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Mobile Health and Medical Care

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Keywords: Health and Medical Care; Cloud Computing; Artificial Intelligence; Health Community; Intelligent Medicine
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Volume 3 Issue 12 December 2019
Mobile Health and Medical Care
Yichi Gu*
Cultigene Medical Technology (Beijing) Ltd., P.R. China
*Corresponding Author: Yichi Gu, Cultigene Medical Technology (Beijing) Ltd., P.R. China.
Review Article
Received: November 18, 2019; Published: November 21, 2019
Keywords: 
Health is always an important theme in human history. There
cient times [1]. The internet service and mobile equipment make
real-time health care, inter-clinic consultation and operation ro-
botics become reality [2-7]-
eral principle plays a vital role so that one therapy can cure people
        
telligent medical care.
[8,9]. Based on the medical re-
search and clinical cases, various vaccinations are invented to
       [10,11]. How-
   
caused by internal or external reasons, such as gene and genetics,
environment, climate, work., et al. Tracking the medical advance
 
modern society [12,13].
    
community and provides integrated and intelligent diagnoses and treatment. The system aims to improve health situation, medical
precaution, etiology analysis and recovery.
chine learning algorithms to construct the model by dealing with
        
    -
dict cancer, segment tumor, inspect bone break etc. The training
process keeps updating the model while iterating data and com-
    
to resolve the situation. Several strategies are invented to prevent
      
in practice. AI mechanism is showing more power in industry and
        -
intelligent medicine in section 3. For health management, we con-
      -
cesses which are data analysis, case analysis, medical imaging and
integrated diagnoses and treatment. The linkage between health
    -
tion 4.
Citation: Yichi Gu. “Mobile Health and Medical Care". 3.12 (2019): 135-139.
Mobile Health and Medical Care
Health management
When reviewing the growth process, human health is not only
 [18]. Digital
       -
   -
dissipation system, et al. [20-23]-
       -
ronment, education and social group [24]. Based on these studies,
    
literature, individuals can record their health data and share their
Health branch
         
       
logs [26,27]
       
       -
Individualized health
Medical research studies human body and the corresponding
        -
        
human body, the everyday interaction and chemical exchange with
outer world have important impact on human as time accumulates.
    -
vironment, water and meals, living and work activities, etc. Hand-
diagnose such as hear bracelet such as heartbeat bracelet.
 
to set up suitable living/work schedule according to the environ-
Figure 1: Health Guidance webpage.
Health community
        
People can communicate with medical experts. Beside medicine
mous traditional book HuangDi Cannon [1] presented this relation-
   
      
 
the experience. Everyone has individual account and publishes
health experiences or comments. This community discusses easy,
     
Intelligent medicine
As we know that medical problem is complicated and tough but
there are always more than one therapy, and doctors make diag-
noses and treatment mainly based on their experience. Intelligent
       
   -
Citation: Yichi Gu. “Mobile Health and Medical Care". 3.12 (2019): 135-139.
Mobile Health and Medical Care
Patient history analysis
        
and treatment. The history data assists to determine the cause
        
        
       
           
not be complete. To deal with incomplete data, statistics learning
Imaging analysis
   
    -
puted tomography, ultrasonic, magnetic resonance imaging, endo-
Statistics analysis
         
network and data science. Statistics studies the distribution or con-
makes prediction, judgement, compare or estimation to approxi-
mate and compute the precision with strategies such as Bayesian,
bootstrap, random processing etc. In medicine, statistics is a pow-
        
Statistics is also an inverse mechanism applied in machine
learning and deep learning. CRF [29,30] combined with neural net-
work shows more details in image segmentation. More statistics
methods will be applied in intelligent medicine.
pitals and intelligent model provides better solution. Traditional
    
noisy data. Their combination which accurately and comprehen-
sively makes predictions is our destination.
tient history analysis, imaging and integrated diagnose and treat-
 
      
         
body, doctors can distinguish the abnormality by vision and experi-
ence. AI training process learns the judgement mechanism to ob-
    
       [32,33]. It
labeled data and unsupervised learning targets on the properties
         -
embedded in the image. The intelligent imaging system will be the
Integrated diagnoses and treatment
In hospitals, diagnose and treatment may be given at the same
The integrated diagnose and treatment system collects the medical
data including history, tests, imaging., et al. and outputs situation
analysis, medical analysis and treatment consultation.
One symptom corresponds to various illnesses and the same in
the other way, one disease has distinctive symptoms. The diversity
         
and sets up multi-solution mechanism.
For the multi-solution system, training data and process will
         
