Skip Navigation LinksHome > December 2003 - Volume 33 - Issue 12 > Understanding the Complexity of Registered Nurse Work in Acu...
Journal of Nursing Administration:

Understanding the Complexity of Registered Nurse Work in Acute Care Settings

Ebright, Patricia R. DNS, RN; Patterson, Emily S. PhD; Chalko, Barbara A. MSN, RN; Render, Marta L. MD

Free Access
Article Outline
Collapse Box

Author Information

Authors’ affiliations: Assistant Professor (Dr Ebright), Clinical Instructor (Ms Chalko), Indiana University School of Nursing, Indianapolis, Ind; Research Physical Scientist (Dr Patterson), Director (Dr Render), Veterans Administration Getting at Patient Safety Center, Cincinnati, Ohio.

Corresponding author: Patricia R. Ebright, DNS, RN, Indiana University School of Nursing, 1111 Middle Dr, NU 412, Indianapolis, IN 46202-5107 (

Collapse Box


Nursing shortages and patient safety mandates require nursing managers and administrators to consider new ways of understanding the complexity of healthcare provider work in actual situations. The authors report findings from a study guided by an innovative research approach to explore factors affecting registered nurse performance during real work on acute care medical-surgical units. Our findings suggest beginning targets for interventions to improve patient safety, as well as recruitment and retention, through support for registered nurse work.

Predictions of significant nursing staff shortages during the next 20 years complicates the mandate for increasing patient safety. 1 In light of the predicted registered nurse (RN) shortages, renewed calls have begun for redesigns in the workplace to recruit and retain nurses. Redesign efforts implemented in the 1990s were criticized for decreasing RN-patient ratios, relying too heavily on nurse extenders, 2 and leading to difficult working conditions. However, those conditions are not understood and, more importantly, whether they also might affect patient safety.

NurseWeek and the American Organization of Nurse Executives reported findings from surveys completed by 4,108 RNs about their perceptions of the nursing shortage, its impact, their career plans, and their work environment. 3 Eighty-three percent of the RNs agreed that improved working environments would help solve the nursing shortage. Respondents reported that the nursing shortage was a problem factor in nurses’ ability to maintain patient safety, detect patient complications, carry out physician orders in a timely manner, and collaborate with other team members.

The expertise of human performance and safety experts and their research in non-healthcare industries, such as the military, nuclear power plants, and space mission control, 4 can help us to understand the complexity of the work environment. According to human performance experts, performance in complex work environments is influenced by human and environmental factors, such as types of information available, worker experience, ambiguity, unpredictability, conflicting goals, and time pressures. 5 Improving patient safety depends on understanding that such work environments require worker flexibility in adapting to variation in patient needs and environmental factors. 6,7 Indeed, research has demonstrated that healthcare workers create safety daily in the presence of multiple “latent failures” and are the resilient factor for preventing accidents in complex systems. 8 A latent failure is a flaw in a system that does not immediately lead to an accident but establishes a situation in which a triggering event leads to failure despite defenses that were built to protect against the failure. 9

Back to Top | Article Outline

The Sharp End and Blunt End Framework

As the human performance framework “Sharp End and Blunt End”10 illustrates (Figure 1), healthcare workers at the point of care delivery (sharp end) are involved in constantly evolving situations. Both supported and constrained by organizational resources from above (blunt end), healthcare workers continuously perform activities influenced by cognitive factors, including knowledge, mindset, and goal conflicts to manage clinical practice situations. Knowledge factors refer to the formal or contextual knowledge base that practitioners use to solve problems in context. 11 Mindset refers to the immediate attentional focus and factors that affect control of attention during real work. 10 Goal conflicts refer to the tradeoff situations that practitioners confront during work and are related to the values or costs placed on possible outcomes or courses of action, that is, between risks of different errors. 12 These factors, in turn, are shaped by complexity in the workplace in the form of multiple goals, obstacles, hazards, missing data, and behaviors surrounding care situations. 10 For example, unpredictability, missing information, clumsy technology, and constant change are common current healthcare environment features that confront staff. To prevent things from going wrong, practitioners anticipate, react, accommodate, adapt, and cope to manage complexity in the midst of a changing environment.

