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36 results on '"KONTIO, ELINA"'

1. Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation

Author

Khan, Muhammad Irfan, Kontio, Elina, Khan, Suleiman A., and Jafaritadi, Mojtaba

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators in dynamic FL environments remains challenging. We present RL-HSimAgg, a novel reinforcement learning (RL) and similarity-weighted aggregation (simAgg) algorithm using harmonic mean to manage outlier data points. This paper proposes applying multi-armed bandit algorithms to improve collaborator selection and model generalization. By balancing exploration-exploitation trade-offs, these RL methods can promote resource-efficient training with diverse datasets. We demonstrate the effectiveness of Epsilon-greedy (EG) and upper confidence bound (UCB) algorithms for federated brain lesion segmentation. In simulation experiments on internal and external validation sets, RL-HSimAgg with UCB collaborator outperformed the EG method across all metrics, achieving higher Dice scores for Enhancing Tumor (0.7334 vs 0.6797), Tumor Core (0.7432 vs 0.6821), and Whole Tumor (0.8252 vs 0.7931) segmentation. Therefore, for the Federated Tumor Segmentation Challenge (FeTS 2024), we consider UCB as our primary client selection approach in federated Glioblastoma lesion segmentation of multi-modal MRIs. In conclusion, our research demonstrates that RL-based collaborator management, e.g. using UCB, can potentially improve model robustness and flexibility in distributed learning environments, particularly in domains like brain tumor segmentation.

Published
2024

2. Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation

Author

Khan, Muhammad Irfan, Kontio, Elina, Khan, Suleiman A., and Jafaritadi, Mojtaba

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

This study presents a robust and efficient client selection protocol designed to optimize the Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2024). In the evolving landscape of FL, the judicious selection of collaborators emerges as a critical determinant for the success and efficiency of collective learning endeavors, particularly in domains requiring high precision. This work introduces a recommender engine framework based on non-negative matrix factorization (NNMF) and a hybrid aggregation approach that blends content-based and collaborative filtering. This method intelligently analyzes historical performance, expertise, and other relevant metrics to identify the most suitable collaborators. This approach not only addresses the cold start problem where new or inactive collaborators pose selection challenges due to limited data but also significantly improves the precision and efficiency of the FL process. Additionally, we propose harmonic similarity weight aggregation (HSimAgg) for adaptive aggregation of model parameters. We utilized a dataset comprising 1,251 multi-parametric magnetic resonance imaging (mpMRI) scans from individuals diagnosed with glioblastoma (GBM) for training purposes and an additional 219 mpMRI scans for external evaluations. Our federated tumor segmentation approach achieved dice scores of 0.7298, 0.7424, and 0.8218 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT) segmentation tasks respectively on the external validation set. In conclusion, this research demonstrates that selecting collaborators with expertise aligned to specific tasks, like brain tumor segmentation, improves the effectiveness of FL networks.

Published
2024

3. Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation

Author

Khan, Muhammad Irfan, Alhoniemi, Esa, Kontio, Elina, Khan, Suleiman A., and Jafaritadi, Mojtaba

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Federated Learning (FL) is a distributed machine learning approach that safeguards privacy by creating an impartial global model while respecting the privacy of individual client data. However, the conventional FL method can introduce security risks when dealing with diverse client data, potentially compromising privacy and data integrity. To address these challenges, we present a differential privacy (DP) federated deep learning framework in medical image segmentation. In this paper, we extend our similarity weight aggregation (SimAgg) method to DP-SimAgg algorithm, a differentially private similarity-weighted aggregation algorithm for brain tumor segmentation in multi-modal magnetic resonance imaging (MRI). Our DP-SimAgg method not only enhances model segmentation capabilities but also provides an additional layer of privacy preservation. Extensive benchmarking and evaluation of our framework, with computational performance as a key consideration, demonstrate that DP-SimAgg enables accurate and robust brain tumor segmentation while minimizing communication costs during model training. This advancement is crucial for preserving the privacy of medical image data and safeguarding sensitive information. In conclusion, adding a differential privacy layer in the global weight aggregation phase of the federated brain tumor segmentation provides a promising solution to privacy concerns without compromising segmentation model efficacy. By leveraging DP, we ensure the protection of client data against adversarial attacks and malicious participants.

