Data decomposition and dual clustering for clinical care management

2015 ◽  
Author(s):  
Shusaku Tsumoto ◽  
Shoji Hirano ◽  
Haruko Iwata
Keyword(s):  
Care Management ◽  
Clinical Care ◽  
Data Decomposition ◽  
Dual Clustering

scholarly journals Datasets collected in general practice: an international comparison using the example of obesity

2018 ◽  
Vol 42 (5) ◽  
pp. 563 ◽  
Author(s):  
Elizabeth Sturgiss ◽  
Kees van Boven
Keyword(s):  
Primary Care ◽  
General Practice ◽  
The Netherlands ◽  
Care Management ◽  
Clinical Care ◽  
International Comparisons ◽  
Health Condition ◽  
Practice Management ◽  
Number Of Patients ◽  
Primary Care Management

International datasets from general practice enable the comparison of how conditions are managed within consultations in different primary healthcare settings. The Australian Bettering the Evaluation and Care of Health (BEACH) and TransHIS from the Netherlands collect in-consultation general practice data that have been used extensively to inform local policy and practice. Obesity is a global health issue with different countries applying varying approaches to management. The objective of the present paper is to compare the primary care management of obesity in Australia and the Netherlands using data collected from consultations. Despite the different prevalence in obesity in the two countries, the number of patients per 1000 patient-years seen with obesity is similar. Patients in Australia with obesity are referred to allied health practitioners more often than Dutch patients. Without quality general practice data, primary care researchers will not have data about the management of conditions within consultations. We use obesity to highlight the strengths of these general practice data sources and to compare their differences. What is known about the topic? Australia had one of the longest-running consecutive datasets about general practice activity in the world, but it has recently lost government funding. The Netherlands has a longitudinal general practice dataset of information collected within consultations since 1985. What does this paper add? We discuss the benefits of general practice-collected data in two countries. Using obesity as a case example, we compare management in general practice between Australia and the Netherlands. This type of analysis should start all international collaborations of primary care management of any health condition. Having a national general practice dataset allows international comparisons of the management of conditions with primary care. Without a current, quality general practice dataset, primary care researchers will not be able to partake in these kinds of comparison studies. What are the implications for practitioners? Australian primary care researchers and clinicians will be at a disadvantage in any international collaboration if they are unable to accurately describe current general practice management. The Netherlands has developed an impressive dataset that requires within-consultation data collection. These datasets allow for person-centred, symptom-specific, longitudinal understanding of general practice management. The possibilities for the quasi-experimental questions that can be answered with such a dataset are limitless. It is only with the ability to answer clinically driven questions that are relevant to primary care that the clinical care of patients can be measured, developed and improved.

48/6 Model of Care for Senior Clinical Care Management

2016 ◽  
Vol 17 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Yoon-Sook Kim ◽  
Seol-Heui Han ◽  
Jong-Min Lee ◽  
Jae-Kyung Choi ◽  
Jae-Min Park ◽  
...  
Keyword(s):  
Care Management ◽  
Clinical Care ◽  
Model Of Care

scholarly journals Pharmacogenomics and Pharmacogenetics in Osteosarcoma: Translational Studies and Clinical Impact

2020 ◽  
Vol 21 (13) ◽  
pp. 4659 ◽  
Author(s):  
Claudia Maria Hattinger ◽  
Maria Pia Patrizio ◽  
Silvia Luppi ◽  
Massimo Serra
Keyword(s):  
Care Management ◽  
Scientific Evidence ◽  
Clinical Care ◽  
Treatment Strategies ◽  
Clinical Settings ◽  
Genetic Biomarkers ◽  
Recent Developments ◽  
High Grade Osteosarcoma ◽  
Genetic Events ◽  
Pharmacogenomic Studies

High-grade osteosarcoma (HGOS) is a very aggressive bone tumor which primarily affects adolescents and young adults. Although not advanced as is the case for other cancers, pharmacogenetic and pharmacogenomic studies applied to HGOS have been providing hope for an improved understanding of the biology and the identification of genetic biomarkers, which may impact on clinical care management. Recent developments of pharmacogenetics and pharmacogenomics in HGOS are expected to: i) highlight genetic events that trigger oncogenesis or which may act as drivers of disease; ii) validate research models that best predict clinical behavior; and iii) indicate genetic biomarkers associated with clinical outcome (in terms of treatment response, survival probability and susceptibility to chemotherapy-related toxicities). The generated body of information may be translated to clinical settings, in order to improve both effectiveness and safety of conventional chemotherapy trials as well as to indicate new tailored treatment strategies. Here, we review and summarize the current scientific evidence for each of the aforementioned issues in view of possible clinical applications.

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