The healthcare industry has huge amounts of data available but it can be difficult to use it in the right way to bring about transformation.
By leveraging in-memory computing, insights from data are helping to change the diagnosis, treatment and prevention of disease.
With the actionable, real-time insights it offers, physicians can make more immediate and better diagnoses, hospitals can improve patient care at lower costs and clinics can promote preventative healthcare.
What is in-memory computing?
In-memory computing enables users to store data in random access memory (RAM) across a cluster of computers and process it in parallel.
This enables the speedy processing of transactions and data analytics, along with the ability to scale seamlessly simply by adding new nodes to the cluster. This technology also comes with high availability and cost savings.
Why is in-memory computing necessary in healthcare?
The healthcare industry needs software technologies to handle large quantities of data with high performance, reliability, scalability, and security.
According to Gigaspaces, traditional technologies like mainframes and relational databases are still being used extensively in healthcare and cannot offer these capabilities.
This is why in-memory computing systems are increasingly being used to handle data processing requirements. It can cost-effectively and efficiently offer the necessary capabilities without having to rip out and replace legacy systems.
Practicing next-generation precision medicine
Next-generation precision medicine is possible with in-memory computing capabilities driving better, more personalized patient treatment. This includes using existing clinical, financial, and claims data as well as emerging sources of data.
More patient-specific data is available today due to home monitors, smartphones and wearable applications. Patients who can’t get to hospitals are also increasingly using telemedicine services for their healthcare needs.
Vital health signs such as heart rate, blood pressure, and temperature are streamed to a centralized data repository which forms another source of useful data.
By using in-memory computing capabilities, doctors are able to personalize treatment plans.
They can prescribe more optimal dosages when they base them on metabolic rates and define treatments according to factors such as a patient’s age, personal habits and genetic information.
When treatment plans are personalized, expenses and resources aren’t wasted and doctors can serve more patients.
The move to personalized medicine will not only reduce healthcare costs but improve patient outcomes in devastating diseases.
Finding potential therapies for complex diseases
In-memory computing allows drug discovery companies to evaluate many potential treatments for a specific disease much faster than previous approaches. They can run a variety of scenarios to identify a broad range of interventions.
By moving data from disk to RAM for processing, there is a great improvement in processing speed and the scalability to deal with many possible permutations.
Drug discovery companies often deal with huge amounts of data and in-memory computing offers them the high speed and performance they require to discover new drugs.
Managing electronic patient records
A patient’s electronic health record (HER) includes all the administrative clinical data relevant to the patient’s care, such as demographics, progress notes, medications, drug allergies and medical problems.
Providers are responsible for maintaining them over time. Combination and integration of data is vital to recover patient history, share information, and pose queries.
Protecting the data is another challenge and data sharing has always been an issue in the healthcare sector. However, this cautiousness when it comes to sharing is decreasing given the increasingly obvious advantages of collaboration.
Collecting, storing, refining, analyzing and exchanging patient data in digital form is increasing and paper-based systems are gradually being replaced.
When all stakeholders can collaborate and share significant data insights, healthcare solutions become more cost-effective and patient care improves and becomes more personalized.
Analyzing patient records
The American Society of Clinical Oncologists (ASCO) is analyzing millions of cancer patient records to try and uncover trends and patterns. The data oncologists normally use for deciding on chemotherapy is often based on a small subset of clinical trial patients.
Most cancer patients are sicker, older and more ethnically diverse than the patients in cancer studies. Oncologists often have to extrapolate because clinical trials exclude patients like those they are treating.
By unlocking value from other patients than those involved in clinical trials, better decision-making is possible based on results of patients that closely match patients being treated.
Thanks to this initiative called CancerLinQ, oncologists will soon have the type of searching, usable information that helps them provide better assessments and more coordinated patient case management.
Using predictive analytics
To effectively manage patient experiences and optimize available resources, insights from big data can help. By using predictive models, healthcare providers can improve patient health and save costs.
For example, by measuring the relationships between patient satisfaction and services, hospitals can better allocate resources and fulfill manpower requirements in different sections of the hospitals.
Utilizing location awareness insights can help with optimizing the use of expensive healthcare equipment in various departments.
Managing health insurance claims
Leading insurance companies are using cost management solutions to reduce benefit costs, prevent issues like double billing, and to maximize the value of benefits.
In-memory computing offers them the ability to add next-generation performance cost-effectively and offers benefits such as being able to use predictive modeling to detect patterns for authentic claims and help prevent abuse.
They can also advocate the most appropriate health plans and introduce innovative business models by capturing customer behavior data in real-time.
In the past, the extensive data collected in the healthcare sector was siloed or not structured in a way to enable efficient decision-making. In-memory computing capabilities now allow a near-real-time analysis of complex datasets in many diverse locations and using different types of data.
The adoption rate of in-memory computing in healthcare is bringing about a transformation.
There’s a greater focus on precision medicine, more accurate diagnostics for many serious medical conditions, new drug therapies for complex diseases and better management of patient records.
Patients are more involved in their own healthcare and insurance companies are introducing more innovative business models.
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