ROCKVILLE, Md., Jan. 4, 2016 /PRNewswire/ -- Nearly 20% of hospitalizations by Medicare beneficiaries are followed by readmission within 30 days, costing billions of dollars in healthcare costs. Existing approaches to identify patients at risk for hospitalization are limited to episodic claims data. These claims-based approaches often fail to detect avoidable and costly admissions in the blind spot between doctor visits. A new study, published today in Perspectives in Health Information Management, demonstrates a novel approach to fill that blind spot by identifying patients at risk for hospitalization by using observations of non-medical workers.
"The survey platform is designed for early detection of dynamic risk factors for admission including medical and psychosocial warning signs," says Dr. Andrey Ostrovsky, study co-author and CEO of Care at Hand.
This technology builds on previous work done by Dr. Kenneth Boockvar who is Professor of Geriatrics at Mt. Sinai School of Medicine. Dr. Boockvar's research led to the creation of the most widely used risk-assessment for assistants in nursing facilities called the STOP and WATCH Tool. Dr. Boockvar notes that "Care at Hand is a 'smart' STOP and WATCH. Making risk assessments dynamic rather than static enables for triage and interventions to be fundamentally more patient-centered, efficient, and data-driven."
According to study co-author, Lori O'Connor, RN, "These findings are significant because non-medical workers, like home health aides, community health workers, or social workers, spend more time with patients and cost much less than doctors and nurses."
The study concludes that frontline staff supervised by nurses may be a key asset in helping Medicare providers endure the growing cost pressures from the shift to value-based payment. Please click here to review the study presented by Care at Hand.
About Care at Hand
Care at Hand is a digital health company on a mission to ensure people can thrive in their homes and avoid unnecessary stays in hospitals. The company's flagship technology is an evidence-based predictive insights platform powered by non-medical staff. For payers and providers struggling to optimize margins and achieve the Triple Aim, Care at Hand provides a smart survey platform that identifies early medical and psychosocial risk factors for hospitalization. Unlike episodic predictive models limited to claims and EMR data, Care at Hand predicts hospitalization risk in the blind spot between doctor visits using the observations of existing frontline staff.
PRLog ID: www.prlog.org/12522013
SOURCE Care at Hand