What Is Big Data In Healthcare?
The phrase “big data in healthcare” is used to refer to the massive quantities of data created by the implementation of digital technology which records and tracks patient information and assists in the running of a hospital too large and sophisticated for conventional methods.
Big data analytics in healthcare can lead to numerous beneficial and critical results. Essentially, big data is the enormous amount of data generated by digitization of all aspects of life, which is then compiled and examined by specialized technology. Applying it to the medical field, this will use the exact health information of a group (or a single individual) and could possibly aid in stopping the spread of contagious diseases, healing sickness, and lowering expenses, etc.
Given our extended lifespans, treatments have been altered and a great deal of these alterations are primarily motivated by statistics. Physicians desire to have as much information about an individual and as early in their life as possible, to detect any potential signs of severe disease as they manifest – treating any ailment in its earliest stages is far simpler and more cost-efficient. By using important metrics in healthcare and healthcare data analysis, it is better to prevent illness than to treat it, and obtaining a complete understanding of an individual will allow insurance companies to offer a customized plan. This is the industry’s effort to resolve the issue of disparate pieces of a patient’s data being stored in various medical facilities such as hospitals, clinics, and surgeries with no way to effectively exchange information.
Health professionals are continually discovering new ways to gain insight into their patients’ health. Typically, the user is faced with a difficulty due to the fact that this data arrives in a variety of formats and sizes. The emphasis is no longer on the size of the data, but rather on how efficiently it is being handled. With the help of the right technology, data can be extracted from the following sources of the big data in healthcare industry in a smart and fast way:
- Patients portals
- Research studies
- EHRs
- Wearable devices
- Search engines
- Generic databases
- Government agencies
- Payer records
- Staffing schedules
- Patient waiting room
It has taken a great deal of money and time to accumulate large quantities of data for medical purposes for a long period of time. With the continual advancement of technology, collecting data, creating thorough healthcare reports, and transforming them into meaningful key findings is becoming simpler, all of which can be used to offer more effective treatment. The aim of healthcare data analysis is to forecast and resolve issues before it is too late, as well as to appraise techniques and treatments quickly, monitor inventory more effectively, get patients engaged in their own health and provide them with the resources to do so.
Big Data Applications In Healthcare
Now that you have grasped the significance of big data in healthcare, let us take a look at 21 examples of real-world use cases that can demonstrate how a data-driven approach can improve operations, enhance patient treatment, and, at the end of the day, save lives.
1) Patients Predictions For Improved Staffing
What is an example of how big data can be used in healthcare? We will look at the classic challenge that shift managers face: determining how many people to assign during any given timeframe. You could be incurring extra labor expenses if you employ too many people. If there is an inadequate number of personnel, customer service can suffer drastically, and this can have deadly consequences for those in the field.
Utilizing big data is aiding in resolving this issue, especially in certain medical institutions in Paris. In a white paper put out by Intel, it was revealed how four hospitals belonging to the Assistance Publique-Hôpitaux de Paris were using data from multiple sources to create daily and hourly forecasts of the estimated number of patients at the respective locations.
Analyzing 10 years’ worth of hospital admission records with time series analysis methods was one of the major datasets. The researchers used these analyses to observe pertinent trends in admission rates. Then, they could employ machine learning to identify the most reliable algorithms that forecasted future admissions trends.
2) Electronic Health Records (EHRs)
Big data is the most widely used in the medical field. Every individual has a unique digital documentation which includes personal information, medical past, sensitivities, laboratory test results, etc. These records are shared utilizing secure data networks and can be accessed by both public and private healthcare providers. Each record is made up of a single editable document, allowing physicians to make alterations to it over time without having to fill out forms or be at risk of duplicating information.
Electronic Health Records can also generate alerts and notifications when a patient should receive a fresh laboratory examination or monitor prescribed medications to determine if they have been adhering to their physician’s instructions.
Despite the potential benefits of electronic health records, numerous nations are still having difficulty fully integrating them. The U.S. has taken a big step forward, with 94% of healthcare facilities adopting electronic health records (EHRs) as per a HITECH study. However, the European Union is still trailing behind. A bold plan created by the European Commission is anticipated to alter the situation.
3) Real-Time Alerting
Other instances of data analysis in healthcare all have one major characteristic – issuing notifications in real-time. At hospitals, Clinical Decision Support (CDS) programs examine medical data instantly, offering guidance to healthcare providers while they create prescriptive choices.
Doctors are urging patients to stay away from hospitals to prevent the expense of treatments that are given within the hospital. This is becoming increasingly popular in the business intelligence world in 2021 and has the capacity to be incorporated into a fresh plan. Wearable devices will constantly gather health information from patients and transmit it to the internet.
This information will also be put into the database regarding the well-being of the general population, so that physicians can look at the data in a socio-economic perspective and adjust the delivery plans accordingly. Organizations and care administrators will utilize advanced tools to track this huge collection of data and respond anytime the outcomes are disconcerting.
For instance, if a patient’s blood pressure jumps significantly, an alert will be sent in real-time to the doctor who will take steps to contact the patient and take steps to bring the pressure down.
