Designing the Alarm Management User Experience for Patient Monitoring

Abstract We present a case study of designing the alarm management user experience for patient monitoring devices in critical care units. Based on contextual inquiry with critical care nurses, we developed novel design concepts that re-imagined the user experience of patient monitoring devices to reduce alarm fatigue. We conducted preliminary evaluations of the design

concepts to examine whether the new features would enable better alarm management. In this case study, we highlight the design process and the lessons learnt.

Author Keywords User-centered design; contextual inquiry; interviews; evaluation; patient monitoring; alarm management.

ACM Classification Keywords H.5.2. User Interfaces.

Introduction In critical care units, the high false alarm rate of patient monitoring devices causes excessive noise [5] and alarm fatigue [3] among care providers. Alarm fatigue leads care providers to ignore or inactivate alarm signals, underutilize monitor features, and miss critical events. It also leads to a lack of caregiver trust in alarming devices and ultimately to desensitization to true alarms [6]. Insufficient attention to human factors and usability of medical devices [1] is a key issue affecting alarm management. Furthermore, alarm management tools often do not map well to nurses’ tasks [4]. Yet, there exist few studies of human alarm-handling behaviour which can inform the user-centred design of patient monitoring devices.

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Sharoda A. Paul GE Global Research San Ramon, CA 94583, USA paul@ge.com Stephen M. Treacy GE Healthcare Milwaukee, WI 53223, USA stephen.treacy@med.ge.com

Alexander K. Carroll GE Global Research San Ramon, CA 94583, USA alex.carroll@ge.com

We conducted a qualitative study to examine the alarm management challenges of nurses in critical care units. In this paper we present three key challenges faced by care providers during alarm management activities – inability to assign ‘normal’ conditions for patients, inability to easily follow trends in patient’s conditions, and lack of predictive alerts that warn about impending alarm conditions. We next present two design concepts for patient monitoring devices to address these challenges. Finally, we discuss findings and lessons learnt from evaluating these design concepts.

Background Alarm fatigue is a richly studied area in the healthcare literature. Human factors issues have been identified as critical to this issue, such as the variety of UIs across vendors and machines and complex monitor displays that often do not convey crucial clinical information [1]. In spite of this, only a handful of research studies [2, 4, 7] have conducted in-depth examination of how healthcare providers respond to alarms. Such studies are important for designing user-centred alarm management interfaces. We wanted to examine the challenges faced by care providers in responding to alarms in critical care units and whether current monitors support user’s alarm management practices.

Contextual Inquiry Methods We conducted contextual inquiry at a 267-bed hospital located in the Western US, which we call WUSH in this paper. WUSH specializes in cancer care and cardiac care. WUSH has three critical care units – a 12-bed cardio-vascular ICU (CVICU), a 10-bed coronary care unit (CCU), and an 8-bed intensive care unit (ICU). It also has 83 telemetry beds, of which 50% operate in

‘combo’ mode, i.e., a combination of telemetry and bedside monitoring. WUSH uses commercial bedside and central monitoring stations, a secondary alert system for telemetry patients, and clinical decision support systems for remote ICU monitoring.

We conducted two site-visits to WUSH in Oct 2013. During the first visit, we spent 2 days on site during which we conducted 11 interviews with healthcare staff. Interviews were 45-60 minutes long. We also conducted walkthroughs of various critical care units where monitoring devices were in use (Figure 1). During the walkthroughs, we focused on the central station and bedside monitoring devices. After our first visit, we conducted a preliminary analysis of the data collected. We conducted follow-up on-site interviews with three of the participants to delve deeper into the themes emerging from our preliminary synthesis.

Participants Table 1 lists the roles we interviewed and the units we visited.

Role (number of participants) Units

visited Biomedical supervisor (1) Cath Lab

Clinical nurse specialist* (1) CVICU

Nurse manager, cardiac & critical care* (1) CCU Director of critical care, eICU manager* (1) ICU

In-unit critical care nurses (4) ER

eICU critical care nurses (2) Telemetry Charge nurse (1) eICU

Table 1: User study participants and units where walkthrough was conducted. * indicates a follow-up participant.

Figure 1: Patient monitoring devices in different settings at WUSH. From top to bottom – critical care bedside monitor, central station monitor, nurses at the Central Station.

Only one participant was male. Participants ranged in clinical experience from 15 years to 37 years. All nurse managers had prior experience as bedside nurses.

Analysis All interview data were recorded and transcribed. We analysed the data using a grounded theory approach [8]. We conducted open coding and axial coding to discover emerging themes and then selective coding to find relationships among the themes. Next, we present the main themes that emerged from our data.

Findings Participants noted a variety of challenges with alarming devices and alarm management, echoing findings from literature. Additionally, we identified three prominent challenges that have not received much attention in past research.

