Feature
Health care providers are turning to artificial intelligence (AI) solutions to improve their operations and provide better patient care. AI is helping health care teams in a range of ways, from data collection and analysis to interpreting results and patient communications and scheduling. Here, we look at some examples of how AI is being used in the health care sector.
AI Health Care Case Studies
- 1. University of Rochester Medical Center
- 2. Valley Medical Center
- 3. OSF HealthCare
- 4. Healthfirst
- 5. University of Alabama at Birmingham Medicine
1. University of Rochester Medical Center
The University of Rochester Medical Center (URMC), the home to Strong Memorial Hospital and centerpiece of the university’s patient care and medical research, worked with Butterfly Network to improve patient access to imaging, according to a case study.
After an initial assessment of enterprise-wide need and identifying medical groups to be the first adopters, URMC decided to provide incoming medical students in certain disciplines with a personal Butterfly IQ probe, distributing 862 of the devices.
The probes use AI and advanced imaging, sharp image quality, rapid data processing and user-centric ergonomics to improve the accuracy and speed of diagnoses of cholecystitis and bladder and other medical issues. URMC plans to have three times the number of Butterfly IQ probes in use by the end of 2026.
“Our phased deployment of Butterfly devices and Compass software has yielded impressive clinical and administrative results at URMC to date,” says Dr. Michael F. Rotondo, CEO of the University of Rochester Medical Faculty Group and SVP of URMC.
Rotondo says URMC is at the “forefront of the wide-scale deployment of innovative point-of-care ultrasound technology, and we believe it shows promise as the right care model for the medical community at large.”
Results
- 116% increase in ultrasound charge capture across health system
- 74% increase in scanning sessions
- 3x increase in ultrasounds sent to electronic health record (EHR) system
2. Valley Medical Center
Xsolis’ Dragonfly Utilize platform, which provides AI-driven medical necessity scores, enabled Valley Medical Center to significantly increase its observation (OBS) rates to keep them more within Centers for Medicare and Medicaid Services (CMS) and other local facilities’ averages, according to a case study.
Within a month’s time of implementation, Valley Medical’s team became proficient at knowing when to review versus when to escalate to physician advisory services. This enabled Valley Medical to more easily keep an eye on their most important cases and make appropriate care and patient status decisions quickly as the clinical indications changed.
The health care facility was also able to reallocate staff for more efficiency and job satisfaction. For example, one lead utilization management (UM) specialist was able to purely focus on appeals and denials resolution management.
“Our nurses were relieved they no longer had to go down the guideline path, fitting squares into circles, waiting on green lights,” says Kim Petram, director of care management, Valley Medical Center.
“They were now empowered to look at clinical merit to guide their patient status determinations.”
Results
- Increased case reviews from 60% to 100%
- Increased observation rate of discharged patients from 4% to 13%
- Improved extended observation rates by 25%
See more: 12 AI Applications in Health Care
3. OSF HealthCare
To improve patient service while also reducing costs, OSF Healthcare and Fabric worked together to customize and implement Fabric’s Digital Front Door software as the AI virtual care navigation assistant Clare on the OSF website.
Clare acted as a single point of contact, allowing patients to navigate to many self-service care options and find information on their timeline. Clare was available 24 hours a day to help patients during and outside of business hours. This allowed patients to directly check symptoms, schedule appointments, including asynchronous and telehealth appointment options, and understand the best online resources for their clinical or non-clinical needs. By navigating patients to what they need, Clare diverted calls from the call center.
“The fact that one in 10 of our patients interacts with Clare during their patient journey speaks volumes to the impact she has made at our health system,” says Jennifer Junis, SVP of digital health, OSF Oncall.
“We are proud that Clare not only empowers patients to find and use the resources they need, but also that she provides instant satisfaction and closure that today’s patients desire.”
Results
- $1.2 million in contact center savings
- Contributed to $1.2 million increase in annual patient net revenue
- Improved patient access to self-service, scheduling and information requests
4. Healthfirst
When its in-house machine learning (ML) and AI development could no longer meet the health care provider’s growing needs, Healthfirst worked with ClosedLoop on a scalable solution to automate most data cleaning, data normalization, feature engineering and model training tasks, according to a case study.
With the ClosedLoop technology, Healthfirst built, shared and reused customized process configurations and machine learning features and predicted outcome definitions. The health care provider also streamlined deployment, enabled continuous model performance monitoring and integrated predictions in existing workflows.
“We’re able to store and operationalize analytics,” says Christer Johnson, chief analytics officer, Healthfirst.
“That’s driving real value. It’s accelerated the implementation of key insights into clinical workflows, and it allows us to more easily account for all of the different factors that influence intervention decisions.”
Results
- Deployed 17 models that predict a variety of outcomes
- Developed 12 ad hoc predictors to assess social determinants of health
- Developed 978 customized machine learning features
See more: 10 Top AI Health Care Companies
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5. University of Alabama at Birmingham Medicine
To leverage its increasing volume of data, University of Alabama at Birmingham (UAB) Medicine worked with the AI-enabled the Sickbay platform by Medical Informatics to develop a large-scale model for data acquisition and synchronization to provide patients with more personalized care, according to a case study.
Deploying the solution in the operating room enabled the anesthesiology and perioperative team to obtain near real-time continuous data monitoring and data to analyze several alterations during different types of cardiac procedures as well as during different stages of the procedures. The results could then be correlated with the various types of management and medications.
“UAB has been a very data-driven institution,” says Dr. Dan Berkowitz, chair of the UAB Department of Anesthesiology and Perioperative Medicine.
Berkowitz says the platform allows the health organization to “capture and integrate this high-resolution information from every monitoring device we have in a completely vendor-agnostic manner. That forms a profoundly useful basis for discovery and real-time monitoring.”
Results
- Improved collection of patient data
- Gained more complete view of patient status on an ongoing basis
- Determined optimal blood pressure for each patient during their procedure
See more: 10 Top AI Health Care Products
