Data Science Assistants

AI tools are part of everyday operations at CedarsSinai, assisting in the lab and the clinic so their human counterparts can work more effectively.

Aliro

Imagine if every clinician had a data science assistant. With Aliro, they can, says Dr. Moore. Aliro allows clinicians and researchers with no machine-learning or coding expertise to run analysis through a sleek web interface. Anyone who can find their way around a spreadsheet or load a data set into a web browser can master this tool. Aliro learns from experience and remembers every analysis it’s ever completed, allowing it to improve over time.

Alex

ALEx is a powerful AI tool aiding Cedars-Sinai professionals in capacities that span many disciplines and departments—from helping with the logistics of placing patients to assisting with research and even finding savings on surgical supplies. Matching resources to ever-fluctuating demands is a daily challenge for medical centers, and ALEx has proved a reliable partner for several years. The AI assistant is used daily in the Capacity Command Center to synthesize thousands of forecasts into useful patterns to aid in determining staffing needs, placing patients, discharge planning and other patient flow operations. During the COVID-19 pandemic, ALEx used public health data to assist with bed planning, staffing, serviceline planning and supply chain needs. ALEx also helped generate models to assist in the transition when the medical center reopened for elective surgeries. ALEx has a role in research, such as predicting which patients are likely to require C-section deliveries (see page 33).

rithm based on data with inherent bias could amplify and magnify those disparities.

“We must always keep the clinician in mind as we develop and evaluate AI tools,” Dr. Moore says. “We need to think first about what’s good for all patients and how we earn the trust of those charged with their health and wellbeing. Our teams are dedicated to capturing the benefits of innovation and applying them equitably.” (See page 28 for insights about optimizing AI for health equity.)

AI Integration

Building AI that will seamlessly blend into the complex workflows of a busy medical center, laboratory or physician’s office and improve the patient experience is key to its adoption, says Michael Thompson, vice president of Enterprise Data Intelligence. That means building programs that span medical and academic disciplines, administration, and technology departments.

“Often, artificial intelligence stops at publishing a paper,” Thompson says. “Many programs are created in universities rather than a hospital setting, causing a massive gap between the mathematical AI models and the people and systems that need to use them.”

One AI tool spanning research and logistical functions at Cedars-Sinai is ALEx, named for “automated learning by example.” ALEx is a helpful colleague, developed to assist in research and fulfill functions that free up nurses and doctors to spend more time with patients. That ALEx was created by and for medical professionals made a difference, Thompson says.

“As we help move AI into a clinical workflow and into a patient setting, our goal always is to make sure that it can add value to patient outcomes. We make sure that the clinician who is using the results of AI knows how to use it appropriately,” he says. “We created a comprehensive strategy and approach to using AI across the board—in patient treatment, in research, in testing and in hospital operations and with our values driving that strategy.”

Managing the human element of AI is essential to deploying it effectively and applying it equitably in healthcare.

“We believe in innovation, in discovery and in moving the needle so the patient benefits,” Dr. Chugh says. “AI is a tool that can do that if— and only if—we keep the core questions we’re asking of it close to the patient.”