scholarly journals Digital healthcare services

2021 ◽  
Author(s):  
Edith Maier ◽  
Ulrich Reimer ◽  
Nilmini Wickramasinghe
Keyword(s):  
Healthcare Services ◽  
Digital Healthcare

scholarly journals The healthcare technology needs you

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Sumarno Adi Subrata ◽  
Jonathan Bayuo ◽  
Busra Sahin
Keyword(s):  
Artificial Intelligence ◽  
3D Printing ◽  
Standard Treatment ◽  
Healthcare Services ◽  
Fundamental Aspect ◽  
Innovative Technology ◽  
Community Settings ◽  
Healthcare Technology ◽  
Health Related ◽  
Digital Healthcare

The growing evidence and technology in healthcare lead to an improvement in the patient's health across a continuum of services in clinical and community settings. A multidisciplinary team should work in tandem on this phenomenon. Therefore, innovative healthcare technology must be designed intensively to optimize productivity and provide new insight along with support the standard treatment for particular diseases. In the coming years, technology is needed to change the way of caring for the patient. This is a fundamental aspect because the recent technology has shaped up in front of our practice with advances in digital healthcare services, such as 3D printing, robotics, nanotechnology and even artificial intelligence (The Medical Futurist, 2021). To respond to this, updated studies should be developed and published focusing on innovative technology including in Medicine, Nursing, Pharmacy, and other health-related topics.

scholarly journals Digi-HTA: Health technology assessment framework for digital healthcare services

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Jari Haverinen ◽  
Niina Keränen ◽  
Petra Falkenbach ◽  
Anna Maijala ◽  
Timo Kolehmainen ◽  
...  
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
State Of The Art ◽  
Health Technology ◽  
Healthcare Services ◽  
The State ◽  
Systematic Evaluation ◽  
Health Technologies ◽  
Digital Healthcare ◽  
And Robotics

Health technology assessment (HTA) refers to the systematic evaluation of the properties, effects, and/or impacts of health technology. The main purpose of the assessment is to inform decisionmakers in order to better support the introduction of new health technologies. New digital healthcare solutions like mHealth, artificial intelligence (AI), and robotics have brought with them a great potential to further develop healthcare services, but their introduction should follow the same criteria as that of other healthcare methods. They must provide evidence-based benefits and be safe to use, and their impacts on patients and organizations need to be clarified. The first objective of this study was to describe the state-of-the-art HTA methods for mHealth, AI, and robotics. The second objective of this study was to evaluate the domains needed in the assessment. The final aim was to develop an HTA framework for digital healthcare services to support the introduction of novel technologies into Finnish healthcare. In this study, the state-of-the-art HTA methods were evaluated using a literature review and interviews. It was noted that some good practices already existed, but the overall picture showed that further development is still needed, especially in the AI and robotics fields. With the cooperation of professionals, key aspects and domains that should be taken into account to make fast but comprehensive assessments were identified. Based on this information, we created a new framework which supports the HTA process for digital healthcare services. The framework was named Digi-HTA.

scholarly journals Healthcare Providers’ Opinions on Digital Healthcare Services

2018 ◽  
Vol 43 (1) ◽  
pp. 54-63
Author(s):  
Seyeon Mun ◽  
Young Mi Yun ◽  
Tae Hwa Han ◽  
Sang Eun Lee ◽  
Hyuk Jae Chang ◽  
...  
Keyword(s):  
Healthcare Providers ◽  
Healthcare Services ◽  
Digital Healthcare

scholarly journals Social impacts of the continuous usage of digital healthcare service: A case of South Korea

2021 ◽  
Vol 17 (2) ◽  
pp. 79-89
Author(s):  
Jaewon Lee ◽  
Boyoung Kim
Keyword(s):  
Social Support ◽  
Social Capital ◽  
Healthcare Service ◽  
Social Impact ◽  
Healthcare Services ◽  
Social Value ◽  
Impact Factors ◽  
Community Members ◽  
Social Community ◽  
Digital Healthcare

