| 1. |
- Boman, Magnus, et al.
(författare)
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Syndromic Surveillance in the United Arab Emirates
- 2012
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Ingår i: International Conference on Innovations in Information Technology (IIT), 2012. - : IEEE conference proceedings. - 9781467311014 ; , s. 31-35
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Konferensbidrag (refereegranskat)abstract
- Opportunities for innovation in view of three complex problems faced by the UAE health care providers are described. The information dissemination problem faced could be approached by creating new channels for providing the population with public health information. These channels are precisely the ones typically used in so-called syndromic surveillance, including care-related data from communicable disease spread indicators, but also tweets and blog posts, for example. Syndromic surveillance could likewise assist the health authorities in addressing the knowledge elicitation problem: how to get more information on the life style, self care, and prevention among individual citizens. To some extent the prediction problem—how to predict the spread of infectious disease in the future and how to mathematically model social behaviour in the case of various health-threatening scenarios—would also be addressed by syndromic surveillance. Fully employed, the solutions proposed would provide new ICT services enabling preparedness for many forms of communicable disease outbreaks, as well as for natural disasters.
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| 2. |
- Commowick, Olivier, et al.
(författare)
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Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
- 2018
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning,.), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
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| 3. |
- Guttmann, Christian, et al.
(författare)
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Intelligent Adherence Support to Manage Contractual Relationships
- 2010
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Ingår i: Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010. - 9780769541914 ; , s. 342-345
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Konferensbidrag (refereegranskat)abstract
- Customer Life Cycle Management (CLCM) is concerned with the advancement of contractual relationships between customers and service providers through different stages. The aim is to eventually reach a stage where a customer is loyal to a product or an institution. To this end, CLCM involves the monitoring of contractual relationships, including the monitoring of activities that are agreed contractually. Changing objectives and uncertain environmental conditions are disruptions that can make the management of a customer life cycle difficult. Our approach aims to prevent behaviour that can jeopardise a contractual relationship. The Belief-Desire-Intention paradigm is used to describe "deficits" in a mental state model of customer and provider agents. We offer a conceptual approach that supports agents with mental deficits to adhere to agreed activities. The approach detects off-track behaviour of agents, identifies the deficit that has caused this behaviour, and applies tailored interventions to move execution back on-track. This paper shows examples of three mental state deficits of agents, and how our approach detects, monitors, and intervenes off-track behaviour.
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| 4. |
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| 5. |
- Ingram Bogusz, Claire, et al.
(författare)
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Don't panic: A human- and problem-centric approach to AI-led innovation.
- 2020
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Ingår i: Sweden through the crisis. - Stockholm : Stockholm School of Economics Institute for Research. - 9789186797386 ; , s. 253-264
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this article, Claire Ingram Bogusz, Sebastian Krakowski and Christian Guttmann have a strong message – Don’t Panic! Highlighting the difference between AI for automation and AI for augmentation, they argue that organizations should focus on AI for augmentation. Seeing COVID-19 as a catalyst for change, the authors provide a concrete method, the problem-centric approach, for how organizations can implement AI and improve their organization’s performance.
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| 6. |
- Keel, George, et al.
(författare)
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Time-driven activity-based costing for patients with multiple chronic conditions : a mixed-method study to cost care in a multidisciplinary and integrated care delivery centre at a university-affiliated tertiary teaching hospital in Stockholm, Sweden
- 2020
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Ingår i: BMJ Open. - London : BMJ Publishing Group Ltd. - 2044-6055. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: This study can be applied to cost the complex non-standardised processes used to treat patients with multiple chronic conditions. DESIGN: A mixed-method approach to cost analysis, following a modified healthcare-specific version of the seven-step Time-Driven Activity-Based Costing (TDABC) approach. SETTING: A multidisciplinary integrated and person-centred care delivery centre at a university-affiliated tertiary teaching hospital in Stockholm, Sweden, designed to improve care coordination for patients with multiple chronic conditions, specifically diabetes, cardiovascular disease and kidney disease. PARTICIPANTS: 314 patients (248 men and 66 women) fit inclusion criteria. Average age was 80 years. RESULTS: This modified TDABC analysis costed outpatient care for patients with multiple chronic conditions. The approach accounted for the difficulty of conceptualising care cycles. The estimated total cost, stratified by resources, can be reviewed together with existing managerial accounting statements to inform management decisions regarding the multidisciplinary centre. CONCLUSIONS: This article demonstrates that the healthcare-specific seven-step approach to TDABC can be applied to cost care for patients with multiple chronic conditions, where pathways are not yet discernable. It became clear that there was a need for slight methodological adaptations for this particular patient group to make it possible to cost these pathways, stratified by activity and resource. The value of this approach can be discerned from the way management incorporated the results of this analysis into the development of their hospital strategy. In the absence of integrated data infrastructures that can link patients and resources across financial, clinical and process data sets, the scalability of this method will be difficult. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
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| 7. |
- Rafiq, Muhammad, et al.
