Basal joint arthroplasty decreases carpal tunnel pressure.

In this study, we propose a novel decision-making framework based on natural-language processing, machine learning and stochastic modeling for the purpose of providing a data-driven perspective to optimize the initiation time for dialysis treatment. When to start dialysis treatment is an important decision for end-stage chronic kidney disease (CKD) care. In the absence of a national guideline, determining the best time to start dialysis remains a challenge. Our decision support framework for optimizing the initiation time includes (a) a comprehensive, efficient "pipeline" for extracting, de-identifying, and standardizing EHR data; (b) an informatics toolkit that couples natural language processing and event mapping, clustering and machine learning to uncover the disease prognosis and treatment effects, and deduce the symptoms and utility rewards for each disease-action stage; (c) a first-of-its-kind, personalized, dialysis-timing stochastic model to determine the optimal initiation time; and (d) a clinical practice guideline (CPG) for systematic testing and implementation in the clinical setting. We evaluate the results using utility rewards for each decision process and published financial costs for each CKD disease and treatment stage. Compared to current clinical policy, the optimal initiation-time policies offer a potential 20.0% to 54.7% mortality reduction, an increase of 6.4% to 14.7% in overall utility reward and a reduction of 9.6% to 16.7% in overall cost. Working with nephrologists and a CKD/ESRD care team, a CPG was developed and tested in the clinic. Initial usage shows promising results. Follow-on clinical trials will gauge the overall effectiveness and impact on patient care of our approach. The new CPG has the potential to become a national standard.