Improving rounding in critical care environments through management of interruptions

Decision Support Systems 55 (2013) 516–527

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Decision Support Systems journal homepage: www.elsevier.com/locate/dss

Improving rounding in critical care environments through management of interruptions Ashish Gupta a,⁎, Ramesh Sharda b, Yue Dong c, Rohit Sharda d, Daniel Asamoah b, Brian Pickering c a

University of Tennessee Chattanooga, United States Oklahoma State University, United States Mayo Clinic Rochester, United States d UT Southwestern Medical School, United States b c

a r t i c l e

i n f o

Available online 6 October 2012 Keywords: Rounding Intensive care units Process Interruptions

a b s t r a c t Efficient and effective functioning of intensive care units (ICU) has a significant impact on the safety of patients who are critically sick, performance of care providers, utilization of clinical resources, and is essential for improving the overall healthcare delivery. This study focuses on developing a better understanding of ICU rounding process, which is a team-based activity and is routinely conducted with the objective of providing an error-free and customized treatment plan for each patient admitted to an ICU. However, rounding process is complex, ill-understood and marred by numerous inefficiencies. In this study, we develop process framework for ICU care delivery that integrates various pathophysiologic, care delivery and intervention processes. We do this by examining the rounding workflow of two major teaching hospitals in the US. One major issue for rounding process is interruptions. We suggest and test strategies for improving ICU rounding workflow by managing interruptions. This is accomplished through the development of simulation models to compare the relative merits of controlling interruptions in ICU with respect to overall rounding completion time. We found that as much as 39% time savings can be realized with alternate interruption control methods. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Multidisciplinary intensive care units (ICU) are among the most complex, critical, and expensive facilities to operate within any hospital. The utilization of highly trained care provider teams, advanced technologies, expensive treatment procedures and drugs in ICUs exert tremendous strain on the healthcare system, its resources and the overall healthcare costs. In 2005, ICU costs represented 13.4% of hospital costs, 4.1% of national healthcare expenditures and 0.66% of the gross domestic product in the US [10]. The ICU work environment is unique, dynamic, and requires collaboration among multidisciplinary team members to provide timely and effective care to patients [16]. The complexity of ICU work is not only compounded by the need for developing customized treatment plans and procedures for each patient in ICU but also by the dynamic and often unpredictable patient state. This requires ICU team members to constantly monitor patients, immediately update treatment plans for the patient and develop quick responses to the evolving state of patient. This makes the nature of ICU work environment highly error-prone and stressful, requiring high attention from the care providers. A few studies have also reported on the interruptive nature ⁎ Corresponding author. E-mail address: [email protected] (A. Gupta). 0167-9236/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.dss.2012.10.009

of the ICU work environment [16]. It is, therefore, imperative that we understand various workflow processes of ICU before we are able to propose solutions to improve the performance of ICUs. Multidisciplinary rounds (MDR, also simply referred to as ‘rounding’) are an important collaborative activity requiring significant time and resource utilization. Many events take place during medical rounds such as starting with the formation of multidisciplinary teams, patient information presentation, discussion and decision making (computerized order entry), meeting and interacting with the patient and their family, teaching residents, etc. The multidisciplinary teams involved in ICU rounding strive to achieve the goals of providing best healthcare and minimizing error by having a full team of experts from different specialties engaged, all while providing clinical teaching for residents and fellows. ICU rounding presents several unique challenges including nonlinearity, high acuity illness, frequent interruptions and processing of large quantities of highly dynamic data by interdisciplinary team members. In this study, we focus on understanding the rounding process embedded within the medical ICUs (other ICUs are cardiac ICU, neonatal ICU, etc.) of two major teaching hospitals, referred as Clinic A and Clinic B in this study. Using various data collection approaches such as direct observations, interviews, and time motion tracking, we studied the workflow process of rounding at a large teaching hospital. We first learned the general process flow at a primary care county hospital drawing patients primarily from the vicinity. Our primary

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study unit is a tertiary referral clinic that attracts patients from all over the country. Understanding the basic differences between the rounding processes employed at two different clinics enables us to identify strategies for improving the performance of rounding processes. Another goal of this study is to understand what non-pharmaceutical interventions (or strategies) can be utilized that can improve the process of ICU rounding, for example, altering the rounding process, controlling for various types of interruptions, etc. The next section of the study provides an overview of extant research on ICU work environment and multidisciplinary rounding (MDR), followed by an explanation of the purpose of rounding within the ICU and the problem of interruptions. Section 3 presents a framework for understanding processes related to ICU rounding. Section 4 describes the data collection phase and presents the workflow of rounding processes at two different hospitals. Section 5 presents a simulation model of rounding process at one of the clinics (tertiary referral care clinic) to test the performance of proposed strategies. Finally, we present the results and discuss the implications of findings.

2. Literature review 2.1. Rounding Intensive care units in hospitals are places where extremely sick patients arrive, and are cared and treated for various ailments. A patient is released from the ICU units or transferred to regular care wards as soon as patient's state improves and care providers decide that a particular patient doesn't require the extensive and expensive care that is provided within ICU environments. Multidisciplinary rounds (MDR) are one of the critical and core ICU activities requiring information exchange and collaboration among several interdisciplinary care providers. In a teaching hospital, MDR also involves an additional teaching component for residents, which happens mostly in unstructured formats, along with providing patient care [14]. Rounding is a routine ICU process occurring on a daily basis and includes a number of health providers from different specialties/disciplines with the aim of providing better patient care, treatment planning and decision making for patients. Little research has been done to decipher the architecture of and the ongoing communication exchanges within a ward round [15] and, therefore, we lack in our understanding of the intervention strategies that could be used to improve the overall performance in various ICU roundings. O'Hare [15] attempted to identify the core routine of a ward round activity, which typically starts with validating history of patient with the referring doctor's note and physical examination of the patient. It is followed by refining the diagnosis and prognosis formation, treatment planning, interdisciplinary team communication, patient communication, interaction with family and teaching [15]. It is difficult to identify a set sequence in which these activities take place during rounding. As such, many of these activities may be revisited in different sequence for each patient. Need for improvements in rounding have been identified. For example, Mcleaod [13] made an early attempt to identify key individual characteristics such as empathy, accessibility, etc. that make up for a successful ward round. Gurses and Xiao [9] performed an extensive literature review on multidisciplinary rounds from a technology perspective and identified three broad areas where the design of such technologies could be improved to elevate the performance in MDR. The technologies that could improve communication and coordination in MDR are: patient-centric information tools such as patient medical records; decision-support tools such as evidence cart, and process-oriented tools such as UWCores. In particular, UWCores, a centralized web based rounding system, was developed by a team of researchers at the University of Washington. This system was found to have positively influenced the resident workflow by improving team communication [19].

