Utility of Telehealth Platforms Applied to Burns Management: A Systematic Review

García-Díaz, Antonio; Vilardell-Roig, Lluís; Novillo-Ortiz, David; Gacto-Sánchez, Purificación; Pereyra‐Rodriguez, José Juan; Saigí-Rubió, Francesc

doi:10.3390/ijerph20043161

Cited by 3 publications

(3 citation statements)

References 45 publications

(133 reference statements)

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“…The financial burden is also substantial, with each burn patient costing the healthcare system approximately US$88,218 on average [ 1 , 2 ]. Despite advancements in technology, the prognosis of a severe burn injury remains poor [ 3 ]. Consequently, there is a significant focus on research and finding ways to alleviate this burden.…”

Section: Introductionmentioning

confidence: 99%

Burn Mortality in an Appalachian Referral Center: An Examination of Mortality Prediction Scores in a 13-Year Retrospective Study

Rahimpour,

Fox,

Kahley

et al. 2024

Cureus

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Introduction Burn injuries have profound implications, prompting the use of various mortality scoring systems. This study aimed to evaluate their effectiveness within our Appalachian burn referral center, which serves as the sole burn center in the state of West Virginia. Given this unique status, understanding the efficacy of mortality scoring systems within our center is crucial for resource allocation and optimizing patient outcomes in our region. Methods A retrospective analysis of patients admitted to Cabell Huntington Hospital Burn Intensive Care Unit (BICU) from January 2010 to June 2023 was conducted, assessing Baux (B), revised Baux (rB), Belgian Outcome in Burn Injury (BOBI), and Abbreviated Burn Severity Index (ABSI) scores. Logistic regression and receiver operating characteristic analysis were employed to examine survival status and determine optimal cut points. Results Among 1,104 patients, 57 died (5% mortality rate). Deceased patients had significantly higher B/rB/BOBI scores (mean: 98/98/92) than survivors (45/46/4.19) (p < 0.001), with ABSI showing no significance (p = 0.079). Each one-point increase in B/rB/BOBI scores correlated with a 1.09/1.09/2.34 times higher mortality risk (p < 0.001). The AUC for B score in predicting mortality was 0.926 (95% CI: 0.890, 0.962), with sensitivity and specificity values of 0.789 and 0.92, respectively, and an optimal cutoff point of 79. The AUC for the rB score was 0.927 (95% CI: 0.892, 0.962), with sensitivity and specificity values of 0.789 and 0.926, respectively, and an optimal cutoff point of 80. The AUC for the BOBI score was 0.901 (95% CI: 0.865, 0.937), with sensitivity and specificity values of 0.895 and 0.775, respectively, and an optimal cutoff point of 2. For patients with B scores above 79, their odds of mortality were 42.6 times higher than those with B scores of 79 or lower (95% CI: 22.6, 85.6, p < 0.001). Similarly, for patients with rB scores exceeding 80, their odds of mortality were 42.9 times higher than those with rB scores of 80 or lower (95% CI: 22.9, 84.8, p < 0.001). Finally, for patients with BOBI scores greater than 2, their odds of mortality were 17.8 times higher than those with BOBI scores of 2 or lower (95% CI: 9.88, 33.4, p < 0.001). Conclusion Our study underscores the vital role of mortality scoring systems in guiding clinical decision-making and resource allocation for burn patients, particularly within the Appalachian region served by the Cabell Huntington Hospital BICU. By leveraging tools such as the Baux, revised Baux, and BOBI scores, healthcare providers can identify high-risk patients early in their treatment course, facilitating personalized interventions and improving overall patient outcomes. Moreover, our findings highlight the significance of age and total body surface area burned as key determinants of mortality risk, emphasizing the need for tailored approaches to care for elderly patients and those with extensive burns. Contin...

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Section: Introductionmentioning

confidence: 99%

Burn Mortality in an Appalachian Referral Center: An Examination of Mortality Prediction Scores in a 13-Year Retrospective Study

Rahimpour,

Fox,

Kahley

et al. 2024

Cureus

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“…Mounting evidence supports the implementation of telehealth systems to reduce the burden of access and improve the efficiency of burn care delivery. [4][5][6] However, factors linked to difficulty accessing in-person burn care, such as poverty and rurality, have also been linked to decreased telehealth utilization rates. 7 Likewise, patients in the South and Midwest, known areas of low burncare access, are less likely than those in the West and Northeast to utilize remote healthcare.…”

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confidence: 99%

“…Mounting evidence supports the implementation of telehealth systems to reduce the burden of access and improve the efficiency of burn care delivery 4–6 . However, factors linked to difficulty accessing in-person burn care, such as poverty and rurality, have also been linked to decreased telehealth utilization rates 7 .…”

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confidence: 99%

Barriers to Remote Burn Care Delivery

Edwards,

Chamoun,

Hecox

et al. 2024

Ann Plast Surg

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Mounting evidence supports the use of telehealth to improve burn care access and efficiency. However, barriers to telehealth use remain throughout the United States and may disproportionately affect specific populations, such as rural and non-English-speaking patients. This study analyzes the association between physical proximity to burn care and determinants of telehealth access. The relationship between telehealth-associated measures and proximity to burn care was analyzed with linear regression analysis. County-level data was sourced from the Agency for Healthcare Research and Quality's Social Determinants of Health Database (2020) and the American Community Survey (2021). County-level distances to the nearest American Burn Association (ABA)-verified burn center were calculated based on verified centers listed in the ABA burn center directory (n = 59). A subsequent analysis was performed on income-stratified datasets available for subset counties. Distance was negatively correlated with access to a smartphone (P < 0.0001), broadband internet (P < 0.0001), and cellular data plan (P < 0.0001) and positively correlated with the percent of households with no computing device (P < 0.0001) and no internet access (P < 0.0001). Analysis of income-stratified data revealed similar results. The percent population not speaking English well (P < 0.0001) at all (P = 0.0009) and the proportion of limited English-speaking households (P = 0.0001) decreased as a function of distance. People living furthest from an ABA-verified burn center in the United States are less likely to have adequate access to critical telehealth infrastructure compared to their counterparts living closer to a burn center. However, income impacts overall access and the degree to which access changes with proximity. Conversely, language-associated barriers decrease as distance increases.

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