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Assessing local capacity to expand rural breast cancer screening and patient navigation: An iterative mixed-method tool.

ABSTRACT: BACKGROUND:Despite federal funding for breast cancer screening, fragmented infrastructure and limited organizational capacity hinder access to the full continuum of breast cancer screening and clinical follow-up procedures among rural-residing women. We proposed a regional hub-and-spoke model, partnering with local providers to expand access across North Texas. We describe development and application of an iterative, mixed-method tool to assess county capacity to conduct community outreach and/or patient navigation in a partnership model. METHODS:Our tool combined publicly-available quantitative data with qualitative assessments during site visits and semi-structured interviews. RESULTS:Application of our tool resulted in shifts in capacity designation in 10 of 17 county partners: 8 implemented local outreach with hub navigation; 9 relied on the hub for both outreach and navigation. Key factors influencing capacity: (1) formal linkages between partner organizations; (2) inter-organizational relationships; (3) existing clinical service protocols; (4) underserved populations. Qualitative data elucidate how our tool captured these capacity changes. CONCLUSIONS:Our capacity assessment tool enabled the hub to establish partnerships with county organizations by tailoring support to local capacity and needs. Absent a vertically integrated provider network for preventive services in these rural counties, our tool facilitated a virtually integrated regional network to extend access to breast cancer screening to underserved women.

SUBMITTER: Inrig SJ

PROVIDER: S-EPMC5323072 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Assessing local capacity to expand rural breast cancer screening and patient navigation: An iterative mixed-method tool.

Inrig Stephen J SJ Higashi Robin T RT Tiro Jasmin A JA Argenbright Keith E KE Lee Simon J Craddock SJ

Evaluation and program planning 20161123

<h4>Background</h4>Despite federal funding for breast cancer screening, fragmented infrastructure and limited organizational capacity hinder access to the full continuum of breast cancer screening and clinical follow-up procedures among rural-residing women. We proposed a regional hub-and-spoke model, partnering with local providers to expand access across North Texas. We describe development and application of an iterative, mixed-method tool to assess county capacity to conduct community outrea ...[more]

PMID: 28011433

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Project description:BackgroundMusculoskeletal conditions account for 16% of global disability, resulting in a negative effect on millions of patients and an increasing burden on health care utilization. Digital technologies that improve health care outcomes and efficiency are considered a priority; however, innovations are often inadequately developed and poorly adopted. Further, they are rarely tested with sufficient rigor in clinical trials-the gold standard for clinical proof of efficacy. We have developed a new musculoskeletal Digital Assessment Routing Tool (DART) that allows users to self-assess and be directed to the right care. DART requires usability testing in preparation for clinical trials.ObjectiveThis study will use the iterative convergent mixed methods design to assess and mitigate all serious usability issues to optimize user experience and adoption. Using this methodology, we will provide justifiable confidence to progress to full-scale randomized controlled trials when DART is integrated into clinical management pathways. This study protocol will provide a blueprint for future usability studies of mobile health solutions.MethodsWe will collect qualitative and quantitative data from 20-30 participants aged 18 years and older for 4 months. The exact number of participants recruited will be dependent on the number of iterative cycles required to reach the study end points. Building on previous internal testing and stakeholder involvement, quantitative data collection is defined by the constructs within the ISO 9241-210-2019 standard and the system usability scale, providing a usability score for DART. Guided by the participant responses to quantitative questioning, the researcher will focus the qualitative data collection on specific usability problems. These will then be graded to provide the rationale for further DART system improvements throughout the iterative cycles.ResultsThis study received approval from the Queen Mary University of London Ethics of Research Committee (QMREC2018/48/048) on June 4, 2020. At manuscript submission, study recruitment was on-going, with data collection to be completed and results published in 2021.ConclusionsThis study will provide evidence concerning mobile health DART system usability and acceptance determining system improvements required to support user adoption and minimize suboptimal system usability as a potential confounder within subsequent noninferiority clinical trials. Success should produce a safe effective system with excellent usability, facilitating quicker and easier patient access to appropriate care while reducing the burden on primary and secondary care musculoskeletal services. This deliberately rigorous approach to mobile health innovation could be used as a guide for other developers of similar apps.International registered report identifier (irrid)DERR1-10.2196/27205.