Published
2023

4. Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation

Author

Khan, Muhammad Irfan, Azeem, Mohammad Ayyaz, Alhoniemi, Esa, Kontio, Elina, Khan, Suleiman A., and Jafaritadi, Mojtaba

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture
Abstract

In federated learning (FL), the global model at the server requires an efficient mechanism for weight aggregation and a systematic strategy for collaboration selection to manage and optimize communication payload. We introduce a practical and cost-efficient method for regularized weight aggregation and propose a laborsaving technique to select collaborators per round. We illustrate the performance of our method, regularized similarity weight aggregation (RegSimAgg), on the Federated Tumor Segmentation (FeTS) 2022 challenge's federated training (weight aggregation) problem. Our scalable approach is principled, frugal, and suitable for heterogeneous non-IID collaborators. Using FeTS2021 evaluation criterion, our proposed algorithm RegSimAgg stands at 3rd position in the final rankings of FeTS2022 challenge in the weight aggregation task. Our solution is open sourced at: \url{https://github.com/dskhanirfan/FeTS2022}

Published
2023

5. Biomedical image analysis competitions: The state of current participation practice

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Published
2022

6. Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation

Author

Khan, Muhammad Irfan, Jafaritadi, Mojtaba, Alhoniemi, Esa, Kontio, Elina, Khan, Suleiman A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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8. List of contributors

Author

AbdulRaheem, Muyideen, primary, Awotunde, Joseph Bamidele, additional, Barsocchi, Paolo, additional, Bhaskar, M.S., additional, Bhoi, Akash Kumar, additional, Canbaz, M. Abdullah, additional, Chowdhury, Shamit Roy, additional, González-Briones, Alfonso, additional, Herath, H.M.K.K.M.B., additional, Ijaz, Muhammed Fazal, additional, Jafaritadi, Mojtaba, additional, Jeyakumar, Vijay, additional, Jimoh, Rasheed Gbenga, additional, Karunasena, G.M.K.B., additional, Klén, Riku, additional, Kontio, Elina, additional, Krishnan, Palani Thanaraj, additional, Le, Anh-Tu, additional, Madhusanka, B.G.D.A., additional, Mishra, Ayaskanta, additional, Munochiveyi, Munyaradzi, additional, Oladipo, Idowu Dauda, additional, Panigrahi, Siba Smarak, additional, Patil, Bhalchandra Sunil, additional, Raj, Alex Noel Joseph, additional, Ray, Arun Kumar, additional, Shubair, Raed M., additional, Singh, Pushpa, additional, Sivaprakasam, Kushika, additional, Sriramalakshmi, P., additional, Subasi, Abdulhamit, additional, Sundaram, Prema, additional, Sur, Samarendra Nath, additional, Swain, Anita, additional, Thakkar, Hiren Kumar, additional, and Tomori, Adekola Rasheed, additional

Published
2022
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11. Concurrent Research and Decentralized Decision Making as an Accelerator from Idea to Business – Case Turku Finland

Author

Reunanen, Tero, Kontio, Elina, Lahtiranta, Janne, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Kantola, Jussi Ilari, editor, Nazir, Salman, editor, and Salminen, Vesa, editor

Published
2020
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12. A short simulated nature experience as an effective way to promote restoration from work‐related stress.

Author

Koivisto, Mika, Koskinen, Juho, Jokiaho, Samu, Vahanne, Tero, Pohjola, Mikko, and Kontio, Elina

Subjects
JOB stress prevention, AUDIOVISUAL materials, SELF-evaluation, NATURE, RESEARCH funding, AUTONOMIC nervous system, SKIN physiology, ELECTROENCEPHALOGRAPHY, WALKING, PSYCHOSOCIAL factors, VIDEO recording, COGNITION
Abstract