Asthmapolis has initiated a program of utilizing inhalers with GPS-activated monitors to pinpoint asthma trends at both the individual and collective level. This information is being used together with information from the Centers for Disease Control and Prevention to devise better management strategies for people with asthma.
4) Enhancing Patient Engagement
Many people who may become patients in the future are already interested in using smart devices that keep track of how many steps they take, their heart rate, and their sleep habits over an extended period of time. This significant data can be combined with other traceable information to recognize potential health hazards that may be present. Persistent sleeplessness and a high pulse can be a sign of potential heart health problems in the future. Patients can take charge of their own health through self-monitoring and can be encouraged to live a healthy life with incentives from health insurance, such as money back for those who use smartwatches.
A different approach involves creating fresh wearables that monitor particular health tendencies and transmit the info to an online platform for medical professionals to observe. Those with asthma or hypertension could gain autonomy and decrease superfluous trips to the physician by taking advantage of this.
5) Prevent Opioid Abuse In The US
The fifth example of how big data is being used to improve healthcare is attempting to address a major issue in the United States. It is a shocking truth that, in the present year, fatalities due to the misuse of opioids have surpassed those caused by car crashes as the leading cause of accidental deaths in the United States.
Bernard Marr, an authority on analysis, wrote an article for Forbes that discussed the issue. Canada has labeled opioid misuse a “major health emergency” and President Obama allocated $1.1 billion to finding solutions to the problem while he was in office.
It appears that Blue Cross Blue Shield and Fuzzy Logix are joining forces to utilize big data analytics to find a solution to the present dilemma. Data scientists from Blue Cross Blue Shield and analytical specialists from Fuzzy Logix have begun collaborating on the challenge. Fuzzy Logix analysts have utilized many years of insurance and pharmacy information to pinpoint 742 risk factors which can accurately determine if someone is in danger of misusing opioids.
It is only right to recognize that it is a difficult task to make contact with people classified as “high risk” and stop them from having a drug problem. This project, however, still offers optimism for reducing a problem that is wrecking the lives of numerous individuals and costing the system a lot of money.
6) Using Health Data For Informed Strategic Planning
Utilizing large volumes of data in healthcare provides the ability to formulate strategic plans due to the improved understanding of people’s motivations. Care managers can examine check-up results among people of various populations and recognize what elements prevent them from engaging in treatment.
The University of Florida employed Google Maps and open health information to build heat maps that focus on a variety of topics, such as population expansion and ongoing health conditions. Afterward, scholars looked at this data in relation to the access to health care in the most highly populated regions. This gave them the opportunity to assess their distribution plan and incorporate more medical facilities to the zones that had the most difficulties.
7) Big Data Might Just Cure Cancer
An intriguing instance of employing massive data in medical care is the Cancer Moonshot initiative. At the end of his two terms in office, President Obama created a plan with the objective of achieving 10 years’ worth of advances in cancer research in five years.
Researchers in the medical field can take advantage of the vast quantity of data on the various cancer treatments and the outcomes of these treatments in order to uncover patterns and treatments which have the best results in actuality. Researchers can investigate tumor samples stored in repositories that have been connected to the medical histories of patients. By analyzing this data, scientists can observe how certain genetic changes and cancer-related molecules respond to different therapies and identify patterns that will result in better results.
This research could lead to an unforeseen advantage, like uncovering that Desipramine, which is an antidepressant, can be used to treat some forms of lung cancer.
5 data trends to watch in 2021:
1. Data storytelling and visualization
Samani suggested that top executives put a great deal of importance on the information that is provided by the data. In order for the data to be of any use, it must be changed into a more understandable form. By illustrating data in a visual format and creating stories through the information, data experts can assist the decision makers of a business (who may not be educated in data analysis) in discovering patterns, perceiving complex concepts quickly, and making decisions.
2. Data literacy for all
As reported by Forrester, those businesses with a limited understanding of data in their staff are at a disadvantage in comparison with their rivals. This year, businesses are taking action to enhance the skills of their staff by introducing data literacy programs. Samani emphasizes that if one wishes to stay current with the latest developments, even those who are not data scientists will need to be knowledgeable about data.
3. MATLAB gaining momentum
MATLAB is a multi-faceted language which combines programming, numerical computing, and visualization for a variety of purposes. It has become increasingly common for companies to make use of this language, as it allows data specialists to drastically reduce the amount of time spent on data preparation and facilitates speedy data cleansing, organization, and visualization.
4. Reliance on DataOps
Samani states that a large amount of effort is being directed towards DataOps. DataOps is a new concept that IBM describes as the procedures that increase the velocity and flexibility of the data pipeline from collection to delivery. Forbes states that DataOps specialists are the new custodians of successful and productive corporate data and that the future of business analysis will highly depend on them.
5. Value of soft skills
The requirement for data professionals who have a combination of both technical expertise and people skills is increasing. Studies have indicated that Google and similar organizations place a heavy emphasis on the development of soft skills like effective communication, the capacity for deep critical thought, and the ability to collaborate in a multi-cultural environment as the main characteristics of success. Samani and Patel both highlighted the necessity of capabilities such as working together and ongoing education.
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