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AlIGNED intelligence for CARE DELIVERY
Ensures healthcare data actually benefits doctors and patients.
It’s where operational efficiency meets the best patient outcomes.
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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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Automated Clinical Documentation and Coding Process
For complex surgeries, there can be a huge variability in documentation and coding and this requires coders to have a great deal of knowledge of anatomy and regulations associated with a specific subspecialty. With CloudMedx, we are looking to automate parts of this process and improve communication between all stakeholders so that this process is streamlined and made efficient.
Richard Capra, Chief Administrative Officer, UCSF Orthopedics Department
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Automated Clinical Documentation and Coding Process
Clinical documentation in surgical specialties is a diverse and difficult area. It requires a lot of manual interventions and is heavily workflow oriented. With our work with CloudMedx, we are looking to automate this process and reduce the manual entry burden and communication between billers, coders and physicians so that this process is streamlined and made efficient, with low documentation errors and queries.
Khalid Mehmood, MD. President of Crescent Medical Center Lancaster
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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, M.D. 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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How it helps
Smart Health Insights for Hospitals, Patients and Care Delivery Organizations
powerful APIs as well as proprietary solutions on
HIPAA Compliant Cloud
Direct Integration with EHRs
CloudMedx Connecting the Dots
Our award winning platform leverages the latest clinical algorithms, machine learning technology, advanced
natural language processing, and a proprietary clinical contextual ontology to improve patient journeys
Data exchanges
Patient encounters analyzed
Readmission reductions
Bonus payments
Chronic patients identified
key features
outcomes driven by data insights
We help health systems streamline and optimize their clinical encounters in real time with our clinical knowledge engine. Overlooked data points come to the forefront, clinical documentation and generation of medical codes from structured and unstructured data is automatically done, thereby enabling the clinician to return to the core of care and patient centered healthcare becomes a reality.

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CloudMedx is working with UCSF to help track progression of disease with patients identified with hepatocellular carcinoma (HCC) who are awaiting liver transplant, as well as predicting treatment outcomes. The goal is to provide tools to risk stratify patients, predict outcomes of certain care plans, and see which patients are at risk of dropping off the transplant wait list due to tumor progression or death.

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CloudMedx is working with the Orthopaedic Department at UCSF (University of California San Francisco) to predict outcomes for patients that have had recent hip and/or knee replacement surgeries. The CloudMedx platform combines EHR data, patient reported outcomes and device data to come up with insights on patient recovery factors. CloudMedx's machine learning models provided could predict outcomes sooner than conventional models and with more accuracy.

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The CloudMedx platform has algorithms for Congestive Heart Failure monitoring and prediction. The goal is to identify high risk individuals and provide appropriate care for those individuals to prevent an adverse event.

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We help health systems and ACOs with prediction around readmissions, cost and resource utilization. These tools assist in achieving quality goals, narrowing gaps in care and reducing costs while providing better care and accessibility to their patients.  

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Clinical Evidence Based Algorithms
We are currently building a portfolio of risk predictors in...
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