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While improvements in healthcare technology used during MDR are critical for achieving any significant improvements in performance, it is important to understand that technology enhancements can bring limited benefit if the rounding is not conducted in an efficient and effective manner. In this study, we make an effort to understand the basic architecture of a MDR and identify interventions that can be used to improve the efficacy of the rounding process. We specifically focus on the interruptions in the rounding process. 2.2. Interruptions Interruptions in healthcare have been defined in several different ways in literature. Brixey et al. [3] defined an interruption as “break in the performance of a human activity initiated by a source internal or external to the recipient, with the occurrence situated within the context of a setting or location” while Chisholm et al. [4] defined interruptions as “any event that briefly required the attention of the subject but did not result in switching to a new task”. Interruptions are prevalent in healthcare environment and have been reported in places such as trauma centers, intensive care units, emergency departments, surgical units, etc. [1,3,20]. While much of the research has focused on emergency department physicians, nurses, and surgical teams, a few studies have been conducted with residents and junior doctors. Studies conducted in the area have predominantly reported on the negative influence of interruptions on various healthcare outcome variables such as patient safety, errors, etc. Drews [7] studied the impact of interruption frequency on patient safety in an ICU setting. The study reported that approximately 30% of activities in ICU were interrupted and in majority of cases led to a compromise in patient safety. Studies focusing on understanding interruptions in ICU have been limited thus far. Part of the reason are the difficulties encountered while trying to track and measure interruptions in ICU environment due to various characteristics of interruptions such as the nature of interruptions, primary task characteristics, work environment, etc. [12]. An exception to this is the exploratory study conducted by Alvarez and Coiera [1] on interruptive communication patterns during ICU ward rounds. This observational study reported that conversation initiating interruptions (intra-event) occurred at the rate of 14 per hour and consumed 37% of overall rounding time while turn-taking interruptions (inter-event) consumed 5.3% of overall rounding time but occurred more frequently and varied with the care-provider role. Clearly, studying these interruptions and their management is important. Controlled experiments can be helpful in studying this issue but it is extremely difficult to interfere with the workflow in ICU rounds due to the critical and time sensitive nature of services provided. Therefore, simulating an ICU environment is very conducive for studying interruptions and evaluating any alterations in ICU workflow. Although high frequency interruptions are associated with increased error and workflow disruption [1], some interruptions might not have a negative impact on the healthcare delivery as it might provide critical information for an ongoing task and improve safety by improving communication and coordination in healthcare (e.g. “time-out” before surgery) [2]. Due to the length of rounding process (1 to 2 h.), the multidisciplinary team (attending, multiple residents, nurses, one pharmacist, and one respiratory therapist) is more likely to be interrupted during the rounding. For example, doctors in emergency department settings were interrupted 6.6 times/h with 11% of all tasks getting interrupted [3]. A few studies also looked at the source of interruptions and noted that these could vary on multiple dimensions [4,5]. These interruptions may originate from various sources (internal, external), have different priorities (low or urgent), exhibit different objectives (consulting, relaying information, discussion, etc.), scale (individual vs. whole team), duration, modality (phone, pager, people/staff, conversation, EMR, family, smartphone) and forms (essential, simple, break and turn taking) [6]. The pattern of the interruptions also seems to vary based on provider role [5]. In

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the ICU, many interruptions cannot be ignored due to the complexity of care delivery processes [8]. For instance, a “code” message is a critical interruption and requires urgent attention by the team. There are several approaches that hospitals may utilize to reduce the frequency of interruptions and, thus, mitigate the negative outcomes associated with interruptions. For example, wearing vests having ‘not to interrupt’ imprinted on back while performing critical tasks (e.g. drug administration to patients through injection), enforcing hospital-wide policies that ensure mechanism to reduce interruptions. However, the viability of implementing such approaches needs to tested in different situations. Additionally, such generic policies cannot be implemented without a complete understanding of the overall impact of such interruption control policies, context of their application and the nature of on-going task. Next section discusses the extant research on rounding. 3. Understanding ICU rounding process Any improvement in a patient's state requires developing a good understanding of the patient's pathophysiology as well as understanding of various processes spanning over different temporal scales (seconds, minutes, hours) and at various hierarchical levels (ICU unit-level, tasks-level, sub-activities-level). Fig. 1 describes a framework for care delivery within the clinical environment of an ICU where care providers interact with a patient through a series of complex care delivery processes that progress temporally and spatially within ICU. These processes represent admission to ICU, rounding, handoffs, resuscitation, and discharge aspects of care delivery processes. Each of these processes consists of a unique subset of activities that are resource intensive, and collectively encompass an entire span of time that a patient spends within an ICU. The outcome of any patient is dependent on the pathophysiologic states of patients, the nature and timing of interventions applied within each of these processes and various embedded sub processes, staffing and resources (medication, blood products, monitors/ devices) constraints, and interventions between those components. For example, the timing and the quantity of antibiotic delivery, the timing of transfusion and the timing of performing critical procedures (CVC and intubation) could determine the outcome of patient undergoing the resuscitation process and could dramatically alter the future outcome of the patient and the system. However, the same intervention when applied to another patient in different state of conditions and

under a different process may lead to errors, adverse events patient or system outcomes due to complex interactions [17]. A modeling approach such as discrete event simulation could be utilized for conducting this type of analysis over varying temporal scale and at different levels of hierarchy. All newly admitted patients have different predispositions to acquiring a disease due to their unique risk factors (life style, smoking habits, etc.). A model can help understand the dynamic states of patient and significantly improve the workflow processes embedded in ICU, resulting in improved patient and system outcomes that are encapsulated in Fig. 1. The framework recognizes that severity of a patient's situation guides care providers in determining the naturespecific interventions and plan of care for each patient. The impact of these interventions will vary depending upon patient's current pathophysiological state and its interaction with care delivery process. Each phase requires an elaborate and in-depth multi-scale (across time and space) study of key ICU processes. The state of each patient may be defined based on the outcome scores from prediction model. As a result of timely or untimely intervention, the current state of patient (n) at time ‘t’ may either improve (n + 1) at the next time period ‘t + 1’ or deteriorate (n − 1). Further, the patient may stay in that state for an elongated period of time requiring state dependent and process dependent interventions. An example focuses on modeling rounding that is used to develop the daily plan of care in an ICU. Daily rounds enable multidisciplinary providers to interact and exchange information regarding patient care. Providers need to achieve multiple goals of management and minimizing error by having more than one professional involved. This process presents several unique challenges including frequent interruptions and processing of large quantities of highly dynamic data. Many events take place during rounds starting from team gathering, through patient information presentation to discussion and decision making (treatment plan, order execution), then interacting with the patient, family and teaching residents (Fig. 2). Process variation resulting from factors such as interruptions and the cognitive overload of care providers could be a major barrier to efficient rounding process. Multiple internal and external interruptions could impede rounding process in the ICU and lead to fragmentation of tasks involving frequent switching. Examples of interruptions from internal sources include those that are team initiated (conversation broken by 3rd member), self-distraction (checking emails, interaction

Fig. 1. ICU clinical environment: complex interaction of pathophysiologic, care delivery and intervention processes.

