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Big data has been key in ushering in the Information Age. Now that we have the technology to process large amounts of data quickly, there is no limit to the number of applications for data analysis across industries. Healthcare, especially, is ripe for advancement with the help of big data.
The healthcare industry has so many moving parts. Additionally, there’s still a lot we don’t know about human health and how to treat certain conditions. Because of this, there are lots of opportunities for waste and errors.
Data can be a huge help in not only making the healthcare industry more efficient but also in advancing medical research. Using the vast amount of data that has already been collected in the healthcare industry, it may be possible to improve patient outcomes and even pioneer new treatments.
Medical research is understandably a slow process. But with the help of big data, researchers may be able to speed up the timeline and advance the field much more quickly. Here’s how.
There are several excellent reasons to collect health data. When used properly, data can be used to improve individual healthcare organizations as well as the industry as a whole.
Many hospitals have already used big data to reduce errors and operate more efficiently. But there are other reasons to use health data analytics, including large-scale industry goals.
First, health data can help to improve public health. By collecting and analyzing data, healthcare organizations can use their findings to create public health awareness campaigns and work toward healthier communities. Health literacy is an important issue and it’s crucial for organizations to have accurate, up-to-date information that is easy for people to understand.
Unfortunately, it’s likely that we will be seeing pandemics more frequently in the future. Many factors are involved in how diseases spread, but as we’ve seen with the COVID-19 pandemic, public education is important for keeping cases under control.
Data analytics can help with virus management as quick action is crucial when a new disease emerges. Not only is this important for developing treatments and vaccines, but for providing information that helps governments and researchers strategize during a crisis. It also provides the information that’s needed to inform the public and provide health recommendations.
Data will also be crucial in driving advanced medical research. So much testing is required in learning more about health, diseases, and treatments. By leveraging existing data, researchers gain certain “shortcuts” and can hone in on solutions that are needed to advance the healthcare industry.
Medical research encompasses all kinds of technologies and areas of study. Researchers work on projects ranging from studies to learn more about public health to new drug development, brand-new medical devices, and innovative therapies for various common and rare health problems.
One of the most important steps of using healthcare data is organizing it and integrating different data sources. This is a crucial first step, and it can be the most difficult, as data can come from different locations and may be presented in various formats. Many organizations struggle to determine the data that will help them reach their goals.
Once the data has been integrated and organized, the quality of the data must be checked. This is a process that must continue throughout the analysis. A data expert will then create data models and interpret the results. The final step is validation.
As a complex multi-step process, most larger organizations have dedicated data scientists and analysts to manage and leverage the data they collect. The many potential benefits can outweigh concerns over complexity and cost. Here are just some of the applications for healthcare data:
Little is known about some diseases, making diagnosis and treatment a challenge. Data analysis can be used to spot patterns within large datasets, allowing researchers to learn more about different health conditions and predict how and when they are most likely to appear.
Learning more about different diseases is key for developing new treatments. In this way, data can give patients new hope and provide doctors with much-needed tools to improve outcomes.
Advanced technology is essential for running a modern hospital. It’s extremely expensive to operate large healthcare organizations and automation can be very helpful in reducing costs and errors. By streamlining operations, hospitals can improve the patient experience and control operating costs.
Data can showcase where automation would be most helpful in the hospital setting. Each organization is unique, serving different communities and experiencing different types of demand. For this reason, having hospital-specific data is key to improving efficiency through automation.
While it’s always important for people to get to the doctor when they need to, many visits simply aren’t necessary or could be conducted via telehealth. With extensive data on patient visits, organizations can more effectively manage doctor’s visits and communicate with patients about when visits are necessary.
Health issues are easier to treat when they have not had a chance to advance and increase in severity, but it is sometimes difficult for patients and doctors to detect the earliest signs. By analyzing large datasets, researchers can often spot important patterns that could lead to increased knowledge and early disease detection. This improves patient outcomes, saves, money, and reduces the need for expensive and painful treatments.
Creating new drugs is a very long and difficult process. Researchers spend years working on different formulas and testing them before they can be used on humans. While big data can’t make new drugs appear overnight, data analysis can provide much-needed insights to make the process easier and faster, benefitting patients who are waiting for relief.
Insurance is all about risk, and predictive analytics have become extremely important in this industry for setting premiums. Accurately calculating risk can reduce rates for patients and increase insurances companies’ ability to manage their costs.
Health data is a great tool for collaboration between patients and their providers. By using data analytics, healthcare providers can increase their diagnostic accuracy and calculate risk factors for individual patients. This kind of personalized care greatly improves outcomes and allows for more effective remote monitoring with the use of apps and wearables.
Despite the importance of health data analytics, many organizations have been slow to adopt big data tools. There are many different challenges that have affected how technology has spread throughout the industry. As a highly regulated industry, it takes some time for new technologies to become widespread.
By this point, electronic health records (EHRs) have become standard in hospitals and other medical organizations. This has really helped to increase the amount of data healthcare providers are collecting, although there are still a few challenges involved.
Many organizations have trouble organizing and effectively leveraging the information they collect. There is so much to sift through that it can be difficult to ask the right questions and gain important insights. Additionally, protected health information (PHI) is strictly controlled under HIPAA (Health Insurance Portability and Accountability Act) to safeguard patients’ privacy.
While HIPAA has offered important protections for patients’ sensitive data, it has certainly affected the industry’s ability to effectively collect and leverage information that could lead to groundbreaking new research and better patient outcomes. Ethical and practical concerns like these have slowed big data adoption and created roadblocks for fully realizing the power of health data analytics.
Raw data may contain all the answers analysts are ultimately looking for, but it can be difficult for them to communicate their findings to health leaders, making it difficult to get buy-in. Health data visualization can help by providing communication tools that are easy for administrators, researchers, and even the general public to understand.
Data alone does not provide recommendations and strategies. It requires human or artificial intelligence to extract the insights and create strategies. To realize the potential of health data in medical research, it’s important for analysts to have visualization tools so they can easily see trends and provide that information to others.
Researchers are unlikely to be data experts. Collaboration is crucial for leveraging the power of data in medical research and communication errors can have huge consequences, potentially setting researchers back months or years on critical healthcare solutions. Careful analysis and visualization can help reduce this risk.
There are so many incredible projects happening in healthcare research. Patients with complex issues like Alzheimer’s, cancer, or organ failure have few current options, despite how far we’ve already come in medical research. After diagnosis, many families face the realities of long waiting times on transplant lists, harsh therapies with only limited effectiveness, or little hope for recovery.
With big data, researchers could have a leg up on creating new solutions for these kinds of medical problems and others. Using the data we already have will change the landscape of medicine, as we know it. A brighter future for healthcare might just be a dataset away!
With a Bachelor’s in Health Science along with an MBA, Sarah Daren has a wealth of knowledge within both the health and business sectors. Her expertise in scaling and identifying ways tech can improve the lives of others has led Sarah to be a consultant for a number of startup businesses, most prominently in the wellness industry, wearable technology and health education. She implements her health knowledge into every aspect of her life with a focus on making America a healthier and safer place for future generations to come.