Spending time in nature, and even watching images or videos of nature, has positive effects on one's mental state. However, cognitively stressful work is often performed indoors, in offices that lack easy access to nature during breaks. In this study, we investigated whether watching a 5‐min audiovisual video that describes a first‐person perspective walk on a forest path could help to restore one's mental state after cognitive stress. Participants were asked to perform cognitive stressor tasks, after which they were shown either a nature walk video or a control video. Subjective restoration was measured using self‐reports before and after the videos, while electrodermal activity (EDA) and electroencephalography (EEG) were measured during the video‐watching session. The results showed that experiencing the nature walk video enhanced subjective restoration more than watching the control video. Arousal of the autonomic nervous system, measured using EDA, decreased more during the nature walk video than during the control video. Additionally, activity in the EEG's upper theta band (6–8 Hz) and lower alpha band (8–10 Hz) increased during the nature walk video, suggesting that it induced a relaxed state of mind. Interestingly, the participants' connection with nature moderated the effects of the nature video. The subjective and physiological measures both suggest that watching a short, simulated nature walk may be beneficial in relaxing the mind and restoring one's mental state after cognitive stress. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Finnish perspective on using synthetic health data to protect privacy: the PRIVASA project.

Author

Pitkämäki, Tinja, Pahikkala, Tapio, Perez, Ileana Montoya, Movahedi, Parisa, Nieminen, Valtteri, Southerington, Tom, Vaiste, Juho, Jafaritadi, Mojtaba, Khan, Muhammad Irfan, Kontio, Elina, Ranttila, Pertti, Pajula, Juha, Pölönen, Harri, Degerli, Aysen, Plomp, Johan, and Airola, Antti

Subjects
GENERATIVE artificial intelligence, MACHINE learning, DATA privacy, DATA protection, PERSONALLY identifiable information
Abstract

The use of synthetic data could facilitate data-driven innovation across industries and applications. Synthetic data can be generated using a range of methods, from statistical modeling to machine learning and generative AI, resulting in datasets of different formats and utility. In the health sector, the use of synthetic data is often motivated by privacy concerns. As generative AI is becoming an everyday tool, there is a need for practice-oriented insights into the prospects and limitations of synthetic data, especially in the privacy sensitive domains. We present an interdisciplinary outlook on the topic, focusing on, but not limited to, the Finnish regulatory context. First, we emphasize the need for working definitions to avoid misplaced assumptions. Second, we consider use cases for synthetic data, viewing it as a helpful tool for experimentation, decision-making, and building data literacy. Yet the complementary uses of synthetic datasets should not diminish the continued efforts to collect and share high-quality real-world data. Third, we discuss how privacy-preserving synthetic datasets fall into the existing data protection frameworks. Neither the process of synthetic data generation nor synthetic datasets are automatically exempt from the regulatory obligations concerning personal data. Finally, we explore the future research directions for generating synthetic data and conclude by discussing potential future developments at the societal level. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Experiences on Telemedicine Solutions for Diabetes Care – Case eMedic Project

Author

Kontio, Elina, Hyrkkänen, Ursula, Saarenpää, Teppo, Junqueira Barbosa, Simone Diniz, Series editor, Chen, Phoebe, Series editor, Cuzzocrea, Alfredo, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Ślęzak, Dominik, Series editor, Washio, Takashi, Series editor, Yang, Xiaokang, Series editor, Saranto, Kaija, editor, Castrén, Maaret, editor, Kuusela, Tiina, editor, Hyrynsalmi, Sami, editor, and Ojala, Stina, editor

Published
2014
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25. National survey focusing on the crucial information needs of intensive care charge nurses and intensivists: same goal, different demands

Author

Lundgrén-Laine Heljä, Kontio Elina, Kauko Tommi, Korvenranta Heikki, Forsström Jari, and Salanterä Sanna

