How big data analytics can aid in population health management

Big data holds great promise of transforming medical and healthcare operations, including clinical decision support, disease monitoring, and population health management. Therefore, the more information we have, the more effectively we can organize ourselves to meet our objectives and deliver better outcomes while lowering costs.

With monumental amounts of data in healthcare being produced at an ever-increasing rate every day, from diverse sources such as Electronic Health Records, sensor data, health information exchanges, patient portals, IoT devices, and more, conventional database systems cannot keep pace with this enormous amount of data flow. Thus, leaving the essential data untapped to its fullest potential.

As a result, the healthcare industry is now leaping over to a modernized approach to managing big data. Moving from paper-based records to electronic health records was the first step. However, the growing population and increasing patient demands have created new challenges in healthcare. Healthcare providers worldwide have difficulty coping with these challenges using the existing disparate systems, which store data in fragments and limit their ability to understand and meet patient needs contextually.

Therefore, healthcare providers are now embracing new technologies with an integrated architecture or open data infrastructure that can unify the whole healthcare continuum, creating a sustainable architecture that orchestrates the flow of data across all the points in a healthcare organization.

The new generation of big data analytics technology is driving a new wave of transformation in healthcare. Moving from volume-based care to value-based care is now within the reach of every physician. Using third-generation big data analytics, healthcare professionals can analyze, manage, and accurately extract valuable information from large data sets pertaining to each patient within a brief period of time. In addition to helping physicians understand symptoms and illnesses, big data analysis enables them to predict diseases early on and assists decision-making.

Big Data Analytics for Population Health Management

Population health management encompasses several health determinants, including patients' behavioral patterns, social and physical environment, and the ability to pay. However, identifying individuals with high risk within your patient population and formulating a personalized care plan for them using disparate data sets proves to be challenging.

Modern Big Data Analytics systems create a central data repository by unifying all the data sources. It weaves a 360-degree view of patient data to detect and close the care gaps and stratifies each layer of patient information to classify them into healthy and high-risk populations. Furthermore, this data provides insights to predict future risks and trends, enabling care providers to deliver personalized care for each patient at a lower cost.

Conclusion

The modern healthcare fraternity has recognized the potential of big data analytics. Consequently, healthcare providers are implementing big data analytics in healthcare and clinical practices in order to extract knowledge that will lead to improvements in the quality of healthcare, reduce costs of care, and help develop effective clinical decision support systems.