Cognitive Performance at Time of AD Diagnosis : A Clinically Augmented Register-Based Study

We aimed to evaluate the feasibility of using real-world register data for identifying persons with mild Alzheimer's disease (AD) and to describe their cognitive performance at the time of diagnosis. Patients diagnosed with AD during 2010-2013 (aged 60-81 years) were identified from the Finnish national health registers and enlarged with a smaller private sector sample (total n = 1,268). Patients with other disorders impacting cognition were excluded. Detailed clinical and cognitive screening data (the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological battery [CERAD-nb]) were obtained from local health records. Adequate cognitive data were available for 389 patients with mild AD (31%) of the entire AD group. The main reasons for not including patients in analyses of cognitive performance were AD diagnosis at a moderate/severe stage (n = 266, 21%), AD diagnosis given before full register coverage (n = 152, 12%), and missing CERAD-nb data (n = 139, 11%). The cognitive performance of persons with late-onset AD (n = 284), mixed cerebrovascular disease and AD (n = 51), and other AD subtypes (n = 54) was compared with that of a non-demented sample (n = 1980) from the general population. Compared with the other AD groups, patients with late-onset AD performed the worst in word list recognition, while patients with mixed cerebrovascular disease and AD performed the worst in constructional praxis and clock drawing tests. A combination of national registers and local health records can be used to collect data relevant for cognitive screening; today, the process is laborious, but it could be improved in the future with refined search algorithms and electronic data.Peer reviewe

ICF Personal Factors Strengthen Commitment to Person-Centered Rehabilitation : A Scoping Review

Background: The International Classification of Functioning, Disability and Health (ICF) classification is a biopsychosocial frame of reference that contributes to a holistic understanding of the functioning of a client and the factors involved. Personal factors (PFs) are not currently classified in the ICF due to large societal and cultural diversity and lack of clarity in the scope of such factors. Aims: To ascertain which factors in the ICF classification have been defined as PFs in different studies and what conclusions have been drawn on their role in the ICF classification. Methods: The study was a scoping review. A systematic search for articles published in 2010–2020 was performed on the Cinahl, Pubmed, ScienceDirect, and Sport Discus databases. The PFs specified in the articles were classified according to the seven categories proposed by Geyh et al. socio-demographic factors; position in the immediate social and physical context; personal history and biography; feelings; thoughts and beliefs; motives; and general patterns of experience and behavior. Results: The search yielded 1,988 studies, of which 226 met the inclusion criteria. The studies had addressed a wide variety of PFs that were linked to all seven categories defined by Geyh et al. Some studies had also defined PFs that were linkable to other components of the ICF or that did not describe functioning. Approximately 22% (51) of the studies discussed the role of PFs in rehabilitation. Conclusions: The range of PFs in the ICF classification addressed in the reviewed studies is wide. PFs play an important role in rehabilitation. However, according to the reviewed studies, a more precise coding of PFs is not yet warranted

E-health and e-welfare of Finland : Check Point 2022

The report provides an overview of progressive nationwide activities towards better e-services in Finland. The information system services of social welfare and health care are monitored by systematic gathering, analysis, and use of data, which allows the tracking of the progress of operations and the realisation of goals. In 2020 and 2021, six data collections were carried out to produce data for the monitoring of the Finnish ‘Information to support well-being and service renewal, eHealth and eSocial Strategy’. Some of the results presented in the report are also openly available in database cubes

Nursing Students’ Learning about an Empowering Discourse In Patient Education

The main purpose of this study was to describe and evaluate nursing students' learning about an empowering discourse in patient education. In Phase 1, the purpose was to describe an empowering discourse between a nurse and a patient. In Phase 2, the purpose was first to create a computer simulation program of an empowering discourse based on the description, and second, the purpose was to evaluate nursing students’ learning of how to conduct an empowering discourse using a computer simulation program. The ultimate goal was to strengthen the knowledge basis on empowering discourse and to develop nursing students’ knowledge about how to conduct an empowering discourse for the development of patient education. In Phase I, empowering discourse was described using a systematic literature review with a metasummary technique (n=15). Data were collected covering a period from January 1995 to October 2005. In Phase 2, the computer simulation program of empowering discourse was created based the description in 2006–2007. A descriptive comparative design was used to evaluate students’ (n=69) process of learning empowering discourse using the computer simulation program and a pretest–post-test design without a control group was used to evaluate students’ (n=43) outcomes of learning. Data were collected in 2007. Empowering discourse was a structured process and it was possible to simulate and learned with the computer simulation program. According to students’ knowledge, empowering discourse was an unstructured process. Process of learning empowering discourse using the computer simulation program was controlled by the students and it changed students’ knowledge. The outcomes of learning empowering discourse appeared as changes of students’ knowledge to more holistic and better-organized or only to more holistic or better-organized. The study strengthened knowledge base of empowering discourse and developed students to more knowledgeable in empowering discourse.Siirretty Doriast

E-health and e-welfare of Finland - Check point 2015

The new e-health and e-welfare strategy in Finland aims to support the renewal of the social welfare and health care services and the active role of citizens in maintaining their own well-being. The means include the development of knowledge management and increasing the provision of online services. The overall structural changes taking place in Finnish health and social care will also influence information and communication technologies (ICT). The report provides information about the change in the services and the service system brought on by ICT over time. The report illustrates the status in 2014 as compared with the strategic outcomes and objectives set on ICT to support performance and renewal of social welfare and health care. The results are condensed from four surveys for a comprehensive view: availability and use of ICT in health care as well as in social care, usability of the systems for physicians, and citizens´ use and anticipations. These are accompanied by a review of Finnish health care system and ICT development. For the international reader, the report provides an overview of progressive nationwide activities towards better e-services in Finland

