COVID-19: How Hospitals Can Create Better Inpatient Bed Capacity through Math

COVID-19: How Hospitals Can Create Better Inpatient Bed Capacity through Math
Dr. Pallabi Sanyal-Dey, Associate Professor of Medicine at UCSF & Director of Client Services at LeanTaaS

Since the beginning of the COVID-19 pandemic, key elements of hospital operations such as managing inpatient bed capacity, and access to ventilators and PPE have taken center stage. The general public got a crash course on what hospitals need in order to function successfully when disaster hits, and daily news and discussions were centered around ICU bed capacity as cases accelerated across the country.

The nightmarish predictions and reality led to the development of creative measures to help meet such catastrophic needs such as popup temporary screening and triage sites, non-medical and medical spaces being repurposed for COVID units, increased patient transfers to hospitals that had more space, and mathematical models to predict upcoming numbers of new COVID-19 cases. 

With the latest surge of COVID-19 cases (see figure 1), some states have or will begin opening up field hospitals (Wisconsin, Texas) while others are considering transfers to other locations (both in and out-of-state), and even the concept of ‘rationing care’ has surfaced. 

1Figure 1. The Covid Tracking Project

This public health crisis intensified what happens when hospitals and healthcare providers run out of the right space and resources. As alarming as it has been to watch this play out, the reality is that these capacity and resource challenges are not unique to the pandemic; they happen often in hospitals across the country, just on a different scale. Bed capacity is something hospital leaders manage every day – only 1 of 3 hospital beds are available on any given day in the U.S., per research by the Robert Wood Johnson Foundation 2.  Of course, there’s further variation when looking at urban versus rural regions. Many systems are forced to go on ‘diversion’ (patients will literally be re-routed to other hospitals) when the reality is that they are bursting at the seams. 

Clearly, the pandemic has been devastating, yet it has (finally) propelled healthcare toward innovation and adoption of technology that was much needed in order to improve access to and utilization of quality and cost-effective care. Although the waves continue, organizations are starting to answer the following questions: What newly applied practices do we keep from the pandemic moving forward as we head into additional COVID-19 waves and the flu season? Can we more vigorously apply lessons of the past and present to tackle our future needs? Are our incentives aligned such that the solutions we pursue can be sustained and still “keep the lights on”?

Delayed access to care and, even worse, lack of access to care, have been among the most devastating consequences of the capacity crises during the pandemic.  Though many of our systems started to transition back to their usual state of affairs by July, other factors in addition to the current surge continued to highlight the ongoing need for creating and sustaining ‘good patient flow’.

Under “normal” circumstances, daily chaos is anticipated and actually expected, as hospitals experience the inability to move patients from the emergency room (ER) or operating room (OR) due to a “lack of beds” in the hospital. While this inevitably requires hospital leadership to ‘do something’ about it, it is a scenario that plays repeatedly throughout the day, every day.  

The chaos that comes from the lack of visibility into available beds, let alone appropriately available levels of care, can have negative downstream impacts not only on the patients but also on the frontline staff. Patients are subject to suffering the consequences of inappropriate levels of care, poor clinical outcomes, and/or poor provider/patient experiences.3 Staff are subject to the stress of caring for patients for whom they are not necessarily appropriately trained to care for.

Despite the known implications, this lose:lose cycle continues. These “risks” plus the impact of significant revenue losses from the pandemic highlight the urgent need to address poor, inefficient patient throughput. We are at a critical point where healthcare systems must do what is necessary to improve existing practices when it comes to bed management.  

Some examples of improvement include: 

– Create machine learning models for all locations and patient movements within the hospital, and adjust space and schedules accordingly

  – Place patients using sophisticated demand-supply model

  – Make data-driven internal transfer decisions

  – Right-Sized unit capacity

  – Look hard at the degree of specialization to pool capacity where possible

  – Smooth the patient flow from the OR

Take a magnifying glass to internal operational workflows – Identify practices that work, areas where support is needed, especially when it comes to discharge planning, and whether or not there are financial implications.

