Multiple Response Sets for Visualizing Clinical Data Trends (811 Patient Visits to General Practitioners)

Author(s):  
Ton J. Cleophas ◽  
Aeilko H. Zwinderman
2002 ◽  
pp. 217-238
Author(s):  
Robert L. Miller ◽  
Ciaran Acton ◽  
Deirdre A. Fullerton ◽  
John Maltby ◽  
Jo Campling

2013 ◽  
pp. 105-113
Author(s):  
Ton J. Cleophas ◽  
Aeilko H. Zwinderman

2009 ◽  
pp. 161-181
Author(s):  
Ciaran Acton ◽  
Robert Miller ◽  
John Maltby ◽  
Deirdre Fullerton

2021 ◽  
Author(s):  
Richard J Varhol ◽  
Sean Randall ◽  
James H Boyd ◽  
Suzanne Robinson

Abstract ObjectiveThe potential for data collected in general practice to be linked and used to address health system challenges of maintaining quality care, accessibility and safety, including pandemic support, has led to an increased interest in public acceptability of data sharing, however practitioners have rarely been asked to share their opinions on the topic. This paper attempts to gain an understanding of general practitioners’ perceptions on routinely sharing practice data for both population health planning and healthcare research both from an Australian and international perspective.Materials and MethodsA mixed methods approach combining an initial online survey followed by face-to-face interviews (before and during COVID-19), designed to identify the barriers and facilitators to sharing data, were conducted on a representative sample of general practitioners across Western Australia (WA).ResultsEighty online surveys and ten face-to-face interviews with general practitioners were conducted from Nov 2020 – May 2021. Although respondents overwhelmingly identified the importance of population health research, their willingness to participate in data sharing programs was determined by a perception of trust associated with the organisation collecting and analysing shared data; a clearly defined purpose and process of collected data; including a governance structure providing confidence in the data sharing initiative simultaneously enabling a process of data sovereignty and autonomy.DiscussionResults indicate strong agreement around the importance of sharing patient’s medical data for population and health research and planning. Concerns pertaining to lack of trust, governance and secondary use of data continue to be a setback to data sharing with implications for primary care business models being raised.ConclusionTo further increase general practitioner’s confidence in sharing their clinical data, efforts should be directed towards implementing a robust data governance structure with an emphasis on transparency and representative stakeholder inclusion as well as identifying the role of government and government funded organisations, as well as building trust with the entities collecting and analysing the data.


2020 ◽  
Author(s):  
Maximiliano Lucius ◽  
Jorge De All ◽  
José Antonio De All ◽  
Martín Belvisi ◽  
Luciana Radizza ◽  
...  

AbstractArtificial intelligence can be a key tool in the context of assisting in the diagnosis of dermatological conditions, particularly when performed by general practitioners with limited or no access to high resolution optical equipment. This study evaluates the performance of deep convolutional neural networks (DNNs) in the classification of seven pigmented skin lesions. Additionally, it assesses the improvement ratio in the classification performance when utilized by general practitioners. Open-source skin images were downloaded from the ISIC archive. Different DNNs (n=8) were trained based on a random dataset constituted by 8,015 images. A test set of 2,003 images has been used to assess the classifiers performance at low (300 × 224 RGB) and high (600 × 450 RGB) image resolution and aggregated clinical data (age, sex and lesion localization). We have also organized two different contests to compare the DNNs performance to that of general practitioners by means of unassisted image observation. Both at low and high image resolution, the DNNs framework being trained differentiated dermatological images with appreciable performance. In all cases, accuracy has been improved when adding clinical data to the framework. Finally, the lowest accurate DNN outperformed general practitioners. Physician’s accuracy was statistically improved when allowed to use the output of this algorithmic framework as guidance. DNNS are proven to be high performers as skin lesion classifiers. The aim is to include these AI tools in the context of general practitioners whilst improving their diagnosis accuracy in a routine clinical scenario when or where the use of high-resolution equipment is not accessible.


2005 ◽  
Vol 173 (4S) ◽  
pp. 10-11
Author(s):  
Markus Fatzer ◽  
Michael Muentener ◽  
Raeto T. Strebel ◽  
Dieter Hauri ◽  
Hubert A. John

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