The nature and occurrence of registration errors in the emergency department

https://doi.org/10.1016/j.ijmedinf.2007.04.011Get rights and content

Abstract

Research into the nature and occurrence of medical errors has shown that these often result from a combination of factors that lead to the breakdown of workflow. Nowhere is this more critical than in the emergency department (ED), where the focus of clinical decisions is on the timely evaluation and stabilization of patients. This paper reports on the nature of errors and their implications for patient safety in an adult ED, using methods of ethnographic observation, interviews, and think-aloud protocols. Data were analyzed using modified “grounded theory,” which refers to a theory developed inductively from a body of data. Analysis revealed four classes of errors, relating to errors of misidentification, ranging from multiple medical record numbers, wrong patient identification or address, and in one case, switching of one patient's identification information with those of another. Further analysis traced the root of the errors to ED registration.

These results indicate that the nature of errors in the emergency department are complex, multi-layered and result from an intertwined web of activity, in which stress in the work environment, high patient volume and the tendency to adopt shortcuts play a significant role. The need for information technology (IT) solutions to these problems as well as the impact of alternative policies is discussed.

Introduction

The emergency department (ED) of today has been described as a “natural laboratory for the study of error” [1]. It constitutes a complex and dynamic environment in which a team, consisting of attending and resident physicians, nurses, students, technicians and support staff, as well as subspecialty consultants, is charged with managing a varying, often overwhelming volume of patients with a wide range of clinical illnesses [2]. Such environments, where decisions are made under pressure and with incomplete information, have been considered more conducive to error [3].

A major theme that has arisen within the context of these errors is the frequency and potential risk of misidentification in the ED—misidentification of patients, study requisitions and patient laboratory specimens [4]. Factors such as the number of patients, the urgency of individual cases, the ability of patients to communicate (e.g. impaired consciousness, language barriers), low staff to patient ratios, and time pressures can all contribute to the risk [4]. In this type of setting, personnel are often prone to “workarounds”, defined as strategies or work patterns that bypass procedural codes in an effort to improve efficiency or productivity, but often with increased risk of error.

To understand the functioning of the healthcare system and to successfully address medical errors, it is necessary to study those components whose complex relationships constitute the system—humans, technologies and their interactions [5], [6], [7], [8], [9]. The study of human factors is an integral part of current safety research [10]. Human error in medicine can range from diagnostic errors to medication errors and has a spectrum of associated cognitive mechanisms [11], [12]. Zhang et al. describe a hierarchy of the healthcare system that elucidates the role of the individual in the causation of error [13]. Although most errors can be traced to actions (or inactions) of an individual, the root causes of errors go beyond a single individual [11], [14]. Patient safety research has expanded to study team interaction and collaborative decision-making [15], [16], the interaction of humans and technology [17], [18], [19], organizational issues [5], [18], institutional functions and government regulations [20]. Even though medical error is rarely due to only one of these factors, traditional patient safety research has not used an integrated approach to study error. As illustrated by Reason's “Swiss cheese” model, medical error is more likely due to a combination of various miscues [11] (like holes in Swiss cheese) that must all line up to allow an error to occur. Perhaps even more alarming is the recognition that near-misses might be more prevalent than previously supposed, because they remain unreported when events do not end in any significant harm [17]. These events are corrected by “filters” in the system, in the form of human operators that act to rectify the results of previous events [17]. The question of whether such filters benefit patient safety needs to be considered in terms of immediate and long-term consequences. In the short term, filters can be effective in avoiding error but do not address larger, system-wide problems that promote error. They do not rectify the root causes of unsafe practices and may even mask the true extent of the problem and allow situations to repeat.

One aspect of medical errors where such filters play a significant role is that of misidentification. Incidents of misidentification or mislabeling are often caught prior to an adverse event and often go unreported. Failure to report misidentifications probably owes to the fact that the personnel fail to note the significance of these errors when caught prior to an adverse event. Some may feel that, if not identified by them, other fail-safes would have prevented an adverse event down the line. Unfortunately, adverse events usually occur as a result of a confluence of several errors – failure of fail – safes or lack of fail-safes [10], [18]. The presence of filters and the failure to report misidentification does not allow for the development or installation of systems or processes that would prevent future occurrences.

Despite increased recognition of the problem of misidentification of patients and samples [21], few researchers have looked at the dynamics of errors at the point of patient registration, where such misidentification usually occurs. At the time of initial registration, the information about the patient presenting to the ED is often far from complete or reliable. In the ED, some patients are unresponsive, unconscious or otherwise incapable of communicating [4], [22]. Some are too ill or unstable to allow collection of this information in a timely fashion. Others communicate inaccurate information for a variety of personal reasons. Finally, some registration personnel noting existing patient demographic data in the database do not reconfirm this information, in order to save time.

The current study was conducted in the naturalistic environment of the ED in order to characterize the factors that compromise patient safety at the point of patient registration. Using ethnographic techniques of observation and interviews, data were gathered in the adult ED of a large academic tertiary care hospital in New York City.

