Integration of technology in healthcare:
In recent years, advancement in computational technologies has reshaped the healthcare sector. In general, data analytics has evolved in multiple healthcare-related domains as a promising method for solving challenges. As per the market database, data has become an important part of healthcare in the new digital world. A new Big Data study estimates that healthcare data’s overall potential would be about USD 300 billion if adopted timely. The entities in the healthcare sector have been gathering large volumes of data for their patients due to the sheer significant developments in data collection and processing technology. Effectively interpreting and building healthcare data awareness includes the creation of sophisticated computational tools that can turn data into useful and actionable insights effectively.
General computational technologies have begun to revolutionize the way patients have access to medical services. The recent market database suggests that data analytics forms a vital part of these computational technologies. When applied to healthcare results, predictive solutions have an incredible ability to turn healthcare delivery from reactive to strategic. The influence of data analytics in the healthcare sector is expected to only increase further. Typically, reviewing health records would help one to understand the trends in the data that are obscured. It can also assist doctors to construct an individualized health record that will precisely measure an individual patient’s risk of suffering from a medical condition shortly.
Voracity of Data generated in the healthcare sector:
In addition, healthcare computational technologies are diverse and produced from a wide range of sources, including sensors, pictures, medical literature/clinical records, document, and conventional electronic data. In both the creation and interpretation of the underlying results, this diversity in the data collection and representation process leads to various difficulties. There is a wide range of techniques needed to examine these various data forms. Furthermore, the diversity of the data inherently poses diverse problems for data integration and data analysis. In many scenarios, it is possible to extract knowledge from different forms of data, which otherwise would not be feasible from a single data source. It is only recently that the enormous potential of such interconnected approaches for data processing has been recognized. The recognition associated with these data techniques is anticipated to further boost technological growth.
Advances from various disciplines including databases, data analysis, data mining, medical researchers, and healthcare practitioners have often been witnessed in the healthcare field. According to the procured market database, while this interdisciplinary nature contributes to the field’s richness, it also contributes to the challenges of making significant progress. In general, data scientists are not qualified in domain-specific medical principles, whereas medical professionals have minimal exposure to the mathematical and methodological context needed in data analytics of computational technologies. This has led to the challenge of developing a cohesive body of work in this area, even though it is apparent that such sophisticated research methods will support most of the available data.
Regional growth associated with Healthcare Analytics:
Although healthcare expenses have been increasingly growing, there has been little substantial change in the standard of services given to patients in the United States. Recently, it was demonstrated through academic research that there was a decrease in death rates, treatment expenses, and surgical risks in multiple hospitals by integrating current healthcare computational technologies. The Health Information Technology for Economic and Clinical Health Act (HITECH) was passed by the US government in 2009, which provided an incentive package of about USD 27 billion for the implementation and practical use of electronic health records (EHRs). The impact of these regulations on the growth associated with the healthcare sector is studied by Global Market Database. The market research platform provides free market data across 600+ verticals in 12 different industries.
The data privacy gap between medical professionals and data analysts is a major challenge in the healthcare sector. Healthcare data is very critical, and it can expose the confidential data of patients. Several regulations in various countries, including the United States Health Care Portability and Accountability Act (HIPAA), specifically restrict the publication of medical records of patients, unless provisions are used to protect privacy.
According to market research reports, Healthcare fraud has been one of the United States’ main issues, costing several billion dollars each year.
The risk of healthcare fraud is rising at an accelerating rate with growing medical costs. The detection of fraud has been at the forefront of attempts to reduce healthcare costs, given the recent scrutiny of failures in the US healthcare system. To address the issue of healthcare fraud, multiple solutions focused on data analytics have been explored. Automatic extraction of fraud patterns and prioritization of suspect cases are the key benefits of data-driven fraud detection. Most of such analysis is performed concerning an episode of care, which is essentially a collection of healthcare provided to a patient under the same health issue.
Owing to hardware and software technologies, which have improved the efficiency of the data collection process, the domain of healthcare analytics has seen major advancements. However, because of its complex nature, privacy limitations, and wide unstructured nature of the data, the development of the field has faced a range of challenges. In certain cases, data analytics can require real-time interpretation and perspective. The data can be complex in some scenarios, which may require advanced recovery and analytical techniques. According to the market database, advances in data collection technologies, which have enabled the field of analytics, also pose new challenges because of their efficiency in collecting large amounts of data.