{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:case1348167637"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:case1348167637","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"A Modern Statistical Approach to Quality Improvement in Health Care using Quantile Regression","abstract":"Quality is difficult to measure and compare among medical providers. First, an appropriate metric must be chosen among many potential process-based and patient outcome measures. Further, when comparing providers, one must take into account the fact that patients are not homogeneous across providers. Accounting for the latter with clinical risk adjustment models is a complicated and controversial topic, as providers are increasingly paid based on their performance with respect to risk-adjusted quality measures. A modern approach to hospital quality improvement is developed by expanding on D.R. Cox's 1958 methodology for calibrating binary outcomes; using this calibration methodology and recent efficient generalized linear modeling algorithms to develop a risk-adjustment model for in-hospital morality with data on 20 million inpatient discharges from the State of California between 2004 and 2008; applying this model to obtain 2009 observed-to-expected mortality ratios for the hospitals across California; and evaluating whether or not performance-dependent relationships between hospital process changes and observed-to-expected mortality ratios exist, based on a novel sparse multiquantile regression technique that incorporates a fused-lasso-type penalty.","abstract_html":"Quality is difficult to measure and compare among medical providers. First, an appropriate metric must be chosen among many potential process-based and patient outcome measures. Further, when comparing providers, one must take into account the fact that patients are not homogeneous across providers. Accounting for the latter with clinical risk adjustment models is a complicated and controversial topic, as providers are increasingly paid based on their performance with respect to risk-adjusted quality measures. A modern approach to hospital quality improvement is developed by expanding on D.R. 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