In Which Healthcare Setting Is A Patient Not Registered Prior To Receiving Care?
Int J Med Inform. Writer manuscript; available in PMC 2009 Mar 1.
Published in final edited course as:
PMCID: PMC2259219
NIHMSID: NIHMS41016
The Nature and Occurrence of Registration Errors in the Emergency Section
A. Forogh Hakimzada
ane Laboratory of Determination Making and Cognition, Section of Biomedical Informatics, Columbia University, New York, NY
Robert A. Green
2 New York-Presbyterian Hospital/Columbia Academy Medical Center, Department of Emergency Medicine, New York, NY
Osman R. Sayan
2 New York-Presbyterian Infirmary/Columbia Academy Medical Center, Department of Emergency Medicine, New York, NY
Jiajie Zhang
3 School of Wellness Information Sciences, Academy of Texas Health Science Center at Houston
Vimla 50. Patel
1 Laboratory of Decision Making and Cognition, Department of Biomedical Computer science, Columbia University, New York, NY
Abstract
Research into the nature and occurrence of medical errors has shown that these often upshot from a combination of factors that lead to the breakdown of workflow. Nowhere is this more than disquisitional than in the emergency section (ED), where the focus of clinical decision is on the timely evaluation and stabilization of patients. This paper reports on the nature of errors and their implications for patient condom in an developed 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. Assay revealed four classes of errors, relating to errors of misidentification, ranging from multiple medical tape numbers, incorrect patient identification or address, and in one instance, switching of one patient's identification information with those of some other. Further analysis traced the root of the errors to ED registration.
These results bespeak that the nature of errors in the emergency department are complex, multi-layered and effect from an intertwined web of action, in which stress of the work environment, high patient volume and the tendency to adopt shortcuts play a significant function. The demand for data technology (Information technology) solutions to these bug besides as the impact of alternative policies is discussed.
Keywords: ED registration, medical errors, misidentification, workarounds, shortcuts, distributed cognition, emergency care
Introduction
The emergency department (ED) of today has been described as a "natural laboratory for the study of mistake" [ane]. It constitutes a complex and dynamic surround in which a team, consisting of attending and resident physicians, nurses, students, technicians and back up staff, as well equally 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 force per unit area and with incomplete data, 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 [iv]. Factors such equally the number of patients, the urgency of individual cases, the ability of patients to communicate (eastward.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 oftentimes prone to "workarounds", divers every bit strategies or work patterns that bypass procedural codes in an endeavor to improve efficiency or productivity, but often with increased risk of error.
To sympathise the functioning of the healthcare arrangement and to successfully accost medical errors, it is necessary to written report those components whose complex relationships constitute the system - humans, technologies and their interactions [5–9]. The written report of man factors is an integral function of current prophylactic research [ten]. Human error in medicine can range from diagnostic errors to medication errors and has a spectrum of associated cognitive mechanisms [xi, 12]. Zhang and colleagues describe a bureaucracy of the healthcare system that elucidates the office of the individual in the causation of error [thirteen]. Although most errors can be traced to actions (or inactions) of an individual, the root causes of errors go beyond a single private [11, fourteen]. Patient safety research has expanded to written report team interaction and collaborative decision-making [15, 16], the interaction of humans and technology [17–xix], organizational problems [v, 18], institutional functions and national regulations [20]. Even though medical mistake is rarely due to simply one of these factors, traditional patient safety inquiry has not used an integrated approach to study error. As illustrated by Reason'due south "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 upwards to allow an error to occur. Perhaps even more alarming is the recognition that virtually-misses might be more prevalent than previously supposed, because they remain unreported when events do non stop in whatever significant harm [17]. These events are corrected by "filters" in the arrangement, in the form of man operators that human action to rectify the results of previous events [17]. The question of whether such filters benefit patient safety needs to exist considered in terms of immediate and long-term consequences. In the brusque term, filters can be constructive in avoiding fault but do non address larger, system-broad 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 permit situations to echo.
