The Latest in Predictive Analytics
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
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.
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...read more
HBI Solutions Joins Iatric® Systems, Inc AI Solutions Center bringing advanced analytics and artificial intelligence to care givers to address risk in real timeread more
Connecticut Department of Social Services Selects HBI Spotlight Analytics to Provide Predictive Insights on Medicaid Population
Medicaid program selects HBI Spotlight Analytics Solution to mitigate health and cost risk of vulnerable population.read more
Authored by: Laura Kanov, Senior Vice President, Product Strategy A couple of years ago (almost to the day) I posted a blog entry on my LinkedIn page about “The Population Health Slide” based on this idea of a sigmoid curve or slippery slope between two extremes. On...read more
Headed to Population Health Colloquium this March? Check out our top session pick: Using Predictive Risk Solutions to Structure Population Care Programs Tuesday, March 19 | 4:15pm | Workshop Track III Presented by: Gray Ellrodt, MD, Chief Medical Officer, Berkshire...read more
Authored by: Laura Kanov, Senior Vice President, Product Strategy We can't let February slip away without emphasizing Heart Health. The month of February was set aside to raise awareness about heart disease and education Americans about the fact that heart disease is...read more
In case you missed it LIVE at #HIMSS19, check out this video of Eric Widen, CEO of HBI Solutions, presenting on AI and Machine Learning in Healthcare.read more
Optimizing opioid management through Health ITread more
Authored by: Laura Kanov, Senior Vice President, Product Strategy As we look back on 2018, it's amazing to see what our HBI team has accomplished in 12 short months! Here's a quick roundup: Spotlight Analytics Platform 2.0 Spotlight was given a sleek and easy makeover...read more
HIMSS18 Presentation: Practical Use Cases for AI & Machine Learning in Healthcare Organization Tuesday, February 12, 2019 at 11:00am | InterSystems Booth #1559 Artificial intelligence (AI) and machine learning (ML) are effective tools to manage healthcare costs...read more