Figure 1
Figure 1
Image Tools

The increasing complexity of healthcare systems compounds the need to understand clearly how best to support decision making. Redesign efforts in healthcare services delivery during the last 10 years were well intentioned but reflect the weakness of implementing change without understanding how healthcare providers organize and structure information cognitively, how they respond appropriately in complex situations, and what environmental conditions support or hinder decision making in actual situations. Increased understanding of the work complexity in acute care environments and the cognitive factors that contribute to RN work management activities will be critical to the successful redesign of environments to improve patient safety and recruit and retain RNs. However, there has been little research exploring the details of actual RN work from a human performance framework.

Back to Top | Article Outline

The Study

The purpose of this study was to increase understanding of RN work complexity in an acute care setting using a human performance framework. The research question that guided the study was, “What human and environmental factors affect decision making by expert RNs on medical-surgical acute care units?” Using the human performance framework represented in Figure 1, this study addressed 3 areas that have had insufficient attention in the nursing literature: (1) human and environmental issues affecting RN work in acute care settings during actual work situations, (2) specific cognitive factors driving RN performance and decision making during actual care situations, and (3) strategies used by experienced RNs to manage work successfully.

Back to Top | Article Outline


For this microethnographic study, the researchers were guided by the human performance framework “Sharp End and Blunt End”10 and used a mixed-method approach. 13 The quantitative and qualitative data collection included field observations, followed by semistructured interviews.

Back to Top | Article Outline


A maximum variation sampling technique was used to select a small but diverse sample. The aim of this sampling technique was to describe unique aspects related to work complexity through a few richly detailed cases and identify important patterns that emerged in the heterogeneous group. 14 The purposive sample included 8 expert RNs recruited from 2 separate facilities working on 7 different units belonging to 1 Midwest healthcare network. The care units included 4 general medical-surgical, 2 medical, 1 postprocedural, and 1 orthopedic unit.

Experienced and clinically expert RNs were studied to increase the likelihood of rich data surrounding alternative care management strategies. Nurses were eligible for participation if they had at least 5 years of medical-surgical experience and were identified as expert clinical nurses by their respective unit supervisors. The sample included 2 diploma-, 1 associate-, and 5 baccalaureate-prepared nurses. RN experience ranged from 5.5 to 39 years. Length of time worked by the RNs on their respective units ranged from 8 months to 24 years.

A second sampling technique was used to select “decision cases” that would be the focus of interviews for each participant. Sandelowski 13 described how 1 dataset in a mixed-method approach can be used to inform a second sampling technique in the same study. In this study, a criterion sampling technique was performed using the observation data to select participants’ directly observed responses to be further explored in the interview. These responses might, for example, be triggered by (1) a person providing new information, (2) a patient’s response to an intervention, or (3) other new information in the immediate environment.

Back to Top | Article Outline


After internal review board approval of the study and RN agreement to participate, a single researcher observed each participant continuously for 3 hours during actual work in the role of staff nurse on 2 different days between 8 am and 3 pm. Two sessions were scheduled for each participant to decrease observer influence on the participant’s work at the second observation. All manually recorded data from the observation periods were entered into a timeline software package to produce sequenced and time-referenced data that were used for recall purposes during the subsequent interview. Participants’ recent events and data retrievable from direct observations guided the interviews, increased data reliability and validity, and enabled discarding of inaccurate speculative and inferential information.

Each participant was interviewed individually using Critical Decision Method (CDM) 15 interviews in the week after the second observation session to elicit detailed information surrounding decisions made during work observed in that session. The CDM interview is a technique based on the recognition-primed decision-making model developed by Klein. 5 The technique enables users to elicit information from an expert about situations that may be difficult to articulate. Decision cases were selected that represented the research phenomenon of interest: work complexity. For example, in this study, CDM interviews (Figure 2) focused on instances where the participant was observed to immediately prioritize and attend to a patient situation in response to some new information. The CDM interview uses several probes as the interview unfolds to elicit cognitive processes that surrounded a specific situation (cues, goals, expectations, and actions). The technique resulted in data related to information gathering, judgments, interventions, and decision-making outcomes. Interview data were recorded manually by the primary interviewer and in more detail by a second recorder who was present at each interview session. Audiotaped recordings of the interviews were used only if needed for follow-up reference.

