Managing alarm systems for quality and safety in the hospital setting

Utilizing Data and Alarm Champions to Enhance Alarm Management: A Pediatric Quality Improvement Initiative

BACKGROUND

Nuisance and false alarms distract clinicians from urgent alerts, raising patient safety risks.

LOCAL PROBLEM

High alarm rates in a pediatric progressive care unit resulted in experiencing 180-250 alarms per day or 1 alarm every 3 to 4 minutes per clinician.

METHODS

Through Plan-Do-Study-Act cycles, environmental, policy, and technology changes were implemented to decrease the average alarms/day/bed and percentage of time in alarm.

INTERVENTIONS

Alarm settings tailored to patient needs using features embedded within the patient monitoring system were implemented and monitored with the assistance of alarm champions.

RESULTS

The average number of alarms/day/bed decreased from 177.69 to 96.94 over the course of 10 years, a 45.45% reduction. The percentage of time in alarm decreased from 7.52% to 2.83%, a 62.37% reduction.

CONCLUSIONS

Arming clinicians with technology to analyze real-time clinical data made alarms meaningful and actionable, decreasing false alarms without compromising patient safety.

Original Research: Alarm Fatigue: Exploring the Adaptive and Maladaptive Coping Strategies of Nurses

BACKGROUND

To improve patient safety, hospitals use alarm notification systems to increase nurses' real-time situational awareness of a patient's condition. Such alarms are critical to nurses' clinical decision-making and prioritization, thus helping to improve patient care and care efficiency. But the frequent and often simultaneous ringing of alarms, including many that are false, nonemergent, or nonactionable, has led to overwhelm, alarm distrust, and desensitization, resulting in alarm fatigue.

PURPOSE

This study aimed to explore oncology nurses' lived experiences with alarms and the adaptive and maladaptive strategies they use to cope with alarm fatigue.

METHODS

This qualitative, phenomenological study was guided by the theoretical framework of the Roy Adaptation Model. A purposive sample of nine nurses was recruited from two oncology units at a large midwestern Magnet hospital in the United States. Qualitative data were collected using a six-question, semistructured interview guide. Interviews were conducted either face-to-face in a private conference room on the unit or via the online videoconferencing platform Zoom.

RESULTS

Data analysis yielded five themes, the most prominent being the high volume and frequency of alarms. Nurse participants reported adopting more maladaptive than adaptive coping strategies. Overall, they felt that the high frequency of false, nonemergent, and nonactionable alarms disrupted their workflow and contributed to a general desensitization to alarms.

CONCLUSIONS

This study's findings offer valuable insight into the problem of alarm fatigue among nurses. Practical measures are urgently needed to reduce nurses' cognitive overload; shift nonnursing responsibilities to other staff; and implement efficiency-focused process changes, such as reengineering workflows to minimize interruptions. Every effort should be made to redesign protocols to reduce alarm fatigue, including by decreasing the number of false, nonemergent, and nonactionable calls and alarms.

Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics

BACKGROUND

In response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units (ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU setting led to a higher alarm burden and more staff with alarm fatigue.

OBJECTIVE

We aimed to compare alarm situations between provisional COVID-19 ICUs and non-COVID-19 ICUs during the second COVID-19 wave in Berlin, Germany. The study focused on measuring alarms per bed per day, identifying medical devices with higher alarm frequencies in COVID-19 settings, evaluating the median duration of alarms in both types of ICUs, and assessing the level of alarm fatigue experienced by health care staff.

METHODS

Our approach involved a comparative analysis of alarm data from 2 provisional COVID-19 ICUs and 2 standard non-COVID-19 ICUs. Through interviews with medical experts, we formulated hypotheses about potential differences in alarm load, alarm duration, alarm types, and staff alarm fatigue between the 2 ICU types. We analyzed alarm log data from the patient monitoring systems of all 4 ICUs to inferentially assess the differences. In addition, we assessed staff alarm fatigue with a questionnaire, aiming to comprehensively understand the impact of the alarm situation on health care personnel.

RESULTS

COVID-19 ICUs had significantly more alarms per bed per day than non-COVID-19 ICUs (P<.001), and the majority of the staff lacked experience with the alarm system. The overall median alarm duration was similar in both ICU types. We found no COVID-19-specific alarm patterns. The alarm fatigue questionnaire results suggest that staff in both types of ICUs experienced alarm fatigue. However, physicians and nurses who were working in COVID-19 ICUs reported a significantly higher level of alarm fatigue (P=.04).

CONCLUSIONS

Staff in COVID-19 ICUs were exposed to a higher alarm load, and the majority lacked experience with alarm management and the alarm system. We recommend training and educating ICU staff in alarm management, emphasizing the importance of alarm management training as part of the preparations for future pandemics. However, the limitations of our study design and the specific pandemic conditions warrant further studies to confirm these findings and to explore effective alarm management strategies in different ICU settings.

Mitigating Alarm Fatigue and Improving the Bedside Experience by Reducing Non-actionable Alarms

OBJECTIVES

To assess whether conditional bedside alarm triggers can reduce the frequency of non-actionable alarms without compromising patient safety and enhance nursing and family satisfaction.

STUDY DESIGN

Single center, quality improvement initiative in an acute care cardiac unit (ACCU) and pediatric intensive care unit (PICU). Following the 4-week pre-intervention baseline period, bedside monitors were programmed with hierarchical time delay and conditional alarm triggers. Bedside alarms were tallied for 4 weeks each in the immediate post intervention period and 2-year follow-up. The primary outcome was alarms per monitored patient day. Nurses and families were surveyed pre- and post-intervention.

RESULTS

A total of 1509 patients contributed to 2034, 1968, and 2043 monitored patient days which were evaluated in the baseline, follow-up, and 2-year follow-up periods, respectively. The median number of alarms per monitored patient day decreased by 75% in the PICU (p<0.001) and 82% in the ACCU (p<0.001) with sustained effect at 2-year follow-up. No increase of rapid response calls, emergent transfers, or code events occurred in either unit. Nursing surveys reported an improved capacity to respond to alarms and fewer perceived non-actionable alarms. Family surveys, however, did not demonstrate improved sleep quality.

CONCLUSIONS

Implemented changes to bedside monitor alarms decreased total alarm frequency in both the acute care cardiac unit and pediatric intensive care unit, improving the care provider experience without compromising safety.

Alarm Management in Intensive Care: Qualitative Triangulation Study

BACKGROUND

The high number of unnecessary alarms in intensive care settings leads to alarm fatigue among staff and threatens patient safety. To develop and implement effective and sustainable solutions for alarm management in intensive care units (ICUs), an understanding of staff interactions with the patient monitoring system and alarm management practices is essential.

OBJECTIVE

This study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions.

METHODS

This explorative qualitative study with an ethnographic, multimethods approach was conducted in an ICU of a German university hospital. Using triangulation in data collection, 102 hours of field observations, 12 semistructured interviews with ICU staff members, and the results of a participatory task were analyzed. The data analysis followed an inductive, grounded theory approach.

RESULTS

Nurses and physicians reported interacting with the continuous vital sign monitoring system for most of their work time and tasks. There were no established standards for alarm management; instead, nurses and physicians stated that alarms were addressed through ad hoc reactions, a practice they viewed as problematic. Staff members' perceptions of intelligent alarm management varied, but they highlighted the importance of understandable and traceable suggestions to increase trust and cognitive ease.

