Session T03: Innovations in Medical Physics

Enhancing Workers' Compensation Claims Management Efficiency with Predictive Maximum Medical Improvement (MMI): A Computational Tool for Standardizing Treatment and Settlement Expectations

We present a computational Maximum Medical Improvement (MMI) tool and its significant impact on managing workers' compensation claims. This innovative tool assimilates a variety of clinical data and utilizes a patented Variable Thread Analytic Computation (VTAC) technology system to accurately forecast key factors such as claim duration and financial outcomes. Its potential to introduce transparency, efficiency, and fairness into the claim process is particularly notable, offering substantial benefits to a diverse group of stakeholders. A key strength of the tool is its reliance on proprietary impairment data, ensuring that forecasts are realistic and fostering more balanced settlement negotiations.  Streamlining the claims process could extend beyond workers' compensation leading to broader healthcare and legal applications. This presentation will spotlight the primary attributes and advantages of this computational tool. It sets a foundation for a thorough investigation into the tool’s features and its broader implications, which will be covered in a future publication.   

Improving Impairment Rating Accuracy: A Novel Computational Approach to Determining Direct Impairment Focusing on Functional Loss

In workers' compensation settlements, assessing impairment is vital when an injured worker reaches Maximum Medical Improvement (MMI). This evaluation process utilizes the individual's medical history, physical examinations, and diagnostic tests to determine a Whole Person Impairment (WPI) score, according to the American Medical Association's Guide to Permanent Impairment, 5^th edition. The WPI score reflects the person's functional loss in daily activities which is crucial for calculating the permanent disability and corresponding financial settlement. The "Functional Loss Rating" (FLR) method introduced here is an innovative computational model designed as an alternative to the traditional "Four Corners Rating" (FCR) method. The FLR approach emphasizes functional loss and aims to deliver WPI assessments more quickly, easily, and cost-effectively than the FCR method. In this study, the FLR and FCR methods were compared for cases involving the shoulder, lumbar spine, and knee. WPI scores obtained with both methods were consistent for shoulder and lumbar spine injuries, and it is proposed here that the FLR method improves upon the FCR method for knee injuries by more accurately focusing on functional loss.   

The molecular mechanism of the core planar cell polarity complex elucidated with single-molecule imaging techniques in live Drosophila wing cells

The planar cell polarity (PCP) signaling pathway polarizes epithelial cells along an axis parallel to the epithelial sheet to generate front-back asymmetry at the tissue level. Failures in PCP signaling underlie developmental defects and diseases such as neural tube and heart defects, deafness, and contribute to cancer. Six core PCP proteins assemble into large, asymmetric complexes at cell-cell junctions, with Van Gogh and Prickle recruited to the proximal side of the cell-cell junction while Frizzled, Diego, and Dishevelled are recruited to the distal side. Flamingo forms asymmetric inter-cellular bridges connecting polarity between adjacent cell. Neither the potential requirement for clusters, nor their detailed organization are understood. To explore these questions, we used total internal reflection fluorescence microscopy to image GFP-tagged PCP proteins in the Drosophila pupal wing disc. Using this approach, we find that the molecular size distribution of PCP complexes follows an exponential function, suggestive of a single underlying growth mechanism. Mutations that block Dishevelled oligomerization decrease average cluster sizes, and also result in PCP phenotypic defects, demonstrating a requirement for large clusters in polarization. In conclusion, our findings provide a quantitative framework for understanding how cluster formation is coupled to the induction of tissue-level asymmetry during early embryonic development.