How Ambient Clinical Intelligence (ACI) May Help Fix Doctor Burnout
Physician burnout has been a significant concern in the healthcare industry for several years, and the COVID-19 pandemic has exacerbated this issue with the burnout rate among doctors in the United States has reached epidemic levels. A 2021 study by the American Medical Association (AMA) found that an alarming 62.8% of physicians reported at least one symptom of burnout, up from 38.2% in 2020. The COVID-19 pandemic exacerbated existing stressors and highlighted the challenges faced by healthcare workers. Burnout is a state of emotional, physical, and mental exhaustion often accompanied by feelings of cynicism and detachment from work. Burnout negatively impacts healthcare quality, patient experience and costs. In addition to the job demands, technological changes in medicine have contributed to burnout. Increasing requirements for documentation and the expansion of the Electronic Health Record (EHR) have been major contributors to burnout. EHRs require typing and dozens of non-intuitive clicks, unique to each individual system, because it requires data entered in a structured format designed for meeting billing and coding requirements rather than enhancing clinical care and relationships. On average, clinicians spend 6 hours documenting care in an EHR.
Over the years, efforts have been made to lessen the documentation burden by using voice recognition technology and medical scribes. However, the costs and technical barriers have not resulted in a universally effective solution. Enter Gen AI — a game changer for the healthcare industry, which may not only reduce burnout but also enhance productivity for clinicians. The feature that distinguishes Gen AI from other AI technologies is that it can generate new content, not just reformat content. It has “cognitive capabilities” such as the ability to “understand” language and concepts. As a result it can be leveraged just by speaking or writing. Previously, in order to access this level of computing power one would have to know a programming language such as Python or R. Now you can access this power just by talking or writing using Natural Language Processing (NLP). Ambient Clinical Intelligence (ACI) is a process that uses advanced AI and voice recognition technology to automatically document patient encounters in real-time, in a natural and free-flowing manner without the need for the clinician to type their note into the computer. They can review and edit the note generated by the computer “listening” to the encounter. It aims to improve clinician satisfaction, patient experience, operational efficiencies, and financial outcomes by reducing the burden of documentation. This technology can help reduce clinician burnout and improve workflows by automating medical transcription and note-taking. This enables the clinician to have a more natural interaction with the patient without the need to turn away to type into the computer. This approach also enables the transcription of notes in real-time and without the transcription costs. ACI also reduces cognitive burden on clinicians as they can naturally interact with the patient, and the system will automatically organize and format their note into pre-defined, configurable, and standard formats. It is important to recognize that leveraging Gen AI is like having a very smart scribe. You can leverage the cognitive capabilities to generate a smart note that can highlight the most important clinical information or red flags and create a note that ensures documentation requirements are met. Using Gen AI for clinical documentation is a slam-dunk use case that I predict will become the norm in 2–3 years.
Some of the early results are impressive and value is created very quickly because there is very little training or setup necessary. In one survey, after only 2 weeks of using the ACI system, clinicians reported an 88% improvement in satisfaction with clinical documentation and 76% believed that it improved the patient experience. In the category of time-savings, a 2023 study found that the implementation of ambient voice technology cut down clinician documentation time by over 28 percent per primary care encounter. The economic value of enabling your most expensive resource to become more productive is significant. As a result, the ACI market is booming, with projections of almost $60 billion in sales expected by 2026.
As the ACI infrastructure becomes more commonplace, the integration of ACI with Clinical Decision Support (CDS) represents a transformative development in healthcare. CDS systems traditionally provide health professionals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance patient care. By incorporating data from ACI, these systems can become more robust and contextually aware. For instance, a CDS tool could analyze a conversation captured by ACI to identify potential diagnostic clues or treatment options that may not have been explicitly noted in the EHR. Statistically, the benefits can be significant. For example, studies have shown that effective CDS tools can reduce medication errors by up to 55%. With the addition of ACI, these tools could become even more effective by capturing a richer, more nuanced patient history. This capability will not only raise the clinical proficiency of all users but enable all users to work at the “top of their license” more reliably with the help of this clinical co-pilot. By incorporating this more complete, consistent, and nuanced data you can also strengthen personalized care and create more effective predictive models.
There are many challenges to the optimal use of ACI. This includes the general risks of using AI for content creation in the healthcare industry, including data privacy, patient consent, bias in the algorithms, and ethical use of Gen AI. To maximize the value of ACI, there will need to be effective integration into workflows, validation of the reliability and accuracy of AI-generated insights, and standardization of clinical note content. The transformative potential of combining ambient computing with Gen AI in healthcare is immense. This integration marks a promising step towards a more efficient, accurate, and patient-centered healthcare delivery model that improves quality, decreases costs AND reduces clinician burnout.