Section snippets

Background

The true extent and seriousness of the impact of medical errors came to the forefront of the healthcare debate with the publication of the Institute of Medicine report in 1999 [5]. In this report, it was estimated that as many as 98,000 deaths per year result from various errors in hospitals. Critical care environments were identified as particularly vulnerable to errors due to their dynamic and complex systems. In addition, the Harvard Medical Practice Study reported that approximately 1.5–3%

Theoretical framework

The theoretical framework that guided this research is the theory of distributed cognition. Distributed cognition is a scientific discipline in cognitive science that is concerned with how cognitive activity is distributed across internal (human cognitive processes) and external cognitive artifacts (computers, telephones, charts), members of groups, and space and time [29], [30], [31], [32], [33]. Distributed cognition provides a unique framework to describe the interactions, processes, and

Study setting

The study site is an adult urban ED in the Washington Heights section of northern Manhattan and has an annual census of approximately 70,000 patients. The population is 61% Hispanic, 18% African American, and 17% White. Twenty-five percent of patients arrive via ambulance. The admission rate for patients seen by a provider is 23%. The ED is staffed with attending emergency physicians and residents. The hospital's Institutional Review Board approved the study and measures were taken to preserve

Results

During the observations, four specific classes of problem were detected which are reflected in the four cases of errors reported in this paper. All of these errors were subsequently recognized and rectified by the filters within the system. As discussed in the following sections, these incidents of error constitute cases of near-misses that could have had potential adverse impact on the patients. All errors, except for case two, were discovered and subsequently corrected during one 3-h

Summary of results

During the course of the observations, four separate types of errors were discovered. All four cases were determined to have originated during the registration process. In each case, the error was eventually caught and no adverse events occurred. Nevertheless, these results represent four cases of “near-miss” that may have resulted in negative outcomes for the patients, if not caught in time.

Errors in distributed cognition

The process of ED registration involves numerous agents and artifacts, including the registration clerks, the patients, the EMS personnel, computers, telephones, charts and wristbands. Like all other aspects of the ED, the artifacts in ED registration are involved in a highly intertwined and dynamically complex fabric of interactions. As a testament to the tremendous workload of ED registration, the registrars must collect several pieces of information on each patient while processing an

Implications

The ED is a unique clinical environment and requires distinctive solutions to address the workflow issues that contribute to the occurrence of medical error. Although long-term solutions must be sought to reduce the root causes of error, the adaptive behavior of the human components of this system must also be bolstered. Although the adoption of technology may benefit the ED, the results of this study suggest that the existing generic electronic tools alone may be ill-suited for this

Conclusions

As the initial step of patient care in the ED, patient registration must be both efficient and accurate. Failure to meet both of these goals can lead to adverse outcomes. Slow registration can impede care by delaying the processing of orders of tests or delaying access to existing medical records and in the long run not providing adequate emergency care. Inaccuracy in gathering information can lead to a myriad of errors, including lack of access to existing medical records, inability to contact

Acknowledgements

This research was supported by grant R01 LM07894 from the National Library of Medicine to Vimla Patel. We thank the nurses, doctors and the administrative staff at the New York Presbyterian Hospital—Columbia University Medical Center Emergency Department for their participation and support of this study. Special thanks are extended to the subjects who participated in the interview portion of our project.

References (42)

  • E. Hutchins

    How a cockpit remembers its speed

    Cognit. Sci.

    (1995)
  • J. Zhang et al.

    Representations in distributed cognitive tasks

    Cog. Sci.

    (1994)
  • T. Cohen et al.

    Distributed cognition in the psychiatric emergency department: a cognitive blueprint of collaboration in context

    Artif. Intell. Med.

    (2006)
  • E. Coiera

    Interaction design theory

    Int. J. Med. Inform.

    (2003)
  • A.W. Kushniruk et al.

    Cognitive and usability engineering methods for the evaluation of clinical information systems

    J. Biomed. Inform.

    (2004)
  • P. Croskerry et al.

    Profiles in patient safety: medication errors in the emergency department

    Acad. Emerg. Med.

    (2004)
  • G. Kovacs et al.

    Clinical decision making: an emergency medicine perspective

    Acad. Emerg. Med.

    (1999)
  • H. Kaplan, Transfusion “Slip”, AHRQ: Morbidity and Mortality Rounds on the Web, 2004, Date Accessed: June 5, 2006, URL:...
  • J.T. Reason

    Human Error

    (1990)
  • D.D. Woods, R.I. Cook, From counting failures to anticipating risks: possible futures for patient safety, in: L.A....
  • Cited by (53)

    • Inaccurate recording of routinely collected data items influences identification of COVID-19 patients

      2022, International Journal of Medical Informatics
      Citation Excerpt :

      As a result, in the Netherlands, secondary registers for COVID-19 intensive care unit (ICU) admissions were put in place, where data were entered manually by healthcare professionals [24,25]. Manually collected data are considered time-intensive but also error-prone [15,26–28], especially since ICUs were under high pressure [29], which can adversely affect analyses leading to potential erroneous conclusions [27]. While ideally data can be extracted automatically and real-time to support, e.g., public health decision-making, this currently may result in under- or overestimation of the prevalence of patients, which could be a significant hindrance for high-quality research, capacity planning and resource management [3,30–33] as governments take measures based on the numbers reported.

    • Transfusion Safety: The Nature and Outcomes of Errors in Patient Registration

      2019, Transfusion Medicine Reviews
      Citation Excerpt :

      The fragmentation of a medical record in this manner can lead to past records being ‘lost’ as they can be listed under a different medical reference number (MRN). Duplicate registrations and their frequency within hospital information systems have been reported in multiple studies [10-15]. Using the number of patients with the same identifiers in the hospital information system as a surrogate for duplicate or potentially duplicate records, McCoy et al (2013) estimated the duplication rate in five-site analysis.

    • Classification of antecedents towards safety use of health information technology: A systematic review

      2015, International Journal of Medical Informatics
      Citation Excerpt :

      Conversely, heavy workloads increased the probability of committing errors [54,72,93]. Besides, heavy workloads promoted multitasking, workarounds, or inhibited the adherence to policy and procedures [59,86,104]. Cognitive load is defined as the level to which cognitive resources, mainly working memory, are used during learning, problem solving, thinking, and reasoning [105].

    View all citing articles on Scopus
    View full text