Identification and Management of Information Problems by Emergency Department Staff Alison R. Murphy, BS, Madhu C. Reddy, PhD The College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA ABSTRACT Patient-care teams frequently encounter information problems during their daily activities. These information problems include wrong, outdated, conflicting, incomplete, or missing information. Information problems can negatively impact the patient-care workflow, lead to misunderstandings about patient information, and potentially lead to medical errors. Existing research focuses on understanding the cause of these information problems and the impact that they can have on the hospital’s workflow. However, there is limited research on how patient-care teams currently identify and manage information problems that they encounter during their work. Through qualitative observations and interviews in an emergency department (ED), we identified the types of information problems encountered by ED staff, and examined how they identified and managed the information problems. We also discuss the impact that these information problems can have on the patient-care teams, including the cascading effects of information problems on workflow and the ambiguous accountability for fixing information problems within collaborative teams. INTRODUCTION Hospitals are highly collaborative, information-intensive environments where patient-care teams rely on the accuracy and availability of information to provide safe and effective patient care. However, hospital staff frequently encounter information problems. In this paper, information problems are defined as any wrong, outdated, conflicting, incomplete, or missing information that may interfere with the ability of hospital staff to do their work. Although these information problems have always existed in paper records,28 there is an increasing need to focus on information problems in electronic records due to the tremendous growth in the use of electronic health record (EHR) systems. Due to recent U.S. government legislation, there has been an acceleration in the transition from paper-based records to EHR systems. These electronic systems also include the use of other information technology systems that can be integrated with EHRs, such as computerized provider order entry (CPOE) systems.25,26 Although these EHRs can provide a number of benefits,3,7,11,24 the use of EHRs do not necessarily eliminate information problems, such as wrong, outdated, conflicting, incomplete, or missing information.3 In some cases, EHRs can actually introduce new types of information problems,3,7 such as the unintentional selection of default values2 and the truncation of data entry fields resulting in the loss of patient data.23 These information problems can lead to issues among the patientcare team including ambiguity about what treatments/procedures were done to a patient,3 medical decisions being made based on wrong or outdated information,16 and even the occurrence of medical errors that could harm patients.3 Current medical informatics research focuses primarily on what causes these information problems within hospitals2,3,6,7,11,16,23,24,29 and the impact that the information problems have on the workflow of hospital staff.1,3,4,8,9,21 However, there is still limited research that explores how these information problems are identified and managed by the patient-care teams who encounter them during their daily work. If they are not properly identified and managed, there may be serious consequences such as medical errors that harm the patient. Therefore, the goal of this paper is to provide a better understanding of the types of information problems encountered by emergency department (ED) staff, and how the staff identified and managed information problems within a highly collaborative emergency department. We will also discuss the impact that information problems have on collaboration, including the cascading effects of information problems on workflow and the ambiguous accountability for fixing information problems in collaborative teams. BACKGROUND Information problems have been extensively studied in the medical informatics community because of the serious impact that they can have on medical errors in patient care. These existing studies primarily focus on the causes of information problems, which include information problems caused by the design of EHR systems3 and by the users
1845
of EHR systems.7 Additionally, researchers also discuss the impact that information problems can have on the workflow of hospital staff. Information Problems Caused by EHR Design It is a challenge to build information systems for the fast-paced and information-intensive environment of hospitals. Researchers describe how EHR systems tend to be overly structured and designed with rigid rules that encourage data standardization (e.g., drop-down menus, text entry restrictions), which can lead to information problems caused by the design of the EHR.3 Koppel et al. 11 discuss how system design can cause problems with medication ordering, including: fragmented displays that prevented a coherent view of patients’ medications, inflexible ordering formats that led to wrong orders, and separation of system functionality that resulted in double dosing or incompatible orders. Abramson et al.2 also describe how a medication ordering system automatically selected the default dosing in medication order forms resulting in inaccurate medication requests. Additionally, Dillion & Lending6 also identify information problems caused by systems preventing users from entering descriptive data into a record and, instead, forcing them to select values from drop-down menus that are not considered intuitive to the user. EHR