I aspect of medical errors where such filters play a meaning part is that of misidentification. Incidents of misidentification or mislabeling are often caught prior to an adverse effect and are often not reported. The 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 agin effect. Some may feel that, if not identified past them, other fail-safes would have prevented an agin event down the line. Unfortunately, adverse events ordinarily 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 forbid future occurrences.
Despite increased recognition of the trouble 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 data about the patient presenting to the ED is oftentimes far from complete or reliable. In the ED, some patients are unresponsive, unconscious or otherwise incapable of communication [4, 22]. Some are as well sick or unstable to allow collection of this information in a timely style. Others communicate inaccurate information for a variety of personal and other reasons. Finally, some registration personnel noting existing patient demographic information in the database practise not reconfirm this information, in order to salve time.
The electric current study was conducted in the naturalistic environment of the ED in order to characterize the factors that compromise patient prophylactic at the signal of patient registration. Using ethnographic techniques of observation and interviews, data were gathered in the adult ED of a big academic tertiary care hospital in New York City.
Groundwork
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 study in 1999 [v]. In this report, it was estimated that as many every bit 98,000 deaths per year result from various errors in hospitals. Critical intendance environments were identified as especially vulnerable to errors due to their dynamic and complex systems. In addition, the Harvard Medical Practice Study reported that approximately 1.5–3% of observed adverse events occurred in the emergency department [23].
A major issue of concern has arisen in the course of misidentification of patients at diverse stages of care. It has formed the basis of many analyses of errors and has been one of the main goals of patient safety movement in the past several years [twenty, 24]. A recent analysis of voluntary near-miss reporting showed that 11% of the near-misses at 54 neonatal intensive intendance units in the The states involved patient misidentification [25]. In addition, during a iii-twelvemonth study, the Veterans Affairs National Center for Patient Safety received more than than 100 reports of patient misidentification. Of these, 22% involved cases of medication assistants (Meet [25]). Unfortunately, the root causes of these errors, such equally the failure to read the chart or wristband and failure to obtain patient information correctly, accept not been adequately studied. It is of import to note that some of these errors occur despite consummate adherence to the policies and procedures for proper identification of patients. A national sample of 712 hospitals reported 5.v% error rates for mislabeling and misidentification of patient's wristbands. Of these, approximately half the cases involved missing wristbands, while the other half included more than one wristband with alien information (18.3%), wristbands with incomplete information (17.v%), erroneous data (viii.six%), or illegible data (five.7%). Less than one percent of cases involved patients wearing wristbands with some other patient's data [26].
Of note, cases of patient misidentification are highly under-reported, rarely appearing in incident reports. This may be partly due to the fact that these errors are frequently defenseless before turning into agin events and are therefore not considered of import for reporting [4]. Furthermore, this lack of reporting may be caused by the fear of reproach or embarrassment [27, 28].
Theoretical Framework
The theoretical framework that guided this research is the theory of distributed cognition. Distributed cognition is a scientific bailiwick in cognitive science that is concerned with how cognitive activeness is distributed across internal (human cognitive processes) and external cognitive artifacts (computers, telephones, charts), members of groups, and space and time [29–33]. Distributed cognition provides a unique framework to describe the interactions, processes, and noesis structures critical to cognitive tasks in the healthcare environment. It allows researchers to focus on complex advisory, social, organizational, and cognitive issues involved in team interactions with information technologies within a distributed, collaborative surround. The core unit of analysis is the functional system composed of human being and artificial agents and their relations, which are distributed across time and infinite [32–34]. Distributed cognition tin lead to discoveries concerning how interactions betwixt agents are coordinated, how artifacts and tools are actually used, and how errors occur every bit a result of interactions among the components of the system.
The ED is a prototypical distributed cognitive system. ED personnel have to manage many patients at the same fourth dimension, constantly prioritizing tasks and dividing their attention between all their patients. They must interact with paper and electronic artifacts, other ED personnel and consultants while also managing interruptions, dealing with time pressure and stress, and communicating information to personnel at the next shift. Human errors in a distributed, dynamic surround such as the ED are viewed as products of cognitive activities in people'south adaptation to their complex physical, social, and cultural environments.
