Transforming Care Delivery Through AI-Powered Predictive Surveillance

Transforming care delivery through AI-powered predictive surveillance
John Langton, Ph.D. Director of Applied Data Science, Wolters Kluwer, Health

Since the onset of the COVID-19 pandemic, hospitals and health systems have pushed forward with innovative technology solutions with great expediency and proficiency. Healthcare organizations were quick to launch telehealth solutions and advance digital health to maintain critical patient relationships and ensure continuity of care. Behind the scenes, hospitals and health systems have been equally adept at advancing technology solutions to support and enhance clinical care delivery. This includes adopting clinical surveillance systems to better predict and prevent an escalation of the coronavirus. 

Clinical surveillance systems use real-time and historical patient data to identify emerging clinical patterns, allowing clinicians to intervene in a timely, effective manner. Over time, these clinical surveillance systems have evolved to help healthcare organizations meet their data analytic, surveillance, and regulatory compliance needs. The adaptability of these systems is evidenced by their expanded use during the pandemic. Healthcare organizations quickly pivoted to incorporate COVID-19 updates into their clinical surveillance activities, providing a centralized, global view of COVID-19 cases. 

To gain insight into the COVID-19 crisis, critical data points include patient age, where the disease was likely contracted, whether the patient was tested, and how long the patient was in the ICU, among other things. Surveillance is also able to factor in whether patients have pre-existing conditions or problems with blood clotting, for example. This data trail is helping providers create a constantly evolving coronavirus profile and provides key data points for healthcare providers to share with state and local governments and public health agencies. In the clinical setting, the data are being used to better predict respiratory and organ failure associated with the virus, as well as flag COVID-19 patients at risk for developing sepsis.

What’s driving these advancements? Clinical surveillance systems powered by artificial intelligence (AI). By refining the use of AI for clinical surveillance, we can proactively identify an expanding range of acute and chronic health conditions with greater speed and accuracy. This has tremendous implications in the clinical setting beyond the current pandemic. AI-powered clinical surveillance can save lives and reduce costs for conditions that have previously proven resistant to prevention.

Eliminating healthcare-associated infections

Despite ongoing prevention efforts, healthcare-associated infections (HAIs) continue to plague the US healthcare system, costing up to $45 billion a year. According to the Centers for Disease Control and Prevention (CDC), about one in 31 hospitalized patients will have at least one HAI on any given day.  AI can analyze millions of data points to predict patients at-risk for HAIs, enabling clinicians to respond more quickly to treat patients before their infection progresses, as well as prevent spread among hospitalized patients. 

Building trust in AI

While the benefits are clear, challenges remain to the widespread adoption and use of AI in the clinical setting. Key among them is a lack of trust among clinicians and patients around the efficacy of AI. Many clinicians remain concerned over the validity of the data, as well as uncertainty over the impact of the use of AI on their workflow. Patients, in turn, express concerns over AI’s ability to address their unique needs, while also maintaining patient privacy. Hospitals and health systems must build trust among clinicians and patients around the use of AI by demonstrating its ability to enhance outcomes, as well as the patient experience.


3 keys to building trust in AI

Building trust among clinicians and patients can be achieved through transparency, expanding data access, and fostering focused collaboration.

1. Support transparency 

Transparency is essential to the successful adoption of AI in the clinical setting. In healthcare, just giving clinicians a black box that spits out answers isn’t helpful. Clinicians need “explainability,” a visual picture of how and why the AI-enabled tool reached its prediction, as well as evidence that the AI solution is effective. AI surveillance solutions are intended to support clinical decision making, not serve as a replacement. 

2. Expand data access

Volume and variety of data are central to AI’s predictive power. The ability to optimize emerging tools depends on comprehensive data access throughout the healthcare ecosystem, no small task as large amounts of essential data remain siloed, unstructured, and proprietary. 

3. Foster focused collaboration

Clinicians and data scientists must collaborate in developing AI tools. In isolation, data scientists don’t have the context for interpreting variables they should be considering or excluding in a solution. Conversely, doctors working alone may bias AI by telling it what patterns to look for. The whole point of AI is how great it is at finding patterns we may not even consider. While subject matter expertise should not bias algorithms,

it is critical in structuring the inputs, evaluating the outputs, and effectively incorporating those outputs in clinical workflows. More open collaboration will enable clinicians to make better diagnostic and treatment decisions by leveraging AI’s ability to comb through millions of data points, find patterns, and surface critically relevant information. 

AI-enabled clinical surveillance has the potential to deliver next-generation decision-support tools that combine the powerful technology, the prevention focus of public health, and the diagnosis and treatment expertise of clinicians. Surveillance is poised to assume a major role in attaining the quality and cost outcomes our industry has long sought.


John Langton is director of applied data science at Wolters Kluwer, Health, where artificial intelligence is being used to fundamentally change approaches to healthcare. @wkhealth


Humana Taps Vida Health to Power Virtual Diabetes Management for Kentucky’s Medicaid Population

Humana Taps Vida Health to Power Virtual Diabetes Management for Kentucky’s Medicaid Population

What You Should Know:

 Humana Healthy Horizons™ in Kentucky announced it has selected Vida Health’s virtual diabetes management program to serve Kentucky’s Medicaid population.

– Vida’s diabetes management program
achieves lasting results for participants. Because chronic conditions like diabetes,
obesity, and hypertension often occur simultaneously, Vida’s unique program was
built from the ground up to treat multiple conditions at the same time.

– The new partnership, which will launch in
January of 2021, allows eligible individuals access to Vida’s group diabetes
coaching, in-app peer group support, digital therapeutics for diabetes and
co-occurring chronic conditions, and more to help them manage their diabetes
and their whole health.

Kentucky has the seventh highest prevalence of diabetes of any state with 13.7% of the
adult population reporting having the disease, well above the U.S. average of
10.9%. The percent of Kentuckians with diabetes has more than doubled since
2000 when only 6.5% of the population reported having been diagnosed.
Additionally, about two thirds of adult Kentuckians are considered overweight or obese
which increases the risk of Type II Diabetes among other chronic illnesses.

– The mobile-first experience is uniquely
personalized to each user through a combination of provider expertise and
machine learning algorithms that utilize data from 100+ app and device
integrations, as well as biometric data, and more to personalize the program
and content. The program addresses the root causes behind an individual’s
diabetes, and, using the power of human connection, psychology, and nutritional
expertise, Vida drives long-term behaviors that shift the course of the
disease.

DoD Awards $2.8M to Philips & BioIntelliSense to Validate Wearable for Early COVID-19 Detection

DoD Awards $2.8M to Philips & BioIntelliSense to Validate Wearable for Early COVID-19 Detection

What You Should Know:

– Philips and BioIntelliSense has been selected by the
U.S. Army Medical Research and Development Command (USAMRDC) to receive nearly $2.8M
from the U.S. Department of Defense (DoD) to validate BioIntelliSense’s
FDA-cleared BioSticker device for the early detection of COVID-19 symptoms.

– Working with the University of Colorado Anschutz
Medical Campus, the clinical study will consist of 2,500 eligible participants
with a recent, known COVID-19 exposure and/or a person experiencing early
COVID-19 symptoms.


Royal
Philips
and BioIntelliSense,
Inc., a continuous health monitoring and clinical intelligence company, today
announced they have been selected by the U.S. Army Medical Research and
Development Command (USAMRDC) to receive nearly $2.8M from the U.S. Department
of Defense (DoD) through a Medical Technology Enterprise Consortium (MTEC)
award to validate BioIntelliSense’s FDA-cleared BioSticker device for the early
detection of COVID-19
symptoms. The goal of the award is to accelerate the use of wearable
diagnostics for the benefit of military and public health through the early
identification and containment of pre-symptomatic COVID-19 cases.

Medical-Grade Wearable for Early COVID-19 Detection

As millions of individuals have been screened and tested, the emerging research on traditional screening methods is revealing how challenging it is to detect the risk of COVID-19 infections early. Temperature checks have proven to be unreliable and even amplified testing (PCR) has proven to be ineffective in identifying the virus in the early days of infection.

The FDA-cleared BioSticker is an advanced on-body sensor
that allows for effortless continuous monitoring of temperature and vital signs
combined with advanced analytics, enables the BioSticker to identify
statistically meaningful trends and screen for early potential COVID-19
infection.

“The medical-grade BioSticker wearable, combined with advanced diagnostic algorithms, may serve as the basis for identifying pre- and very early symptomatic COVID-19 cases, allow for earlier treatment for infected individuals, as well as reduce the spread of the virus to others,” said James Mault, MD, Founder and CEO of BioIntelliSense.

Clinical Trial Details

Working with the University of Colorado Anschutz Medical Campus, the
clinical study will consist of 2,500 eligible participants with a recent, known
COVID-19 exposure and/or a person experiencing early COVID-19 symptoms.
Individuals may learn more about the study eligibility and enroll online
at www.BioStickerCOVIDstudy.com.
The research will focus on the validation of BioIntelliSense’s BioSticker for
early detection of COVID-like symptoms, as well as assessment of scalability,
reliability, software interface, and user environment testing. 

Turning Data into Actionable Insights

While previous studies have shown potential using consumer wearables in relation to COVID-19, this study will leverage BioIntelliSense’s medical-grade wearable, the BioSticker, which enables continuous multi-parameter vital signs monitoring for 30 days and captures data across a broad set of vital signs, physiological biometrics and symptomatic events, including those directly associated with COVID-19.  With its integration into Philips’ remote patient monitoring offerings, this is another example of how cloud-based data collection takes place seamlessly, across multiple settings, from the hospital to the home. Allowing data to be turned into actionable insights and care interventions, while providing connected, patient-centered care across the health continuum. 

Dr. Vik Bebarta, the Founder and Director of the CU Center for COMBAT Research and Professor of Emergency Medicine on the CU Anschutz Medical Campus added: “The University of Colorado School of Medicine and the CU Center for COMBAT Research in the Department of Emergency Medicine are excited to be a lead in this effort that will change how we care for our service members in garrison and our civilians in our communities.  The COMBAT Center aims to solve the DoD’s toughest clinical challenges, and the pandemic is certainly one example. With this progressive solution, we aim to detect COVID in the pre-symptomatic or early symptomatic phase to reduce the spread and initiate early treatment. This trusted military-academic-industry partnership is our strength, as we optimize military readiness and reduce this COVID burden in our community and with frontline healthcare workers.” 

