Focusing attention to prevent complications

Presaj is an Iowa-based IT innovation leader that specializes in creating personalized risk data for healthcare providers by combining artificial intelligence and machine learning with engineering principles and clinical expertise to prevent patient safety-related adverse events such as complications and errors.
Presaj augments healthcare’s existing standardized, outcome-oriented tools with cost-effective customized, process-oriented tools.
We use techniques from high-risk industries (e.g., aerospace, nuclear power) to analyze all the factors that contribute to patient risk—from medical history (e.g., diabetes) and provider fatigue to understaffing and social determinants of health—and identify the specific care processes/steps with the most risk of leading to adverse events for each patient at the point of care.

Nearly 25% of all U.S. hospitalizations involve an adverse event, totaling over 58 million incidents annually and incurring costs of nearly $170 billion. Alarmingly, up to 70% of these adverse events are deemed avoidable.
Despite the use of evidence-based practices for risk mitigation (e.g., risk calculators, databases, root cause analyses, checklists, patient safety huddles), the frequency of avoidable adverse events continues to increase.
Record rates of burnout, staff turnover, and financial challenges for hospitals amplify the urgent need for new patient safety solutions. Doctors and nurses need a better way to proactively identify the risks for each patient before harm occurs. To address this need, we're developing a system that seamlessly incorporates patient safety into daily clinical workflow.

Patient risk cannot be assessed using only patient risk factors—they must be combined with an understanding of the patient experience at the point of care, which reveals risks created by human factors, staffing, workflow, system conditions, and social determinants of health.
Presaj uses artificial intelligence to analyze all the factors that contribute to patient risk and their interactions for common medical and surgical procedures.
The output is a set of patient-specific risk scores that indicates the risk of an adverse event at each step in care.
These data are available to providers in all care settings at the beginning of the care episode
By focusing the attention of doctors and nurses on the steps with the most risk, our system reduces information overload and prevents adverse events before they happen.