The Promise of Care Orchestration in Healthcare
Chilmark Research recently authored an illuminating white paper entitled Automation and
Care Orchestration. The focus of the paper is on how care orchestration “can play a role in
improving outcomes, lowering costs, and improving the working conditions for both clinical
and administrative workers.”
In laying out the case for investing in solutions to these pressing needs, Chilmark discusses
both the prerequisites for effective automation and the distinctions between various
approaches to automation.
But what does it mean for healthcare delivery organizations?
“Automating administrative and some clinical processes in the past has been difficult,”
Chilmark writes,“ due mainly to the lack of interoperability across electronic health records
(EHRs), which limited the ability to share data across platforms within an enterprise. Over
the past decade, the growth in adoption of standards — such as HL7 FHIR and APIs — is
improving interoperability and our ability to exchange data with systems that can perform
value-added services such as automation.”
Now, with interoperability becoming a lower hurdle, the automation conversation is
evolving. Clearly robotic process automation (RPA), artificial intelligence (AI) and machine
learning (ML) are sucking up much of the oxygen. And while RPA has been successful in
areas such as revenue cycle or intake processes, Chilmark says, “clinical workflows have
proven more challenging due to the lack of reliable and trustworthy data on which to act.
This lack of trust is typically mitigated through rework and revalidation performed by highly
licensed care team members who find themselves ‘coordinating’ the care of patients
through careful step-by-step planning and verification. It is here that a different approach is
needed.”
Enter care orchestration – integrating real-time data, applying clinical knowledge, and
employing intelligent automation to individualize the care of every patient at scale. Using
this approach to automation, care coordination becomes less manual, saving clinicians and
staff valuable time just when there are fewer people to divide the work among.
Furthermore, it addresses the vexing issue of “trust.” Care orchestration uses deterministic
logic; every decision the system takes is explainable and adheres to established protocols
and governance structures. Clinicians know with 100% certainty that given condition “x”, “y”
will happen; they can fully trust the system’s actions. There is nothing “predictive” or
“probabilistic” about it, as would be the case using an AI algorithm. Chilmark puts it this
way: In clinical workflows, and care coordination specifically, “Many of the tasks that can be
readily automated are rules-based and can be automated without the use of AI/ML, carrying
fewer risks than AI-enabled tools.”
The “trust” issue also applies to patient data. Even with greater interoperability, clinicians
cannot count on the EHR or patients themselves to provide complete and accurate data;
after all, keyboard inputs and people’s memories are fallible. However, by distributing trust
across multiple different actors – by comparing what’s in the EHR with what the patient says
– providers can identify gaps up front that allow care coordinators to take action to
reconcile inconsistencies, effectively “federating” the trust by creating a composite system
that ultimately offers a more complete and accurate picture of each patient.
Chilmark concludes, “The intelligent automation of care coordination is now a technological
reality with the capacity to produce results and a positive ROI in a relatively short amount of
time, particularly around the biggest challenges health systems have seen in decades –
workforce, cost reduction and the need to generate revenue.”
Download the Chilmark Report – “Automation and Care Orchestration: The Key to
Addressing Today’s Workforce Burden While Improving Patient Journeys and Outcomes”
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