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HEALTHCARE | Medical Devices & Equipment / Patient Monitoring
physiq.com

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Founded Year

2011

Stage

Loan | Alive

Total Raised

$27.09M

Last Raised

$680K | 2 yrs ago

About PhysIQ

PhysIQ enables proactive care delivery models through its proprietary, FDA-cleared Personalized Physiology Analytics technology. PhysIQ offers a platform designed to process multiple vital signs from biosensors to create a personalized dynamic baseline for each individual. By mapping vital sign relationships, physIQ's analytics detect subtle deviations that may be a precursor to disease exacerbation or change in health.

PhysIQ Headquarter Location

200 W Jackson Suite 550

Chicago, Illinois, 60606,

United States

800-516-7902

Latest PhysIQ News

Newly Published physIQ and UI Health Study on the Efficacy of Novel COVID-19 Decompensation Index

Dec 8, 2021

NIH-funded DeCODe clinical study evaluates FDA-cleared AI analytics platform (pinpointIQTM) and a wearable biosensor to remotely monitor patients with COVID-19 First-in-Kind COVID-19 Decompensation Index (CDI) identifies early signs of worsening health with a lower false alarm rate compared to standard-of-care modalities Findings suggests the potential for wearable sensor-based analytics to detect exacerbation of infectious disease December 08, 2021 08:03 AM Eastern Standard Time CHICAGO--( BUSINESS WIRE )--A leader in digital medicine, physIQ announced the publication of a peer-reviewed paper describing the performance of a novel COVID-19 Decompensation Index (CDI) model in Phase I of a two-Phase NIH supported clinical study. The Wearable Sensor Derived Decompensation Index for Continuous Remote Monitoring of COVID-19 Diagnosed Patients article was published in npj Digital Medicine this week. The DeCODe study proposed to develop and test a novel machine-learning CDI based on chest patch-derived continuous biosensor data in patients diagnosed with COVID-19. Performance of the physIQ CDI was retrospectively compared to standard of care (SOC) modalities to detect COVID-19 exacerbation. The study demonstrated that the CDI outperformed SOC modalities with a lower false alarm rate. “Traditional remote monitoring systems involve `spot-check' measurements taken by the patient, at most only a few times a day, which could lead to missing subtle and emerging indications of worsening health,” said Stephan Wegerich, physIQ Chief Science Officer. “At physIQ, we integrate artificial intelligence with wearable biosensing technology to enable continuous monitoring of patients during activities of daily living. These data provide rapid and continuous insights into physiology that may be impossible to gain from intermittent measurements. We have collected millions of hours of continuous biosensor data that our team has used with our CDI algorithms to detect early signs of possible decompensation due to COVID-19. We anticipate this approach may be applicable to other infectious diseases, as well.” The DeCODe study was funded by the National Cancer Institute of the National Institutes of Health (NIH) in support of the Health and Human Services and NIH Digital Health Solutions for COVID-19 initiative. physIQ was awarded an NIH contract to develop an artificial intelligence (AI)-based CDI digital biomarker to detect a rapid decline of COVID-19 patients as well as to contribute to a digital data hub to support further research on COVID-19. The Phase I study enrolled 400 adults from the University of Illinois Health System (UI Health) diagnosed with COVID-19 in largely underserved Chicago areas. “We purposely collaborated with UI Health because of their mission to engage diverse communities. To that end, we were able to build this index using data from a truly representative population including Hispanic and non-Hispanic Black individuals who are the hardest hit by COVID-19,” Karen Larimer PhD, ACNP-BC, Study Principal Investigator, explained. physIQ’s FDA-cleared AI analytics platform ( pinpointIQ™ ) was used in the remote monitoring of COVID-positive study patients. The pinpointIQ solution continuously monitors a patient’s vital signs through a wearable biosensor, applies artificial intelligence analytics to the sensor data and alerts clinicians of any relevant physiologic changes. Clinicians review these signals and determine whether intervention is required. “Up to one in five people who are infected with COVID-19 are at risk for severe worsening of the illness resulting in hospitalization. Novel digital health technologies hold the potential to expand our understanding of COVID-19, improve care delivery and produce better health outcomes. The Phase I results of the DeCODe study present a highly promising application of AI-driven personalized analytics within a continuous remote patient monitoring system,” added study co-author Dr. Terry Vanden Hoek, Chief Medical Officer at UI Health and Head of the Department of Emergency Medicine at the College of Medicine. For the full study results published in npj Digital Medicine, visit: https://rdcu.be/cA0tj . ClinicalTrials.gov Identifier: NCT04575532 The pinpointIQ system continues to monitor patients in health systems across the country. An additional study of 1,000 patients has been completed and results are forthcoming. About physIQ PhysIQ is the leader in digital medicine, dedicated to generating unprecedented health insight using continuous wearable biosensor data and advanced analytics. Its industry leading enterprise-ready cloud platform continuously collects and processes data from any wearable biosensor using a deep portfolio of FDA-cleared analytics. The company has published one of the most rigorous clinical studies to date in digital medicine and are pioneers in developing, validating and achieving regulatory approval of artificial intelligence-based analytics. With applications in both clinical trial support and healthcare, physIQ is transforming continuous physiological data into insight for health systems, payers and pharmaceutical companies. For more information, please visit www.physIQ.com . Follow us on Twitter and LinkedIn . Contacts

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Research containing PhysIQ

Get data-driven expert analysis from the CB Insights Intelligence Unit.

CB Insights Intelligence Analysts have mentioned PhysIQ in 4 CB Insights research briefs, most recently on Dec 23, 2020.

Expert Collections containing PhysIQ

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

PhysIQ is included in 8 Expert Collections, including Internet of Things ( IoT ).

I

Internet of Things ( IoT )

3,149 items

A

Artificial Intelligence

8,317 items

This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.

M

Medical Devices

11,176 items

Companies developing medical devices (per the IMDRF's definition of "medical device"). Includes software, lab-developed tests (LDTs), and combination products. *Columns updated as regularly as possible.

C

Conference Exhibitors

5,302 items

D

Digital Health

12,254 items

Technologies, platforms, and systems that engage consumers for lifestyle, wellness, or health-related purposes; capture, store, or transmit health data; and/or support life science and clinical operations. (DiME, DTA, HealthXL, & NODE.Health)

T

Telehealth

2,732 items

Companies developing, offering, or using electronic and telecommunication technologies to facilitate the delivery of health & wellness services from a distance. *Columns updated as regularly as possible; priority given to companies with the most and/or most recent funding.

PhysIQ Patents

PhysIQ has filed 4 patents.

The 3 most popular patent topics include:

  • Artificial intelligence
  • Artificial neural networks
  • Exercise physiology
patents chart

Application Date

Grant Date

Title

Related Topics

Status

7/25/2017

7/28/2020

Artificial neural networks, Machine learning, Medical terminology, Health informatics, Artificial intelligence

Grant

Application Date

7/25/2017

Grant Date

7/28/2020

Title

Related Topics

Artificial neural networks, Machine learning, Medical terminology, Health informatics, Artificial intelligence

Status

Grant

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