Analytics for big data has rapidly become indispensable for nearly all clinical and operational processes in the healthcare industry. Such functions include support for clinical decisions, predictive analytics for big data, management of revenue cycles, measurement of quality, and management of the health of populations.
Healthcare organizations only just mastered placing data into electronic health records. They now must extract actionable insights from their electronic health records. Moreover, they now must apply learnings to complex initiatives that affect their reimbursement rates.
Incorporating data-driven learning into operational and clinical processes can result in significant rewards for healthcare organizations. Converting assets of data to insights from data provides many advantages. Such benefits include higher satisfaction rates of end-users and staff, increased visibility into performance, reduced costs of care, and healthier patients.
Extracting actionable insights from analytics is a complex problem for healthcare organizations. Obtaining meaningful analytics for big data for healthcare is challenging due to the multifaceted and cumbersome contents. It requires healthcare organizations to carefully inspect their methods to collect, store, analyze, and present their data to patients, organizational partners of the healthcare organization, and staff. Unfortunately, the complexity of the analytics for big data is difficult to divide into manageable parts. Healthcare organizations must comprehend and resolve each analytics property for big data to succeed with their selected projects. Consequently, analytics from big data is a serious endeavor for the healthcare industry.
Properties of Big Data
Relevance
Relevance refers to whether data is pertinent to specific use cases. Data scientists often cite the fact that correlation does not equal causation. Comprehending which data elements are connected to measuring and predicting the desired outcomes is essential for producing dependable results. For this purpose, healthcare organizations must grasp their features, whether these elements are sufficiently robust for analysis, and whether results are truly informative or merely exciting diversions. Establishing the viability of specific metrics and features requires trial and error. Many projects for predictive analytics for big data currently emphasize identifying innovative variables for detailing certain behaviors of patients and clinical outcomes. This emphasis continues to be prevalent as more data sets become available.
Reliability
Reliability refers to whether data can be trusted. Dependability is more critical than access in the context of patient care. The integrity of datasets is challenging to confirm. However, healthcare organizations cannot utilize insights data analysts may have derived from noisy, biased, and incomplete data. Data scientists generally spend most of their time cleaning up data before applying it. Healthcare organizations are continuously struggling to improve the quality and integrity of their data. For example, healthcare systems allow unstructured entries such as free text and scanned images. Governance of data and information is a vital strategy that healthcare organizations must follow to ensure that their data is readily available, standardized, complete, and clean.
Soundness
Soundness refers to whether data is correct and accurate. Datasets may not tell end-users what they are trying to determine, even when datasets are completed. The fundamentals of soundness include proper responsibility for stewarding and curating data, the information generated by applying accepted scientific methods and protocols, and current values.
Data sets in the healthcare industry must consist of precise metadata describing the author, the process, and the date and time concerning the data creation. Metadata ensures that the analytics for big data are repeatable, data analysts comprehend each other, and future data scientists can query data and find what they attempt to determine.
Diversity
Diversity refers to the number of different types of data sources. Meaningful data comes in many forms and sizes. Healthcare organizations need more variety. The meaning of analytics for big data may be separated from its volume.
The disorganized development of information technology over long periods left healthcare organizations with data silos that are nearly impossible to penetrate. Significant barriers to practical data management include various locations, inconsistent semantics, non-aligned data structures, and incompatible data formats. Such restrictions make it nearly impossible to compare datasets. The inability to compare datasets constrains the insights that healthcare organizations can gain concerning their patients and operations. Developers in healthcare are now dividing problems via application programming interfaces and
new standards (such as Fast Healthcare Interoperability Resources). These two techniques make it easier to penetrate silos and increase variety.
Stability
Stability refers to how frequently data changes. Data in the healthcare industry changes quickly. The rapid changes raise questions of how long data is relevant, which historical metrics to include in analyses, and how long to store data before archiving and deleting it. Datasets with higher rates of turnover and less relevance to analytics for big data may be more eligible for deletion than those that remain stable and reusable for long periods. Such regular datasets include genomic test results of patients. Such decisions become increasingly important as the volume of data continues to grow daily.
The cost of storing data is significant for most healthcare organizations. The Health Insurance Portability and Accountability Act requires healthcare organizations to retain specific patient data for at least six years. This constraint complicates the cost of data storage.
Speed
Speed refers to how quickly data is generated, accessed, and migrated. Some three billion gigabytes of data are produced daily. Data in the healthcare industry accounts for a significant proportion of the existing and newly created data. The speed increases as new techniques to develop and process data evolve. Such novel techniques include the processing of natural language, machine learning, testing of genomes, medical devices, and the Internet of Things. Determining what data sources are essential to access in days and weeks rather than months benefit healthcare organizations by reporting on quality and improving practices.
Rapidly changing data must update in real-time at the point of care and display immediately. Such data includes patient vital signs in the intensive care unit. In such cases, the response time of systems is an essential metric for healthcare organizations. The response time may also be a competitive differentiator for vendors that develop relevant products. Slowly changing data can slowly pass through healthcare organizations without negative impact. Such data includes collection rates of patients and reports of readmissions. Consequently, attempting to make all data streams as fast as possible is not an appropriate use of resources, and this may not even be feasible.
Capacity
Capacity refers to how much data exists. Analytics refers to extensive datasets. The amount of data generated doubles each year. Some forty trillion gigabytes of data are expected to live in three years.
Most data is short-lived. Such transient data is rarely or never analyzed for insights. Examples of transient data are streaming audio and video. In contrast, more than one-third of the data may be helpful for big data analytics in three years. This data must be appropriately curated and tagged for it to be beneficial. Data in the healthcare industry tend to be full of information. Also, data in the healthcare industry becomes even more helpful when combined in novel ways to produce brand-new insights. Such data includes imaging studies, data from medical devices, gene sequences, laboratory results, claims data, and clinical notes.
Healthcare organizations must develop techniques to store data to handle large amounts of relevant data. Such storage may be located on-premise and in the cloud. Healthcare organizations must also ensure that their infrastructure keeps up with the properties of analytics for big data without slowing down critical functions (such as access to electronic health records) and communications between healthcare organizations.
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