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Background: There are an estimated 1.4 million colorectal cancer (CRC) survivors in the United States. Research on endocrine and metabolic diseases over the long term in CRC survivors is limited. Obesity is a risk factor for CRC; thus it is of interest to investigate diseases that may share this risk factor, such as diabetes, for long-term health outcomes among CRC survivors.Methods: A total of 7114 CRC patients were identified from the Utah Population Database and matched to a general population cohort of 25 979 individuals on birth year, sex, and birth state. Disease diagnoses (assessed over three time periods of 1-5 years, 5-10 years, and >10 years) were identified using electronic medical records and statewide ambulatory and inpatient discharge data. Cox proportional hazard models were used to estimate the risk of endocrine and metabolic disease.Results: Across all three time periods, risks for endocrine and metabolic diseases were statistically significantly greater for CRC survivors compared with the general population cohort. At 1-5 years postdiagnosis, CRC survivors' risk for diabetes mellitus with complications was statistically significantly elevated (hazard ratio [HR] = 1.36, 99% confidence interval [CI] = 1.09 to 1.70). CRC survivors also experienced a 40% increased risk of obesity at 1-5 years postcancer diagnosis (HR= 1.40, 99% CI= 1.66 to 2.18) and a 50% increased risk at 5-10 years postdiagnosis (HR = 1.50, 99% CI= 1.16 to 1.95).Conclusions: Endocrine and metabolic diseases were statistically significantly higher in CRC survivors throughout the follow-up periods of 1-5 years, 5-10 years, and more than 10 years postdiagnosis. As the number of CRC survivors increases, understanding the long-term trajectory is critical for improved survivorship care. [ABSTRACT FROM AUTHOR]

Objective: To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE).Materials and Methods: A 5-year retrospective pre-post study evaluated 66 186 patients and 104 153 admissions (49 683 pre-CPOE, 54 470 post-CPOE) at an academic medical center. Generalized linear mixed statistical tests controlled for 17 potential confounders with 2 models per outcome.Results: After controlling for covariates, CPOE remained a significant statistical predictor of decreased LOS and mortality. LOS decreased by 0.90 days, P  < .0001. Mortality decrease varied by model: 1 death per 1000 admissions (pre = 0.006, post = 0.0005, P  < .001) or 3 deaths (pre = 0.008, post = 0.005, P  < .01). Mortality and LOS decreased in medical and surgical units but increased in intensive care units.Discussion: This study examined CPOE at multiple levels. Given the inability to randomize CPOE assignment, these results may only be applicable to the local setting. Temporal trends found in this study suggest that hospital-wide implementations may have impacted nursing staff and new residents. Differences in the results were noted at the patient care unit and room levels. These differences may partly explain the mixed results from previous studies.Conclusion: Controlling for confounders, CPOE implementation remained a statistically significant predictor of LOS and mortality at this site. Mortality appears to be a sensitive outcome indicator with regard to hospital-wide implementations and should be further studied. [ABSTRACT FROM AUTHOR]

Objective To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes). Materials and methods In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology. Evaluation consisted of measurement against project objectives, including implementation timeliness, system performance, completeness, accuracy, extensibility, adoption, satisfaction, and the ability to support value improvement. Results A modular, extensible framework was developed to allocate clinical care costs to individual patient encounters. For example, labor costs in a hospital unit are allocated to patients based on the hours they spent in the unit; actual medication acquisition costs are allocated to patients based on utilization; and radiology costs are allocated based on the minutes required for study performance. Relevant process and outcome measures are also available. A visualization layer facilitates the identification of value improvement opportunities, such as high-volume, high-cost case types with high variability in costs across providers. Initial implementation was completed within 6 months, and all project objectives were fulfilled. The framework has been improved iteratively and is now a foundational tool for delivering high-value care. Conclusions The framework described can be expeditiously implemented to provide a pragmatic, modular, and extensible approach to understanding and improving healthcare value. [ABSTRACT FROM AUTHOR]