In the News: PCCI Data Scientist Talks With Healthcare IT News About Sepsis Prevention
Yusuf Tamer, PhD, principal data and applied scientist at the Parkland Center for Clinical Innovation, offers a sneak preview of his HIMSS24 session, which offers a detailed look at one of artificial intelligence’s most promising use cases.
Suicide, a lack of psychiatric bed capacity, lack of access to psychiatry services, and other challenges are all too real in today’s healthcare environment. Using technology, however, to facilitate rapid diagnosis, treatment, and identification of resources is a welcomed relief to patients and clinicians alike. With a focus on various technologies, attendees will realize the difference that technology makes in the lives of patients and their families.
SPEAKERS:
Jacqueline Naeem, MD, Senior Medical Director at PCCI
While 80-85 percent of sepsis cases present within the first 48 hours of admission (ED), they have lower mortality (5-10 percent) as compared to 15-20 percent of cases that present later and have higher mortality (15-30 percent). To better (and earlier) identify sepsis cases not present on admission, at a large safety-net hospital, an end-to-end early sepsis prediction and response workflow was created in the inpatient setting. First, an ML model was built to predict the risk of a patient becoming septic in real-time. Next, the model baked into clinical workflows through FHIR APIs to make the model actionable at point of care. The model accesses EMR every 15 minutes and alerts the care providers when the risk exceeds a certain threshold, which can be tailored to local populations. Finally, an EHR-integrated decision support app (ISLET) was added to enable clinicians to easily view and understand model output to improve actionability. Prediction, alerting, visualizing the root causes and acting on the case completes the workflow. This full workflow has been running for thousands of patients every 15 minutes in the last year. This session will focus on the challenges, achievements and impact of this workflow on healthcare outcomes.
SPEAKERS:
George Oliver, MD, PhD, Vice President, Clinical Informatics at PCCI
Nainesh Shah, Assistant Professor, Health Informatician at Parkland Health
Yusuf Tamer, Principal Data and Applied Scientist at PCCI
The presenters built an AI- ML-driven pediatric asthma surveillance system (PASS) to monitor the clinical and social risk of pediatric asthma at the census tract level in Dallas County. First, they developed a novel AI/ML pediatric asthma risk index, combining clinical and social risk factors from multiple data sources to accurately predict census-tract risk of asthma-related emergency department visits and hospitalizations. Subsequent analyses identified actionable risk drivers which, combined with the novel asthma risk index, painted a wholesome, countywide picture of pediatric asthma risk disparities. PASS is an interactive, community-facing dashboard that maps and compares the distribution of the asthma risk index and other risk drivers across Dallas County. PASS is hosted on the Dallas County Health and Human Services website and is readily accessible to community stakeholders. Launched in January 2023, PASS was introduced to the community through training sessions and dissemination events to engage key stakeholders. PASS is being leveraged to advance health equity through diverse use cases ranging from environmental advocacy to city planning, clinical resources deployment, school-based interventions and corporate social responsibility. Lessons learned from PASS provide a blueprint for other scalable AI/ML-driven chronic disease surveillance systems such as diabetes and hypertension.
SPEAKERS:
Teresita Oaks, Director, Community Health Programs at Parkland Health
Yolande Pengetnze, MD, Vice President, Clinical Leadership at PCCI
Yusuf Tamer, PhD, Principal Data and Applied Scientist at PCCI
Occupational Safety and Health Administration (OSHA) defines workplace violence (WPV) as any act or threat of physical violence, harassment, intimidation or other threatening disruptive behavior occurring at work. Healthcare and social service workers are five times more likely to be injured than other workers and WPV rates continue to rise. Due in part to poor reporting systems, and the common misperception that violent events should be expected while working in healthcare, prevention measures rarely match the issue’s severity and often go unreported. This session will focus on efforts, in a large safety-net hospital, to address an important gap impacting WPV prevention efforts through the development of a predictive model to more accurately identify―in an inpatient healthcare setting―potentially violent patients, thus enabling healthcare workers to mitigate risks of impending WPV incidents.
SPEAKERS:
Karen Garvey, Vice President, Safety and Clinical Risk Management at Parkland Health
Alex Treacher, PhD, Senior Data and Applied Scientist at PCCI
Reshma Suresh, MS/MA, Data and Applied Scientist at PCCI
U.S. suicide rates increased by 27.6 percent over the past 15 years and suicide remains a leading U.S. cause of death, with 48,183 deaths in 2021, according to the CDC’s National Center for Health Statistics and the United Health Foundation. Development of evidence-based practices has dramatically increased over the past 20 years; however, suicide rates continue to increase in part due to broad variability in adoption of, and consistent adherence to, suicide prevention practices. As a vital first step in understanding suicide prevention from a population health perspective, and improving risk recognition for treatment application, a large safety-net hospital implemented a universal suicide screening program (SSP) in 2015, in which all patients ages 10 and older are screened for suicide risk during every provider encounter. This session will determine if the SSP reduces the number of patients falsely identified as not at risk of death by suicide in our cohort by linking mortality data to healthcare utilization data from five years pre- and post- SSP implementation. Despite suicide being a relatively low base-rate event (13-14/100,000 in the U.S.), the massive dataset size provides enough power for statistically meaningful changes to be detected.