AI Algorithms Can Predict Outcomes of COVID-19 Patients with Mild Symptoms in ER

AI Algorithms Can Predict Outcomes of COVID-19 Patients with Mild Symptoms in ER

What You Should Know:

– Artificial intelligence algorithms can predict outcomes
of COVID-19 patients with mild symptoms in emergency rooms, according to recent
research findings published in Radiology: Artificial Intelligence journal.

– Researchers trained the algorithm from data on 338
positive COVID-19 patients between the ages of 21 and 50 by using diverse
patient data from emergency departments within Mount Sinai Health System
hospitals (The Mount Sinai Hospital in Manhattan, Mount Sinai Queens, and Mount
Sinai Brooklyn) between March 10 and March 26.


Mount Sinai researchers have developed an artificial intelligence algorithm to rapidly predict outcomes of COVID-19 patients in the emergency room based on test and imaging results. Published in the journal, Radiology: Artificial Intelligence, the research reveals that if the AI algorithms were implemented in the clinical setting, hospital doctors can identify patients at high risk of developing severe cases of COVID-19 based on the severity score.  This can lead to closer observation and more aggressive and quicker treatment.

Research Background/Protocols

They trained the algorithm using electronic medical records (EMRs) of patients between 21 and 50 years old and combined their lab tests and chest X-rays to create this deep learning model. Investigators came up with a severity score to determine who is at the highest risk of intubation or death within 30 days of arriving at the hospital. If applied in a clinical setting, this deep learning model could help emergency room staff better identify which patients may become sicker and lead to closer observation and quicker triage, and could expedite treatment before hospital admission.

Led by Fred Kwon, Ph.D., Biomedical Sciences at the Icahn School of Medicine at Mount Sinai, researchers trained the algorithm from data on 338 positive COVID-19 patients between the ages of 21 and 50 by using diverse patient data from emergency departments within Mount Sinai Health System hospitals (The Mount Sinai Hospital in Manhattan, Mount Sinai Queens, and Mount Sinai Brooklyn) between March 10 and March 26. Data from the emergency room including chest X-rays, bloodwork (basic metabolic panel, complete blood counts), and blood pressure were used to develop a severity score and predict the disease course of COVID-19. 

Patients with a higher severity score would require
closer observation. The researchers then tested the algorithm using patient data on other patients in all adult age groups and
ethnicities.  The algorithm has an 82 percent sensitivity to predict intubation and death within 30 days of
arriving at the hospital. 

Why It
Matters

Many patients with COVID-19, especially younger ones, may show non-specific symptoms when they arrive at the emergency room, including cough, fever, and
respiratory issues that don’t provide any indication of disease severity. As a
result, clinicians cannot easily identify patients who get worse quickly. This algorithm can provide the probability that a patient may
require intubation before they get worse. That way clinicians can make more accurate decisions for appropriate
care.

Algorithms that predict outcomes of patients with COVID-19 do exist, but they are used in admitted patients who have already developed more severe symptoms and have additional imaging and laboratory
data taken after hospital admission.  This algorithm is different since it predicts outcomes in COVID-19 patients while they’re in the emergency room—even in those with mild symptoms. It only uses information from the initial
patient encounter in the hospital emergency department. 

“Our algorithm demonstrates that initial imaging and laboratory tests contain sufficient information to predict outcomes of patients with COVID-19. The algorithm can help clinicians anticipate acute worsening (decompensation) of patients, even those who present without any symptoms, to make sure resources are appropriately allocated,” explains Dr. Kwon. “We are working to incorporate this algorithm-generated severity score into the clinical workflow to inform treatment decisions and flag high-risk patients in the future.”

Mayo Clinic Researchers to Validate Interoperability of Encrypted Algorithms and Training on Encrypted Data

TripleBlind Collaborates with Mayo Clinic on Next Generation Algorithm Sharing and Training on Encrypted Data

What You Should Know:

–  Mayo Clinic researchers are collaborating with TripleBlind on next generation algorithm sharing and training on encrypted data.

– TripleBlind’s solution functions as the innovative data
encryption conduit that keeps the data and intellectual property in the algorithm
secure.


TripleBlind announced
today it is collaborating with Mayo Clinic researchers
who will use TripleBlind tools to validate interoperability
of encrypted algorithms
on encrypted data and the training of new algorithms on encrypted data. TripleBlind
has created a rapid, efficient and cost effective data privacy focused solution
based on breakthroughs in advanced mathematics, which will be used and
validated by the Mayo team. No Mayo data will be accessed by TripleBlind.

Why It Matters

Today, healthcare systems have to either transfer data or
algorithms outside their institution for experts to train or conduct research.
The encryption conduit being evaluated will eliminate the need for data
transfer or for sharing the algorithm, thus protecting intellectual property.
TripleBlind’s solution functions as the innovative data encryption conduit that
keeps the data and intellectual property in the algorithm secure.

The aim of this collaboration is also to demonstrate that
TripleBlind’s toolset can be applied to train entirely new algorithms from
independent entities anywhere in the world without the need to share raw data,
thus preserving privacy and security while meeting regulatory standards.

“Training novel algorithms on encrypted data sets and
facilitating trust between independent parties is critical to the future of AI
in medicine. By using advanced mathematical encryption technologies, we will
greatly enhance scientific collaboration between groups and allow for more
rapid development and scalable implementation of AI-driven tools to advance
healthcare,” said Suraj Kapa, M.D., a practicing cardiologist and director of
AI for knowledge management and delivery at Mayo Clinic.

Mayo Clinic and Dr. Kapa have financial interest in the
technology referenced in this release. Mayo Clinic will use any revenue it
receives to support its not-for-profit mission in patient care, education and
research.

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.

AliveCor Receives FDA Clearance of Next-Gen EKG Algorithms

AliveCor Receives FDA Clearance of Next-Gen EKG Algorithms

What You Should Know:

– AliveCor announced they received FDA clearance of new
algorithms for use with their personal EKG devices, KardiaMobile and
KardiaMobile 6L. These additional determinations will be available via a
software upgrade for the Kardia devices in 2021.

– The additional FDA-cleared algorithms double the number
of heart rhythm disturbances that AliveCor’s Kardia devices can detect,
broadening the number of patients who are able to use their remote monitoring
devices.


AliveCor, an AI-based
personal ECG technology and provider of enterprise cardiology solutions, today
announced that the US FDA had given clearance to the company’s next generation
of interpretive ECG algorithms. AliveCor’s KardiaMobile and KardiaMobile 6L
devices, along with the Kardia app, allow users to take a 30-second ECG and
receive instant determinations of multiple cardiac conditions.

Why It Matters

This new FDA clearance positions AliveCor to deliver
AI-based remote cardiological services for the vast majority of cases when
cardiac patients are not in front of their doctor. AliveCor’s goal is to help
cardiologists efficiently provide the best possible 24/7 service to their
patients.

New Generation of AI-Powered Remote Cardiology

This new FDA 510(K) clearance provides detail and fidelity
unlike any previously seen in personal ECG devices including:

– A “Sinus Rhythm with Premature Ventricular
Contractions (PVCs)” determination if two or more ventricular ectopic
beats are detected. PVCs are a common occurrence where extra heartbeats
originate in the bottom chamber of the heart and occur sooner than the next
expected regular heartbeat. After the PVC beat, a pause usually occurs, which
causes the next normal heartbeat to be more forceful. When one feels the heart
“skip a beat,” it is this more forceful beat that is felt.

– A “Sinus Rhythm with Supraventricular Ectopy
(SVE)” determination if narrow-complex ectopy, such as premature atrial
contractions (PACs), are detected. PACs are similar to PVCs, but these beats
originate in the top chamber of the heart, however not in the heart’s natural
pacemaker, the Sinus Node.

– A “Sinus Rhythm with Wide QRS,” determination
for QRS intervals of 120ms or longer. 
Wide QRS indicates that the activation of the bottom chamber of the
heart is taking longer than expected. This could indicate a bundle branch block
in which there is a delay in the passage of heart’s electrical signals along
the bottom of the heart.

– A reduced number of “Unclassified” readings,
thereby giving users more reliable insight into their heart rhythms.

– Improved sensitivity and specificity on the company’s
“Normal” and “Atrial Fibrillation” algorithms, giving users
fewer false positives, fewer false negatives, and even greater confidence in
Kardia determinations.

– New visualizations, including average beat, PVC
identification, and a tachogram.

Kardia AI V2 is the most sophisticated AI ever brought to personal ECG,” said AliveCor CEO Priya Abani. “This suite of algorithms and visualizations will provide the platform for delivery of new consumer and professional service offerings beyond AFib, by allowing a much wider range of cardiac conditions to be determined on a personal ECG device.”

Availability

Today, KardiaMobile and KardiaMobile 6L are the most
clinically validated personal ECG devices in the world, and provide instant
detection of Normal Sinus Rhythm, Atrial Fibrillation, Bradycardia, and
Tachycardia. The new determinations and services will be available in 2021.

Change Healthcare Unveils Social Determinants of Health Analytics Solution

Change Healthcare Acquires Credentialing Tech Docufill to Improve Administrative Efficiency

What You Should Know:

– Change Healthcare launches national data resource on
social determinants of health (SDoH) for doctors, insurers and life sciences
organizations to better understand the connection between where a person lives
and how they live their life to the care a patient receives and their health
outcome.

– 80% of U.S. health outcomes are tied to a patient’s
social and economic situation, ranging from food, housing, and transportation
insecurity to ethnicity.


Change Healthcare, today announced the launch of Social Determinants of Health (SDoH) Analytics solution that will serve as an innovative national data resource that connects the circumstances of people’s lives to the care they receive. The SDoH Analytics solution is designed for health systems, insurers, and life sciences organizations to explore how geodemographic factors affect patient outcomes.


