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AnteayerJournal of Nursing Scholarship

Low‐value and high‐value care recommendations in nursing: A systematic assessment of clinical practice guidelines

Abstract

Introduction

The World Health Organization defines quality of care as providing effective, evidence-based care, and avoiding harm. Low-value care provides little or no benefit to the patient, causes harm, and wastes limited resources. In 2017, shortly after the start of the International Choosing Wisely campaign, the first Dutch nursing “Do-not-do” list was published and has become a widely used practical tool for nurses working in daily practice. However, over the last years new guidelines are published. Therefore, an update of the list is necessary with an addition of high-value care recommendations as alternative care practices for low-value care.

Design/methods

In this study, a combination of designs was used. First, we searched Dutch clinical practice guidelines for low-value or high-value care recommendations. All nursing care recommendations were assessed and specified to several healthcare sectors, including hospital care, district care, nursing home care, disability care, and mental health care. Second, a prioritization among nurses regarding low-value care recommendations was done by a cross-sectional survey for each healthcare sector.

Results

In total, 66 low-value care recommendations were found, for example, “avoid unnecessary layers under the patient at risk of pressure ulcers” and “never flush the bladder to prevent urinary tract infection.” Furthermore, 414 high-value care recommendations were selected, such as “use the Barthel Index to assess and to evaluate the degree of ADL independence” and “application of cold therapy may be considered for oncological patients with pain.” In total, 539 nurses from all healthcare sectors prioritized the low-value care recommendations, resulting in a top five low-value care practices per healthcare sector. The top five low-value care recommendations differed per healthcare sector, although “do not use physical restraints in case of a delirium” was prioritized by four out of five sectors.

Conclusions

Assessing low-value and high-value care recommendations for nurses will help and inspire nurses to deliver fundamental care for their patients. These initiatives regarding low-value and high-value care are essential to generate a culture of continuous quality improvement based on evidence. This is also essential to meeting the current challenges of the healthcare delivery system.

Clinical relevance

This paper provides an update of low-value care recommendations for nurses based on Dutch guidelines from 2017 to 2023, specified to five healthcare sectors, including hospital care, district care, nursing home care, disability care and mental health care, with an accompanying prioritization of these low-value care recommendations to facilitate de-implementation. This paper provides a first overview of high-value care recommendations to reflect on and create alternative care practices for low-value care. The recommendations regarding low-value and high-value care are essential to generate a culture of continuous improvement of appropriateness based on evidence, finally leading to better quality of care and improving patient outcomes.

Automating sedation state assessments using natural language processing

Abstract

Introduction

Common goals for procedural sedation are to control pain and ensure the patient is not moving to an extent that is impeding safe progress or completion of the procedure. Clinicians perform regular assessments of the adequacy of procedural sedation in accordance with these goals to inform their decision-making around sedation titration and also for documentation of the care provided. Natural language processing could be applied to real-time transcriptions of audio recordings made during procedures in order to classify sedation states that involve movement and pain, which could then be integrated into clinical documentation systems. The aim of this study was to determine whether natural language processing algorithms will work with sufficient accuracy to detect sedation states during procedural sedation.

Design

A prospective observational study was conducted.

Methods

Audio recordings from consenting participants undergoing elective procedures performed in the interventional radiology suite at a large academic hospital were transcribed using an automated speech recognition model. Sentences of transcribed text were used to train and evaluate several different NLP pipelines for a text classification task. The NLP pipelines we evaluated included a simple Bag-of-Words (BOW) model, an ensemble architecture combining a linear BOW model and a “token-to-vector” (Tok2Vec) component, and a transformer-based architecture using the RoBERTa pre-trained model.

Results

A total of 15,936 sentences from transcriptions of 82 procedures was included in the analysis. The RoBERTa model achieved the highest performance among the three models with an area under the ROC curve (AUC-ROC) of 0.97, an F1 score of 0.87, a precision of 0.86, and a recall of 0.89. The Ensemble model showed a similarly high AUC-ROC of 0.96, but lower F1 score of 0.79, precision of 0.83, and recall of 0.77. The BOW approach achieved an AUC-ROC of 0.97 and the F1 score was 0.7, precision was 0.83 and recall was 0.66.

Conclusion

The transformer-based architecture using the RoBERTa pre-trained model achieved the best classification performance. Further research is required to confirm the that this natural language processing pipeline can accurately perform text classifications with real-time audio data to allow for automated sedation state assessments.

Clinical Relevance

Automating sedation state assessments using natural language processing pipelines would allow for more timely documentation of the care received by sedated patients, and, at the same time, decrease documentation burden for clinicians. Downstream applications can also be generated from the classifications, including for example real-time visualizations of sedation state, which may facilitate improved communication of the adequacy of the sedation between clinicians, who may be performing supervision remotely. Also, accumulation of sedation state assessments from multiple procedures may reveal insights into the efficacy of particular sedative medications or identify procedures where the current approach for sedation and analgesia is not optimal (i.e. a significant amount of time spent in “pain” or “movement” sedation states).

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