Patient Safety in Pediatrics: a Developing Discipline
Bibliographic record
Abstract
__Abstract__ \n \nThe publication of the breakthrough report “To Err is Human” by the Institute of Medicine was the \nlaunch of patient safety initiatives all over the world. In the intensive care unit (ICU) of the Erasmus \nMC-Sophia Children’s Hospital this resulted in the institution of a multimodal patient safety management \nsystem under the name Safety First in 2005. This system now includes nine major elements, \nrepresenting monitoring and intervention activities. In this thesis we report on the results and the \nimplementation of the patient safety management system called Safety First. \n \n__Outline of this thesis:__ \nIn part I the concept of patient safety and the Safety First project are introduced. The rationale for \nselecting the elements of the patient safety management system is explained. As preventable mortality \nand morbidity are the public focus as outcome parameters for quality and safety of care, we \nhave studied very long stay patients in our ICU (chapter 2). The goal of this study was to determine \ncharacteristics and mortality in these patients as well as modes of death. Chapter 3 presents an evaluation \nof potentially preventable deaths in our ICU. An important question was whether five years \nof patient safety efforts had resulted in fewer potentially preventable deaths. \nPart II reflects on the difficulties in monitoring adverse events. In chapter 4 we present numbers and \ntypes of adverse events identified with real time physicians’ registration during a 3-month period in \ngeneral pediatric practice. The next chapter is a study into adverse events in the surgical pediatric \nICU in a 2-year period. We combined the physicians’ registration with the Trigger Tool methodology \nas developed by the Institute for Healthcare, Boston, USA. The goals were to determine the rate and \nnature of the adverse events and to compare the two methods. \nIn part III a number of elements of Safety First are described, as well as other studies into patient \nsafety issues relevant to bedside ICU care. Chapter 6 brings the results of critical incident analysis \nwith a focus on the factors contributing to the incident and the resultant recommendations. The \nnext study evaluated the availability and reliability of drug formularies used in our ICU, which are \ncrucial in safe drug prescription. In chapter 8 we discuss the safety of routine MRI scans in preterm \ninfants at 30 weeks gestational age, as reflected by safety incidents and adverse events. In the next \nchapter, safety focused Mortality and Morbidity conference reports were scrutinized for numbers \nand types of recommendations stemming from these meetings. Chapter 10 is a study about nursing \nprotocol violations established with the Critical Nursing Situation Index. \nPart IV describes a study of safety culture in the ICU, as it emerged from a safety attitude questionnaire \nadministered to all staff. We aimed to compare findings to benchmark data and explore any \ndeficiencies. \nIn the general discussion in part V the results of the studies are commented on and future directions \nare given, including guidelines for optimal implementation of a patient safety management system \nand future benchmarking.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".