Understanding Social Determinants of Health

SDoH includes factors such as socioeconomic status, education, demographics, employment, health behaviors, social support networks, and access to healthcare. Individuals who experience challenges in any of these areas can face significant risks to their overall health.

“All the work I do—for Mayo Clinic, the COVID-19 Healthcare Coalition, and The Fight Is In Us— is predicated on equity,” said John Halamka, president, Mayo Clinic Platform. “The only way we can eliminate racism and disparities in care is to better understand the challenges. Creating a national data resource on the social determinants of health is an impactful first step.”

The SDoH Portrait Analysis includes financial attributes, education
attributes, housing attributes, ethnicity, and health behavior attributes.

3 Ways Healthcare Organizations Can Leverage SDoH
Analytics

Healthcare organizations can now use SDoH Analytics to
assess, select, and implement effective programs to help reduce costs and
improve patient outcomes. Organizations can choose one of three ways to use
SDOH Analytics:

1. Receive customized reports identifying SDoH factors that
impact emergency room, inpatient, and outpatient visits across diverse
population health segments.

2. Append existing systems with SDoH data to close
information gaps and help optimize both patient engagement and outcomes.

3. Leverage a secure, hosted environment with ongoing
compliance monitoring for the development of unique data analytics, models, or
algorithms.

Why It Matters

Scientific research has shown that 80% of health outcomes
are SDoH-related. Barriers such as food and housing availability,
transportation insecurity, and education inequity must be addressed to reduce
health disparities and improve outcomes. Change Healthcare’s SDoH Analytics
links deidentified claims with factors such as financial stability, education
level, ethnicity, housing status, and household characteristics to reveal the
correlations between SDoH, clinical care, and patient outcomes. The resulting
dataset is de-identified in accordance with HIPAA privacy regulations.

“Health systems, insurers, and scientists can now use SDoH Analytics to make a direct connection between life’s circumstances and health outcomes,” said Tim Suther, senior vice president of Data Solutions at Change Healthcare. “This helps optimize healthcare utilization, member engagement, and employer wellness programs. Medical affairs and research are transformed. And most importantly, patient outcomes improve. SDoH Analytics makes these data-driven insights affordable and actionable.”

Cleveland Clinic Develop COVID-19 Risk Prediction Model through Epic MyChart

Cleveland Clinic Develop COVID-19 Risk Prediction Model through Epic MyChart

What You Should Know:

– Cleveland Clinic develops the COVID-19 risk prediction model through Epic MyChart that is now available to health systems around the world.

– Healthcare organizations can present the clinically
validated model to patients in MyChart to assess their risk of having COVID-19.


Cleveland Clinic researchers have developed a COVID-19 risk prediction model that uses information from the patient’s comprehensive health records combined with patient-centered information in Epic’s patient-facing app, MyChart, to show an individual’s likelihood of testing positive for COVID-19. The COVID-19 risk prediction model is now available to health systems around the world through Epic.

 A COVID-19 risk prediction model designed by Cleveland Clinic researchers is
now available to health systems around the world through EpicDeveloped and tested using
clinical data from more than 11,000 Cleveland Clinic patients
, the model
uses information from patients’

COVID-19 Risk Prediction Model Development

Predicting positive COVID-19 tests could help direct limited healthcare resources, encourage those who are likely to have the virus to get tested, and tailor decision-making about care. Cleveland Clinic’s model was developed and validated using retrospective patient data from more than 11k patients tested for COVID-19 at Cleveland Clinic locations in Northeast Ohio and Florida. Data scientists used statistical algorithms to transform data from patients’ electronic medical records into the first-of-its-kind risk-prediction model. All data collected was housed in a secure database.

How It Works

Patients complete a short self-assessment in MyChart,
documenting information like symptoms they are experiencing and potential
exposure to COVID-19. The model uses that information, as well as clinical and
demographic data already in their electronic chart, to calculate their score.
Patients with high risk for having COVID-19 are advised to receive a test, and
their care team members can be automatically notified of a high-risk score.

Other healthcare providers around the country also have
developed risk prediction models, which they can integrate with Epic. For
organizations that want to use an existing model rather than developing their
own, they can quickly turn on the model designed, developed, and tested–and now
being shared–by Cleveland Clinic researchers.

“We have developed the first validated prediction model that can forecast an individual’s risk for testing positive with COVID-19 and then simplified this tool while retaining exceptional accuracy for easy adoption,” said Lara Jehi, M.D., Chief Research Information Officer at Cleveland Clinic. “We are excited to make this tool available to the 250 million patients around the world who have a record in Epic. The ability to accurately predict which patients are likely to test positive will be paramount in effectively managing a patient’s care as well as allocating our resources.”

M&A: Centene to Acquire AI Healthcare Analytics Platform Apixio

M&A: Centene to Acquire AI Healthcare Analytics Platform Apixio

What You Should Know:

– Centene Corporation acquires AI healthcare analytics platform
Apixio to additional data and AI capability to technology portfolio.

– Apixio will remain an operationally independent entity
as part of Centene’s Health Care Enterprises group to continue bringing value
to its clients and the industry.

Centene Corporation, today
announced it has signed a definitive agreement to acquire
Apixio Inc., a AI healthcare analytics company offering Artificial
Intelligence (AI)
technology solutions. The transaction is subject to
regulatory approvals and is expected to close by the end of 2020.

Better Data. Better Healthcare

More than 1.2 billion clinical documents are generated each year in the U.S., but there is very little analysis of that unstructured information. Founded in 2009. Apixio helps organizations use their data for knowledge about patient health. This ultimately translates into more effective care delivery, lower costs and streamlined processes. Apixio’s machine learning and deep learning algorithms analyze unstructured data embedded in electronic health records, scanned notes, facsimiles, and handwritten notes to produce high-quality predictions for measurement, care, and discovery.

The Apixio Platform

M&A: Centene to Acquire AI Healthcare Analytics Platform Apixio

The Apixio Platform can mine textual data and combine its generated insights with available structured data to craft computable individual health profiles or phenotypes. We analyze our assembled phenotypes in real-time using a flexible rules engine. This automates the execution of clinical guidelines, quality and risk measures, payment or reimbursement policies, and other operational and administrative rules, to support critical healthcare activities.

Acquisition Complements Centene’s Existing Data Analytics
Products

“Centene is committed to accelerating innovation, modernization and digitization across the enterprise and solidify its position as a technology company focused on healthcare. Apixio’s capabilities are closely aligned with our plans to digitize the administration of healthcare and to leverage comprehensive data to help improve the lives of our members,” said Michael F. Neidorff, Chairman, President and Chief Executive Officer for Centene. “Apixio’s technology will complement existing data analytics products including Interpreta, creating a differentiated platform to broaden support for value-based healthcare payment and delivery with actionable intelligence.”

As part of the acquisition, Apixio will remain an
operationally independent entity as part of Centene’s Health Care Enterprises
group to continue bringing value to its clients and the industry, while also
realizing the benefits of enhanced scale with Centene. Financial details of the
acquisition were not disclosed.

Eko Lands $65M to Expand AI-Powered Telehealth Platform for Virtual Pulmonary and Cardiac Exam

Eko Lands $65M to Expand AI-Powered Telehealth Platform for Virtual Pulmonary and Cardiac Exam

What You Should Know:

– Cardiopulmonary digital health company Eko raises $65M
in Series C funding to close the gap between virtual and in-person heart and
lung care.

– The latest round of funding will enable Eko to expand
in-clinic use of its platform of telehealth and AI algorithms for disease
screening and to launch a monitoring program for cardiopulmonary patients at
home.

Eko, a
cardiopulmonary digital
health
company,
today announced $65 million in Series C funding led by Highland Capital
Partners and Questa Capital, with participation from Artis Ventures, DigiTx
Partners, NTTVC, 3M Ventures, and other new and existing investors. The new
funding will be used to expand in-clinic use of the company’s platform of telehealth
and AI
algorithms for disease screening, and to launch a monitoring program for
cardiopulmonary patients at home.

Eko was founded in 2013 to improve heart and lung care for
patients through advanced sensors, digital technology, and novel AI algorithms.
The company reinvented the stethoscope and introduced the first combined
handheld digital stethoscope and electrocardiogram (ECG). Eko’s FDA-cleared AI
analysis algorithms help detect heart rhythm abnormalities and structural heart
disease. Eko seeks to make AI analysis the standard for every physical exam. The
company recently launched Eko AI and Eko Telehealth to combat the needs of the COVID-19
pandemic.

Eko Telehealth delivers:

– AI-powered and FDA-cleared identification of heart murmurs
and atrial fibrillation (AFib), assisting providers in the detection and
monitoring of heart disease during virtual visits

– Lung and heart sound live-streaming for a thorough virtual
examination

– Single-lead ECG live-streaming, enabling providers to
assess for rhythm abnormalities

– Embedded HIPAA-compliant video conferencing, or can work
alongside the video conferencing platform a health system has in place

Symptoms of valvular heart disease and AFib often go
undiagnosed during routine physical exams. With the development of Eko’s AI
screening algorithms, clinicians are able to harness state-of-the-art machine
learning to detect heart disease at the earliest point of care regardless if
the patient visit is in-person or remote.

“We are thrilled that our new investors have joined our journey and our existing investors have reaffirmed their support for Eko,” said Connor Landgraf, CEO and co-founder at Eko. “The explosion in demand for virtual cardiac and pulmonary care has driven Eko’s rapid expansion at thousands of hospitals and healthcare facilities, and we are excited for how this funding will accelerate the growth of our cardiopulmonary platform.”

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.”

AI Leads Way to Less False Positives on Remote Cardiac Monitoring Devices, Improved Results

What You Should Know:

– Cardiac patients and their cardiologists are
experiencing a high number of false positives with remote patient monitoring
devices as a result of signal artifact providing inaccurate data, which can
lead to many complications—other than medical, such as unnecessary tests and
increased medical costs.

– Ambulatory cardiac monitoring provider InfoBionic has devised a way to decrease false positives and increase efficiency.


Remote cardiac monitoring’s false positives—especially on atrial fibrillation (Afib)—hurt everyone, from the patient to the boss who will have to go without an employee when he or she has to go in for unnecessary tests. An estimated 12.1 million people in the United States will have Afib by 2030; Afib increases the risk of stroke, heart failure, and death, and is one of the few cardiac conditions that continue to rise.(1) “We must give the clinician more effective diagnoses, while at the same time increasing confidence in our healthcare technology systems with respect to the accuracy of the same patient data,” expressed Stuart Long, CEO of InfoBionic, a provider of ambulatory cardiac monitoring services.

Impact of Remote
Patient Monitoring on Afib

Afib is a “fluttering feeling that can point to a quivering heart muscle, a notable skipped beat as the mark of a palpitation, and a racing heart rate that sparks other discomforts.” (2) With the rise of remote patient monitoring (RPM) as an effective and economical modality to treat and monitor patients, false positives continue to rise to generate a lack of confidence in the accurate clinical data captured through RPM. False positives can overwhelm the clinician and result in the increased use of resources and downstream costs, and false negatives could have detrimental clinical consequences.(3) 

Without a reliable RPM supported by powerful AI solutions, healthcare payers experience higher costs. Heart disease takes an economic toll, as well, costing the nation’s healthcare system $214 billion per year and consuming $138 billion in lost productivity on the job. (4) The cascading effect of false positives run the gamut of the human experience—from the physical and emotional health of the patient to the added out-of-pocket expenses of unnecessary and avoidable tests.

The increased risks of hospital readmissions at a time when healthcare systems are overtaxed and understaffed adds another factor of what could have been an unneeded situation. “InfoBionic AI has all but eliminated the need for physicians to deal with false positives. In fact, 100% of Atrial Fibrillation events longer than 30 seconds are detected accurately (true positive) by InfoBionic’s AI system(6),” said Long.

By
leveraging cloud computing with continuous arrhythmia monitoring to create a
reliable platform with accurate data collection, an ambulatory cardiac monitor,
such as the MoMe® Kardia device, optimizes AI solutions,
allowing for consistency in the treatment. Integrated sensor measures have been
shown to predict heart failure and might have the potential to
empower patients to participate in their own care.(5) Offering
24-hour monitoring through RPM technology that reduces false positives leads to
the patient becoming more comfortable with the RPM service, which increases the
likelihood the patient will adopt the practice of self-care well into the
future. Cardiac patients with pulmonary or electrolyte problems may need
continuous cardiac monitoring to screen for arrhythmias.

A primary feature of our MoMe® Kardia is its ability to leverage technology in a way that makes physicians feel more confident via analysis precision that verifies detected cardiac episodes through the algorithm,” said Long. Another distinct advantage is the ability to provide 6 lead analysis instead of the 1 or 2 leads provided by other systems. This affords the physician a much better view of each heartbeat, thereby increasing physician confidence in the accuracy of diagnosis.

The
AI
provides valuable clinical statistics that guide treatment with the best
patient outcomes. As the leading provider to collect every heartbeat and
transmit it to the cloud in near real time, explains Long, InfoBionic’s AI
algorithms are informed by over 15 million hours of electrocardiogram (ECG)
collected from the entire patient population. With full disclosure transmission
that allows AI algorithms to run on powerful servers in the cloud, the system
utilizes much more intensive processing than could be accomplished on other
patient-worn devices. Multiple patented algorithms are run concurrently on the
ECG stream, each with superior performance on a variety of clinical conditions.

KēlaHealth Lands $12.9M to Expand AI-Powered Surgical Intelligence Platform

KēlaHealth Lands $12.9M to Expand Surgical AI Platform

What You Should Know:

–  KēlaHealth
announced their Series A round (combined seed and Series A) of $12.9M led by
Sante Ventures and new innovation VC, Intuitive Ventures.

– KēlaHealth, is a surgical intelligence engine that
applies a dynamic cycle of patient-specific predictions, stratified
interventions, and outcomes tracking to reduce surgical complications

– KēlaHealth is the first investment for Intuitive
Ventures, a new innovation fund spun out of Intuitive Surgical, Inc.


KēlaHealth, Inc.,
a San Francisco, CA-based surgical intelligence platform that applies a dynamic
cycle of patient-specific predictions, stratified interventions, and outcomes
tracking to reduce surgical complications, today announced the closing of
a $2.9 million Seed financing and milestone-based $10 million Series
A financing led by Santé Ventures and Intuitive Ventures, and inclusive of grant
funding from the National Science Foundation Small Business Innovation Research
(SBIR) Program. These funds will accelerate the expansion of the KēlaHealth
platform to hospitals and surgical partners across the United States. KēlaHealth
is the first investment for Intuitive Ventures, a new innovation fund spun out
of Intuitive Surgical, Inc.

Learning Ecosystem to Improve Surgical Care Outcomes

Founded by Bora Chang, MD, with a goal of harnessing machine
learning algorithms to reduce patient surgical complications and improve
outcomes. KēlaHealth uses advanced artificial intelligence techniques to
deliver a cloud-based software-as-a-service solution to healthcare providers,
surgeons, and hospital systems.

In the U.S., 51 million surgeries are performed annually, with an average complication rate of 15 percent. This results in millions of patients suffering harm and loss after a procedure. Tragically, half of these complications are known to be avoidable and contribute to $77 billion in wasted healthcare costs each year

KēlaHealth helps to prevent these avoidable complications
while enhancing surgical care by delivering stratified patient risk scoring.
The company’s state-of-the-art platform uses machine learning algorithms to
match individual risk levels with graduated pathways of care that align with
the unique needs of each surgical patient.

These personalized efforts bring surgery into a new era of
precision medicine: with KēlaHealth, surgeons can match the right patient with
the right procedure with the right precautions at the right time, leading to
improved patient outcomes and significant hospital savings.

To date, KēlaHealth’s hospital partners have applied the
company’s AI-powered platform in colorectal, vascular, cardiac, and orthopedics
surgical specialties.

The company has participated in highly selective accelerator
programs such as Cedars-Sinai Techstars Accelerator, Healthbox Studio, and Plug
and Play.

Dr. Chang, CEO of KēlaHealth added: “Our vision is to apply the lessons learned from millions of previous surgeries for the benefit of every patient undergoing a procedure. Patients and their families, clinicians, and hospitals deserve the assurance that the risks of any surgery will be safely navigated by surgical teams with the best information available to them at every point in the surgical journey. We are thrilled to have a stellar group of surgeons, hospital centers, investors, and advisors working with us to realize the opportunity of precision surgery.”

The Future of the ICU? How Clinical Decision Support Is Advancing Care

The Future of the ICU? How Clinical Decision Support Is Advancing Care
Kelly Patrick, Principal Analyst at Signify Research

Without a doubt 2020 has been a devastating year for many; the impact of COVID-19 on both personal lives and businesses has had long-term consequences. At the end of September, the number of COVID-19 cases fell just short of 350 million, with just over 1 million deaths reported. The expectation of a second peak in many countries exposed to the deadly illness is being handled with care, with many governments attempting to minimize the impact of an extreme rise in cases.  

COVID-19 the aftermath will be the new normal?

Despite the chaotic attempts to dampen the impact of a second peak, it is inevitable that healthcare facilities will be stretched once again. However, there are key learnings to be had from the first few months of the pandemic, with several healthcare providers opting to be armed with as much information to tackle the likely imminent surge of patients with COVID-19 head-on. The interest in solutions that offer support to clinicians through data analysis is starting to emerge with several COVID-19 specific Artificial Intelligence (AI) algorithms filtering through the medical imaging space. 

Stepping into the ICU, the use of analytics and AI-based clinical applications is drawing more attention. Solutions that collect relevant patient information, dissect the information, and offer clinical decision support are paving the way to a more informed clinical environment. Already, early-warning scoring, sepsis detection, and predictive analytics were becoming a focus. The recent COVID-19 outbreak has also driven further interest in COVID-19 specific applications, and tele-ICU solutions, that offer an alternative way to ensure high-risk patients are monitored appropriately in the ICU. 

What does the future hold?

Signify Research is currently in the process of assessing the uptake of clinical decision support and AI-based applications in the high acuity and perinatal care settings. An initial assessment has highlighted various solutions that help improve not only the efficiency of care but also improve its quality. Some of the core areas of focus include:

Clinical Decision Support & Predictive Analytics

Due to the abundance of patient data and information required to be regularly assessed and monitored, the high-acuity and perinatal care settings benefit from solutions offering clinical decision support. 

The ICU specifically has been a focus of many AI solution providers, with real-time analysis and support of data to provide actionable clinical decision support in time-critical situations. Clinical decision support solutions can collate data and identify missing pieces of information to provide a complete picture of the patient’s status and to support the treatment pathway. Some of the key vendors pathing the way for AI in clinical decision support in the ICU include AiiNTENSE; Ambient Clinical Analytics; Etiometry; BetterCare; AlertWatch; and Vigilanz Corp.

Early-warning

Early-warning protocols are commonly used in hospitals to flag patient deterioration. However, in many hospitals this is often a manual process, utilizing color coding of patient status on a whiteboard in the nurse’s station. Interest in automated early-warning systems that flag patient deterioration using vital signs information is increasing with the mounting pressure on stretched hospital staff.

Examples of early-warning software solutions include the Philips IntelliVue Guardian Solution and the Capsule Early Warning Scoring System (EWSS). Perigen’s PeriWatch Vigilance is the only AI-based early-warning scoring system that is developed to enhance clinical efficiency, timely intervention, and standardization of perinatal care.

The need for solutions that support resource-restricted hospitals has been further exacerbated during the COVID-19 pandemic. Many existing early-warning vendors have updated their surveillance systems to enable more specific capabilities for COVID-19 patients, specifically for ventilated patients. Companies such as Vigilanz Corp’s COVID Quick Start and Capsule Tech’s Clinical Surveillance module for ventilated patients enables healthcare professionals to respond to COVID-19 and other viral respiratory illnesses with customizable rules, reports, and real-time alerts.

