A simple filter model to guide the allocation of healthcare resources for improving the treatment of depression among cancer patients

Background: Depression is highly prevalent yet often poorly detected and treated among cancer patients. In light of the move towards evidence-based healthcare policy, we have developed a simple tool that can assist policy makers, organisations and researchers to logically think through the steps involved in improving patient outcomes, and to help guide decisions about where to allocate resources. Methods: The model assumes that a series of filters operate to determine outcomes and cost-effectiveness associated with depression care for cancer patients, including: detection of depression, provider response to detection, patient acceptance of treatment, and effectiveness of treatment provided. To illustrate the utility of the model, hypothetical data for baseline and four scenarios in which filter outcomes were improved by 15% were entered into the model. Results: The model provides outcomes including: number of people successfully treated, total costs per scenario, and the incremental cost-effectiveness ratio per scenario compared to baseline. The hypothetical data entered into the model illustrate the relative effectiveness (in terms of the number of additional incremental successes) and relative cost-effectiveness (in terms of cost per successful outcome and total cost) of making changes at each step or filter. Conclusions: The model provides a readily accessible tool to assist decision makers to think through the steps involved in improving depression outcomes for cancer patents. It provides transparent guidance about how to best allocate resources, and highlights areas where more reliable data are needed. The filter model presents an opportunity to improve on current practice by ensuring that a logical approach, which takes into account the available evidence, is applied to decision making.