  -
tween two destinations with new constructions. It is the advantage
       
 -
Citation: Yichi Gu. “Mobile Health and Medical Care". 3.12 (2019): 135-139.
Mobile Health and Medical Care
1. 
2. 
      Revue Médicale De
Liège 74.2 (2019): 104-110.
3. et al. “Electronic Health Records in the Cloud:
       
 245 (2017): 35-
4. Prerna Mohit., et al.A Standard Mutual Authentication Proto-
 (2017).
5. Gao Fangjian., et al.    -
  
      
6.     -
 (2019).
7. Medical Robotics Daniel S Elson., et al  
Engineering (2018).
8. Ndu Anne C. “Standard precaution knowledge and adher-
 
Malawi 29.4 (2017): 294-300.
9. Bard Denis. “Health and environment, prevention or precau-
 
        -
illness and clinics to cure patients, but also provides academic
and medical advances to replenish health knowledge and health
       
      
ments is more desired than taking medicine. Health is in mind bet-
ter than in clinics.
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nation behavior under myopic update rule on complex net-
       
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ing priority Cochrane Reviews in health communication and
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 (2019).
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Citation: Yichi Gu. “Mobile Health and Medical Care". 3.12 (2019): 135-139.
Mobile Health and Medical Care
Volume 3 Issue 12 December 2019
© All rights are reserved by Yichi Gu.
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Citation: Yichi Gu. “Mobile Health and Medical Care". 3.12 (2019): 135-139.
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Background: Priority-setting partnerships between researchers and stakeholders (meaning consumers, health professionals and health decision-makers) may improve research relevance and value. The Cochrane Consumers and Communication Group (CCCG) publishes systematic reviews in 'health communication and participation', which includes concepts such as shared decision-making, patient-centred care and health literacy. We aimed to select and refine priority topics for systematic reviews in health communication and participation, and use these to identify five priority CCCG Cochrane Reviews. Methods: Twenty-eight participants (14 consumers, 14 health professionals/decision-makers) attended a 1-day workshop in Australia. Using large-group activities and voting, participants discussed, revised and then selected 12 priority topics from a list of 21 previously identified topics. In mixed small groups, participants refined these topics, exploring underlying problems, who they affect and potential solutions. Thematic analysis identified cross-cutting themes, in addition to key populations and potential interventions for future Cochrane Reviews. We mapped these against CCCG's existing review portfolio to identify five priority reviews. Results: Priority topics included poor understanding and implementation of patient-centred care by health services, the fact that health information can be a low priority for health professionals, communication and coordination breakdowns in health services, and inadequate consumer involvement in health service design. The four themes underpinning the topics were culture and organisational structures, health professional attitudes and assumptions, inconsistent experiences of care, and lack of shared understanding in the sector. Key populations for future reviews were described in terms of social health characteristics (e.g. people from indigenous or culturally and linguistically diverse backgrounds, elderly people, and people experiencing socioeconomic disadvantage) more than individual health characteristics. Potential interventions included health professional education, interventions to change health service/health professional culture and attitudes, and health service policies and standards. The resulting five priority Cochrane Reviews identified were improving end-of-life care communication, patient/family involvement in patient safety, improving future doctors' communication skills, consumer engagement strategies, and promoting patient-centred care. Conclusions: Stakeholders identified priority topics for systematic reviews associated with structural and cultural challenges underlying health communication and participation, and were concerned that issues of equity be addressed. Priority-setting with stakeholders presents opportunities and challenges for review producers.
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Background: A body of knowledge continues to grow regarding Aboriginal perspectives on current challenges and barriers to health literacy and access to health services. However, less is known from the perspectives of health professionals who work in cardiac care. Given their role in delivering patient education, health practitioners could provide useful insights into potential solutions to improve patient-practitioner communication. The primary aim was to explore perspectives of health professionals who work in coronary care units regarding the enablers, barriers and potential solutions for patient-practitioner communication with patients of Aboriginal and Torres Strait Islanders descent. The secondary aim was to evaluate the acceptability and value of two videos developed with key stakeholders to provide culturally appropriate education. Methods: Participants were recruited from two major regional hospitals. In-depth, semi-structured interviews were conducted with 17 health professionals (11 Nurses, five Cardiologists and one Aboriginal Health Worker). Interviews were recorded, de-identified and transcribed verbatim. Transcripts were analysed