Sepsis Detection

Sepsis is the primary cause of death from infection, accounting for 20% of global deaths worldwide. Sepsis frequently occurs from infections acquired in health care settings, which are one of the most frequent adverse events during care delivery and affect hundreds of millions of patients worldwide every year. As death from Sepsis can be prevented, there is a significant focus around monitoring at-risk patients.

Several health systems employ their own early-warning scoring protocol utilizing in-house AI models to help to target sepsis. HCA Healthcare, an American for-profit operator of health care facilities, claims that its own Sepsis AI algorithm (SPOT) can detect sepsis 18-hours before even the best clinician. Commercial AI developers are also focusing their efforts to provide supporting solutions.

The Sepsis DART™ solution from Ambient Clinical Analytics uses AI to automate early detection of potential sepsis conditions and provides smart notifications to improve critical timeliness of care and elimination of errors. Philips ProtocolWatch, installed on Philips IntelliVue bedside patient monitors, simplifies the implementation of evidence-based sepsis care protocols to enable surveillance of post-ICU patients. 

Tele-ICU

The influx of patients into the ICU during the early part of 2020 because of COVID-19 placed not only great strain on the number of ICU beds but also the number of healthcare physicians to support them. Due to the nature of the illness, the number of patients that were monitored through tele-ICU technology increased, although the complex nature of implementing a new tele-ICU solution has meant the increase has not been as pronounced as that of telehealth in primary care settings.

However, its use has enabled physicians to visit and monitor ICU patients virtually, decreasing the frequency and need for them to physically enter an isolation room. As the provision of healthcare is reviewed following the pandemic, it is likely that tele-ICU models will increase in popularity, to protect both the patient and the hospital staff providing direct patient care. Philips provides one of the largest national programs across the US with its eICU program.

Most recently, GE Healthcare has worked with Decisio Health to incorporate its DECISIOInsight® into GE Healthcare’s Mural virtual care solution, to prioritize and optimize ventilator case management. Other vendors active within the tele-ICU space include Ambient Clinical Analytics, Capsule Health, CLEW Med, and iMDsoft.

Figure 1 Signify Research projects the global tele-ICU market to reach just under $1 billion by 2024.

Interoperable Solutions

More and more solutions are targeted toward improving the quality of patient care and reducing the cost of care provision. With this, the requirement for devices and software to be interoperable is becoming more apparent. Vendors are looking to work collaboratively to find solutions to common problems within the hospital. HIMMS 2020 showcased several collaborations between core vendors within the high acuity market. Of note, two separate groups demonstrated their capabilities to work together to manage and distribute alarms within a critical care environment, resulting in a quieter experience to aid patient recovery. These included:

– Trauma Recovery in the Quiet ICU – Ascom, B Braun, Epic, Getinge, GuardRFID, Philips

– The Quiet Hospital – Draeger, Epic, ICU Medical, Smiths Medical, Spok​


About Kelly Patrick, Principal Analyst at Signify Research

The Future of the ICU? How Clinical Decision Support Is Advancing Care
Kelly Patrick, Principal Analyst at Signify Research

Kelly Patrick is the Principal Analyst at Signify Research, a UK-based market research firm focusing on health IT, digital health, and medical imaging. She joined Signify Research in 2020 and brings with her 12 years’ experience covering a range of healthcare technology research at IHS Markit/Omdia. Kelly’s core focus has been on the clinical care space, including patient monitoring, respiratory care and infusion.


Can Technology Help Reduce Cases of Hospital Negligence?

Can Technology Help Reduce Cases of Hospital Negligence?

For most healthcare professionals, providing care to their patients is mandatory. However, there are times when their desire to give patients the best care possible becomes a necessity for compliance, particularly now that hospital negligence has been a constant stress factor for both professionals and patients. 

It is a given that any medical treatment has the potential to go wrong. There is never a perfect process, and healthcare workers are very much aware of this, especially when the burden falls on their shoulders to make sure everything goes right. Patients, on the other hand, place their trust in healthcare professionals, as they believe that they know what’s best for them. With all this considered, it’s common for both doctors and patients to have hospital negligence as the least of their concerns. Sadly, it is a reality that happens to many people.

According to http://www.tariolaw.com/, medical malpractice cases are still fairly common, and it’s why many people still refuse to allow technology to be a part of their treatment process. However, recent advancements in healthcare technology have led to the development of applications that can effectively reduce and eliminate the incidence of negligence. 

Machine Learning in Healthcare

The use of artificial intelligence (AI), particularly machine learning (ML) in healthcare, has been making great progress in revolutionizing medicine and incidents of medical negligence. 

Diagnostic Algorithms

Several startups and enterprises are now leveraging the power of ML to develop algorithms with capabilities that can help doctors predict potential medical problems and come up with effective treatment processes.

The healthcare industry is continually evolving, and there are new illnesses that scientists and epidemiologists are discovering. However, not every doctor can be aware of every published journal. Machine learning tools can scan these journals, match the presenting symptoms, and make diagnostic and therapeutic recommendations based on their readings. In fact, many experts now believe that the spread of COVID-19 could have been prevented had leading doctors been able to use ML to scan for journals about an earlier study that discussed a SARS-like virus with the potential to cause an epidemic. 

Removing Specialty Bias

Most cases of medical malpractice arise due to limited knowledge. You cannot expect a dermatologist to diagnose certain infectious diseases of the lungs, for example. Extensive coordination between specialists may cause a patient’s condition to worsen as time passes. With AI technology, computers can process information and suggest possible diagnoses. Doctors can take these recommendations to help narrow down their choices. It will then be easier for a dermatologist to know if they should refer your case to a hematologist or an infectious diseases expert. 

Eliminating the Blame Game

Perhaps one of the most vital contributions of technology in hospital negligence is eliminating the blame game. Suppose for example that both the doctor and machine arrive at a misdiagnosis. In this case, it may be easier for the patient to accept that there was no negligence and that their particular case is so rare that there isn’t enough information for diagnosis or treatment. With the full acceptance of their medical condition, it would be easier for patients to welcome adjuvant therapies that can help them get better. 

AI in healthcare is still young. There are many facets of medical care that still need refining, bugs to address, and tons of privacy issues to fix. These medical innovations still need time to fully come to fruition and need to be developed in a way that will not cause additional negligence.  For now, patients need to place their full trust in their doctors, who, in turn, should care for their patients to the best of their capacity. 

Amwell, Tyto Care Expand Partnership to Power Augmented Virtual Care Experiences

Amwell, Tyto Care Expand Partnership to Power Augmented Virtual Care Experiences

What You Should Know:

Tyto
Care
 and Amwell® announced an expanded partnership, allowing
the companies to develop new integrations to enhance virtual care offerings for
providers.

– By pairing Tyto Care’s TytoHome device and platform
with Amwell’s platform, the two companies will together provide patients
and providers with augmented virtual care experiences and
broadly enrich the capabilities and satisfaction with healthcare organizations’
virtual care applications.


Telehealth
provider Amwell, today
announced it is expanding its partnership with Tyto Care, the
healthcare industry’s first all-in-one modular device and examination platform
for AI-powered, on-demand, remote medical exams. Together the companies will
introduce exclusive integrations and newly designed workflows and tools to
enhance the ability for providers using the Amwell platform to examine and
diagnose patients virtually. Additionally, Amwell will become a reseller of Tyto Care’s
integrated devices
.   

Tyto Care Background

Tyto Care seamlessly connects people to clinicians to provide
the best virtual home examination and diagnosis solutions. Its solutions are
designed to enable a comprehensive medical exam from any location and include a
hand-held, all-in-one tool for examining the heart, lungs, skin, ears, throat,
abdomen, and body temperature; a complete telehealth platform for sharing exam
data, conducting live video exams, and scheduling visits; a cloud-based data
repository with analytics; and built-in guidance technology and machine
learning algorithms to ensure accuracy and ease of use for patients and
insights for healthcare providers.

Conduct Exams and Diagnoses

By pairing the TytoHome handheld examination
device – which enables on-demand examinations of the heart, lungs, abdomen,
skin, throat, ears, heart rate, and body temperature – with Amwell’s telehealth
platform, providers can guide patients through
virtual health examinations and together gain real-time insight into a
patient’s health data and status directly in the visit. For patients and
providers, this will augment the virtual care experience and more broadly
enrich the capabilities and overall satisfaction associated with healthcare
organizations’ virtual care applications. This enriched workflow will be available to
thousands of Amwell hospitals, health systems, health plans
and employer clients who collectively serve millions of patients.  

“As COVID-19 wages on and more patients and providers adopt telehealth, it’s critical that we accelerate the depth of care that can be provided in the home – to keep patients and providers safe,” said Roy Schoenberg, President and Co-CEO, Amwell. “Our latest integration with Tyto Care will allow providers to clinically come closer than ever before to patients during telehealth encounters, allowing them to see, interact, examine and deliver care in ways that growingly resemble in-person care.”

Offering More Holistic Care for Patients

“Our longstanding partnership with Amwell exemplifies our shared goal of providing deeply integrated telehealth solutions that put health in the hands of consumers, creating a more impactful and seamless healthcare experience for both patients and providers,” said Dedi Gilad, CEO and Co-Founder, Tyto Care. “The integration with TytoHome will enable Amwell to offer more holistic care for patients, especially for urgent and primary care needs, as well as help to enable better adherence to treatment plans. We look forward to continuing our work together as we realize the full potential of clinic-quality, at-home care in this new era of telehealth.”

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.


Innovaccer Launches Perioperative Optimization Platform for Surgeons

Innovaccer Launches Perioperative Optimization Platform for Surgeons

What You Should Know:

– Innovaccer launches a perioperative
optimization solution for surgeons to realize clinical and financial goals with
patient-risk analysis.

– The solution redefines surgical planning and
post-surgical recovery with machine learning-based patient stratification for
optimized surgery experience and personalized patient care management.