design can also prevent the entry of important psycho-social information about patients, which nurses argue, “provided continuity of care…[and] a richer picture of the patient’s situation” (p. 2065).29 Furthermore, EHR researchers describe system bugs that cause information problems. This includes EHR systems that truncate data entry fields resulting in lost patient data and EHR systems where two buttons on a screen have the same label but different functionalities, which caused information problems within the system.23 These studies highlight how EHR design can lead to information problems that users must manage. Information Problems Caused by EHR Users Users are also the cause of certain information problems within EHRs. These problems can include information entry errors, delayed information entry, and discrepancies between multiple sources of information. Researchers have often described how users tend to copy-and-paste information from prior notes in the system in order to cut down on their data entry efforts.3,7,22 However, as Embi et al.7 highlight, this can lead to outdated or incorrect information being proliferated throughout the system. Siegler & Adelman22 also discuss how the copy-and-paste function leads to, “reducing the credibility of the recorded findings, clouding clinical thinking, limiting proper coding, and robbing the chart of its narrative flow and function” (p. 495). Other researchers describe how delayed information entry occurs when clinicians are too busy or tightly scheduled to enter patient data into the system directly after seeing the patient, which leads to information in the EHR being outdated or incomplete for extended periods of time.16 This negatively affects any other patient-care team member who needs access to updated, accurate patient records. As Ash et al.3 describe, delayed information entry could result in the patient being given the same medication twice by another member of the patient-care team who relies on the patient record for information. Furthermore, information discrepancies are another issue that can occur during the use of EHR systems. Turchin, Shubina, & Goldberg24 discuss how EHR users encountered situations where medication information provided in the system’s structured fields (e.g., medication name, dosage) contradicted information found in the free-text description field. Therefore, these studies highlight how information problems can also be caused by the users themselves. Impact of Information Problems on Workflow Within a hospital, a number of clinical and non-clinical staff members must work together to gather, document, and share information in order to provide effective patient care. This workflow of activities is highly collaborative, complex, and prone to interruptions. This is especially true in fast-paced hospital environments like the emergency department where patients require immediate attention. However, it is important that whatever can negatively affect the hospital workflow is minimized in order to reduce impacts to the patient-care process. Medical informatics researchers have studied how information problems can have a negative impact on the hospital workflow. Researchers discuss how the design of EHR systems can increase the time it takes to perform workflow activities. Holden8 discusses how issues with information accessibility (e.g., system login, system response time) can negatively impact the workflow of physicians. Additionally, Shachak et al.21 describe how there are communication interruptions when physicians try to navigate EHR systems to find or enter patient information while also talking with their patients. This can lead to inaccurate information being entered into the system. Other researchers also describe the additional time and effort spent finding and updating information in multiple systems or on different screens.1,9 The time and effort that users often do not have.
1846
The existing medical informatics literature on information problems describes what causes information problems and the impacts that information problems can have on workflow. In this paper, we seek to extend this understanding to include how patient-care teams identify and manage the information problems that they encounter during their daily work. METHODS Research Site We conducted this study in the emergency department of a large teaching hospital in northeastern United States. The ED has approximately 55,000 visits per year. The first author conducted 54 hours of observations and 4 hours of semi-structured interviews with 7 clinical and non-clinical staff in the ED. We observed approximately 85 ED staff members (Table 1). Table 1. Observed ED Staff ED Staff Role
Number Observed
Nurses (including Charge Nurses) Registration Assistants Physicians Residents Emergency Medical Technicians (EMTs) ED Technicians Transporters ED Volunteers Chaplains (spiritual advisors) Sanitation/Cleaning Care Coordinators Social Workers Maintenance Pharmacists TOTAL
18 14 12 10 6 4 4 4 3 3 2 2 2 1 85 TOTA