Figure 2
Figure 2
Image Tools
Back to Top | Article Outline

Data Analysis

Members of the research team, which included 2 nurses, 1 physician safety expert, and 1 human performance expert, started data analysis immediately after each interview session. Guided by a “start list” of codes 16 based on the Sharp End and Blunt End framework, content analysis was used to sort and categorize individual interview and observation data and then identify common meanings and patterns across participants. Agreement was reached regarding patterns in the observation and interview data that reflected (1) human and environmental factors affecting work in the form of multiple goals, conflicts, obstacles, hazards, data, and behaviors; (2) cognitive factors that influenced performance and decision making; and (3) specific management strategies participants used to adapt, anticipate, accommodate, react, and cope to provide and coordinate care.

Back to Top | Article Outline

Work Complexity, Cognition, and Care Management Strategies

Twenty-two patterns (Figure 3) emerged from analysis of observation and interview data that reflected work complexity (8 patterns), cognitive factors driving performance and decision making (8 patterns), and strategies participants used to manage care situations (6 patterns). Each pattern described emerged from data retrieved from at least 4 of the 8 participants, except for geography of assignment, for which 2 contrasting cases are described.

Figure 3
Figure 3
Image Tools
Back to Top | Article Outline

Patterns of Work Complexity

Eight patterns contributed to complexity of work. The pattern of disjointed supply sources was identified whenever supplies or equipment needed for an aspect of care were located in another area of the unit. For example, in every observation session, participants retrieved different types of medications from different locations. On several units, syringes were stored in one area and subcutaneous or intramuscular medications were located in another. Other examples of disjointed supply sources included pillows separate from linens, oral fluids separate from ice and from cups, portable oxygen saturation monitors separate from disposable finger attachments, and equipment for drawing laboratory blood samples separate from labels.

All observation sessions revealed at least 1 situation in which participants encountered missing equipment or supplies; examples included a thermometer, medications, equipment for drawing blood, isolation gowns, dressing tape, saline, appropriate-sized collar for patient for discharge, stockings, tube feeding, lubricant, paper towels, soap, patient name plate, soda requested by patient, nail clippers, bedside commode, and a breakfast tray. In 4 of the 8 observations, participants found the following equipment to be nonfunctioning or needing repair or adjustment before using for care: EKG machine, patient television control, autodispensing device for medications, flashlight, and Doppler device.

Repetitive travel by participants was identified as an activity where repeated trips during the 3-hour observation period were made to the same supply areas on the unit. In all cases, few supplies were stored in the patient room, and most everything that was needed was centralized in separate rooms off the hallway or in or near the nursing station.

Every participant had multiple interruptions during the 3-hour sessions. An interruption was counted every time the participant was distracted from the immediate task or issue on which she was focused. Interruptions per participant ranged from 7 to 31 (M = 19). Persons initiating interruptions included patients, physicians, nursing technicians, other RNs, patients’ families, social workers, pharmacists, physical therapists, unit managers, unit secretaries, students, laboratory technicians, housekeeping personnel, and other department personnel. Most interruptions occurred as participants traveled to and from patient rooms, while in the process of retrieving supplies or equipment, and during patient medication retrieval in the hall or at the autodispensing device.

The 2 participants with the most interruptions provided an interesting contrast related to geography of assignment. The latter was defined as the distance between patients in one participant’s assignment. For example, 1 participant had 4 patients on 1 wing, each in a private room, 2 rooms directly opposite the other 2. She was able to chart directly outside the rooms and to observe all 4 patients at the same time. In contrast, another participant had 5 patients on 4 different wings. The first participant was interrupted 31 times, but 15 of the interruptions were directly from her assigned patients and she was able to respond immediately. The participant with patients on 4 wings was interrupted 25 times but only once by her own patient, with the remaining 24 interruptions by other people as she traveled the halls.

In 7 out of the 8 observations, participants delayed decisions or the delivery of care because they were waiting for systems or processes that were not available when initial attempts were made. These incidents included waiting for access to the autodispensing device for medications, access to hall medication carts, a response to a page to a physician or other resource, another healthcare provider to finish with the patient, medication delivery through the tube system, delivery of equipment or supply, and a bag of tube feeding.