CONCLUSIONS

Staff members' interactions with the omnipresent patient monitoring system and its alarms are essential parts of ICU workflows and clinical decision-making. Alarm management standards and workflows have been shown to be deficient. Our observations, as well as staff feedback, suggest that changes are warranted. Solutions for alarm management should be designed and implemented with users, workflows, and real-world data at the core.

Evaluation of a Clinical Decision Support Tool to Guide Adoption of the American Heart Association Telemetry Monitoring Practice Standards

BACKGROUND

The objectives of this study were to (1) evaluate telemetry use pre- and postimplementation of clinical decision support tools to support American Heart Association practice standards for telemetry monitoring and (2) understand the factors that may contribute to variation of telemetry monitoring in practice.

METHODS AND RESULTS

First, we captured overall variability in telemetry use pre- and postimplementation of the clinical decision support intervention. We then conducted semistructured interviews with telemetry-ordering providers to identify key barriers and facilitators to adoption. During the study period, 399 physicians met criteria for inclusion and were divided into excessive and nonexcessive orderers. Distribution of telemetry use was bimodal. Among nonexcessive users, 24.4% of patient days were with telemetry compared with 51.6% among excessive users. On average, both excessive (6.1% reduction) and nonexcessive users (2.8% reduction) decreased telemetry use postimplementation, and these reductions were sustained over a 16-month period. Sixteen interviews were conducted. Physicians believed that the tool was successful because it caused them to more closely consider if telemetry was indicated for each patient. Physicians also voiced frustration with interruptions to their workflow, and some noted that they commonly use telemetry outside of practice standards to monitor patients who were acutely but not critically ill.

CONCLUSIONS

Embedding telemetry practice standards into the electronic health record in the form of clinical decision support is effective at reducing excess telemetry use. Although the intervention was well received, there are persistent barriers, such as preexisting views on telemetry and existing workflow habits, that may inhibit higher adoption of standards.

Pro-Con Debate: Universal Versus Selective Continuous Monitoring of Postoperative Patients

In this Pro-Con commentary article, we discuss use of continuous physiologic monitoring for clinical deterioration, specifically respiratory depression in the postoperative population. The Pro position advocates for 24/7 continuous surveillance monitoring of all patients starting in the postanesthesia care unit until discharge from the hospital. The strongest arguments for universal monitoring relate to inadequate assessment and algorithms for patient risk. We argue that the need for hospitalization in and of itself is a sufficient predictor of an individual's risk for unexpected respiratory deterioration. In addition, general care units carry the added risk that even the most severe respiratory events will not be recognized in a timely fashion, largely due to higher patient to nurse staffing ratios and limited intermittent vital signs assessments (e.g., every 4 hours). Continuous monitoring configured properly using a "surveillance model" can adequately detect patients' respiratory deterioration while minimizing alarm fatigue and the costs of the surveillance systems. The Con position advocates for a mixed approach of time-limited continuous pulse oximetry monitoring for all patients receiving opioids, with additional remote pulse oximetry monitoring for patients identified as having a high risk of respiratory depression. Alarm fatigue, clinical resource limitations, and cost are the strongest arguments for selective monitoring, which is a more targeted approach. The proponents of the con position acknowledge that postoperative respiratory monitoring is certainly indicated for all patients, but not all patients need the same level of monitoring. The analysis and discussion of each point of view describes who, when, where, and how continuous monitoring should be implemented. Consideration of various system-level factors are addressed, including clinical resource availability, alarm design, system costs, patient and staff acceptance, risk-assessment algorithms, and respiratory event detection. Literature is reviewed, findings are described, and recommendations for design of monitoring systems and implementation of monitoring are described for the pro and con positions.

Nurses' alarm fatigue, influencing factors, and its relationship with burnout in the critical care units: A cross-sectional study

BACKGROUND

Excessive number of alarms and false and nonactionable alarms may lead to alarm fatigue. Alarm fatigue could easily contribute to burnout. Burnout may reduce nurses' sensitivity to alarms, thus affecting patients' safety due to insufficient response to the alarms. However, no study has examined nurses' alarm fatigue in Ghana.

OBJECTIVES

The objective of this study was to investigate the level of alarm fatigue and its associated factors, as well as determine its relationship with burnout among nurses working in the critical care units of hospitals in Ghana.

METHODS

The cross-sectional study was conducted in critical care units of five hospitals in Ghana from November 2021 to January 2022. A total of 364 nurses were recruited and completed the questionnaire. Alarm fatigue was assessed by the alarm fatigue questionnaire, which was originally developed in Chinese and was translated into English using a standard protocol. Burnout was assessed using the Maslach Burnout Inventory.

RESULTS

The overall alarm fatigue score was 76.43 ± 27.80 out of 124. Longer years working at the critical care unit (B = -2.50, 95% confidence interval [CI]: -4.62, -0.37) and having policies related to alarm management (B = -10.77, 95% CI: -3.50, -18.04) were associated with a decreased risk of alarm fatigue, while working in neonatal intensive care unit (B = 16.35, 95% CI: 2.48, 30.21) and postanesthesia care unit (B = 15.16; 95% CI: 0.32, 30.01), and having anxiety and stress (B = 8.15, 95% CI: 1.30, 15.00) were associated with an increased risk of alarm fatigue. In addition, alarm fatigue was positively associated with emotional exhaustion (r = 0.52, P < 0.001) and depersonalisation (r = 0.43, P < 0.001) but not personal accomplishment (r = -0.09, P = 0.100).

CONCLUSION

Critical care nurses in Ghana experienced higher levels of alarm fatigue, which is affected by multiple factors. There is a significant link between nurses' alarm fatigue and burnout. Our findings provide important guidance for future intervention programs to improve critical care nurses' alarm fatigue by introducing policies on alarm management and improving nurses' psychological health, with a special focus on nurses with shorter working years and working in neonatal intensive care unit and postanesthesia care unit.

Levels and Factors of Nurses' Alarm Fatigue in Critical Care Settings in Saudi Arabia: A Multicenter Cross-Sectional Study

Background

A continuous and high frequency of alarms from monitoring and treatment devices can lead to nurses' sensory exhaustion and alarm fatigue in critical care settings.

Aim

The purpose of this study was to evaluate the level of alarm fatigue and determine the relationship between nurses' sociodemographic and work-related factors and the level of alarm fatigue in critical care settings in Hail City, Saudi Arabia.

Methods

Between May and July 2023, 298 nurses who worked in the emergency, intensive care, and critical care units of all the public hospitals in Hail City participated in a cross-sectional survey. Sociodemographic and work-related sheet and the Nurses' Alarm Fatigue Questionnaire were used to collect data.

Results

The total mean score of alarm fatigue was 26.38±8.30 out of 44. The highest score was observed for the item "I pay more attention to the alarms in certain", while the lowest score were observed for the items "I turn off the alarms at the beginning of every shift" with mean scores of 2.51 and 1.61, respectively. Nurses who were males, older than 30 years and Saudi citizens had significantly higher mean scores of alarm fatigue than their counterparts. In addition, significantly higher mean scores of alarm fatigue were noticed for nurses experienced for 10 years or more and who had regular morning shifts. Multiple linear regression showed that male (p=0.014), age (p=0.012), and Saudi nationality (p <0.029) were the independent factors affecting the level of fatigue alarm among nurses.