SPEAKERS:
Jacqueline Naeem, MD, Senior Medical Director at PCCI
Alex Treacher, PhD, Senior Data and Applied Scientist at PCCI
This Spring, HIMSS (Healthcare Information and Management Systems Society), a global thought leader and member-based society committed to reforming the global health ecosystem with information and technology, offered several platforms for PCCI’s experts to share their innovative programs with thousands of healthcare IT professionals.
PCCI in collaboration with leaders from Parkland Health, Parkland Community Health Plan and Dallas County, have delivered eight presentations, four at the national HIMSS conference in Chicago and four at the Texas HIMSS regional conference in Dallas. These eight presentations highlight the impactful and diverse applications of artificial intelligence and social determinants of health in clinical and populations health programs.
These presentations include:
HIMSS Annual National Convention:
“ML-AI-Driven Community Coalition, Digital Outreach Improves Asthma Among Low-Income Children”
Teresita Oaks, MPH, Director of Community Health Programs, Parkland Health
Description: PCCI partnered with Parkland Health to develop a Machine Learning/Artificial Intelligence asthma risk prediction model, using diverse social and clinical data sources to identify rising risk for asthma-related ED visits/hospitalizations among low-income children with asthma. Monthly risk-reports were leveraged to develop a multidisciplinary coalition across six high-asthma-morbidity Dallas County zip codes, for communitywide interventions.
“Using Data-Driven Insights to Prioritize Programs Addressing Social Vulnerabilities”
Natasha Goburdhun, MS, MPH, Vice President, Connected Communities of Care, PCCI
Paula Turicchi, FACHE, Chief Strategy Officer at Parkland Community Health Plan
Description: Despite the increasing awareness of the importance of social determinants of health (SDoH) and their influence on health outcomes, few organizations have adequate, contextualized insights to better address interrelated, social needs across a community. This presentation demonstrated how the Parkland Community Health Plan leveraged a three-tiered strategic analysis of SDoH and patient-level data to identify and prioritize programs to meet their members’ social needs.
“Integrating Universal Suicide Screening in EMR Improving Detection of Risk”
Jacqueline Naeem, MD, Senior Medical Director/Program Director AHC, PCCI
Kim Roaten, PhD., UT Southwestern Medical Center
Description:
In 2015, a universal suicide screening program was implemented at Parkland Health in which all patients 10 and older are screened for suicide risk during every provider encounter. Analysis was completed on over 3 million unique patients to understand the distribution of levels of risk in the population, as well as insights around the impact of the pandemic on patients identified with suicidal ideation.
“Patient Segmentation and Clustering Using ML to Develop Holistic, Patient Programs and Treatment Plans”
Yusuf Tamer, PhD, Senior Data and Applied Scientist, PCCI
Albert Karam, MS, MBA, Vice President, Data Strategy Analytics, PCCI
Brett Moran, MD, Chief Medical Informatics Officer, Parkland Health
Description:
In close collaboration with Parkland, PCCI developed a novel, advanced analytics process called Know-Thy-Patient (KTP) to group patients other than by their primary disease or diagnoses (e.g., diabetes, hypertension). By integrating and analyzing metrics associated with barriers to health care access — social vulnerabilities, transportation barriers, lack of insurance coverage — into the clinical context, health strategies adopting these cohort-similarity approaches can more readily incorporate a wider variety of patient-centered, whole-person approaches to care, such as integrated practice units, targeted digital programs, virtual and in-person support groups, and focused outreach and communication.
HIMSS Texas Regional Conference
“AI-Driven Interventions Improve Preterm-Birth Among Medicaid Pregnant Members” PCCI’s Yolande Pengetnze, MD, VP Clinical Leadership joins Amrita Waingankar, MD, MBA, Senior Medical Director at Parkland Community Health Plan to share how they successfully implemented preterm birth prevention program in Dallas.
“Predicting Mortality of Trauma Patients” PCCI’s Albert Karam, Vice President of Data Strategy and Analytics presents with Parkland’s Adam J. Starr, MD, will discuss this innovative predictive trauma model.
“Data Driven Approach to Addressing Health Related Social Needs (HRSN)” PCCI’s Jacqueline Naeem, MD, Medical Director will present with Vidya Ayyr, MPH, CHW, Director Social Impact at Parkland about the Accountable Health Community model recently and successfully completed in Dallas.
“Implementing Inpatient Sepsis Early Prediction Model” PCCI’s Yusuf Talha Tamer, PhD, Principal Data and Applied Scientist, will present with Parkland’s Nainesh Shah, Hospital Physician, DMIO, will share how their predictive sepsis model is changing how sepsis can be addressed.
Understanding community need has been a core aspect of hospital operations, especially for organizations with a non-profit status. As we gain greater insights into the impact of non-medical determinants and their impact on positive health outcomes, there is a heightened imperative to revamp how CHNA activities are undertaken and the type of data that are collected. This session will speak to how organizations who have been on the front lines of SDOH work have altered their approach to their CHNA to gain deeper insights to better contextualize the true needs of their communities. This webcast features PCCI and Healthbox leaders:
Please have a look at the full set of HIMSS Webcasts featuring PCCI and Healthbox discussing how to implement SDOH principles via connected communities of care: