Measuring interdependence in ambulatory care

Rationale, aims and objectives: Complex systems differ from complicated systems in that they are nonlinear, unpredictable and lacking clear cause-and-effect relationships, largely due to the interdependence of their components (effects of interconnectedness on system behaviour and consequences). The purpose of this study was to demonstrate the potential for network density to serve as a measure of interdependence, assess its concurrent validity and test whether the use of valued or binary ties yields better results. Method: This secondary analysis used the 2010 National Ambulatory Care Medical Survey to assess interdependence of ‘top 20’ diagnoses seen and medications prescribed for 14 specialties. The degree of interdependence was measured as the level of association between diagnoses and drug interactions among medications. Both valued and binary network densities were computed for each specialty. To assess concurrent validity, these measures were correlated with previously-derived valid measures of complexity of care using the same database, adjusting for diagnosis and medication diversity. Results: Partial correlations between diagnosis density, and both diagnosis and total input complexity, were significant, as were those between medication density and both medication and total output complexity; for both diagnosis and medication densities, adjusted correlations were higher for binary rather than valued densities. Conclusion: This study demonstrated the feasibility and validity of using network density as a measure of interdependence. When adjusted for measure diversity, density-complexity correlations were significant and higher for binary than valued density. This approach complements other methods of estimating complexity of care and may be applicable to unique settings.