Conclusion

Nurses working in critical care settings at hospitals in Hail city are exposed to average levels of alarm fatigue, which can be influenced by sex, age, nationality, and experience of nurses. Therefore, it is imperative to manage alarm fatigue in critical care units by considering work-related and personality-related factors to ensure patient safety.

The Neonatal Intensive Care Unit (NICU) Context and the Perceived Soundscape: A Grounded Theory Approach

BACKGROUND

Studies address excessive sound levels and their adverse effects on infants in neonatal intensive care units (NICUs). However, objective measurements represent merely one aspect of the acoustic environment, and investigations into staff's perceptions of the acoustic environment remain scarce in the NICU context. A holistic approach, soundscape, is needed to explore the acoustic environment in-depth.

AIM

This study aims to (1) contribute to indoor soundscape literature and inform decision-makers of future NICU design and research by focusing on staff members' perceptions of the soundscape and (2) explore whether there is a relationship between staff members' perceptions of soundscapes and the built and acoustic environments of one NICU.

METHODS

Following the ISO/TS 12913-2/3 protocols, semi-structured interviews were conducted with 10 NICU staff members and analyzed using the grounded theory to generate a conceptual framework for NICU soundscapes.

RESULTS

The results indicated that the task-related sounds, including false alarms, were neutrally responded to as they evoked acceptance and adaptation behaviors. The sound sources perceived as irrelevant were responded to negatively. NICU staff indicated that although they expect to hear alarms clearly, specific features of alarms caused several physiological and psychological problems.

CONCLUSIONS

The findings of the study revealed the importance of conducting a soundscape approach to investigate NICU acoustic environment in detail. The study showed that staff members' perceptions and responses primarily depend on the context rather than on sound levels.

Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure

Aims

Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD).

Methods and results

In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods.

Conclusion

The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement.

A grounded theory study of alarm fatigue among nurses in intensive care units

OBJECTIVES

The aim of this study was to explore the process of how nurses experienced and dealt with alarm fatigue in intensive care units based on Iranian nurses' perceptions and experiences.

BACKGROUND

Alarm fatigue is the overstimulation of senses due to the constant ringing of alarms in intensive care units. It is associated with nurses' desensitization to critical alarms that can directly influence patient safety and quality of care.

METHODS

A qualitative exploratory study using the grounded theory approach by Strauss and Corbin was carried out. Participants were 20 nurses working in intensive care units. The sampling process was started purposively and continued theoretically. Data were collected using semi-structured, in-depth, and individual interviews and continued to data saturation. The constant comparative analysis approach was used consisting of the following steps: open coding, developing concepts, analysing the context, entering the process into data analysis, integrating categories.

FINDINGS

The participants' main concern in the exposure to alarm fatigue was 'threat to personal balance'. The core category in this research was 'trying to create a holistic balance', which reflected a set of strategies that the nurses consistently and continuously used to deal with alarm fatigue and consisted of four main categories as follows: 'smart care', 'deliberate balancing', 'conditional prioritisation', and 'negligent performance'. Threat to personal balance was strengthened by 'inappropriate circuit of individual roles', 'distortion of the organisational structure', and 'insecurity of the infrastructure'. The consequences of this process was harm to the patient, burnout among nurse, and damage to the healthcare organisation.

CONCLUSIONS

The research findings have practical implications for healthcare management, policymaking, nursing education, research, and clinical practice. Mitigating staff shortages, improving staff competencies, enhancing nurses' authority for responding to alarms, modifying care routines, improving the physical environment, and removing problems related to alarm equipment can prevent alarm fatigue and its unappropriated consequences.

An Autonomous Continuous Bladder Irrigation System

Introduction and Objective: Continuous bladder irrigation (CBI) is used in a variety of clinical settings, including post-transurethral surgery and the emergency department. Currently, CBI administration relies on nurses to diligently monitor and switch irrigation bags, as well as titrate the inflow rate based on effluent color. Inappropriate administration can result in discomfort to patients, clot urinary retention, repeat injury to the pathologic or surgical site, extended hospital stays, and even operative management. Our objective was to create an autonomous CBI system that decreases the incidence of disrupted irrigation flow and monitors the outflow to alert clinicians of critical events. Methods: 3D printing and off-the-shelf microcontrollers were used to design a device to fit the needs identified by stakeholders at our institution. An in vitro model of the bladder was created to test our design. The mechanical, electrical, and software subsystems were adjusted accordingly to meet our design requirements. Results: Our in vitro CBI model was able to simulate routine CBI administration with sudden bleeding. Bovine blood was used to simulate the bleeding events. A device was created that met identified stakeholder needs. Accurate detection of critical bleeding events, catheter blockage, and empty irrigation bags were achieved. The device responds to bleeding appropriately by increasing the irrigation rate. When the catheter is blocked, it stops the irrigation and alerts the nurse. Our system accurately titrated the irrigation rate to match a set outflow blood level parameter, conserving irrigation and minimizing nursing workload. Continuous monitoring of CBI effluent was recorded. Conclusions: We anticipate our device will decrease the cognitive load on nurses in busy clinical settings and improve workflow. Moreover, the detection of critical events will likely decrease patient morbidity. Continuous monitoring of the CBI outflow may prove to be a new clinical decision-making tool for ongoing hematuria. Clinical trial is pending.

Synthetic biomedical data generation in support of In Silico Clinical Trials

Living in the era of Big Data, one may advocate that the additional synthetic generation of data is redundant. However, to be able to truly say whether it is valid or not, one needs to focus more on the meaning and quality of data than on the quantity. In some domains, such as biomedical and translational sciences, data privacy still holds a higher importance than data sharing. This by default limits access to valuable research data. Intensive discussion, agreements, and conventions among different medical research players, as well as effective techniques and regulations for data anonymization, already made a big step toward simplification of data sharing. However, the situation with the availability of data about rare diseases or outcomes of novel treatments still requires costly and risky clinical trials and, thus, would greatly benefit from smart data generation. Clinical trials and tests on animals initiate a cyclic procedure that may involve multiple redesigns and retesting, which typically takes two or three years for medical devices and up to eight years for novel medicines, and costs between 10 and 20 million euros. The US Food and Drug Administration (FDA) acknowledges that for many novel devices, practical limitations require alternative approaches, such as computer modeling and engineering tests, to conduct large, randomized studies. In this article, we give an overview of global initiatives advocating for computer simulations in support of the 3R principles (Replacement, Reduction, and Refinement) in humane experimentation. We also present several research works that have developed methodologies of smart and comprehensive generation of synthetic biomedical data, such as virtual cohorts of patients, in support of In Silico Clinical Trials (ISCT) and discuss their common ground.

The effects of electronic nursing handover on patient safety in the general (non-COVID-19) and COVID-19 intensive care units: a quasi-experimental study

BACKGROUND

The unprecedented increase in the nurses' workload is one of the issues affecting the quality and safety of patient care in the Intensive Care Units (ICUs). The electronic nursing handover can share sufficient, relevant, and necessary data about patients with greater efficiency and accuracy and prevent their information from being deleted. Therefore, this study aimed to determine and compare the effect of the Electronic Nursing Handover System (ENHS) on patient safety in General ICU and COVID-19 ICU.

