Improving accessibility through referral management: setting targets for specialist care

A Simulation Modelling Study of Referral Distribution Policies in a Centralized Intake System for Surgical Consultation

Delays beyond recommended wait times, especially for specialist services, are associated with adverse health outcomes. The Alberta Surgical Initiative aims to improve the referral wait time-the time between a referral is received at the central intake to the time a specialist sees the patient. Using the discrete event simulation modelling approach, we evaluated and compared the impact of four referral distribution policies in a central intake system on three system performance measures (number of consultations, referral wait time and surgeon utilization). The model was co-designed with clinicians and clinic staff to represent the flow of patients through the system. We used data from the Facilitated Access to Surgical Treatment (FAST) centralized intake referral program for General Surgery to parameterize the model. Four distribution policies were evaluated - next-available-surgeon, sequential, "blackjack," and "kanban." A sequential distribution of referrals for surgical consultation among the surgeons resulted in the worst performance in terms of the number of consultations, referral wait time and surgeon utilization. The three other distribution policies are comparable in performance. The "next available surgeon" model provided the most efficient and robust model, with approximately 1,000 more consultations, 100 days shorter referral time and a 14% increase in surgeon utilization. Discrete event simulation (DES) modelling can be an effective tool to illustrate and communicate the impact of the referral distribution policy on system performance in terms of the number of consultations, referral wait time and surgeon utilization.

Musculoskeletal care pathways for adults with hip and knee pain referred for specialist opinion: a systematic review

OBJECTIVE

Musculoskeletal care pathways are variable and inconsistent. The aim of this systematic review was to evaluate the evidence for the clinical and/or cost effectiveness of current care pathways for adults with hip and/or knee pain referred for specialist opinion.

DESIGN

Systematic review.

DATA SOURCES

Electronic database searches were carried out in MEDLINE, MEDLINE In-Process, Cumulative Index of Nursing and Allied Health Literature, Embase, PEDro, PubMed, Web of Science, Cochrane Central and Health Management Information Consortium without language restriction from 1990 onwards. Websites were reviewed for grey literature.

ELIGIBILITY CRITERIA

All study designs and documents that considered care pathways for adults with musculoskeletal hip and/or knee pain referred for specialist opinion were screened by two reviewers. Risk of bias was assessed using The Critical Appraisal Skills Programme checklist for randomised controlled trials and the Joanna Briggs Institute checklists.

DATA EXTRACTION AND SYNTHESIS

Data extraction and quality assessment were performed by one reviewer and checked by a second. Findings are reported narratively.

RESULTS

The titles and abstracts of 1248 articles were screened and 140 full-text articles retrieved. 19 papers reporting 17 studies met the study inclusion criteria. Quality was low due to study design and methodological flaws. Most of the outcomes relate to organisational process at the 'meso' level of a whole systems approach.

CONCLUSION

It can be concluded that the pathway is not linear, containing variations and activity loops. The available evidence suggests that, from the point of referral for specialist opinion, a model is required that integrates the skills of all the different healthcare professionals and streamlining is required to ensure that individuals are seen by the healthcare professional that best meets their needs. There is very limited evidence of patient experience informing knee and hip care pathways.

PROSPERO REGISTRATION NUMBER

CRD42016035510.

The voice of patients in system redesign: A case study of redesigning a centralized system for intake of referrals from primary care to rheumatologists for patients with suspected rheumatoid arthritis

BACKGROUND

The published literature demands examples of health-care systems designed with the active engagement of patients to explore the application of this complex phenomenon in practice.

METHODS

This case study explored how the voice of patients was incorporated into the process of redesigning an element of the health-care system, a centralized system for intake of referrals from primary care to rheumatologists for patients with suspected rheumatoid arthritis (RA)-centralized intake. The phenomenon of patient engagement using "patient and community engagement researchers" (PaCERs) in research and the process of redesigning centralized intake were selected as the case. In-depth evaluation of the case was undertaken through the triangulation of findings from the document review and participants' reflection on the case.

RESULTS

In this case, patients and PaCERs participated in multiple activities including an initial meeting of key stakeholders to develop the project vision; a patient-to-patient PaCERs study to gather perspectives of patients with RA on the challenges they face in accessing and navigating the health-care system, and what they see as key elements of an effective system that would be responsive to their needs; the development of an evaluation framework for future centralized intake; and the choice of candidate centralized intake strategies to be evaluated.

CONCLUSIONS

The described feasible multistep approach to active patient engagement in health-care system redesign contributes to an understanding of the application of this complex phenomenon in practice. Therefore, the manuscript serves as one more step towards a patient-centred health-care system that is redesigned with active patient engagement.

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.

Summary

Complexity necessitates a greater role for mathematical models, especially those that capture the dynamics of human actions and interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0149-8) contains supplementary material, which is available to authorized users.