Huddle up for safer healthcare: how frontline teams can work together to improve patient safety

Cracknell, Alison; Lovatt, Alison; Winfield, Anna; Arkhipkina, Sofia; McDonagh, Eileen; Green, Angela; Rooney, M.

doi:10.7861/futurehosp.3-2-s31

Cited by 12 publications

(6 citation statements)

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“…We are also developing an English version of our system for testing in English-speaking countries. Studies have reported that intervention is more successful when various health care professionals are involved as a team rather than taking a nursing-centric approach [17,37,38]. Thus, the output of data and risk factors provided by the system could be helpful for information sharing among teams of health care professionals at safety huddles or during handover.…”

Section: Discussionmentioning

confidence: 99%

Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study

Nakatani

Nakao

Uchiyama³

et al. 2020

JMIR Med Inform

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Background Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. Objective The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input—unstructured nursing records obtained from Japanese electronic medical records (EMRs)—using a natural language processing (NLP) algorithm and machine learning. Methods The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. Results The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. Conclusions We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

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Section: Discussionmentioning

confidence: 99%

Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study

Nakatani

Nakao

Uchiyama³

et al. 2020

JMIR Med Inform

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“…A systematic review of safety briefings in various healthcare settings suggests positive outcomes after safety huddle introduction, including improved risk identification, enhanced relationships, increased incident reporting and ability to voice concerns (Ryan et al, 2019). In the United Kingdom, safety huddles have been introduced into secondary care with some promising early evaluation (Cracknell et al, 2016). However, the feasibility and acceptability of similar meetings in primary care are unknown and there have been no formal evaluations.…”

Section: Introductionmentioning

confidence: 99%

Exploring the feasibility of patient safety huddles in general practice

Panayiotou

Higgs

Foy

2020

Prim Health Care Res Dev

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Background: Patient safety is a key priority for healthcare systems. Patient safety huddles have been advocated as a way to improve safety. We explored the feasibility of huddles in general practice. Methods: We invited all general practices in West Yorkshire to complete an online survey and interviewed practice staff. Results: Thirty-four out of 306 practices (11.1%) responded to our survey. Of these, 22 practices (64.7%) reported having breaks for staff to meet and eight (23.5%) reported no longer having breaks in their practices. Seven interviewees identified several barriers to safety huddles including time and current culture; individuals felt meetings or breaks would not be easily integrated into current primary care structure. Discussion: Despite their initial promise, there are major challenges to introducing patient safety huddles within the current context of UK general practice. General practice staff may need more convincing of potential benefits.

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“…The huddles studied changed over time in the face of both internal and external disruption, and as a result of trial and error. This demonstrates a second key factor: the adaptability and flexibility of the huddle (Cracknell et al 2016). Staff commitment to the PSH is high and so change is embraced rather than viewed as a threat.…”

Section: Discussionmentioning

confidence: 99%

“…A final factor is celebrating success and offering reward. An important element in this is the routine use of harms data to evidence their reduction (Cracknell et al 2016;Crosswaite, Faisal, et al 2018;Crosswaite, Montague, et al 2018). This demonstrates the benefits for patient safety.…”

Section: Discussionmentioning

confidence: 99%

Sustaining the commitment to patient safety huddles: insights from eight acute hospital ward teams

et al. 2019

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Background: A recent initiative in hospital settings is the patient safety huddle (PSH): a brief multidisciplinary meeting held to highlight patient safety issues and actions to mitigate identified risks. Aim: The authors studied eight ward teams that had sustained PSHs for over 2 years in order to identify key contributory factors. Methods: Unannounced observations of the PSH on eight acute wards in one UK hospital were undertaken. Interviews and focus groups were also conducted. These were recorded and transcribed for framework analysis. Findings: A range of factors contributes to the sustainability of the PSH including a high degree of belief and consensus in purpose, adaptability, determination, multidisciplinary team involvement, a non-judgemental space, committed leadership and consistent reward and celebration. Conclusion: The huddles studied have developed and been shaped over time through a process of trial and error, and persistence. Overall this study offers insights into the factors that contribute to this sustainability.

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Huddle up for safer healthcare: how frontline teams can work together to improve patient safety

Cited by 12 publications

References 0 publications

Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study

Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study

Exploring the feasibility of patient safety huddles in general practice

Sustaining the commitment to patient safety huddles: insights from eight acute hospital ward teams

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