IT professionals who are supporting healthcare providers today must be aware of many key strategic initiatives that share the promise of improved patient outcomes, reduced enterprise risk, and increased efficiency.
But data management challenges are a constant source of frustration for healthcare IT professionals, inhibiting their ability to deliver value and consistent, scalable results. These challenges include:
Let’s examine each of these data frustrations for healthcare providers and payers.
Healthcare provider organizations “grew up” with departmental applications that had been integrated using HL7 messaging. Back in the day, HL7 messaging was a tremendous innovation, since it allowed sharing registration data, orders and results, along with all forms of other data between applications. Because HL7 was so flexible, it was easy to adopt and became broadly adopted by the application vendor community. Unfortunately, the flexibility that led to HL7’s wide adoption also meant each application vendor was then free to adjust the message content for their own purposes—which greatly complicated the process of implementing and maintaining the interfaces between applications, as most data requires a lot of manual cleanup and mapping to integrate across apps.
Similarly, many healthcare payers have lived with monolithic claims processing apps for decades. These applications have been significantly customized over many years, which effectively limited the number of people who fully understood how the apps were being used, or how to best leverage the data inside those apps. The result: healthcare providers have relatively deep data insights about patients, but only when they have provided the care to the patient, with (at best) minimal data access to holistic perspectives across all the other care a patient may be receiving.
In contrast, while healthcare payers have very broad data across all patient care (primarily because they’re the ones paying for it), they often lack the detailed clinical data about the care received.
Frustrating, right? If only they had interoperability across provider and payer organizations!
Healthcare has arguably the most complex data management issues of any industry. There are any number of necessary data points to capture: symptoms, medical history, diagnoses, procedures, and more, across individual use cases, with literally millions of different concepts and values regarding how care was delivered, billed and paid for. Unfortunately, the quality of this data is often extremely poor, for a variety of human and clinical factors, along with the additional challenges created by various healthcare app vendors capturing data in different formats.
After decades of working with inefficient, error-prone and unavailable paper records, providers have become so accustomed to poor data quality that they’ve figured out how to deliver care and get paid for it, regardless of the data’s quality. Unfortunately, this resilience that served them so well in an era of paper records has persisted into the era of electronic health records. Resilient and resourceful clinicians have become remarkably adept at filtering through what makes sense and interpreting what’s true at the point of care, but this filtering and decision-making does not translate well when you’re dealing with huge volumes and types of poor-quality data that exist in applications and analytics systems.
On the payer side, much of the data content of claims was dictated by HIPAA guidelines in 1996. Thanks to this regulation, payers have relatively clean data (at least within HIPAA-defined parameters). But data is less trustworthy where it’s not dictated, particularly in areas like provider data/membership rosters and standardized code sets and locations (reference data).
Here’s a common example of the hazards of untrustworthy data that frustrates consumers and healthcare professionals alike: the inability of insurance plans or medical groups to publish a reliable directory of participating providers. This makes it especially challenging for consumers to find providers that accept their insurance even on the insurer’s own website. And being able to determine which providersare accepting new patients? That’s also notoriously unreliable.
For decades, providers have tried to uniquely identify individual patients and obtain a single source of truth. Yet despite numerous efforts to deliver on an enterprise master patient index, duplicate patient IDs are still pervasive in most systems. Why does this inconsistent system persist? Likely because providers can still deliver, bill, and get paid for care, even if the provider has multiple different identifiers for an individual patient. Where the existence of multiple identifiers for the same patient really does matter is quality of care, where creating a comprehensive 360-degree view of a patient—and ensuring that the patient receives safe, high-quality care—requires consolidating and aggregating all available information about a patient, their conditions, and their care.
Payers have also struggled mightily to uniquely identify their members so they can appropriately stratify patient risk and proactively manage patients. However, because payer applications have typically been policy- and product-focused, rather than member focused, aggregating a member’s data across products has been problematic. In the past, this has meant that when a member changed products, the payer system would then treat a current member as though they were a new individual. Although this is changing as payers focus on mastering member data across products and systems, it remains a significant challenge, particularly in the face of rampant merger and acquisition activity in the payer market.
Frustration could be reduced if payers were able to deliver a single, trusted holistic (360 degree) view of patients, members, providers, and other critical entities.
Healthcare providers and payers have both struggled to effectively embrace data governance best practices, but they’re inhibited by a variety of system, data, care delivery, and other silos. So much effort has gone into figuring out how to deliver care around siloed data processes, it has been nearly impossible to govern grassroots manual improvement processes through healthcare’s distributed system. Also, early attempts at healthcare data governance were ineffective, highly bureaucratic, and heavy-handed top-down approaches.
It’s frustrating how data governance is finally hitting its stride delivering real value across many other industries, yet healthcare organizations continue to struggle getting their efforts off the ground.
Although HIPAA had a significant positive impact on data privacy, it sets a relatively low bar regarding regulated requirements for data privacy and protection. Data silos make it hard to know where sensitive data resides, and sensitive data proliferates broadly within the healthcare enterprise. For example, it is common in many healthcare organizations to use full copies of data from production applications for test and development, even as data sharing across organizations has become more prevalent.
It isn’t that healthcare organizations don’t recognize the importance of protecting sensitive data. The frustration lies in the fact that protecting and ensuring the privacy of data has become an insurmountable task with challenges such as silos, complexity, and mergers and acquisitions.
All of these data management competencies fall under the master concept of managing data as a true organizational asset. And while managing data as an asset has often been a throwaway phrase, significant disruptions to the healthcare industry highlight the urgency of this need.
Everyone in the provider and payer space shares the desire to provide safe, high-quality, and affordable care. We now have more data than ever before to aid in this mission. We are also in the midst of a generational market shift in what’s possible with analytics, artificial intelligence, and machine learning to gain insights from this data. With the data and insights available to the healthcare industry, we can now make possible what never existed before. If we can just get it in order, we can go from data frustration to data freedom!
More on what data freedom can look like in my next post.