Innovaccer, Inc., a San Francisco, CA-based healthcare technology company, recently launched its perioperative optimization solution for health systems. The solution optimizes surgeries and ramps up volumes by identifying high-risk patients for pre-surgical intervention while reducing the length of stay, readmissions, and cost. The solution uses advanced analytics and machine learning-based algorithms to proactively identify patients at greater risk for post-surgical complications. Patients are then referred to the pre-surgical optimization clinic for pre-surgical strategies which are personalized for individual patients and specifically designed to minimize post-surgical complications.

Impact of COVID-19 on Elective Surgeries, Non-Essential
Medical Care

COVID-19
has challenged traditional healthcare delivery systems and caused the
postponement of elective surgeries and other non-essential medical care. As
patients wait for their surgeries, it is likely their conditions could
deteriorate and/or patients would return to clinics during a pandemic surge.
Health systems will need to be prepared to address the potential for more
complicated patient health conditions with careful risk assessment.

Pre-Surgical Optimization Platform Features

Innovaccer’s “Pre-Surgical Optimization” solution guides patient prioritization based on an algorithm that factors medical history, patient demographics, allergies, chronic conditions, history, and social determinants of health. Based on the previous data on these patients from the electronic medical record, claims, and the individual’s risk factors, the algorithm estimates the future cost of care for the patient. The algorithm also assigns patients to appropriate case managers using a smart rule engine that assesses a variety of factors including the number of appointments, and the surgeon’s expertise to map the patient to the provider. This approach helps hospitals identify high-risk patients and focus on the patients that will benefit most from pre-surgical interventions. 

Return on Investment Model for Healthcare Organizations

Innovaccer has also incorporated a refined return on
investment model designed to make the optimization process revenue positive for
healthcare organizations. The three key pillars of the exclusive model are
sensitivity analysis tools, deep data insights, and performance analytics.
Using this solution, hospitals can track their return on investment in
real-time on a customizable dashboard with metrics including reduced
readmissions, reduced length of stay, and emergency department visits with
their associated costs. 

“With about 28 million surgeries canceled worldwide, non-COVID medical care has suffered tremendously. Canceled elective surgeries have impacted patient health conditions and the economic sustainability of health systems,” says Abhinav Shashank, CEO and Co-founder of Innovaccer. “As health systems plan to resume surgical procedures, care managers will need to engage the patient remotely for pre-surgical interventions. Our solution is created to redefine the entire process of optimizing surgery planning and to become more patient-centered and adaptable to the changing care environment. We want to ensure exemplary pre-optimization and post-discharge engagement to reduce readmissions and improve the hospital’s financial impact using the pre-surgical optimization process.”

To Combat COVID-19, Philips Launches Rapid Equipment Deployment Kits

To Combat COVID-19, Philips Launches Rapid Equipment Deployment Kits

What You Should Know:

– Philips today announced the launch of its Rapid
Equipment Deployment Kits, which provide doctors with critical care patient
monitoring solutions they can quickly implement in the ICU. The Rapid Equipment
Deployment Kits use advanced patient monitoring technology to enable care teams
to swiftly scale up critical care capabilities within just a few hours, and
help hospitals meet on-demand access during these pressing times of COVID-19.

– Arriving at hospitals fully configured and
ready-to-deploy, the kits are pre-built and pre-packed into sturdy cases and
can be transferred from hospital to hospital as needed. After a crisis/surge
has passed, the kits are disinfected and stored to have available in
preparation for future emergencies.


Royal Philips, today introduced its Rapid Equipment Deployment Kit for ICU ramp-ups, allowing doctors, nurses, technicians, and hospital staff to quickly support critical care patient monitoring capabilities during the COVID-19 pandemic.  Currently successfully in use in the first health systems across the US, the Rapid Equipment Deployment Kit combines Philips advanced patient monitoring technology with predictive patient-centric algorithms enabling care teams to quickly scale up critical care patient monitoring capabilities within a few hours.  As health systems in the U.S. continue to experience surges in critical care and emergency care demand related to the COVID-19 crisis, the kit provides hospitals a way to quickly and easily expand their critical care capacity.

The Rapid Equipment Deployment Initiative for COVID-19
Response

To Combat COVID-19, Philips Launches Rapid Equipment Deployment Kits

The Philips Rapid Equipment Deployment Kit is a fully configured and ready-to-deploy ICU patient monitoring solution, which includes 20 ICU monitors, 20 measurement servers and one central management monitoring station. The kits are pre-built, pre-configured and pre-packed into sturdy cases that can elevate a hospital’s general care area to a critical care level in a matter of hours. Kits are complete with step-by-step instructions allowing the pre-configured system to be deployed by hospital staff, with remote technical and clinical support from Philips. Kits can be transferred from hospital to hospital as needed. Once a crisis/surge passes, the kits are disinfected, packed up and stored to have available in preparation for future emergencies.

 Why It Matters

“The current health crisis has demonstrated a clear need for
us to deliver innovative solutions to our customers that provide a complete
critical care monitoring solution with all of the equipment they require on
demand. This eliminates the need to source and configure individual pieces of
high-demand equipment during a crisis,” said Peter Ziese, General Manager of
Monitoring Analytics at Philips.  “To help ensure economical and more
efficient use of hospital resources, the Rapid Equipment Deployment Kits
provide the speed, flexibility and ease of implementation for advanced critical
care patient monitoring that many of our customers must have during this most
pressing time.”

In June, Philips announced it had received Emergency Use Authorization from the FDA for Philips’
IntelliVue Patient Monitors MX750/MX850
 and its IntelliVue Active
Displays AD75/AD85, for use in the US during the COVID-19 health emergency.
These patient monitoring solutions support infection-control protocols and
remotely provide critical patient information when caring for hospitalized
COVID-19 patients. The MX750 and MX850 monitors are the latest additions
to Philips’ portfolio of integrated patient monitoring solutions to help
support improved clinical and operational workflows. Updated features, include
enhancements to monitor and assess clinical and network device performance, and
additional functionalities to strengthen cybersecurity.

Medtronic Acquires Smart Insulin Pen Company Companion Medical

Medtronic Acquires Smart Insulin Pen Company Companion Medical

What You Should Know:

– Today, Medtronic announced that it will
acquire Companion Medical, the manufacturer of InPen, a smart insulin pen
system paired with an integrated diabetes management app.

– The addition of Companion’s InPen system builds
upon Medtronic’s strategic acquisitions of Nutrino and Klue to further
improve overall automated decision-making capabilities and optimizes dosing
decisions using algorithms and AI for patients.


Medtronic,
today announced the planned acquisition
of privately-held Companion
Medical,
manufacturer of InPen — the only U.S. FDA-cleared smart insulin
pen system paired with an integrated diabetes management app on the market. The
addition of Companion Medical’s InPen to the Medtronic portfolio
expands the company’s ability to serve people where they are in their diabetes
journey and offer them a unique and expansive ecosystem of support — regardless
of how insulin is delivered.

FDA-Cleared Smart Pen System

The InPen dose calculator recommends a personalized dose
based on your blood glucose, the carbohydrates you’re eating, and your active
insulin. The calculator is customizable — choose between three different modes
depending on whether you count carbs or not.

InPen is the only smart pen system that tracks active
insulin. At any time, you can check the app and see how much insulin is still
active in your body from previous doses. InPen uses this information to let you
know if you need a correction dose and to help you avoid stacking.

Acquisition
Expands 
Medtronic Capabilities to Serve People Using Multiple Daily Injections (MDI)
to Manage Diabetes

The acquisition of Companion Medical builds upon prior Medtronic strategic
acquisitions, including Nutrino and Klue, that form the building blocks to
design powerful algorithms leveraging the company’s deep data science and AI
capabilities. With this latest acquisition, Medtronic will work to
further advance the automation of insights and dosing capabilities to help
alleviate burden regardless of the technology that’s preferred for insulin
delivery. In addition, Medtronic will look to expand the availability
of InPen globally.

“This acquisition is an ideal strategic fit for Medtronic as we further simplify diabetes management and improve outcomes by optimizing dosing decisions for the large number of people using multiple daily injection (MDI). We look forward to building upon the success of the InPen by combining it with our intelligent algorithms to deliver proactive dosing advice personalized to each individual. This smart CGM system can help people think less about diabetes and be able to live life with more freedom, on their own terms,” said Sean Salmon, executive vice president and president of the Diabetes Group at Medtronic. ”Our goal is to become a trusted partner that offers consistent support whether an individual wants to stay on MDI, transition to automated insulin delivery or take a break from their pump.”

Financial terms of the acquisition were not disclosed. The
acquisition is expected to close within one to two months – subject to the
satisfaction of certain customary closing conditions. The transaction is
expected to be neutral to Medtronic’s adjusted earnings per share in the
current fiscal year, and accretive thereafter.

“We are thrilled to be combining our strengths and differentiated product portfolios to work towards serving even more people around the world living with diabetes in the ways that matter most to them,” said Sean Saint, CEO and co-founder of Companion Medical. “Simplifying diabetes management to reduce burden and improve outcomes has always been our goal, and through a respected global leader like Medtronic, we’ll now be able to take InPen to this next phase of growth which is great news for people with diabetes who stand to benefit most.”

$5M Research Grant Launches to Evaluate Efficacy of Virtual Diabetes Care

diabetes management qualcomm medtronic diabetes

What You Should Know:

– DreaMed Diabetes, the developer of personalized,
AI-based diabetes management solutions, will participate in a clinical study,
led by Jaeb Center for Health Research Foundation, to evaluate the efficacy of virtual
treatment for diabetes patients. The research is funded by a $5 million grant
from The Leona M. and Harry B. Helmsley Charitable Trust. 

– DreaMed uses AI-powered technology to seamlessly treat
patients remotely with its virtual diabetes management service by providing
personalized recommendations on insulin dosage for people with type 1 diabetes.

– As part of Helmsley’s research, the study will
determine if the virtual clinic model improves clinical and psychological
outcomes for people with diabetes. DreaMed, together with Aetna and Cecilia
Health, will provide a multi-faceted platform to fully evaluate the
effectiveness of virtual clinics using DreaMed’s technology. 