The ED staff communicated and received information using a variety of sources, including: verbal communication, cell phones, pagers, desktop computers, laptops, computers-on-wheels (COWs), paper documents, white boards, and mounted electronic screens that displayed the “tracking board” (i.e. a chart of ED rooms with patient information and the assigned ED staff). The ED staff primarily used an electronic health record (EHR) system to document patient information. This system included the patients’ medical records, laboratory and medication orders, laboratory results, clinical team narratives, registration information, and tracking board. One group of users, registration assistants, also used an Admissions Discharge and Transfer (ADT) system that was integrated with the EHR to document information when patients first arrived in the ED. This ADT system created the patients’ unique identification number and included their name, date of birth, social security number, zip code, and complaint (i.e. symptoms, why they were there). Data Collection In this study, we used qualitative data collection methods including observations and semi-structured interviews. We used this methodological approach because qualitative research includes an immersion into a field site, which allows the researcher to observe the naturalistic processes and activities of the participants.12 Our ED observations resulted in detailed descriptions of participants’ behaviors and interactions related to information problems. These problems were situated within the context of the participants’ busy ED setting and everyday work activities.13 These qualitative methods have also been used in other informatics studies for a similar purpose.3,4,19 The first author conducted 54 hours of observations in two ED areas that had the largest amount of communication and information exchange among the staff: the registration area and the main nurses’ station. We also observed the ED staff in hallways and at smaller nurses’ stations. Detailed field notes were taken about the workflow,
1847
communication, collaboration, and technology used by both clinical and non-clinical staff. During the observations, the first author also had short, informal discussions with participants in order to clarify assumptions or gain additional information about specific situations. The field notes were then transcribed into an electronic document for analysis (Table 2). The first author also conducted 7 semi-structured interviews in order to better understand the ED staff’s roles and responsibilities, as well as their use and perceptions of the EHR system. The first author interviewed 1 physician, 1 registered nurse, 3 registration assistants, 1 care coordinator, and 1 social worker. The interview data was then transcribed into an electronic document for analysis (Table 2). Table 2. Data Collection Method, Participants, and Data Method
Focus
Hours
Participants
Transcribed Data
Observations
ED Workflow
54 hours
85 staff
175 pages
Semi-structured Interviews
ED Staff
4 hours
7 staff
28 pages
Data Analysis The transcriptions resulted in 203 pages of data. This data was analyzed by the first author using Braun & Clarke’s5 six-phase thematic analysis approach (Table 3). This approach facilitates the process of becoming familiar with the data, systematically identifying codes and themes, and then defining and naming the common themes found across the entire data set. The analysis resulted in three main themes, as described in the results section. Table 3. Braun & Clarke Six-Phase Thematic Analysis Approach Phase (1) Familiarizing ourselves with the data
Description Transcribe the notes taken during observations/interviews and read through the transcriptions to ensure a general understanding of the data.
(2) Generating initial codes
Label segments of data in a systematic way across all of the data.
(3) Searching for themes
Review individual codes and identify preliminary themes.
(4) Reviewing themes
Review preliminary themes to ensure that they make sense across the entire data set.
(5) Defining and naming themes
Continuously refine each theme, identify a specific name for each theme, and define the boundaries of the theme.
(6) Producing the report
Present themes with interesting examples from the data that illustrate the individual themes.
RESULTS Through our data analysis, we identified three main findings: the types of information problems encountered by ED staff, how the staff identified the information problems, and how the staff managed the information problems. Types of Information Problems We identified the following information problems in the ED: wrong, outdated, conflicting, incomplete, and missing information (Table 4). These information problems identified in the ED are similar to information problems described by other researchers.3,11,16,29
1848
Table 4. Types of Information Problems in the ED Information Problem Type
Description
Example from Data
Wrong
Information is not accurate.
“For example, if you put allergies in the wrong spot, they’re put in the problems list instead of the allergies list – which happens – [and it led to] another physician making patient care decisions off of the wrong information.” [physician interview]
Outdated
Information is no longer accurate and has not been updated.
“We had a patient who was in here because of abuse injuries and the boyfriend was listed as the emergency contact from a previous visit. The boyfriend was called, but the boyfriend was the abuser. So this created a potentially dangerous situation for the patient and the hospital because the information was not updated.” [social worker interview]
Conflicting
Information that should be the same but it is not the same.
In the check-out area, a physician tells the registration assistants that his patient’s insurance had been stolen. He says that while scanning the patient’s registration information he realized something was wrong because the record said the patient’s race was listed as African American and the man was Caucasian. The patient then confirmed that the [EHR] record did not have his correct social security number. [observational field notes]
Incomplete
Only part of the information is provided.
“Some of my information about my patient does not get into the patient’s record because I can’t edit [in the system]. And there’s no place for me to put some of my notes about the patient, which could help others, like the next shift.” [social worker interview]
Missing
Information is not available.
