Analytics

Getting to the unique root cause for each patient has enabled the healthcare organization to reduce hospitalizations, improve medication adherence, reduce healthcare costs, reduce medications and help patients lose weight.

The clinical analytics tool is improving engagement metrics at Tennessee-based Psychiatric Medical Care, helping report aggregate data to the board and The Joint Commission, informing performance evaluations – and offering something quantifiable to show patients and families.

Thierry Marchal of Ansys and researcher María Angeles Pérez discuss how combining clinical data and mathematical models to create digital simulations can increase precision across specialties and expedite patient treatment.  

This marks the Australian company's expansion into Saudi's private health sector after cementing its presence in its public health system.

Videha Sharma, clinical innovation lead at the University of Manchester, explains the NHS’s plan to expand pharmacogenetics, or how people’s genetic codes influence the effects of drugs, beyond specialist care to general practice.

Dr. Tim O'Connell, cofounder and CEO of Emtelligent, explains how NLP can quickly surface relevant data information buried in patient charts, helping guide clinicians' treatment decisions.  

Using 14 key performance indicators (KPIs), decision-makers receive an overview of surgeon performance. This increased transparency aims to enhance the quality of care and identify potential areas for improvement.

Researchers compare machine learning models to traditional alerts for critically ill adults, while a new real-world GLP-1 data trove incorporates unstructured data. Also, RapidAI earns another FDA clearance for rapid stroke imaging.

Dr. Gauri Puri, chief business officer for healthcare at WNS, a global business process management company, offers a deep dive that can lead IT leaders at hospitals and health systems on the road to digital success.

Trained using more than 100,000 echocardiograms from atrial fibrillation cases, the algorithm has shown its ability to predict which patients could develop irregular heart rhythms within 90 days.