METHOD

This is a quasi-experimental study conducted during an 8-month period from 22 to 2021 to 26 June 2022 using a test-retest design. A total of 29 nurses working in the General and COVID-19 ICUs participated in this study. Data were collected using a five-part questionnaire consisting of demographic information, handover quality, handover efficiency, error reduction, and handover time. Data analysis was conducted in IBM SPSS Statistics for Windows, version 26 (IBM Corp., Armonk, N.Y., USA) using the chi-squared test, paired t-test, and Analysis of Covariance (ANCOVA).

RESULTS

The results showed that the mean scores of handover quality and efficiency, reduction of clinical error, and handover time in the electronic handover were significantly higher than those obtained in the paper-based method. The results showed that the mean score of patient safety in the COVID-19 ICU was 177.40 ± 30.416 for the paper-based handover and 251.40 ± 29.049 for the electronic handover (p = .0001). Moreover, the mean score of patient safety in the general ICU was 209.21 ± 23.072 for the paper-based handover and 251.93 ± 23.381 for the electronic one (p = .0001).

CONCLUSION

The use of ENHS significantly improved the quality and efficiency of shift handover, reduced the possibility of clinical error, saved handover time, and finally increased patient safety compared to the paper-based method. The results also showed the positive perspectives of ICU nurses toward the positive effect of ENHS on the patient safety improvement.

Implementing a Unit-Based Alarm Management Bundle for Critical Care Nurses

Background

Clinical alarms are designed to signal an unsatisfactory patient physiological state and alert staff members to malfunctioning medical equipment. Alarm desensitization and fatigue can occur when clinicians are exposed to an overwhelming number of clinical alarms, particularly nonactionable alarms.

Local Problem

Alarm fatigue and alarm management competency related to use of Philips monitoring systems were noted to be problematic among nurses working in the 27-bed surgical intensive care unit of a teaching hospital in northeastern Florida.

Methods

A quality improvement project was conducted to reduce alarm fatigue and improve nursing competency in managing alarms. The CEASE (Communication, Electrodes, Appropriateness, Setup, and Education) evidence-based alarm management bundle was implemented on the unit, and a representative from the alarm manufacturer conducted in-service training sessions. A clinical alarms survey developed by the Healthcare Technology Foundation was distributed both before and after the intervention to 115 nurses working in the unit. Descriptive and inferential statistics were used to assess for differences between the preintervention and postintervention periods.

Results

Nurse participants demonstrated improved alarm management competency, resulting in significant improvements in their perceptions of alarm functionality, settings, response time, and policy adherence. There was a statistically significant decrease in self-reported alarm fatigue, and the CEASE bundle was found to influence nursing practice.

Conclusions

The Joint Commission’s 2022 goal of improving clinical alarm safety remains a top priority nationwide. Implementation of the CEASE alarm management bundle was effective in reducing surgical intensive care unit nurses’ alarm fatigue and improving their alarm management practices.

Polish Adaptation of the Alarm Fatigue Assessment Questionnaire as an Element of Improving Patient Safety

Medical personnel, working in medical intensive care units, are exposed to fatigue associated with alarms emitted by numerous medical devices used for diagnosing, treating, and monitoring patients. Alarm fatigue is a safety and quality problem in patient care and actions should be taken to reduce this by, among other measures, building an effective safety culture. In the present study, an adaptation of a questionnaire to assess alarm fatigue was carried out. The study obtained good reliability of the questionnaire at Cronbach's alpha level of 0.88. The Polish research team has successfully adapted the Alarm Fatigue Assessment Questionnaire so that it can be used in healthcare settings as a tool to improve patient safety.

Alarm fatigue among nurses working in intensive care and other inpatient clinics

BACKGROUND

Alarm fatigue is an important technological hazard that adversely affects patient safety and the healthcare team. Nurses can be exposed to an excessive amount of alarms during their work which may lead to alarm fatigue.

OBJECTIVE

To determine the experiences of alarm fatigue among nurses working in intensive care units and other inpatient clinics.

METHODS

This descriptive study was conducted in university hospitals in five cities in Turkey between August and December 2019. A total of 592 nurses participated in this study. The data was collected using questionnaires and the Visual Analog Scale (0 to 10 points) was used to determine the level of alarm fatigue.

RESULTS

More than half of the nurses experienced problems, especially false alarms, caused by devices. Alarm fatigue decreased with increasing age and working years. Nurses reported appropriate actions in solving problems, but also had practices that may increase the risk of error, such as turning off or muting alarms or turning off equipment.

CONCLUSION

Alarm fatigue is mostly caused by false alarms. It can lead to physical fatigue, increased workload and decreased concentration, resulting in an increased possibility of error. Management of alarm fatigue is necessary in preventing a compromise in patients' safety and improving quality of care.

Determinants of Medical Equipment Alarm Fatigue in Practicing Nurses: A Systematic Review

Objective

This study aimed to systematically evaluate the level of medical equipment alarm fatigue and its influencing factors among clinical nurses.

Methods

PubMed, Embase, CNKI, and Wanfang databases were systematically searched to identify articles on alarm fatigue of clinical nurses published before September 25, 2022. According to the evaluation criteria of prevalence studies recommended by JBI Evidence-Based Health Care Center, the quality of the literature meeting the inclusion criteria was evaluated, and Stata MP17 software was used for meta-analysis.

Results

A total of 14 cross-sectional studies were included, with a total sample of 2,848 nurses. The results showed that the alarm fatigue score of clinical nurses was 21.76 (95% CI [20.27, 23.25]). Subgroup analysis showed that the nurses who worked night shift and had lower professional title had higher alarm fatigue.

Conclusion

The alarm fatigue of clinical nurses was at a moderate level. To reduce the alarm fatigue level of clinical nurses, nursing managers should strengthen the alarm safety awareness of nurses, rationally arrange nurse manpower, carry out training to actively improve the alarm management ability of nurses, and optimize the alarm level and frequency of alarm equipment.

Assessment of Alarm Fatigue Risk Among Oncology Nurses in Jordan

Introduction

Using technology in the clinical setting where clinical alarms frequently occur, resulting in many false alarms, which is called alarm fatigue, alarm fatigue may increase nurses' distraction, and that might negatively affect patient safety.

Objective

This study aimed to assess alarm fatigue among oncology nurses in Jordan.

Methods

A descriptive cross-sectional design was used in a non-profit specialized cancer center. A self-reported questionnaire was answered by nurses who participated in the study.

Results

A total of 222 questionnaires were analyzed with a more than 95% response rate. More than half of the sample (60.4%) were females. The participants were young nurses with a mean age of 25.18 ± 3.33 years. The total mean score of alarm fatigue was 31.62 ± 7.14 on a scale ranging from zero to 52. Post-hoc analysis showed that the palliative unit (25.73 ± 7.22) and emergency room (28.73 ± 6.62) had low scores of total mean alarm fatigue than remaining area of practice, such as the ICU (33.92 ± 6.99); p-value: .004.

Conclusion

Alarm fatigue is a global issue affecting many practice areas. An educational program is recommended for nurses to learn how to deal with alarm fatigue. In order to effectively manage alarms, nurses' education and individual training are crucial.

A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting

BACKGROUND

Seamless data integration between point-of-care medical devices and the electronic health record (EHR) can be central to clinical decision support systems (CDSS).

