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Utilizing Machine Learning to Combat Clinician Burnout

March 31, 2023

Burnout, which is defined as “emotional exhaustion, depersonalization, and a decreased sense of accomplishment,” is widespread among clinicians as our aging population continues to outnumber working medical professionals in the field. The Association of American Medical Colleges predicts a shortage of up to 124,000 physicians by 2034. In addition, a 2022 analysis report showed that over 230,000 physicians, nurse practitioners, physician assistants, and other clinicians quit their jobs just last year. 

Healthcare professionals are among some of the most overburdened workers in the world and their subsequent burnout can be caused by a variety of factors including: 

- Inadequate support due to clinical and non-clinical staff shortages 

- Outdated technology systems such as EMRs and CRMs 

- The administrative burden of time-consuming paperwork, billing, and electronic health record (EHR) documentation 

- And a lack of efficiency in clinical workflows and administrative tasks.

How can Diagnostic Robotics solutions help?

Reducing Administrative Burden

Diagnostic Robotics' AI-powered population health management solutions help combat clinician burnout by reducing the administrative burden on healthcare plans and providers. They automate many administrative tasks associated with risk stratification, patient outreach, and follow-up. This frees up clinicians' time to focus on providing high-quality care to their patients, rather than spending hours on paperwork and administrative tasks.

Diagnostic Robotics also offers a navigation and intake solution that can help reduce administrative burden by automating tasks, providing real-time analytics, and optimizing resource allocation. This triage solution automates tasks such as collecting patient data, assessing the severity of their symptoms, and recommending appropriate next steps. This reduces the time and resources healthcare providers need to spend on manual data collection and analysis.

Both population health and intake solutions can help reduce the burden by providing real-time analytics and insights that identify bottlenecks in the patient care process, optimize resource allocation, and improve overall efficiency. The solution’s user-friendly dashboard can help healthcare providers make data-driven decisions and streamline their operations.

Providing Actionable Insights

Another way Diagnostic Robotics' solutions help combat clinician burnout is by providing clinicians with actionable insights based on patient data. They use machine learning algorithms to analyze patient data and identify patterns that may indicate a patient is at risk of having an avoidable health incident or developing a chronic condition. This information is then presented to the clinician in an easy-to-understand format, allowing them to make informed decisions about patient care.

For example, rather than relying on medical professionals to manually analyze charts and medical history to identify patients with risk factors for diabetes, such as high body mass index (BMI) and elevated blood glucose levels, Diagnostic Robotics’ machine learning algorithms evaluate patient data and conduct automated risk assessments for these factors. Based on this information, they may determine if a patient needs to be referred to a nutritionist for dietary counseling and provided with a home glucose monitor to track their blood glucose levels. This type of proactive care can prevent the onset of diabetes and improve the patient's overall health.

Diagnostic Robotics' solutions help combat clinician burnout and improve patient outcomes by leveraging machine learning to automate administrative tasks and provide actionable insights. They have been tested in multiple clinical settings, including primary care clinics and hospital systems, and have significantly improved patient outcomes and clinician satisfaction.

A regional Blues plan worked with Diagnostic Robotics to identify impactable future-risk members within its CHF program and generated a $1,600 PMPM cost of care reduction. Patients who received care through the solution had a lower risk of developing CHF than those who did not receive care through it.

Optimizing Workflows 

Communication is key to understanding patients' individual needs across a variety of medical practice areas to be able to offer whole-body care. Oftentimes, overburdened clinicians experience a lack of resources that allow them to effectively share information with relevant parties who are responsible for providing or coordinating care. 

By analyzing patient data and identifying individual patient needs, Diagnostic Robotics population health solutions can provide healthcare professionals with recommendations for personalized care plans so that patients receive the care and support that they need to manage their health conditions and improve their outcomes.

Diagnostic Robotics’ intake solution provides similar benefits as it is an aggregator for patient history, treatment scheduling, and risk scores. When a patient must visit multiple provider locations or even multiple clinicians within the same practice, this solution can serve as a streamlined platform to ensure that no information is missed. Automated patient summaries also ensure that people across the patient's care pathway can clearly understand the diagnoses and interventions at hand.


Clinician burnout is a growing issue affecting the entire healthcare industry, but machine learning can help combat it. Diagnostic Robotics' AI-powered population health management solution is one example of how machine learning can be used to automate administrative tasks and provide actionable insights to clinicians, improving patient outcomes and reducing burnout. As the healthcare industry continues to embrace technology, we can expect more health plans and providers to adopt innovative population health solutions like Diagnostic Robotics' that use machine learning to improve patient care and clinician efficiency.

Lower Care Costs and Improve Outcomes with Intelligent Care Journeys

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