Consistency - Clinical Productivity - Quality
Rapid Precise Metrics (RPM)™ functionality enables them all at the Point of Read
Traditionally, radiologists or anyone else obtaining a measurement of a lesion, have been required to use digital “calipers” provided by the Picture Archiving and Communication System (PACS). Clinical studies have conclusively determined that inter / intra reader variability is an inevitable byproduct of these tools.
Without consistency, quality is almost impossible to achieve. HealthMyne's Rapid Precise Metrics (RPM) is designed to solve the consistency issue while also bringing greater efficiency to the process of identifying, measuring, and tracking lesions across studies.
RPM is also at the heart of the enhanced Clinical Decision Support (CDS) Workflows built into the HealthMyne Platform. Metrics are combined with data from the EHR and other clinical systems to improve clinical decision making and enable precise patient management at the point-of-care.
You identify, we quantify
HealthMyne addresses this issue with its Rapid Precise Metrics (RPM)™ functionality, which empowers the Radiologist or any other clinician to identify a lesion once with a simple mouse gesture. Upon this initial determination, HealthMyne takes over to deliver:
- True, consistent Long/Short values delivered immediately to the report without verbal dictation minimizing inter/intra reader variability
- Measurements propagated forward on follow-up studies, eliminating incomplete follow-up (RPM can propagate back in time as well) and saving the time of re-measurement
- Evidence Based Metrics beyond Long/Short available to support the clinical decisions of downstream physicians: % Change in Volume, Density, Mass, %GGO, Doubling Time, Texture and many more customized by physician preference
Proven Accuracy and Consistency
Using the LIDC dataset RPM was compared to the 3D segmentations of the four expert radiologists in the LIDC database. We found that QIDS’ quantitative volumetric delineation was significantly more consistent (93% vs 85%) and matched each individual radiologist decidedly better than they matched each other (0.87 vs 0.68). Proof that RPM can greatly reduce intra- and inter-reader variability.
Faster Follow-up Reads
Automation of manual tasks for greater productivity
The QIDS platform and RPM functionality automates many manual processes for you, allowing you to focus on the things that require your expertise such as identifying abnormalities in the study. The platform automates:
- Fetching of relevant prior studies
- Registration of current and priors
- Locking Pan/Zoom of current and priors
- Locating and identifying lesions that have been previously identified on earlier studies (saving you from having to identify these same lesions on the current study)
- Propagating previously identified lesions to today's images with delineations and Long and Short measurements waiting for your approval
With improved consistency of lesion measurement with RPM, QIDS also eliminates the need for you to go back and re-measure previous lesions saving additional time and effort.
Intuitive longitudinal display of clinically relevant patient information
The PatientCare Timeline® integrates EMR, PACS, Radiotherapy, and other clinical systems data into a patient-centric view to enable multidisciplinary collaboration.
This robust clinical dashboard displays vital information at the Point-of-Read to provide clinical context and to ensure that dictated impressions are as accurate as possible.
Multidisciplinary clinicians at the Point-of-Care gain valuable, metric-driven clinical decision support (CDS) to make informed patient management decisions. The preparation and execution of critical clinical multidisciplinary care processes, such as the Tumor Board, are streamlined and enlivened with the PatientCare Timeline and the QIDS platform.
Clinical Decision Support at The Point-of-Read
Radiotherapy Dose Overlay
Could that be recurrence? Is that only fibrosis? Does anyone know where the RT dose was delivered?
These are questions asked daily in reading rooms, clinics, and tumor boards around the country, and can be attributed to the divide between Radiation Oncology data and the rest of the multidisciplinary team. HealthMyne has developed a CDS workflow to extract and display the radiotherapy dose distribution curve as a relevant prior, enabling radiologists and clinicians to see precisely where the dose was delivered. This can inform their interpretation of post-treatment studies and guide appropriate follow-up and treatment.
MRI Series Overlay
The QIDS platform provides functionality that allows automated and semi-automated tracking of lesions across various series within MRI image sets (separate measurements and tracking for T1, T2, Flair, etc.), and tools to show visual overlay across series and across time points. This functionality can save time at the point-of-read and can assist in clinical assessment and diagnosis of complex lesions in MRI.
The QIDS platform provides functionality for PET/CT visual overlay and automated determination of SUV max and mean within lesions, in addition to the selection of reference regions. These tools provide decision support and efficiency for the interpretation of PET images and can provide access to PET images for others in the organization when required.
Deeper Metrics and Radiomics
RPM creates a full complement of quantitative metrics to better manage patient care
Dr. Robert Gillies of Moffitt Cancer Center is often called the 'Father of Radiomics' and in his paper, "Radiomics: Images Are More than Pictures, They Are Data" he predicts that Radiomics are the next frontier in clinical decision making. He sees a Reading Room of the Future in which radiologists use software to identify, segment and extract this useful data that has predictive capabilities in diagnosis, prognosis, and prescription. This allows radiology to provide key information for clinical decision support at the Point-of-Care.
Additionally, every lesion identified with Rapid Precise Metrics has over 500 radiomic metrics extracted and added to the discoverable database which becomes a valuable asset for Precision Medicine initiatives, data mining, analytics, and research.