OBJECTIVE

The objective of this scoping review is to (1) examine the existing evidence related to integrated medical devices, primarily medication pump devices, and associated clinical decision support (CDS) in acute care settings and (2) to identify how acute care clinicians may use device CDS in clinical decision-making. The rationale for this review is that integrated devices are ubiquitous in the acute care setting, and they generate data that may help to contribute to the situational awareness of the clinical team necessary to provide individualized patient care.

METHODS

This scoping review was conducted using the Joanna Briggs Institute Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extensions for Scoping Review guidelines. PubMed, CINAHL, IEEE Xplore, and Scopus databases were searched for scholarly, peer-reviewed journals indexed between January 1, 2010 and December 31, 2020. A priori inclusion criteria were established.

RESULTS

Of the 1,924 articles screened, 18 were ultimately included for synthesis, and primarily included articles on devices such as intravenous medication pumps and vital signs machines. Clinical alarm burden was mentioned in most of the articles, and despite not including the term "medication" there were many articles about smart pumps being integrated with the EHR. The Revised Technology, Nursing & Patient Safety Conceptual Model provided the organizational framework. Ten articles described patient assessment, monitoring, or surveillance use. Three articles described patient protection from harm. Four articles described direct care use scenarios, all of which described insulin administration. One article described a hybrid situation of patient communication and monitoring. Most of the articles described devices and decision support primarily used by registered nurses (RNs).

CONCLUSION

The articles in this review discussed devices and the associated CDSS that are used by clinicians, primarily RNs, in the daily provision of care for patients. Integrated device data provide insight into user-device interactions and help to illustrate health care processes, especially the activities when providing direct care to patients in an acute care setting. While there are CDSS designed to support the clinician while working with devices, RNs and providers may disregard this guidance, and defer to their own expertise. Additionally, if clinicians perceive CDSS as intrusive, they are at risk for alarm and alert fatigue if CDSS are not tailored to sync with the workflow of the end-user. Areas for future research include refining inclusion criteria to examine the evidence for devices and their CDS that are most likely used by other groups' health care professionals (i.e., doctors and therapists), using integrated device metadata and deep learning analytics to identify patterns in care delivery, and decision support tools for patients using their own personal data.

Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review

Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.

Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233461, identifier: CRD42021233461.

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients

The evolution of intracranial pressure (ICP) of critically ill patients admitted to a neurointensive care unit (ICU) is difficult to predict. Besides the underlying disease and compromised intracranial space, ICP is affected by a multitude of factors, many of which are monitored on the ICU, but the complexity of the resulting patterns limits their clinical use. This paves the way for new machine learning (ML) techniques to assist clinical management of patients undergoing invasive ICP monitoring independent of the underlying disease. An institutional cohort (ICP-ICU) of patients with invasive ICP monitoring (n = 1346) was used to train recurrent ML models to predict the occurrence of ICP increases of ≥ 22mmHg over a long (> 2 hours) time period in the upcoming hours. External validation was performed on patients undergoing invasive ICP measurement in two publicly available datasets (Medical Information Mart for Intensive Care (MIMIC, n = 998) and eICU Collaborative Research Database (eICU, n = 1634)). Different distances (1h-24 h) between prediction time point and upcoming critical phase were evaluated, demonstrating a decrease in performance but still robust AUC-ROC with larger distances (24 h AUC-ROC: ICP-ICU 0.826 ± 0.0071, MIMIC 0.836 ± 0.0063, eICU 0.779 ± 0.0046, 1 h AUC-ROC: ICP-ICU 0.982 ± 0.0008, MIMIC 0.965 ± 0.0010, eICU 0.941 ± 0.0025). The model operates on sparse hourly data and is stable in handling variable input lengths and missingness through its nature of recurrence and internal memory. Calculation of gradient-based feature importance revealed individual underlying decisions for our Long Short Time Memory (LSTM) based model and thereby provided improved clinical interpretability. Recurrent ML models have the potential to be an effective tool for the prediction of ICP increases with high translational potential.

Determining the Impact of an Alarm Management Program on Alarm Fatigue among ICU and Telemetry RNs: An Evidence Based Research Project

This evidence-based research project provides an appraisal of current research on how an alarm management program impacts alarm fatigue among registered nurses (RNs) in both intensive care units (ICUs) and telemetry units. Alarm fatigue is a major problem recognized by both the American Association of Critical-Care Nurses (AACN) and the Joint Commission. RNs are the primary caretakers of critically ill patients in ICUs and telemetry units and therefore are at the greatest risk for alarm fatigue. The researchers performed an evidence synthesis to determine the impact of an alarm management program on alarm fatigue among ICU and telemetry RNs. A literature search was conducted using scientific databases such as PubMed, CINAHL, Trip, Cochrane Review, and Google Scholar. Our search strategy included the following terms: adult registered nurse, inpatient registered nurse, ICU registered nurses, RNs, Nurse Practitioners, alarm fatigue, alarm management strategy, education, cardiac monitor alarm, alarm strategies, alarm bundle, telemetry alarm, and cardiac monitor. Any studies involving the pediatric population, pulse oximeter alarms, and ventilator alarms were excluded. Due to the lack of available randomized control trials and cohort studies, the authors included two quality improvement (QI) projects. Finally, six studies were taken into consideration for review. The authors appraised each of the six articles using the Critical Appraisal Skills Programme Checklist (CASP) Tool. This tool allowed the authors to synthesize information based on the outcomes and determine the level of the evidence of each article in order to make evidence-based practice recommendations on implementing alarm management programs. Conclusion: Despite extensive literature highlighting the astronomical prevalence of alarm fatigue in RNs, there was a lack of current high-quality data related to implementing alarm management programs. Therefore, more research is needed to support the utilization of alarm management programs in ICUs and telemetry units to improve alarm fatigue among RNs.

Clinical alarms and alarm fatigue in a University Hospital Emergency Department-A retrospective data analysis

BACKGROUND

Alarm fatigue is hypothesized to be caused by vast amount of patient monitor alarms. Objectives were to study the frequency and types of patient monitor alarms, to evaluate alarm fatigue, and to find unit specific alarm threshold values in a university hospital emergency department.

METHODS

We retrospectively gathered alarm data from 9 September to 6 October 2019, in Jorvi Hospital Emergency department, Finland. The department treats surgical, internal and general medicine patients aged 16 and older. The number of patients is on average 4600 to 5000 per month. Eight out of 46 monitors were used for data gathering and the monitored modalities included electrocardiography, respiratory rate, blood pressure, and pulse oximetry.

RESULTS

Total number of alarms in the study monitors was 28 176. Number of acknowledged alarms (ie acknowledgement indicator pressed in the monitor) was 695 (2.5%). The most common alarm types were: Respiratory rate high, 9077 (32.2%), pulse oximetry low, 4572 (16.2%) and pulse oximetry probe off, 4036 (14.3%). Number of alarms with duration under 10 s was 14 936 (53%). Number of individual alarm sounds was 105 000, 469 per monitor per day. Of respiratory rate high alarms, 2846 (31.4%) had initial value below 30 breaths min^-1 . Of pulse oximetry low alarms, 2421 (53.0%) had initial value above 88%.

CONCLUSIONS

Alarm sound load, from individual alarm sounds, was nearly continuous in an emergency department observation room equipped with nine monitors. Intervention by the staff to the alarms was infrequent. More than half of the alarms were momentary.

Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions

BACKGROUND

As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients.

OBJECTIVE

This study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies.

METHODS

This observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU's alarm situation.

RESULTS

We developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed).

CONCLUSIONS

Analyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff's work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data.

Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study

BACKGROUND

Clinical decision support systems (CDSS) have the potential to lower the patient mortality and morbidity rates. However, signal artifacts present in physiological data affect the reliability and accuracy of the CDSS. Moreover, patient monitors and other medical devices generate false alarms while processing physiological data, further leading to alarm fatigue because of increased noise levels, staff disruption, and staff desensitization in busy critical care environments. This adversely affects the quality of care at the patient bedside. Hence, artifact detection (AD) algorithms play a crucial role in assessing the quality of physiological data and mitigating the impact of these artifacts.

OBJECTIVE

The aim of this study is to evaluate a novel AD framework for integrating AD algorithms with CDSS. We designed the framework with features that support real-time implementation within critical care. In this study, we evaluated the framework and its features in a false alarm reduction study. We developed static framework component models, followed by dynamic framework compositions to formulate four CDSS. We evaluated these formulations using neonatal patient data and validated the six framework features: flexibility, reusability, signal quality indicator standardization, scalability, customizability, and real-time implementation support.

METHODS

We developed four exemplar static AD components with standardized requirements and provisions interfaces that facilitate the interoperability of framework components. These AD components were mixed and matched into four different AD compositions to mitigate the artifacts' effects. We developed a novel static clinical event detection component that is integrated with each AD composition to formulate and evaluate a dynamic CDSS for peripheral oxygen saturation (SpO2) alarm generation. This study collected data from 11 patients with diverse pathologies in the neonatal intensive care unit. Collected data streams and corresponding alarms include pulse rate and SpO2 measured from a pulse oximeter (Masimo SET SmartPod) integrated with an Infinity Delta monitor and the heart rate derived from electrocardiography leads attached to a second Infinity Delta monitor.

RESULTS

A total of 119 SpO2 alarms were evaluated. The lowest achievable SpO2 false alarm rate was 39%, with a sensitivity of 80%. This demonstrates the framework's utility in identifying the best possible dynamic composition to serve the clinical need for false SpO2 alarm reduction and subsequent alarm fatigue, given the limitations of a small sample size.

CONCLUSIONS

The framework features, including reusability, signal quality indicator standardization, scalability, and customizability, allow the evaluation and comparison of novel CDSS formulations. The optimal solution for a CDSS can then be hard-coded and integrated within clinical workflows for real-time implementation. The flexibility to serve different clinical needs and standardized component interoperability of the framework supports the potential for a real-time clinical implementation of AD.

Low Blood Flow Continuous Veno-Venous Haemodialysis Compared with Higher Blood Flow Continuous Veno-Venous Haemodiafiltration: Effect on Alarm Rates, Filter Life, and Azotaemic Control

TITLE

Low blood flow continuous veno-venous haemodialysis (CVVHD) compared with higher blood flow continuous veno-venous haemodiafiltration (CVVHDF): effect on alarm rates, filter life, and azotaemic control.

INTRODUCTION

Continuous renal replacement therapy (CRRT) can be delivered via convective, diffusive, or mixed approaches. Higher blood flows have been advocated for convective clearance efficiency and promotion of filter life. It is unclear whether a lower blood flow predominantly diffusive approach may benefit filter life and alarm rates.

MATERIALS AND METHODS

Sequential cohort study of 284 patients undergoing 874 CRRT circuits from January 2015 to August 2018 in a single university-associated tertiary referral hospital in Australia. Patients underwent a protocol of either CVVHDF at blood flow 200-250 mL/min or CVVHD at blood flow 100-130 mL/min. Machine and patient data were analysed. Outcomes of azotaemic control, filter life, and warning alarm rates were log transformed and analysed with mixed linear modelling with patient as a random effect.

RESULTS

Both groups had similar azotaemic control (effect estimate on log creatinine CVVHD vs. CVVHDF 1.04 [0.87-1.25], p = 0.68) and median filter life (CVVHDF 16.8 [8.4-90.5] h and CVVHD 16.4 [9.4-82.3] h, p = 0.97). However, circuit pressures were less extreme with a narrower distribution during CVVHD. Multivariate analysis showed CVVHD had a reduced risk of warning alarms (incidence risk ratio [IRR] 0.51 [0.38-0.70]) and femoral access placement also had a reduced risk of alarms (IRR 0.55 [0.41-0.73]).

CONCLUSION

Low blood flow CVVHD and femoral vascular access reduce alarms while maintaining azotaemic control and circuit patency thus minimizing bedside clinician workload.

Reducing medical device alarms by an order of magnitude: A human factors approach

The intensive care unit (ICU) is one of the most technically advanced environments in healthcare, using a multitude of medical devices for drug administration, mechanical ventilation and patient monitoring. However, these technologies currently come with disadvantages, namely noise pollution, information overload and alarm fatigue—all caused by too many alarms. Individual medical devices currently generate alarms independently, without any coordination or prioritisation with other devices, leading to a cacophony where important alarms can be lost amongst trivial ones, occasionally with serious or even fatal consequences for patients. We have called this approach to the design of medical devices the single-device paradigm, and believe it is obsolete in modern hospitals where patients are typically connected to several devices simultaneously. Alarm rates of one alarm every four minutes for only the physiological monitors (as recorded in the ICUs of two hospitals contributing to this paper) degrades the quality of the patient’s healing environment and threatens patient safety by constantly distracting healthcare professionals. We outline a new approach to medical device design involving the application of human factors principles which have been successful in eliminating alarm fatigue in commercial aviation. Our approach comprises the networked-device paradigm, comprehensive alarms and humaniform information displays. Instead of each medical device alarming separately at the patient’s bedside, our proposed approach will integrate, prioritise and optimise alarms across all devices attached to each patient, display information more intuitively and hence increase alarm quality while reducing the number of alarms by an order of magnitude below current levels.

"Noise Factory": A qualitative study exploring healthcare providers' perceptions of noise in the intensive care unit

OBJECTIVES

This study aimed to explore healthcare providers' perceptions of noise in the intensive care unit.

DESIGN

A qualitative exploratory study was conducted using group interviews.

SETTING

The setting comprised a total of 15 participants (five physicians and ten registered nurses) working in an 18-bed medical surgical intensive care unit at a teaching hospital in Istanbul, Turkey. Semi-structured questions were formulated and used in focus group interviews, after which the recorded interviews were transcribed by the researchers. Thematic analysis was used to identify significant statements and initial codes.

FINDINGS

Four themes were identified: the meaning of noise, sources of noise, effects of noise and prevention and management of noise. It was found that noise was an inevitable feature of the intensive care unit. The most common sources of noise were human-induced. It was also determined that device-induced noise, such as alarms, did not produce a lot of noise; however, when staff were late in responding, the sound transformed into noise. Furthermore, it was observed that efforts to decrease noise levels taken by staff had only a momentary effect, changing nothing in the long term because the entire team failed to implement any initiatives consistently. The majority of nurses stated that they were now becoming insensitive to the noise due to the constant exposure to device-induced noise.