Project description:BackgroundMusculoskeletal disorders negatively affect millions of patients worldwide, placing significant demand on health care systems. Digital technologies that improve clinical outcomes and efficiency across the care pathway are development priorities. We developed the musculoskeletal Digital Assessment Routing Tool (DART) to enable self-assessment and immediate direction to the right care.ObjectiveWe aimed to assess and resolve all serious DART usability issues to create a positive user experience and enhance system adoption before conducting randomized controlled trials for the integration of DART into musculoskeletal management pathways.MethodsAn iterative, convergent mixed methods design was used, with 22 adult participants assessing 50 different clinical presentations over 5 testing rounds across 4 DART iterations. Participants were recruited using purposive sampling, with quotas for age, habitual internet use, and English-language ability. Quantitative data collection was defined by the constructs within the International Organization for Standardization 9241-210-2019 standard, with user satisfaction measured by the System Usability Scale. Study end points were resolution of all grade 1 and 2 usability problems and a mean System Usability Scale score of ≥80 across a minimum of 3 user group sessions.ResultsAll participants (mean age 48.6, SD 15.2; range 20-77 years) completed the study. Every assessment resulted in a recommendation with no DART system errors and a mean completion time of 5.2 (SD 4.44, range 1-18) minutes. Usability problems were reduced from 12 to 0, with trust and intention to act improving during the study. The relationship between eHealth literacy and age, as explored with a scatter plot and calculation of the Pearson correlation coefficient, was performed for all participants (r=-0.2; 20/22, 91%) and repeated with a potential outlier removed (r=-0.23), with no meaningful relationships observed or found for either. The mean satisfaction for daily internet users was highest (19/22, 86%; mean 86.5, SD 4.48; 90% confidence level [CL] 1.78 or -1.78), with nonnative English speakers (6/22, 27%; mean 78.1, SD 4.60; 90% CL 3.79 or -3.79) and infrequent internet users scoring the lowest (3/22, 14%; mean 70.8, SD 5.44; 90% CL 9.17 or -9.17), although the CIs overlap. The mean score across all groups was 84.3 (SD 4.67), corresponding to an excellent system, with qualitative data from all participants confirming that DART was simple to use.ConclusionsAll serious DART usability issues were resolved, and a good level of satisfaction, trust, and willingness to act on the DART recommendation was demonstrated, thus allowing progression to randomized controlled trials that assess safety and effectiveness against usual care comparators. The iterative, convergent mixed methods design proved highly effective in fully evaluating DART from a user perspective and could provide a blueprint for other researchers of mobile health systems.International registered report identifier (irrid)RR2-10.2196/27205.

Project description:BACKGROUND:In many low- and middle-income countries, data on the prevalence of surgical diseases have been derived primarily from hospital-based studies, which may lead to an underestimation of disease burden within the community. Community-based prevalence studies may provide better estimates of surgical need to enable proper resource allocation and prioritization of needs. This study aims to assess the prevalence of common surgical conditions among children in a diverse rural and urban population in Nigeria. METHODS:Descriptive cross-sectional, community-based study to determine the prevalence of congenital and acquired surgical conditions among children in a diverse rural-urban area of Nigeria was conducted. Households, defined as one or more persons 'who eat from the same pot' or slept under the same roof the night before the interview, were randomized for inclusion in the study. Data was collected using an adapted and modified version of the interviewer-administered questionnaire-Surgeons OverSeas Assessment of Surgical Need (SOSAS) survey tool and analysed using the REDCap web-based analytic application. MAIN RESULTS:Eight-hundred-and-fifty-six households were surveyed, comprising 1,883 children. Eighty-one conditions were identified, the most common being umbilical hernias (20), inguinal hernias (13), and wound injuries to the extremities (9). The prevalence per 10,000 children was 85 for umbilical hernias (95% CI: 47, 123), and 61 for inguinal hernias (95% CI: 34, 88). The prevalence of hydroceles and undescended testes was comparable at 22 and 26 per 10,000 children, respectively. Children with surgical conditions had similar sociodemographic characteristics to healthy children in the study population. CONCLUSION:The most common congenital surgical conditions in our setting were umbilical hernias, while injuries were the most common acquired conditions. From our study, it is estimated that there will be about 2.9 million children with surgically correctable conditions in the nation. This suggests an acute need for training more paediatric surgeons.

Project description:Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived from local and global topological information respectively has been shown to better predict nodal influence than using a single metric. In this work, we investigate to what extent local and global topological information around a node contributes to the prediction of nodal influence and whether relatively local information is sufficient for the prediction. We show that by leveraging the iterative process used to derive a classical nodal centrality such as eigenvector centrality, we can define an iterative metric set that progressively incorporates more global information around the node. We propose to predict nodal influence using an iterative metric set that consists of an iterative metric from order 1 to K produced in an iterative process, encoding gradually more global information as K increases. Three iterative metrics are considered, which converge to three classical node centrality metrics, respectively. In various real-world networks and synthetic networks with community structures, we find that the prediction quality of each iterative based model converges to its optimal when the metric of relatively low orders ( K∼4 ) are included and increases only marginally when further increasing K. This fast convergence of prediction quality with K is further explained by analyzing the correlation between the iterative metric and nodal influence, the convergence rate of each iterative process and network properties. The prediction quality of the best performing iterative metric set with K=4 is comparable with the benchmark method that combines seven centrality metrics: their prediction quality ratio is within the range [91%,106%] across all three quality measures and networks. In two spatially embedded networks with an extremely large diameter, however, iterative metric of higher orders, thus a large K, is needed to achieve comparable prediction quality with the benchmark.

Dataset Information

Assessing local capacity to expand rural breast cancer screening and patient navigation: An iterative mixed-method tool.

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Assessing local capacity to expand rural breast cancer screening and patient navigation: An iterative mixed-method tool.

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