world scenarios

Markus Bylund, IT-direktör på Region Gävleborg, och Jakob Algulin, digitaliseringschef på E-hälsomyndigheten, ger utifrån erfarenheter från arbetet med regionernas anslutning till nationell läkemedelslista en kort introduktion till vikten av samverkan mellan olika offentliga aktörer för att åstadkomma en gemensam digital infrastruktur för sektorn. Vidare utveckling av det arbete kommer att presenteras under presentationen En gemensam digital infrastruktur i Sverige Torsdag 25 maj 2023 10:30 - 10:55

Max Herulf, chefsläkare på E-hälsomyndigheten, ger en inledning till spåret där han beskriver hur en välfungerande digital infrastruktur kan bidra med möjligheter för alla aktörerna i sektorn. Det handlar förstås om vård och omsorg, men också forskning, innovation, styrning och beslutsfattande som alla behöver bra underlag för att största möjliga nytta ska kunna skapas. I detta spår ska en rad olika aktörer beskriva möjligheter som finns för att tillsammans driva på utvecklingen för en väl fungerande nationell digital infrastruktur.

Snomed CT omfattar ungefär 2000 termer som kommer från International Classification of Nursing Practice (ICNP) för beskrivning av omvårdnad. Termerna finns i hierarkierna Kliniskt fynd respektive Åtgärd, företrädesvis inom domänerna findings, situation och procedure. Det behövs dock också en annan kategorisering av ICNP-termer för att på ett pedagogiskt sätt hjälpa användare att få syn på innehållet. Exempel på, och användning av, kategorisering beskrivs i presentationen.

Östergötland; 13 kommuner + 1 region. Fjorton huvudmän som bestämt sig för att göra resan mot Nära vård tillsammans.Åsa och Maria berättar om avgörande moment som hjälpt länet att navigera framåt i den komplexa omställningen. Startskottet var den visuella målbilden som togs fram med stöd av tjänstedesign. Det resulterade i en lavin av insikter, nya förhållningssätt och aktiviteter. Nu lever, andas (och ibland hyperventilerar) Östergötland Nära vård!