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 Table of Contents  
Year : 2022  |  Volume : 1  |  Issue : 1  |  Page : 5-6

The evolving landscape of digital health using big data analytics for personalized healthcare

Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates

Date of Submission20-Dec-2021
Date of Acceptance23-Dec-2021
Date of Web Publication19-Jan-2022

Correspondence Address:
Salman Yousuf Guraya
Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah.
United Arab Emirates
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/abhs.abhs_22_21

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How to cite this article:
Guraya SY. The evolving landscape of digital health using big data analytics for personalized healthcare. Adv Biomed Health Sci 2022;1:5-6

How to cite this URL:
Guraya SY. The evolving landscape of digital health using big data analytics for personalized healthcare. Adv Biomed Health Sci [serial online] 2022 [cited 2022 Aug 11];1:5-6. Available from: http://www.abhsjournal.net/text.asp?2022/1/1/5/335723

Digital health entails an evolutionary transformation of the art and science of medicine toward more prevalent information and communication technologies [1]. The rapid developments in health-care technology have shown a great promise in a vast range of medical disciplines including robotics, image-guided diagnostics and therapeutics, patient-care protocols, computer-assisted algorithms, and safety protocols. Digital health has rapidly expanded to modernize the scope of scientific concepts and technologies, including genomics, artificial intelligence, big data analytics, digital interfaces, and telehealth. In this endeavor, digital health essentially implies diverse Internet-based applications to enhance biomedical content, commerce, and connectivity [2].

Currently, a wide range of mobile technologies with self-tracking and wearable devices, directly interacting with social networking sites, have significantly engaged the clients in their pursuit for healthy lifestyles. These technological advancements have empowered healthy populations and patients to independently monitor and self-regulate their inherent needs. MC10, Proteus, and HealBe are some examples of the commercially available wearables with delicate sensors [3]. Despite these impressive developments, more evidence-based research is essential to create computer-based algorithms using bioinformatics that would assist the clients to set their goals for their physical and metabolic fitness.

The liberal integration of Web science, medical ontologies, and taxonomies has now offered a great access to accurate biomedical information via Internet navigation and semantic search. The use of social media allows instant access, dissemination, medical advice, and smart personalized systems for patients and health-care professionals. Recently, there have been serious attempts for customized and tailored profiling of users’ needs that would provide dedicated personalized healthcare [4]. These state-of-the-art technologies have been introduced using the concept of big data, which utilizes extremely large and complex datasets that traditional software cannot accurately store and analyze. Big data analytics are rapidly gaining popularity in health-care systems where bioengineers and researchers can collect, store, curate, process, and interpret medical data for personalized care. Unfortunately, there is an overarching need to standardize computer-assisted programs for their clinical definitions and interpretations. The accuracy of health-care solutions for optimizing various clinical conditions remains questionable. In addition, there are growing concerns about the reported violations of privacy and confidentiality of clients in the digital realm [4]. The breach in e-professionalism by medical students, physicians, and patients demands a strict policy about professional codes of conduct [5].

Robotics technologies in the medical field have been introduced by the integration of computing, biology, and physics. Per se, the area of medical robotics does not directly fall under digital health; it is imperative to touch base this novel master-class invention, which uses artificial intelligence and virtual reality for sensing, connectivity, quantum computing, three-dimensional printing, nanotechnology, and biotechnology. Recently, the use of robotics has been witnessed in all medical subspecialties with a reported positive impact on patient safety and health-related outcomes [6]. Another developing component of digital health is the game-based learning which encompasses computational integration of clinical data for tangible modifications of personal knowledge, health, and behaviors. All these changes are perceived to lead to positive patient-related health outcomes [7]. A unique feature of game-based learning is the use of sensor-based technologies which records facial expression analytics and gaze tracking for a deeper understanding of the impact of learning. Nevertheless, the long-term impacts of these innovations are still awaited.