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Background Although information technology adequately supports clinical care in many intensive care units (ICUs), it provides much poorer support for the managerial information needed to coordinate multi-professional care. To gain a general view of the most crucial multi-professional information needs of ICU shift leaders a national survey was conducted, focusing on the information needs of charge nurses and intensivists. Methods Based on our previous observation study an online survey was developed, containing 122 information need statements related to the decision-making of ICU shift leaders. Information need statements were divided into six dimensions: patient admission, organisation and management of work, allocation of staff and material resources, special treatments, and patient discharge. This survey involved all ICU shift leaders (n = 738) who worked in any of the 17 highest level ICUs for adults in university hospitals in Finland during the autumn of 2009. Both charge nurses’ and intensivists’ crucial information needs for care coordination were evaluated. Results Two hundred and fifty-seven (50%) charge nurses and 96 (43%) intensivists responded to the survey. The consistency of the survey was found to be good (Cronbach’s α scores between .87–.97, with a total explanatory power of 64.53%). Altogether, 57 crucial information needs for care coordination were found; 22 of which were shared between shift leaders. The most crucial of these information needs were related to organisation and management, patient admission, and allocation of staff resources. The associations between working experience, or shift leader acting frequencies, and crucial information needs were not statistically significant. However, a statistically significant difference was found between the number of ICU beds and the ICU experience of charge nurses with information needs, under the dimension of organisation and management of work. The information needs of charge nurses and intensivists differed. Charge nurses’ information needs related to care coordination, were more varied, and concerned issues at a unit level, whereas intensivists focused on direct patient care. Conclusions The reliability and validity of our survey was found to be good. Our study findings show that care coordination at an ICU is a collaborative process among ICU shift leaders with multiprofessional information needs related to organisation and management, patient admission, and allocation of staff resources. Study findings can be used to identify the most crucial information needs of ICU shift leaders when new information technology is developed to support managerial decision-making during care coordination.

Published
2013
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27. Potilaspalautetta QR‐koodilla ‐ Käyttöönottopilotti Varsinais‐Suomen sairaanhoitopiirissä

Author

Lundgrén‐Laine, Heljä, Eloranta, Sini, Danielsson-Ojala, Riitta, and Kontio, Elina

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, potilaspalaute, kehittäminen, Public aspects of medicine, RA1-1270, Tieteelliset artikkelit / Scientific papers, QR-koodi, käyttöönotto
Abstract

Potilaspalautteet ovat yksi tärkeä menetelmä terveydenhuollon asiakaslähtöisen toimintakulttuurin luomisessa ja hoidon laadun jatkuvassa kehittämisessä. Vahva perusta potilaspalautteen merkitykselle tulee muun muassa terveydenhuoltolaista ja sosiaali- ja terveydenhuollon kansallisesta kehittämisohjelmasta. Potilaspalautetta kerätään terveydenhuollossa yleisesti manuaalisesti erilaisilla kyselyillä. Useimmat terveydenhuollon yksiköt keräävät palautetta myös sähköisesti internetsivujen kautta. Potilaille ja heidän läheisilleen palautteen antamisen tulisi olla mahdollisimman helppoa. Terveydenhuollon ammattilaisille puolestaan palautteen reaaliaikaisuus ja raportointijärjestelmän sujuva hyödyntäminen hoidon kehittämisessä ovat tärkeitä tekijöitä. Tämän artikkelin tarkoituksena on kuvata uuden potilaspalautemenetelmän, QR-koodin käyttöönottopilottia sekä verrata palautteenkeräysmenetelmää nykykäytäntöön Varsinais-Suomen sairaanhoitopiirissä (VSSHP). Käyttöönottopilotti tehtiin yhteistyössä VSSHP:n kehittämispalveluiden, sydäntoimialueen, sairaanhoidollisten palveluiden, lasten ja nuorten klinikan sekä TapIn Solutions Oy:n kanssa. Pilotti toteutettiin Turun yliopistollisen keskussairaalan neljässä toimipisteessä: kuvantamisessa, lasten päiväsairaalassa, sydänpoliklinikalla ja -vuodeosastolla. Käyttöönottopilotti kesti noin 1,5 kuukautta. Pilotti tukee erilaisten potilaspalautemenetelmien käytettävyyden ja hyödynnettävyyden arviointia tulevaisuudessa. Arvioimme tuloksissa uuteen palautemenetelmään liittyviä käyttäjäkokemuksia loppukäyttäjien, henkilökunnan ja lähiesimiesten näkökulmasta suhteessa nykykäytäntöön. Pilotoitu menetelmä näyttäisi lisäävän palautteiden määrää. Muita uuden menetelmän vahvuuksia ovat palautteiden reaaliaikaisuus, palauteväittämien sujuva muokattavuus ja niiden liittäminen osaksi kehittämistä ja laadun parantamista, manuaalisen työn väheneminen ja kustannustehokkuus sekä reaaliaikaiset valmiit raportit. Suurin este potilaspalautteen keräämiselle ja hyödyntämiselle näyttäisi liittyvän enemmän henkilökunnan asenteisiin kuin käytettyihin menetelmiin. Potilaat ja / tai heidän läheisensä näkevät palautteen tärkeänä osana toiminnan kehittämistä. Patient feedback is an important tool in creating customer-oriented culture of public health services and in constant development of the quality of care. The health care act and the national development program for social welfare and public health service form a strong foundation for the significance of patient feedback. Quite often patient feedback is manually collected with different questionnaires in the public health care. In addition, most public health service units collect patient feedback electronically through their web pages. Giving feedback should be as easy as possible for the patients and their next of kin. As for the professionals of the public health services, the most important factors are real time feedback and the fluent use of the feedback reports in the development of patient care. The purpose of this article is to describe the implementation of a new patient feedback tool, QR code, and to compare it to the present practice in the hospital district of South West Finland (VSSHP). This pilot study was made in cooperation with the hospital district’s Development Unit, Cardiological Clinic, Medical Services, Children and Adolescent Clinic, and with TapIn Solutions Inc. The pilot was carried out in four units of the University Central Hospital of Turku: in the imaging unit, in the children’s day hospital, in the heart outpatient department and in the cardiological ward. The pilot study lasted about one and a half months and will support future evaluation of the usability and usefulness of different methods on gathering patient feedback. We evaluate the user experience of the new feedback tool from the end users', staff's and managers’ point of view in relation to the present practice. The piloted tool seems to increase the amount of patient feedback. Other strengths of the new method are: real time feedback, fluent revise of statements in order to develop and improve the quality of care, decrease of manual work and cost efficiency, and real time reports. One of the biggest impediments for collecting and utilizing patient feedback seems to be more connected to the attitudes of the staff than to the methods used. The patients and/or their next of kin see the feedback as an important part of the process of developing the care.

