A categorisation of problems and solutions to improve patient referrals from primary to specialty care.

BACKGROUND: Improving access to specialty care has been identified as a critical issue in the delivery of health services, especially given an increasing burden of chronic disease. Identifying and addressing problems that impact access to specialty care for patients referred to speciality care for non-emergent procedures and how these deficiencies can be managed via health system delivery interventions is important to improve care for patients with chronic conditions. However, the primary-specialty care interface is complex and may be impacted by a variety of potential health services delivery deficiencies; with an equal range of interventions developed to correct them. Consequently, the literature is also diverse and difficult to navigate. We present a narrative review to identify existing literature, and provide a conceptual map that categorizes problems at the primary-specialty care interface with linkages to corresponding interventions aimed at ensuring that patient transitions across the primary-specialty care interface are necessary, appropriate, timely and well communicated. METHODS: We searched MEDLINE and EMBASE databases from January 1, 2005 until Dec 31, 2014, grey literature and reference lists to identify articles that report on interventions implemented to improve the primary-specialty care interface. Selected articles were categorized to describe: 1) the intervention context, including the deficiency addressed, and the objective of the intervention 2) intervention activities, and 3) intervention outcomes. RESULTS: We identified 106 articles, producing four categories of health services delivery deficiencies based in: 1) clinical decision making; 2) information management; 3) the system level management of patient flows between primary and secondary care; and 4) quality-of-care monitoring. Interventions were divided into seven categories and fourteen sub-categories based on the deficiencies addressed and the intervention strategies used. Potential synergies and trade-offs among interventions are discussed. Little evidence exists regarding the synergistic and antagonistic interactions of alternative intervention strategies. CONCLUSION: The categorization acts as an aid in identifying why the primary-specialty care interface may be failing and which interventions may produce improvements. Overlap and interconnectedness between interventions creates potential synergies and conflicts among co-implemented interventions.

Complex intervention modelling should capture the dynamics of adaptation.

BACKGROUND: Complexity has been linked to health interventions in two ways: first as a property of the intervention, and secondly as a property of the system into which the intervention is implemented. The former recognizes that interventions may consist of multiple components that act both independently and interdependently, making it difficult to identify the components or combinations of components (and their contexts) that are important mechanisms of change. The latter recognizes that interventions are implemented in complex adaptive systems comprised of intelligent agents who modify their behaviour (including any actions required to implement the intervention) in an effort to improve outcomes relative to their own perspective and objectives. Although an intervention may be intended to take a particular form, its implementation and impact within the system may deviate from its original intentions as a result of adaptation. Complexity highlights the challenge in developing interventions as effective health solutions. The UK Medical Research Council provides guidelines on the development and evaluation of complex interventions. While mathematical modelling is included in the guidelines, there is potential for mathematical modeling to play a greater role. DISCUSSION: The dynamic non-linear nature of complex adaptive systems makes mathematical modelling crucial. However, the tendency is for models of interventions to limit focus on the ecology of the system - the 'real-time' operation of the system and impacts of the intervention. These models are deficient by not modelling the way the system reacts to the intervention via agent adaptation. Complex intervention modelling needs to capture the consequences of adaptation through the inclusion of an evolutionary dynamic to describe the long-term emergent outcomes that result as agents respond to the ecological changes introduced by intervention in an effort to produce better outcomes for themselves. Mathematical approaches such as those found in economics in evolutionary game theory and mechanism design can inform the design and evaluation of health interventions. As an illustration, the introduction of a central screening clinic is modeled as an example of a health services delivery intervention. Complexity necessitates a greater role for mathematical models, especially those that capture the dynamics of human actions and interactions.

Development of key performance indicators to evaluate centralized intake for patients with osteoarthritis and rheumatoid arthritis.

INTRODUCTION: Centralized intake is integral to healthcare systems to support timely access to appropriate health services. The aim of this study was to develop key performance indicators (KPIs) to evaluate centralized intake systems for patients with osteoarthritis (OA) and rheumatoid arthritis (RA). METHODS: Phase 1 involved stakeholder meetings including healthcare providers, managers, researchers and patients to obtain input on candidate KPIs, aligned along six quality dimensions: appropriateness, accessibility, acceptability, efficiency, effectiveness, and safety. Phase 2 involved literature reviews to ensure KPIs were based on best practices and harmonized with existing measures. Phase 3 involved a three-round, online modified Delphi panel to finalize the KPIs. The panel consisted of two rounds of rating and a round of online and in-person discussions. KPIs rated as valid and important (≥7 on a 9-point Likert scale) were included in the final set. RESULTS: Twenty-five KPIs identified and substantiated during Phases 1 and 2 were submitted to 27 panellists including healthcare providers, managers, researchers, and patients in Phase 3. After the in-person meeting, three KPIs were removed and six were suggested. The final set includes 9 OA KPIs, 10 RA KPIs and 9 relating to centralized intake processes for both conditions. All 28 KPIs were rated as valid and important. CONCLUSIONS: Arthritis stakeholders have proposed 28 KPIs that should be used in quality improvement efforts when evaluating centralized intake for OA and RA. The KPIs measure five of the six dimensions of quality and are relevant to patients, practitioners and health systems.

Sex allocation and dispersal in a heterogeneous two-patch environment.

We investigate the evolution of sex allocation and dispersal in a two-habitat environment using a game theoretic analysis. One habitat is of better quality than the other and increased habitat quality influences the competitive ability of offspring in a sex-specific manner. Unlike previous work, we allow incomplete mixing of the population during mating. We discuss three special cases involving the evolution of sex allocation under fixed levels of dispersal between habitats. In these special cases, stable sex-allocation behaviors can be both biased and unbiased. When sex-allocation behavior and dispersal rates co-evolve we identify two basic outcomes. First-when sex-specific differences in the consequences of spatial heterogeneity are large-we predict the evolution of biased sex-allocation behavior in both habitats, with dispersal by males in one direction and dispersal by females in the other direction. Second-when sex-specific differences are small-unbiased sex-allocation is predicted with no dispersal between habitats.