The Latest in Predictive Analytics
Amid the COVID-19 crisis sweeping the nation, HBI clients are able to identify at-risk populations for outreach or resource planning using robust filters in Spotlight Population Risk Management.
Available now in Spotlight Analytics 2.0 and greater, clients can select from among (28) HEDIS® Health Plan and HEDIS® Allowable Adjustments Measures certified for 2020. HBI Solutions combines quality compliance and gaps in care with future risk for a unique population stratification strategy and whole person view
Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records
Suicide is a leading cause of death in the US, particularly for young people, veterans and Native Americans. An early warning system can be an effective way to identify those in need before a crisis is upon them. See how AI and machine learning on longitudinal health records can be used to identify those at risk – up to 60+ times as likely to attempt a suicide in a future 1 year period.
HIMSS20 Presentation: Unleash the Power to Predict in the ED Thursday, March 12, 2020 | 12:00 pm | InterSystems Booth #3301 Delivering the revisit risk scores to the emergency department (ED) supports critical discharge, admit, and follow up decisions. ED physicians,...
The FDA announced its preliminary guidance on clinical decision support software, including predictive analytics this month. Be sure your predictive analytics are going to meet the new regulations.
In the hospital setting, I knew that sepsis accounted for 1 in 3 deaths, increased lengths of stay and increased costs. But I was unaware how many patients were admitted with sepsis already brewing. According to research in Critical Care Medicine, in the United States, over 970,000 sepsis cases are admitted annually, and the numbers have been rising year over year. That’s about 3% of all admission and 60% of the 1.7 million cases reported by the CDC. The Sepsis Alliance reports as many as 87% of sepsis cases originate in the community!
HBI Solutions’ Spotlight powers TruCare Analytics for focused care and population health management.
We study what is already in the published domain for insights on risk models & modeling methods, as well as clinical challenges to learn from other authors. Then, we contribute to the body of knowledge by publishing our methods and results so that others can learn, too.
Authored by: Laura Kanov, Senior Vice President, Product Strategy Every 65 seconds, someone in the United States develops Alzheimer’s Disease and everyone in that family is impacted. More than 16 million Americans provide unpaid care for people with Alzheimer’s or...
HBI Solutions Joins Iatric® Systems, Inc AI Solutions Center bringing advanced analytics and artificial intelligence to care givers to address risk in real time
Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine
Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate.
Using data from individual patient electronic health records (EHR’s), we retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify high-risk patients, which can be used in population health to inform early detection, preventive interventions and/or enable more focused diagnostics and surveillance.
A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data
The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. In this study, data collected from a system-wide electronic medical record (EMR) were exposed to multiple machine learning methods. The prospectively validated algorithm scored patients’ daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups.
We demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions