Intention estimation based on biosignal and operating force for cooperative operation by ultrasound diagnosis and treatment support robot

2018 ◽  
Vol 2018 (0) ◽  
pp. 2A1-C18
Author(s):  
Genta TAKAHASHI ◽  
Daichi YOSHIMURA ◽  
Yusuke AOKI
Keyword(s):  
Diagnosis And Treatment ◽  
Ultrasound Diagnosis ◽  
Treatment Support ◽  
Intention Estimation

scholarly journals Availability and Use of Mobile Health Technology for Disease Diagnosis and Treatment Support by Health Workers in the Ashanti Region of Ghana: A Cross-Sectional Survey

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1233
Author(s):  
Ernest Osei ◽  
Kwasi Agyei ◽  
Boikhutso Tlou ◽  
Tivani P. Mashamba-Thompson
Keyword(s):  
Healthcare Professionals ◽  
Healthcare Delivery ◽  
Disease Diagnosis ◽  
Text Messages ◽  
Diagnosis And Treatment ◽  
Cross Sectional Survey ◽  
Mobile Wireless ◽  
Ashanti Region ◽  
Phone Calls ◽  
Treatment Support

Mobile health (mHealth) technologies have been identified as promising strategies for improving access to healthcare delivery and patient outcomes. However, the extent of availability and use of mHealth among healthcare professionals in Ghana is not known. The study’s main objective was to examine the availability and use of mHealth for disease diagnosis and treatment support by healthcare professionals in the Ashanti Region of Ghana. A cross-sectional survey was carried out among 285 healthcare professionals across 100 primary healthcare clinics in the Ashanti Region with an adopted survey tool. We obtained data on the participants’ background, available health infrastructure, healthcare workforce competency, ownership of a mobile wireless device, usefulness of mHealth, ease of use of mHealth, user satisfaction, and behavioural intention to use mHealth. Descriptive statistics were conducted to characterise healthcare professionals’ demographics and clinical features. Multivariate logistic regression analysis was performed to explore the influence of the demographic factors on the availability and use of mHealth for disease diagnosis and treatment support. STATA version 15 was used to complete all the statistical analyses. Out of the 285 healthcare professionals, 64.91% indicated that mHealth is available to them, while 35.08% have no access to mHealth. Of the 185 healthcare professionals who have access to mHealth, 98.4% are currently using mHealth to support healthcare delivery. Logistic regression model analysis significantly (p < 0.05) identified that factors such as the availability of mobile wireless devices, phone calls, text messages, and mobile apps are associated with HIV, TB, medication adherence, clinic appointments, and others. There is a significant association between the availability of mobile wireless devices, text messages, phone calls, mobile apps, and their use for disease diagnosis and treatment compliance from the chi-square test analysis. The findings demonstrate a low level of mHealth use for disease diagnosis and treatment support by healthcare professionals at rural clinics. We encourage policymakers to promote the implementation of mHealth in rural clinics.

Ultrasound diagnosis and treatment outcome of cystic ovaries in cattle

2001 ◽  
Vol 148 (13) ◽  
pp. 411-413 ◽  
Author(s):  
J. E. Tebble ◽  
M. J. O'Donnell ◽  
H. Dobson
Keyword(s):  
Treatment Outcome ◽  
Diagnosis And Treatment ◽  
Ultrasound Diagnosis

scholarly journals Mapping Evidence of Mobile Health Technologies for Disease Diagnosis and Treatment Support by Health Workers in Sub-Saharan Africa: A Scoping Review

2019 ◽  
Author(s):  
Ernest Osei ◽  
Desmond Kuupiel ◽  
Portia Nelisiwe Vezi ◽  
Tivani P. Mashamba-Thompson
Keyword(s):  
Mobile Health ◽  
Scoping Review ◽  
Full Text ◽  
Methodological Quality ◽  
Health Workers ◽  
Disease Diagnosis ◽  
Sub Saharan Africa ◽  
Diagnosis And Treatment ◽  
Treatment Support ◽  
Sub Saharan

Abstract Background: The rapid growth of mobile technology has given rise to the development of mobile health programmes aimed at treating and preventing a wide range of health conditions. However, evidence on the use of mHealth in high disease burdened settings such as SSA is not clear. We systematically mapped evidence on mHealth for disease diagnosis and treatment support by health workers in SSA. Methods: We conducted a scoping review study guided by the Arksey and O’Malley’s framework, Levac et al recommendations, and Joanna Briggs Institute guidelines. We thoroughly searched the following databases: MEDLINE and CINAHL with full-text via EBSCOhost; PubMed; Science Direct and Google Scholar for relevant articles from inception to July 2019. Two independent reviewers screened abstracts and full-text articles using the eligibility criteria as reference. This study employed the mixed methods appraisal tool version 2018 to assess the methodological quality of the included studies. Results: Out of the 572 articles identified , only 10 published articles presented evidence on mHealth for treatment support by health workers in SSA since 2010. No studies reported evidence on mHealth for disease diagnosis by health workers in SSA. Of the 10 studies, four studies were conducted in Kenya; one in South Africa; one in Malawi; one in Zimbabwe; one in Mozambique; one in Nigeria and one in Lesotho. Of the 10 studies: three reported the use of mHealth to manage HIV; two on the management of HIV/TB; two on treatment of malaria; one each on the management of hypertension; cervical cancer; and one was not specific on any disease condition. All the 10 included studies underwent methodological quality appraisal with a scored between 70 and 100%. Conclusions: The study shows that there is limited research on the availability and use of mHealth by health workers for treatment support in SSA. This study also shows there is no literature on the availability and use of mHealth by health workers for disease diagnosis in SSA. We, therefore, recommend primary studies focusing on the use of mHealth by health workers for disease diagnosis in SSA. Keywords: Mobile Health; Disease diagnosis; Treatment support; sub-Saharan Africa

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