Four participants had difficulty accessing resources to continue or complete care. On a day when residents were switching services, multiple pages were needed to locate the appropriate physician who was covering the patient. Other difficulties in access included inability to find information on a new tubing and drainage device after checking several sources, a physician who directed the participant to call another physician, needing assistance from a nursing technician but unable to locate her, and inability to identify correct telephone number for reaching special services needed by patients after 2 and 3 inquiries.

Inconsistencies in care communication across care providers and/or patient was a pattern identified in all 8 observations in which information about the patient was not consistent across caregivers, patients, and/or families. Examples included the patient asking the participant about time of discharge and no order had been written for the participant to act on, inability to respond to an anxious family’s question about bed placement after same-day surgery, participant delivering admission supplies to a new patient room and finding that the nursing technician had already completed the preparation, and participant having to obtain an order for additional laboratory work when blood should have been drawn but orders were not on the computer screen.

The pattern of breakdown in communication process/communication medium was identified to describe those situations for 5 out of the 8 participants in which inability to communicate resulted from processes involving paper and labeling. Examples included inability to read a physician’s signature, medication sheets filed in the wrong chart, no date marked on a medication patch on the patient’s arm, and receiving a call from a pharmacist saying she could not read the carbon copy of new medication orders.

Back to Top | Article Outline

Patterns of Cognitive Factors Driving Performance and Decision Making

Eight patterns were identified, primarily from interview data, related to cognitive factors that guided participant activities. Five patterns reflected goals that potentially conflict, and 3 patterns were related to the types of knowledge that guided performance activities in the context of managing an assignment.

Back to Top | Article Outline
Goal Patterns

In 4 of the 8 participant interviews, maintaining patient safety was identified as a goal. For example, a participant who delegated vital signs to a nursing technician for patients returning from procedures talked about “Always being there [herself] when the patient first returns from a procedure...I have to get a baseline...” picture of the patient to recognize later subtle changes if they occur.

For 5 of the 8 participants, statements focused on preventing getting behind in work. “If I get behind by 10 am, I can predict the whole day will be ruined...Working to keep ahead is an 8 (on a scale from 1 to 10) as a big driver of work.”

Making decisions to avoid increasing the complexity of situations was a pattern described by 5 of the 8 participants. Examples of statements in this pattern were “I didn’t push the patient regarding the procedure and getting ready...I anticipated more complexity if I pushed...I don’t want to underreact because I could miss something, but I don’t want to overreact because I can get a lot of people upset.”

Statements from 4 of the 8 participants reflecting the pattern appearing competent and efficient to coworkers follow: “Don’t want to hold up everyone else’s day...In the back of my mind, I’ll have to tell someone in report that I didn’t get something done [if the RN does not get something completed on shift]...but you value others’ opinions...I may stay over to finish because I don’t want the bad opinion.”

The following statements were examples of a pattern from 6 of the 8 participants related to maintaining patient/family satisfaction: “When patients don’t see you it can decrease other outcomes and satisfaction with their care because they wonder where their nurse is...I know that if I start out on a good foot with patients, it makes for a better day...When I introduce myself it tells them I care, I’m here, if you put on your light I’ll come.”

Back to Top | Article Outline
Knowledge Patterns

A pattern identified in 6 of the 8 interviews was knowing individual patient information that the participant used to guide care activities and decisions. One participant “Knew that the patient had a previous cardiac arrest from earlier in the current admission....” and responded immediately to a new-onset complaint of chest pain, despite no other clinical manifestations of cardiac involvement and the young age of the patient. One participant spent more time preparing a patient for a procedure “because I knew she had had a bad experience on her previous procedure.”

Another participant did not leave medications with a patient when responding to a second patient emergency because “I knew the patient from the day before...he had impaired vision and tended to be drowsy...” and if he dropped a pill she would not know if he had taken it. Another participant took immediate action to lay a patient down during an attempt to sit up the first time post-op when the patient expressed discomfort because “I knew she was not a ‘whiner’ from my interactions with her earlier in the morning.”

