With the extensive use and constant up-keeping of various management systems, reporting tools, and electronic records that come with being part of the healthcare profession, the demand for better business intelligence applications in these areas has skyrocketed. Access to informative and systematic analytical tools is a necessary commodity within the medical field.
The amount of time and effort it takes to execute healthcare data management is growing exponentially. Physicians and healthcare employees struggle to balance between time spent on data management and other tasks they must complete at work—spent with patients, a team of coworkers, or taking part in meetings. The current environment taxes all healthcare employees involved.
Healthcare organizations are noticing a significant spike in employee burnout stemming from the time spent on data reporting. Overall employee satisfaction is plummeting due to the difficult task of analyzing electronic heath records, documentation, and straining to look at large amounts of data for long hours.
Effects of employee burnout are numerous, and include fatigue and/or general indifference which leads to mistakes, which is detrimental to the process.
Being attentive when dealing with such sensitive material is paramount to maintaining a functional, faultless data governance program. However, because of the steadily rising numbers of patients requiring numerous medications, doctor visits, and the like—and the long hours it takes to document each case—human error is almost unavoidable.
The need to produce an easier, more streamlined way of collecting and reporting sensitive data within the medical profession provides healthcare analytic companies the opportunity to create efficient and intuitive reporting tools to highlight data and clean up inaccurate, unneeded information. Analytic companies are beginning to solve current struggles through the use of artificial intelligence. Relying on a program specifically designed with healthcare in mind takes the weakness of data governance, and puts it into the virtual hands of a platform that accomplishes all the heavy lifting. Artificial Intelligence focuses on all the intricate details of data reporting and collection, so healthcare professionals are able to focus and prioritize daily issues.
When faced with what policies should be in place when dealing with data governance policies, there are four key points that should make up the foundation of what is needed to fulfill the needs of the client:
Getting data to the correct place: While this seems obvious, it has been reported that spending time resolving patient file discrepancies is alarmingly common in healthcare. There are similar ways of matching client’s files to their person, such as birth date or home address, but not every system is the same, which results in mismatched data. Matching data across platforms is crucial, as insufficient or outdated data adversely affects a patient’s treatment.
Excellent visualizations quickly summarize data: A healthcare organization’s data grows larger by the day. A robust governance tool (or business intelligence application) quickly sifts through data to present quick and actionable information to decision makers.
Workflow optimization reduces the number of clicks and drill paths: Navigating systems in a quick and meaningful way is a vital step in the right direction towards fulfilling client’s needs when regarding data governance. Access to correct data and viewing information in a concise and direct manner is important. If information is confusing or difficult to navigate, there’s a good chance a crucial detail will be missed.
Reducing number of data silos and errors: This step could be seen as having a virtual maid for unnecessary information. Creating an intuitive system that can take all the overwhelming data and cleans it up into a neatly presented, precise package helps immensely in data governance.
Successes in data governance ultimately depends on proper patient engagement, data access, dynamic analytics, and population health. Seeing the meaningful details is crucial, and with the expansive healthcare system, the ways in which this is possible will continue to evolve and grow with the needs of medical professionals, Implementing the four data governance policies allows healthcare companies the effortlessly accomplish this feat.
Healthcare Business Intelligence Market
Global Healthcare Business Intelligence Market is forecasted to reach $8.9 billion by 2023 from $4.4 Billion in 2018, at a compound annual growth rate of 15.3%. Business intelligence, or BI is an umbrella term for analytics, data warehousing, and visualization tools, but the phrase is used as a form of strategy. BI was created to present information to end users that assists them in making educated business decisions. BI is used to provide analytics for an array of business resolve, whether it is basic operating decisions, such as pricing, or strategic business decisions, such as priorities and goals.
North America is projected to grow at the fastest rate with the largest share of healthcare business intelligence, due to the increased demand for improved healthcare and lower costs. The healthcare industry is experiencing a rapid shift towards analytics and BI. For the healthcare industry to keep up with the demands of a quickly evolving world, focus needs to be on making sure data warehousing is keeping up with the current technological advances. The data warehouse must maintain a totally secure platform that is simple to navigate and supports a wide variety of data.
According to a 2014 report, Top Actions for Healthcare Delivery Organization CIOs, 2014: Avoid 25 Years of Mistakes in Enterprise Data Warehousing, not employing a BI strategy is one of the “fatal flaws in business operations improvement” in health care. Healthcare is one of the most complex data sources of any industry, but organizations have yet to utilize their full wealth of data. The biggest flaw is “lack of a documented BI strategy, or the use of a poorly developed or socialized one.”