As untact communication is promoted in the era of the COVID-19 pandemic, special attention is paid to remote medical examination and customized healthcare trends. General digital healthcare services among social community members positively affect individuals’ healthcare and reduce medical social services’ burden, contributing to the development of society. Accordingly, it is necessary to induce healthcare behaviors through the continuous usage of digital healthcare services among social community members and to examine significant social impact factors in this regard. This study empirically analyzes the impact of three social impact factors – social capital, social support, and social value – on the continuous usage of digital healthcare service with healthcare behaviors and e-health literacy as media. To this end, a survey was conducted among 363 individuals who had used digital healthcare services in Korea, and the statistical data were analyzed. Social capital and social value were found to affect healthcare behaviors, e-health literacy, and continuous usage intentions, but social support did not. Based on this result, it was confirmed that the factors regarded by digital healthcare service users as necessary were the values and perceptions shared in society and the group, information and active communication rather than direct public support.

scholarly journals Attitudes Towards Digital Health Technology: Introducing the Digital Health Scale

2021 ◽  
Author(s):  
Ridley Cassidy
Keyword(s):  
Digital Health ◽  
Health Technology ◽  
Healthcare Services ◽  
Older Individuals ◽  
Cross Sectional ◽  
Frequent Use ◽  
Age Related ◽  
Frequency Of Use ◽  
The Uk ◽  
Digital Healthcare

Objective: The study sought to investigate the relationship between attitude towards digital health technology and age, gender and frequency of use of digital health technology and to consider whether age, gender and frequency of use present potential barriers to accessing future healthcare in the UK. Differences in technological affinity are likely to lead to differences in the adoption of digital health technology and subsequent inequalities in healthcare between older and younger people and between men and women. Design: The study represents an example of a technology adoption study employing a survey-based cross sectional correlational design. Attitude towards digital health technology was measured using the 20 item Digital Health Scale. Age, gender, frequency of use of health technology and employment status data were gathered using a demographics questionnaire. The opportunity sample (N = 247) included volunteer participants aged 16-84 years (M = 31.7, SD = 19.35, 156 females and 91 males). Results: Results indicated a significant negative correlation between age and positive attitude towards digital health technology (r = -0.24, p < .01). Gender differences in attitudes towards digital health technology were non-significant (p > .05). Significant differences in frequency of use were also found, where occasional and frequent use resulted in more positive attitudes than never having used digital health technology (p < 0.05) and participants reporting frequent use were significantly older than those reporting never or occasional use (p < .05) Conclusion: Findings identified age, but not gender, as a significant factor in attitude towards digital health technology, suggesting that continued and increased reliance on digital technology in healthcare may lead to age, but not gender, related inequalities in access to healthcare in the UK. That frequent users of digital health technology were also older, highlights the greater demand for healthcare services by older individuals and is further evidence for the potential of digital healthcare to lead to age related inequalities in access to and provision of healthcare. Recommendations for successful application of digital healthcare technology are considered in the light of these findings.

scholarly journals Medication Name Comprehension of Intelligent Virtual Assistants: A Comparison of Amazon Alexa, Google Assistant, and Apple Siri Between 2019 and 2021

2021 ◽  
Vol 3 ◽  
Author(s):  
Adam Palanica ◽  
Yan Fossat
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Healthcare Services ◽  
The United States ◽  
Brand Names ◽  
The Past ◽  
Speech Characteristics ◽  
Software Algorithms ◽  
Digital Healthcare ◽  
Comprehension Accuracy

The current study was a replication and comparison of our previous research which examined the comprehension accuracy of popular intelligent virtual assistants, including Amazon Alexa, Google Assistant, and Apple Siri for recognizing the generic and brand names of the top 50 most dispensed medications in the United States. Using the exact same voice recordings from 2019, audio clips of 46 participants were played back to each device in 2021. Google Assistant achieved the highest comprehension accuracy for both brand medication names (86.0%) and generic medication names (84.3%), followed by Apple Siri (brand names = 78.4%, generic names = 75.0%), and the lowest accuracy by Amazon Alexa (brand names 64.2%, generic names = 66.7%). These findings represent the same trend of results as our previous research, but reveal significant increases of ~10–24% in performance for Amazon Alexa and Apple Siri over the past 2 years. This indicates that the artificial intelligence software algorithms have improved to better recognize the speech characteristics of complex medication names, which has important implications for telemedicine and digital healthcare services.

