CMS Sematrix: A Tool to Aid the Development of Clinical Quality Measures (CQMs)
As part of the effort to improve quality and to reduce national healthcare costs, the Centers for Medicare and Medicaid Services (CMS) are responsible for creating and maintaining an array of clinical quality measures (CQMs) for assessing healthcare structure, process, outcome, and patient experience across various conditions, clinical specialties, and settings. The development and maintenance of CQMs involves substantial and ongoing evaluation of the evidence on the measure's properties: importance, reliability, validity, feasibility, and usability. As such, CMS conducts monthly environmental scans of the published clinical and health service literature. Conducting time consuming, exhaustive evaluations of the ever-changing healthcare literature presents one of the largest challenges to an evidence-based approach to healthcare quality improvement. Thus, it is imperative to leverage automated techniques to aid CMS in the identification of clinical and health services literature relevant to CQMs. Additionally, the estimated labor hours and related cost savings of using CMS Sematrix compared to a traditional literature review are roughly 818 hours and 122,000 dollars for a single monthly environmental scan.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction
This paper introduces InverseMatrixVT3D, an efficient method for transforming multi-view image features into 3D feature volumes for 3D semantic occupancy prediction. Existing methods for constructing 3D volumes often rel…
3D Semantic Occupancy PredictionAutonomous DrivingDepth EstimationGPUInteroperable synthetic health data with SyntHIR to enable the development of CDSS tools
There is a great opportunity to use high-quality patient journals and health registers to develop machine learning-based Clinical Decision Support Systems (CDSS). To implement a CDSS tool in a clinical workflow, there is…
A study of why we need to reassess full reference image quality assessment with medical images
Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well as in the development stage of machine learning algorithms that operate on medical images. The popular full reference…
Full reference image quality assessmentFull-Reference Image Quality AssessmentImage Quality AssessmentSSIMQuantifying Itch and its Impact on Sleep Using Machine Learning and Radio Signals
Chronic itch affects 13% of the US population, is highly debilitating, and underlies many medical conditions. A major challenge in clinical care and new therapeutics development is the lack of an objective measure for qu…
Sleep QualitySpecificityTorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio
Measuring quality and intelligibility of a speech signal is usually a critical step in development of speech processing systems. To enable this, a variety of metrics to measure quality and intelligibility under different…