– Improve provider workflow

– Don’t let “a dime hold up a dollar”: take a hard look at staffing, hours of operations, and transportation

– Use predictive discharge planning to focus on case teams and social services

Identify clinical workup that can be prioritized according to disposition, treat outpatient setting 

– Prioritize discharge patients in queues for labs/clinical procedures

– Transition some procedures to outpatient

With the recent surge of COVID-19 cases across the nation and the impending flu season, hospitalizations will continue to rise.  Although health systems will be able to resurface earlier crafted emergency plans from previous surges, set up incident command centers more quickly, and have a more stable supply inventory, they will likely continue to manage their bed capacity through a very manual process.  It is imperative that we start to do things differently to achieve better outcomes!

Implementing operational change and deploying new but proven technologies that incorporate both artificial intelligence and lean principles will increase patient access, improve provider, patient, and staff experience, and, of course, smooth inpatient capacity. As a result, terms such as chaos and crisis can, in time, become things of the past. 


References:

1. The Covid Tracking Project Nov. 10, 2020. Retrieved from https://covidtracking.com/data/charts/us-currently-hospitalized

2. Blavin F., (March 1, 2020). Hospital Readiness for Covid-19: Analysis of Bed Capacity and How It Varies Cross The Country The Robert Wood Johnson Foundation. https://www.rwjf.org/en/library/research/2020/03/hospital-readiness-for-covid19-analysis-of-bed-capacity-and-how-it-varies-across-the-country.html

3. Mohr et al., Boarding of Critically Ill Patients in the Emergency Department. Critical Care Medicine 2020; 48(8): 1180–1187

4. Agrawal S., Giridharadas M., (2020) Better Healthcare Through Math: Bending the Access and Cost Curves. Forbes, Inc. 


About Dr. Pallabi Sanyal-Dey

Dr. Pallabi Sanyal-Dey is the director of client services for ‘iQueue for Beds’ Product at LeanTaaS, a Silicon Valley software innovator that increases patient access and transforms operational performance for more than 300 hospitals across the U.S. Dr. Sanyal-Dey is also a visiting associate professor of medicine, providing career mentorship to trainees at the University of California, San Francisco Medical Center (UCSF) where she attends on the internal medicine inpatient teaching service. Prior to joining LeanTaaS, Dr. Sanyal-Dey was at UCSF, as an assistant clinical professor and an academic hospitalist at Zuckerberg San Francisco General Hospital where she directed clinical operations for the Division of Hospital Medicine, and oversaw the faculty inpatient services.


Hospitals Hit with Low Volumes, High Expenses, Poor Margins as COVID-19 Cases Mount

Hospitals Hit with Low Volumes, High Expenses, Poor Margins as COVID-19 Cases Mount

What You Should Know:

– October was a challenging month for hospitals and
health systems nationwide amid ongoing instability spurred by the COVID-19
pandemic, according to Kaufman Hall’s latest Hospital Flash Report.

– Margins and volumes fell, revenues flattened, and
expenses rose as COVID metrics continued to climb and some states moved to
retighten social distancing guidelines.

– As of Oct. 31, the number of daily U.S. COVID cases
reached a high of more than 90,500 and related hospitalizations surpassed
47,400.


Instability spurred by the COVID-19 pandemic
continued to hit hospitals and health systems nationwide in October. Margins
fell, revenues flattened, and expenses rose as organizations saw an eighth
consecutive month of shrinking volumes, according to the November
issue of Kaufman Hall’s National Hospital Flash Report
.

Rising COVID-19 rates are expected to exacerbate volume
declines as many local and state governments reinstate stricter social
distancing policies, causing many to delay non-urgent procedures and outpatient
care. The result threatens to further destabilize hospitals financially in the
coming months.


Key Findings

– Eight months into the pandemic, the Kaufman Hall median
hospital Operating Margin Index was –1.6% for January through October, not
including federal funding from the Coronavirus Aid, Relief, and Economic
Security Act (CARES Act). With the funding, the median margin was 2.4%
year-to-date.

– Operating Margin fell 69.4% year-to-date (6.0 percentage
points) compared to the same period last year, and 9.2% year-over-year (1.4
percentage points) without CARES Act funding. With the federal aid, Operating
Margin fell 18.7% year-to-date (1.7 percentage points) and 8.5% (1.2 percentage
points) below October 2019 levels.

– Declining volumes and rising expenses contributed to the
month’s low margins. Adjusted Discharges fell 11.2% year-to-date and 9.3%
year-over-year, while Adjusted Patient Days dropped 7.7% year-to-date and 2.9%
year-over-year. Operating Room Minutes fell 11.7% year-to-date and 5.6%
compared to October 2019, as many patients opted to delay non-urgent
procedures.