(författare)
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Extreme Consumers of Health Care: Patterns of Care Utilization in Patients with Multiple Chronic Conditions Admitted to a Novel Integrated Clinic
- 2019
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Ingår i: Journal of Multidisciplinary Healthcare. - Macclesfield : Dove Medical Press Ltd.. - 1178-2390 .- 1178-2390. ; 12, s. 1075-1083
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Patients with multiple chronic conditions (MCC) of diabetes, cardiovascular and kidney diseases; hereafter referred to as HND (heart/cardiac-, nephrology-, diabetes mellitus-) patients, are high utilizers of health care. However, the care received is often insufficiently coordinated between different specialties and health-care providers. This study aims to describe the characteristics of HND patients and to explore the initial effects of a multidisciplinary and person-centered care on total care utilization.Patients and Methods: We conducted a sub-study of HND patients recruited in an ongoing randomized trial CareHND (NCT03362983). Descriptive statistics of patient characteristics, including diagnostic data and Charlson Comorbidity Index scores, informed a comparison of care utilization patterns between HND patient care and traditional care. Diagnostic and care utilization data were collected from a regional database. Wilcoxon signed ranked sum tests were performed to compare care utilization frequencies between the two groups.Results: Patients included in the study were care-intensive with several diagnoses and experienced a high level of variation in care utilization and diagnoses profiles. HND patients were sicker than their counterparts in the control group. Utilization indicators were similar between the two arms. There was some indication that the HND center is beginning to perform as expected, but no results were statistically significant.Conclusion: This study sits among many studies reporting difficulties obtaining statistically significant findings for MCC patients. However, previous research has shown that the key components of this intervention, such as integrated, multidisciplinary, inter-professional collaboration within patient-centered care have had a positive effect on health-care outcomes. More innovative methods beyond the RCT, such as machine learning should be explored to evaluate the impact of integrated care interventions on care utilization.
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| 8. |
- Rafiq, Muhammad, et al.
(författare)
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Predictive analytics support for complex chronic medical conditions : An experience-based co-design study of physician managers’ needs and preferences
- 2024
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Ingår i: International Journal of Medical Informatics. - Shannon : Elsevier. - 1386-5056 .- 1872-8243. ; 187, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The literature suggests predictive technology applications in health care would benefit from physician and manager input during design and development. The aim was to explore the needs and preferences of physician managers regarding the role of predictive analytics in decision support for patients with the highly complex yet common combination of multiple chronic conditions of cardiovascular (Heart) and kidney (Nephrology) diseases and diabetes (HND). Methods: This qualitative study employed an experience-based co-design model comprised of three data gathering phases: 1. Patient mapping through non-participant observations informed by process mining of electronic health records data, 2. Semi-structured experience-based interviews, and 3. A co-design workshop. Data collection was conducted with physician managers working at or collaborating with the HND center, Danderyd University Hospital (DSAB), in Stockholm, Sweden. HND center is an integrated practice unit offering comprehensive person-centered multidisciplinary care to stabilize disease progression, reduce visits, and develop treatment strategies that enables a transition to primary care. Results: Interview and workshop data described a complex challenge due to the interaction of underlying pathophysiologies and the subsequent need for multiple care givers that hindered care continuity. The HND center partly met this challenge by coordinating care through multiple interprofessional and interdisciplinary shared decision-making interfaces. The large patient datasets were difficult to operationalize in daily practice due to data entry and retrieval issues. Predictive analytics was seen as a potentially effective approach to support decision-making, calculate risks, and improve resource utilization, especially in the context of complex chronic care, and the HND center a good place for pilot testing and development. Simplicity of visual interfaces, a better understanding of the algorithms by the health care professionals, and the need to address professional concerns, were identified as key factors to increase adoption and facilitate implementation. Conclusions: The HND center serves as a comprehensive integrated practice unit that integrates different medical disciplinary perspectives in a person-centered care process to address the needs of patients with multiple complex comorbidities. Therefore, piloting predictive technologies at the same time with a high potential for improving care represents an extreme, demanding, and complex case. The study findings show that health care professionals’ involvement in the design of predictive technologies right from the outset can facilitate the implementation and adoption of such technologies, as well as enhance their predictive effectiveness and performance. Simplicity in the design of predictive technologies and better understanding of the concept and interpretation of the algorithms may result in implementation of predictive technologies in health care. Institutional efforts are needed to enhance collaboration among the health care professionals and IT professionals for effective development, implementation, and adoption of predictive analytics in health care. © 2024 The Author(s)
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