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The goal of CloudMedx is to combine the latest machine learning, natural language processing and a clinical ontology to provide insights at the point of care. All data - clinical and non clinical, structured and unstructured - may be taken into account with the goal to improve outcomes
Clinical Analyzer
for optimal care delivery
The Clinical Analyzer analyzes patient's record to provide clinicians, nurses, and front line staff with insights to improve patient outcomes.  

For example, when a provider types a diagnosis such as “hypertension” or “HTN”,  the CloudMedx Clinical Analyzer detects the data and immediately puts it to use by:

a. Recording the ICD-10 code for it
b. Generating the risk using evidence based algorithms that use hypertension as a component
c. Returning a probability for the patient having other associated diseases (such as obesity and diabetes)
d. Generating a care pathway as well as disease progression for that patient based on his/her entire medical history

Clinicians have the potential to save time by viewing all relevant patient information from past notes, intelligent point of care alerts  and course of action - based on big data, machine learning and evidence based medicine, thereby enabling clinicians to do what they went into medicine to do – focus on their patients.

Predictive Analytics for Management of Liver Cancer Transplants

Working with CloudMedx provides us with insights from data and augment workflows, treatment options and planning (for liver cancer patients). Most of this work is currently done manually and adds to administrative burden. Some of this heavy lifting may be done by a technology that can assist physicians. CloudMedx provides its AI powered tools and services that are healthcare specific.

Bilal Hameed, MD Hepatologist, UCSF
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Predictive Analytics and AI models for Management of ALS

As a heterogenous disease that varies from patient to patient, ALS poses numerous challenges to clinicians seeking better treatments...this leads to long delays in diagnosis because ALS can mimic other more common diseases. Additionally, the unpredictability in rate of progression makes treatment challenging. Our goal in working with Cloudmedx is to see if we can identify clinical features that will help us identify patients earlier and improve our ability to predict the course of their disease.

Shafeeq Ladha, Director, Greg Fulton ALS and Neuromuscular Disorders Center
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Clinical Insights and AI models for Orthopaedic Surgery Outcomes

We wanted to combine Patient Reported Outcomes, data from Electronic Medical Records and Sensor data that patients wear to see how patients were doing. For this we partnered with CloudMedx as they could handle large sets of data and give us a perspective on how patients were doing with very strong predictive analytics.

Stefano Bini, M.D. Orthopaedic Surgeon, UCSF
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Predictive Analytics for Congestive Heart Failure

As an industry, we do not have a sufficiently sophisticated tool to predict certain things such as disease progression and resulting readmissions in hospitals. We are working with CloudMedx to use new guidelines and algorithms, using clinical data to determine these risks and predictors. CloudMedx has a fast, scalable platform that can allows us to do just that. We found CloudMedx to be very intuitive and useful.

Ashish Atreja, MD, MPH, Chief Innovation and Engagement Officer, Mount Sinai
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Joint Application of Artificial Intelligence to Patient Care

As the healthcare industry transitions to value-based care and risk-based reimbursement models, provider and payer organizations are looking for solutions to help identify patients with chronic health conditions within their populations and actively manage their care. Our partnership with CloudMedx allows us to do that effectively.

John Bennett, Chief of Business Development at Sutter Physician Services
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Key Features
Gaps in care
Real-time Collaboration
Real-time Risk Assessment
Automated Care Plans
Intelligent Clinical Alerts