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The Changing Healthcare Landscape

Patient engagement lies at the confluence of two dominant trends in U.S. healthcare:

1. The proliferation of data
2. The sector’s unwavering shift toward value-based care

Data is omnipresent in healthcare, and with the advent of technologies such as generative artificial intelligence (AI) models penetrating all corners of the sector, its ubiquity is poised only to accelerate.

Many industry experts and popular business publications, such as The Economist, have asserted that data – not oil – has, in the past decade, become the world’s most valuable resource. As my colleague, Brenden McGlinchey, recently wrote, “Data is the lifeblood of today’s world. It’s essential to running key company operations, driving decisions, and informing responses in almost all industries. As its scope expands, the organizations that manage it must improve and adapt.”

As the volume of data has proliferated in healthcare, so, too, has the need for greater data quality to support a variety of advancements across the sector, such as the movement toward value-based care.

Value-based care is a payment and care delivery model that seeks to improve the quality of care patients receive while reducing costs by aligning incentives between payers and providers. Investment in value-based payment (VBP) models is accelerating, with over half of healthcare payments in 2022 made through a value-based reimbursement model.

For more than two centuries, physicians have practiced in a fee-for-service (FFS) reimbursement environment. Workflow, care delivery, business plan, staffing, documentation, health information technology, scheduling, billing, goals, productivity, policies and procedures, and communication have all been designed and implemented around a care and financing model that emphasizes volume over value. The transition to value-based care has the potential to change each of these dynamics in a significant way.

Core to the mission of value-based care is providing the right care, at the right time, to the right patient.

Consider the following example – courtesy of Aetna – of James, a 47-year-old with chronic kidney disease (CKD) living in Houston, Texas. James is supported by a team within the Aetna Memorial Hermann Accountable Care Network. He visited the hospital with shortness of breath and swelling in both legs, indications that the symptoms of his disease were not controlled.

Through this illustration, it becomes clear how patients would benefit from healthcare payers, providers, and other organizations collaborating to securely share their data and apply increasingly advanced data analysis techniques (e.g., data science, artificial intelligence, machine learning) to proactively engage patients throughout their unique care journeys.

Enter patient engagement.

The Delicate Nature of Patient Engagement

Patient engagement can be defined as efforts to promote patient access to high-quality, evidence-based medicine during a particular episode of care. Engagement itself often takes the form of bidirectional communication with patients through modalities such as SMS (text), email, phone call, and direct mail along personalized care pathways such as the following:

For investments in patient engagement to be worthwhile, communications must be executed at the scale of, at minimum, tens, if not hundreds, of thousands of patients. Therefore, by its very nature, patient engagement is a delicate capability that requires critical thought and careful planning.

For any organization considering investing in patient engagement, they should consider investing commensurately in their data governance capabilities. Without strong data governance, it is all but guaranteed that messaging with patients will be miscommunicated. Trust will be lost as quickly as it is built, data will be misused, and hidden costs will balloon.

The Case for Data Governance in Patient Engagement

Let’s first define data governance. The American Health Information Management Association (AHIMA) defines data governance in healthcare as:

The overall administration, through clearly defined procedures and plans, that assures the availability, integrity, security, and usability of the structured and unstructured data available to an organization.

Healthcare data governance programs include the people, processes, and systems used to manage data throughout the data lifecycle, allowing data to benefit the organization.

In layman’s terms, data governance is the mindset an organization adopts when approaching data, and the supporting behaviors (processes) and tools (technologies) applied to tend to it, care for it, and, ultimately, maximize its clinical and financial value.

As it relates to patient engagement, data governance is acutely important, if not vital, given the vast amount of business rules and patient data managed across a variety of simultaneously active journeys.

Organizations investing in patient engagement will discover quickly how the surrounding data ecosystem is particularly dynamic. Consider the following, all of which have data governance implications (not a comprehensive list):

• Multiple journeys will be active concurrently, and many patients may be eligible for more than one journey at a time (e.g., mammogram appointment, annual PCP visit), but government regulations restrict how frequently patients can be contacted.
• At any given time, patients may opt out of a modality (e.g., SMS), but still be eligible for alternative modalities (e.g., email). Patients may also opt back in to communication modalities at any time.
• To a surprising degree, patients will respond with unstructured, “out-of-bounds” communications (e.g., “Stop”, “stope”, “enough”) that will need to be evaluated manually or with trusted AI.
• Due to the dynamic nature of social norms, core data elements such as demographic information will evolve over time (e.g., male vs. cismale).
• It goes without saying that all data must be handled in compliance with existing privacy and security regulations and standards such as Fast Healthcare Interoperability Resources (FHIR), Health Level Seven Version Two (HL7 v2), X12 for electronic data interchange (EDI), the Health Insurance Portability and Accountability Act (HIPAA) and, if applicable, the General Data Protection Regulation (GDPR). Noncompliance can be financially and reputationally ruinous for firms weathering the storms of privacy violations.
• Do not forget the original purpose of patient engagement: improving patient health outcomes while reducing costs of care. In the broader value-based care ecosystem, clear reporting metrics must be established to demonstrate engagement efforts are indeed resulting in improved health outcomes, a necessity for firms seeking to be reimbursed adequately by entities such as the Centers for Medicare and Medicaid Services (CMS).

Best Practices: Applying Data Governance to Patient Engagement

To address the challenges above, data governance will be crucial. While no two patient engagement solutions are the same, organizations should consider the following principles when developing their data governance capabilities alongside their patient engagement investments.

• Apply data governance surgically instead of radically – Avoid the pitfall of overzealously committing to writing long, complex policies and standards without adequately considering what can reasonably be implemented in the short to medium term.
• Begin with mindsets and behaviors – Firms do not need to prioritize expensive data management solutions, mobilizing big teams, or mandating rigorous data literacy programs to execute patient engagement. Instead, begin treating data as a product itself that has customers, can be innovated over time, and can be democratized for data scientists and engineers to advance the firm’s patient engagement products and services. This is often referred to as “data as a product” thinking.
• Centralize rather than federate – Given the nascency of firms’ patient engagement capabilities when first embarking on these initiatives, centralizing data governance responsibilities within the team can streamline decision-making, quicken the resolution of data issues, and offer organizations the opportunity to test data governance fundamentals before federating responsibilities over time.
• Prioritize with use cases – Target specific use cases that relate directly to the critical data that determine and demonstrate how a patient engagement solution uniquely delivers clinical and business value, starting small and iterating over time.
• Embed data governance by design – Incorporate data governance decision-making and practices into existing processes, positioning data governance as a strategic imperative and necessary piece of the patient engagement puzzle.

As mentioned above, not all patient engagement solutions are created equally, so firms should tailor their data governance approaches accordingly. Be skeptical of adopting a one-size-fits-all data governance solution.

At Kenway, we’ve helped a variety of organizations assess, define, build, and execute their patient engagement aspirations while building and maturing their data governance capabilities.

If you’re interested in exploring a patient engagement and/or data governance journey together, connect with us. We’d be happy to help.