Every bit mentioned earlier, little attending has been paid to the offset step in the process of patient identification, namely, registration [27]. Every bit the information point, these errors occur more oft than one expects and certainly more often than are reported. Therefore, the aim of this report is to analyze a similar set of errors and look more than closely at the underlying causes from the perspective of distributed cognition. In our previous piece of work, we developed a model for the function of interruptions, multitasking and handoffs as critical factors contributing to medical errors [10]. The focus of the current study is on specific cases of errors that occurred as a upshot of multiple factors in the distributed cognitive system. The goal is to determine the root causes of these errors and the processes in the distributed cognitive system, the procedures and the mitigating circumstances that led to these errors.
Methods
Study Setting
The study site is an developed urban ED in the Washington Heights section of northern Manhattan and has an almanac census of approximately 70,000 patients. The population is 61% Hispanic, 18% African American, and 17% White. Xx-five per centum of patients go far via ambulance. The admission rate for patients seen by a provider is 23%. The ED is staffed with attention emergency physicians and residents. The hospital's Institutional Review Lath approved the study and measures were taken to preserve the confidentiality of the data sources and patients.
Data Drove
Data were nerveless using ethnographic techniques of observations and interviews. Brusque-term observational studies focus on grouping behaviors and findings of a brusque-term qualitative study are presented based on the recorded observations [x]. Detailed, semi-structured interviews were also conducted to supplement the observation data [10].
Observation
The key aspect of qualitative research is the observation of phenomena in natural settings. During a period of 3 months, 2 researchers (i a doctor) observed the activities unfolding in the emergency department and recorded their observations at different representative times of the day. In order to be as objective as possible, the physician researcher was non responsible for patient care and did non have part in the conclusion-making process. Observational data were collected in club to provide insight into the cognitive workflow, data transfer and decision-making processes in the ED [35, 36]. Each set of observations occurred for approximately three hours, usually in two dissever shifts (morning and afternoon).
Interviews
Interviews were guided conversations where open up-ended questions were asked. New questions were allowed to arise as a effect of the discussion. The prepared questions focused on 2 broad themes including the general policies and procedures within ED registration, and the coping machinery of the registration staff with regard to handling the large number of patients and timely processing of incoming cases.
Data Assay
Observational and interview data were afterward analyzed. Commencement, a basic workflow model of ED registration was synthesized. Each mistake was analyzed individually in order to trace the origin of the errors and, more importantly, the point at which the ideal workflow of the registration broke down, resulting in the occurrence of the errors. This was achieved by thorough analysis of the ascertainment information and the record-recorded activities at the point of detection of the error and through the encounter with the patient. The observational data was after supplemented with the interview data in order to form a comprehensive view of the class of events. Furthermore, the policies and procedures transmission for ED registration was reviewed in society to fully empathise the safeguards in identify confronting such errors. Interviews with members of the ED registration staff were aimed at gaining an understanding of the workarounds and shortcuts taken by the staff in order to proceed speedily of the volume of patients.
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 past the filters within the organisation. As discussed in the following sections, these incidents of fault establish cases of near-misses that could take had potential adverse impact on the patients. All errors, except for case two, were discovered and subsequently corrected during ane three-hr observation session on May 28, 2005 (case two was discovered in an earlier shift that 24-hour interval). It is of import to note that the actual initiation and incremental proliferation of these errors were on-going processes that occurred upward to about 24 hours earlier discovery.
Example one
This example was brought to the attention of the physician by the ED nurse. On examining the patient'due south record, the nurse realized that the patient had 2 medical tape numbers. The ED nautical chart, all tests and prescriptions for this ED visit were under one medical record number while the patient'south previous medical records were nether another number. This error was caused by the failure of the initial registration clerk to obtain the required identification information. The patient was assumed to be new to the hospital and a new medical tape number was generated. As a result, none of the prior medical information was available to the dr. for this visit. Upon detecting the problem, the physician contacted the registration clerk and the two record numbers were combined.
