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Journal of pediatric hematology/oncology · 2026

Implementation of a Digital Clinical Management Platform in a Tertiary Pediatric Hemato-Oncology Center: Impact on Clinical Outcomes and Patient Safety.

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Singer Michal, Ilan Uri, Dolinski Guy, Halabi Nitsan, Edri Yuliana, Rahman Nisim, Sina Sigal, Sagi Maor, Tam Sivan, Ashkenazi Moshe

Аннотация

INTRODUCTION: Healthcare delivery is increasingly challenged by rising demand, escalating costs, and the need for efficiency. Contributing factors such as the nursing workforce crisis, fragmented and excessive clinical data, outdated IT infrastructures, and the growing complexity of care exacerbate the risk of medical errors, treatment delays, and staff burnout. To address these challenges, we evaluated AMIT, a novel digital platform designed to support nursing practice in pediatric oncology.

METHODS: AMIT is a web-based platform developed in 2022 and implemented in Q4 2023 in the pediatric oncology day care unit. It integrates data from 5 hospital IT systems, applying predefined rules based on local practice to highlight only clinically relevant information requiring intervention or safety event prevention. In addition, analytic and diagnostic alerts were generated. Primary outcomes included prevention of safety events, reduction in urgent PICU referrals, and shorter patient journey times; the secondary outcome was nursing satisfaction, assessed by questionnaire.

RESULTS: Following implementation, 61 safety events, including 2 potentially fatal cases, were identified and addressed. The incidence of urgent PICU referrals decreased by 78%, timely antibiotic administration (≤3 h) improved by 47%, and patient journey times for electrolytes and transfusions decreased by 58% (104 to 44 min). Nurse surveys indicated 100% satisfaction with workflow integration.

CONCLUSIONS: AMIT improved patient safety, accelerated treatment, and streamlined nursing workflow, underscoring the value of rule-based digital platforms in clinical care. Future integration of large language models will enable automated translation of guidelines into validated clinical rules and support feedback loops to identify patient-specific deterioration patterns, fostering more harmonized and timely interventions across institutions.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты