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Sepsis is a life-threatening condition with significant long-term impacts for survivors and their families. The known benefits of patient engagement have led to increased efforts globally to involve survivors in sepsis research. This study aimed to characterize the experiences of sepsis survivors and their families in patient advisory councils (PACs) for two Canadian sepsis research networks (Action on Sepsis and Sepsis Canada) and explore how PAC involvement supports long-term recovery. This mixed-methods cross-sectional study consisted of a structured survey, ten interviews, and one focus group discussion. All current members of the Sepsis Canada and Action on Sepsis PACs (n = 29) were invited to participate. The results of the survey were analyzed descriptively and used to inform the development of the semi-structured interview guide. Qualitative data were analyzed using a thematic approach. Overall, 16 PAC members participated. The majority of participants were women and over 40 years old. Survey scores showed that most participants felt meaningfully engaged, while the qualitative findings highlighted how PACs supported recovery and fostered community connections between survivors, families, and researchers. Major themes included sepsis experience, recovery journey, characteristics of PACs, characteristics of PAC participation, and impacts of PAC involvement. Our findings demonstrate that PACs provide critical benefits that extend beyond feeling valued or appreciated for contributing to a specific project. These findings highlight the value of patient-oriented research in shaping evidence-based practices and policies and emphasize the need for trauma-informed approaches and improved post-sepsis care pathways to enhance recovery outcomes.

Automated messaging through text (SMS) and instant messaging services (IMS) offers low-cost solutions for patient follow-up in resource-constrained contexts. This study aims to evaluate a quality improvement (QI) initiative to improve caregiver response rates to an automated messaging system for post-discharge follow-up of children in rural Uganda. From June 2022 to June 2024, caregivers of children triaged through the Smart Triage platform at Gulu Regional Referral Hospital were invited to participate in an automated follow-up program. Messages were sent seven days after discharge via SMS and IMS (WhatsApp), prompting caregivers to report if their child had \"improved\" or \"not improved\". Non-responders and \"not improved\" cases were followed up with a phone call from a study nurse. From April 2023 to November 2023, a QI initiative refined the messaging system to improve response rates and a post-QI period then continued the intervention with no changes until June 2024. Response rates were analyzed over three periods: historical (pre-QI, June 2022 - March 2023), QI intervention, and post-QI. Additionally, data on message delivery rates, improvement strategies, and health outcomes were analyzed. Of 6826 participants, 6469 (95%) messages were successfully delivered. Response rates improved from 20% in April 2023 to 40% in June 2024 and remained stable between 33% and 41% during the post-QI period. Compared to the historical period, post-QI response rates were significantly higher (95% CI: 12.5% to 18.2%, p < 0.001). This improvement reflected a statistically significant positive trend during the QI period. Overall, 1856 caregivers responded: 1244 (67%) reported improvement and 612 (33%) reported no improvement. Follow-up phone calls for those \"not improved\" revealed 58 (9%) sought care, 12 (2%) were readmitted, and no deaths occurred. For non-responders, 206 (5%) sought care, 33 (0.7%) were readmitted, and 3 (0.07%) deaths occurred. Automated two-way text messages for post-discharge pediatric follow-up yielded high delivery and moderate response rates. Iterative QI efforts increased response rates, highlighting the importance of tailored communication strategies. Automated messages can facilitate timely intervention for high-risk children and enable efficient collection of health outcomes, offering a viable alternative to in-person follow-up in resource-poor settings.

This is a retrospective review of the feasibility study and implementation of the Smart Triage and Quality Improvement (QI) initiative at Holy Innocents Children's Hospital (HICH), a dedicated pediatric hospital in Mbarara, Uganda, over a 5-year period. The aim of this QI initiative was to improve triaging rates and the time-to-antimicrobials in HICH's outpatient department (OPD). Smart Triage is a risk prediction algorithm and digital platform that enables healthcare workers to triage patients and track treatments effectively. Following the feasibility study, the QI program was implemented in September 2021 using three Plan-Do-Study-Act cycles: 1) Standardize Training, 2) Adjust Workflows, and 3) QI Team Communication. Data sources were triage and hospital reports. Monthly run charts of OPD attendance, acuity of illness, triaging rates, median-time-to-antimicrobials, and mortality rates of admitted patients were created. The trajectories of the variables were assessed using linear regression with time as the explanatory variable. 121,521 children attended HICH OPD from November 2018 to October 2023. The OPD triaging rate increased to 91% by October 2023, with a sustained plateau above 90% since July 2022. There was a significant reduction in the median time-to-antimicrobials during the 5-year period, from 77.6 to 53.6 minutes, with a slope of -0.4 minutes per month (CI: -0.73 to -0.04, p-value: 0.029). The inpatient mortality rate decreased from 5.1% in August 2018 to 2.6% in October 2023, with a significant increase in the number of cases with comparable illness severity. The impact of Smart Triage was sustained beyond the end of the feasibility trial and showed sustained improvements in processes such as treatment times and clinical outcomes including a reduction in mortality. HICH's leadership integrated a culture of QI across disciplines and departments, contributing to this initiative's sustainability and impact.

