The Power of Real-Time Analytics in Healthcare

The Power of Real-Time Analytics in Healthcare
The Power of Real-Time Analytics in Healthcare

The Power of Real-Time Analytics in Healthcare : Today almost 30% of the data volume in the world is generated by the healthcare industry. All over the world, growing aging populations are living with more chronic diseases and the increasing demands on the healthcare industry require more efficient high-quality healthcare.

The potential of data in healthcare relies on being able to detect patterns and find actionable insights to suggest solutions and improve treatment. A lack of speedy access to data can leave patients and healthcare providers in the dark, which greatly limits any efforts to improve the quality and efficiency of health care.

By using real-time analytics, health care systems can access the most up-to-date information, which enables providers to assess patient-specific eligibility, risk scores, gaps in care and historical medical information right at the point of care. This helps to improve the quality of performance, ensure regulatory compliance, reduce waste, and decrease costs.

Some challenges of using Big Data in healthcare

Big Data is unwieldy and complex so it requires a close look at approaches to its collection, storage, analysis, and presentation. It demands cost-effective, innovative forms of information processing in order to deliver actionable insights.

  • Capturing clean, accurate, and correctly formatted information is often an issue and it’s imperative that healthcare information is trustworthy, timely, and meaningful.
  • When it comes to querying, health organizations have to overcome data silos that prevent access to an entire repository of information. Healthcare information is often not connected or easy to access in a centralized manner.
  • Healthcare data isn’t static but ever-changing. Most elements require frequent updates in order to remain relevant.
  • There may be fundamental differences in the way electronic health records are designed and implemented. This affects how data moves between organizations which may mean clinicians cannot access the information they need when they need it.

Memory-based solutions are the most viable way to increase network speed, computer power, and memory capacity. An In Memory Data Grid (IMDG) is cost-effective, easy to deploy, highly distributed solution. It minimizes access to high-latency, hard disk drive-based storage and co-locates business logic with data. It’s possible to insert an IMDG between an existing application and a disk-based database without major changes to either layer.

The distributed design means data and application loads are scalable just by adding a new node to the cluster. As processing is distributed across the cluster, it allows for very fast performance and huge scalability. An IMDG can also support semi-structured and unstructured data.

Benefits of real-time analytics in healthcare delivery

Healthier patients, lower care costs and more visibility into performance are some of the many benefits of real-time insights into big data. A shift is happening from volume to value-based care. Real-time analytics is helping to address many of the challenges head-on, such as providing access to the right information at the right time.

Benefits for patients

When healthcare providers have access to up-to-date health data for patients, they can provide safer, higher quality, more efficient personalized care for patients. Real-time analytics offers instant and accurate insight into their medical history, including their past conditions, diagnoses, treatments, and outcomes.

Real-time insights can also help clinicians to identify unnecessary costs related to tests and to avoid duplication. They can only make such decisions if they have access to the right information at the right time.

Patients who have access to their own health data in real-time understand more about their health and how to adapt their lifestyles to impact their quality of life. They have more motivation to be compliant because they can see how their habits contribute to the condition of their health.

Health data insights can also speed up the development of new treatments and medical products for patients. Wearable devices can help considerably with preventative care for patients because they can measure their vital signs, like their heart rate and blood pressure. A smart monitoring system may give a mobile phone alert to a patient whose blood pressure is too high.

Healthcare systems

Analyzing health data enables many healthcare improvements, such as identifying risk factors and speeding up diagnosis. It is possible to quickly identify disease transmission pathways to prevent serious conditions or diseases.

Predicting outcomes and increasing effectiveness of treatments, improvement of the safety and quality of treatments, dissemination of knowledge and enhancing public health strategies are all made possible by being able to gain real-time insights from health care data. With a 360-degree view of patients, it is possible to deliver proactive care that can improve health outcomes, reduce hospital admissions and increase efficiency all around.

Individual healthcare providers

Healthcare providers can use real-time data analytics to design better patient care pathways, improve their strategic planning and use healthcare resources more efficiently. It can provide them with reports outlining where patients stand, how to improve their care, achieve compliance, and also be fully reimbursed for their services. They can make better-informed decisions at the point of care, which enables them to provide the most appropriate care for patients.

An individual healthcare provider running a business needs it to be financially viable. Having access to real-time analytics empowers providers not only to improve care and save time but to achieve critical metrics that can impact their financial performance. With real-time patient scheduling, virtual visits and AI serving as the first point of care, there is more convenience and efficiency in systems that used to be slow and ineffective.

Conclusion

Smarter, more effective healthcare is needed to deliver better care to patients and the real-time analytics from health data can help to support this. Digital advances in the healthcare industry are bringing together many industries, including genomics, wearables, sensors, AI, analytics, implantables, clinical data and electronic health records. All the relevant stakeholders need to collaborate and adapt the design and performance of their systems if they want to build the type of technological infrastructure that supports the use of real-time analytics in health care.

In-memory computing adoption is likely to accelerate in the coming years as healthcare and other industries rely on it to achieve more application speed, scalability, and real-time data access.

 

 

 

 

 

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