Healthcare’s biggest challenges are often framed in terms of staffing shortages, rising costs, administrative burden, and growing complexity. Those challenges are real, but many are compounded by a more fundamental issue: friction.
Every day, healthcare professionals—and patients—experience delays and unnecessary complexity across the healthcare system, often due to reliance on disjointed, or even contradictory, information sources when making decisions.
As healthcare organizations continue to embrace AI and digital transformation, there is a clear opportunity to reduce some of the friction in healthcare through an infrastructure of consistent clinical intelligence.
At this year’s HLTH conference, Wolters Kluwer is demonstrating how our healthcare technology partnerships are the enablement engine that brings trusted intelligence to the moment decisions are made.
The challenge in medicine is no longer finding information
Healthcare has never had more evidence, data and tools available to support decision-making. The challenge is taking information from fragmented sources and synthesizing it into intelligence that can be applied at the right moment, with the right patient or member context, and within the workflow. That context, such as comorbidities that can influence the choice of medication, authorization, or treatment path, is often disconnected from the guidance used to support decisions, leaving decision-makers without the full clinical context of the patient at the moment of action.
Right now, decision-makers must draw from different systems to put together the full clinical context for a patient healthcare decision, which can consume extra time and make it easy to miss important clinical context. More than half of healthcare workers spend at least 11% of their workweek—the equivalent of more than four hours—dealing with problems caused by fragmented systems.
That fragmented information creates variation, and variation creates error. However, given the scale and complexity of this fragmentation, clinical intelligence alone will not solve this challenge. The content has to appear in workflows without disruption, and those workflows are likewise varied and complex. This means we are actively pursuing partners that can leverage our content at the right moment in their normal workflow. Partnerships are making it possible to bring patient context into the workflow, helping clinicians access guidance that is specific to the patient and the complexity of the situation.
Removing variation and making evidence-based intelligence the driving factor can help align decisions around a common standard of care that has been validated, tested, assessed, curated, and constantly reviewed.
Reducing friction across the healthcare ecosystem
Friction rarely appears as a single obstacle. More often, it shows up as a series of small interruptions and workarounds across the healthcare ecosystem. A clinician is asked to clarify documentation. An administrator tracks down supporting evidence for a prior authorization request. A pharmacist validates information that already exists elsewhere in the system.
Each step may seem minor on its own, but may require consulting multiple systems and people, and together they create a significant cognitive and administrative burden and increase the margin for error. Time is spent searching, validating, and reconciling information rather than advancing decisions.
Leading health systems are increasingly adopting an enterprise approach to trusted intelligence, creating a single foundation of evidence to support consistent decision-making across care delivery, operations, pharmacy, revenue cycle, and patient engagement.
A more connected approach allows evidence-based guidance to be embedded throughout the healthcare ecosystem to reduce that friction. We see this moving beyond just the point of care and into administrative workflows, where trusted intelligence can support activities such as prior authorization, claims preparation, and documentation review.
The value of a shared source of truth
As patients increasingly become members of their own care teams, the need for clinical intelligence that can power both clinician-facing and patient-facing experiences grows. AI and partnerships can help make it possible to adapt that intelligence for different audiences while maintaining consistency in the underlying evidence.
A shared source of truth does not mean every stakeholder receives the same experience. A physician may require detailed clinical recommendations; a nurse may need a more tactical procedure breakdown; a patient may want to engage with that same evidence, but written for a non-clinical audience.
When clinicians, pharmacists, payers, administrators, and patients are informed by the same trusted evidence base, organizations can reduce unnecessary variation and support more coordinated decision-making.
What healthcare organizations need is the ability to trust the answer, regardless of where it appears. Whether information is surfaced in an EHR, an ambient documentation platform, an AI assistant, or a patient-facing application, stakeholders need confidence that it is grounded in the same trusted evidence base. A shared source of truth makes that possible.