CONCLUSION

The data obtained from this study showed that especially human-induced noise threatened healthcare providers' cognitive task functions, concentration and job performance, impaired communication and negatively affected patient safety. In addition, it was determined that any precautions taken to reduce noise were not fully effective. A team approach should be used in managing noise in intensive care units with better awareness.

Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study

Background

Owing to an increase in digital technologies in health care, recently leveraged by the COVID-19 pandemic, physicians are required to use these technologies appropriately and to be familiar with their implications on patient care, the health system, and society. Therefore, medical students should be confronted with digital health during their medical education. However, corresponding teaching formats and concepts are still largely lacking in the medical curricula.

Objective

This study aims to introduce digital health as a curricular module at a German medical school and to identify undergraduate medical competencies in digital health and their suitable teaching methods.

Methods

We developed a 3-week curricular module on digital health for third-year medical students at a large German medical school, taking place for the first time in January 2020. Semistructured interviews with 5 digital health experts were recorded, transcribed, and analyzed using an abductive approach. We obtained feedback from the participating students and lecturers of the module through a 17-item survey questionnaire.

Results

The module received overall positive feedback from both students and lecturers who expressed the need for further digital health education and stated that the field is very important for clinical care and is underrepresented in the current medical curriculum. We extracted a detailed overview of digital health competencies, skills, and knowledge to teach the students from the expert interviews. They also contained suggestions for teaching methods and statements supporting the urgency of the implementation of digital health education in the mandatory curriculum.

Conclusions

An elective class seems to be a suitable format for the timely introduction of digital health education. However, a longitudinal implementation in the mandatory curriculum should be the goal. Beyond training future physicians in digital skills and teaching them digital health’s ethical, legal, and social implications, the experience-based development of a critical digital health mindset with openness to innovation and the ability to assess ever-changing health technologies through a broad transdisciplinary approach to translate research into clinical routine seem more important. Therefore, the teaching of digital health should be as practice-based as possible and involve the educational cooperation of different institutions and academic disciplines.

Team-Based Intervention to Reduce the Impact of Nonactionable Alarms in an Adult Intensive Care Unit

BACKGROUND

Nonactionable alarms comprise over 70% of alarms and contribute a threat to patient safety. Few studies have reported approaches to translate and sustain these interventions in clinical settings.

PURPOSE

This study tested whether an interprofessional team-based approach can translate and implement effective alarm reduction interventions in the adult intensive care unit.

METHODS

The study was a prospective, cohort, pre- and postdesign with repeated measures at baseline (preintervention) and post-phase I and II intervention periods. The settings for the most prevalent nonactionable arrhythmia and bedside parameter alarms were adjusted during phases I and II, respectively.

RESULTS

The number of total alarms was reduced by 40% over a 14-day period after both intervention phases were implemented. The most prevalent nonactionable parameter alarms decreased by 47% and arrhythmia alarms decreased by 46%.

CONCLUSIONS

It is feasible to translate and sustain system-level alarm management interventions addressing alarm fatigue using an interprofessional team-based approach.

Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study

BACKGROUND

Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development.

OBJECTIVE

This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups.

METHODS

This survey study was performed with ICU staff from 4 ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire, by analyzing a preceding qualitative interview study with ICU staff, about the clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and chi-square tests to compare the distributions of item responses of the professional groups.

RESULTS

In total, 86 of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated they felt confident using the patient monitoring equipment, but that high rates of false-positive alarms and the many sensor cables interrupted patient care. Regarding future improvements, respondents asked for wireless sensors, a reduction in the number of false-positive alarms, and hospital standard operating procedures for alarm management. Responses to the display devices proposed for remote patient monitoring were divided. Most respondents indicated it would be useful for earlier alerting or when they were responsible for multiple wards. AI for ICUs would be useful for early detection of complications and an increased risk of mortality; in addition, the AI could propose guidelines for therapy and diagnostics. Transparency, interoperability, usability, and staff training were essential to promote the use of AI. The majority wanted to learn more about new technologies for the ICU and required more time for learning. Physicians had fewer reservations than nurses about AI-based intelligent alarm management and using mobile phones for remote monitoring.

CONCLUSIONS

This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173.

Nurses' Perception of the Bed Alarm System in Acute-Care Hospitals

Introduction

In hospitals, the nurse uses the bed alarm system for patients' safety, which may have some forms of physical restraints included, depending on the situation. However, the nurses' perceptions of the bed alarm system with reference to restraints are yet to be clarified. Moreover, there were no reports that can shed light upon the factors that relate to nurses' perceptions about the bed alarm system in Japan. The objective of this study is to investigate the nurses' perception on whether the bed alarm can be considered as a form of physical restraint and to elucidate the factors that pertain to the nurses' perceptions regarding the bed alarm.

Methods

This study conducted a quantitative cross-sectional survey. We used bivariate logistic regression analyses to investigate the nurses' perception and the factors affecting these perception. Ethical approval was obtained from the research ethics committee of the Kyoto University. Participants opted for answering the questionnaire voluntarily.

Results

The sample population comprised of 289 nurses from 10 acute-care hospitals. Out of these, 214 (74.0%) nurses considered the bed alarm system as a form of restraint, and 75 nurses (26.0%) did not. Furthermore, the nurses' perception was relevant to the hospitals that they belonged to, their years of experience, and the content of education. It was especially interesting that the group of nurses with little experience had the consciousness of being considered the bed alarm as restraint compared with nurses with many years of experience.

Conclusion

The alarm systems are gradually being considered to be classified as a restraint. Hospitals should ensure providing an ethically sensitive climate and appropriate educational opportunities to help nurses build these perceptions for patient care. An ethically sensitive climate and appropriate educational opportunities would lead to an environment that nurtures nurses with the ability to recognize problems in daily care.

Nurses' responses to monitor alarms in an intensive care unit: An observational study

OBJECTIVE

The present study aimed to determine the types of monitor alarms and nurses' responses to them in an adult intensive care unit.

DESIGN AND METHODS

This was an observational descriptive research study conducted in the adult intensive care unit of a university hospital in the Mediterranean region of Turkey. The nonparticipant observation method was used. Data were collected by two observers using a semi-structured observation form developed according to literature.

RESULTS

Between August 2016 and January 2017, 13 registered nurses were observed for 328 hours. There were 1781 alarms, which included alarms for blood pressure (37.6%), respiration and oxygen saturation (35.3%) and heart rate and arrhythmia (27.1%). Nurses responded to approximately half (46.9%) of the alarms that required a response. Responses to alarms included silencing them, responding to the patient's clinical condition and solving contact and transmission problems.

CONCLUSION

In the present study, according to response requirement, the division of the alarms was different. The number of alarms that do not reflect the clinical status of the patient was high. It was found that as the false alarm rate increased, the response rate of nurses to these alarms decreased.

Educational Program for Physiologic Monitor Use and Alarm Systems Safety: A Toolkit

Clinical alarm systems safety is a national healthcare concern in the United States. Physiologic monitors are the medical devices associated with the highest number of false and clinically insignificant alarms, producing alarm fatigue and a challenge to meet the national clinical alarm systems safety goal. Modern physiologic monitors are high-tech complex devices with multimeasurement modalities and high sensitivity for alarms. This complexity hinders safe operation of the monitors by nurses and appropriate management of associated alarms. Nurses need to integrate cognitive knowledge, psychomotor skills, and critical thinking to safely operate the monitors and support clinical decisions. Limited resources are available to support clinical education for nurses on physiologic monitor use and alarm management. This toolkit presents an educational framework for physiologic monitor use and alarm safety guided by adult learning principles. The components of the program are (1) knowledge, skills, and attitude of physiologic monitor use; (2) scenario-based learning model to support the knowledge, skills, and attitude necessary for safe monitor use; and (3) a framework for evaluating the educational program. Education should be ongoing and customized per facility to ensure safe use of complex technology and to decrease alarm fatigue, the leading cause of alarm-related sentinel events.