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Fig. 2. ICU rounding process and interruptions.

with other team members in cliques). External sources of interruptions could include pagers, non-rounding team members, new patient arrival/departures (transfers to operation theater), and face-to-face interactions with consulting services. We use this framework to guide our broad understanding of various processes that interact with rounding and utilize this knowledge to develop the simulation model of rounding process for evaluating interruptions control strategies. Next section discusses data collection and development of rounding process at two different clinics. 4. Understanding ICU rounding workflow 4.1. Data collection We first began with understanding the ICU rounding process at Clinic A. Then we conducted a detailed study at Clinic B, the primary site for this study. Several ICU team members engaged in the rounding process were shadowed and observed at the medical ICU at Clinic B. These included attending physicians, nurses, pharmacists, residents, fellows, and respiratory therapists. The site provided qualitative as well as quantitative data needed to develop the workflow structure of ICU rounding process and capture various parameters needed to develop the simulation model of ICU rounding process. Patients and their family members were not monitored in the rounding process. Multiple team members were engaged in making simultaneous observations of the rounding team. Data was collected for rounding process covering sixteen patients at Clinic B. We made observations of the rounding process on two randomly selected days that were separated by a margin of least a week. This was done to ensure that we captured any variations in the rounding process resulting from different rounding teams. Direct observations made at Clinic B also focused on identifying interruptions from external and internal sources as well as noting different tasks performed by team members during the rounding. 4.2. Rounding at Clinic A Various tasks related to the rounding process typically start at 0500 h. For example, residents arrive at the hospital and begin patient record compilation (e.g. “look up” patient data) to prepare for Phase 1 of rounding. This happens in a dedicated physician/resident workroom (hereafter referred to as “rounding room”) located in the ICU itself and containing desktop computers and mobile laptop units. The post-call resident (who was in charge of patient care overnight) updates all teams in the morning about their respective patients and any

noteworthy events that occurred during the night. Depending on a resident's style, basic order entry can also be done at this stage to correct common lab abnormalities, such as prescribing a potassium or sodium IV drip for a patient requiring electrolyte replenishment. More advanced order entry for the patient is done during a later step (after rounds are completed) (Fig. 3). 4.2.1. Phase 1 Residents visit patients separately during this phase, which typically occurs between 0600 and 0800 h. This visit entails performing a physical examination and checking vital signs at the bedside. Each patient visit normally takes between 5 and 10 min. After checking on patients, residents and interns return to the rounding room and begin entering orders and writing daily progress notes. This order entry encompasses: (i) requesting consultations from other medical services (nephrology, cardiology, etc.), (ii) making changes to ventilator settings, and (iii) ordering additional labs/medications for the day. Answering pagers is an ongoing activity but normally does not begin until at least 0600 as the nurses are not sure when exactly the primary team arrives and re-assumes care of their patients from the overnight resident. 4.2.2. Phase II Any newly arrived patient is discussed with the attending physician during this phase, which typically lasts for 2 h, between 0800 and 1000 h. Different ICU teams (four at this hospital) gather in the rounding room and listen to every on-call team's presentations. They discuss new patients that were admitted over the preceding 24 h. Generally, however, they discuss patients that were admitted from 5 pm (1700 h on previous day)–7 am (0700 h on the following day). Patients admitted before 1700 h are usually seen and discussed with the attending physician on the day of admission, which is a staple of many different services, including the wards and specialty services. The goal is two-fold here. ICU patients are obviously quite sick, and thus may benefit from an attending physician's involvement earlier in the process. Secondly, the more patients that are seen and discussed in the afternoon, the faster morning rounds proceed the next day, which frees up time for residents to attend conferences, perform other clinical duties, and comply with work hour restrictions (no more than 28 continuous hours on call). After the new patients have been discussed and updates have been given on patients that were seen with the attending the previous afternoon, the attending, all residents, and the pulmonary fellow gather to examine new X-rays obtained on ICU patients (of note, it is fairly common practice to obtain a chest X-ray every morning for patients on ventilators.). These “X-ray rounds” allow the attending physician – a trained pulmonologist – to

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quickly review new lung findings compared to the previous day, and also serve as a valuable time to teach residents how to critically analyze X-rays. 4.2.3. Phase III Actual rounding happens during this phase, at which time teams physically visit patients from 1000 to 1200 h. Four ICU teams are split into two groups: half accompany the attending (attending bedside rounds), and the other half accompany the Pulmonary/Critical care fellow (fellow bedside rounds). Attending bedside rounds consists of the post-call team (team that was on call the previous night) and their “buddy” team, which is on call the following day. The teams' on-call days are staggered 2 days apart in order for them to round with either the attending or the

fellow for the whole month. The end result is that two ICU teams round together for the whole month, and the other two teams round together for the whole month. This schedule is flexible and subject to the daily vicissitudes of the ICU. If there is a challenging case that was just admitted, all the teams may see that patient first and then break off. Thus, a strong framework is in place that can be tweaked by the attending in special circumstances. Fellow bedside rounds consist of the on-call team and their buddy team. These teams round on their patients with the fellow. The fellow later meets with the attending to once again round on these patients 4.2.4. Phase IV This is typically the lunch time (1200–1300). Typically, a resident/ intern either attends a conference or works through lunch.

Patient admission

Data gathering and handoffs Pagers

Order entry and reports preparation Cell phone Presentation and x-ray discussions Patients and Family

Interns’ teaching session

Fellow staff

ICU rounding begins

Fellow and attendant physician post rounding discussion

Sources of interruptions

Points of interruptions Unattended patients?

Yes ICU rounding ends

Fig. 3. ICU rounding process at Clinic A.