– Emergency Department (ED) Visits remained the hardest hit,
falling 16% both year-to-date and year-over-year in October, but increased 1.9%
from September. The month-over-month increase was due in part to rising
COVID-19 infections, which also contributed to a 7.6% month-over-month increase
in Discharges, reflecting higher numbers of inpatients.       

– Gross Operating Revenue (not including CARES Act funding)
fell 4.8% from January to October compared to the same period in 2019, but was
flat compared to October 2019. Fewer outpatient visits were a major
contributor, driving Outpatient Revenue down 6.6% year-to-date and 2.6%
year-over-year. Inpatient Revenue declined 2.4% year-to-date but rose 2.6%
year-over-year.

– Expenses rose as hospitals continued to bring back
furloughed workers, and purchased drugs, personal protective equipment, and
other supplies needed to care for COVID-19 patients. Total Expense per Adjusted
Discharge rose 13.5% year-to-date and 12.2% year-over-year in October. Labor
Expense and Non-Labor Expense per Adjusted Discharge rose 15.2% and 13%
year-to-date, respectively. Such increases will put hospitals in a tenuous
situation if volumes continue to decline.

Why It Matters

“The next few months will be a grave period for our country, and for our nation’s hospitals and health systems,” said Jim Blake, a managing director at Kaufman Hall and publisher of the National Hospital Flash Report, which draws on data from more than 900 U.S. hospitals. “If unchecked, the virus is projected to continue its rapid spread through communities as families gather for the holidays, and as colder weather pushes more activities indoors. The potential public health implications and financial impacts for our hospitals could be dire.”

For more information, click here
to download the report.

GE Healthcare Unveils First X-Ray AI Algorithm to Assess ETT Placement for COVID-19 Patients

Why GE Healthcare Won’t Sell its Health IT Business

What You Should Know:

– GE Healthcare announced a new artificial intelligence
(AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a
necessary and important step when ventilating critically ill COVID-19 patients.

– The AI solution is one of five included in GE Healthcare’s Critical Care Suite 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization, and quality control.


GE Healthcare today announced a new artificial intelligence (AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a necessary and important step when ventilating critically ill COVID-19 patients. The AI solution is one of five included in GE Healthcare’s Critical Care Suite 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization, and quality control. GE Healthcare and UC San Francisco co-developed Critical Care Suite 2.0 using GE Healthcare’s Edison platform, which helps deploy AI algorithms quickly and securely. Critical Care Suite 2.0 is available on the company’s AMX 240 mobile x-ray system.

The on-device AI offers several benefits to radiologists and
technologists, including:

– ETT positioning and critical findings: GE
Healthcare’s algorithms are a fast and reliable way to ensure AI results are
generated within seconds of image acquisition, without any dependency on
connectivity or transfer speeds to produce the AI results.

– Eliminating processing delays: Results are then
sent to the radiologist while the device sends the original diagnostic image,
ensuring no additional processing delay.

– Ensuring quality: The AI suite also includes several quality-focused AI algorithms to analyze and flag protocol and field of view errors, as well as auto, rotate the images on-device. By automatically running these quality checks on-device, it integrates them into the technologist’s standard workflow and enables technologist actions – such as rejections or reprocessing – to occur at the patient’s bedside and before the images are sent to PACS.

Impact of ETTs

Up to 45% of ICU patients, including severe COVID-19 cases, receive ETT intubation for ventilation. While proper ETT placement can be difficult, Critical Care Suite 2.0 uses AI to automatically detect ETTs in chest x-ray images and provides an accurate and automated measurement of ETT positioning to clinicians within seconds of image acquisition, right on the monitor of the x-ray system. In 94% of cases, the ET Tube tip-to-Carina distance calculation is accurate to within 1.0 cm. With these measurements, clinicians can determine if the ETT is placed correctly or if additional attention is required for proper placement. The AI-generated measurements – along with an image overlay – are then made accessible in a picture archiving and communication system (PACS).