Participants reported use of knowing typical patient profiles to guide their work. “I knew from her signals that she was ‘anxious,’” “Certain personality types need more validation about what is to happen to have to pull information from them...” “...I was considering the possibility of a clot because of her chest pain and her anxiety...if someone throws a clot, [being] anxious fits with a clot picture.” Another participant described the typical profile of a patient on the unit whose pain management had not been adequate: “The patient cannot do exercises...they end up using more have to try other alternatives to supplement meds...their joints stiffen because they are unable to do physical therapy...I will have to be in the room more often checking on them...and I will be calling the MD more often for them.” The participant placed a high priority on keeping up with pain management.

In addition to knowing the patient or typical patient profiles, another pattern that was identified was knowing unit routines and workflow to guide management of care and decision making. To allow her to move on from an unstable patient situation, 1 participant included in her decision making the needs assessment of her assigned patients, knowledge of available help from the nursing technician, and the geographic location of her patients: “It was on a day when I felt like I couldn’t be in there more...I didn’t feel comfortable being in a patient’s room for 30 minutes...if I hadn’t had a lot going on I could have monitored another patient with pain more often.”

Another participant talked about what having a patient fall means to the workflow: “Ruins the whole day if a patient falls...means constant checking on patient afterward...means time away from other patients...missing other orders...passing on unfinished work to next lunch break.” And from the same participant: “By 10 am, charts are back on the rack since the residents have finished rounds and have stopped adding orders...if I try to check earlier...would have to check multiple times.” Another participant talked about working with a nursing technician. “When I work with a tech...I get a feel for her whole assignment...the numbers of patients and geographic distribution...then I know how much I can depend on the tech and what other RNs will be using the tech for...”

Back to Top | Article Outline

Patterns of Care Management Strategies

Six patterns identified from the data reflected the decision-making and coordination strategies used by participants to manage and provide patient care. Every participant used stacking of activities, or moving on to other activities to prevent down time when not able to complete something because of waiting for processes or inability to access resources. For example, at one point during the 3-hour observation period, 1 participant with 5 patients had stacked all of the following: discharge completion of a patient without an order, responding to a student nurse request for information regarding tube management for her patient because of inability to find the information, telling another patient the scheduled time of a procedure when requested because the procedure had not yet been ordered, and moving on with routine care of other patients because she was waiting for a physician call-back for a patient with new-onset chest pain.

A pattern of anticipating or forward thinking as part of workload management was identified in the observation and interview data. Participants were observed to prioritize and delegate work based on their ability to anticipate what might happen if certain activities were completed, or not completed, and the effect on their workload assignment. A simple example included the participant who carried the ECG strip around with her because she anticipated needing the information when the physician responded to her page. When another participant heard from a nursing technician about a central line being out, she described thinking about the possibilities of what “out” might mean and what actions might be needed even before reaching the room. Anticipating the interventions needed for a first-time-up postoperative patient and what would happen if the patient did not get up appropriately, a participant chose to be at the bedside to assist the new nursing technician.

Strategies to proactively monitor patient status were seen frequently as participants traveled up and down hallways and turned to look repeatedly in rooms for patients’ current conditions and activities in the rooms. Other examples of proactive monitoring included the following: traveling down the hall to respond to another patient problem, a participant reminded a student nurse that she had not forgotten her question and would get back; while on the way to another patient, a participant poked her head into a patient room and let the patient and wife know the status of the discharge order and equipment. Participants used simple checks and balances continuously during the process of travel between patients and during the care of individual patients to manage workload. Having a dry-erase board in each patient room helped another participant “Organize care...if nothing is written on the board, I have to check the chart, but if written...helps to know what to do next, it helps to get you started and keep track of pain meds with the patient.”

Examples of understanding the patients and the flow of unit activities resulted in strategic delegation and hand-off decisions. Anticipating that a student would need help moving a patient in the course of the morning routine care, a participant directed the student, before she asked, to get the nursing technician when she was ready. Another participant stated: “I always give the patient and family a call light, always let them know how to reach you.” To explain when she calls a physician, a participant responded, “ I do not call the physician at the drop of a hat. Bottom line...the physician wants to know that the patient is or isn’t stable.”