No

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4.2.5. Phase V Several activities take place during this phase, which typically starts after lunch time. The post-call resident has left the hospital by 11 am, and the remaining teams continue to work. For the on-call team, new admissions start rolling in at 7 am. After they have rounded with the fellow for bedside morning rounds on their old patients, the on-call team waits for new admissions. As mentioned previously, patients that arrive before 5 pm (1700 h) are seen by the attending on the call day itself, in order to streamline the morning routine the following morning. When the other three ICU teams leave the hospital (post-call leaves at 1100 while other teams leave whenever they have finished their work), the first-year residents from those teams “check out” to the on-call intern. Checking out entails leaving a sheet of paper with brief descriptions about one's patients, their active problems, and issues that need to be followed upon such as lab values in the evening, imaging studies, or ventilator settings. In addition to leaving the sheet, each first-year resident verbally discusses each patient briefly with the on-call first-year resident, hopefully emphasizing what items truly are important to follow up on during the evening. One purpose of team rounds in the morning is to inform everyone about all ICU patients, which is vitally important in maintaining continuity of care. This joint rounding does not occur on the wards. 4.3. Rounding at Clinic B Armed by our general understanding of the ICU rounding process at Clinic A, we began a thorough study of the rounding process at Clinic B. We noticed several differences in how rounding was accomplished at these two clinics. At Clinic B, the entire process of multidisciplinary rounding at Clinic B could be divided into several steps. The day begins with pre-rounding and hand-off activities. Handoffs involve a joint review and evaluation of patient's recent state, treatment history, order charts at the time of care provider shift change and performing a set of several other activities that are needed for seamless transfer of knowledge, responsibilities and charge of admitted patients from outgoing care provider to the incoming care provider. Hand-off activities usually precedes pre-rounding activities and may take approximately 30 min. Attending physician (or simply ‘attending’) and fellows start at 7:00 am and end at 7:00 pm, while residents arrive in two separate shifts. One shift starts at 6:00 am and lasts until noon of next day and the other shift starts at 3:00 pm and lasts until 5:00 pm of the next day. This provides for significant overlap among resident teams. Handoffs occur immediately before the start of a new shift. Pre-rounding activities typically start early in the day with individual team members making separate visits to each patient in ICU. Residents start visiting patients around 6:00 am in a particular ICU in small groups to collect initial data, meet with patients and enter information collected in EMR systems. Nurses start visiting patients around 7:00 am and go over all the reports. Nurses, pharmacists, residents, fellows, attending and respiratory therapists start pre-rounding all the patients separately around 7:00 am. The fellow and attending typically go through all the patients separately with their peers from night shift. The entire clinic has several ICU units. Each rounding team is comprised of a attending, a fellow who leads the rounding, two senior residents, two junior residents, one pharmacist, one room nurse, one charge nurse (unit nurse), and one respiratory therapist (The respiratory therapist does not always attend team rounding due to time conflicts as they cover several units during the day). A typical ICU at Clinic B has a maximum occupancy of 24 patients. Rounding typically starts at 8:30 am. Each patient is visited sequentially. After discussing each patient's case, recent changes in patient's state, the team brainstorms the best treatment plan and places the computerized order.

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The team then moves on to the next patient while the attending might stays behind to meet the patient and (or) family as necessary. This is an important part of the rounding process. The attending later rejoins the team discussion at the next patient. This visitation with the family by the attending appears to be a major difference between the rounding process at Clinics A and B. While family visit time at Clinic A is kept as a separate block of time after team rounding, family time at Clinic B is squeezed between the two successive patient team visits. This often becomes a cause for interruption and longer rounding time as family visit time at Clinic B serves to interrupt the ongoing rounding process. The attending is pulled out of the team workflow before switching to the next patient. We observed that when the attending rejoins the team, the rounding continues as usual. However, the information that the attending missed while processing the interruption is gathered through the goal alignment accomplished with the help of plan summary discussed at the end for each patient. Fig. 4 describes the rounding workflow at Clinic B. A particular patient is visited by several different care providers individually as well as in a team. Individual visits typically occur during the pre-rounding process. A team rounding happens after various care providers have evaluated patient charts separately and collected necessary data needed for next phase of treatment planning. Various members of the team convene immediately outside of ICU room to start the rounding process at a set time. Various activities performed during the rounding include review of patient data and reports, team discussion, search for optimal treatment plan, patient examination, family visit, computerized physician order entry, updating notes, etc. Our observations at Clinic B suggest that these activities are non-linear in nature i.e. in no particular sequence or structure and are typically performed multiple times during each patient visit during the rounding process. 4.4. Interruptions in rounding processes Although there may be many opportunities for improving the ICU rounding process, our major focus is on managing the interruptions that are common in ICU rounding. For example, in Clinic A, interruptions are prevalent during the entire rounding process. The interns, residents, and often times, the attending physician themselves were frequently paged in the middle of bedside rounds, which was disruptive to the workflow. Attending physicians were infrequently paged relative to interns in particular. The on-call intern gets paged all the time, especially once the other teams have left. This on-call intern is called to check on patients from other ICU teams and is also notified (depending on the nurse) about abnormal lab values through text pages. We conducted extensive interviews with care providers engaged in conducting rounds at Clinic B to identify all the sources of interruptions. It was realized that team members were interrupted by a wide variety of sources. We found that the reported sources of interruptions have an overlap but had several dissimilarities. These are reported in Table 1. When an interruption occurs, the interrupted team members are preempted from the rounding process while other members continue with various tasks related to the rounding process and do not wait for the interrupted member to rejoin, which is in accordance with the observations made by our observation team. We next describe our modeling of the rounding process and the interruptions in Clinic B where we were able to collect detailed process data. 5. Modeling ICU rounding at Clinic B We use Simio [11], a discrete event simulation software package, to understand the flow and management of interruptions within the Clinic B ICU environment. We were not able to collect quantitative data at Clinic A due to lack of sufficient resources at that site. Data collection related to the rounding process required several individuals to

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Pre-Rounding, Handoffs

Patient Admission

ICU Rounding Team Formation X-Ray, MRI Patient Data and Report Review EMR Team Discussion

Resident Report

Repository EMR, hand notes,

Search Solutions

Prepare Treatment Plan Personal Notes

Treatment Plan Implementation (Lab test, drugs, etc.)

Patient Examination

Service List Printout

Family Consulting

Team Communic-ation

Unattended Patients

Yes

Heterogeneous Information Sources

No

ICU Rounding Ends (Rounding Team Disperses)

Fig. 4. ICU rounding process at Clinic B.

simultaneously observe the process, make immediate notes and record observations. Hence, while modeling Clinic A will be useful, we did not simulate Clinic A in this study. The first scenario that we model mimics the current work environment of an ICU at Clinic B where interruptions are processed as soon as they arrive. Such processing patterns lead to a higher frequency of switching between the tasks, which may result in ineffective utilization of tasks due to the compounded effects of learning and forgetting processes taking place. In this study, we propose and test the hypotheses that strategies for controlling interruptions at different rates offer better prospects for improving the rounding performance. These interruption-control strategies are based on the principle that not all interruptions are of equal priority and, therefore, non-urgent interruptions can wait till the completion of the rounding process. This will result in early completion of the rounding process as the cognitive workflow of the rounding process will be less fragmented. We model several scenarios representing control-interruption strategy that are based on varying proportion of interruptions requiring immediate attention from members of an ICU rounding team. These proportions represent cases with 20%, 40%, 60% and 80% distributions of urgent interruptions. We now describe the logic of rounding process implemented in the model.