DreaMed Diabetes Ltd., the developer of personalized, AI-based diabetes management solutions, announces it will be serving as a subcontractor on a $5,025,099 grant from The Leona M. and Harry B. Helmsley Charitable Trust to the Jaeb Center for Health Research Foundation.  DreaMed’s technology will include a comprehensive data system that can pull information from CGM, SMBG, insulin pumps, and connected insulin pens. It will visualize this information for the healthcare provider and for the participants through web and mobile applications as well as operate decision support algorithms to optimize insulin treatment plans for people with type 1/type 2 on insulin pumps or multiple daily injections therapy. 

Growing Need for Virtual Diabetes Care

Due to the sheer size of the United States, many people with
diabetes lack the necessary medical support required, particularly access to
endocrinologists who provide them with critical guidance and information. This
includes the overseeing of continuous glucose monitoring (CGM), insulin-dosing
support, and pertinent mental health support. The lack of support alongside
today’s social distancing guidelines has encouraged a host of remote medical
initiatives, many of which aim to provide people with diabetes a comprehensive
virtual solution. While virtual solutions have proved exciting, questions
remain as to whether virtual care, including CGM, provides better outcomes than
traditional methods.

Jaeb Center’s Pilot Study

In January 2019, the Jaeb Center’s pilot study assessed
whether CGM could be successfully introduced outside of a clinic over a
three-month period. The results found participants using CGM received
personalized support, enabling them to improve their glycemic outcomes and
quality of life. While the study proved invaluable, there was a need for a
larger study that truly evaluates the efficacy of remote diabetes care, which
will be accomplished within the scope of this new grant from the Helmsley
Charitable Trust to the Jaeb Center.

New Study Explores the Efficacy of Virtual Diabetes Care

The new study represents a more rigorous model for analyzing
virtual diabetes management, because it will evaluate CGM use over time,
glycemic- and participant-reported outcomes, healthcare utilization and cost,
the use of decision support tools, and the impact of mental health support.

Jaeb will evaluate the efficacy of a virtual specialty
clinic model for improving clinical and psycho-social outcomes for people with
diabetes. This evaluation will include 300 patients who don’t currently utilize
CGM, with type 1 and type 2 diabetes nationwide over the course of a six-month
period. To integrate the virtual management of insulin dosage, the initiative
will utilize the DreaMed Advisor Platform, which gives providers a way to view
and manage recommendations.

For the first time, DreaMed Advisor’s state-of-the-art
cloud-based technology will provide, in this study, personalized
recommendations on insulin dosage for people with type 1 or type 2 diabetes
treated with insulin pumps or multiple daily injections. The investigational
version of DreaMed Advisor will also manage the virtual presentation of
data  derived from CGM, glucometers, insulin pumps, and connected insulin
pens. Furthermore, the platform supports virtual communications of new
treatment plans as well as a virtual clinic team, which provides behavioral
health coaching to help participants address certain diabetes-related
challenges.

“We are thrilled to be involved in Jaeb’s VDiSC study,” says Eran Agmon, Chief Product Officer of DreaMed Diabetes. “Virtual care is the future of medicine, and the technology is ripe for deployment in diabetes. We are confident in the model and proud that our technology is providing the support necessary to enable its implementation”

AstraZeneca, Eko Collaborate to Advance Innovation Around Heart Failure

FDA Breakthrough Status Granted for Heart Failure Algorithm by Eko

What You Should Know:

– Eko today announced a global collaboration with
AstraZeneca to accelerate the development of digital health tools for the
earlier screening of cardiovascular diseases, including heart failure. 

– Through the collaboration, AstraZeneca and Eko will explore accelerating the development of Eko algorithms, enhancing clinical trials with Eko technology, and potentially building new heart failure detection solutions.


Eko, a digital health company building AI-powered screening and telehealth solutions to fight cardiovascular disease, today announced with AstraZeneca to accelerate the development of digital health tools for the earlier screening of cardiovascular diseases, including heart failure. Through the collaboration, AstraZeneca and Eko will explore accelerating the development of Eko algorithms, enhancing clinical trials with Eko technology, and potentially building new heart failure detection solutions.

Why It Matters

Heart failure is one of the leading causes of morbidity and
mortality, affecting approximately 64 million people worldwide. Heart failure
happens when the heart cannot pump enough blood into the body and is most
commonly detected by echocardiogram imaging tests that are not normally
conducted during a physical exam. Because of the limited access to
echocardiography or other diagnostic tests, heart failure is frequently
diagnosed late, making life-prolonging treatment more challenging. Heart
failure remains as fatal as some of the most common cancers and is the leading
cause of hospitalization for those over the age of 65, representing a
significant clinical and economic burden.

Eko’s AI-Powered telehealth platform for virtual pulmonary
and cardiac exams, providing clinicians within-person level exam capabilities
during video visits. The platform is already deployed by more than 200 health
systems for telehealth, the platform goes beyond standard video conferencing to
facilitate stethoscope audio, ECG live-streaming, and FDA-cleared
identification of atrial fibrillation (AFib) and heart murmurs.

“Eko was founded to provide a better way to understand our heart and lung health and to improve cardiopulmonary care for patients through digital technology and novel algorithms,” said Connor Landgraf, CEO and co-founder of Eko. “Eko’s collaboration with AstraZeneca will allow us to expand the capability of our technology, generate real-world data, and explore disease management solutions while leveraging AstraZeneca’s global expertise and existing relationships across the treatment continuum for heart failure.”

Eko Awarded $2.7M NIH Grant for Heart Murmur & Valvular Heart Disease Detection Algorithms

FDA Breakthrough Status Granted for Heart Failure Algorithm by Eko

What You Should Know:

– The National Institutes of Health (NIH) has granted next-generation
cardiac AI company Eko an award totaling $2.7 million to support continued
collaborative work with Northwestern Medicine Bluhm Cardiovascular Institute

– The grant will focus on validating algorithms and help
more accurately screen for heart murmurs and valvular heart disease during
routine office visits with Northwestern Medicine.

– By incorporating data from tens of thousands of heart
patterns into Eko sensors and algorithms, clinicians will have
cardiologist-level precision in detecting subtle abnormalities from normal
sounds.


Eko, a digital health company
building AI-powered screening
and telehealth solutions to
fight cardiovascular disease, today announced it has been awarded a $2.7
million Small Business Innovation Research (SBIR) grant by the National
Institutes of Health (NIH). The grant will fund the continued collaborative
work with Northwestern Medicine Bluhm Cardiovascular Institute to validate
algorithms that help providers screen for pathologic heart murmurs and valvular
heart disease during routine office visits.

Eko and Northwestern first announced their collaboration in
March 2019 to provide a simpler, lower-cost way for clinicians to identify
patients with heart disease without the use of screening tools such as
echocardiograms which are typically only available at specialty clinics. By
incorporating data from tens of thousands of heart patterns into the
stethoscope and its algorithms, clinicians will have cardiologist-level
precision in detecting subtle abnormalities from normal sounds.

“Cardiovascular disease is the leading cause of death in the U.S., and valvular heart disease often goes undetected because of the challenge of hearing murmurs with traditional stethoscopes, particularly in noisy or busy environments. A highly accurate clinical decision support algorithm that is able to detect and classify valvular heart disease will help improve accuracy of diagnosis and the detection of potential cardiac abnormalities at the earliest possible time, allowing for timely intervention,” said James D. Thomas, MD, director of the Center for Heart Valve Disease at Northwestern Medicine and the clinical study’s principal investigator. “Our work with Eko aspires to extend the auscultatory expertise of cardiologists to more general practitioners to better serve our patient community, playing a pivotal role in growing the future of cardiovascular medicine.”

Recent FDA Clearance and Telehealth Platform Launch

This recognition comes on the heels of several key company
milestones, including the clearance
of Eko’s cardiac AI algorithms by the U.S. Food and Drug Administration and the
launch
of Eko’s AI-powered telehealth
platform. Eko’s ECG-based deep learning algorithm, developed on a large
clinical dataset in collaboration with the Mayo Clinic, can help efficiently
identify signs of possible heart failure in patients.

Eko’s AI-Powered telehealth platform for virtual pulmonary and cardiac exams, providing clinicians within-person level exam capabilities during video visits. The platform is already deployed by more than 200 health systems for telehealth, the platform goes beyond standard video conferencing to facilitate stethoscope audio, ECG live-streaming, and FDA-cleared identification of atrial fibrillation (AFib) and heart murmurs.

Virtually or In-Person, Automation Improves The Healthcare Experience

Virtually or In-Person, Automation Improves The Healthcare Experience
Muthu Alagappan, MD, Medical Director at Notable Health

The COVID-19 pandemic has caused an unprecedented shift in the way consumers view and access a variety of goods and services—and healthcare is no exception. Recent studies show that many patients, including vulnerable populations like those living with cancer, are delaying recommended care and procedures—and will continue to do so for at least several months amid fears over the safety of in-person visits. In response, reports of providers adapting to offer care virtually are all the more commonplace, with almost half of physicians now treating patients through telemedicine platforms, up from just 18 percent in 2018.

These trends have solidified virtual care as a mainstay, and as a result, the virtual visit has become a commodity—a service that can be provided by many capable vendors. However, the logistics that power the adoption of virtual care are often overlooked. As healthcare administrators turn to telemedicine to resume “non-urgent” healthcare services, we must ensure that best-in-class technology solutions are utilized to improve the virtual care experience—for providers, clinical staff, and, importantly, patients.

Health systems and their networks face significant operational issues when delivering care in a remote setting, due to the range of potential interactions and diversity of devices—adding to the already recognized administrative burden that comes with routine patient care. With each patient visit comes over a dozen manual tasks, including patient intake and registration, in-visit clinical note writing, as well as back-office billing and claims processing. The virtual visit adds even more steps, such as helping patients access the appropriate technology for a two-way video interface or sending custom links to a “virtual waiting room” at the right time.

Facilitating a seamless virtual care experience before, during, and after a patient’s visit should be top-of-mind—particularly as patient expectations have heightened and healthcare has progressed towards a technology-enabled future. Fortunately, the automation of operational workflows can help healthcare administrators smooth the friction around conducting virtual visits at scale.

