Jun 15 2023
Data Analytics

Healthcare Automation Reduces Burnout

While clinicians are facing high levels of stress, health systems can implement data-driven automation to provide insights that enable streamlined workflows and revitalize care teams.

The healthcare industry is experiencing a concerning rise in staff burnout, a critical issue that affects both healthcare providers and patient care. However, the integration of data-driven automation solutions holds immense potential to address this challenge.

Data-driven automation provides real-time insights and predictive analytics, enabling informed decision-making and proactive patient management. By leveraging the power of automation and data analysis, healthcare organizations can alleviate the burden on their staff, optimize workflows, improve the overall well-being of healthcare professionals and improve patient care.

Kristin Myers, executive vice president, chief digital and information officer, and dean of digital and information technology at Mount Sinai Health System, explains that data-driven automation helps improve the workforce experience by automating administrative work for clinical teams so they can focus on patients.

“Data-driven automation can positively impact and address burnout for them,” she says. “It’s about being able to support clinicians in their decision-making and trying to reduce the administrative burden.”

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She points to robotic process automation, which can help automate data entry, populating electronic health records and extracting information.

“We also saw the announcement around ChatGPT and integration with the EHR, specifically around the patient messaging function and the ability to respond to a patient with a draft message,” Myers says. “This must first be reviewed by a physician or a nurse, but can be an efficiency play, assisting clinicians overwhelmed with the number of messages they’re receiving from patients.”

She points to the benefits derived from the capability of artificial intelligence and machine learning algorithms to analyze large volumes of data, identify patterns, make predictions and generate insights.

“We have a clinical innovation data science team that builds and operationalizes decision support applications to improve clinical quality and safety and the patient experience,” she says. “We have models for sepsis, malnutrition or falls that can predict behavior.”

Using Automation to Analyze Patient Flow, Wait Times

Myers says when it comes to optimizing clinical workflows, automation can help analyze patient flow, wait times and time spent doing certain tasks.

“This helps us understand resource allocation through patient demand schedules, availability and patient volumes to determine adequate staffing levels,” she explains.

She adds that clinical documentation is another area where natural language processing or voice recognition technology helps physicians dictate information or automatically document care in a patient visit as part of the clinical note.

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Clinical Involvement in Algorithm Development

Michael Pencina, vice dean of data science at the Duke University School of Medicine, says recent discussions about data-driven automation have focused on how to decrease burnout and increase efficiency by helping clinicians do what they want and spend less time worrying about administrative tasks.

He adds that it’s very important for clinicians to be involved in the planning of automation initiatives and the development of the algorithms themselves.

“The tool must be fit for purpose, address a real clinical operational need and be created with the practitioners, who are contributing throughout the process,” he says. “This isn’t a data science exercise; it must be created out of a need rather than just for the sake of technology.”

He adds that his team spends a lot of time identifying the top-priority use cases for digital innovation and AI technology.

“We are prioritizing the low-hanging fruit — where the risks are low and the gains can help in reducing burnout, improving the patient experience and, ultimately, improving the patient’s health,” says Pencina.

WATCH: Clinical automation offers relief amid staff shortages and rising burnout.

Analyzing Patient Behavior Using Predictive Modeling and Automation

Data-drive automation can also give insight into patient behavior, allowing healthcare organizations to make better operational decisions. Myers points to models that can analyze past patient behavior and determine whether a patient is going to be a no-show for an appointment.

“If you know a patient frequently does not turn up for their appointment, you can predict that and also double book the appointment,” she says. “This is where you can use predictive modeling and automation to ensure all of the patient slots are ultimately being filled, which ties back to revenue.”

For health systems seeking to implement data-driven clinical automation solutions, he recommends focusing on data governance, data transparency and identification of the areas that will benefit most.

“Governance means making sure it’s not just any algorithm, but it’s evaluated, and it’s done in a risk-based manner,” he explains. “Transparency is knowing what’s in place and understanding where the tools are coming from and what data they’ve been trained on.”

Pencina points out that it may not be beneficial — and would likely be impossible — to transform every area through automation all at once.

“Focus on identifying the use cases and measuring the progress,” he advises. “I think the stakes are high, but the opportunity is absolutely there.”

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