Some work strategies represented a pattern in which participants used specific actions to stabilize patient situations so that they could move on with other assignments. The following are examples of the pattern stabilizing and moving on. When a participant was asked about her reasoning in relation to obtaining an electrocardiogram on a young patient whose chest pain she believed was not related to cardiac problems, she explained that “there was nothing to lose from doing an wasn’t invasive and didn’t cause was one extra piece of assessment information and reassurance...I could move on with other patients’ care...” After assessing a patient who fell, another participant returned immediately to another patient to continue administering medications. She stated “He would be finished for now...and I could then move on to deal with the patient who fell.”

Every participant had some type of manual recording system or memory aid to help keep track of work. Paper documents were, in some cases, computer-generated lists of patient medications and treatments. Participants carried the aids on clipboards, in notebooks, or in binders. Each participant had her own method for tracking activities that involved various forms of shorthand, colored markers, and symbols. The following were observed uses of the memory aids: checking for specific patient information, checking for work needing to be done, for direct recording of patient information (vital signs, weights, laboratory results, and intake and output), for marking off completed work, for managing medications; as a reference for later formal charting, and for recording new verbal orders by physicians.

Back to Top | Article Outline


The purpose of this study was to identify and describe work complexity for RNs in acute care units and its relationship to cognitive factors and performance behaviors of RNs during actual work situations. The design of the study for examining the actual work of RNs was unique in its use of a human performance framework to categorize observation and interview data. Although the complexity and demands of the work environment have been cited as contributors to patient safety and RN retention and recruitment issues, 2 little research about real-time work complexity for RNs in acute care settings has been conducted.

Understanding RN work in the context of patient assignments in the midst of complex system characteristics, such as unpredictability, missing information, and unreliable access to resources and processes, provides a different picture from the role descriptions, nursing process, and strict policy and procedural guidelines that have been used in the past to design systems of care delivery. Findings from this study suggest multiple ways to redesign systems to decrease work complexity, obviously starting with the relatively simple redesign of units so that, for example, water, ice, and cups would be kept in the same area.

Findings from this study cannot be generalized because of the small and nonrandomized sample. Although data were collected from 8 RNs on 7 different units, all were part of 1 healthcare network. Data collection was limited to only expert RNs and during the day shift. The care delivery models on the study units were either total patient care or an RN with nursing technician support. Patterns with larger and more varied samples and settings will be identified with future research.

Eight patterns were identified that related to complexity of work, including disjointed supply sources, missing or nonfunctioning supplies and equipment, repetitive travel, interruptions, waiting for systems/processes, difficulty in accessing resources to continue care, breakdown in communication, and breakdowns in communication processes or mediums. For the nurses on the research team with recent clinical experience across multiple settings, these patterns were no surprise. The insight gained from using the human performance framework was that these issues were clearly patterns of work complexity that threaten continuity in patient care, have the potential to contribute to medical errors, and decrease RN work satisfaction.

In other words, what for many RNs is a routine aspect of the daily management of work in the care setting became, when seen through the lens of a human performance framework, a series of gaps and discontinuities that distracted the RN from focusing on critical clinical reasoning about individual patients. For example, disjointed supply sources and missing and nonfunctioning equipment and supplies required constant travel. Instead of focusing on individualized patient assessment and interventions, RNs in the study were wasting a large amount of valuable time managing and working around systems that created, rather than supported, work. Given the limits of human performance capabilities in an environment of multiple distractions, it is reasonable to infer that interventions directed toward minimizing these distractions would benefit patient safety and increase RN satisfaction.

Five goal patterns that represented areas for potential goal conflicts included maintaining patient safety, preventing getting behind, avoiding increasing complexity, appearing competent and efficient to patients/families and coworkers, and maintaining patient/family satisfaction. Complex system failure experts argue that even with the best improvements in systems and technological support, workers will continue to deal with competing goals in work environments because of the continuing introduction of change, new people, and rapid pace of activities. 17 To achieve desired patient care outcomes in the acute care environment, RNs balanced tradeoffs as part of their work to achieve personal and organizational goals. What redesign interventions might decrease the frequency or risk in tradeoff situations? Healthcare organizations strongly emphasize educating staff about guidelines, standards, policies, and procedures. However, if tradeoffs are unavoidable, this must be discussed openly with staff to help them learn to apply the types of reasoning and prioritizing that the expert clinicians in this study exhibited.