Nursing staff's evaluation of facilitators and barriers during implementation of wireless nurse call systems in residential care facilities. A cross-sectional study

Background

Traditional nurse call systems used in residential care facilities rely on patients to summon assistance for routine or emergency needs. Wireless nurse call systems (WNCS) offer new affordances for persons unable to actively or consciously engage with the system, allowing detection of hazardous situations, prevention and timely treatment, as well as enhanced nurse workflows. This study aimed to explore facilitators and barriers of implementation of WNCSs in residential care facilities.

Methods

The study had a cross-sectional descriptive design. We collected data from care providers (n = 98) based on the Measurement Instrument for Determinants of Innovation (MIDI) framework in five Norwegian residential care facilities during the first year of WNCS implementation. The self-reporting MIDI questionnaire was adapted to the contexts. Descriptive statistics were used to explore participant characteristics and MIDI item and determinant scores. MIDI items to which ≥20% of participants disagreed/totally disagreed were regarded as barriers and items to which ≥80% of participants agreed/totally agreed were regarded as facilitators for implementation.

Results

More facilitators (n = 22) than barriers (n = 6) were identified. The greatest facilitators, reported by 98% of the care providers, were the expected outcomes: the importance and probability of achieving prompt call responses and increased safety, and the normative belief of unit managers. During the implementation process, 87% became familiar with the systems, and 86 and 90%, respectively regarded themselves and their colleagues as competent users of the WNCS. The most salient barriers, reported by 37%, were their lack of prior knowledge and that they found the WNCS difficult to learn. No features of the technology were identified as barriers.

Conclusions

Overall, the care providers gave a positive evaluation of the WNCS implementation. The barriers to implementation were addressed by training and practicing technological skills, facilitated by the influence and support by the manager and the colleagues within the residential care unit. WNCSs offer a range of advanced applications and services, and further research is needed as more WNCS functionalities are implemented into residential care services.

Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital

BACKGROUND10: The Joint Commission identified inpatient alarm reduction as an opportunity to improve patient safety; enhance patient, family and nursing satisfaction; and optimise workflow. We used quality improvement (QI) methods to safely decrease non-actionable alarm notifications to bedside providers.

METHODS

In a paediatric tertiary care centre, we convened a multidisciplinary team to address alarm notifications in our acute care cardiology unit. Alarm notification was defined as any alert to bedside providers for each patient-triggered monitor alarm. Our aim was to decrease alarm notifications per monitored bed per day by 60%. Plan-Do-Study-Act testing cycles included updating notification technology, establishing alarm logic and modifying bedside workflow processes, including silencing the volume on all bedside monitors. Our secondary outcome measure was nursing satisfaction. Balancing safety measures included floor to intensive care unit transfers and patient acuity level.

RESULTS

At baseline, there was an average of 71 initial alarm notifications per monitored bed per day. Over a 3.5-year improvement period (2014-2017), the rate decreased by 68% to 22 initial alarm notifications per monitored bed per day. The proportion of initial to total alarm notifications remained stable, decreasing slightly from 51% to 40%. There was a significant improvement in subjective nursing satisfaction. At baseline, 32% of nurses agreed they were able to respond to alarms appropriately and quickly. Following interventions, agreement increased to 76% (p<0.001). We sustained these improvements over a year without a change in monitored balancing measures.

CONCLUSION

We successfully reduced alarm notifications while preserving patient safety over a 4-year period in a complex paediatric patient population using technological advances and QI methodology. Continued efforts are needed to further optimise monitor use across paediatric hospital units.

Smart alarms towards optimizing patient ventilation in intensive care: the driving pressure case

OBJECTIVE

Alarms are a substantial part of clinical practice, warning clinicians of patient complications. In this paper, we focus on alarms in the intensive care unit and especially on the use of machine learning techniques for the creation of alarms for the ventilator support of patients. The aim is to study a method to enable timely interventions for intubated patients and prevent complications induced by high driving pressure (ΔP) and lung strain during mechanical ventilation.

APPROACH

The relation between the ΔP and the total set of the ventilator parameters was examined and resulted in a predictive model with bimodal implementation for the short-term prediction of the ΔP level (high/low). The proposed method includes two sub-models for the prediction of future ΔP level based on the current level being high or low, named cH and cL, respectively. Based on this method, for both sub-models, an alarm will be triggered when the predicted ΔP level is considered to be high. In this vein, three classifiers (the random forest, linear support vector machine, and kernel support vector machine methods) were tested for each sub-model. To adjust the highly unbalanced classes, four different sampling methods were considered: downsampling, upsampling, synthetic minority over-sampling technique (SMOTE) sampling, and random over-sampling examples (ROSE) sampling.

MAIN RESULTS

For the cL sub-model the combination of linear support vector machine with SMOTE sampling showed the best performance, resulting in accuracy of 93%, while the cH sub-model reached the best performance, with accuracy of 73%, with kernel support vector machine combined with the downsampling method.

SIGNIFICANCE

The results are positive in terms of the generation of new alarms in mechanical ventilation. The technical and organizational possibility of integrating data from multiple modalities is expected to further advance this line of work.

Reduction of false alarms in the intensive care unit using an optimized machine learning based approach

This work attempts to reduce the number of false alarms generated by bedside monitors in the intensive care unit (ICU), as a majority of current alarms are false. In this study, we applied methods that can be categorized into three stages: signal processing, feature extraction, and optimized machine learning. At the stage of signal processing, we ensured that the heartbeats were properly annotated. During feature extraction, besides extracting features that are relevant to the arrhythmic alarms, we also extracted a set of signal quality indices (SQIs), which we used to distinguish noise/artifact from normal physiological signals. When applying a machine learning algorithm (Random Forest), we performed feature selection in order to reduce the complexity of the models and improve the efficiency of the algorithm. The dataset used is from Reducing False Arrhythmia Alarms in the ICU: the PhysioNet/Computing in Cardiology Challenge 2015. Using the performance metric "score" from the Challenge, we achieved a score of 83.08 in the real-time category on the hidden test set, which is the highest in all published work.

Can Nursing Drive Technological Advances in Healthcare in the Asia-Pacific?

The Asia-Pacific healthcare industry is expected to grow at 11.1% in 2018. This has been considered one of the fastest growing regions in the world. The positive growth occurring in the Asia-Pacific region is due to the increasing adoption of technology. While it is understood that technology drives advances in nursing and the health sciences, would it be possible that nursing can or will also drive technological advancements in human caring? All too often, nurses are employed in health care as simply the end-users of technologies. It is the purpose of this paper to engage a discourse towards advancing nursing as driving technological improvements aimed for human caring. How can nursing facilitate this powerful dynamic, and what will it take for nursing as a discipline and a profession to occupy a primary role in this all too often unrecognized view, that nursing can and will drive technological advancements for human caring?