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 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.

A roadmap for remote digital health technology for Motor Neuron Disease (Preprint)

2021 ◽  
Author(s):  
Ruben P.A. van Eijk ◽  
Anita Beelen ◽  
Esther T. Kruitwagen ◽  
Deirdre Murray ◽  
Ratko Radakovic ◽  
...  
Keyword(s):  
Motor Neuron ◽  
Motor Neuron Disease ◽  
Real World ◽  
Clinical Decision Making ◽  
Digital Health ◽  
Health Technology ◽  
Innovative Technology ◽  
Progression Rate ◽  
Healthcare Technology ◽  
Digital Healthcare

UNSTRUCTURED Despite recent and compelling technological advances, the real-world implementation of remote digital health technology in care and monitoring of patients with motor neuron disease (MND) has not yet been realized. Digital health technology may increase the accessibility to and personalization of care, whereas remote biosensors could optimize the collection of vital clinical parameters, irrespective of the patients’ ability to visit the clinic. To facilitate wide-scale adoption of digital healthcare technology, and to align current initiatives, we outline a roadmap that (1) will identify clinically relevant digital parameters, (2) mediate the development of benefit-to-burden criteria for innovative technology and (3) direct the validation, harmonization and adoption of digital healthcare technology in real-world settings. We define two key end-products of the roadmap: (1) a set of reliable digital parameters to capture data, collected under free-living conditions, that reflect patient-centric measures and facilitate clinical decision-making, and (2) an integrated, open-source system that provides personalized feedback to patients, healthcare providers, clinical researchers and caregivers, linked to a flexible and adaptable ICT platform that integrates patient data in real time. Given the ever-changing care needs of patients and the relentless progression rate of MND, the adoption of digital healthcare technology will significantly benefit the delivery of care and accelerate the development of effective treatments.

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.

scholarly journals Extra Health and Health-Related Costs of Living With Frailty Among Rural and Urban Elders in China

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 78-78
Author(s):  
Yalu Zhang ◽  
Ada Mui
Keyword(s):  
Chronic Diseases ◽  
Household Income ◽  
Statistical Significance ◽  
Healthcare Services ◽  
Standard Of Living ◽  
Medical Needs ◽  
Rural And Urban ◽  
Need Support ◽  
Health Related ◽  
Rural Elders

Abstract Growing attention has been focused on how to improve the affordability and accessibility of healthcare services, especially for elders (aged 55 and above) who have higher levels of medical needs. Following the standard of living approach, which assumes that people’s standard of living would be negatively affected if additional needs (i.e., healthcare) arise at a given level of household income, this secondary research examines elders’ extra health and health-related costs of having chronic diseases and disabilities in rural (n=5,509) and urban (n=3,225) areas of China. Bivariate analyses show there were no significant differences between rural and urban groups in terms of the prevalence of having one or more chronic diseases (56% vs. 58%) and at least one type of disability (15% vs. 13%). Multivariate analyses indicate that living with chronic diseases incurred more extra costs for rural elders than their urban peers, after controlling for individual and household characteristics. On average, rural elders who had at least three chronic medical conditions would spend 108.3% more on medical services than those who had no chronic disease; elders with at least two types of disabilities would spend 59.8% more than those with no disability. The extra health-related costs were boosted when people had at least one type of disability (63.6%), but this was not the case for those who had chronic diseases. Statistical significance was not found among urban elders in China regarding both health and health-related expenditures. The results suggest that rural elders need support to manage their chronic health conditions.

Artificial Intelligence, Social Media and Depression. ‘Patient’ Autonomy Revisited

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 133-133
Author(s):  
Regina Mueller ◽  
◽  
Sebastian Laacke ◽  
Georg Schomerus ◽  
Sabine Salloch ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Mental Disorders ◽  
Patient Autonomy ◽  
Ethical Challenges ◽  
Traditional Concept ◽  
Ethical Implications ◽  
Health Related ◽  
Twitter Users ◽  
Using Data

"Artificial Intelligence (AI) systems are increasingly being developed and various applications are already used in medical practice. This development promises improvements in prediction, diagnostics and treatment decisions. As one example, in the field of psychiatry, AI systems can already successfully detect markers of mental disorders such as depression. By using data from social media (e.g. Instagram or Twitter), users who are at risk of mental disorders can be identified. This potential of AI-based depression detectors (AIDD) opens chances, such as quick and inexpensive diagnoses, but also leads to ethical challenges especially regarding users’ autonomy. The focus of the presentation is on autonomy-related ethical implications of AI systems using social media data to identify users with a high risk of suffering from depression. First, technical examples and potential usage scenarios of AIDD are introduced. Second, it is demonstrated that the traditional concept of patient autonomy according to Beauchamp and Childress does not fully account for the ethical implications associated with AIDD. Third, an extended concept of “Health-Related Digital Autonomy” (HRDA) is presented. Conceptual aspects and normative criteria of HRDA are discussed. As a result, HRDA covers the elusive area between social media users and patients. "

scholarly journals The impact of music on health-related quality of life, as quantified by the SF-36: A systematic review and meta-analysis

2021 ◽  
Author(s):  
J. Matt McCrary ◽  
Eckart Altenmuller ◽  
Clara Kretschmer ◽  
Daniel S. Scholz
Keyword(s):  
Standard Treatment ◽  
Meta Analysis ◽  
Music Listening ◽  
Health Related Quality ◽  
Post Intervention ◽  
Sf 36 ◽  
Related Quality ◽  
Health Related ◽  
The Impact

Background/Objectives: Increasing evidence supports the ability of music to broadly promote wellbeing and health-related quality of life (HRQOL). However, the magnitude of music effects on HRQOL is still unclear, particularly relative to established interventions, limiting inclusion of music interventions in health policy and care. The SF-36 is the most widely used instrument to evaluate HRQOL, with broad validity in evaluating the effects of a range of interventions. This study aims to synthesize and contextualize the impact of music interventions on HRQOL, as assessed by the SF-36. Methods: MEDLINE; EMBASE; Web of Science; PsycINFO, clinicaltrials.gov, and ICTRP databases were searched on 30 July 2021. Randomized and single-group studies of music interventions which reported SF-36 data at pre- and post-intervention timepoints were included. Observational studies were excluded. The quality and certainty of evidence provided by included articles and meta-analysis results was appraised using GRADE. Inverse variance random effects meta-analyses quantified changes in SF-36 mental and physical component summary scores (respectively, MCS and PCS) pre- to post-intervention and vs. common control groups. Results: Analyses included 764 participants from 25 studies. Music interventions (music listening, 10 studies; music therapy, 7 studies; singing, 7 studies; gospel music, 1 study) significantly improved MCS (Mean difference (MD) [95% confidence interval]=3.0 [1.4, 4.6]; p<.001) and PCS (MD=1.0 [0.1, 2.0; p<.04) scores. In a subgroup (8 studies; music group, N=254; control, N=257) addition of music to standard treatment for a range of conditions significantly improved MCS scores vs. standard treatment alone (MD=3.7 [0.4, 7.1; p<.03). Effects did not vary between music listening, therapy and singing intervention types or doses (p>.12); no evidence of small study or publication biases was present in any analysis (p>.31). Music impact on MCS scores meets SF-36 minimum important difference thresholds (MD>/=3) and is within the range of established interventions. Conclusions: This study provides Moderate quality evidence that music interventions can generally be used to provide clinically meaningful improvements in HRQOL. Further study is needed to determine optimal music interventions and doses for distinct clinical and public health scenarios. Funding: Alexander von Humboldt Foundation Registration: PROSPERO (ID: CRD42021276204)

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.

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