Published
2015

32. Intensive Care Admission and Discharge – Critical Decision-Making Points.

Author

Saranto, Kaija, Brennan, Patricia Flatley, Park, Hyeoun-Ae, Tallberg, Marianne, Ensio, Anneli, Lundgrén-Laine, Heljä, Suominen, Hanna, Kontio, Elina, and Salanterä, Sanna

Abstract

Delivery of intensive care has many critical points impacting the outcomes of critically ill patients. Two important key events in intensive care are patients' admission and discharge procedures. The decision making of intensive care experts should be supported in these two points, in order to attain good quality and safe care. We hypothesize that in the future this decision-making process can be effectively supported with information technology. To reveal the complex decision-making, we studied the decision-making processes and information needs of intensive care charge nurses during patients' admission and discharge procedures. We identified several interconnected decision-making steps during these procedures. [ABSTRACT FROM AUTHOR]

Published
2009

33. Key Elements of Successful Care Process of Patients with Heart Symptoms in an Emergency Care – Could an ERP System Help?

Author

Saranto, Kaija, Brennan, Patricia Flatley, Park, Hyeoun-Ae, Tallberg, Marianne, Ensio, Anneli, Kontio, Elina, Korvenranta, Heikki, Lundgren-Laine, Heljä, and Salanterä, Sanna

Abstract

The aim of the study was to identify key elements of successful care process of patients with heart symptoms from the nursing management viewpoint in an emergency care. Through these descriptions, we aimed at identifying possibilities for using enterprise resource planning (ERP) systems to support decision making in emergency care. Hospitals are increasingly moving to process-based workings and at the same time new information system in healthcare are developed and therefore it is essential to understand the strengths and weaknesses of current processes better. A qualitative descriptive design using critical incident technique was employed. Critical Incidents were collected with an open-ended questionnaire. The sample (n=50), 13 head nurses and 37 registered nurses, was purposeful selected from three acute hospitals in southern Finland. The process of patients with heart symptoms in emergency care was described. We identified three competence categories where special focus should be placed to achieve successful process of patients with heart symptoms: process-oriented competencies, personal/management competencies and logistics oriented competencies. Improvement of decision making requires that the care processes are defined and modeled. The research showed that there are several happenings in emergency care where an ERP system could help and support decision making. These happenings can be categorized in two groups: 1) administrative related happenings and 2) patient processes related happenings. [ABSTRACT FROM AUTHOR]

