Hoitoisuusluokituksen kriteeristö : kehittämishanke Palokan terveyskeskuksen osastolle 2

Aihe opinnäytetyölle valittiin työelämän tarpeita ajatellen ja se toteutettiin yhteistyössä Palokan terveyskeskuksen osasto 2:n kanssa. Opinnäytetyön tavoitteena oli luoda osastolle 2 oma OPC-mallin mukainen hoitoisuusluokituksen kriteeristö, joka palvelisi henkilökuntaa päivittäisessä hoitotyössä. Kriteeristö piti sisällään 6 hoitotyön osa-aluetta, joissa kussakin oli 4 (A, B, C ja D) hoitoisuuden vaativuuden osa-aluetta. A oli vaativuudeltaan helpoin ja D vaativuudeltaan hoitoisin osa-alue. Opinnäytetyön tarkoituksena on saada näkymätön hoitotyö näkyväksi Palokan terveyskeskuksen osastolla 2 sekä tuoda hoitoisuusluokitus tutuksi hoitohenkilökunnalle. Luodun hoitoisuusluokituskriteeristön tarkoituksena oli herättää dialogeja hoitohenkilökunnan keskuudessa potilaiden hoitoisuutta arvioitaessa. Dialogien tarkoituksena oli yhtenäistää työtapoja ja ajattelumalleja. Kriteeristön haluttiin palvelevan juuri osasto 2:n tarpeita, jonka vuoksi kriteeristöä tehtäessä käytettiin apuna osastolla hoidettavia potilasesimerkkejä. Hoitoisuusluokituskriteeristöä koekäytettiin osastolla 9 päivää. Näin saatiin hoitohenkilökunnan näkökulma sen toimivuudesta käytännön hoitotyössä. Kumpikin tiimi (A, B) valitsi jokaiselle päivälle 2 hoitajaa, jotka rinnakkaisluokittelivat 5 samaa potilasta toistensa luokittelua tietämättä. Hoitajan ammattinimike ei vaikuttanut luokittelun tekoon. Potilaat oli valittu järjestyksessä osaston vuodepaikkojen mukaan, jolloin saatiin luokittelun kohteeksi mahdollisimman erilaisia potilaita joka päivälle. Potilaat säilyivät anonyymeina koekäytön ajan. Rinnakkaisluokittelun koekäytön tuloksiin oltiin tyytyväisiä. Hoitajat olivat sisäistäneet kriteeristön ohjeistuksen etupäässä hyvin. Hoitotyön eri osa-alueita (6) pidettiin pääsääntöisesti selkeinä ja hajonta hoitoisuuden osa-alueiden (A-D) kesken oli vähäistä. Osa luokittelijapareista kävi yhdessä läpi luokittelun jälkeen luokittelujensa tulokset.The subject for the thesis was selected considering working life and was executed in collaboration with Palokka Health Care Center ward 2. The aim of the thesis was to create an individual OPC-pattern patient classification criteria for ward 2, which was to serve the ward staff in their daily nursing. The criteria included 6 nursing divisions which each had 4 (A, B, C and D) patient classification sub-divisions assorted by the rate of requirement . A was the least and D the most demanding division. The purpose of the thesis is to make invisible nursing work visible in Palokka Health Care Center ward 2 and to make the concept of patient classification known among the nursing staff. Once created patient classification criteria was meant to inspire dialog between nursing staff while evaluating nursing demands. The meaning of the dialog was to standardize procedures and paradigms. The criteria was meant to serve precisely the needs of ward 2, therefore patient examples from ward 2 was used to create the criteria. Patient classification criteria was tested in ward 2 for 9 days. This way it was possible to receive the nursing staff point of view of criteria´s functionality in daily nursing. Each team (A, B) picked 2 nurses for each day who collaterally classified 5 same patients without knowing each other´s classifications. Staff member´s job description did not affect the classification. Patients were picked in order of beds in the ward, thus a wide variety of different kind of patients were obtained for classification each day. Patients remained anonymous during testing. The test results of collateral classification were satisfying. Nurses assimilated the criteria directions mostly well. Different divisions of nursing (6) were mainly considered clear and dispersion between sub-divisions (A – D) was minor. Some of the classification pairs discussed the results after classification

Migrating from a Centralized Data Warehouse to a Decentralized Data Platform Architecture

To an increasing degree, data is a driving force for digitization, and hence also a key asset for numerous companies. In many businesses, various sources of data exist, which are isolated from one another in different domains, across a heterogeneous application landscape. Well-known centralized solution technologies, such as data warehouses and data lakes, exist to integrate data into one system, but they do not always scale well. Therefore, robust and decentralized ways to manage data can provide the companies with better value give companies a competitive edge over a single central repository. In this paper, we address why and when a monolithic data storage should be decentralized for improved scalability, and how to perform the decentralization. The paper is based on industrial experiences and the findings show empirically the potential of a distributed system as well as pinpoint the core pieces that are needed for its central management.Peer reviewe

Gap-filling methods for 3D PlanTIS data

The range of positron emitters and their labeled compounds have led to high-resolution PET scanners becoming widely used, not only in clinical and pre-clinical studies but also in plant studies. A high-resolution PET scanner, plant tomographic imaging system (PlanTIS), was designed to study metabolic and physiological functions of plants noninvasively. The gantry of the PlanTIS scanner has detector-free regions. Even when the gantry of the PlanTIS is rotated during the scan, these regions result in missing sinogram bins in the acquired data. Missing data need to be estimated prior to the analytical image reconstructions in order to avoid artifacts in the final reconstructed images. In this study, we propose three gap-filling methods for estimation of the unique gaps existing in the 3D PlanTIS sinogram data. The 3D sinogram data were gap-filled either by linear interpolation in the transaxial planes or by the bicubic interpolation method (proposed for the ECAT high-resolution research tomograph) in the transradial planes or by the inpainting method in the transangular planes. Each gap-filling method independently compensates for slices in one of three orthogonal sinogram planes (transaxial, transradial and transangular planes). A 3D numerical Shepp-Logan phantom and the NEMA image quality phantom were used to evaluate the methods. The gap-filled sinograms were reconstructed using the analytical 3D reprojection (3DRP) method. The NEMA phantom sinograms were also reconstructed by the iterative reconstruction method, ordered subsets maximum a posteriori one step late (OSMAPOSL), to compare the results of gap filling followed by 3DRP with the results of OSMAPOSL reconstruction without gap filling. The three methods were evaluated quantitatively (by mean square error and coefficients of variation) over the selected regions of the 3D numerical Shepp-Logan phantom at eight different Poisson noise levels. Moreover, the NEMA phantom scan data were used in visual assessments of the methods. We observed that all methods improved the reconstructed images both quantitatively and visually. Therefore, the proposed gap-filling methods followed by the analytical 3DRP are alternative for the reconstructions of not only the 3D PlanTIS data, but also other PET scanner data of the ClearPET family