Health Care Providers’ Perspectives of Clinical Decision Support Tools for Pediatric Sepsis in Bangladesh: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Sepsis, a life-threatening condition resulting from a dysregulated immune response to infection, disproportionately affects children in low- and middle-income countries (LMICs). Children with sepsis in LMICs face high mortality rates, with early detection and clinical monitoring posing significant challenges to effective management. There is great potential for digital technologies, such as wearable biosensor devices and mobile health (mHealth) clinical decision support (CDS) tools, together referred to as clinical decision support systems (CDSSs), to enable closer monitoring and more prompt recognition of children at risk of advanced sepsis and death. However, little is known about the perceptions of health care providers (HCPs) regarding the introduction of new digital health tools for pediatric sepsis care in LMICs. OBJECTIVE: The objective of this study was to assess HCPs' understanding, perceptions, and recommendations regarding the design and implementation of digital CDSSs for pediatric sepsis care in Bangladesh. METHODS: Between February and May 2024, 18 individual semistructured in-depth interviews were conducted with HCPs (nurses and physicians) at 3 urban hospitals in Bangladesh. The data were transcribed, translated from Bangla to English, and analyzed using a framework matrix analysis approach. Participants were asked about familiarity with digital health tools, feedback on CDSS design, perceptions of the system's utility, and barriers and facilitators to use of similar tools in clinical settings in Bangladesh. RESULTS: Participants reported overall positive perceptions toward the potential implementation of a CDSS for pediatric sepsis care in Bangladesh. Some key priorities for the design of a CDSS were durability, reusability, cost considerations, reliability, and accuracy. Clinicians desired the CDS tool to also have customizable alarm parameters and include additional functions such as glucose monitoring. Many favored audio (ringtone) or visual (light) alarms to alert about changes in captured vital signs. HCPs believed that a CDSS could enhance patient care by allowing greater staff capacity to monitor patients, reducing management time, and aiding in faster clinical decision-making, with some suggesting it could lower mortality rates. Concerns regarding implementation included internet availability, affordability of the wearable devices, and trust in the CDSS outputs compared to expert clinician judgement. CONCLUSIONS: The findings of this study highlight HCPs' perceptions toward the potential of wearable biosensor devices and CDS tools (CDSSs) for improving pediatric sepsis outcomes in LMICs and highlight the need to address implementation challenges to ensure the effective integration of CDSSs into health care systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".