Published
2009

34. Information utilization in tactical decision making of middle management health managers.

Author

Kontio E, Lundgren-Laine H, Kontio J, Korvenranta H, and Salanterä S

Subjects
Health Care Rationing, Heart Diseases therapy, Humans, Nursing Administration Research, Nursing Informatics, Qualitative Research, Decision Making, Hospital Administrators psychology, Information Systems statistics & numerical data, Nurse Administrators psychology
Abstract

Healthcare is an information-intensive field, as information is needed to make strategic, tactical, and operational decisions. The purpose of this study was to identify the tactical decisions that middle management healthcare managers make, the information that is available, and the necessary information that is missing using the cardiac care process as an example. Data were collected through focused interviews of nurses and physicians who work in middle management in a secondary healthcare field. The interviews were coded and analyzed using the thematic content analysis method. We identified two main categories of tactical decisions: those concerning the process of care and those concerning the resources for the care. We termed the categories "process decisions" and "resource decisions." The availability of information varied. Much of the necessary information was created and processed manually. Our results show that the collection, mining, and systematic use of information are difficult because of the existence of many types of information systems and their varying abilities to produce and report information. Finally, much of the important information is missing. In conclusion, the information management process in healthcare settings needs to be improved, and a new generation of information system is needed to support tactical decision making in middle management.

Published
2013
Full Text
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35. Key elements of successful care process of patients with heart symptoms in an emergency care - could an ERP system help?

Author

Kontio E, Korvenranta H, Lundgren-Laine H, and Salanterä S

Subjects
Cardiovascular Diseases physiopathology, Decision Making, Humans, Nursing Process organization & administration, Surveys and Questionnaires, Cardiovascular Diseases nursing, Emergency Medical Services standards, Nursing Process standards
Abstract

The aim of the study was to identify key elements of successful care process of patients with heart symptoms from the nursing management viewpoint in an emergency care. Through these descriptions, we aimed at identifying possibilities for using enterprise resource planning (ERP) systems to support decision making in emergency care. Hospitals are increasingly moving to process-based workings and at the same time new information system in healthcare are developed and therefore it is essential to understand the strengths and weaknesses of current processes better. A qualitative descriptive design using critical incident technique was employed. Critical Incidents were collected with an open-ended questionnaire. The sample (n=50), 13 head nurses and 37 registered nurses, was purposeful selected from three acute hospitals in southern Finland. The process of patients with heart symptoms in emergency care was described. We identified three competence categories where special focus should be placed to achieve successful process of patients with heart symptoms: process-oriented competencies, personal/management competencies and logistics oriented competencies. Improvement of decision making requires that the care processes are defined and modeled. The research showed that there are several happenings in emergency care where an ERP system could help and support decision making. These happenings can be categorized in two groups: 1) administrative related happenings and 2) patient processes related happenings.

Published
2009

36. Intensive care admission and discharge - critical decision-making points.

Author

Lundgrén-Laine H, Suominen H, Kontio E, and Salanterä S

Subjects
Finland, Humans, Nurse Administrators, Decision Making, Intensive Care Units, Patient Admission, Patient Transfer organization & administration
Abstract

Delivery of intensive care has many critical points impacting the outcomes of critically ill patients. Two important key events in intensive care are patients' admission and discharge procedures. The decision making of intensive care experts should be supported in these two points, in order to attain good quality and safe care. We hypothesize that in the future this decision-making process can be effectively supported with information technology. To reveal the complex decision-making, we studied the decision-making processes and information needs of intensive care charge nurses during patients' admission and discharge procedures. We identified several interconnected decision-making steps during these procedures.

Published
2009

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