One Platform: A Host of Clinical Decision Support Possibilities
Achieve insights at the point-of-care to ensure the best outcomes
As a member of the multidisciplinary care team you rely on information contained in images to make decisions on the most appropriate care for your patients. You need:
- Consistent quantitative data for every lesion
- Enhanced clinical decision support (CDS) workflows that drive precise patient management and higher quality care
- Actionable quantitative reports with easy to digest data including today's measurements and difference metrics from previous studies
- A robust database of quantitative imaging features for precision medicine and research initiatives
The HealthMyne QIDS platform can accomplish all of these and much more!
By utilizing quantitative data created at the Point-of-Read and combining that with clinical information from the EMR and other systems the QIDS platform enables enhanced CDS workflows that impact the entire Multidisciplinary Team's ability to provide precise patient management. Click on a Module below to learn more about them.
Data you can trust
In the American Journal of Roentgenology's (AJR) article series, "Quantitative Imaging in Oncology Patients" 93% of oncologists reported, "(they) think that patient management is affected by the inclusion of tumor measurements in radiologic interpretations." Yet in the same series, only 7% of radiologists report including those measurements.
The HealthMyne QIDS Platform and Rapid Precise Metrics (RPM)™ functionality makes it easy for Radiologists at the Point-of-Read to obtain true, consistent Long / Short values delivered immediately to the report without verbal dictation. This minimizes inter/intra reader variability and provides more confidence at the Point-of-Care.
Intrigued? Let's talk and find out what's important to you
Once we understand your needs we can customize a demo specifically for you and your team
Go Beyond the Long and Short
Evidence-based metrics such as volume, density, and mass at your fingertips
Metrics such as volume, mass, and density have extensive published data exhibiting their value beyond the Long and Short in oncology care. Unfortunately, these valuable data points are difficult to extract and until now were mostly unavailable to you when evaluating your patient's diagnosis or response to treatment.
With RPM, not only is the true Long and Short determined, evidence-based metrics including % change from prior and baseline are calculated within seconds and can be included in reports or viewed in the application by anyone on the multidisciplinary team:
- % GGO
- Doubling Times
- Over 600 others!
Check out the Long and Short of it e-book series to learn more
COMING IN JUNE 2019* - Clinical Decision Support Alerts Module that allows clinicians to specify a metric or combination of metrics and receive an alert when the results of RPM match, advanced brain segmentation on MRI that utilizes multiple series to enhance the accuracy of segmentation of brain tumors, and organ segmentation for the liver.
*Subject to change without notice
More Meaningful Reports
Quantitative Tumor Burden Report® (QTBR)®
Assessment of total tumor burden requires aggregation of lesion measurements throughout the body, either in a single report or via reports from different sub-specialty radiologists. Rather than the different disciplines having to compile the information manually and from several sources, the QTBR is able to capture the required quantitative information and present it in a consolidated form.
The QTBR shows all studies for the patient, separated into different study types by body part or modality and is ideal for preparing for, and being used in Tumor Conference.
Currently, if the radiology report contains quantitative information it is buried within the text as the Radiologist dictates their findings. This can make it difficult to find and interpret especially when it is a follow up study. The HealthMyne platform integrates with dictation systems so that metrics created with RPM automatically flow to the report in a tabular format. Not only is this easier to read, your choice of metrics beyond the Long and Short, and difference metrics can easily be included and viewed in paper or electronic copies, the PACS system, or the EHR.
HealthMyne’s Thoracic Module automatically segments CT images of lung tissue into normal- and low-density regions and provides quantitative metrics for assessment of emphysema. Easily create both provider- and patient-facing reports with a click of the mouse. Reports can feature your facility logo and customized text to meet your specific needs. Patient-facing reports generated by HealthMyne showing the extent of emphysematous damage to lung tissue can help convince patients to quit smoking and can give their care team information needed to better guide their long-term treatment.
Robust Imaging Data
Discoverable database for research and precision medicine efforts
Cancer patients undergo imaging as part of their care, and images contain a wealth of information that can be used to determine diagnoses, prognoses, and even what treatment will work best. Yet, in almost every hospital and clinic, that data is lost because it cannot easily be mined from the dictated radiology report or is not mined from the image to begin with.
The HealthMyne Platform creates 600+ metrics for every lesion identified and saves that data to a discoverable database. Simple data such as the long and short, evidence-based metrics such as volume, density, mass, and doubling time, and even more complex radiomics such as texture and spiculation are now easily mineable and available for inclusion in precision medicine initiatives and for clinical, translational, and commercial research.
How is AI used in HealthMyne?
A common question we get is how are we deploying Artificial Intelligence in our software? Currently we don’t directly put AI or Machine Learning (ML) in front of our users such as using it for Computer Aided Detection (CAD), but in the background there are multiple ways that these methods are utilized in the software and in development work today.
Determining the correct exam groups for retrieving priors and organizing image files – AI is utilized to identify body areas and correctly identify relevant priors in both CT and MR
Rapid Precise Metrics (RPM)™ - AI is utilized to create the initial 2D outlines of the lesion based on the readers input
Rapid Precise Metrics (RPM)™ - AI learns from the initial and subsequent inputs from the reader to create the full 3D segmentation
Automated Liver Segmentation – AI and ML are being utilized for training and testing this functionality for a future release
Automated brain tissue segmentation – AI and ML are being utilized for training and testing this functionality for a future release
Some of our future plans for AI and ML include:
Liver segment and vessel identification for surgical prep, fatty liver disease (NAFLD), and sclerosis metrics and identification
Brain ventricular volumes for Multiple Sclerosis, Parkinsons, and Alzheimers metrics and identification
Lung vessel maps and fissure identification