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Big data management in healthcare

The healthcare industry is increasingly turning to big data to help improve patient care and outcomes. By analyzing large datasets, healthcare providers can identify patterns and trends that enable better understanding of the factors currently impacting wellness and disease.

Leveraging big data for the healthcare industry

Big data can be used to track the spread of infectious diseases, predict outbreaks of illnesses, and pinpoint areas where prevention efforts are most needed. In addition, big data can also be used to improve individualized care by providing insights into a patient’s health history and risk factors. As the healthcare industry continues to harness the power of big data, we can expect to see even more advances in the quality of care.

How healthcare organizations use big data

With a constant intake of both structured and unstructured data, big data enables a number of different elements capable of transforming a healthcare organization. This range covers the complete spectrum of the healthcare workflow, from internal operations to the way patients access records.

Electronic health records: Since 2000, the healthcare industry has shifted toward electronic records. This creates an accessible, transferable, and standardized data format that is easy to update from both internal and external locations. Centralizing this data optimizes workflows and accuracy for both patients and providers.

Device monitoring: Medical devices record data at regular intervals, sometimes at high frequency. Not only does this data need to be securely stored for review, it must be accessible and up to date. This allows patients and healthcare providers to stay on top of conditions in case there is need for adjustment or intervention.

Patient interactions: Cloud-based data management for healthcare organizations opens the door to a more customized patient experience, from apps to web-based interactions. This can be anything from retrieving and reviewing records to interacting with chatbots for intake to personalized wellness suggestions based on individual criteria and background.

Internal operations: With big data management, the entire healthcare organization gains access to a single source of truth. This produces deeper insights that ultimately impact all departments, from human capital management to finance. As this data consolidates across the organization, it opens the door to data-driven insights and decisions across all departments.

Research: Global collaboration has significantly expedited research, both by connecting massive volumes of data and providing machine learning tools to process data. This feeds into healthcare analytics tools that can perform data mining, leading to insights and visualizations for greater understanding.

Supply chain: Across the entire healthcare workflow, logistics and procurement can mean the difference between life and death. With supply chain data consolidated into a single source of truth, organizations across functions can optimize for manufacturing, delivery, and quality. The result is a smoother process that allows for increased transparency and lead time.

Data management in healthcare

Data management is a critical component of healthcare. With the advent of electronic health records, organizations have increasingly focused on the ability to securely and accessibly store and manage patient data. Various data management solutions are available, ranging from simple, file-storage solutions to more sophisticated enterprise-wide systems. The choice of solution depends on the needs of the organization and the volume of data to be managed.

Considerations for big data in healthcare

As your healthcare organization approaches embracing big data, several key points must be considered, including:

Security: An important consideration in data management is security. Patient data must be protected from unauthorized access and theft. Healthcare organizations must have in place policies and procedures for managing data security.

Compliance: Regulations by governing agencies set standards for electronic health records. These evolve over time with technology, meaning that any cloud provider must be able to support these specific needs.

Scalability: Every enterprise must keep an eye on scalability, but for healthcare data management, scalability is critical for using technology to improve patient health and preventing disease. Beyond simple usability and availability, scalability is critical for any artificial intelligence and machine learning initiatives used to proactively identify health issues or assist with research patterns.

Availability: As electronic health records connect internally and externally, availability becomes one of the top priorities. Records will need to be accessed by providers at the office, patients via their phone’s app, insurance employees, and more. A robust redundancy and backup plan must be used to ensure availability.

Data management is a complex issue, but it is essential to the delivery of quality healthcare. By carefully selecting a data management solution and implementing security measures, healthcare organizations can ensure that patient data is properly managed and protected.

Healthcare data analytics: use cases

Healthcare organizations, regardless of size or scope, have many uses for big data,. Some of these include:

Organizational optimization: In recent years, the healthcare industry has further relied on data analytics to improve patient care and drive down costs. By collecting and analyzing large amounts of data, healthcare providers can identify trends and patterns that would otherwise be difficult to spot.

Medical record accuracy: Processing electronic health records using artificial intelligence and machine learning can help flag errors and near misses in the healthcare system. These flags can be sent for manual review and updating as necessary to ensure each patient’s records are as accurate as possible.

Individual preventative care: Data analytics can help individuals work proactively with their doctors. Artificial intelligence and machine learning can be used to identify patterns in records that might work as early warning signs for conditions, enabling patients to get ahead of any developing diseases or conditions.

Systemic preventative care: On a larger scope, processing large volumes of patient records through analytics enables a big-picture perspective of disease trends. With the power of machine learning and cloud-based collaboration, deeper insights can be reached to identify risk factors for certain conditions or optimize treatment plans.

As more and more data is collected, organizations gain a deeper understanding of the complex relationships between different factors in the healthcare system. Having that deeper understanding will ultimately allow healthcare providers to provide better care for patients and make more informed decisions about the use of resources.