Improper positioning of the ETT during intubation can lead
to various complications, including a pneumothorax, a type of collapsed lung.
While the chest x-ray images of a suspected pneumothorax patient are often
marked “STAT,” they can sit waiting for up to eight hours for a radiologist’s
review. However, when a patient is scanned on a device with Critical Care Suite
2.0, the system automatically analyzes images and sends an alert for cases with
a suspected pneumothorax – along with the original chest x-ray – to the
radiologist for review via PACS. The technologist also receives a subsequent
on-device notification to provide awareness of the prioritized cases.

“Seconds and minutes matter when dealing with a collapsed lung or assessing endotracheal tube positioning in a critically ill patient,” explains Dr. Amit Gupta, Modality Director of Diagnostic Radiography at University Hospital Cleveland Medical Center and Assistant Professor of Radiology at Case Western Reserve University, Cleveland. “In several COVID-19 patient cases, the pneumothorax AI algorithm has proved prophetic – accurately identifying pneumothoraces/barotrauma in intubated COVID-19 patients, flagging them to radiologist and radiology residents, and enabling expedited patient treatment. Altogether, this technology is a game-changer, helping us operate more efficiently as a practice, without compromising diagnostic precision. We soon will evaluate the new ETT placement AI algorithm, which we hope will be equally valuable tool as we continue caring for critically ill COVID-19 patients.”

Research shows that up to 25 percent of patients intubated
outside of the operating room have misplaced ETTs on chest x-rays, which can
lead to severe complications for patients, including hyperinflation,
pneumothorax, cardiac arrest and death. Moreover, as COVID-19 cases climb, with
more than 50 million confirmed worldwide, anywhere from 5-15 percent require
intensive care surveillance and intubation for ventilatory support.

Sony Updates NUCLeUS Medical Imaging Platform to Support Remote Patient Observation

Sony Updates NUCLeUS Medical Imaging Platform to Support Remote Patient Observation

What You Should Know:

– Today, Sony announced an update to our NUCLeUS medical
imaging platform, which improves support for remote patient observation.

– NUCLeUS has added new functionality and features,
including powerful bi-directional telestration capabilities allowing multiple
remote users to simultaneously annotate, draw or highlight areas of interest in
a live stream video or still image.

Sony today
announced an update to its vendor-neutral medical imaging platform NUCLeUS. The latest release introduces Remote
Patient Monitoring (observation) functionality with recording functionalities
for use in the operating room (OR), Intensive Care Units (ICU), endoscopy
suites, procedure rooms or anywhere else in the hospital.

The Smart Digital Imaging Platform for Medical Environments

Sony Updates NUCLeUS Medical Imaging Platform to Support Remote Patient Observation

Developed in consultation with leading surgeons and with vendor
neutrality in mind, NUCLeUS guides clinical staff through the planning,
recording and sharing of video, still images and other patient-related data.
Seamlessly linking Sony and third-party devices, applications, video and most
importantly, people, NUCLeUS focuses on hospital staff requirements and use
cases, adding value to imaging workflows.

New
Bi-Directional Telestration Capabilities

NUCLeUS has added
new functionality and features, including powerful bi-directional telestration
capabilities allowing multiple remote users to simultaneously annotate, draw or
highlight areas of interest in a live stream video or still image. This can be
securely shared with authorized viewers to discuss as a group in real time,
ideally suited for socially distanced environments.  Equipped with a full
set of recording functionalities, NUCLeUS is also a valuable tool for
hospitals, outpatient surgery centers and private practices serving a variety
of specialties including Urology, ENT, Obstetrics, Ophthalmic, Plastic surgery,
and Robotics

New NUCLeUS Functionality Features

New functionalities of NUCLeUS include:

Mosaic
Video Wall,

presenting video streams from image sources in multiple ORs and ICUs
simultaneously on a single display, thus providing a situational overview of
activity in a tiled or mosaic format.

An
iPad Streaming function,
allowing clinical staff to access images from any modality via
an iPad in virtual real time within the OR, so medical staff can follow the
intervention on their handheld device.

High
quality 4K conversion
, allowing any HD resolution video content to be converted to 4K
using advanced resolution-augmentation algorithms superior to conventional
upscaling, giving a crisp ultra-high resolution view of converted video
footage.

Customizable
Expanded Patient Distraction
– helping to reduce patient anxiety through music tracks and
video imagery that can be played in the OR to create a more relaxing and
comfortable atmosphere.