5.1. Model processes and parameters at Clinic B A thorough understanding of the rounding process at Clinic B was first established before the simulation model was built. A typical rounding process takes anywhere between 2 and 3 h each morning to complete. The medical ICU being modeled has a bed capacity of 24. We developed the simulation model with the assumption that patient occupancy in a typical ICU varies randomly in the range of between 75% and 100% of the maximum capacity on a daily basis, which translates to the range of approximately 18 to 24 patients. The rounding team comprises a seven member inter-disciplinary team with each team member treated as a resource in the model (Table 1). Thus the rounding team has a single instance of an attending physician, fellow, pharmacist, two instances of residents (senior and junior residents) and two instances of nurses (room and charge nurses). Each provider rule (as there are multiple nurses and residents) was modeled as an individual entity with characteristics such as frequency of interruption and the duration of interruptions. Various activities performed during the rounding process for each patient is treated as a primary task. Examples of such tasks include presentation of patient case history, discussion among team members, development

A. Gupta et al. / Decision Support Systems 55 (2013) 516–527 Table 1 Sources of interruptions in rounding process.

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Table 2 Generalized description of rounding tasks.

Multidisciplinary rounding team member

Sources of interruptions in rounding

Multidisciplinary rounding team member

Rounding task descriptions

Bedside nurse

Patient-driven interruptions Interruptions from hospital staff (secretary, charge, etc.) Attending phone calls, pagers Assisting fellow nurses with care of other patients Pagers, phone calls Calls for patients needing emergency car. Family/patient conference time New admissions during rounding New admission during rounding Pagers Unstable patients Getting called for other procedures Emergencies Admitting new patients Change in critically-ill patient's state Pagers from RN Variability in rounding style of attending physician Ambient noise Multiple ongoing conversations Slow or malfunctioning computer Slow computers New admissions during rounds Lack of team focus Interruptions from team members Interventions during rounds Emergency situations and unexpected tasks Pages for emergencies, procedures

Bedside nurse

Responds to MDT questions Conveys patient and family concerns Provides an update on patient's vitals and assessment Suggest plan for day considering patient's needs Voice input from nurse perspective Advocates for patient and family's concerns Develops plan of care Reviews patient data and listens to presentations Communication with nurses, patient and family Teaches residents Teaches residents Leads the round with attending physician Supervises and performs procedures related to patient care plan Discusses care plan with RN, pharmacist, RT and others Use Computerized Physician Order Entry (CPOE) to input orders Presents patient data and communicates with nursing staff and pharmacists Visits with patient/family (pre-rounding) Answers pagers Discuss plan of care, informs team of active issues Updates the team on the progress of the patient during past 24 h Suggest on medication (pharmacotherapy plans, review meds list with team) Reviews patient details and lab information Discusses evidence based medicine as it relates to individual patients Makes recommendations for medication selections doses, and potential complications Changes ventilator's settings

Charge nurse Attending physician

Fellow

Residents/intern

Pharmacist

Respiratory therapist

of care plan, and order entry. We conducted extensive interviews of rounding care providers to develop an in-depth understanding of the nature of tasks conducted by various team members during the rounding process (Table 1). Some of these tasks are simple and require less time while other tasks require more cognitive engagement and, therefore, may be associated with larger penalties as a result of interruptions. Each member of the team is engaged in processing the primary tasks and is immediately seized by interruptions when no controls are placed on the flow of interruptions. At the start of the rounding process in our model, the team visits their first patient and reviews the patient's medical case. The attending leads the team and coordinates the primary task related discussions among all the team members. Besides attending to a patient, the rounding process also offers a pedagogic environment for the attending, fellows and residents. Due to this, relatively more time is spent in patient case discussion during the rounding. The specific steps and tasks taken for each patient by team members are discussed in Table 2. Based on our observation during rounding sessions at the ICU, it was observed that the rounding team usually spent between 10 and 15 min on each patient. Most cases required about 12 min of rounding times. However, patients demonstrating aggravated critical conditions or a significant change in their state since previous rounding will require more time and are, therefore, treated as complex patients. Based on the observation, the patient processing time was modeled with a triangular distribution with a minimum time of 10 min, a maximum time of 15 min and a mode of 12 min. After a patient's case has been reviewed, the team moves on to the next patient. The rounding process ends when cases of all patients in the ICU have been discussed. 5.2. Modeling interruptions Interruptions were treated as randomly occurring events during the rounding process, which is in compliance with prior research on interruptions [4,8]. The value-added time spent on a patient is not affected by the time spent on interruptions but the overall rounding completion

Charge nurse Attending physician

Fellow

Resident/intern

Pharmacist

Respiratory therapist

time could be prolonged due to additional time being spent on switching from interruption tasks to rounding tasks. There is a difference between the value-added time spent on reviewing a particular patient (TV) and the overall rounding time spent on each patient before the next patient is reviewed by the team (TW). Once a team member ends an interruption, a small amount of time is spent to revert back to the rounding task [8,18]. The team must update the team member on relevant information that was missed while he or she was away attending to the interruption. This is known as the switching time and usually takes between 1 and 3 min with most instances taking about 2 min. This extra time, TS, accounts for the reason why TW could become larger than TV. The switching time also includes a small component of time that an interrupted resource needs to re-focus on the ongoing rounding event. The overall rounding time (TW) per patient accounts for this switching time TS for each resource that gets interrupted during the rounding process for that particular patient. While the number of interruptions that occurs per each patient does not affect TV, the resultant TS created causes Tw to increase as the actual rounding process per patient takes longer to complete due to inefficiencies resulting from task switching and processing of interruptions task by team members. Total time consumed before the next patient is reviewedðTW Þ ¼ TV þ T S ð1Þ The rounding team disengages after processing all the patients in the model. 5.3. Model details and patient processing The simulation models patients as moving entities while the team is performing rounding operation. The model requires patients waiting to