Instance 2
Ms. G was a 22-twelvemonth-former meaning adult female who came to the ED for a fetal ultrasound. While being interviewed, it was discovered that the patient had been treated at the infirmary two days earlier and had some tests done. However, none of this data was nowadays on the hospital'southward online medical information organization under her proper name or medical record number. Upon further investigation, it was discovered that she had been given ii medical record numbers, and that the test results were under the second number.
Case three
A 20-year-erstwhile adult female with sickle jail cell disease came to the ED with symptoms of bacterial pharyngitis. The patient was prescribed a five-day course of the antibiotic, azithromycin. The doctor discussed the medication and the follow-up plan with the patient. He told her that a nurse would phone call her to check on the condition of her illness. Upon inspecting her prescription, the patient noticed that the address recorded was incorrect. Investigation revealed that the patient was never given a registration form to complete upon arrival to the ED. Since the patient was plant to accept existing data in the hospital computer system, she was not asked for updated data. After being prompted past the medico, the registration clerk updated the patient's demographic data.
Instance four
A immature woman was brought into the emergency department after a motor-vehicle accident. The patient was fully witting and communicative. While at the patient's bedside, the attending and training physicians undertook a word of her example. During this period of discussion, the patient interrupted the attending-resident chat to note that she was not the patient of whom they were speaking. The physicians referred to the patient's chart and the wristband and discover that the name on both the chart and the wristband were incorrect. Information technology was discovered that two patients were brought to the ED at approximately the same time with the outcome that the registration staff mismatched their identities. Corrections were fabricated to the ED charts and wristbands of both patients. Fortunately, no adverse effects resulted from this error.
Summary of Results
During the course of the observations, 4 separate types of errors were discovered. All four cases were adamant to take originated during the registration procedure. In each instance, the mistake was eventually caught and no adverse events occurred. All the same, these results represent four cases of "most-miss" that may have resulted in negative outcomes for the patients, if non caught in time.
Discussion and Commentary
Errors in Distributed Cognition
The procedure of ED registration involves numerous agents and artifacts, including the registration clerks, the patients, the European monetary system 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 cloth of interactions. As a testament to the tremendous workload of ED registration, the registrars must collect several pieces of data on each patient while processing an average of 200 patients/day. In addition, the registration clerks have different avenues via which they can attain their goal - retrieval of the patient'southward demographic information (fig. 1). Most often the information is provided past the patient directly. On other occasions, when the patient cannot provide the information, information technology is obtained through a third party - a relative or caretaker, or, in some cases, the EMS personnel. Third party information collection creates a susceptibility to breakdown of communication and potential errors (Stride 1). The retrieved patient information is subsequently entered into the registration computer system (Pace 2). The information entered at this level consists of a prepare of data required for proper patient identification. It is of import to note, however, that not all patients possess or are willing to share the required set of information necessary for registration. In such cases, the patient is admitted with little or no identifying information. This is one of the major factors in introduction of errors into the process of registration. Furthermore, the computer system is designed to run the patient information against the infirmary database to find if the patient has any previous records in the hospital. If then, the patient is registered nether the existing medical record number. However, if no matching information is institute, then the patient is given a new tape number and is registered every bit a new patient. Once registration information is entered, ED registration procedure dictates that the information is read dorsum to the patient and the patient is asked to confirm the data. Finally, the registration clerk imprints the paper ED chart with the patient'southward data and creates a patient wristband. The registration clerk must return to the patient, reconfirm his identity and apply the wristband to the patient (Step 3).
The dotted lines represent the points of communication that are prone to error.
