Big data in healthcare: a discussion on the big challenges

A perspective on computer vision in biosensing

Computer vision has become a powerful tool in the field of biosensing, aiding in the development of innovative and precise systems for the analysis and interpretation of biological data. This interdisciplinary approach harnesses the capabilities of computer vision algorithms and techniques to extract valuable information from various biosensing applications, including medical diagnostics, environmental monitoring, and food health. Despite years of development, there is still significant room for improvement in this area. In this perspective, we outline how computer vision is applied to raw sensor data in biosensors and its advantages to biosensing applications. We then discuss ongoing research and developments in the field and subsequently explore the challenges and opportunities that computer vision faces in biosensor applications. We also suggest directions for future work, ultimately underscoring the significant impact of computer vision on advancing biosensing technologies and their applications.

Big data-driven public health policy making: Potential for the healthcare industry

The use of healthcare data analytics is anticipated to play a significant role in future public health policy formulation. Therefore, this study examines how big data analytics (BDA) may be methodically incorporated into various phases of the health policy cycle for fact-based and precise health policy decision-making. So, this study explores the potential of BDA for accurate and rapid policy-making processes in the healthcare industry. A systematic review of literature spanning 22 years (from January 2001 to January 2023) has been conducted using the PRISMA approach to develop a conceptual framework. The study introduces the emerging topic of BDA in healthcare policy, goes over the advantages, presents a framework, advances instances from the literature, reveals difficulties and provides recommendations. This study argues that BDA has the ability to transform the conventional policy-making process into data-driven process, which helps to make accurate health policy decision. In addition, this study contends that BDA is applicable to the different stages of health policy cycle, namely policy identification, agenda setting as well as policy formulation, implementation and evaluation. Currently, descriptive, predictive and prescriptive analytics are used for public health policy decisions on data obtained from several common health-related big data sources like electronic health reports, public health records, patient and clinical data, and government and social networking sites. To effectively utilize all of the data, it is necessary to overcome the computational, algorithmic and technological obstacles that define today's extremely heterogeneous data landscape, as well as a variety of legal, normative, governance and policy limitations. Big data can only fulfill its full potential if data are made available and shared. This enables public health institutions and policymakers to evaluate the impact and risk of policy changes at the population level.

Perception and Utilization of Artificial Intelligence (AI) among Dental Professionals in Saudi Arabia

Objective:

Artificial intelligence (AI) is the new buzzword that is trendy in multiple branches of dentistry. The aim of this study was to assess perceptions and utilization of AI among dental professionals in Saudi Arabia.

Methods:

This was a cross-sectional study investigating 389 dental students and dentists from 22 cities in Saudi Arabia using a validated self-reported questionnaire.

Results:

A total of 49.4% of participants reported knowing what AI is; 44.5% reported having basic knowledge of AI principles, and 42.2% know of AI uses in dentistry. The most common AI information source was social media (66.07%). Out of 17 AI attitude items, 16 were scored above the midpoint. A total of 75.0% of participants agreed or strongly agreed AI will lead to major advances in dentistry. In contrast, 49.1% agreed or strongly agreed that AI could replace dentists in the future. There were no significant differences by gender or region, but students and interns had significantly higher attitude scores than did dentists. There was a widespread desire to take professional courses in dental AI use (69.7%), and some had used dental AI applications (25.4%) or taken an AI course (18.5%–20.3%). The most common barriers to dental AI use were non-availability of courses (73%) and lack of time (68.9%).

Conclusion:

Dental professionals in Saudi Arabia have moderate awareness levels and high rates of good attitudes about AI in dentistry. However, AI use in practice is limited. Incorporating AI in dental curricula is crucial due to the worldwide digital transformation.

Which Framework to Use? A Systematic Review of Ethical Frameworks for the Screening or Evaluation of Health Technology Innovations

Innovations permeate healthcare settings on an ever-increasing scale. Health technology innovations (HTIs) impact our perceptions and experiences of health, care, disease, etc. Because of the fast pace these HTIs are being introduced in different healthcare settings, there is a growing societal consensus that these HTIs need to be governed by ethical reflection. This paper reports a systematic review of argument-based literature which focused on articles reporting on ethical frameworks to screen or evaluate HTIs. To do this a four step methodology was followed: (1) Literature search conducted in five electronic literature databases; (2) Identification of relevant articles; (3) Development of data-extraction tool to analyze the included articles; (4) Analysis, synthesis of data and reporting of results. Fifty-seven articles were included, each reporting on a specific ethical framework. These ethical frameworks existed out of characteristics which were grouped into five common ones: (1) Motivations for development and use of frameworks; (2) Objectives of using frameworks; (3) Specific characteristics of frameworks (background context, scope, and focus); (4) Ethical approaches and concepts used in the frameworks; (5) Methods to use the frameworks. Although this multiplicity of ethical frameworks shows an increasing importance of ethically analyzing HTIs, it remains unclear what the specific role is of these analyses. An ethics of caution, on which ethical frameworks rely, guides HTIs in their design, development, implementation, without questioning their technological paradigm. An ethics of desirability questions this paradigm, without guiding HTIs. In the end, a place needs to be found in-between, to critically assess HTIs.

Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review

Background

“Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them.

Methods

We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health.

Results and analysis

Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement.

Conclusion

Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.

Cancer in Saudi Arabia (CSA): Web-Based Application to Study Cancer Data Among Saudis Using Waterfall Model

BACKGROUND

Information technology (IT) has emerged as a promising enabler to address the issue of big data in health care. Despite the urgent need for an IT-based tool to tackle this issue, one is not available to specifically study the massive data related to cancer among Saudis.

OBJECTIVE

To develop a web-based application, which we named "Cancer in Saudi Arabia (CSA)" to provide an interactive, quick, and easy method to reach, extract, compare, and visualize cancer data from Saudi Cancer Incidence Reports (SCIRs).

METHODS

We used waterfall model to develop CSA. Next, we used CSA to study the data of non-Hodgkin lymphoma (NHL) in Saudis reported in the SCIRs (1999-2015).

RESULTS

CSA-based analysis showed that NHL incidence rate increased with age and the disease was more common among males compared with females. In addition, NHL was most predominant in the regions of Riyadh and Eastern, while it was the least prevalent in Jazan Region. Interestingly, the largest proportion of NHL patients was diagnosed in the late stage, and malignant lymphoma, large B-cell diffuse, OS (DLBCL) were the most frequent subtypes of NHL.

CONCLUSION

As a user-friendly application, we believe that CSA will be a useful tool for studying cancer data in Saudis and will make the data published in SCIRs more reachable and usable. Our findings of NHL provided an almost comprehensive view of the epidemiology of the disease in Saudis for 17 years.

The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees

Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.