Background Effective triage at hospitals can improve outcomes for children globally by helping identify and prioritize care for those most at-risk of death. Paper-based pediatric triage guidelines have been developed to support frontline health workers in low-resource settings, but these guidelines can be challenging to implement. Smart Triage is a digital triaging platform for quality improvement (QI) that aims to address this challenge. Smart Triage represents a major cultural and behavioural shift in terms of managing patients at health facilities in low-and middle-income countries. The purpose of this study is to understand user perspectives on the usability, feasibility, and acceptability of Smart Triage to inform ongoing and future implementation. Methods This was a descriptive qualitative study comprising of face-to-face interviews with health workers (n = 15) at a regional referral hospital in Eastern Uganda, conducted as a sub-study of a larger clinical trial to evaluate Smart Triage (NCT04304235). Thematic analysis was used to assess the usability, feasibility, and acceptability of the platform, focusing on its use in stratifying and prioritizing patients according to their risk and informing QI initiatives implemented by health workers. Results With appropriate training and experience, health workers found most features of Smart Triage usable and feasible to implement, and reported the platform was acceptable due to its positive impact on reducing the time to treatment for emergency pediatric cases and its use in informing QI initiatives within the pediatric ward. Several factors that reduced the feasibility and acceptability were identified, including high staff turnover, a lack of medical supplies at the hospital, and challenges with staff attitudes. Conclusion Health workers can use the Smart Triage digital triaging platform to identify and prioritize care for severely ill children and improve quality of care at health facilities in low-resource settings. Future innovation is needed to address identified feasibility and acceptability challenges; however, this platform could potentially address some of the challenges to implementing current paper-based systems.

Sepsis occurs predominantly in low-middle-income countries. Sub-optimal triage contributes to poor early case recognition and outcomes from sepsis. Improved recognition and quality of care can lead to improved outcomes. We evaluated the impact of Smart Triage using improved time to intravenous antimicrobial administration in a multisite interventional study. Smart Triage, a digital platform with a risk score and clinical dashboard, was implemented (with control sites) in Kenya (February 2021-December 2022) and Uganda (April 2020-April 2022). Children presenting to the outpatient departments with an acute illness were enrolled. A controlled interrupted time series was used to assess the effect on time from arrival at the facility to intravenous antimicrobial administration. Secondary analyses included antimicrobial use, admission rates and mortality (NCT04304235). During the baseline period, the time to antimicrobials decreased significantly in Kenya (132 and 58 minutes) at control and intervention sites. In Uganda, the time to antimicrobials marginally decreased (3 minutes) at the intervention site. Then, during the implementation period in Kenya, the time to antimicrobials at the intervention site decreased by 98 min (57%, 95% CI 81-114) but increased by 49 min (21%, 95% CI: 23-76) at the control site. In Uganda, the time to antimicrobials initially decreased but was not sustained and there was no significant difference between intervention and control sites. At both intervention sites, there was a significant reduction in antimicrobial utilization of 47% (Kenya) and 33% (Uganda) compared to baseline. There was a reduction in admission rates of 47% (Kenya) and 33% (Uganda) compared to baseline. Mortality reduced by 25% (Kenya) and 75% (Uganda) compared to the baseline period. We showed significant improvements in time to intravenous antibiotics in Kenya but not Uganda, likely due to COVID-19, a short study period and resource constraints. The reduced antimicrobial use and admission and mortality rates are remarkable and welcome benefits. The admission and mortality rates should be interpreted cautiously as these were secondary outcomes. This study underlines the difficulty of implementing technologies and sustaining quality improvement in health systems.

Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low-and-middle income country. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referral Hospital in Eastern Uganda. Variables were labeled as direct and quasi-identifiers based on conditions of replicability, distinguishability, and knowability with consensus from two independent evaluators. Direct identifiers were removed from the data sets, while a statistical risk-based de-identification approach using the k-anonymity model was applied to quasi-identifiers. Qualitative assessment of the level of privacy invasion associated with data set disclosure was used to determine an acceptable re-identification risk threshold, and corresponding k-anonymity requirement. A de-identification model using generalization, followed by suppression was applied using a logical stepwise approach to achieve k-anonymity. The utility of the de-identified data was demonstrated using a typical clinical regression example. The de-identified data sets was published on the Pediatric Sepsis Data CoLaboratory Dataverse which provides moderated data access. Researchers are faced with many challenges when providing access to clinical data. We provide a standardized de-identification framework that can be adapted and refined based on specific context and risks. This process will be combined with moderated access to foster coordination and collaboration in the clinical research community.

ObjectiveThis is a retrospective review of the feasibility study and implementation of the Smart Triage and Quality Improvement (QI) initiative at Holy Innocents Children's Hospital (HICH), a dedicated pediatric hospital in Mbarara, Uganda, over a 5-year period. The aim of this QI initiative was to improve triaging rates and the time-to-antimicrobials in HICH's outpatient department (OPD).MethodsSmart Triage is a risk prediction algorithm and digital platform that enables healthcare workers to triage patients and track treatments effectively. Following the feasibility study, the QI program was implemented in September 2021 using three Plan-Do-Study-Act cycles: 1) Standardize Training, 2) Adjust Workflows, and 3) QI Team Communication. Data sources were triage and hospital reports. Monthly run charts of OPD attendance, acuity of illness, triaging rates, median-time-to-antimicrobials, and mortality rates of admitted patients were created. The trajectories of the variables were assessed using linear regression with time as the explanatory variable.Results121,521 children attended HICH OPD from November 2018 to October 2023. The OPD triaging rate increased to 91% by October 2023, with a sustained plateau above 90% since July 2022. There was a significant reduction in the median time-to-antimicrobials during the 5-year period, from 77.6 to 53.6 minutes, with a slope of -0.4 minutes per month (CI: -0.73 to -0.04, p-value: 0.029). The inpatient mortality rate decreased from 5.1% in August 2018 to 2.6% in October 2023, with a significant increase in the number of cases with comparable illness severity.ConclusionThe impact of Smart Triage was sustained beyond the end of the feasibility trial and showed sustained improvements in processes such as treatment times and clinical outcomes including a reduction in mortality. HICH's leadership integrated a culture of QI across disciplines and departments, contributing to this initiative's sustainability and impact.

Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low-and-middle income country. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referral Hospital in Eastern Uganda. Variables were labeled as direct and quasi-identifiers based on conditions of replicability, distinguishability, and knowability with consensus from two independent evaluators. Direct identifiers were removed from the data sets, while a statistical risk-based de-identification approach using the k-anonymity model was applied to quasi-identifiers. Qualitative assessment of the level of privacy invasion associated with data set disclosure was used to determine an acceptable re-identification risk threshold, and corresponding k-anonymity requirement. A de-identification model using generalization, followed by suppression was applied using a logical stepwise approach to achieve k-anonymity. The utility of the de-identified data was demonstrated using a typical clinical regression example. The de-identified data sets was published on the Pediatric Sepsis Data CoLaboratory Dataverse which provides moderated data access. Researchers are faced with many challenges when providing access to clinical data. We provide a standardized de-identification framework that can be adapted and refined based on specific context and risks. This process will be combined with moderated access to foster coordination and collaboration in the clinical research community. Author summary Open Data is data that anyone can access, use, and share. Open Data has the potential to facilitate collaboration, enrich research, and advance the analytic capacity to inform decisions. Importantly, Open Data plays a role in fulfilling obligations to research participants and honoring the nature of medical research as a public good. Leaders in industry, academia, and regulatory agencies recognize the value in increased transparency and are focusing on how to openly share data while minimizing the safety risks to research participants. For example, making data open can pose a privacy risk to research participants who have shared personal health information. This risk can be mitigated using data de-identification, a process of removing personal information from a data sets so that an individual’s identity is no longer apparent or cannot be reasonably ascertained from the data. We introduce a simple, statistical risk-based framework for de-identification of clinical data that can be followed by any researcher. This framework will guide open data sharing while improving the protection of research participants.