A comparison of diabetes patient's self-reported health status with hemoglobin A1c test results in 11 California health plans.

PURPOSE: To examine the relationship between hemoglobin A1c (HbA1c) test rates and values and various self-reported measures of health status within a sample of diabetes patients drawn from 11 California health plans, with a focus on improving diabetes care in this patient population. DESIGN: The analysis relies on data obtained from medical records of a sample population of 4,747 diabetes patients and a patient survey mailed to a large subsample of patients included in the medical-records analysis. METHODS: Descriptive methods were used to compare the medical records and survey-data results. PRINCIPAL FINDINGS: There were substantive differences noted between diabetes patients' self-reported health status, their level of satisfaction with the care they received, and the actual care they received. There was a large discrepancy between diabetes patients' perceptions of the care they received for their diabetes, which was overwhelmingly positive, and the HbA1c test-frequency rates observed across the 11 health plans studied, which were low. CONCLUSIONS: Patients' self-reports of health status, satisfaction with care, and extent of control over diabetes--a chronic condition that may have few perceptible symptoms--are associated with significant methodological limitations. Our examination of the relationship between perceived levels of self-management of diabetes and test status indicated that for patients who had at least one HbA1c test, some education during that process may have resulted in behavioral change. Patients who received no tests, however, may remain unaware of their glycemic control and the long-term consequences associated with even mild hyperglycemia. A clear need thus exists to educate diabetes patients about their health status. Health plan and provider group investments in educational efforts aimed at increasing testing rates are likely to lead to improved glycemic control and a reduction in the incidence of diabetes-related complications and related expenditures.

CALINX (California Information Exchange): a multi-stakeholder statewide initiative to improve healthcare information flows.

Given the diffusion of responsibilities for gathering and reporting healthcare information in a managed care environment, California stakeholders are taking concrete steps to break the deadlock on data and information flows that has characterized the industry for some time. The California Information Exchange (CALINX) was established to facilitate the implementation of the Health Insurance Portability and Accountability Act (HIPAA) standards in California and to create trust for data exchange between trading partners, without which data exchange still will not occur. Strategic directions are set by the chief executives of key associations and organizations representing purchasers, plans, providers, and consumers. Multi-stakeholder workgroups have produced detailed data guidelines for the HIPAA standards along with rules for exchange of key data sets between trading partners. These rules address frequency, timeliness, and accuracy of data submission. Both the data guidelines and the rules have been tested in live demonstration projects, and the results of these projects have been reported to substantiate the business case for implementation. Further incentives are being built into contracts between purchasers and plans, and between plans and providers. CALINX is currently promoting widespread adoption of the data guidelines and rules for exchange with all members of the industry.

A technique for improved maxillary record base adaptation through controlled polymerization of light-activated dental resins.

A technique for the fabrication of light-activated maxillary record bases is described. The use of a segmental polymerization process provides improved palatal adaptation by minimizing the effects of polymerization shrinkage. Utilization of this technique results in record bases that are well adapted to the corresponding master casts.

A patient mix model for hospital financial planning.

A linear programming model was formulated to examine the impact on hospital finances and resource use of changes in patient mix under current and proposed government reimbursement regulations. The model specifically incorporated the costs, revenues, and resource consumption patterns associated with patients drawn from different intensity level and payer class combinations. The necessary data were obtained from financial reports and other records, and the model was used in policy analysis at a major university teaching hospital.

A financial planning model for estimating hospital debt capacity.

A computer-based financial planning model was formulated to measure the impact of a major capital improvement project on the fiscal health of Stanford University Hospital. The model had to be responsive to many variables and easy to use, so as to allow for the testing of numerous alternatives. Special efforts were made to identify the key variables that needed to be presented in the model and to include all known links between capital investment, debt, and hospital operating expenses. Growth in the number of patient days of care was singled out as a major source of uncertainty that would have profound effects on the hospital's finances. Therefore this variable was subjected to special scrutiny in terms of efforts to gauge expected demographic trends and market forces. In addition, alternative base runs of the model were made under three distinct patient-demand assumptions. Use of the model enabled planners at the Stanford University Hospital (a) to determine that a proposed modernization plan was financially feasible under a reasonable (that is, not unduly optimistic) set of assumptions and (b) to examine the major sources of risk. Other than patient demand, these sources were found to be gross revenues per patient, operating costs, and future limitations on government reimbursement programs. When the likely financial consequences of these risks were estimated, both separately and in combination, it was determined that even if two or more assumptions took a somewhat more negative turn than was expected, the hospital would be able to offset adverse consequences by a relatively minor reduction in operating costs.