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BackgroundVisual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots. ObjectiveThis scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR). MethodsUsing the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences. ResultsAfter screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard. ConclusionsWith the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage. International Registered Report Identifier (IRRID)RR2-10.2196/14019

Rationale: Timely assessment of a chronic condition is critical to prevent long‐term irreversible consequences. Patients with inflammatory arthritis (IA) symptoms require diagnosis by a rheumatologist and intervention initiation to minimize potential joint damage. With limited rheumatologist capacity, meeting urgency wait time benchmarks can be challenging. We investigate the impact of the maximum wait time guarantee (MWTG) policy and referral volume changes in a rheumatology central intake (CI) system on meeting this challenge. Methods: We applied a system simulation approach to model a high‐volume CI rheumatology clinic. Model parameters were based on the referral and triage data from the CI and clinic appointment data. We compare the wait time performance of the current distribution policy MWTG and when referral volumes change. Results: The MWTG policy ensures 100% of new patients see a rheumatologist within their urgency wait time benchmark. However, the average wait time for new patients increased by 51% (178–269 days). A 10% decrease in referrals resulted in a 76% decrease on average wait times (178–43 days) for new patients and an increase in the number of patients seen by a rheumatologist within 1 year of the initial visit. Conclusion: An MWTG policy can result in intended and unintended consequences—ensuring that all patients meet the wait time benchmarks but increasing wait times overall. Relatively small changes in referral volume significantly impact wait times. These relationships can assist clinic managers and policymakers decide on the best approach to manage referrals for better system performance. [ABSTRACT FROM AUTHOR]

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]

ABSTRACT Objectives The objective of our study is to evaluate the effectiveness of dementia case management compared with usual care on reducing long-term care placement, hospitalization, and emergency department visits for adult patients with dementia. We also sought to evaluate the effectiveness of this intervention on delaying time to long-term care placement and hospitalization. Methods We searched electronic databases supplemented by bibliographies and conference proceedings for randomized controlled trials testing the effectiveness of dementia case management in reducing resource utilization in a population of caregiver-care recipient dyads living in the community. We meta-analyzed the risk ratio (RR) and weighted mean differences of long-term care placement and the RR of hospital admissions. Pooled estimates were further stratified by study characteristics and measures of study quality. Results Seventeen studies were included in the meta-analysis. The overall pooled RR of long-term care placement was 0.94 (95% confidence interval [0.85, 1.03]; p = 0.227) for dementia case management compared with usual care. Stratification by follow-up duration indicated a statistically significant reduction in risk of long-term care placement when follow-up duration was less than 18 months (RR 0.61, 95% confidence interval [0.41, 0.91], p = 0.015). There was no effect of dementia case management compared with usual care for the other outcomes. Conclusion Dementia case management demonstrated a short-term positive effect on reducing the risk of long-term care placement among older people with dementia residing in the community. However, other sources of resource utilization and more extended effects of dementia case management on risk of long-term care placement warrant further investigation. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Demands on gastroenterology are growing, as a result of the high prevalence of digestive diseases, the impact of colon cancer screening programs and an aging population. Prioritizing referrals to gastroenterology would assist in managing wait times. Our objectives were (1) to assess whether there were consistent criteria to guide referrals from family physicians for gastroenterological outpatient consultation and (2) to determine if there were different levels of urgency or priority in referral criteria., Methods: We conducted a scoping review, searching Medline, Embase and Cochrane databases from 1997 to 2009, using the terms referral, triage, consultation and at least one from a list of gastroenterology-specific search terms. Of 2978 initial results, 51 papers were retrieved, and 20 were retained after review by two reviewers. Additional publications were identified through hand searches of retained papers, website searches and nomination by a panel of specialists., Results: Thirty-four papers, reports or websites were retained. No referral criteria covered the spectrum of disorders that might be referred by family physicians to gastroenterologists. Criteria for referral were most commonly listed for suspected colorectal cancer, followed by suspected upper GI cancer, hepatitis, and functional disorders., Conclusions: A clinical panel comprised of gastroenterologists and primary care providers, informed by this literature review, are completing the work of formulating a Gastroenterology Priority Referral Score, and plan to test the reliability and validity of the tool for determining the relative urgency for referral from primary care to gastroenterology.