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be served by the rounding team to wait in buffer at a separate holding place as if they are placed in their ICU wards until the team is done processing previous patients. Patients are released from buffer when processing of a previous patient is completed. Upon release from buffer, patients will immediately seize all rounding team resources. The time spent by rounding team per patient was modeled as triangular distribution with a minimum time of 10 min, a maximum time of 15 min and a mode of 12 min. These parameters were based on observations made by the research team members collecting data at the ICU (described in previous section). We implemented the logic for holding patients in ICU within the simulation model by setting the link between the ICU object and the rounding team server to allow only one patient to travel on it at any given point in time. Once a patient reaches rounding team, the link is triggered to shut off. It is triggered to restart after a patient releases the rounding team's resources. Further, we implemented several decision rules to ensure proper rounding process for each patient. For example, at the start of rounding for each patient, the patient needs to seize all the rounding team resources. Hence, none of the team members can be in an interrupted state so as to allow a patient (entity) to seize it. The decision rule used to ensure rounding team availability for each patient is implemented through pseudo code below. RoomNurseIsAvailable&&ChargeNurseIsAvailable&&Pharmacist IsAvailable&&SnrResidentIsAvailable&&JnrResidentv&&Fellow IsAvailable&&AttendingIsAvailable==1 Any team member modeled in the simulation experiment could have one of the three possible states: busy, interrupted or idle. The above rule implicitly allows the model to ensure that all team members are concurrently available before the rounding process on the first patient. A team member in the model may get interrupted during the course of patient processing depending on the interruption processing strategy in place during the rounding process. The strategy representing controlled processing of interruptions will allow only urgent interruptions to be processed immediately as soon as they arrive while other nonurgent interruptions can wait. While a particular team member is engaged in processing interruptions, the remaining team members continue with the rounding process. A team member joins the ongoing rounding process after completing the interruption task but only after an additional delay in rounding process is interjected to reflect the extension in rounding completion time for ongoing patient. This occurs as a result of the additional time that the rounding team spends in updating the care provider on what was missed while he or she was away processing interruptions. This is implemented in the model using the concept of an interruption count. Whenever a member gets interrupted, an interruption count is generated. The number of interruptions generated during the rounding process for each patient is stored (Wi). Assuming ‘n’ number of patients, the total number of interruptions (Wt) is, therefore, aggregated over the rounding process for the entire ICU ward to account for all the patients as shown in Eq. (2). n

Wt ¼ W1 þ W2 þ W3 þ …… þ Wn ¼ ∑i¼1 W i :

ð2Þ

During the uncontrolled interruption scenario described above, all interruptions are allowed to go through soon as they arrive. In the next phase, we modeled the rounding process with interruption-control policy in effect, which essentially allows only a certain proportion of urgent interruptions to immediately be allowed through while the remaining non-urgent interruptions wait to be processed after the team disperses at the end of the entire rounding process. In the controlled interruption policy, we modeled several scenarios reflecting the variation in the proportion of urgent category interruptions, beginning with 80% of interruptions allowed and 20% held for later processing. In subsequent scenarios,

60%, 40% and 20% of overall interruptions were allowed to occur immediately. 6. Results and discussion The model was run with 20 replications for each scenario in Simio version 4 for 40 days with a warm-up period of 10 days. The average time spent on core rounding activities in the scenario representing unlimited processing of interruptions was noted to be 9 min. The average switching time per patient (TS) was found to be approximately 2.5 min. As explained in Section 5.2, this switching may occur due to numerous sources identified in Table 1. Thus, the average time spent on rounding for each patient is approximately 11.5 min. This implies that on an average 22% of the overall time spent on each patient per provider is spent on tasks other than rounding. Given that rounding is a team activity, this 22% distraction time, which comprises of time spent processing interruptions and time lost as a result of task switching, cumulates to a significantly large value of approximately 15 min of overall provider time (accounting for multiple providers) for the entire rounding team per patient. This effect adds up to a significantly large value when all the ICU patient beds are accounted for the multiple rounding (in morning and afternoon) that occur over the span of each day for the entire year. Further, each hospital typically has multiple types of ICU such as medical ICU, cardiac ICU, psychiatry ICU, etc. where rounding process is performed on a daily routine. Therefore, the overall impact of interruptions on the entire organization is formidable and translates into huge annual cost when dollar amounts are assigned to the time lost in terms of salary and wages. It is, therefore, imperative to evaluate the performance and relative merits of strategies that control the processing of interruptions during the rounding process. The graph in Fig. 5 indicates that as the volume of interruptions decreases during the rounding process, the overall rounding time per patient decreases steadily from approximately 11.5 min per patient to about 7 min per patient, which is a 39% decrease in rounding time. This significant decrease in rounding time is due to eliminating the extra time spent on processing interruptions during the rounding process. As the frequency of interruptions decreases depending on the degree of control on processing interruptions as soon as they arrive, the rounding can be completed in less time. The average time spent on processing interruptions during rounding for each scenario is shown in Fig. 6 below. This includes the time spent on processing interruptions as well as time wasted due to switching. For example, in the scenario representing 20% of interruption processing, the average time spent on processing interruptions is 0.37 min. Various technological approaches such as rule based expert systems or smart technology based on learning algorithms could be used to implement such controls on the flow and processing of interruptions. Other human based filters could also be used for such purposes. Certain residents or nurse staff not directly involved with the rounding process could manually process these interruptions and allow only non-urgent interruptions to flow to the appropriate target. These interruptions are then allowed to freely flow through once the entire rounding process for a group of patients is accomplished. However, the use and optimal design of such technology filters in controlling interruptions needs to be approached scientifically using design science principles. As suggested by Figs. 5 and 6, average rounding and switching time per patient decreased with the volume of interruptions (from 100% to 20% interruption scenario). The largest decrease in average rounding time (1.36 min) occurred when the volume of interruptions decreased from 100% to 80%. Subsequently, the gain in rounding time occurred at a smaller rate when volume of interruptions decreased from 80% to 20 (1.03 min with 80% scenario, 1.1 min with 60% scenario and 1.04 min with 20% scenario). Similar trend was observed for the average switching times for different interruption volumes. Although we expect the average rounding completion time to decrease with the

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Fig. 5. Average patient rounding times for different volumes of interruptions.

reduction in interruption frequency, the pattern of this decrease is not so well understood. As noted in this research, control of interruptions may be more effective (in terms of time savings) to implement in situations or teams dealing with high frequency of interruptions than scenarios with minimal interruptions. However, the efficacy of these controls needs to be evaluated with respect to utilities associated with various rewards tied to the processing of interruptions as soon as they arrive. For example, interruption related to code blue for a patient may lead to timely resuscitation efforts by the attending physician engaged in rounding process but leads to the disruption of care for patients covered during the rounding. 7. Conclusion Critical care is expensive, complex and marred with interruptions. This study provides several opportunities for pursuing a rich stream of research in critical care delivery. First, we have focused on understanding only a single aspect of improving the ICU rounding process in this study—i.e. evaluating the performance of using control strategies that regulates the flow, timing and processing of interruptions. Further research needs to be conducted to account for other characteristics of interruptions such as complexity of interruptions, sender–

Fig. 6. Average switching times per patient for different interruption policies.