Learning the patient’s normal One of the key issues leading to alarm fatigue was the difficulty of customizing alarms to individual patients. For instance, when arrhythmia alarms are triggered, the baseline electrocardiogram waveform that the algorithm compares against may not be ‘normal’ for the given patient. An example provided by a participant was of a patient who had a normal heart rate but had a “wide and bizarre QRS” which led the machine to alarm continuously even though this was not a life-threatening condition for the particular patient.

“I’m sure it was atrial fib and aberrant but [the monitor] was alarming. We went through a whole roll of paper printing out this rhythm… It was aberrantly conducted and we couldn’t get the machine to stop alarming. Yes, I saw it. I knew what it was… That to

me is one of the frustrations because now had the guy really gone on to V Tach, or had a rate fast enough to where he wasn’t perfusing, that I would really want to know. Instead every time he went into this slow rate, wide and bizzare, it alarmed and we couldn’t get it to relearn and say ‘Yup, that we know’.” – Clinical nurse specialist

Under such situations, participants wanted the monitoring device to ‘learn’ the rhythm as the patient’s normal so that it wouldn't sound an alarm for that condition. It would be useful to allow nurses to designate multiple ‘normal’ rhythms for a patient.

“These people are sick and they go from one rhythm to another, and if they’ve been doing it, I want to learn all of those rhythms as normal” – critical care nurse 2

According to participants, about 20% patients have rhythms that are normal for them but are outside the normal range, i.e., the rhythms don’t meet QRS criteria. However, often these patients are the sickest and tend to contribute a high volume of alarms to the overall alarm burden of the nurse.

For such situations, participants wanted to be prompted at regular intervals to confirm that the ‘learned’ rhythms were still normal for the patient. They also wanted to specify to the monitors to ‘unlearn’ these normal rhythms if required. Finally, participants wanted to see trends for how long or often the patient was in a certain rhythm. As one participant said –

“I want to be able to scroll back and forth, what was it doing before and how long did it last…It

would be nice to know this rhythm occurred every min for the last 24 hours.” – critical care nurse 4

Along with the trends of the designated ‘normal’ rhythms, participants wanted to visualize patients’ physiological parameters and drug interventions and their effects on the EKG rhythm.

Predictive alerts While alarms help providers to respond to critical situations, a key challenge with alarm response is prioritizing and planning patient tasks. Participants felt that providing certain predictive alerts would be helpful so that they could respond to a developing situation before an alarm went off. As one participant said,

“A crisis alarm is great, that obviously has to be responded to, but changes in patient rhythm are important. A patient goes into atrofib, we want to know about that and we want to jump on that, and notify a physician in a timely manner.” – clinical nurse specialist

Converting potential alarm conditions into predictive alerts would enable nurses to react proactively. Also, the distinction between alerts and alarms would help them prioritize tasks across different patients they were managing. Examples of useful predictive alerts were:

§ Sepsis alert that this patient might be entering a septic state

§ Trending blood pressure

§ Abnormal lab values or physiological parameters

Detecting trends The notion of predictive alerts was closely tied to trend monitoring. Current central station monitors provide basic graphical trends of patients’ physiological parameters (such as temperature or blood pressure). However, participants wanted to be alerted to subtle changes in patient’s condition such as – gradual increase in heart rate, prolonged QT interval, blood pressure change, duration of time that the patient been in various rhythms, and changes in volume of technical artefact.

Trending of artefact could indicate that a serious issue was impending. For instance, one of our participants mentioned a case where a cardiac telemetry patient went into VT after having “frequent artefact”. Since artefact can arise from benign things such as the patient moving around or brushing their teeth, care providers tend to ignore them. However, frequent artefact can indicate impending crisis situations; participants wanted monitors to provide predictive alerts for such events.

Participants also wanted better visualization of trends in alarm conditions (such as PVCs, PVTachs, ST segments) to understand “has it happened before?”, “how often is it happening?”, “when did it start?”, etc. Understanding trends and correlating them with lab results would help them notify the physician in a timely manner. Trending information would be important to have at the central station as well as the bedside monitor.

Figure 2: Design concept 1. Left panel: Nurse can select an alarm condition, the waveform associated with the alarm appears in the middle panel. Middle panel: Nurse can indicate to the monitor to learn the rhythm as normal, name it, and set a reminder to be reminded by the monitor to review or change the normal. Right panel: Nurse can review the normal rhythms and their trends over time.

Design The findings from our field research led us to re-design the user experience of the existing GE Central station product. The Central Station consists of a multi-patient view that allows nurses to monitor multiple patients. The user can select an alarm displayed by the Central Station for further review and switch to the single patient view. Most of our findings were related to monitoring a single patient, hence we created the following design concepts for the single-patient monitoring screen. The design concepts were low-fidelity, created using Balsamiq wire-framing software. The primary persona for our designs was the critical care nurse.