A Coherent Theme
The errors discussed above represent a unified theme and source of mistake, namely ED registration. Our observations and interview information revealed the nature of ED registration as i of loftier stress and high patient volume surroundings where individual patients must be processed quickly and efficiently [37]. As such, the registration staff is often interrupted and need to constantly reprioritize their set of tasks in lodge to meet the changing demands of their work surroundings [4]. This results in a considerable amount of multitasking, and a consequently loftier level of cognitive load on the staff. In addition, in lodge to process the patients in the shortest time possible, staff members tend to prefer a number of workaround protocols [22, 38]. It should exist noted that study observations took place only during morning and afternoon shifts, although the types of errors described in this paper could occur during whatsoever shift. As we have pointed out, times of work overload and high patient volume promote increased error rates.
As is the case with all errors of this nature, the responsibleness for the error cannot be entirely placed at whatsoever one point [xi, 14]. Although it is ultimately the omission by one individual that results in the cosmos of the fault [thirteen], information technology is often a result of the overall organization failure particularly that of the cerebral workload placed upon an individual intendance provider at a point in time [eleven, 22, 38]. Similarly, the discovery and subsequent correction of these errors consequence from the so-called "Swiss Cheese" model where, in most cases, the errors are finally caught and rectified past the organization [11]. Equally such, these errors are called "about-miss" events because they are defenseless earlier resulting in an adverse upshot [39].
As figure one indicates, each error represents a breakdown in the overall distributed cognitive system that defines the registration desk-bound and the emergency room. Cases ane and two represent possible failures in communication and/or information entry into the computer (Pace 1: figure. 1). The fact that the patients were given multiple record numbers suggests two possible scenarios: 1) information technology is possible that the registration clerk failed to get all the necessary information from the patients or their caretaker/EMS personnel. Equally a result, wrong or incomplete data was entered into the reckoner, resulting in the failure of the arrangement to recognize that the patient had visited the infirmary on a prior occasion; 2) the other possibility is that while the information was correctly obtained from the patient, information technology was subsequently entered into the figurer incorrectly. Interviews with the registration staff revealed that the arrangement can only match records if the content is an exact match. Otherwise, the patient is given a new record number. In either scenario, when the computer compared the entered information with the infirmary database, no matches were plant and the patients were consequently assigned new tape numbers.
On the other mitt, the tertiary case is a clear instance of the failure of the registration staff to update patient data, by failing to ask for any changes with regard to the patient's data. As a result, the modify in address was never noticed until the patient realized that the accost on her file was an old one (Step 1).
In the outset three cases of error (two cases with multiple medical record numbers and one case with wrong patient accost), it tin can be inferred that the problem had arisen due to an incompatibility between the skills of the staff and the computer system used for registration [40]. This fact was reflected in the interviews with the registration staff, all of whom expressed their frustration with the system and the fact that "the system was too exact and not user-friendly". In two cases (Cases ane and 2), although the patients had already been to ED on prior occasions, ED registration had assigned them a new medical tape number. As such, although all of their previous medical information including the laboratory and other test results were present within the organization, the physicians were unaware of their existence. In the third instance, although the patient had just 1 record number, the registration staff did non reconfirm the patient's demographic information on the second visit and hence omitted to update the patient's address and telephone number. It was stated during the interviews that when patients mention that they have previously visited the ED, their data is oft non updated in order to salve time. As evident in these cases, the procedure of shortcuts and workarounds, combined with the inherent difficulty in the functionality of the computer system, results in the occurrence of errors and potentially adverse events.
In these cases, it is possible that improvements in the computer system as well as better preparation of the staff to utilise the organization can result in pregnant reduction of such errors. Indeed, if the computer systems used at ED registration are as difficult to piece of work with as expressed by the registration staff, then the engineering is actually facilitating the errors rather than preventing them [40].
