Axes of a revolution: challenges and promises of big data in healthcare

Furthermore, emerging innovations such as AI-powered dashboards and health Internet of Things (H-IoT) applications are enabling more personalized and efficient care delivery. Despite its potential, the adoption of BDA in healthcare faces several challenges, such as lack of standardization, inconsistent data quality, resistance to change, and fast-paced technological evolution. The analysis of the latest data reveals that data analytics increase the accuracy of diagnoses. Physicians can use predictive algorithms to help them make more accurate diagnoses 45.

Applications in big data analysis

The scope of BDA is also being extended beyond core clinical applications, as observed by Guo and http://eyesvisions.com/bates-medical-articles-blindness-relieved Chen 91, who emphasized the development of scalable knowledge systems and interoperable platforms for health data management. These developments align with Semantic Web technologies and the use of knowledge graphs in healthcare, which facilitate structured data linkage and enhance machine interpretability. The emergence of standards like HL7 FHIR supports interoperable electronic health records (EHRs) and seamless data exchange across diverse platforms. Similarly, Alsmadi et al. 92 discussed the role of innovation-driven data ecosystems, which are crucial for the long-term sustainability of digital health infrastructures. Such ecosystems rely on open innovation, public–private partnerships, and shared data repositories to enable collaborative analytics and rapid solution deployment. According to OECD (2021), data ecosystems thrive when supported by regulatory clarity, trust mechanisms, and active stakeholder engagement.

On the basis of the literature analysis and research study, a set of questions and statements related to the researched area was formulated. The results from the surveys show that medical facilities use a variety of data sources in their operations. Prolaio is a clinical intelligence platform that aims to deliver continuous, predictive and shareable heart data. It applies analytics and AI to large-scale healthcare data, supporting clinical decision making and improving patient outcomes. Socially Determined takes a more holistic approach to population health by supplying healthcare organizations with social risk intelligence.

Recent Healthcare Technology Articles

Furthermore, the framework enabled consistent application of objective inclusion and exclusion criteria for the inclusion of required studies. By adopting the PRISMA framework, this study aligns with globally accepted best practices for systematic reviews. This strengthens the validity and reliability of the findings and enhances their utility for future researchers and policymakers. In order to introduce new management methods and new solutions in terms of effectiveness and transparency, it becomes necessary to make data more accessible, digital, searchable, as well as analyzed and visualized. In turn, Knapp perceived Big Data as tools, processes and procedures that allow an organization to create, manipulate and manage very large data sets and storage facilities 38. From this point of view, Big Data is identified as a tool to gather information from different databases and processes, allowing users to manage large amounts of data.

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Thus, starting from this background the discussion on the future perspectives on BDA development in the healthcare organizations appears as a need. Our systematic literature review revealed both challenges and opportunities that big data offers to the health care industry. The literature mentioned the challenges of data structure and security in at least 50% of the articles reviewed. The literature also mentioned the opportunities of increased quality, better management of population health, early detection of disease, and data quality structure and accessibility in at least 50% of the articles reviewed.

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In addition, there is a need for computationally efficient algorithms to handle the heterogeneity, noise, and massive size of structured BD. Healthcare big data refers to collecting, analyzing, and leveraging consumer, patient, physical, and clinical data that is too vast or complex to be understood by traditional means of data processing. Instead, big data is often processed by machine learning algorithms and data scientists.

Healthcare professionals analyze such data for targeted abnormalities using appropriate ML approaches. The huge size and highly heterogeneous nature of big data in healthcare renders it relatively less informative using the conventional technologies. The most common platforms for operating the software framework that assists big data analysis are high power computing clusters accessed via grid computing infrastructures. Cloud computing is such a system that has virtualized storage technologies and provides reliable services. It offers high reliability, scalability and autonomy along with ubiquitous access, dynamic resource discovery and https://business-exclusive.com/essential-tools-and-equipment-for-a-modern-dental-lab.html composability. Such platforms can act as a receiver of data from the ubiquitous sensors, as a computer to analyze and interpret the data, as well as providing the user with easy to understand web-based visualization.

• This output confirms a growing interest in the research field of BDA applied to healthcare organizations (Fig. 2).
• Data privacy 18, 19, security 20, 21, and surveillance 22, 23 are a few of the concerns sparked by the usage of BDA in healthcare.
• The most relevant studies having an alignment with the study’s objectives are selected through rigor methods and eligibility criteria.
• Data science can effectively manage, evaluate, and comprehend huge data by opening up new possibilities for comprehensive medical treatment.
• It assists in the extraction of core studies to facilitate the systematic literature review effectively and efficiently.

In the field of digital health, big data is being utilized to create prediction models and algorithms that may lead to better patient outcomes. On massive data sets, machine learning algorithms can be taught, for example, to better inform decision‐making and illness management 6. The development of DNA sequencing technologies has allowed for the quick and inexpensive collection of a vast quantity of genetic information that can be used to determine the genetics of diseases and guide the creation of personalized treatments. The simultaneous measurement of hundreds of molecules by RNA sequencing and quantitative proteome profiling offers the opportunity to understand the transcriptome and proteomic landscapes of cohort specimens for scientific and medical research. Doctors recommend the use of telemedicine to patients for personalized treatment solutions and to prevent readmissions. Healthcare big data analytics can then be linked to a predictive analytics program to predict medical events and improve the overall quality of patient care.

Since telemedicine is highly accessible, doctors can monitor patient activity from anywhere and at any time. The overall objective of healthcare business intelligence is to give doctors the ability to make quick data-driven decisions. In the case of patients who suffer from complex, rare illnesses, this ability becomes very useful. There is evidence to suggest that the healthcare industry is far more likely to experience a data breach when compared to any other. This information can be used to identify potential health risks that may not be easily detectable. Furthermore, when patients take more control over their health, they can be encouraged by payers and other organizations to live a healthier lifestyle.

Automated digital data flow

Scorekeeping method was applied for the searched studies according to pre-developed questions. The searched articles were allocated a score having an alignment with specific categories. The score mapping consisted of the options of ‘yes’, ‘no’, ‘partially’, ‘barely’, and ‘satisfactorily’. An adequate score was essential for the studies for their inclusion in the current investigation. This process supported in selecting the most relevant studies to carry out the systematic literature review on the topic under investigation.