Patient
Time-Out Functionality
, featuring checklists that simplify time out of safety
standards at the start, during and end of an operation.

Enhanced
Printing capabilities
, allowing hard copies of still images captured by NUCLeUS to be
created inside the OR using an optional UP-DR80MD A4 digital color printer. The
Auto Print function also extends CMS (Content Management System) print
functionality to collect a preconfigured number of stills, printing them
automatically.

Full
compatibility
with the latest Sony PTZ and fixed cameras including HD and 4K
models.

“Sony is committed to developing NUCLeUS to suit the needs of patients and medical staff at all times,” said Theresa Alesso, pro division President, Sony Electronics.  “The Remote Patient Monitoring capabilities within NUCLeUS are a primary example of this and were developed to help hospitals manage day-to-day requirements through the COVID-19 pandemic.  We are committed to helping hospitals and healthcare providers reinvent their workflows and provide medical staff with the tools they need to continue delivering excellent patient care.”

FundamentalVR Expands VR Surgical Capabilities to Ophthalmology

FundamentalVR Expands into VR Surgical Capabilities to Ophthalmology

What
You Should Know:

– Today, FundamentalVR, global pioneers in immersive VR
surgical skills training and skills data analytics, announced the expansion of
its surgical specialty capabilities with the addition of ophthalmology. The new
capabilities allow it to support medical device and life science companies in
improving the way they bring new tools and procedures to market.

– Powered by the company’s patented HapticVR&#8482
technology that mimics the physical cues of surgical actions, medical tools,
and tissue variations, FundamentalVR can now create educational simulations for
ophthalmology as well as orthopedics on its Fundamental Surgery platform.


FundamentalVR, global pioneers
in immersive VR surgical skills training and skills data analytics, today
announced the expansion of its surgical specialty capabilities with the
addition of ophthalmology. Powered by the company’s patented HapticVR&#8482
technology architecture that mimics the physical cues of surgical actions,
medical tools, and tissue variations, FundamentalVR can now create immersive,
data-driven medical educational simulations for ophthalmology as well as
orthopedic device and pharmaceutical brands.

Why It Matters

FundamentalVR Expands into VR Surgical Capabilities to Ophthalmology

Traditional ophthalmology teaching methods and the way Life
Science brands, medical institutions, and students interact, typically include;
classroom lectures, instructional videos, medical meetings, operating room (OR)
observations, and tissue-based wet lab training, which is considered the gold
standard for medical training. Low-cost immersive simulations now offer
solutions to continue remote, socially distant learning, while accelerating
skills transfers, thanks to the ability to collect and objectively measure
performance data previously unattainable.

Simulations, featuring the interactions with human tissue
essential for learning, can be created to cover various ophthalmology
procedures. These interactions include incisions, trocar placement, scleral
tissue manipulation, lens manipulation, lens implant insertion, posterior
chamber manipulations, bimanual manipulation of the eyeball, and subretinal
injections.

The ophthalmology capabilities were first developed in
collaboration with international eye charity, Orbis Flying Eye Hospital. Orbis is currently deploying cataract surgical simulations
created by FundamentalVR in select residency training programs and prospective
digital training hubs to further software developments.

“Industry analysts now estimate adoption curves for immersive technologies have accelerated by around three years as COVID-19 permanently changes traditional teaching methods,” said Richard Vincent, co-founder and CEO of FundamentalVR. “With the addition of ophthalmology capacities, we are meeting this increased demand with proven technology that allows medical device companies and medical educators to more effectively train the next generation of surgeons and bringing innovative new procedures and equipment to market permanently.”

4 Areas Driving AI Adoption in Hospital Operations and Patient Safety

4 Reasons Why Now Is the Time for Hospitals to Embrace AI
Renee Yao, Global Healthcare AI Startups Lead at NVIDIA

COVID-19 has put a tremendous burden on hospitals, and the clinicians, nurses, and medical staff who make them run. 

Many hospitals have suffered financially as they did not anticipate the severity of the disease. The extended duration of patient stays in ICUs, the need for more isolated rooms and beds, and the need for better supplies to reduce infections have all added costs. Some hospitals did not have adequate staff to check-in patients, take their temperature, monitor them regularly, or quickly recruit nurses and doctors to help.