Next we describe the design concepts and the challenges they addressed.

Alarm reduction through tracking rhythm changes The goal of design concept 1 (Figure 2) was to allow nurses to designate normal rhythms and track them. More specifically, the nurse would be able to –

§ Assign certain alarm conditions as ‘normal’ for a given patient.

§ Indicate to the monitor to learn ‘normal’ rhythms.

§ Be prompted by monitors at regular intervals about whether the ‘normal’ needs to be re-learned by setting reminders.

§ Review historical trends of how often the designated ‘normals’ occurred.

§ Be alerted if frequency or duration of normal rhythms changed.

Figure 3: Design concept 2. The left area shows trends for various patient parameters in facets that can be collapsed or expanded. It allows nurses to examine multiple patient variables together. The right area shows alerts that could signal impending alarm events.

Alerts and trend detection The goal of design concept 2 (Figure 3) was to provide predictive alerts and trend-monitoring that would allow nurses to take action and prioritize tasks so that certain alarm conditions can be prevented. More specifically, nurses would be able to view visual alerts for the following –

§ Changes in a patient’s rhythm, before a crisis alarm goes off

§ Sudden and sharp changes in vitals

§ Prolonged QT intervals

§ Sepsis

§ Changes in lab values

§ Frequent PVCs.

Also, nurses could view trends for additional information such as, –

§ Arrhythmia events – starting time, duration, frequency.

§ Lab values

§ Artefact

Preliminary Evaluation of Design Concepts We conducted preliminary evaluations of the design concepts to get initial feedback and refine the concepts. The goal was also to understand whether to invest more time and effort to incorporate new features into the Central Station product. Due to time and participant availability constraints, we tested only two features – setting normal alarms and trend monitoring.

Methods and participants We conducted evaluations at WUSH with two critical care nurses. We conducted the evaluation session as a focus group where participants were first introduced to the goals of the re-design and then provided overviews of the two design concepts. Then we handed paper prototypes of the concepts to each of the nurses and described the designs in detail. This was followed with questions that evoked their responses to the concepts in terms of how useful the additional features would be and whether they would help them manage alarms better. We also asked specific questions related to the usability and usefulness of various features.

Responses were audio recorded and transcribed and analyzed using the same open coding technique as in the formative contextual inquiry.

Findings

Participants liked the ability to track rhythms and designate certain rhythms as normal. This feature would be especially useful for patients with pace makers who are not paced all the time. For such patients, every time the patient changes from the paced rhythm to their own rhythm, the monitors sound alarms. So, it would be useful to designate the paced rhythm as normal to prevent alarms and reduce alarm overload.

Participants liked the reminder feature that would remind them that a rhythm was set as normal and ask whether they wanted to change it. One request was the ability to set up multiple rhythms as ‘normal’ for a given patient.

Participants felt that monitoring trends would be useful for some patients but for a majority of patients, the trends screen would lead to information overload. As one participant said –

“It would be hard to manage all that information with all the stuff we have to do. If this is the only thing I had to do, then it’s not that much information. But if I’m managing 10 patients and their families, then I might not go into it. ” – Nurse 2

Though trend monitoring may not be used for typical patients, it could be useful for retrospective analysis of critical patients or for critical incidents. As one participant said –

“If you have the physician come up and say ‘what happened with so-and-so?’ then this would be useful. ” – Nurse 1

Thus, examining trends in details was deemed too time consuming for most patients, but for some patients it might be useful where intense monitoring or in-depth analysis was needed.

Discussion The feedback from preliminary evaluations of the design concepts suggest that a useful feature for patient monitoring devices would be the ability to track and designate ‘normal’ rhythms for certain patients. This would reduce alarm overload for the sickest patients.

Further examination is needed for designing trends and alerts. The preliminary evaluation suggests that trends

for multiple variables related to the patient would be overwhelming for most nurses to analyze during the normal course of patient monitoring. Hence, further research is needed to understand how much trend information would be valuable and what would be the best way to represent it.

The evaluation results helped our business to prioritize new feature-introduction for enhancing the user experience for the Central Station product.

Lessons learnt As with most human subjects research related to healthcare, a primary challenge for our study was to find healthcare providers to evaluate our design ideas with. Some things we found helpful were –

§ Leveraging business customer relationships for getting access to user research site and participants.

§ Having goals that supported and benefited participants’ work goals helped us gain participants’ trust and time commitment for the study.

§ Using paper prototypes of design concepts for quick, informal participant feedback.

Acknowledgements We thank the participants from WUSH for their time and our business partners for their support.

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