Inpatient complications and their consequences cost over $88 billion each year, which averages about $14 million per hospital per year. Medical errors alone cost over $20 billion each year and represent the third leading cause of death in the US. These adverse events occur when flaws inherent to the system of care delivery predispose individuals to mistakes. For example, doctors and nurses are routinely overworked, exhausted, inundated with alarms, overwhelmed by information from health records, and pressured to see more patients in less time.
The current industry standards for risk assessment and mitigation in healthcare do not address unreported adverse events or inherent yet unrealized risks. Although numerous institutions collect and manipulate “big data” related to risk, no clinician can keep pace with the constant alarms, perpetually-evolving guidelines, and complex datasets. Furthermore, patients undergoing the same procedures can experience different risks. Similarly, workflow and resource availability can vary drastically across care facilities.
Presaj establishes a new standard for personalized risk assessment in healthcare by using machine learning and subject-matter expertise to identify methods of avoiding surgical and medical risks in real time. The Presaj system quantifies the risk at each step in care using the best-practices from engineering and customizes this risk data for each patient and each care facility to reflect their unique needs and risk profiles. The system presents this process-level data to providers at the point of care in order to focus their attention on the high-risk steps for each patient. Presaj can evaluate risk in high-impact surgical procedures and medical conditions such as COVID-19.
Presaj uses machine learning to optimize workflow at the process level by combining patient and facility data with national, state, institutional, and actuarial datasets. While large datasets exist for most procedures, they provide only high-level conclusions regarding the impact of patient factors on risk and do not pinpoint the specific steps in each care process with high risk, which is the essence of the Presaj system. By enhancing situational awareness, Presaj leverages the clinical judgment of providers to minimize distractions, oversights, miscommunications, and overall risk. Presaj represents the first attempt to use machine learning in this way.
Copyright © 2021 Presaj - All Rights Reserved.
Powered by GoDaddy
We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.