The patterns of knowledge factors driving RN activities in this study were particularly interesting, given the shortage of RNs and the search for the ideal RN-patient ratio. Interview data revealed the use of specific and sometimes subtle cues regarding individual patients that resulted in important decisions about next steps with patients who were clinically unstable. Knowing specific information about patients and typical profiles enabled RNs to anticipate likely outcomes, which, in turn, guided decision making.

The RNs, who were responsible for as many as 5 acutely ill patients, used knowledge of unit routines and resources to manage work and make prioritizing decisions. Their ability to imagine outcomes based on previous experiences or knowledge also was helpful. How are RNs who float to new units best supported to make clinical decisions effectively? How is safety maintained with agency and travel nurses who are unfamiliar with their assigned patients and specific unit routines?

The expert RNs in this study demonstrated effective strategies to cope and adapt in work situations to manage workload demands. Although nursing school curricula have a great deal of content related to disease processes, procedural techniques, and critical-thinking processes, little time is devoted to managing workload complexities in care situations, and students must be taught these skills.

Back to Top | Article Outline


Making progress in both patient safety and RN recruitment and retention may depend on redesign of environments to support care providers in work situations. Using a human performance framework and innovative data collection techniques, multiple patterns characterizing RN work during work situations on medical surgical acute care units were identified. Findings from this small study support the future use of research approaches not used traditionally in healthcare to understand factors related to the work complexity of healthcare environments, cognitive factors driving performance and decision making, strategies used by healthcare workers to manage care in demanding environments, and potential areas for redesign and education.

Back to Top | Article Outline


The authors thank the Indiana University School of Nursing, Center for Nursing Research, which provided funding for this project, as well as the 8 registered nurses who contributed immensely by agreeing to participate in the study.

Back to Top | Article Outline


1. Buerhaus P, Staiger D, Auerbach D. Implications of an aging registered nurse workforce. JAMA. 2000; 283:2948–2954.

2. American Association of Colleges of Nursing. Hallmarks of the professional nursing practice environment. Washington, DC: Author; 2002.

3. NurseWeek. AONE/ NurseWeek survey of registered nurses. Available at: Accessed December 10, 2002.

4. Woods DD, Roth EM. Cognitive engineering: human problem solving with tools. Hum Factors. 1988; 30:415–430.

5. Klein G. Sources of Power: How People Make Decisions. Cambridge, Mass: Massachusetts Institute of Technology; 1998.

6. Institute of Medicine. Committee on Quality Healthcare in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001.

7. Cook RI, Woods DD. Operating at the ‘sharp end:’ The complexity of human error. In: Bogner MS, ed. Human Error in Medicine. Hillsdale, NJ: Lawrence Erlbaum; 1994:255–310.

8. Leape LL. Error in medicine. JAMA. 1994; 272:1851–1857.

9. Reason J. Human Error. Cambridge, Mass: Cambridge University Press; 1990.

10. Woods DD, Johannesen LJ, Cook RI, Sarter NB. Behind Human Error: Cognitive Systems, Computers, and Hindsight. Wright-Patterson AFB, Ohio: Crew Systems Ergonomic Information and Analysis Center (CSERIAC); 1994.

11. Feltovich PJ, Ford KM, Hoffman RR. Expertise in Context: Human and Machine. Cambridge, Mass: Massachusetts Institute of Technology Press; 1997.

12. Klein G, Orasanu J, Calderwood R, Zsambok C. Decision Making in Action: Models and Methods. Norwood, Conn: Ablex Publishing Company; 1993.

13. Sandelowski M. Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed-method studies. Res Nurs Health. 2000; 23:246–256.

14. Patton MQ. Qualitative Evaluation and Research Methods. 2nd ed. Newbury Park, Calif: Sage; 1990.

15. Klein GA, Calderwood R, MacGregor D. Critical decision method for eliciting knowledge. IEEE Trans Systems Man Cybernetics. 1989; 19:462–472.

16. Miles MB, Huberman, AM. Qualitative Data Analysis. 2nd ed. Thousand Oaks, Calif: Sage; 1994.

17. Cook R, Render M, Woods D. Gaps in the continuity of care and progress on patient safety. Br Med J. 2000; 320:791–794.

© 2003 Lippincott Williams & Wilkins, Inc.