using constant comparison, interpreted through inductive thematic analysis and final themes were agreed through consensus with secondary researcher. Results: Health professionals acknowledged that existing barriers resulted from organisational structures entrenched in the healthcare system, impacted on the practitioners' ability to provide culturally appropriate, patient-centred care. Lack of time, availability of culturally appropriate resources and the disconnection between Western medical and Aboriginal views of health were the most common challenges reported. The two videos evaluated as part of this study were found to be a useful addition to practice. Strengths in the videos design were the use of Aboriginal and Torres Strait Islander actors and positive messaging to convey health related topics. Further improvements included additional information related to common tests and procedures to allow for realistic expectations of patient care. Conclusion: Re-modelling of organisational structures is required in order to promote a more culturally-friendly and welcoming environment to encourage Aboriginal and Torres Strait Islanders to engage with mainstream cardiac care services. The videos that were developed using principles that are sensitive to Aboriginal health views, may offer an additional way in which to overcome existing barriers to effective patient-practitioner communication with Aboriginal and Torres Strait Islanders.
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Background: Cloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications. Researchers claim that information technology (IT) services delivered via the cloud computing paradigm (ie, cloud computing services) provide major benefits for health care. However, due to a mismatch between our conceptual understanding of cloud computing for health care and the actual phenomenon in practice, the meaningful use of it for the health care industry cannot always be ensured. Although some studies have tried to conceptualize cloud computing or interpret this phenomenon for health care settings, they have mainly relied on its interpretation in a common context or have been heavily based on a general understanding of traditional health IT artifacts, leading to an insufficient or unspecific conceptual understanding of cloud computing for health care. Objective: We aim to generate insights into the concept of cloud computing for health IT research. We propose a taxonomy that can serve as a fundamental mechanism for organizing knowledge about cloud computing services in health care organizations to gain a deepened, specific understanding of cloud computing in health care. With the taxonomy, we focus on conceptualizing the relevant properties of cloud computing for service delivery to health care organizations and highlighting their specific meanings for health care. Methods: We employed a 2-stage approach in developing a taxonomy of cloud computing services for health care organizations. We conducted a structured literature review and 24 semistructured expert interviews in stage 1, drawing on data from theory and practice. In stage 2, we applied a systematic approach and relied on data from stage 1 to develop and evaluate the taxonomy using 14 iterations. Results: Our taxonomy is composed of 8 dimensions and 28 characteristics that are relevant for cloud computing services in health care organizations. By applying the taxonomy to classify existing cloud computing services identified from the literature and expert interviews, which also serves as a part of the taxonomy, we identified 7 specificities of cloud computing in health care. These specificities challenge what we have learned about cloud computing in general contexts or in traditional health IT from the previous literature. The summarized specificities suggest research opportunities and exemplary research questions for future health IT research on cloud computing. Conclusions: By relying on perspectives from a taxonomy for cloud computing services for health care organizations, this study provides a solid conceptual cornerstone for cloud computing in health care. Moreover, the identified specificities of cloud computing and the related future research opportunities will serve as a valuable roadmap to facilitate more research into cloud computing in health care.
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Background: Doctors and laboratory scientists are at risk of infection from blood borne pathogens during routine clinical duties. After over 20 years of standard precautions, health care workers knowledge and compliance is not adequate. Aim: This study is aimed at comparing adherence and knowledge of standard precautions (SP) among Medical Laboratory Scientists (MLSs) and doctors. Methods: It was a cross sectional study done at University of Nigeria Teaching Hospital, ItukuOzalla. A semi structured pre-tested questionnaire was the study instrument. Results: General knowledge of SP was high,76.2% in doctors and 67.6% in MLSs although there were differences between the two groups on the knowledge of components of SP. Safe injection practices, use of personal protective equipment as well as safe handling of contaminated equipment or surfaces was higher amongst doctors. Even though more than half of respondents in both groups, 53.1 % among doctors and 58.1% among MLSs had received training on standard precautions, this did not reflect in the practice. MLS reported more use of personal protective equipment such as gloves and coveralls (100% in MLS and 35% of doctors), P<0.001. Recapping of syringes was higher amongst doctors (63.6%) than MLS (55.1%).The doctors practiced better hand hygiene than MLS (P<0.001). Constraints that affected SP included non-availability of PPEs and emergency situations for both groups. Conclusion: SP knowledge and practice are still low, and as such, there is a need to train doctors and MLS on the components of SP. Policies on SP need to be enforced and facilities for practice regularly supplied.
