Imagine you have 15-20 meetings per day at work. Each one requires extensive notetaking, information analysis, and complex decision-making. Afterward, you need to address time-sensitive, post-meeting action items and inbound follow-up questions from the participants.
This is the daily reality for today’s clinician. The administrative burden of information collection and consumption, documentation, complex decision-making and patient/provider communication has reached new heights. Further, it is contributing to unprecedented levels of reported burnout across the industry.
Clinician burnout is a pressing issue that impacts healthcare worldwide. It can cause physicians to feel overwhelmed, stressed, and disengaged, leading to reduced job satisfaction and lower quality patient care. While electronic health record (EHR) systems have existing functionality to help address some of the underlying causes of healthcare burnout, they have limitations. AI, on the other hand, promises to change everything.
The AI movement took a major leap forward with the widely publicized efforts of OpenAI. Advancements in this technology are expected to progress rapidly this year. This evolution will have a significant impact on technology solutions, including those in the healthcare industry.
How can AI help address the rising problem of clinician burnout? Here are four specific and practical applications to look for in the coming year.
1. AI-powered Virtual Assistants to Prevent Burnout
One promising application of AI to reduce job burnout for healthcare workers is AI-powered virtual assistants. These assistants can help clinicians with routine tasks.
Such tasks include scheduling appointments, responding to patient inquiries, and providing reminders for medication and follow-up appointments. For example, an AI-powered chatbot can triage patient inquiries and respond to commonly asked questions. This allows clinicians to focus on more complex cases.
Another example is using voice recognition and natural language processing to automate clinical documentation and progress note creation during patient visits. This is complemented by advanced features that discretely capture and document key data points in the designated fields of the medical record such as diagnoses, medications, labs and procedures. Virtual assistants can identify referral requests and schedule follow-up appointments to reduce the administrative burden.
2. Predictive Analytics for Personalized Patient Care
In healthcare, predictive analytics models have been primarily limited to structured data. AI-powered predictive analytics enables the use of unstructured data in predictive models providing more accurate and comprehensive identification of patients at risk of developing certain conditions or complications.
This has the potential to allow clinicians to provide proactive care and prevent complications before they occur. For example, AI can be used to analyze EHRs and flag patients at risk of developing diabetes or sepsis, allowing clinicians to take early interventions to limit negative outcomes. Another potential use of AI in this case would be to predict patient readmissions and target interventions to reduce the risk and likelihood of readmission.
3. Enhanced Decision Support for Personalized Treatment
Decision support has been driving patient care improvements since the introduction of the EHR. The addition of AI-powered decision support systems can enhance traditional systems that analyze historical patient data. These support systems can then recommend treatment decisions at the point of care.
This is done by delivering personalized treatment options that also consider the most common treatment decisions made by clinicians at the organization for patients with similar conditions, demographics and medical history. AI can also provide a comparison of treatment outcomes trended over time for the best practice treatment recommendation, the most common treatment and alternate treatment methods.
Furthermore, AI can analyze a patient’s medical history, lab results and imaging data to recommend personalized treatment options including analyzing a patient’s genomic data to identify targeted therapies based on genetic profile.
4. Remote Monitoring and Telehealth
In-person visits are taxing on patients and providers. AI is advancing the complexity of care that can be delivered outside the office through improvements in wearable devices, remote patient monitoring systems and chatbots.
Advancements in each take on a portion of the complexity of information collection and consumption, clinical decision-making and timely communication to reduce clinician burden.
Wearable devices such as fitness trackers, smartwatches and health monitors can collect data on a patient’s overall health. This can include vital signs, activity levels, sleep patterns and other lifestyle behaviors. These markers are beyond what’s traditionally available in a clinical setting. Thus, more data is made available for provider and patient to determine next steps.
AI can be used to analyze this data, identify trends or anomalies that could indicate health issues. It can then notify patients and clinicians when further action is required. For example, AI can analyze a patient’s heart rate and respiratory rate to identify early signs of heart failure.
Remote patient monitoring systems can be used to collect data on a patient’s wellbeing from home or another remote location. This is completed through a combination of wearable devices and at-home medical equipment. AI can be used to analyze this data and alert healthcare providers to any changes that require attention.
AI for Remote Patient Monitoring
For example, AI can analyze a patient’s vitals and alert healthcare providers if they’re outside of the patient’s normal range. This preserves providers’ time for care decisions requiring more complex analysis.
AI-powered chatbots play an important role in the remote care experience as well and can be used to:
- Communicate with patients remotely to collect patient data, such as updates to symptoms, medication adherence, activity levels and diet
- Provide condition-specific patient education and management techniques (e.g., breathing exercises, avoiding triggers and proper use of inhalers for asthmatics)
- Deliver reminders and alerts for patient appointments, medication refills and what time to take medications
- Monitor patient progress by tracking vitals, blood glucose levels, and changes in reported symptoms
- Provide decision support by synthesizing the data with the patient’s chart to suggest personalized treatment options
Looking Ahead: AI and Burnout in the Healthcare Field
Healthcare is turning a critical corner in the adoption and deployment of technological advances. The possibilities and applications for AI are expanding, and these advancements show promise in mitigating prevalent problems like clinician burnout.
In the few examples noted above, AI can reduce administrative burdens. This includes information collection and consumption, documentation, complex decision-making, and patient/provider communication that challenge clinicians daily.
Leaders should leave room in their roadmaps to quench clinician burnout by evaluating ethical, innovative and effective AI-driven solutions. Doing so can and will improve the overall healthcare experience for both providers and patients.
