CloudMedx provides a risk calculator for patients so that doctors can address their care, which allows doctors to treat for their patient cohorts.
Big data health analytics specialist CloudMedx announced the latest version of its analytics platform, which provides real-time visual insights and predictive analytics around population clinical risk and outcomes based on machine learning and natural language processing.
CloudMedx provides a risk calculator for patients so that doctors can address their care, which allows doctors to treat and intervene properly for their patient cohorts.
Both value- based and fee for service address risk in different ways, but both have penalties for over-coding, misdiagnosis, or over treatment.
On top of this, the value-based payments offer a reward for better performance, better clinical outcomes, and reducing costs. All of them are directly tied to patient risk management and require analytics software.
The CloudMedx analytics platform can process both structured and unstructured data from healthcare workflows, including medical labs, pharmacies and hospitals to capture all relevant clinical and financial information at the point of care.
Programs such as Medicare shared savings where cost savings are shared directly with doctors as incentives or bonus payments, or transitional care management, or gaps in care closures are directly tied to improved revenue for doctors.
“This requires a 360 degree analysis of patient profiles through analytics. CloudMedx provides this 360 degree risk assessment for patient profiles,” Tashfeen Suleman, CEO of CloudMedx, told eWEEK. “This allows doctors not only to improve their clinical outcomes by interventions at the right point and time, but also capture the additional revenue that is associated to better performance. Most doctors know who their high risk patients are but they get this information retrospectively and by that time a patient has already had an adverse event, it is after the fact.”
In addition, the CloudMedx APIs integrate with existing healthcare IT workflows and use natural language processing and machine learning on clinical notes to produce a multi-dimensional assessment of patient risk.
One of the goals of CloudMedx is readmission reduction, in addition to helping doctors capture other value- based incentives such as gaps in care closures, transitional care and care coordination programs to improve clinical outcomes.
“Readmission reduction is important for MSSP, DSRIP, and commercial payer programs as a fifth of patients get readmitted within 30 days in certain high risk, high comorbidity cases,” Suleman said. “It’s a high cost event and Medicare, Medicaid, and other commercial payers have incentivized doctors to reduce these avoidable readmissions so that they can help reduce costs. It’s one of the goals of the U.S. health system as it moves towards value- based care.”