From the logistic and economic viewpoint, digital health has coined the possibility of eHealth, interchangeable with telemedicine, which provides cost-effective solutions with less visits to health-care facilities and a significant drop in unplanned admissions due to regular follow-ups. This utility of eHealth became more advantageous during the COVID-19 outbreak, owing to a remarkable progress in the digital health-care ecosystem. The creation of specific call centers, self-registration, virtual visits systems (eConsult), mobile health applications for self-assessment, electronic medical records, auto-renewal of chronic medications and day-to-day record of bed status of health-care facilities are some of the clinical applications of eHealth. Such smart use of technologies during the COVID-19 pandemic has been rightly stated as “turning crisis into an opportunity” [8].

The shift of the health-care system from a conventional and orthodox version toward a digitalized climate commemorates a turning point toward a right direction. This paradigm shift underpins the need to ensure that the underserved and under-resourced societies are not ignored. The establishment of patient-centered health equity for digital health requires a multifaceted approach by governmental agencies, vendors, institutions, clinical teams, and patients. Development of robust regulations to safeguard e-professional ethics, safe and secure Internet bandwidth, creation of culturally and linguistically acceptable tools, sociodemographic needs of the society, training and awareness campaigns, and equitable implementation of digital health-care system are the bare minimum to execute a well-accepted health-care ecosystem in the digital realm [9].

Digital health is not without challenges and pitfalls. As it stands, there is no standard framework for the identification of a validated digital health-care system. Internet is replete with inaccurate and unreliable information without regulatory guidance. Furthermore, enormous funding, specialized resources, and digital literacy of all stakeholders are essential. Poverty, poor access to Internet, old devices with less compatibility with smart applications, and the lack of a standard and unified system for accurate and reliable biomedical datasets pose challenges. A tangible solution is the Digital Health Equity Framework with its associated personalized care and digital health equity [10]. Lastly, a well-thought concept of e-professionalism should be inculcated into the clients’ policies to appraise all stakeholders about data privacy, confidentiality, accountability, duty of care, and respect for others.

With immense pride and joy, I am delighted to be a part of the editorial team of the Advances in Biomedical and Health Sciences Journal which will certainly facilitate the dissemination of novel and relevant research in biomedical fields. This will promote the concept of digital health by distributing the emerging evidence-based knowledge in the medical sphere.

Financial support and sponsorship

Not applicable.

Conflicts of interest

There are no conflicts of interest

  References Top

Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digital Med 2020;3:1-7.  Back to cited text no. 1
Frank SR Digital health care: The convergence of health care and the internet. J Ambul Care Manag 2000;23:8-17.  Back to cited text no. 2
Amft O How wearable computing is shaping digital health. IEEE Pervas Comput 2018;17:92-8.  Back to cited text no. 3
Hulsen T, editor. Challenges and solutions for big data in personalized healthcare. In Big Data in Psychiatry: Neurology. Amsterdam: Elsevier; 2021. 69-94.  Back to cited text no. 4
Guraya SS, Guraya SY, Harkin DW, Ryan Á, Mat Nor MZ, Yusoff MS Medical education e-professionalism (MEeP) framework: From conception to development. Med Educ Online 2021;26:1-25.  Back to cited text no. 5
McKendrick M, Yang S, McLeod GA The use of artificial intelligence and robotics in regional anaesthesia. Anaesthesia 2021;76:171-81.  Back to cited text no. 6
Emerson A, Cloude EB, Azevedo R, Lester J Multimodal learning analytics for game-based learning. Br J Educ Technol 2020;51: 1505-26.  Back to cited text no. 7
Sust PP, Solans O, Fajardo JC, Peralta MM, Rodenas P, Gabaldà J, et al. Turning the crisis into an opportunity: Digital health strategies deployed during the COVID-19 outbreak. JMIR Public Health Surveil 2020;6:1-8.  Back to cited text no. 8
Papavasiliou S, Reaiche C, Papavasiliou S Digital health and patient-centred care: A digital systems view. Syst Res Behav Sci 2021;38:231-45.  Back to cited text no. 9
Rodriguez JA, Clark CR, Bates DW Digital health equity as a necessity in the 21st century cures act era. JAMA 2020;323:2381-2.  Back to cited text no. 10


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