The fourth example of registration fault is of a different and conceivably more serious nature. In hindsight, information technology appears that this error resulted from the fact that two patients with similar physical and demographic characteristics and complaints arrived during the same, high volume time. The registration clerk generated the proper ED charts with right information merely, in the blitz to process the charts through ED registration, wrong name stamps were placed on the charts and the patients were incorrectly wrist-banded (Stride 3). A failsafe exists to prevent this type of error. Whatever personnel applying a wristband to a patient is required to identify the patient by ii means. In this scenario, this process was apparently not followed and it required another intervention (in this example, past the patient) to correct the error. This is clearly a cognitive mistake resulting from the high volume of patients, the constant distractions and, more importantly, the tendency of ED registration staff to work around the safety protocols established to avert precisely this sort of fault. It is also important to note that although in this particular case the error was identified by an circumspect warning patient, this is not always the instance. The patient may be unresponsive or uncooperative. On the other hand, at times, even confirmation with the patient may non be plenty of a failsafe. Rosenthal reported the case of an anxious patient who mistakenly confirmed the incorrect name, incorrect diagnosis and wrong procedure, as the nurse read the information to her, hence creating a state of affairs of "Uninformed Consent" [27, 41]. In such cases, boosted steps must exist taken to ensure patient prophylactic.
Whereas in the previous cases, the use of engineering could event in significant improvements, in this case technology comeback could just provide a partial solution. The upshot here is the work demand pressuring the registration personnel to cut corners. The primary solution is to accost the intense piece of work environment. The employment of more than registration personnel would reduce the volume of work and promote adherence to policies and procedures with less need for workarounds. The implementation of wireless computers to do bedside registration would encourage the registration personnel to concentrate on one patient at a time and would probable reduce mistake.
Implications
The ED is a unique clinical environment and requires distinctive solutions to accost the workflow issues that contribute to the occurrence of medical error. Although long-term solutions must be sought to reduce the root causes of fault, the adaptive beliefs of the human components of this arrangement must likewise exist bolstered. Although the adoption of technology may benefit the ED, the results of this study suggest that the existing generic electronic tools lone may be ill-suited for this environment. These tools must be tailored to support adaptive processes similar multitasking and handoffs that occur in a time-constrained surroundings [10]. In study interviews, the registration staff expressed a consistent pattern of frustration with the existing applied science. In every interview, the system was referred to as "likewise exact", "not user friendly" and "difficult". This suggests that steps must be taken to amend the usability of the computer system, coupled with proper preparation of the staff, in order to foreclose errors of this nature. It is vital that usability studies be conducted in order to fully understand the nature and extent of this problem [42].
Other important issues arising from this study are the trouble of ED over-crowding and the loftier patient/staff ratio. These issues are especially meaning at ED registration - a crucial component of ED functioning. Subsequently, any error created at this level would propagate for as long every bit it goes undiscovered. It is therefore vital that attending be paid to these issues and considerable effort must be made at the policy level to increase the number of staff in ED registration.
Conclusions
As the initial stride of patient care in the ED, patient registration must exist both efficient and accurate. Failure to meet both of these goals can lead to agin 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 intendance. Inaccuracy in gathering information tin can lead to a myriad of errors, including lack of admission to existing medical records, inability to contact a patient after discharge and even implementation of an incorrect treatment regimen with dangerous consequences to the rubber of the patient.
Errors frequently occur, not equally a result of the failure of a unmarried entity inside the system, only every bit a result of the breakdown of the system [13]. These failures have many underlying causes, including but not express to the heavy patient volume, the need to multitask and the inevitable trend to work around the established procedural codes [10]. Whereas in some cases technological solutions can improve the state of affairs, in others, they could very well lead to more errors or automate the errors that exist within the arrangement.
Acknowledgments
This research was supported by grant R01 LM07894 from the National Library of Medicine to Vimla Patel. We give thanks the nurses, doctors and the administrative staff at the New York Presbyterian Hospital - Columbia University Medical Centre Emergency Section for their participation and support of this study. Special thank you are extended to the subjects who participated in the interview portion of our project.
