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Smart Mind Maps in Promoting Safe Administration of High-Alert Medications

The Effectiveness of Smart Mind Maps in Promoting Safe Administration of High-Alert Medications Among Nurses in the Pediatric Intensive Care Unit

Status
Not yet recruiting
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT07320196
Acronym
HAMs- PICU
Enrollment
70
Registered
2026-01-06
Start date
2026-01-04
Completion date
2026-12-31
Last updated
2026-01-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Nurses, PICU

Keywords

HAMs, PICU, Nurses, Safe administration

Brief summary

In PICU setting, drug administration, monitoring, and prescribing errors made up most cases (54%) of MEs . Emphasizes these issues requires a multifaceted approach, including staff training, creation of innovative cognitive frameworks, use of electronic prescribing systems, and the promotion of a safety-awareness within healthcare settings. Recently, mind mapping has been applied in the field of nursing education as an advanced conceptual tool. It uses a technique of combining drawings with words to build memory associations between a topic keyword and image, color, or other link allowing learners to effectively store and extract information

Detailed description

This study aims to evaluate the effectiveness of smart mind maps in promoting safe administration of high-alert medications among nurses in the pediatric intensive care unit. Designing a mind map using AI holds significant value for nurses working in the PICU. AI-powered mind maps can help nurses organize complex patient information more efficiently, enabling quicker decision-making and improved care planning. By integrating AI, the mind map can automatically update with real-time data, alert nurses to critical changes, and even suggest evidence-based interventions. This not only enhances patient safety and care quality but also reduces cognitive load and streamlines communication among the healthcare team .

Interventions

OTHERsmart mind mapsde

designing a smart mind maps as an advanced conceptual tool to help nurses understand well about HAMs

Sponsors

Mansoura University
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE

Intervention model description

The study will include a convenience sample comprising of all nurses available and on duty in the PICU (who directly handled HAM

Eligibility

Sex/Gender
ALL
Healthy volunteers
No

Inclusion criteria

* Nurses on duty in the PICU (who directly handled HAM) irrespectively to their age, sex, educational level.

Exclusion criteria

* Nurses who do not handle with HAM

Design outcomes

Primary

MeasureTime frameDescription
Tool I: A Structured Assessment Questionnaire2 weeksIt will be developed by the researcher which will consist of two parts: Part 1: Socio-demographic Characteristics and Professional Data of Studied Nurses part 2: Knowledge Assessment Tool on AI and Mind Mapping for Nurses (pre/posttest)
Tool II: Nurses' Knowledge Regarding Safe Use of High-Alert Medications (HAMs)1 monthIt will be developed to collect data necessary for evaluating nurses' knowledge related to safe use of high-alert medications. The assessment will encompass key domains such as the definition and classification of HAMs, safety precautions during drug administration (including delivery routes and dosage accuracy), and drug regulation (including proper storage, documentation, and handling procedures).
Tool III: Observational Checklist for Safe Handling of High Alert Medications2 monthIt will be developed by the researcherto objectively assess nurses' practices related to the safe handling of high-alert medications in PICUs according to the international recognized guidelines and evidence-based protocols.

Contacts

Primary Contactdoaa osman ibrahim doaa ibrahim- assistant lecturer, assistant lecturer
doaaibrahim93@mans.edu.eg020-01069637084

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026