The breadth and depth of data that healthcare organizations keep up with daily is ever-expanding. This is nothing new. But, now healthcare organizations finally have access to healthcare tools that can not only help them keep pace, but can revolutionize the race to immediate and reliable insights.
For example, the UK’s National Health Service (NHS) is now seeking to improve patient outcomes through employing a NHS Federated Data Platform. And, as the largest nationalized health service in the world, this is a significant feat. By consolidating data into one unified system, NHS will expect to experience rapid insights on a massive scale. It’s no surprise many other organizations will soon follow suit to avoid costly data silos and open the door to improved operational efficiencies and patient outcomes.
In this guide, we’ll explore the basics of a modern healthcare data platform and unveil its larger role as a strategic accelerator for growth. Together, get to the bottom of the following questions:
- What is a modern healthcare data platform?
- What features make a healthcare data platform modern?
- What are the benefits of a modern healthcare data platform?
- What are common healthcare data platform integration challenges?
Keep in mind that a platform solution will look different for each organization. Align your team and coordinate your goals before conducting research and committing. From there, you can select a path forward whether that means building a platform in-house or finding a reliable vendor.
What is a modern healthcare data platform?
A healthcare data platform is a digital solution that houses an organized repository of healthcare data including EHR, member data, claims data, care coordination data and more. That said, a “healthcare data platform” is a broad term that can be used to describe three separate digital solutions—the healthcare data warehouse, the data lake, and the modern healthcare data lakehouse.
A healthcare data warehouse describes a more structured solution that typically uses Extract, Transform, Load (ETL) processes to prepare and load structured data. Warehouses are more performant than data lakes, but are relatively inflexible and difficult to scale operationally.
On the other hand, data lakes represent a fluid digital solution that easily captures raw, structured, unstructured, and even semi-structured data. This storage environment is relatively low-cost, but doesn’t have the same management capabilities as a warehouse.
A healthcare data lakehouse blends the advantages of both platforms into one data management solution.This opens the door to more advanced analytics through the use of one unified storage system. Check out this video for a deeper dive into Arcadia’s data lakehouse:
An agile and expansive data lakehouse offers flexible interactions, various data type support, and simplifies complex queries to speed up workflows. Tasks like data entry, cleaning, transformation, and analysis can be streamlined with built-in data validation processes.
What features make a healthcare data platform modern?
As alluded to above, functionally efficient healthcare data platforms must complete several tasks in a timely manner. Some data platforms excel at this while others lack core functionalities.
If your organization is looking for a platform solution, you’ll want to prioritize one with modern features that will meet both your current and future needs. Specifically, you should consider selecting a platform with these built-in features:
- Cloud-based integration: Cloud-based platforms that scale horizontally will maintain reliability even with multiple users.
- Daily updates and syncs: Daily data refreshes ensure the most relevant and recent data is prepared at the time of use for speedy recovery and synchronization.
- Security measures and access control: Predefined security measures support data access restriction according to user roles and privileges.
- Web-based integrated tools: Built-in development tools keep data querying, uploads, and downloads simple and straightforward.
- Adaptable data sourcing: The right platform will balance the breadth and depth of healthcare data from raw files to detailed data models.
- Rapid turnaround and recovery: Fast retrieval (hours, not days) during unexpected workflows is a must for the right people to make informed decisions quickly.
- Granular data distribution: Enable teams to access their own adaptable “data lakehouses” to foster collaboration across enterprise networks.
Quick, reliable, and low-latency healthcare data platforms can help any provider or analyst find what they need when they need it, whether that be a roundup of all patients with Diabetes or an up-to-date hospital readmission rate.
What are the benefits of a modern healthcare data platform?
Integrating a brand new healthcare data platform, like a datalake, might give you pause either due to the cost or the seeming complexity of adopting a new system.
However, the tangible benefits, such as the combined flexibility and organizational capacity of a healthcare data lakehouse open new opportunities. On a ground level, this means faster pipelines through simple creation of reports and dashboards. For instance, a provider could easily access a breakdown members per cost or SDOH data overview from diverse data sources quickly.
Additionally, by employing machine learning models to understand user behavior, highly intelligent data platforms can tailor content and feature releases accordingly. Therefore, organizations can introduce new content on an ongoing basis without having to consider increased computing demands. Thus, teams can count on rapid updates instead of having to await major release cycles which can be more administratively disruptive and give way to security risks.
Rapid insights and adaptable data sourcing power teams to enhance care coordination and simplify patient stratification. Consequently, care teams can make game-changing interventions and close expanding patient gaps.
What are common healthcare data platform integration challenges?
Before you decide on a platform, you need to ensure that your team is ready to adjust its workflows. This means establishing operational best practices before committing to building or buying a solution. To start, your analyst teams should be prepared to adapt to the following challenges:
- New SQL (structured query language) variants to ensure your teams are ready for any linguistic shifts. This involves understanding the syntax, function, and capabilities to the new variant. Training programs, workshops, and online resources can all be applied to make sure your team is all on the same page before transitioning.
- Content migration demands that require analysts to transfer existing data onto a new one while maintaining data integrity. Train your team to perform data validation checks to ensure data is not compromised. Then, experiment using retrieval queries, reports, and dashboards.
- New analytics tools which include new user interfaces and advanced features. Encourage analysts to play around with web-based environments and collaborate with other stakeholders to test the usability, efficiency, and effectiveness of the new tools to usher in positive outcomes.
As healthcare organizations begin their implementation journey, they should take note of which workflows best meet their clinical and operational goals. Then, they’ll need to update their internal documentation and guidelines to reflect these best practices.
How Arcadia’s healthcare data model can help
Even the most sophisticated healthcare organizations can experience tangled data which can spread analysts thin and slow progress. Arcadia’s data model, Foundry, takes these transition pain points into account.
A platform written by analysts for analysts, Foundry can help answer more complex questions faster and more consistently. Our interoperable platform enables:
- Elegant reporting based on wide-table schema for quick analytics.
- Sandbox database capabilities to upload your own data and experiment with workflows.
- Rich data details including clinical and claims models.
- Preferred third-party app integration to access Business intel, query/ERL and data science transfer.
Analyze quality, risk, financials, clinicals, and contracts data using Business Intelligence, or statistical model of your choice. Our hosted data-base provides you with all you need to achieve value-based care at scale with a simplified schema.
Driving healthcare forward with advanced analytics and AI
Looking ahead, modern healthcare data platforms are propelling organizations forward with advanced analytics and AI. Predictive analysis and natural language processing align care teams and aggregate disparate data into a unified, usable asset.
No longer are organizations left in the dust by siloed and tangled data. Now, care teams are mobilized with reliable insights so they can intervene with informative, preventive care.