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recipient relationship, context of interruptions, relevance of interruptions, etc. Use of design science principles to develop smart technologies that lead to fewer interruptions at the team level will be an interesting related problem to pursue. The impact of various modalities of interruptions such as pagers needs to be investigated as well. Receiving multiple pages from two or three different nurses located at the same nursing station, all in succession can lead to additional overload. These nurses may be covering different patients and have no coordination among them. Further research is needed to develop methods that could be devised to coordinate page transmissions such that multiple non-acute pages could be queued and transmitted simultaneously instead of sequentially. Furthermore, it would be interesting to explore the impact of two-way paging mechanism, similar to text messaging, where a nurse could text a request to place a routine order for a patient. This will be less disruptive to the workflow as the recipient of the texted message could let the sender know that the order has been completed without having to call to multiple senders again using a different gadget. Another related topic that needs to be studied is the quantity and quality of information contained in the interruption that may facilitate decision making and result in different actions at the receiver's end. For example, some nurses only text pages with certain tagged information. A page may carry a patient's name and a callback number, but does not typically contain the level of urgency, which does not provide sufficient information to the recipient on whether to process the interruption now or later. Certain page messages only carry a number without any mention of who sent the page or what the page is about. These are most interruptive and require immediate processing due to lack of information contained in the message. A third area worth exploring is to investigate how rounding processes can be further streamlined and improved. In this study, we developed an initial understanding of two different ICU rounding workflow processes at two hospitals. Both workflow processes significantly deviate from each other in their structure due to inherent practice variation at these clinics and lack of government regulatory protocol or guidelines. Our initial insights into the ICU workflow processes suggest that there are several mechanisms that could be used to improve the current rounding process. For example, evaluating the relative merits of the impact of variations in patient and family encounter times within the rounding workflow with respect to improving physician, patient and family outcomes is not done in this study and is an interesting issue to further investigate. The patient and family time at Clinic B is arranged in between two patients while it is situated at the end of overall rounding process at Clinic A. At Clinic B, the attending temporarily parts from the team reviewing nth patient and stays back to interact with this nth patient and their family but the rest of the rounding team moves on to discuss the next patient (n + 1) after they have reviewed the previous patient's case (n). The attending later rejoins the ongoing rounding process. This process is different from Clinic A where patient and family time is scheduled at the end of the entire rounding process. Fourth, we focused only on the workflow processes of medical ICUs in this study. The workflow processes, challenges and structure for other types of ICUs such as cardiac ICU and psychiatric ICU varies and needs to be studied. Finally, we also need to investigate the impact of interruptions on the handoff process that precedes the rounding process. Handoffs require a change in care provider team and are occurring more frequently at the hospitals due to government imposed limit reducing the length of resident duty hours. Critical patient information may easily be lost during handoffs as a result of unanticipated cognitive distractions or interruptions, which may lead to medical errors and poor decision making. Critical care delivery in the U.S. is fragmented, complex and illunderstood. The challenges encountered with gaining access to the ICU environment and hurdles involved with collecting data from fragmented nature of ICU care delivery make this preliminary study an important first step in this domain. We focused on understanding

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the rounding workflow at two large teaching hospitals located in different geographic regions of US and catering to different patient demographics (Clinic A attends more to localized patients while Clinic B primarily caters to referral patients) to describe the workflow, similarities and dissimilarities among them. We then developed the simulation model of rounding process at Clinic B to understand the impact of various interruption control strategies that regulate the frequency of interruptions on the duration of rounding completion time. Reducing the number of interruptions can decrease the time spent in rounding by as much as 39% and lead towards an ‘interruption-free’ critical care environment. However, the suitability of these strategies needs to be evaluated carefully against the nature of interruptions and characteristics of critical care environment. Appendix A. Interview questionnaire Questions related to understanding rounding process Describe the typical steps in the rounding process that takes place within the medical ICU in the morning? Elaborate on all the activities and sub-activities (patient specific) that you generally perform stepwise a) during the rounding process. How much time each of these activities take? Do you find some consistencies (and inconsistencies) in the procedure? Do you see a huge variation in terms of time consumed in these activities? On an average how many interruptions do you receive while performing rounding process? Do you feel interrupted or distracted during rounding? Do you think these interruptions have an impact on your performance or time it takes to complete the process? Do you find external interruptions (originating from outside of rounding team e.g. email, pager call) or internal interruptions (originating from team members e.g. you get interrupted by comment made by another team member) more distracting? Can you name some key sources of internal and external interruption sources? Does your pager calls bother you when conducting rounding? What about the pager calls received by other rounding team members? References [1] G. Alvarez, E. Coiera, Interruptive communication patterns in the intensive care unit ward round, International Journal of Medical Informatics 74 (2005) 791–796. [2] D.W. Bates, I. Larizgoitia, N. Prasopa-Plaizier, A.K. Jha, Global priorities for patient safety research, BMJ 338 (2009) 1242–1244. [3] J.J. Brixey, Z. Tang, D.J. Robinson, C.W. Johnson, T.R. Johnson, J.P. Turley, V.L. Patel, J. Zhang, Interruptions in a level one trauma center: a case study, International Journal of Medical Informatics 77 (2008) 235–241. [4] C.D. Chisholm, A.M. Dornfeld, D.R. Nelson, W.H. Cordell, Work interrupted: a comparison of workplace interruptions in emergency departments and primary care offices, Annals of Emergency Medicine 8 (2) (2001). [5] Y. Donchin, A look into the nature and causes of human errors in the intensive care unit, Quality & Safety in Health Care 12 (2003) 143–147. [6] Y. Dong, N. Chbat, A. Gupta, M. Hadzikadic, O. Gajic, Systems modeling and simulation applications for critical care medicine, Annals of Intensive Care 2 (18) (2012). [7] F.A. Drews, The frequency and impact of task interruptions in the ICU, in: Proceedings of the 51th Annual Meeting of the Human Factors and Ergonomics Society, Baltimore, MD, 2007. [8] A. Gupta, R. Sharda, SIMONE: a Simulator for Interruptions and Message Overload in Network Environments, International Journal of Simulation and Process Modeling 4 (3–4) (2009) 237–247. [9] A.P. Gurses, Y. Xiao, A systematic review of the literature on multidisciplinary rounds to design information technology, Journal of the American Medical Informatics Association 13 (3) (2006) 267–276. [10] N. Halpern, S. Pastores, Critical care medicine in the United States 2000–2005: an Analysis of bed numbers, occupancy rates, payer mix, and costs, Critical Care Medicine 38 (1) (2010) 65–71. [11] W.D. Kelton, J.S. Smith, D.T. Sturrock, Simio and Simulation: Modeling, Analysis, Applications, McGraw Hill Learning Solutions, Boston, 2011. [12] F. Magrabi, S.Y. Li, A.G. Dunn, E. Coeira, Challenges in measuring the impact of interruption on patient safety and workflow outcomes, Methods of Information in Medicine 50 (5) (2011). [13] P.J. McLeod, A successful formula for ward rounds, CMAJ 134 (8) (1986) 902–904. [14] L.J. Morrison, L.A. Headrick, Teaching residents about practice-based learning and improvement, Journal on Quality and Patient Safety 34 (8) (2008) 453–459.

