The Latest in Predictive Analytics
NEW YORK, Dec. 9, 2020 /PRNewswire/ -- Ready Computing, a leader in healthcare IT services and solutions, will integrate its Channels Care Coordination Management module with HBI Solutions' Spotlight Analytics Platform. Through this new partnership, Ready Computing...
The current environment of the COVID-19 pandemic and social unrest has unarguably created unprecedented crises for individuals with mental health conditions and those that care for them, though the extent and severity of the consequences remains unknown.
Policy makers and businesses across the globe are struggling to determine when and how to return to normalcy, balancing health safety with economic pressures and needs. And while most are focusing on susceptibility, case count, deaths and recovery rates one critical piece of information is noticeably missing from the equation – re-emergence. Can individuals get re-infected? Does the virus remain dormant? Can they once again become infectious? A 16% relapse rate suggests we should be considering it.
Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm
Falls in our elder population are a major cause of injury, hospitalization and mortality worldwide, and a major source of medical costs. Current risk screening and assessment methods are often manual, include a limited number of intrinsic factors, exclude environmental factors and/or lack discriminatory capabilities that a robust machine-learned model can provide. See how machine learning can provide better discrimination for intervention and prevention.
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.