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Background: Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity.Objectives: In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available.Conclusions: Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives: Strong health systems are said to be paramount to achieving effective and equitable health care. The World Health Organization has been advocating for using system-wide approaches such as 'systems thinking' to guide intervention design and evaluation. In this paper we report the system-wide effects of a complex health system intervention in Zambia known as Better Health Outcome through Mentorship and Assessment (BHOMA) that aimed to improve service quality. Methods: We conducted a qualitative study in three target districts. We used a systems thinking conceptual framework to guide the analysis focusing on intended and unintended consequences of the intervention. NVivo version 10 was used for data analysis. Results: The addressed community responded positively to the BHOMA intervention. The indications were that in the short term there was increased demand for services but the health worker capacity was not severely affected. This means that the prediction that service demand would increase with implementation of BHOMA was correct and the workload also increased, but the help of clinic lay supporters meant that some of the work of clinicians was transferred to these lay workers. However, from a systems perspective, unintended consequences also occurred during the implementation of the BHOMA. Conclusions: We applied an innovative approach to evaluate a complex intervention in low-income settings, exploring empirically how systems thinking can be applied in the context of health system strengthening. Although the intervention had some positive outcomes by employing system-wide approaches, we also noted unintended consequences. [ABSTRACT FROM AUTHOR]

Background: Proliferation of terms describing the science of effectively promoting and supporting the use of research evidence in healthcare policy and practice has hampered understanding and development of the field. To address this, an international Terminology Working Group developed and published a simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies. This paper presents results of validation work and a second international workgroup meeting, culminating in the updated AIMD framework [Aims, Ingredients, Mechanism, Delivery].Methods: Framework validity was evaluated against terminology schemas (n = 51); primary studies (n = 37); and reporting guidelines (n = 10). Framework components were independently categorized as fully represented, partly represented, or absent by two researchers. Opportunities to refine the framework were systematically recorded. A meeting of the expanded international Terminology Working Group updated the framework by reviewing and deliberating upon validation findings and refinement proposals.Results: There was variation in representativeness of the components across the three types of literature, in particular for the component 'causal mechanisms'. Analysis of primary studies revealed that representativeness of this concept lowered from 92 to 68% if only explicit, rather than explicit and non-explicit references to causal mechanisms were included. All components were very well represented in reporting guidelines, however the level of description of these was lower than in other types of literature. Twelve opportunities were identified to improve the framework, 9 of which were operationalized at the meeting. The updated AIMD framework comprises four components: (1) Aims: what do you want your intervention to achieve and for whom? (2) Ingredients: what comprises the intervention? (3) Mechanisms: how do you propose the intervention will work? and (4) Delivery: how will you deliver the intervention?Conclusions: The draft simplified framework was validated with reference to a wide range of relevant literature and improvements have enhanced useability. The AIMD framework could aid in the promotion of evidence into practice, remove barriers to understanding how interventions work, enhance communication of interventions and support knowledge synthesis. Future work needs to focus on developing and testing resources and educational initiatives to optimize use of the AIMD framework in collaboration with relevant end-user groups. [ABSTRACT FROM AUTHOR]

Background: The evaluation of payment for performance (P4P) programmes has focused mainly on understanding contributions to health service coverage, without unpacking causal mechanisms. The overall aim of the paper is to test the causal pathways through which P4P schemes may (or may not) influence maternal care outcomes.Methods: We used data from an evaluation of a P4P programme in Tanzania. Data were collected from a sample of 3000 women who delivered in the 12 months prior to interview and 200 health workers at 150 health facilities from seven intervention and four comparison districts in Tanzania in January 2012 and in February 2013. We applied causal mediation analysis using a linear structural equation model to identify direct and indirect effects of P4P on institutional delivery rates and on the uptake of two doses of an antimalarial drug during pregnancy. We first ran a series of linear difference-in-difference regression models to test the effect of P4P on potential mediators, which we then included in a linear difference-in-difference model evaluating the impact of P4P on the outcome. We tested the robustness of our results to unmeasured confounding using semi-parametric methods.Results: P4P reduced the probability of women paying for delivery care (-4.5 percentage points) which mediates the total effect of P4P on institutional deliveries (by 48%) and on deliveries in a public health facility (by 78%). P4P reduced the stock-out rate for some essential drugs, specifically oxytocin (-36 percentage points), which mediated the total effect of P4P on institutional deliveries (by 22%) and deliveries in a public health facility (by 30%). P4P increased kindness at delivery (5 percentage points), which mediated the effect of P4P on institutional deliveries (by 48%) and on deliveries in a public health facility (by 49%). P4P increased the likelihood of supervision visits taking place within the last 90 days (18 percentage points), which mediated 15% of the total P4P effect on the uptake of two antimalarial doses during antenatal care (IPT2). Kindness during deliveries and the probability of paying out of pocket for delivery care were the mediators most robust to unmeasured confounding.Conclusions: The effect of P4P on institutional deliveries is mediated by financing and human resources factors, while uptake of antimalarials in pregnancy is mediated by governance factors. Further research is required to explore additional and more complex causal pathways. [ABSTRACT FROM AUTHOR]

Racial-ethnic minorities receive lower quality and intensity of health care compared with whites across a wide range of preventive, diagnostic, and therapeutic services and disease entities. These disparities in health care contribute to continuing racial-ethnic disparities in the burden of illness and death. Several national medical organizations and the Institute of Medicine have issued position papers and recommendations for the elimination of health care disparities. However, physicians in practice are often at a loss for how to translate these principles and recommendations into specific interventions in their own clinical practices. This paper serves as a blueprint for translating principles for the elimination of racial-ethnic disparities in health care into specific actions that are relevant for individual clinical practices. We describe what is known about reducing racial-ethnic disparities in clinical practice and make recommendations for how clinician leaders can apply this evidence to transform their own practices. [ABSTRACT FROM AUTHOR]