Data collection in helicopter emergency medical services

Prehospital critical care, especially helicopter emergency medical services (HEMS), is a costly but vital part in the chain of survival for a critically deteriorated patient. The quality assessment and outcome measures of this service are important for targeting the limited resources accurately. Clinical registries are a key element of this system follow-up and quality assurance. In addition, they are a vast resource for scientific objectives. Therefore, the data reliability in these clinical registries needs to be assured. The aims of this thesis were to evaluate the accuracy and reliability of clinical data collection in a national HEMS service. In addition, to study the accuracy of prognostication based on prehospital patient classification and registry data. And finally, to revise a prehospital patient scoring system, the HEMS Benefit Score, to meet the modern standards of prehospital emergency medical services. This scoring is used in all Finnish HEMS units to evaluate the benefit of prehospital emergency medical services for patients treated on HEMS missions. Inter-rater reliability was evaluated among HEMS clinicians as they registered written mission scenarios into the FinnHEMS database. Furthermore, the accuracy of prognostication was evaluated in a retrospective patient population of 6219 HEMS patients. Finally, a revision for the HEMS Benefit Score was performed with Delphi method. The overall inter-rater reliability of data collected from the written mission scenarios was on an adequate level, however, vital signs documentation was shown to be poor. In addition, documentation of time-related parameters had a moderate inter-rater reliability. Patient scoring and classification indicated an overall poor inter-rater reliability among study participants. Prognostication in the HEMS setting had a moderate accuracy, and both futile and non-futile patients were treated with similar intensity. The revision of the HEMS Benefit Score resulted in a restructured and modernised version of a scoring for prehospital use, the EMS Benefit Score. As a conclusion, the reliability and accuracy of data collection among Finnish HEMS clinicians is on an adequate level. The reliability of a prehospitally set futile prognosis is at least questionable, therefore, decisions to limit treatment in a prehospital setting should be made with caution. Delphi method was established as a suitable process for implementation of a prehospital scoring system.Tiedon keruu ensihoidon helikopteritoiminnassa Lääkärijohtoiset ensihoidon helikopteriyksiköt (HEMS) ovat tärkeä osa kriittisesti sairastuneiden potilaiden hoitojärjestelmää. Jotta rajallisia resursseja voidaan kohdistaa oikealla tavalla, on tärkeää arvioida HEMS-toiminnan laatua ja vaikuttavuutta. Kliiniset laaturekisterit ovat olennainen osa toiminnan laadun arviointia, ja rekisterit toimivat myös tieteellisen tutkimuksen pohjana. Tästä syystä kliinisiin rekistereihin kerätyn tiedon luotettavuus tulee varmistaa. Tämän väitöskirjan tavoitteena oli tutkia kansallisissa HEMS-yksiköissä toimivien ensihoitolääkärien ja ensihoitajien kirjauskäytäntöjen luotettavuutta ja yhtenäisyyttä. Lisäksi tutkittiin potilaiden luokittelun ja ennustearvion osuvuutta HEMS-tehtävissä. Väitöskirjan viimeisenä osaprojektina päivitettiin kansallisten HEMS-yksiköiden käyttämä pisteytysjärjestelmä, HEMS Benefit Score, vastaamaan nykyaikaisia ensihoidon käytäntöjä. HEMS Benefit Score on ensihoidon yksittäiselle potilaalle tuottamaa hyötyä arvioiva pisteytysjärjestelmä, joka on käytössä kaikissa suomalaisissa HEMS-yksiköissä. Kirjausten luotettavuutta tutkittiin kuvitteellisten ensihoidon tehtävien avulla. Luotettavuutta arvioitiin erikseen sekä tehtäväkohtaisten muuttujien että potilasluokitus- ja pisteytysjärjestelmien osalta. Väitöskirjan kolmannessa osatyössä tutkittiin ennustearvion luotettavuutta 6219 potilaan retrospektiivisessä tutkimusasetelmassa. Viimeisessä osatyössä HEMS Benefit Score päivitettiin Delphimenetelmää käyttäen. Tulosten perusteella kirjaamisen luotettavuus oli kaiken kaikkiaan kohtalaisella, mutta peruselintoimintojen kirjaamisen osalta huonolla tasolla. Väitöskirjassa tutkittujen pisteytysjärjestelmien luotettavuus osoitettiin olevan vaihtelevaa vastaajien välillä. Ennustearvion teko onnistui kohtalaisen luotettavasti, ja sekä toivottomaksi arvioituja että todennäköisesti selviytyviksi arvioituja potilaita hoidettiin yhtä intensiivisesti. Väitöskirjan tulosten perusteella ensihoidossa asetetun toivottoman ennusteen osuvuus ei ole merkittävän korkea, joten päätöksiin rajoittaa hoitoa jo ensihoitotilanteessa tulisi suhtautua varovaisuudella. HEMS Benefit Score päivitettiin Delphi-menetelmällä vastaamaan nykyaikaisia hoitokäytäntöjä, ja nimettiin uudelleen EMS Benefit Scoreksi

FinnGen provides genetic insights from a well-phenotyped isolated population

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10–11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.publishedVersionPeer reviewe

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.Siirretty Doriast