How Adaptive Analytics Can Optimize Cloud Migration for the Healthcare Industry

The healthcare industry is enthusiastically undergoing digital transformation and embracing hybrid cloud architectures. However, cloud migration in the healthcare industry comes with significant challenges. 

If there is any disruption to the migration process, or any compatibility issues between old and new data storage systems, PHI and other sensitive data could be lost or stolen, and your organization could run afoul of compliance regulations. Disruptions also cause downtime, which could halt daily user activity and force the organization to burn valuable time and resources to get up and running again. And while cloud migration promises savings in data storage and maintenance costs, those savings are only possible when resource usage is comprehensively understood and well managed.

To harness the power of the hybrid cloud and unlock its full potential, without the risks of disruption or downtime during cloud migration, healthcare organizations need a way to seamlessly access, manage, and utilize data across both cloud and on-premise systems, and ensure it is protected and in compliance with HIPAA and all other healthcare data privacy regulations. 

Fortunately, next-generation adaptive analytics fabrics have come online to alleviate these issues.

Leveraging an Adaptive Analytics Fabric  

An adaptive analytics fabric ensures all of your data across all of your systems is readily accessible from a single virtualized source, even while the data remains distributed across backend platforms. This enables you to seamlessly conduct your cloud migration at your own pace as you see fit, without worrying about downtime, data loss, or security breaches. Not only will your cloud migration be accelerated and simplified, but your data will be optimized for insights, decision-making, budgeting, and patient care. 

Adaptive analytics optimizes healthcare organizations’ migration to the cloud in many ways, including: 

1. Improved data agility and performance
2. Enhanced data privacy, security, and compliance
3. Streamlined operations 
4. Cost efficiency and predictability
5. Improved patient care 

Improved data agility and performance through autonomous engineering

With an adaptive analytics fabric, machine learning observes query needs and builds acceleration structures that serve needed data for queries in a fraction of the time; queries that used to take hours or days to run return results in minutes or seconds.

Moreover, when data is virtualized and accessed through an adaptive analytics fabric, business users have the freedom to combine data from multiple sources and in multiple formats without needing to wait for IT to prepare and deliver the data. Analyses can be run and decisions can be made on current, atomic-level data, and a semantic layer that models the data enables  consistent results across teams no matter what BI tools are used.

Enhanced data privacy, security, and compliance

Due to stringent compliance laws and regulations in the healthcare industry, the single biggest security issue for many healthcare providers is securing PHI and other sensitive data. Being able to provide insight into data provenance and lineage in a searchable interface to ensure complete visibility into data access patterns across the organization has tremendous value to them.  

An adaptive analytics fabric preserves the security policies of individual databases, orchestrates seamless merging of policies when users are working with multiple databases that may have different security policies, and applies global security policies across all data, such as federally-mandated HIPAA and HITECH data privacy regulations. 

Streamlined operations 

An adaptive analytics fabric unifies data that is spread across multiple systems, breaks down data silos, and provides access to all of an organization’s aggregated data through a single common interface. Data analysts can then use their own preferred BI tools to analyze and compare data across the organization to discover redundant, inefficient processes that might be negatively affecting operations, revenue, or patient care. They can also glean insights into where processes could be improved or new processes could be implemented in areas such as utilization rates, inventory, supply chain management, staffing requirements, and claims management. 

An adaptive analytics fabric also supports streamlined payment and reimbursement structures. New value-based reimbursement models, shrinking reimbursement rates, increases in patient financial responsibility, and an uptick in government mandates are exacerbating financial issues by making it more difficult for healthcare providers to get paid. Without a comprehensive view on all patient data, it is likely that reporting will be inaccurate and reimbursements will fall through the cracks. An adaptive analytics fabric enables collection of accurate data and survey analytics, as well as up-to-date information about patient readmissions and aftercare adherence. 

Cost efficiency and predictability

Cost efficiency is a critical piece of successfully migrating from legacy on-premise data platforms to cloud data warehouses. But if you aren’t careful, it is very easy to spend a lot of money very quickly, outstripping the cost benefits expected from the migration.

Cloud data warehouse pricing is generally resource usage based, making it critical to follow best practices such as avoiding scanning raw data and limiting expensive operations such as joins whenever possible. Taking these measures enables organizations to scale their cloud data warehouses for BI use and roll them out to more data consumers, resulting in a more data-driven organization. An adaptive analytics fabric automatically optimizes data layout and query efficiency to lower the use of resources (and dollars).

Improved patient care

When patient data can be accessed and read no matter where it came from, what format it’s in, or where it resides—either in the cloud or on-premise—that data can be thoroughly checked against volumes of external medical data, including case studies and new treatment options. Connections can be made that could easily have gone undetected if some data were incomplete or inaccessible. Patients themselves can also be empowered with access to their own data through their own devices. 

As the healthcare industry shifts toward patient-centered models, providers will need to fully understand patient satisfaction measures and how they affect their practices. When all administrative data is integrated and easily available for analysis, health care providers can better address issues such as long wait times and avoidable readmissions. Data from patient satisfaction surveys across every branch and area of the network can be used to see which areas of patient care and communication could be improved, increasing patient satisfaction and HCAHPS scores.

Why wait to optimize your operations and patient care? 

At any stage of your cloud migration journey, an adaptive analytics fabric provides the confidence you need to move data from legacy systems, accelerate queries with legacy systems, avoid disruptions in operations, ensure patient data is secure and compliance with laws such as HIPAA are always met, and, most importantly, ensure your operations and health care decisions are optimized. By having all data available for the best possible analyses and insights, healthcare organizations can rest assured they are performing in the most efficient and cost-effective manner and—most importantly—providing the highest quality care to their patients.

For more information on how an adaptive analytics fabric can transform your healthcare operations, get the full paper here.