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The integration of artificial intelligence in the radiation oncologist's workflow has multiple applications and significant potential. From the initial patient encounter, artificial intelligence may aid in pretreatment disease outcome and toxicity prediction. It may subsequently aid in treatment planning, and enhanced dose optimization. Artificial intelligence may also optimize the quality assurance process and support a higher level of safety, quality, and efficiency of care. This article describes components of the radiation consultation, planning, and treatment process and how the thoughtful integration of artificial intelligence may improve shared decision making, planning efficiency, planning quality, patient safety, and patient outcomes.
Exercise is a powerful means to maintain health, prevent disease, and even act as medicine for a wide range of non-communicable diseases. The key effects by which exercise benefits our metabolic health include (i) events that occur during exercise and in the hours to days following exercise, and (ii) the adaptations that occur following long-term repeated exercise training. Here, we provide a contemporary overview of recent significant advances in our knowledge of exercise as medicine in metabolic disease with a focus on muscle glucose metabolism.
A revised Electronic Health Record (HER) is one of the most important keystones for the building of a new ecosystem. The latter is characterized by keywords such as big data, artificial intelligence, and cloud technology. The HER, which is used nowadays, does not fit within this purpose and - by the way - is considered as one of the most important reasons for frustration and burnout in the medical profession. Very often, the HER is mainly designed to collect data useful for billing purposes. However, it has to evolve rapidly to a tool which allows both storage of controlled and validated data, and analysis resulting in useful information. This information can help the professional both in diagnosis and prevention, at an individual level as well as at the level of population health. It should also be of potential use for organization and management of the health care sector as a whole. This deep facelift is an absolute requirement, if we want to cope with the major challenges of our exhausted health care sector.
Objective: Extracorporeal life support has traditionally been used as a supportive platform for patients with cardiopulmonary failure. Many of these patients require endovascular access for the performance of diagnostic or therapeutic procedures, and obtaining vascular access in these patients can be problematic. We sought to develop a novel system that allows the extracorporeal life support circuit to serve as an access point to the cardiovascular system. Methods: By using computer-aided design, modeling, and 3-dimensional printing, a novel adaptor that can be easily inserted and removed from an extracorporeal life support circuit was developed. A mock loop was used to measure flow and pressure at various pump speeds with insertion of guidewires and catheters through the adaptor. The ability of the system to enable performance of endovascular procedures in vivo was then tested in a porcine extracorporeal life support model. Results: By using a small arterial cannula (15F) at 3500 RPM and 3.2 LPM, 15% and 24% decrements in circuit flow were observed when a 0.035" guidewire and 5F angiography catheter, respectively, were passed through the adaptor (P < .001). However, when using a larger arterial cannula (23F) at 3500 RPM and 4.7 LPM, only 3% and 5% decrements in flow were observed (P < .001), respectively, with intermediate changes when using 17F to 21F cannulas. In vivo testing confirmed that this system enables the performance of a variety of endovascular procedures, including left ventriculography, aortic root and coronary angiography, and descending aortography. Conclusions: This novel system successfully enables endovascular access through an extracorporeal life support circuit. This technology may transform extracorporeal life support from a purely supportive strategy to a platform for endovascular intervention.
This paper conducts a survey on iterative learning control ( ILC ) with incomplete information and associated control system design, which is a frontier of the ILC field. The incomplete information, including passive and active types, can cause data loss or fragment due to various factors. Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection, storage, transmission, and processing, such as data dropouts, delays, disordering, and limited transmission bandwidth. Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied, such as sampling and quantization. This survey emphasizes two aspects: the first one is how to guarantee good learning performance and tracking performance with passive incomplete data, and the second is how to balance the control performance index and data demand by active means. The promising research directions along this topic are also addressed, where data robustness is highly emphasized. This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance, quantitatively, and promote further developments of ILC theory. IEEE