One study from the University of Florida focuses on cancer. Academics used Google Maps and free public health data to create heat maps and compared them to the locations of medical services.
Their insights led the researchers to rethink their delivery model and add additional care units to problem areas.
This study was part of President Obama’s Cancer Moonshot program, which aims to double progress in the fight against cancer.
How Big Data Improves Patient Outcomes
Read on to learn more about how big data improves patient outcomes.
Interoperability helps analyze healthcare system information
Data interoperability, or the ability of healthcare systems to share and analyze information, is the key to reducing errors.
By allowing the data to be shared between applications and systems, care providers can identify patterns in patient behavior and learn how to avoid them.
However, data standardization alone is not sufficient; it must happen across organizations. When only partial data is available, care providers can only interpret a limited set of data, leading to fatal errors.
Data sharing is an essential part of the continuity of care, whether the patient is suffering from a chronic illness or is moving from hospital to community care.
In addition, interoperability facilitates safer care transitions between different healthcare providers rooted in the healthcare big data and the promise of value-based care.
For instance, a GP can access the patient’s medical history and learn about any recent hospital procedures and medications that the patient took, saving both the GP and the patient time and energy.
Machine Learning for efficiency.
A large dataset can be daunting, but ML and extensive data analysis can help improve the efficiency of healthcare. The primary task is to annotate, integrate, and present the data meaningfully.
Big data is not just a mess – it’s a gold mine! The following are ways that ML and big data analytics can help you make sense of it all. And with the help of intelligent web applications, you can make sense of it, too.
A recent study evaluated various ML methods used in genomic studies of head and neck cancers, and the results showed an extensive range of accuracy rates.
ML techniques can predict prognosis based on cancer genomic data. For example, deep learning algorithms can detect pulmonary nodules based on CT images.
The accuracy rate ranged from 68% to 99.6% in two studies. While these results may not be statistically significant, they represent a step forward.
Artificial Intelligence to aid healthcare providers
With a growing body of data about patients and their health, it is possible to understand better how AI can help healthcare providers.
With the help of artificial intelligence, pathologists can better identify diseases, treat patients, and improve health outcomes.
This technology can drill down to the pixel level to identify nuances that a human eye might miss. AI also allows physicians to focus on patient care rather than analyzing data.
In addition to increasing healthcare efficiency, AI can improve patient outcomes through big data analytics. Big data analysis helps uncover patterns in inpatient data and reveal innovative healthcare strategies.
The latest technological developments pave the way for a revolution in personalized medicine. However, these developments are not without their challenges.
For example, integrating healthcare data can be a complex task with multiple stakeholders and multiple levels of regulation.
Big data analysis in healthcare presents several challenges related to data curation, sharing, privacy, and search.
On the other hand, big data analytics is a powerful tool that offers healthcare organizations more accurate data than traditional methods, such as face-to-face consultations and measurements.
SaaS BI tools to track and analyze patient data
Healthcare organizations have many advantages when it comes to deploying SaaS BI tools. These tools provide organizations with the ability to track and analyze patient data.
As a result, they can help healthcare professionals understand patterns and make better decisions, resulting in improved patient outcomes. But before implementing such tools, organizations must first determine their needs.
There are many considerations to choosing the right BI tool for your organization. Listed below are some of the most important factors to consider.
Healthcare organizations are under constant pressure to save money. While implementing complicated data integration strategies is beneficial, waiting for a comprehensive vocabulary mapping process is not practical.
Quick solutions are needed to meet the demands of the organization. And SaaS BI tools can help improve patient outcomes. Listed below are a few of the benefits of SaaS BI tools. Let’s discuss some of these benefits in detail.
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