Footnotes
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References
1. Croskerry P, Shapiro One thousand, Campbell S, LeBlanc C, Sinclair D, Wren P, Marcoux M. Profiles in Patient Safety: Medication Errors in the Emergency Department. Acad Emerg Med. 2004;11(iii):289–99. [PubMed] [Google Scholar]
2. Kovacs G, Croskerry P. Clinical decision making: an emergency medicine perspective. Acad Emerg Med. 1999;half-dozen(9):947–952. [PubMed] [Google Scholar]
3. Brixey JJ, Robinson DJ, Johnson CW, Johnson TR, Turley JP, Patel VL, et al. Towards a hybrid method to categorize interruptions and activities in healthcare. Int J Med Inform. 00:000–00. in press. [PMC free commodity] [PubMed] [Google Scholar]
5. Kohn LT, Corrigan JM, Donaldson MS, editors. To err is human: Building a safer health system. Washington, DC: National Academies Press; 2000. [Google Scholar]
six. Alvarez Thou, Coiera E. Interruptive communication patterns in the intensive care unit ward round. Int J Med Inform. 2005;74:791–half-dozen. [PubMed] [Google Scholar]
7. Anderson JG, Ramanujam R, Hensel D, Anderson MM, Sirio CA. The need for organizational change in patient safety initiatives. Int J Med Inform. 2006;75(12):809–817. [PubMed] [Google Scholar]
8. Ash JS, Sittig DF, Dykstra RH, Guappone K, Carpenter JD, Seshadri 5. Categorizing the unintended sociotechnical consequences of computerized provider social club entry. Int J Med Inform. 00:000–000. in press. [PubMed] [Google Scholar]
ix. Balka Due east, Doyle-Waters Grand, Lecznarowicz D, FitzGerald JM. Technology, governance and patient safe: Systems issues in technology and patient safety. Int J Med Inform. 00:000–000. in press. [PubMed] [Google Scholar]
ten. Laxmisan A, Hakimzada F, Sayan O, Green R, Zhang J, Patel V. The multitasking clinician: Decision-making and cognitive demand during and after squad handoffs in emergency intendance. Int J Med Inform. 00:000–000. in press. [PubMed] [Google Scholar]
eleven. Reason JT. Human being mistake. Cambridge, England: Cambridge Academy Printing; 1990. [Google Scholar]
12. Beuscart-Zéphir M-C, Pelayo S, Anceaux F, Maxwell D, Guerlinger Southward. Cognitive analysis of physicians and nurses cooperation in the medication ordering and administration process. Int J Med Inform. 00:000–000. in printing. [PubMed] [Google Scholar]
13. Zhang J, Patel VL, Johnson TR, Shortliffe EH. A cognitive taxonomy of medical errors. Journal of Biomedical Computer science. 2004 Jun;37(3):193–204. [PubMed] [Google Scholar]
xiv. Woods DD, Cook RI. National Patient Condom Foundation (U.S.) From Counting Failures to Anticipating Risks: Possible Futures for Patient Safety. In: Zipperer LA, Cushman South, editors. Lessons in patient safety. Chicago, IL: National Patient Prophylactic Foundation; 2001. p. xiv.p. 134. [Google Scholar]
15. Patterson ES, Roth EM, Wood DD, Chow R, Orlando J. Handoff strategies in settings with loftier consequences for failure: lessons for health care operations. International Journal for Quality in Health Care. 2004;16(two):125–32. [PubMed] [Google Scholar]
xvi. Patel VL, Cytryn KN, Shortliffe EH, Safran C. The Collaborative Wellness Care Squad: The Role of Private and Group Expertise. Instruction and Learning in Medicine. 2000;12(3):117–32. [PubMed] [Google Scholar]
17. Laxmisan A, Malhotra S, Keselman A, Johnson TR, Patel VL. Decisions about critical events in device-related scenarios equally a part of expertise. J Biomed Inform. 2005;38(3):200–12. [PubMed] [Google Scholar]
