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Explore effects of comorbidity and prior health care utilization on choice of employee health plans with different levels of cost sharing.Mayo Clinic employees in Rochester, Minnesota (MCR) under age 65 in January 2004; N = 20,379.Assessment of a natural experiment where self-funded medical care benefit options were changed to contain costs within a large medical group practice. Before the change, most employees were enrolled in a plan with first dollar coverage, while 18% had a plan with copays and deductibles. In 2004, 3 existing plans were replaced by 2 new options, one with lower premiums and higher out-of-pocket costs and the other with higher premiums, a lower coinsurance rate, and lower out-of-pocket maximums.Data on employees were merged across insurance claims, medical records, eligibility files, and employment files for 2003 and 2004.As the number of chronic comorbidities among family members increased, the probability of choosing high-premium option also increased. Seventy-two percent of employees with at least 1 family member with comorbidity chose the high-cost option versus 54.7% of employees with no comorbidities. High-premium and low-premium plans seem to subdivide population into discrete risk categories, which may adversely affect the future stability of the insurance plan options.Various factors affect decision making of employees regarding the choice of plan with different levels of cost-sharing. In a natural experiment setting where all options were redesigned, the health status of employees and their dependents played a very significant role in plan choice.

Context: Many attempts to identify hospital complications rely on secondary diagnoses from billing data. To be meaningful, diagnosis codes must distinguish between diagnoses after admission and those existing before admission. Objective: To assess the influence of diagnoses at admission on patient safety, comorbidity, severity measures, and case mix groupings for Medicare reimbursement. Design: Cross-sectional association of various diagnosis-based clinical and performance measures with and without diagnosis present on admission. Setting: Hospital discharges from Mayo Clinic Rochester hospitals in 2005 (N = 60,599). Patients: All hospital inpatients including surgical, medical, pediatric, maternity, psychiatric, and rehabilitation patients. About 33% of patients traveled more than 120 miles for care. Main Outcome Measures: Hospital patient safety indicators, comorbidity, severity, and case mix measures with and without diagnoses present at admission. Results: Over 90% of all diagnoses were present at admission whereas 27.1% of all inpatients had a secondary diagnosis coded in-hospital. About one-third of discharges with a safety indicator were flagged because of a diagnosis already present at admission, more likely among referral patients. In contrast, 87% of postoperative hemorrhage, 22% of postoperative hip fractures, and 54% of foreign bodies left in wounds were coded as in-hospital conditions. Severity changes during hospitalization were observed in less than 8% of discharges. Slightly over 3% of discharges were assigned to higher weight diagnosis-related groups based on an in-hospital complication. Conclusions: In general, many patient safety indicators do not reliably identify adverse hospital events, especially when applied to academic referral centers. Except as noted, conditions recorded after admission have minimal impact on comorbidity and severity measures or on Medicare reimbursement.