In this story from HealthIT Analytics, how PCCI is using data analytics technologies to identify at-risk individuals and alleviate health disparities during the COVID-19 pandemic is examined. Please click on the image below to read the whole story.
Steve Miff, president and chief executive officer of Parkland Center for Clinical Innovation, shares how to use data analytics to identify at-risk individuals. He describes how the center’s proximity risk index can help physicians decide when to direct patients to telehealth, identify patients facing social determinants of health and health disparities, and provide evidence to guide policy measures.
Click on the image below to listen to the podcast for healthcare professionals seeking solutions to today’s and tomorrow’s top challenges.
In Healthcare IT News, the media arm of HIMSS, an article about how the data science and clinical teams at PCCI, in collaboration with Parkland informaticists, have developed an AI-driven predictive model that predicts for individual COVID-19 exposure risk, based on population density and their proximity to positive cases. Click on the image below to read the entire story:
PCCI CEO, Steve Miff has been included in a HealthLeaders story based on his participation in its executive round table. The story, “9 STRATEGIC INSIGHTS INTO DEVELOPING THE HEALTHCARE SYSTEM OF THE FUTURE,” includes comments from top healthcare leaders from around the country on strategies related to payment, re-imagining models of care, applying real-time data, and addressing social determinants of health. Click on the image below to read the full story:
To learn more about PCCI’s experts or to inquire about its healthcare technology leaders presenting to your organization virtually, please contact PCCI HERE.
During 2019, the team at PCCI and was recognized for its results, leadership and community participation, earning several awards, nominations and important appointments. The recognitions include:
Among its accomplishments this year, was how PCCI’s predictive modeling helped to prevent adverse drug events. See how this was applied at Healthcare IT News.
Among its accomplishments this year, PCCI reported how its predictive modeling helped prevent preterm births. See how this was applied at D CEO Healthcare.
Sepsis opens in a new window is a word that can evoke feelings of dread for most people and yet, many fail to understand its meaning and implications fully. It is a life-threatening condition that arises when the body’s response to infection results in organ dysfunction or failure. It is one of the leading causes of deaths with 6 to 9 million deaths per year worldwide and 250,000 deaths per year in the United States. To make matters worse, sepsis is also one of the leading causes of preventable deaths worldwide. The key to prevention is early detection since every hour of delay increases the odds of death by 20%. However, sepsis can be very hard to detect especially in its early stages when it is reversible. The symptoms often point to other conditions and patients can deteriorate rapidly. Traditional risk models such as SIRS criteria to detect sepsis generate a lot of false positives leading to inefficient or ineffective care and nursing fatigue.
Due to PCCI’s close collaboration opens in a new window with Parkland Health & Hospital System, we recognized sepsis detection and prevention as an area that could benefit from an advanced machine learning-based algorithm. To be effective, the model needed to fulfill the following criteria:
PCCI developed a predictive model to fulfill the criteria identified as effective. This real-time model is designed to predict an individual’s risk of becoming septic within the next 12 hours.
Instead of relying on traditional ways of detecting the risk of sepsis through a handful of physiologic variables, we cast our net wide and started out with approximately 120 variables as potential predictors, such as socio-demographic, vital signs, co-morbidities, hospital utilization, medical conditions, clinical history, and lab results.
We trained and tested a Lasso Logistic Regression model, a decision tree and a neural net on 27 months-worth of Inpatient encounters from Parkland. Initial data sets included 54,629 encounters and 30,922 patients. We chose the Lasso Logistic Regression model as the final model because of its high performance and interpretability by clinical users. The resulting model that compared favorably to other community models resulted in:
A model is useful only if it is actionable and available at point of care. PCCI’s Sepsis model is incorporated into clinical workflows through industry-standard APIs. The model accesses EHR data in real-time every five minutes and alerts the clinician if the risk is above a certain threshold. The risk threshold can be tailored to local populations and clinical best practices of an organization. The alerting mechanism can vary based on EHR functionality, but it can include a simple alert, queuing up of order sets based on the risk threshold, and sending a real-time alert to a secure mobile device or pager for a member of the rapid response team.
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