Critical Care Nurses' Cognitive Ergonomics Related to Medical Device Alarms

The development and preliminary evaluation of a clinician e-learning training platform for a neonatal sepsis risk monitor for use in ICU settings

BACKGROUND

Training clinicians on the use of hospital-based patient monitoring systems (PMS) is vital to mitigate the risk of use errors and of frustration using these devices, especially when used in ICU settings. PMS training is typically delivered through face-to-face training sessions in the hospital. However, it is not always feasible to deliver training in this format to all clinical staff given some constraints (e.g., availability of staff and trainers to attend in-person training sessions and the costs associated with face-to-face training).

OBJECTIVE

The literature indicates that E-learning has the potential to mitigate barriers associated with time restrictions for trainers and trainees and evidence shows it to be more flexible, and convenient for learners in healthcare settings. This study aimed to develop and carry out a preliminary evaluation via a case study of an e-learning training platform designed for a novel neonatal sepsis risk monitor system (Digi-NewB).

METHODS

A multi-modal qualitative research case study approach was used, including the analysis of three qualitative data sources: (i) audio/video recordings of simulation sessions in which participants were asked to operate the system as intended (e.g., update the clinical observations and monitor the sepsis risk), (ii) interviews with the simulation participants and an attending key opinion leader (KOL), who observed all simulation sessions, and (iii) post-simulation survey.

RESULTS

After receiving ethical approval for the study, nine neonatal intensive care unit (NICU) nurses completed the online training and participated in the simulation and follow-up interview sessions. The KOL was also interviewed, and seven out of the nine NICU nurses answered the post-simulation survey. The video/audio analysis of the simulations revealed that participants were able to use and interpret the Digi-NewB interface. Interviews with simulation participants and the KOL, and feedback extracted from the survey, revealed that participants were overall satisfied with the training platform and perceived it as an efficient and effective method to deliver medical device training.

CONCLUSIONS

This study developed an online training platform to train clinicians in the use of a critical care medical device and carried out a preliminary evaluation of the platform via a case study. The e-learning platform was designed to supplement and enhance other training approaches. Further research is required to evaluate the effectiveness of this approach.

Alarms in a neurocritical care unit: a prospective study

The contemporary practice of monitoring physiologic parameters in the critical care setting is based on alarm systems with high sensitivity but low specificity. A natural consequence of this approach is a massive amount of alarms, which potentially leads to fatigue in the personnel and negatively impacts the quality of care provided. The study objective is to determine the prevalence, types, and determinants of alarms in a neurological critical care unit (NCCU) prototype. During a one-month period corresponding to 272 days of monitoring in 34 patients, nursing staff recorded the type and number of sounding alarms in a university NCCU. Alarms were categorized into three types as type-A alarms that were merely handled by the nursing staff, type-B alarms that were primarily managed by nurses, but the physician was also notified, and type-C alarms that were principally handled by NCCU physicians. There were a total of 9439 alarms, with an average of daily 34.7 alarms per bed, corresponding to one alarm every 41.4 min. Most of the alarms were type-A (57.7%), followed by type-B (39.2%) and type-C (3.1%) alarms. Alarms originated from electrocardiogram (34.6%), pulse oximeter (33.7%), noninvasive blood pressure monitoring (9.8%), respiratory monitoring (9.7%), intravenous fluid pumps (4.5%), ventilator (3.9%), enteral pumps (2.1%) and invasive blood pressure systems (1.7%). A noticeable diurnal variation was observed for type-A pulse oximeter, type-A and -B ECG alarms (increase during morning shifts), and type-A ventilator alarms (decrease during morning shifts). Alarms are highly prevalent in NCCUs and can correspond to an important portion of the workload.

[Factors influencing the nurse’s decision to set and modulate alarm parameters of bedside monitoring in intensive care : an integrative review of literature]

Introduction : In critical care units, improper alarm settings for bedside monitoring systems promotes numerous alarms and may lead to nurse fatigue. Purpose : Achieve an integrative review of the literature to identify the factors that influence the nurse in her decision to set and modulate the alarm parameters of bedside monitoring systems in the intensive care unit. Method : An integrative review of the literature according to the five steps of the Whittemore and Knafl method was carried out from the literature related to the field of critical care. Results : The factors related to the nurse’s decision to set or modulate the alarm parameters of the monitoring systems are divided into two themes : personal factors and contextual factors. Discussion : Certain factors identified in this integrative review were also identified as elements that contribute to the clinical surveillance process carried out by expert nurses in a critical care context. Conclusion : The results of this study suggest the need to focus empirically on the factors influencing the nurse’s decision to adjust and modulate alarm parameters in critical care monitoring systems.

Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring

Patients who deteriorate while on the acute care ward and are emergently transferred to the Intensive Care Unit (ICU) experience high rates of mortality. To date, risk scores for clinical deterioration applied to the acute care wards rely on static or intermittent inputs of vital sign and assessment parameters. We propose the use of continuous predictive analytics monitoring, or data that relies on real-time physiologic monitoring data captured from ECG, documented vital signs, laboratory results, and other clinical assessments to predict clinical deterioration. A necessary step in translation to practice is understanding how an alert threshold would perform if applied to a continuous predictive analytic that was trained to detect clinical deterioration. The purpose of this study was to evaluate the positive predictive value of 'risk spikes', or large abrupt increases in the output of a statistical model of risk predicting clinical deterioration. We studied 8111 consecutive patient admissions to a cardiovascular medicine and surgery ward with continuous ECG data. We first trained a multivariable logistic regression model for emergent ICU transfer in a test set and tested the characteristics of the model in a validation set of 4059 patient admissions. Then, in a nested analysis we identified large, abrupt spikes in risk (increase by three units over the prior 6 h; a unit is the fold-increase in risk of ICU transfer in the next 24 h) and reviewed hospital records of 91 patients for clinical events such as emergent ICU transfer. We compared results to 59 control patients at times when they were matched for baseline risk including the National Warning Score (NEWS). There was a 3.4-fold higher event rate for patients with risk spikes (positive predictive value 24% compared to 7%, p = 0.006). If we were to use risk spikes as an alert, they would fire about once per day on a 73-bed acute care ward. Risk spikes that were primarily driven by respiratory changes (ECG-derived respiration (EDR) or charted respiratory rate) had highest PPV (30-35%) while risk spikes driven by heart rate had the lowest (7%). Alert thresholds derived from continuous predictive analytics monitoring are able to be operationalized as a degree of change from the person's own baseline rather than arbitrary threshold cut-points, which can likely better account for the individual's own inherent acuity levels. Point of care clinicians in the acute care ward settings need tailored alert strategies that promote a balance in recognition of clinical deterioration and assessment of the utility of the alert approach.

Critical care nurses' clinical reasoning about physiologic monitor alarm customisation: An interpretive descriptive study

AIMS AND OBJECTIVES

To explore clinical reasoning about alarm customisation among nurses in intensive care units.

BACKGROUND

Critical care nurses are responsible for detecting and rapidly acting upon changes in patients' clinical condition. Nurses use medical devices including bedside physiologic monitors to assist them in their practice. Customising alarm settings on these devices can help nurses better monitor their patients and reduce the number of clinically irrelevant alarms. As a result, customisation may also help address the problem of alarm fatigue. However, little is known about nurses' clinical reasoning with respect to customising physiologic monitor alarm settings.

DESIGN

This article is an in-depth report of the qualitative arm of a mixed methods study conducted using an interpretive descriptive methodological approach.

METHODS

Twenty-seven nurses were purposively sampled from three intensive care units in an academic medical centre. Semi-structured interviews were conducted by telephone and were analysed using thematic analysis. Consolidated Criteria for Reporting Qualitative Research (COREQ) reporting guidelines were used.

RESULTS

Four themes were identified from the interview data: unit alarm culture and context, nurse attributes, motivation to customise and customisation "know-how." A conceptual model demonstrating the relationship of these themes was developed to portray the factors that affect nurses' customisation of alarms.

CONCLUSIONS

In addition to drawing on clinical data, nurses customised physiologic monitor alarms based on their level of clinical expertise and comfort. Nurses were influenced by the alarm culture on their clinical unit and colleagues' and patients' responses to alarms, as well as their own technical understanding of the physiologic monitors.

RELEVANCE TO CLINICAL PRACTICE

The results of this study can be used to design strategies to support the application of clinical reasoning to alarm management, which may contribute to more appropriate alarm customisation practices and improvements in safety.

Challenges of customizing electrocardiography alarms in intensive care units: A mixed methods study

BACKGROUND

Customizing monitor alarm settings to individual patients can reduce alarm fatigue in intensive care units (ICUs), but has not been widely studied.

OBJECTIVES

To understand ICU nurses' approaches to customization of electrocardiographic (ECG) monitor alarms.

METHODS

A convergent mixed methods study was conducted in 3 ICUs in 1 hospital. Data on the type and frequency of ECG alarm customization were collected from patient monitors (n=298). Nurses' customization clinical reasoning was explored through semi-structured interviews (n=27).

RESULTS

Of the 298 patients, 58.7% had ≥1 alarm(s) customized. Heart rate limits, irregular heart rate, and atrial fibrillation were the most commonly customized alarms. Interviews revealed that customization practices varied widely and were influenced by factors including clinical expertise, lack of customization education, and negative experiences.

CONCLUSION

Alarm customization is nuanced and requires adequate support to develop safe and effective practices. The challenges identified can inform development of strategies to improve alarm customization.