Pediatric care involves a different approach for diagnosis, treatment and support. This specialized approach is now being enhanced by predictive analytics and Big Health Data. Routine healthcare generates large volumes of data through medical records, vaccination registries, pharmacy dispensing and hospitalization.
Data that used to be discarded or sit in silos is now revolutionizing child healthcare. This is made possible through effective data governance and predictive analytics. In fact, Statista projects that the global market for clinical, financial and operational analytics in healthcare will be worth nearly $30 billion USD by 2025.
Here are a few benefits of Big Data, data governance and predictive analytics in Pediatric Care.
Identify Illnesses in the Patient’s Geographic Location
Precision public health uses big data technology to balance personalized care and health information with the overall health of populations. The Bill and Melinda Gates Foundation has highlighted the importance of primary surveillance data and analytics in healthcare. This is done through sophisticated predictive analytical models that track infected individuals, carriers and patients. In fact, this information can be drilled down to the level of each school, providing intervention at a granular level. This is, perhaps, best illustrated by the flu season that takes a toll on attendance each year. Proactive, data-driven measures could curtail the spread by identifying infected individuals even before they can step into the school.
Predictive analytics is all about access to huge volumes of real-time data that can continuously update and create near real-time monitoring. A great example of this is in asthma care, one of the chief reasons that children visit emergency rooms. National Children’s Hospital was able to correlate 20 years of air quality data from the Environmental Protection Agency with patient data to understand the potential triggers. It is now able to deliver interactive analysis to its healthcare workers, thus improving the outcomes.
Use Genomic Medicine to Identify Effective Treatments
Genomic medicine considers an individual’s genomic information to develop personalized diagnoses and care. It is a more holistic approach than genetics, which considers genes individually. In the U.S., newborn babies are tested for severe yet treatable genetic diseases. Rapid genome sequencing technology can provide meaningful diagnostics for genetic disorders over a greater range than the 29-50 disorders babies are tested for currently. The efficiency of genome frequency continues to increase.
Genome medicine is the new kid on the block when it comes to new technology. Its effects are already being seen by pediatricians who have long been confounded by the cause of seizure disorders in infants. Treatments usually include trying different medications until reaching one that works. With Genome medicine, the cause of the disorder can be pinpointed and treatment is far faster and effective. Rady Children’s Hospital San Diego uses genomic sequencing that integrates AI and Big Data to make such rapid analysis possible and provide treatment plans sooner.
Especially when dealing with sensitive patient data, it is important for hospitals to prioritize data security. Data encryption, user access roles and permissions, data de-identification and proper data analytics procedures are all essential parts of data governance for health data.
Preventing Hospital Readmissions
Hospital readmissions are often considered to signify the quality of care at a location and can result in financial penalties. Reducing this number is a priority. Outcomes for each patient can now be predicted using advanced data science tools that analyze hundreds of thousands of data points.
Due to the vastness of this data, the power of predictive analytics is reliant on effective data governance. In order to gather data-driven insights, the data must be organized and stored in a way that allows hospitals and healthcare organizations to easily find the data they need when they need it most.
A 2016 study from the University of Texas Southwestern found that certain events during hospitalization can lead to a higher chance of readmissions within 30 days. With this knowledge, health managers can focus hospital resources, guide intervention during hospitalization and monitor post-discharge procedures.
Managing the Practice’s Staffing and Supply Chain Needs
The largest expense of healthcare facilities is labor, followed by supply chain costs. Predictive analytics provides the opportunity to trim unnecessary spending and improve process efficiency. Data-driven decisions can save a hospital up to $11 million each year, so it is surprising that only 17% of hospitals are using data-driven solutions to manage their supply chain. Managing pricing, inventory and ordering patterns are some of the ways that technology makes data visible and connects to everyday processes.
Predictive analytics is also becoming a driver in more efficient staff scheduling. In many healthcare organizations, experience and intuition drive staffing solutions. This can lead to understaffed shifts, unplanned overtime and in short, staff not matching the patient volume. This uncertainty is what makes staffing one of the biggest spends. Healthcare organizations are now turning towards a scientific method to handle staffing challenges.
Predictive analytics and data governance benefit healthcare in many ways. However, this technology is also revolutionizing many other industries. If you are interested in making the most of your data, schedule a demo of 7T’s data governance platform, Sertics. Sertics provides businesses with the tools they need for data lake creation, data governance and data management. The platform also integrates with leading data visualization tools to enable advanced analytics.
To discuss your development project or to schedule a Sertics demo, reach out to 7T today.