Enterprise data warehouse, or EDW, is essential to the growth and maintenance of the healthcare organization. The capacity to consolidate data from multiple sources and providing decision makers with correct information is a key factor in becoming better fit for the expanding healthcare market. Main drivers for market growth is due to government mandated electronic health records, reductions in healthcare spending, and big data in healthcare. Becoming and staying organized with an EDW could pose as challenging, because of the initial data input and potential learning curve, but in the long run, an EDW would improve efficiency in three key ways:
Enabling an effective and easily changeable reporting process: Instead of spending time manually setting up, compiling, and running data, an EDW would streamline the process by interpreting data, running visualizations and/or reports, and obtain insights to achieve goals.
Making sure all data is consistent and precise: Data differs from person to person because of the numerous ways in which data can be collected. With a healthcare EDW, one single source is established, and permits healthcare analytics, transforming healthcare into a detailed data driven culture. An EDW assists in developing and maintaining a data governance program so that professionals could identify and resolve data issues, while determining who needs access to the data, and defining the best data access path.
An EDW platform supplies access to data, real-time answers, and secures the integrity of data. Implementing an EDW into the healthcare system is crucial to the ever-growing market.
Deep Learning, Blockchain, Big Data to see Huge Growth in Healthcare
Deep learning, also referred to as hierarchical learning or deep structured learning, is a type of AI that uses a layered algorithm structure to analyze data. Using deep learning, data is filtered through multiple layers, gaining and processing information in a way that becomes more and more accurate—essentially learning from previous results to make connections within the data. These layers, when translated, turn raw input into meaningful output. In an article titled, What is Deep Learning and How Will It Change Healthcare, on healthitanalytics.com, it’s noted that “this branch of artificial intelligence has very quickly become transformative for healthcare, offering the ability to analyze data with a speed and precision never seen before.”
The deep learning strategy makes decisions with less involvement from humans, unlike other types of AI. Machine learning requires a programmer to assess and agree with the conclusions of the data, but deep learning—with its multiple layers of complex data—calculates accuracy of the response on its own.
Deep learning requires less data processing since the network utilizes its filtering system instead of relying on programmers. Evolution and complex details of deep learning are explained “the mathematics involved in developing deep learning models are extraordinarily intricate, and there are many different variations of networks that leverage different sub-strategies within the field. The science of deep learning is evolving very quickly to power some of the most advanced computing capabilities in the world, spanning every industry and adding significant value to user experiences and competitive decision-making.”
Deep learning is projected to increase 42% compound annual growth rate until 2024 and healthcare companies will spend an estimated $18 billion on deep learning analytics in the next few years.
Possessing the knowledge to analyze images, infer data from unstructured figures, and supporting decision making will keep the healthcare framework at peak performance. Investing in data aggregation, analysis, validation, and reporting is crucial for organizations to meet their long term goals. Deep learning is a concrete step in the right direction to guarantee healthcare companies meet and exceed their goals.
Big data analytics are projected to be worth more than $68.03 billion by 2024, because of North America’s investment in electronic health records, and other tools used in the healthcare market. Currently, there are 2.5 quintillion bytes of data created each day.
Over the last two years alone, 90% of the data in the world was generated. According to one study, 79% of enterprise executives agree that companies that don’t adopt Big Data will lose their competitive position.
Aside from deep learning strategies, but still part of big data analytics, healthcare organizations are looking towards blockchain capabilities. Blockchain is “a digital database containing information (such as records of financial transactions) that can be simultaneously used and shared within a large decentralized, publicly accessible network,” and refers to technology which creates a database.
The idea behind using blockchain begins with a need to reduce issues caused by data siloes and convoluted administrative arrangements. Blockchain is a simple way of organizing complex data so that all transactions may be verified and recorded through the agreement of all parties involved, similar to an event logbook.
Due to the large volume of big data and pressures to reduce wasteful spending, healthcare organizations look towards blockchain for a positive change in data management. In addition to streamlining current systems, blockchain ensures complete security of patient data, while preventing fraud and data tampering. Because any change must be verified and approved, security is strengthened, and greatly reduces any unauthorized changes. Blockchainis a relatively new methodology, but expected to grow exponentially over the next six years, at around a 70.45% compound annual growth rate until 2024, which is a $1.4 billion market.
Having peace of mind that all data is secure, authentic, and verified makes block chain an integral part of the healthcare system expansion.