18. Bates DW, Cohen Thousand, Leape LL, Overhage JM, Shabot MM, Sheridan T. Reducing the frequency of errors in medicine using it. J Am Med Inform Assoc. 2001;viii(4):299–308. [PMC costless article] [PubMed] [Google Scholar]
19. McAlearney Every bit, Chisolm DJ, Schweikhart S, Medow MA, Kelleher Chiliad. The story behind the story: Physician skepticism most relying on clinical information technologies to reduce medical errors. Int J Med Inform. 00:000–000. in press. [PubMed] [Google Scholar]
22. Pape T. Applying airline safety practices to medication administration. Medsurg Nurs. 2003;12:77–93. [PubMed] [Google Scholar]
23. Leape LL, Brennan TA, Laird NM, Lawthers AG, Localio AR, Barnes BA, et al. The nature of adverse events in hospitalized patients. Results of the Harvard Medical Practice Study Two. Northward Engl J Med. 1991;324(6):377–84. [PubMed] [Google Scholar]
25. Suresh G, Horbar J, P P, et al. Voluntary anonymous reporting of medical errors for neonatal intensive care. Pediatrics. 2004;113:1609–xviii. [PubMed] [Google Scholar]
26. Renner SW, Howanitz PJ, P B. Wristband identification fault reporting in 712 hospitals. A College of American Pathologists' Q-Probes study of quality bug in transfusion practice. Arch Pathol Lab Med. 1993;117:573–vii. [PubMed] [Google Scholar]
28. Osborne J, Blais K, JS H. Nurses' perceptions: when is it a medication mistake? J Nurs Adm. 1999;29:33–8. [PubMed] [Google Scholar]
29. Hutchins Eastward. How a cockpit remembers its speed. Cognitive Scientific discipline. 1995;19:265–88. [Google Scholar]
30. Norman DA. Cognitive Artifacts. In: Carroll JM, editor. Designing interaction: Psychology at the human-estimator interface. New York: Cambridge University Press; 1991. [Google Scholar]
31. Zhang J. A distributed representation approach to group trouble solving. J Am Society Inform Sci. 1998;49(9):801–9. [Google Scholar]
32. Zhang J, Norman DA. Representations in distributed cognitive tasks. Cog Sci. 1994;18(1):87–122. [Google Scholar]
33. Cohen T, Blatter B, Almeida C, Shortliffe EH, Patel VL. Distributed cognition in the psychiatric emergency department: A cerebral blueprint of collaboration in context. Artif Intell Med. 2006;37(2):73–83. [PubMed] [Google Scholar]
34. Hutchins E. Knowledge in the wild. Cambridge, MA: MIT Printing; 1995. [Google Scholar]
35. Patel VL, Kaufman D, Magder SA. The Road To Excellence: The Conquering Of Expert Performance In The Arts And Sciences Sports And Games. Hillsdale, NJ: Lawrence Erlbaum publishers; 1996. The acquisition of medical expertise in complex dynamic medical environments; pp. 127–65. [Google Scholar]
36. Strauss A, Corbin J. Basics of qualitative inquiry: Techniques and procedures for developing grounded theory. 2. Thousand Oaks, CA, U.s.a.: Sage Publications, Inc; 1998. [Google Scholar]
37. Morrissey J. Quality vs. quantity. IOM report: Hopsitals must cutting back workload and hours of nurses to maintain patient prophylactic. Mod Healthc. 2003;33:8–11. [PubMed] [Google Scholar]
twoscore. Coiera E. Interaction design theory. Int J Med Inform. 2003;69:205–22. [PubMed] [Google Scholar]
41. Chassin M, Becher Due east. The wrong patient. Ann Intern Med. 2002;136:826–33. [PubMed] [Google Scholar]
42. Kushniruk AW, Patel VL. Cognitive and usability engineering methods for the evaluation of clinical data systems. J Biomed Inform. 2004;37(1):56–76. [PubMed] [Google Scholar]
In Which Healthcare Setting Is A Patient Not Registered Prior To Receiving Care?,
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2259219/
Posted by: shipmanthermser.blogspot.com
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