[15] J.A. O'Hare, Anatomy of the ward round, European Journal of Internal Medicine 19 (5) (2008) 309–313. [16] V.L. Patel, J. Zhang, N.A. Yoskowitz, R. Green, O.R. Sayan, Translational cognition for decision support in critical care environments: a review, Journal of Biomedical Informatics 41 (3) (2008) 413–431. [17] T.A. Shamliyan, S. Duval, J. Du, R.L. Kane, Just what the doctor ordered. Review of the evidence of the impact of computerized physician order entry system on medication errors, Health Services Research 43 (1 pt 1) (2008) 32–53. [18] J.G. Trafton, E.M. Altmann, D.P. Brock, F.E. Mintz, Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal, International Journal of Human Computer Studies 58 (5) (2003) 583–603. [19] E.G. Van Eaton, K.D. Horvath, W.B. Lober, A.J. Rossini, C.A. Pellegrini, A randomized, controlled trial evaluating the impact of a computerized rounding and sign-out system on continuity of care and resident work hours, Journal of the American College of Surgeons 200 (4) (2005) 538–545. [20] M. Weigl, A. Muller, C. Vincent, P. Angerer, N. Sevdalis, The association of workflow interruptions and hospital doctors' workload: a prospective observational study, BMJ Quality and Safety 21 (5) (2012) 399–407.

Ashish Gupta is an Associate Professor in the College of Business at the University of Tennessee Chattanooga and holds visiting appointment in Biomedical Informatics Department at Arizona State University. He has been a visiting Research Scientist at Mayo Clinic. He received his PhD in Management Science and Information Systems from Oklahoma State University. He has published in several journals including Decision Support Systems, European Journal of Information Systems, Communications of AIS, Information Systems Frontiers, etc. He serves on the editorial boards of journals such IJDSST, IJITSA and is guest editing special issue of Decision Support Systems on healthcare modeling. He serves on NIH and PICORI review panels and has chaired numerous conferences such as Symposium on Healthcare Advancement in Research & Practice (SHARP) −1.0, 2.0, EMOAS 2011 (London). Ashish is the current President of MidwestAIS (2012–2013). His interests include critical care, EMR, patient portals, simulation, virtual world in health, clinical informatics and healthcare delivery.

Ramesh Sharda is Director of the Institute for Research in Information Systems (IRIS), ConocoPhillips Chair of Management of Technology, and a Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. He received his Ph.D. from the University of Wisconsin-Madison. His research has been published in major journals in management science and information systems including Management Science, Information Systems Research, Decision Support Systems, Interfaces, INFORMS Journal on Computing, Computers and Operations Research, and many others. He serves on the editorial boards of journals such as the INFORMS Journal on Computing, Decision Support Systems (Area Editor), Information Systems Frontiers, and OR/MS Today. His research interests are in decision support systems, especially neural network applications, and technologies for managing information overload. His team's work on forecasting box office revenue of movies has received a lot of press. Defense Ammunitions Center, NSF, the US Department of Education, Marketing Science Institute, and other organizations have funded his research. Ramesh is also a cofounder of a company that produces virtual trade fairs, iTradeFair.com.

Yue Dong is an Assistant Professor of Medicine at the College of Medicine, Mayo Clinic. He is currently a patient safety researcher and educator at the Mayo Clinic Multidisciplinary Simulation Center (MCMSC) and the Multidisciplinary Epidemiology and Translational Research in Intensive Care (METRIC) group at the Mayo Clinic Rochester. Dr. Dong's primary research interests are: using systems engineering approaches (discrete event simulation) to study and improve healthcare process and system performance in the ICU; study the effectiveness of simulation-based medical education (SBME) by developing valid outcome assessment instruments, and provide highly reliable data for decision support and high-stakes testing; using simulation as a tool, conduct human factor research to study healthcare provider performance variation under different “stress conditions” and conduct usability testing of software, devices and care processes. Dr. Dong also teaches several simulation courses (central line, airway, sedation, system thinking) at the MCMSC for clinical fellows, residents and medical students. He serves on research committee, technology and standard committee of Society for Simulation in Healthcare.

A. Gupta et al. / Decision Support Systems 55 (2013) 516–527 Rohit Sharda, MD is a third-year resident in internal medicine at the University of Texas Southwestern Medical School. He earned his BS from Stanford University and an MD from UT-Southwestern Medical School. He is interested in resource utilization in healthcare and plans to pursue a fellowship in gastroenterology.

Daniel Adomako Asamoah is a PhD student in the Management Science and Information Systems Department in the Spears School of Business at Oklahoma State University. He received his Master of Science degree in Telecommunications Management from the Oklahoma State University. Prior to his graduate studies, he completed a Bachelor of Science degree in Electrical and Electronic Engineering at the Kwame Nkrumah University of Science and Technology, Ghana. His current research interests are in advanced business analytics, decisions support systems in healthcare and the use of analytical modeling solving operations management and information systems problems.

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Brian Pickering graduated from Trinity College Dublin, Ireland in 1996. He holds a fellowship from the College of Anesthetists in Ireland, MSc. in molecular neuroscience from Bristol University, UK and another fellowship in critical care medicine at Mayo Clinic. He currently works as a consultant in anesthesiology-critical care medicine in Mayo Clinic, Rochester. As part of the METRIC group at Mayo Clinic, Dr. Pickering works closely with experts in programming, clinical informatics, cognitive science, psychology, system engineering, complex adaptive system modeling, technology dissemination, intellectual property protection, nursing and allied health to achieve the objective of improving patient outcomes in the acute care setting. His work focuses on the ergonomics and science of health care delivery, design of interventions which, when introduced into the clinical setting optimize the impact of provider-system interactions on patient-centered outcomes. Dr. Pickering and his group received a center for medicare/medicaid innovation award valued at $16 M to disseminate cloud based technologies designed to reduce medical error and information overload in the ICU setting.