The relentless rise in healthcare costs in the United States has been a longstanding concern for patients, providers, and policymakers alike. While many factors contribute to this trend, one economic phenomenon, known as Baumol’s Cost Disease, offers a compelling explanation for the persistent escalation of healthcare expenses.
Baumol’s Cost Disease: The Root of the Problem
Named after the economist William J. Baumol, this theory explains why specific labor-intensive sectors, like healthcare and education, experience disproportionate cost increases over time.
At the core of this phenomenon is that some industries, particularly those reliant on human labor, struggle to increase productivity at the same rate as more technology-driven sectors.
The personal nature of patient care and the complexity of documenting and sharing patient data have traditionally relied on human labor to perform most of the related work. This human factor has limited the potential for significant productivity gains through technology automation or process optimization.
As a result, healthcare stakeholders continually increased wages to retain skilled professionals, leading to higher costs that outpaced economic growth.
The effects of Baumol’s Cost Disease on healthcare costs are profound. As the demand for healthcare services continues to rise due to an aging population and the industry continues to rely on human labor, healthcare costs continue to escalate at an alarming rate.
Embracing Innovation: Artificial Intelligence (AI) and Infrastructure as Potential Solutions
While Baumol’s Cost Disease presents a formidable challenge, advancements in artificial intelligence (AI), machine learning, and other cutting-edge technologies offer promising solutions to mitigate its impact on healthcare costs.
By combining human expertise with AI, you achieve the automation needed to reduce administrative burden and paperwork and reduce non-clinical staff hours focused on tasks unrelated to actual human beings and patients.
However, innovators must consider where and how technology and innovation are applied to move past incremental change. The hypothesis is that unless we can insert AI-powered innovation and machine learning in the right places and for the right tasks, we won’t overcome factors such as low provider and clinical adoption of technology and patient mistrust of AI and AI-powered tech.
The infrastructure of healthcare is grounded in patient data, but this data is often not standardized, lives in silos, and ultimately feeds broken processes. For AI to address endemic cost-sensitive issues outlined above, innovators must insert it where the provider or the caregiver meets the patient – at the point of care (note: this does not have to be in-person doctor-to-patient care).
This means applying AI in traditional record systems, such as EHRs and clinical workflow solutions that are nearly ubiquitous across the US and provide the fundamental context from which AI models can advance. But how do you connect innovation across EHRs and get it in more hands faster?
EHR and clinical workflow integrators seemingly solve this challenge, but not all integrators are created equal or built for scale.
For example, consider incorporating AI-powered ambient scribe technology into clinical workflow. This innovation offers an audio recording of a provider’s conversation with a patient, transcript generation, and subsequent note creation that is then reviewed by the provider and populated within the system of record—the patient chart within the EHR—through various modalities.
This automation saves hours from clinicians transcribing notes and allows the physician to engage with the patient without the distraction of data input in the EHR during the office visit.
Ambient scribe technology has the potential to significantly impact clinical workflow. However, we are observing a trend where single EHRs create or integrate with single ambient scribe technology. This one-to-one integration limits end user (provider) access to innovation and arguably makes the solution category more expensive.
Additionally, many ambient scribe innovators create custom integrations for each EHR system or avoid integration altogether by building their tools outside the EHR. This one-off or avoidant approach significantly slows down widespread provider adoption and can inflate costs.
However, suppose this tech or any AI-powered innovation is connected to the point of care faster and at a fraction of the cost through the right infrastructure—middleware platforms connected to the most popular EHRs.
This enables technology vendors to focus on building the best version of their product while relying on the flexible and scalable integration of middleware platforms to connect them to providers directly at the point of care.
This open ecosystem democratizes access to healthcare innovation and unlocks more sophisticated and previously unconnected workflows while ensuring adoption by end users— the caregivers. In that case, the applied technology has the potential to become pervasive, efficiency reigns supreme, the human touch is refocused where it belongs—with patients, and costs go down.
Conclusion
Integrating AI, machine learning, and innovation with traditional healthcare delivery models presents a promising path forward in the battle against rising healthcare costs and the effects of Baumol’s Cost Disease.
By embracing innovation and fostering collaboration among stakeholders, the healthcare industry can leverage these powerful tools to improve patient care, enhance efficiency, and ultimately bend the cost curve.
