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Abstract: Background: The increase in the incidence of dementia in the aging population and the decrease in the availability of informal caregivers put pressure on continuing care systems to care for a growing number of people with disabilities. Policy changes in the continuing care system need to address this shift in the population structure. One of the most effective tools for assessing policies in complex systems is system dynamics. Nevertheless, this method is underused in continuing care capacity planning. Methods: A system dynamics model of the Alberta Continuing Care System was developed using stylized data. Sensitivity analyses and policy evaluations were conducted to demonstrate the use of system dynamics modelling in this area of public health planning. We focused our policy exploration on introducing staff/resident benchmarks in both supportive living and long‐term care (LTC). Results: The sensitivity analyses presented in this paper help identify leverage points in the system that need to be acknowledged when policy decisions are made. Our policy explorations showed that the deficits of staff increase dramatically when benchmarks are introduced, as expected, but at the end of the simulation period, the difference in deficits of both nurses and health care aids are similar between the 2 scenarios tested. Modifying the benchmarks in LTC only versus in both supportive living and LTC has similar effects on staff deficits in long term, under the assumptions of this particular model. Conclusion: The continuing care system dynamics model can be used to test various policy scenarios, allowing decision makers to visualize the effect of a certain policy choice on different system variables and to compare different policy options. Our exploration illustrates the use of system dynamics models for policy making in complex health care systems. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives Persistently long waiting times for hip and knee total joint arthroplasty ( TJA) specialist consultations have been identified as a problem. This study described referral processes and practices, and their impact on the waiting time from referral to consultation for TJA. Methods A mixed-methods retrospective study incorporating semi-structured interviews, patient chart reviews and observational studies was conducted at three clinic sites in Alberta, Canada. A total of 218 charts were selected for analysis. Standardized definitions were applied to key event dates. Performance measures included waiting times percentage of referrals initially accepted. Voluntary (patient-related) and involuntary (health system-related) waiting times were quantified. Results All three clinics had defined, but differing, referral processing rules. The mean time from referral to consultation ranged from 51 to 139 business days. Choosing a specific surgeon for consultation rather than a next available surgeon lengthened waits by 10-47 business days. Involuntary waiting times accounted for at least 11% of total waiting time. Approximately 40-80% of the time patients with TJA wait for surgery was in the consultation period. Fifty-four per cent of new referrals were initially rejected, prolonging patient waits by 8-46 business days. Conclusions Our results suggest that variation in referral processing led to increased waiting times for patients. The large proportion of total wait attributable to waiting for a surgical consultation makes failure to measure and evaluate this period a significant omission. Improving referral processes and decreasing variation between clinics would improve patient access to these specialist referrals in Alberta. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives A collaborative, multidisciplinary guideline adaptation process was developed to construct a single overarching, evidence-based clinical practice guideline (CPG) for all primary care practitioners responsible for the management of low back pain (LBP) to curb the use of ineffective treatments and improve patient outcomes. Methods The adaptation strategy, which involved multiple committees and partnerships, leveraged existing knowledge transfer connections to recruit guideline development group (GDG) members and ensure that all stakeholders had a voice in the guideline development process. Videoconferencing was used to coordinate the large, geographically dispersed GDG. Information services and health technology assessment experts were used throughout the process to lighten the GDG's workload. Results The GDG reviewed seven seed guidelines and drafted an Alberta-specific guideline during 10 half-day meetings over a 12-month period. The use of ad hoc subcommittees to resolve uncertainties or disagreements regarding evidence interpretation expedited the process. Challenges were encountered in dealing with subjectivity, guideline appraisal tools, evidence source limitations and inconsistencies, and the lack of sophisticated evidence analysis inherent in guideline adaptation. Strategies for overcoming these difficulties are discussed. Conclusion Guideline adaptation is useful when resources are limited and good-quality seed CPGs exist. The Ambassador Program successfully utilized existing stakeholder interest to create an overarching guideline that aligned guidance for LBP management across multiple primary care disciplines. Unforeseen challenges in guideline adaptation can be overcome with credible seed guidelines, a consistently applied and transparent methodology, and clear documentation of the subjective contextualization process. Multidisciplinary stakeholder input and an open, trusting relationship among all contributors will ensure that the end product is clinically meaningful. [ABSTRACT FROM AUTHOR]