Value Add Intermediaries in FHIR®

However, this data must be represented responsibly and used only for appropriate uses that are tolerant of the inherent risks. The FHIR standard needs to evolve to allow the capture of this data, along with the original data.

This paper looks at three areas in which FHIR can evolve:

However, the purpose of this paper is not to propose any particular change but instead raise awareness of the powerful possibilities that VAIs bring to the table.

Why Value Add Intermediaries are Needed

Current State of Healthcare Data

Health data is extremely fragmented. Healthcare is, arguably, the second oldest profession in the world. It should be no surprise that documentation in healthcare is so varied. After all, for a long time this documentation only served to help the provider, as one patient only had one doctor. Even as the idea of medical institutions grew and doctors formed practices where more than one doctor might see a patient, these were islands to themselves. However, today’s world is far more mobile and complex. Patients often see providers in other parts of the country or even other parts of the world. On average, patients see two primary care providers and seven specialists from four practices each year.¹ This has made interoperability extremely important to the continuity of care.

Problems arise when we try to use data from one institution in another, because each institution has its own practices and procedures for recording healthcare data. This stems both from divergent histories and from licensing, specialty, and regulatory variations. One institution may have licensed one code system, while the institution down the street licensed another. Additionally, one code system may support cardiology better than orthopedics, or the like. These variations make it impossible to exchange the data in a meaningful way.

While many thought that electronic health records (EHR) systems would solve this problem, the reality was that EHRs were entering a fairly entrenched world that was understandably resistant to change. In many cases, the EHR had to adapt to the realities on the ground, instead of imposing conformity. EHRs are a massive step forward, but they do not answer all the problems of interoperability.

New standards, like FHIR, certainly help. FHIR provides a consistent format for the data, merging the variations of shapes into a single, recognizable shape. However, this only answers for the form of the data. It does not help with the contents within. To follow the analogy: FHIR may specify that all data of a certain type must be represented in a circle, but, for the most part, it doesn’t say what the difference between a red and green circle is. This means that FHIR provides a standard format for organizing and exchanging data for a specific type, but it doesn’t dictate the interpretation or significance of different variations or aspects of that data.

Additionally, a lot of healthcare data is trapped in narrative notes, reports, and images. Not to mention the quantity of data that is outright missing for any number of reasons, from patients not disclosing certain information to errors in data entry and inadequate data collection procedures. New technologies are springing up every day in the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), that can help unlock that data. They can also enrich the data, with analysis and predictions that can greatly aid doctors and patients in making more informed decisions. However, the results from these systems are probabilistic in nature and great care must be taken to use them appropriately.

AI systems can be seen as one type of a larger classification of VAI. VAIs sit between producers and consumers of data and work to make the data more understandable and actionable. Below are some scenarios that VAIs address:

Types of VAIs: From Deterministic to Futuristic

VAIs can range from highly deterministic, for use in regulated environments,
to more probabilistic systems, which need to be handled with more care.

Data Quality Improvement

There are often very deterministic ways to move data from one standard to another. This includes code mapping, where the system understands that code A in system 1 maps to code B in system 2. However, even more advanced techniques can be applied, such as using key words in descriptions to match uncoded values to codes in a code system. At Availity, we call this process Upcycling Data™, which, in addition to this normalization, includes enriching, reorganizing, and deduplicating data via our API-based Availity Fusion™ technology engine.

In cases where data is coming in from an EHR, lab system, or other electronic system, Upcycling Data can often add tremendous value to the data. It can determine that while source A has a red circle and source B has a green circle, in reality both are blue circles in the national standard for how circles are understood. Further, it can decorate the blue circle with information, tell where the blue circle should appear in the record, and determine if two blue circles are the same. All of this results in data that is far easier to leverage in the consuming system.

To put it into perspective, Upcycling Data can help to ensure that the clinical data being exchanged through a Health Information Exchange (HIE) is consistent, complete, and accurate, regardless of where it was sourced. For example, a patient’s medication record may be represented differently in the EHR system used by their primary care physician and their specialist, resulting in discrepancies or inconsistencies in the data. By Upcycling Data, the HIE can identify and reconcile any differences, ensuring that the data is represented consistently and accurately across both systems. The upcycled data can also be enriched with additional metadata, such as the source of the data, the date of the medication order, and the dosage instructions, making it more valuable for clinical decision-making.

