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Big Data Analytics in Healthcare Data Processing
Published in Punit Gupta, Dinesh Kumar Saini, Rohit Verma, Healthcare Solutions Using Machine Learning and Informatics, 2023
Tanveer Ahmed, Rishav Singh, Ritika Singh
Veracity: Data veracity refers to the degree of certainty that a data interpretation is consistent [11]. Varied data sources have different levels of data reliability and dependability [12]. Unsupervised machine learning algorithms, on the other hand, are used in healthcare for automated machines to make decisions based on data which may be deceptive or useless [10].The purpose of healthcare analytics is to obtain useful information that can be used to make better decisions and provide better patient care.
Case Study-Based Big Data and IoT in Healthcare
Published in Govind Singh Patel, Seema Nayak, Sunil Kumar Chaudhary, Machine Learning, Deep Learning, Big Data, and Internet of Things for Healthcare, 2023
Arun Kumar Garov, A.K. Awasthi
In healthcare, data analysis is making a big change in this current era. This is helpful for the newcomers and patient treatment. Big data is useful for finding new ways to treat diseases. It can benefit patients and providers for healthcare in different ways, including the following: Effect on Patients: Big data is used to understand the personal and community trends of the patient and create a plan for useful treatment or forecast of patient risk.Staff and Operating Systems: Big data can be used to relieve overloaded of staff and the requirement of new staff members in healthcare and shift-wise arrangement of staff members. It is also used to forecast the long-term treatment of patients coming to the healthcare centre.Development of Product: Big data can help in researching a new product, new therapy, and medicine for healthcare.Strategic Planning: Healthcare analytics can be used to identify harmful diseases and in plans for solving problem. Sources of big data in the healthcare sector are shown in Figure 2.2.
Sensors and the Internet of Things
Published in Connie White Delaney, Charlotte A. Weaver, Joyce Sensmeier, Lisiane Pruinelli, Patrick Weber, Nursing and Informatics for the 21st Century – Embracing a Digital World, 3rd Edition, Book 3, 2022
Imagine a world where data is ubiquitous in a healthcare ecosystem comprised of clinical providers, patients, payers, medical device makers, regulatory institutions and so forth. For any healthcare provider, clinical, demographic, environmental and other data would be available in real time for their patients through large, integrated databases. Advanced healthcare analytics software could mine these databases and discover patterns that lead to new knowledge regarding diagnosis and treatment of disease conditions, ways to improve efficiency and reduce cost, prediction of patients at high risk for safety issues, patient empowerment and self-care and population health. The IoT has the potential to achieve this vision and in many healthcare organizations, it already has.
Meeting report: an exploration into the scientific and regulatory aspects of pharmaceutical drug quality in the United States
Published in Expert Opinion on Drug Safety, 2022
Bridget M. Flavin, Laura E. Happe, Randy C. Hatton
A market-based example presented was Pharm3r, a company that uses healthcare analytics and artificial intelligence to identify signals and patterns from real-world quality data and aggregates them into usable facility quality scores that can be compared throughout the global supply chain. Scoring a facility versus a product allows a broader perspective as individual products can be made at multiple facilities with varying quality, but a low scoring facility indicates all its products should be scrutinized. A demonstration of Pharm3r’s capabilities supported additional key points about drug quality issues: they occur with generic and brand drugs, they are not limited to specific geographic regions, they occur in U.S. facilities, and they occur with APIs and finished products. Their data also provide a nuanced view of the relationship between drug shortages and quality by differentiating other root causes like increased demand or manufacturing delays. Further, Pharm3r assesses the recency of issues to identify trends and offers predictability of future issues.
mHealth for pediatric chronic pain: state of the art and future directions
Published in Expert Review of Neurotherapeutics, 2020
Patricia A. Richardson, Lauren E. Harrison, Lauren C. Heathcote, Gillian Rush, Deborah Shear, Chitra Lalloo, Korey Hood, Rikard K. Wicksell, Jennifer Stinson, Laura E. Simons
Data generated by advancements in digital health technologies, in concert with other healthcare data, require sophisticated analyses. Such ‘Big Data’ approaches need to be able to process a multitude of data points on differing scales, including data from wearable sensors, apps, administrative healthcare data, clinical registries, electronic health record, diagnostic imaging, among others [116]. To process Big Data, machine learning algorithms and artificial intelligence (AI) are employed with the goal of predicting, preventing, and optimally treating target health outcomes. In one recent example within the context of chronic pain, a measure of pain volatility (i.e. variability in pain intensity over time) was developed for patients using a pain management app at 1 and 6 months (Manage My Pain app) [117]. A total of 130 demographic, clinical, and application usage variables were collected within the first month of app use. Machine learning algorithms were then employed to analyze the 130 variables to successfully predict pain volatility 6 months later. The above-described study by Rahman and colleagues [117] is a promising example of how mHealth and Big Data can be synthesized to assess and ultimately improve personalized care for patients with chronic pain. The healthcare industry is only beginning to explore the pragmatics of healthcare analytics [118]. A literature base is needed to evaluate if Big Data approaches will be able to improve upon existing assessment and interventional paradigms in the treatment of pediatric chronic pain.
Artificial intelligence: improving the efficiency of cardiovascular imaging
Published in Expert Review of Medical Devices, 2020
Andrew Lin, Márton Kolossváry, Ivana Išgum, Pál Maurovich-Horvat, Piotr J Slomka, Damini Dey
Despite the potential pitfalls, the incorporation of AI into cardiology is not a change that clinicians should fear, but rather, one that should be embraced. Many technology companies, such as IBM, Google, and Apple, are investing heavily in health care analytics. Furthermore, both the American Heart Association and American College of Cardiology have established precision medicine and digital innovation platforms to develop and implement AI technologies to improve cardiovascular care. AI-augmented medical systems will serve to streamline workflow and provide more reproducible and objective quantitative results which can inform clinical decisions. Physicians will be able to focus on tasks best performed by human intelligence without being distracted by the tedium of many tasks (such as manual measurement of imaging parameters and interacting with EHRs) which can be fully automated. This may lead to higher physician job satisfaction by avoiding data fatigue, lower healthcare costs through the reduction in response time to critical data and avoiding unnecessary hospital admissions, and improved quality of care by providing prompt, personalized treatment.