This year, we are joining healthcare executives and companies, along with our partner NVIDIA, in the first virtual HLTH conference. Ouva and NVIDIA has been working together to create one of the most advanced automated care on top of NVIDIA's recently announced Clara Guardian platform.

The new conference format is not overshadowed by 2019, where with over 300 speakers, it was one the industry’s largest events. This year, HLTH gathers nearly the same number and quality of speakers and attendees, but with even more opportunities to learn and network, thanks to the virtual format.

The Need for Virtual Care

The reasons why HLTH 2020 is virtual are the same reasons why we’ve launched the Ouva Care Automation Platform. Amidst the COVID-19 pandemic, we are witnessing the immediate shift towards virtual care with the adoption of remote monitoring and telehealth solutions allowing providers to care for patients from their homes as well as hospital rooms and doctors’ offices. These systems allow caregivers to proactively monitor the condition of their patients, take precautionary measures, and act upon in case of an emergency or change in their condition. Smart wearables such as watches, insulin monitors, and voice assistants reminding patients to take their pills are the most commonly used tools today by patients with chronic conditions. Thus, hospitals started investing in such smart solutions to provide quality, cost-effective inpatient care with fewer resources in an efficient manner.

The prospect of virtual care brings hope to an otherwise grim outlook. The population is aging very rapidly. By 2035, there will be 78.0 million people 65 years and older compared to 76.7 million (previously 76.4 million) under the age of 18. There will be far more demand for healthcare services from patients with chronic health conditions. Consequently, healthcare costs will increase due to staff shortages and longer stays. Healthcare spending is expected to hit $5 billion by 2025, according to the U.S. Census Bureau and the Centers for Medicare and Medicaid Services.

Predictive and Preventative Care with AI

Hospitals of the future will be able to monitor the entire care process via smart IoT sensors at all times to predict and prevent costly issues, delays and medical errors that negatively impact the wellbeing of patients and healthcare workers and efficiency of care. In a way, hospital systems should have their own centralized artificial intelligence that is capable of making these informed decisions on the fly that are critical for care delivery based on historical and real-time data. In order to do so, they need to have the proper machine learning tools and infrastructure. Soon these systems will recommend custom tailored treatment plans based on the insights coming from the past patient outcomes with similar demographics and health conditions. 

Today, innovative healthcare systems typically deploy many smart independent systems and sensors. Each comes with their own dashboard and alert mechanisms hence, making it very difficult for hospital staff to maintain and keep up with. Alarm fatigue is real! These systems are there to assist caregivers yet in many cases, they add more effort and stress to their daily workflows. Patient care should be fully autonomous which can only be accomplished through the full interoperability of all smart systems. 

Our vision at Ouva is to integrate with all data sources coming from sensors and EHR (Electronic Health Record) systems to have full visibility into patient care and make a precise assessment of the patient’s condition as well as ensuring the effective care continuum. Predicting potential surges, safety &  infection hazards, and patient deterioration and delays between care stages to improve the quality and efficacy of care. Similarly, to make a car fully autonomous, companies today intake data coming from various data sources such as cameras, radar, and GPS. With more quality, diverse data the accuracy of predictions will rise.

Yes AI is the future, however, are the hospital systems ready for this transformation?