This study was conducted to analyze this process closer focusing on a case of Cardiology. Conducting a comprehensive literature review and qualitative expert interviews, the impact of big data in the field of Cardiology was explored. The result of the study shows that big data can play a positive role in three aspects: prediction of disease, prevention of disease and management of disease. Big data enables us to build models that can be used to predict the occurrence of disease.
Based on this information, actions can be taken to prevent the disease. Data also helps to manage the disease by offering helpful insights. Medical personnel can retrieve the patient data, with the help of AI, they can make faster decisions allowing them to spend more quality time with the patients and reduce cognitive errors.
Through the interviews, it was understood that even though the positive role of big data has been acknowledged, the implementation is still a challenge due to various limitations. The challenges lie mainly on technical know-how and domain knowledge. Further challenges were data security and privacy issues that need to be addressed to mitigate the risks that can be caused by them. The examples of big data implementation in various cases like in heart failure prediction or prevention shows a positive picture.
The overwhelming majority of case studies analyzed in this regard show an optimistic picture. Due to growing importance and use of smart devices, IoT, genomics and the recent developments in the field of ICTs, it is expected that big data will not only leave a positive influence on the field of Cardiology, it will also change the way medicine is practiced and healthcare is offered.
The statement ‘Data is the new oil’ has been broadly acknowledged due to its wide-ranging importance. Utilizing big data offers a variety of benefits. Although the health sector was late in terms of exploiting the benefits of big data, currently, the adoption is accelerating. Healthcare is increasingly becoming an information science and the implementation of electronic medical records (EMR) and other information systems is growing rapidly. The patient data originating from smart devices and other sources like genomic databases are supporting the healthcare sector offering better healthcare delivery and increasing efficiency, hence saving costs.
Table of Contents
1. Introduction
1.1 Purpose Statement and Research Questions
1.2 Topic Justification
1.3 Scope and Limitations
1.4 Definition of Big Data
1.4.1 Healthcare Big Data Sources
1.4.2 Techniques and tools to analyze Healthcare Big Data
1.4.3 Application of Big Data in Healthcare
2. Methodology
2.1 Methodological Tradition
2.2 Methodological Approach
2.3 Data Collection
2.4 Methodology Applied for literature review
2.4.1 Inclusion and Exclusion Criteria
2.4.2 Search Procedures
2.4.3 Methods for Data Analysis
2.4.4. Validity, Reliability and Generalizability
2.4.5 Ethical Considerations
3. Results
3.1 Cardiology
3.2 Results from Comprehensive Literature Review
3.2.1 Prevention
3.2.2 Prediction
3.2.3 Management of Disease
3.2.4 Future Trends and Directions
3.2.5 Challenges
3.3 Results from Qualitative Interviews
3.3.1 Theme 1
3.3.2 Theme 2
3.3.3 Theme 3
3.3.4 Theme 4
3.3.5 Theme 5
3.3.6 Theme 6
4. Discussion
4.1 Part 1
4.2 Part 2
4.3 Part 3
4.4 Part 4
5. Conclusion
Research Objectives and Themes
The research aims to explore the role of big data within the healthcare sector, specifically focusing on the field of Cardiology, to determine how data analytics can support disease prediction, prevention, and management from a provider’s perspective.
- Impact of big data on cardiovascular disease burden and patient care quality.
- Integration of smart devices, IoT, and AI in clinical cardiology workflows.
- Challenges regarding data security, privacy, and technical domain knowledge.
- The shift toward precision medicine and patient-centric healthcare delivery.
- The influence of demographic and generational gaps on digital health adoption.
Excerpt from the Book
3.3.1 Theme 1: Inaccuracies in patient reported data can be mitigated by data captured by sensors and smart devices
When patients report about their symptoms and the extent of their pain to medical personnel, they are mostly subjective. A severe pain might mean something different for somebody who is experiencing the sort of pain for the first time than the ones who are used to that and have experienced that already. By nature, some people tend to exaggerate the event or express it in a more dramatic way than the other. Medical personnel often face the challenge to bring the information into context and to quantify the patient information. Although there is nothing wrong with the behavior of a patient, a uniform standard would be more accurate to make decisions. Furthermore, there might be other medical conditions that are important to know to treat a certain type of symptoms or disease. When patients forget the relevant information or skip the vital facts, the impact will be directly on the quality of care. In this regard, several participants have shared their thoughts and experiences.
Summary of Chapters
1. Introduction: This chapter introduces the motivation for the study, highlights the increasing pressure on global healthcare systems, and defines the research question regarding the role and impact of big data in Cardiology.
2. Methodology: This section details the exploratory mixed-method qualitative research approach, including the literature review process, the selection of interview participants, and the ethical considerations maintained throughout the study.
3. Results: This chapter provides an overview of Cardiology diagnostics, summarizes findings from the comprehensive literature review on big data applications, and categorizes insights from qualitative interviews into six recurring themes.
4. Discussion: This part analyzes the study findings by connecting the literature review with the empirical data from interviews, discussing the role of big data in prevention, prediction, management, and current implementation challenges.
5. Conclusion: The final chapter synthesizes the findings, confirming that big data and AI have a significant positive role in Cardiology, and provides recommendations for future research and policy-making.
Keywords
Big Data, Healthcare, Health, Cardiology, Cardiovascular Diseases, Medicine, Information Systems, Information and Communication Technology, Predictive Analytics, Patient-centric care, IoT, Smart Wearables, EMR, Data Privacy, Precision Medicine
Frequently Asked Questions
What is the core focus of this dissertation?
The dissertation assesses the role of big data in the healthcare sector, specifically examining its impact on the field of Cardiology, from the perspective of health providers.
What are the primary themes discussed in this work?
The study explores disease prevention, predictive analytics, disease management, implementation challenges, and future trends driven by technological advancements.
What is the primary research question?
The research asks: What is the role of big data and how is it impacting the field of Cardiology in terms of predicting, preventing and managing the diseases?
Which scientific methodology was utilized?
A mixed-method exploratory qualitative approach was used, consisting of a comprehensive literature review and semi-structured interviews with domain experts.
What topics does the main body of the work cover?
It covers healthcare data sources, predictive modeling techniques, the role of IoT and smart wearables in data collection, and the ethical and technical challenges of integrating big data into clinical practice.
What are the defining keywords for this study?
The study is characterized by terms such as Big Data, Cardiology, Cardiovascular Diseases, Precision Medicine, AI, IoT, and Patient-centric care.
How does the integration of smart sensors influence the role of the Cardiologist?
According to the interviewees, smart sensors allow for automated data collection and real-time monitoring, which can reduce administrative tasks for medical staff and allow them to spend more quality time with patients.
What is the significance of the "generational gap" identified in the research?
The study highlights that older generations of medical professionals are often more reluctant to trust and utilize new digital health tools compared to younger, "digital native" practitioners, which acts as a barrier to rapid technology adoption.
Why is "caution" mentioned regarding the integration of AI in healthcare?
Participants emphasized caution because AI and big data models are based on existing data; if this data is biased or incomplete, it could lead to erroneous clinical decisions, posing potential risks to patient safety.
- Quote paper
- Bikal Dhungel (Author), 2020, Big Data in Cardiology. Predicting, Preventing and Managing Diseases, Munich, GRIN Verlag, https://www.grin.com/document/920382