AI can greatly improve hospital efficiency, improve patient satisfaction, and help keep costs from ballooning. Autonomous robots can help with surgeries and deliver items to patient’s rooms. Smart video sensors can determine if patients are wearing masks or monitor their temperature. Conversational tools can help to directly input patient information right into medical records or help to explain surgical procedures or side effects.

Here are four key areas where artificial intelligence (AI) is getting traction in hospital operations and enhancing patient safety:

1- Patient Screening

We’ve become familiar with devices in and around our homes that use AI for image and speech recognition, such as speakers that listen to our commands to play our favorite songs. This same technology can be used in hospitals to screen patients, monitor them, help them understand procedures, and help them get supplies.

Screening is an important step in identifying patients who may need medical care or isolation to stop the spread of COVID-19. Temporal thermometers are widely used to measure temperatures via the temporal artery in the forehead, but medical staff has to screen patients one by one. 

Temperature screening applications powered by AI can automate and dramatically speed up this process, scanning over 100 patients a minute. These systems free up staff, who can perform other functions, and then notify them of patients who have a fever, so they can be isolated. Patients without a fever can check-in for their appointments instead of waiting in line to be scanned. 

AI systems can also perform other screening functions, such as helping monitor if patients are wearing masks and keeping six feet apart. They can even check staff to ensure they are wearing proper safety equipment before interacting with patients.  

2. Virtual Nurse Assistant 

Hospitals are dynamic environments. Patients have questions that can crop up or evolve as circumstances change. Staff have many patients and tasks to attend to and regularly change shifts. 

Sensor fusion technology combines video and voice data to allow nurses to monitor patients remotely. AI can automatically observe a patient’s behavior, determining whether they are at risk of a fall or are in distress. Conversational AI, such as automatic speech recognition, text-to-speech, and natural language processing, can help understand what patients need, answer their questions, and then take appropriate action, whether it’s replying with an answer or alerting staff.

Furthermore, the information recorded from patients in conversational AI tools can be directly inputted into patients’ medical records, reducing the documentation burden for nurses and medical staff.

3. Surgery Optimization 

Surgery can be risky and less invasive procedures are optimal for patients to speed up recovery, reduce blood loss, and reduce pain. AI can help surgeons monitor blood flow, anatomy, and physiology in real-time. 

Connected sensors can help optimize the operating room. Everything from patient flow, time, instrument use, and staffing can be captured. Using machine learning algorithms and real-time data, AI can reduce hospital costs and allow clinicians to focus on safe patient throughput.

But it’s not just the overall operations. AI will allow surgeons to better prepare for upcoming procedures with access to simulations beforehand. They will also be able to augment procedures as they happen, incorporating AI models in real-time, allowing them to identify missing or unexpected steps.

Contactless control will allow surgeons to utilize gestures and voice commands to easily access relevant patient information like medical images, before making a critical next move. AI can also be of assistance following procedures. It can, for example, automatically document key information like equipment and supplies used, as well as staff times. 

4. Telehealth

During COVID-19, telehealth has helped patients access their clinicians when they cannot physically go to the office. Patients’ adoption of telehealth has soared, from 11% usage in 2019 in the US to 46% usage in 2020. Clinicians have rapidly scaled offerings and are seeing 50 to 175 times the number of patients via telehealth than they did before. Pre-COVID-19, the total annual revenue of US telehealth was an estimated $3 billion, with the largest vendors focused on the “virtual urgent care” segment. With the acceleration of consumer and provider adoption of telehealth, up to $250 billion of current US healthcare spend could potentially be virtualized.

Examples of the role of AI in the delivery of health care remotely include the use of tele-assessment, telediagnosis, tele-interactions, and telemonitoring.

AI-enabled self-triage tools allow patients to go through diagnostic assessments and receive real-time care recommendations. This allows less sick patients to avoid crowded hospitals. After the virtual visit, AI can improve documentation and reimbursement processes.

Rapidly developing real-time secure and scalable AI intelligence is fundamental to transforming our hospitals so that they are safe, more efficient, and meet the needs of patients and medical staff. 


About Renee Yao

Renee Yao leads global healthcare AI startups at NVIDIA, managing 1000+ healthcare startups in digital health, medical instrument, medical imaging, genomics, and drug discovery segments. Most Recently, she is responsible for Clara Guardian, a smart hospital ecosystem of AI solutions for hospital public safety and patient monitoring.