Upcycled Data In Action: Accurate Medication Data For Critical Decision Making

AI Enrichments

AI has been exploding in the last few years. While it is laughable to suggest that AI will replace doctors, it does have tremendous potential to revolutionize the practice of medicine. As my colleague, Paulo Pinho, M.D., Vice President, and Medical Director of Innovation at Availity, says, “The black bags of physicians today contain stethoscopes, reflex hammers, and ophthalmoscopes. The black bags of the future will need to contain AI tools in order to meet the demands of an evolving medical body of knowledge.”

However, the power of AI is not limited to what it will do for doctors. In the increasingly complicated world of medicine, it is critical that patients and their loved ones become highly involved in their healthcare. Most of these people are not medical professionals and need help understanding the nuanced data available in health records. AI can take this quantitative data and represent it in a qualitative narrative to help non-medical professionals make good decisions.

Nevertheless, AI is far from foolproof and the majority of it is probabilistic in nature. Great care must be taken to use this data responsibly. Different uses of this data will have different tolerances for the inherent risk of this data being incorrect. For example, population health analytics across large populations often have a high-risk tolerance for use of this data. In a large population, the added data can erase biases, regional inconsistencies, and so on, while the inaccuracies are reduced due to the sheer volume of data. On the other hand, highly regulated programs, like HEDIS® and Risk Adjustment Factor (RAF), may have far less risk tolerance for AI data. Below we will discuss in detail “responsibly” representing data.

Unbounded Future

If there is one thing that can be certain in medicine and technology, it is that we have no idea where the future will take us. Above we outlined two scenarios for VAI, but there is no doubt that there will be others. While we can’t predict what these VAIs will do and how they will do it, we can prepare for their arrival. By building into our standards and interoperability flows the room for VAIs, we leave the door open for others to be inserted with far less pain.

For example, if we adopt some of the suggestions around the use of the FHIR meta tag below, we make it possible for future VAIs to also identify resources they have generated or touched.

While future proofing is often a foolhardy undertaking, it still makes sense to leave hooks in our standards to support the future. The difference lies in creating something that may not have present-day applications, as opposed to designing a useful structure for today with flexibility to adapt for future needs. VAIs exist today and the need for them will only grow. Therefore, by supporting VAIs today in an open way, we can lay a solid foundation for the future.

Achieving the Value-Added Potential of Intermediaries in Healthcare

If VAIs are going to take their place as a standard part of our data pipelines, then we need the industry to take action.

The Role of Regulatory Bodies

Organizations like the Centers for Medicare & Medicaid Services (CMS) have a huge impact on how healthcare data is exchanged. Both because of regulations like the Patient Access Final Rule and because of the way they consume data, like HEDIS and RAF. Some regulatory bodies are resistant to VAIs because they are concerned that any intermediate process might “game the system” by changing the data in a way that makes the results (quality scores for example) better. However, regulatory bodies who are resistant to VAIs may miss out on valuable data because of the fragmentation discussed above. As technology progresses, the amount of value being missed will only increase. Instead of ignoring VAIs, regulatory bodies should embrace them, but do so responsibly, by leveraging some of the ideas presented here. For example, regulations may allow for the use of data from VAIs as long as it is properly tagged, and its importance is weighted appropriately.

The Role of Patients, Providers, Health Plans and Loved Ones

Everyone involved in healthcare needs to demand better access to higher quality data and the ability to understand that data. No one should be satisfied with simply passing on bad data because that is how it came to them. All the players need to understand the value that VAIs bring, along with the risks. They need to think hard about their use cases and when the value of VAI data outweighs the risk. Then they need to demand that VAIs be allowed to responsibly drive better outcomes and contain costs.

Your Role

Whether you are a healthcare industry leader, provider, or healthcare consumer, you have a role to play. It’s crucial to understand the value vs. risk of VAIs and recognize the limitations of the data you are currently using today. You should also think about the data you’re missing that could be helpful. Then you need to raise your voice and talk to providers, health plans, standards bodies, and regulatory bodies and demand the highest quality data be made available to you when you need it, in a responsible way. There are many players in the world of healthcare, but in the end, you are the one primarily responsible for your well-being. Take steps to make sure you have the high-quality, understandable, and actionable data you need to take on that responsibility.

---

Don’t Wait: With up to 5 billion exchanges of digital health information annually, the tide of clinical data keeps rising. Start upcycling your data into a clean, concentrated, usable force today.