This second in a series focusing on the challenges and changes facing healthcare payers and providers focuses on how data management holds the promise of a cure for what ails the healthcare industry.
Every transformational healthcare initiative today has a significant reliance on the availability of accessible, trusted and secure data. In my previous post, I explored the variety of data frustrations that healthcare technology professionals from both provider and payer organizations have contended with over the years.
Enter 2020: It’s a new year—a new decade—and we can now say goodbye to the data frustrations of the past. It’s time to celebrate and say hello to our newfound data freedom that current trends, best practices, and modern technologies afford us.
Let’s walk through the same healthcare data management challenges discussed in my last post, and I will share why we’re now in a position to say hello to data freedom for each scenario.
The first step for most health data management initiatives is to provide access to and integrate all the necessary and relevant data. And standards accelerate this step, making access and integration even more effective and efficient. We’re on the path to saying goodbye to data that is inaccessible in data silos and the gaps in data insights that have persisted for so long across both provider and payer environments.
Instead, thanks to the proliferation of electronic health records, we have access to more valuable data than ever before. Thanks to the innovative interoperability initiatives led by HL7, which is bringing the FHIR standard to fruition with modern, API integrations to healthcare, and mandated by CMS (The Centers for Medicare and Medicaid Services) as well as the ONC (Office of the National Coordinator for Healthcare Information Technology), the road to accessing and bridging healthcare’s data siloes has finally opened.
From a technology perspective, the availability of virtually unlimited compute capacity and storage in the cloud is a game-changer. We’ve seen the emergence of artificial intelligence (AI) and machine learning (ML)- driven automation and scale, along with cloud, SaaS apps and new capabilities to enable self-service analytics by business consumers. These trends have driven innovation leading to today’s rich landscape of data integration and interoperability. Modern APIs are the most effective way to optimize integration and make it easier than ever before.
We’ve also seen the emergence of the iPaaS (integration platform as a service) market category that is leading integration innovation. iPaaS solutions offer multi-tenant cloud managed integration services which incorporate both traditional and modern techniques, and include API management, data integration, application integration, B2B exchange and more, along with a new focus on business self-service and user-friendly access to these techniques. In other words, iPaaS services finally enable citizen integrators.
Data freedom means faster onboarding of new data sources, the ability to scale with the speed and agility of the cloud, faster and easier API integration, and greater ease of use. Because a larger number of business and clinical users can now access their data without waiting for IT, this new data freedom results in faster time to value on every data-driven initiative. (Which of course is every business initiative!)
So now that we can integrate and access our data better than ever before, the next question is: can we trust it?
As discussed in my last post, data quality in healthcare has always been a challenge, in large part because in the past it was easier to be reactive and clean it up only when data quality problems became obvious and unavoidable. Proactively trying to get the data quality right the first time takes so much more effort: who’s going to prioritize that? But everyone in healthcare has a shared interest in getting data quality right the first time, since trustworthy data is integral to delivering safe, high-quality and cost-effective care.
The consumerization of healthcare has changed the game. Consumers have now become active participants in their own care. As they often are now the ones selecting their own insurance and providers, these organizations have begun to create differentiated consumer experiences, understanding that they must engage effectively with patients before and after they visit the doctor or hospital–not just while they’re sick. Then too, because the consumer experience has become more than ever a digital experience, every digital transformation initiative requires trustworthy data—providers and payers alike need high quality, trusted data to create the most positive and effective consumer experiences. Healthcare finally has the incentive and charter to ensure the quality of their data is very high from the start.
From the technology perspective, there is very good news. The data quality tools and services are extremely mature and are more than capable of serving your real-time, proactive data quality requirements. And now with cloud, artificial intelligence and machine learning driving significant innovations, the scale and automation of data quality is even greater than ever before.
Master data management (MDM) initiatives and technology have changed significantly over the years. In the bad old days, it could take years to master a single master domain (e.g., patient, member, location). These implementations were fraught with organizational and process risk and too often delivered less value than anticipated.
But today’s MDM solutions are not only more mature and capable, they are further enhanced by considerable experience and best practices in how to use them. As a result, we can now master multiple data domains within just a few months and when paired with the emergence of enterprise data governance as a strategic imperative, MDM solutions can begin to deliver value immediately.
The value of the single source of truth is now accessible in shorter time. And we’ve entered a new MDM frontier where we are expanding from an internally-focused perspective on MDM to a more inclusive external perspective. We can use AI and ML to associate all of the interaction data that exists across social, internet of things (IoT) devices, and many other external sources with internal transaction, reference, and master data. And this is what can finally deliver that elusive 360-degree view of virtually anything you really need to know.
It’s time to say goodbye to the heavy-handed, top-down, controlling and seldom-successful data governance initiatives of old. Instead, we can say hello to collaborative, easy-to-engage data governance programs that effectively leverage the data expertise that exists throughout the enterprise.
This new world of data governance recognizes that many employees who use data have expertise to offer in what that data means and how the organization might best leverage the data. Instead of relying on a bureaucratic program, we now have data governance programs that allow existing experts to collaborate and educate the rest of the organization.
Key to this new approach to collaboration and data governance is the ability to minimize the manual effort required to create and maintain required documentation and move aggressively to automate as many tasks as possible. From a technology landscape, we now have AI- and metadata-driven solutions that provide the necessary transparency and automation to update all of your data governance assets, processes, and policies. Cross-functional collaboration plus automatic creation of a holistic enterprise data catalog of all assets and consumers across the enterprise is the promised land of data governance.
Goodbye Visio diagrams of dataflows, and manual administration of spreadsheets and SharePoint sites built to house disconnected business glossaries. Hello to a dramatic reduction in the amount of time it takes a new analyst to become as productive as a 25-year industry veteran.
As data proliferates inside and outside an organization, the seemingly insurmountable task of protecting and ensuring the privacy of your most sensitive data has never been more important. After all, even though data consumption through self-service holds the promise of delivering significantly greater business value, it also creates a need to protect this information due to the increased number of people with access.
Even here, there’s good news: innovations across AI, data cataloging, and a seamless platform supporting all integration and data management needs, allows us to actually holistically manage data privacy with full confidence that we have insight into where data risk lies. Mitigating data privacy risk manually was never a viable solution. Automation of privacy and protection is the data freedom we’ve needed all along.
Driven by a combination of industry trends prioritizing the need for better data management competencies, improved healthcare data standards, as well as technology innovations that span AI/ML, cloud, and beyond, we can finally see a path to saying goodbye to data frustration and hello to data freedom!
I’d love to hear your thoughts on what data frustrations you’re still facing, and what are some of the barriers to achieving data freedom within your own organization.
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