Patient complexity in quality comparisons for glycemic control: An observational study

<p>Abstract</p> <p>Background</p> <p>Patient complexity is not incorporated into quality of care comparisons for glycemic control. We developed a method to adjust hemoglobin A1c levels for patient characteristics that reflect complexity, and examined the effect of using adjusted A1c values on quality comparisons.</p> <p>Methods</p> <p>This cross-sectional observational study used 1999 national VA (US Department of Veterans Affairs) pharmacy, inpatient and outpatient utilization, and laboratory data on diabetic veterans. We adjusted individual A1c levels for available domains of complexity: age, social support (marital status), comorbid illnesses, and severity of disease (insulin use). We used adjusted A1c values to generate VA medical center level performance measures, and compared medical center ranks using adjusted versus unadjusted A1c levels across several thresholds of A1c (8.0%, 8.5%, 9.0%, and 9.5%).</p> <p>Results</p> <p>The adjustment model had R²= 8.3% with stable parameter estimates on thirty random 50% resamples. Adjustment for patient complexity resulted in the greatest rank differences in the best and worst performing deciles, with similar patterns across all tested thresholds.</p> <p>Conclusion</p> <p>Adjustment for complexity resulted in large differences in identified best and worst performers at all tested thresholds. Current performance measures of glycemic control may not be reliably identifying quality problems, and tying reimbursements to such measures may compromise the care of complex patients.</p

Patient Complexity: More Than Comorbidity. The Vector Model of Complexity

BACKGROUND: The conceptualization of patient complexity is just beginning in clinical medicine. OBJECTIVES: This study aims (1) to propose a conceptual approach to complex patients; (2) to demonstrate how this approach promotes achieving congruence between patient and provider, a critical step in the development of maximally effective treatment plans; and (3) to examine availability of evidence to guide trade-off decisions and assess healthcare quality for complex patients. METHODS/RESULTS: The Vector Model of Complexity portrays interactions between biological, socioeconomic, cultural, environmental and behavioral forces as health determinants. These forces are not easily discerned but exert profound influences on processes and outcomes of care for chronic medical conditions. Achieving congruence between patient, physician, and healthcare system is essential for effective, patient-centered care; requires assessment of all axes of the Vector Model; and, frequently, requires trade-off decisions to develop a tailored treatment plan. Most evidence-based guidelines rarely provide guidance for trade-off decisions. Quality measures often exclude complex patients and are not designed explicitly to assess their overall healthcare. CONCLUSIONS/RECOMMENDATIONS: We urgently need to expand the evidence base to inform the care of complex patients of all kinds, especially for the clinical trade-off decisions that are central to tailoring care. We offer long- and short-term strategies to begin to incorporate complexity into quality measurement and performance profiling, guided by the Vector Model. Interdisciplinary research should lay the foundation for a deeper understanding of the multiple sources of patient complexity and their interactions, and how provision of healthcare should be harmonized with complexity to optimize health