Intelligent automation extends our capacity in healthcare by enabling us to do more with the same workforce and technology infrastructure. In fact, digital medical assistants can use artificial intelligence to automate repetitive, cognitively tiring, and error-prone tasks. This technology can support the influx of virtual visits by offloading administrative processes, such as co-payment collection, clinical documentation, and pre-population of common clinical orders. 

For patients not as familiar with digital interactions and the variety of telemedicine modalities, which can include platforms like Amwell, Doctor on Demand, and Teladoc or video conference solutions like RingCentral and Zoom, participating in virtual visits can be a daunting change. Additional technological challenges associated with virtual care can result in heightened frustration, increased no-show rates, or decreased activation, so maintaining patient engagement throughout the patient journey is even more important in a virtual environment. Digital medical assistants can automate appointment reminders, offer detailed setup guidance for patients, and provide “just-in-time” virtual visit links to ensure patients and providers can make the most of their time together.

The COVID-19 pandemic has also introduced new variables and risks that patients, providers, and healthcare institutions at-large must consider when seeking and delivering care. Until recently, it was a relatively straightforward process to determine where a patient should receive routine care. Now given the risk of disease spread, providers find themselves considering which patients to see when to see them and whether to see them virtually or in-person.

This creates additional complexity in determining when to schedule patients and in which medium to conduct the visit. Platforms that leverage intelligent automation can help clinical teams to pre-screen all scheduled patients, collect a thorough medical history, intelligently segment patients into risk cohorts and triage each cohort to an individualized destination, be it a return to in-person care or a virtual environment.

In the “virtual exam room,” things also look a little different. From the provider’s perspective, one of the oft-cited drawbacks of virtual visits is the limited ability to measure vital signs, perform a physical exam or order point-of-care diagnostics. At-home diagnostics, wearable devices and remote patient monitoring tools allow providers to collect continuous clinical data that can be gathered asynchronously and quickly, resulting in a more comprehensive picture of a patient’s health. Further, platforms that use intelligent automation algorithms to organize data collected across the care continuum can parse these data streams to identify at-risk patients and then automate outreach and care management to follow clinical care pathways.

The COVID-19 pandemic has given us a unique opportunity to reimagine healthcare using a modern suite of technology for patients, providers and staff that does away with outdated and inefficient processes. But we also have a responsibility to replace them with solutions that improve digital experiences by supporting patients before visits, automating repetitive workflows, and parsing large amounts of data to support clinical decision-making.

Combining intelligent automation with virtual visits creates a powerful tool to efficiently manage patient populations and offer an experience that feels intuitive while enabling healthcare systems to do more with less. By accelerating the digital transformation of healthcare today, we can position ourselves for a future of increased capacity, decreased overhead, and improved quality.


Muthu Alagappan, MD, is an attending physician at Massachusetts General Hospital, a trained engineer, and medical director at Notable Health, a healthcare experience automation company. 

COVID-19: 4 Essential Patient Payment Strategy Components to Accelerate Cash Flow

Accelerating Cash Flow Amid COVID-19
David Shelton, PatientMatters CEO

In the past few months, the COVID-19 pandemic has shaken societies, economies, and human wellbeing to the core. While protecting public health and welfare are top priorities for hospitals, the harsh reality is that it takes cash to keep the doors open and serve patients effectively. Revenue is down significantly as a result of canceled elective surgeries, while the costs of medical supplies and in-demand personal protective equipment for workers have skyrocketed. Hospitals’ operating challenges are expected to continue, with Moody’s Investors Service predicting cash flow will remain low into next year.

Further exacerbating hospitals’ financial woes is the rise in coronavirus-related unemployment and part-time employment, and the subsequent loss of patients’ job-based health insurance. The Bureau of Labor Statistics reported that unemployment fell 2.2 percentage points to 11.1 percent in June, as businesses began reopening, however, even with this bit of good news, nearly 18 million people in America are still unemployed. Many more face financial uncertainty as regional spikes in virus cases threaten to slow rehiring and a return to normal. 

As consumer income goes down and debt goes up, many utility companies, auto lenders, credit card issuers, and mortgage holders are offering debt relief options to their customers. On the flip side, other organizations, including some hospitals, have attracted attention for the aggressive collection of unpaid bills, prompting several states to limit actions such as suing, coercive payment plans, and wage garnishment during the pandemic. Critics of these practices say there are better ways for hospitals to collect unpaid debt, especially when patients are dealing with the unprecedented financial and emotional stress caused by COVID-19. 

A Better Approach to Patient Collections

Experience shows that a personalized, patient-friendly approach to the financial side of healthcare yields better results. Hospitals that create a positive patient financial experience often see higher front-end collections, total collections, and patient satisfaction; and lower accounts receivable (AR) days and bad debt. 

To be most effective, personalized patient payment strategies must be comprehensive and incorporate four essential components to balance patient needs with hospital revenue goals: 1) data-driven technology, 2) customizable workflows, 3) staff training and 4) ongoing analytics. Considering the urgent need for hospitals to accelerate cash flow amid the pandemic, payment strategy implementation should also be done quickly and without detracting from other operational and clinical priorities.   

Essential component #1: Data-driven technology

A truly personalized payment solution relies on providing accurate bill estimates and determining patients’ ability and likelihood to pay prior to care. Advanced tools use current financial data and algorithms to assign scores based on credit information, payment history, and residual income. These results help registration staff understand each patient’s unique character traits so they can quickly identify and accurately explain personalized payment options to help patients meet their financial responsibilities.

Essential Component #2: Customizable Workflows

Payment solution technology cannot deliver results unless it is seamlessly integrated into existing hospital systems. Key functions from registration and bill estimation to payment planning and billing should be custom designed to create unified workflows for staff and streamlined experiences for patients.  

Essential Component #3: Staff Training

Talking about financial obligations can be the most confusing, frustrating, and stressful part of healthcare, for patients as well as registration staff. Scripting and guidance on how to tailor conversations to individual circumstances can increase patient satisfaction and trust, improve staff’s job satisfaction and productivity, and reduce staff turnover.

Essential Component #4: Ongoing Analytics

Cash flow will continue to be a challenge for hospitals long after the current COVID-19 crisis is past. Patient payment strategies should provide reporting and dashboards that allow leaders to monitor and manage staff and collection opportunities by shift, registrar, and other custom parameters. Disposition reports should show productivity and performance to ensure high-performance teams and optimum results over the long term.     

Positive Outcomes

Personalized patient payment strategies have been proven to increase collections by guiding patients through the financial maze and offering realistic ways to meet financial obligations. In the current landscape of record low margins for hospitals and extraordinary financial hardship for patients, these solutions provide a path toward increased revenue, higher up-front collections, lower bad debt, and improved patient satisfaction and peace of mind.  


About David Shelton

David Shelton serves as Chief Executive Officer for PatientMatters. He has served in senior healthcare management for more than 15 years, with experience in operations, technology development, and manufacturing. His expertise includes delivering business growth, streamlining operational management, and generating profitability for PatientMatters and its healthcare clients.


Cohere Health Launches with $10M to Increase Transparency Across Care Journey

Cohere Health Launches with $10M to Increase Transparency Across Care Journey

What You Should Know:

– Boston-based health IT start-up Cohere Health announced the official launch of its company with a $10 million Series A funding round led by Flare Capital Partners.

– The company’s patient journey-focused platform improves the notoriously difficult prior authorization process and replaces an existing patchwork of legacy, siloed processes, and antiquated technologies that contribute to the enormous administrative burden for physicians and health plans.

– The end goal is transparent, high-value care alignment across the entire patient care journey, to improve the quality of care delivered, the lower total cost of care, and transform the patient and physician experience.


Cohere Health, a
Boston, MA-based health
IT
startup,
today announced its company launch, with the mission of aligning the
relationship between physicians and health plans around the care journey in a
way that is appropriate for each patient. Cohere Health’s launch is
flanked by the news of its recent $10 million Series A funding round led by Flare Capital Partners, with Define Ventures as an investor and partner
as well as participation from an additional leading national strategic partner.

Every Health Journey Should Be A Win-Win-Win

Confusion and complexity shouldn’t define a patient’s journey. Patients deserve clearer paths to health, more transparency, and fewer bumps on the way. Assessing every transaction without any context creates an undue burden for physicians and health plans alike. Administrative complexity should never stand in the way of patient care.

Led by Co-Founder and CEO Siva Namasivayam, Cohere Health aims to improve the quality of care delivered, the lower total cost of care, and transform the patient and physician experience. Cohere’s patient journey platform, CohereNext replaces the existing patchwork of legacy, siloed processes, and antiquated technologies that contribute to enormous administrative burden for physicians and health plans. It further facilitates the transition from fee-based services to value-based arrangements by providing an evolutionary path that can support all payment arrangements while reducing unnecessary variation in clinical outcomes.

Benefits of Care Journey Recommendations

Cohere leverages care journey recommendations to fundamentally change the healthcare system through:

Evidence-based care paths: increase transparency and trust among patients and their physicians.

Advanced analytics and rules: help identify high-value care and, ultimately, improved outcomes

Payments and incentives: incentivize physicians by ensuring that behavior leading to optimal patient outcomes also benefits practice economics

Proven Leadership Team

Cohere Health has assembled an impressive team of proven
leaders, including Gary Gottlieb, MD, former CEO of Partners Healthcare (now
MassGeneral Brigham) as Chairperson of the Board of Directors. “As a physician,
it’s clear to me that the administrative complexity of the current system gets
in the way of delivering the care that patients deserve, that physicians want
to provide, and that health plans want for their members,” Gottlieb said. “If
we’re going to realize the promise of value-based care, we need an approach
that is based on all available evidence, shared expectations and transparency.”

Siva Namasivayam, CEO of Cohere Health, explained: “The current system is resource-intensive, unwieldy and creates a frustrating experience for patients, physicians and health plans. There’s no reason it should be so miserable for everyone involved. We have the evidence-based clinical algorithms, human-centered design and innovative technology to improve the system dramatically.” Namasivayam, who has more than 20 years of experience working on transformational healthcare businesses, was previously the CEO/Founder of SCIO Health Analytics.