Modeling Digital Healthcare Services Using NLP and IoT in Smart Cities

2022 ◽  
pp. 138-155
Author(s):  
Kalpana Verma
Keyword(s):  
Language Processing ◽  
Smart Cities ◽  
Healthcare Services ◽  
Health Issues ◽  
Medical Reports ◽  
Monitoring Devices ◽  
The Internet Of Things ◽  
Digital Healthcare

Smart cities shall have computerized health services to help the treatment of health issues based on a centralized framework. These computerized healthcare services would involve the patient, medical support staff, and doctors. They would have voice recognition clinical staff management, smart health monitoring devices, smart medical reports, and smart medical treatment based on the internet of things (IoT) and natural language processing (NLP). This chapter shall describe the digital healthcare services in the context of NLP and IoT that would help in the improvement of quality of life for people living in smart cities. The authors shall discuss a possible case study on NLP and IoT in digital healthcare in the smart cities.

scholarly journals A Blockchain and Artificial Intelligence-Based, Patient-Centric Healthcare System for Combating the COVID-19 Pandemic: Opportunities and Applications

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1019
Author(s):  
Mohamed Yaseen Jabarulla ◽  
Heung-No Lee
Keyword(s):  
Artificial Intelligence ◽  
Healthcare Delivery ◽  
Healthcare Services ◽  
Future Research ◽  
Public Healthcare ◽  
Healthcare Data ◽  
Disruptive Technologies ◽  
Drug Manufacturing ◽  
Digital Healthcare ◽  
Patient Centric

The world is facing multiple healthcare challenges because of the emergence of the COVID-19 (coronavirus) pandemic. The pandemic has exposed the limitations of handling public healthcare emergencies using existing digital healthcare technologies. Thus, the COVID-19 situation has forced research institutes and countries to rethink healthcare delivery solutions to ensure continuity of services while people stay at home and practice social distancing. Recently, several researchers have focused on disruptive technologies, such as blockchain and artificial intelligence (AI), to improve the digital healthcare workflow during COVID-19. Blockchain could combat pandemics by enabling decentralized healthcare data sharing, protecting users’ privacy, providing data empowerment, and ensuring reliable data management during outbreak tracking. In addition, AI provides intelligent computer-aided solutions by analyzing a patient’s medical images and symptoms caused by coronavirus for efficient treatments, future outbreak prediction, and drug manufacturing. Integrating both blockchain and AI could transform the existing healthcare ecosystem by democratizing and optimizing clinical workflows. In this article, we begin with an overview of digital healthcare services and problems that have arisen during the COVID-19 pandemic. Next, we conceptually propose a decentralized, patient-centric healthcare framework based on blockchain and AI to mitigate COVID-19 challenges. Then, we explore the significant applications of integrated blockchain and AI technologies to augment existing public healthcare strategies for tackling COVID-19. Finally, we highlight the challenges and implications for future research within a patient-centric paradigm.

MEDBIZ Big Data Platform Based on Enterprise Support for Digital Healthcare Service using Internet of Medical Things (Preprint)

2021 ◽  
Author(s):  
Hee Young LEE ◽  
Kang Hyun LEE ◽  
Kyu Hee LEE ◽  
Urtnasan Erdenbayar ◽  
Sangwon HWANG ◽  
...  
Keyword(s):  
Big Data ◽  
Real World ◽  
Digital Health ◽  
Healthcare Services ◽  
Mobile App ◽  
Chronic Obstructive ◽  
Real World Data ◽  
Data Platform ◽  
Internet Of Medical Things ◽  
Digital Healthcare

UNSTRUCTURED The aim of this study is to introduce the implemented big data platform (MEDBIZ) based on the internet of medical things (IoMT) supporting digital healthcare services and to discuss about application cases of this platform. Implemented MEDBIZ platform based on the IoMT devices and big data to provide digital healthcare services to the enterprise and users. The big data platform is consisting of four main components: IoMT, Core, Analytics, and Services. IoMT component is used for lifelog data acquisition and collection from the IoMT devices. The core components are composed of the main functional operations including the metadata, resource brokers and computing elements, virtual file system, security, and system logs. Analytics component is covered data analyzing frameworks such as Hadoop, Spark, R, and TensorFlow. Finally, the service component can support the web-based or mobile app-based digital healthcare services through the Open API to the end-users. As a result, an implemented big data platform can provide various digital healthcare services using various IoMT devices. Among them, we are focusing on detailed empirical studies about chronic obstructive pulmonary disease (COPD), metabolic syndrome, vital sign, arrhythmia, and diabetes monitoring services. We demonstrated the implemented MEDBIZ platform based on IoMT supporting digital healthcare services by acquiring real-world data for getting the real-world evidence. And then through this platform, we are developing Software as a Medical Device (SaMD), digital therapeutics, and digital healthcare services, and contributing to the development of the digital health ecosystem.

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