In the registration area, a doctor is talking to an EMT and the family of his patient. He says that the patient is refusing to tell him anything about his medical conditions, medications, and what happened in his accident. The doctor says there is no existing record for this patient and he needs any information they can provide so he can proceed with his patient care plan. [observational field notes]
These information problems occurred quite frequently in the ED. Even though participants discussed the importance of ensuring information accuracy in their work, they also stated that certain problems, such as data entry errors or incompleteness, are often an inherent aspect of the highly dynamic hospital environment. A participant described this: "There’s always the possibility of errors, that’s just the nature of entering information. And you only know what you know. Sometimes you don’t have the whole story" [social worker interview]. Identification of Information Problems The ED staff recognized the information problems when the problems interfered with their ability to do work, and they were subsequently identified as information problems. Any of the information problems described in the previous section could have persisted within the EHR system if no one had identified the problem. Therefore, it is important to understand how ED staff identifies information problems because the information problem must first be identified before it can be managed or fixed by the team. The ED staff identified information problems in two ways: comparing the EHR system’s information to other information sources, and trying to make sense of information that did not “look right.” Comparing Sources: The ED staff frequently identified information problems when they were comparing the EHR system information to other sources of information. These other sources included other ED staff members, patients, visitors, or paper records. For example:
1849
A registration assistant identifies that a patient’s first and last names are switched in the system while she is talking with a visitor who requested to see the patient [observational field notes]. The registration assistant was comparing the EHR system to the information that the visitor was verbally providing about the patient when she identified this as an information problem. Additionally, a social worker identified an information problem when comparing the information in the EHR system to a patient’s statement: "The other problem with the system is people checking the wrong information. So the nurses will ask [patients] questions, like “has anyone ever forced you to have sex against your will?” One time a nurse checked the wrong box for that question, then I am called in [through a notification in the EHR system] to talk to the patient about it. When I addressed them about the sensitive topic, the patient became irate saying that they never said that and demanding that their record be changed” [social worker interview]. Furthermore, the participants also identified information problems when comparing the EHR information to something they were physically seeing. For instance, the care coordinator identified an information problem when she noticed that the patient’s pill color was not the same color that the patient normally received: “I had a patient who was elderly that had an INR of 16 and it should have been 3.5 at most. I went in to talk to the patient and she said, “the color changed” in reference to her pills. So I looked in the records and found that the drug she was taking was 5 times the amount she should have been taking. So [comparing the pill to] the record in the system helped me identify that there was an issue” [care coordinator interview]. Sensemaking: Participants also described identifying information problems when they encountered information that did not look right, which then caused them to think about why that information did not look right – or to make sense of the information. Sensemaking is “the process of encoding information into external representations to answer task-specific questions” (p. 322).17 When the ED staff tried to make sense of information that did not look right, they were questioning the accuracy of information based on their previous experiences, medical knowledge, or other training or skills that they have. The staff described this as a “gut instinct” or simply that the information just “didn’t look right.” The following two examples illustrate the identification of information problems while making ED staff was trying to make sense of the information: The care coordinator described a time when the EHR system showed that the diabetes patient was using a glucometer, but noticed that her “[glucose] numbers were off.” The care coordinator stated: “A lot of times I have a gut instinct, you know? It didn’t look right. So I talked to the patient and she said, ‘I had a glucometer but my son stepped on it’ so she wasn’t using one at home, even though her record said she was [using a glucometer]” [care coordinator interview]. A registration assistant (RA) questions another RA about something she had entered into the EHR system. The second RA said she could not read her handwriting from her notes when she was transcribing the information into the system, so the information in the system may be wrong. The first RA said: “Yeah, it just didn’t look right. So I had to check with you” [observational field notes]. In both of these examples, the social worker and the RA identified the information problem by making sense of the information and discussing it with a patient or other ED staff member. Management of Information Problems After the ED staff identified information problems, they then had to respond to the problem. The participants managed information problems in one of three ways: by fixing the problem, finding the right person to fix the problem, or finding a way to work around the problem. Fixing the problem: When the participants identified an information problem and knew how to correct the problem, they typically just fixed the issue in the EHR system themselves. This is exemplified in the following two scenarios: In the check-out area, a physician tells the registration assistants that his patient’s insurance had been stolen. He says that while scanning the patient’s registration information he realized something was wrong because the record said the patient’s race was listed as African American and the man was Caucasian. The patient then confirmed that the [EHR] record did not have his correct social security number. The physician says he then asked the patient for his correct information and entered it into the EHR system so that the patient could be billed correctly for the visit” [observational field notes].
1850
A registration assistant identifies that a patient’s first and last names are switched in the system while she is talking with a visitor who requested to see the patient. She then asks the visitor additional questions about the patient (date of birth, home address) to verify the patient’s identity and asks other registration assistants about the patient’s correct name. The registration assistant realizes that the first and last names of the patient are mistakenly switched, so she fixes the information error in the system [observational field notes]. Finding someone to fix the problem: There were other times where the participant who identified the information problem did not have the knowledge or the editing rights to fix the problem in the EHR system. For instance, in the previous example, a social worker realized that a nurse had checked the wrong box in the system after the patient verified that the system was incorrect. However, the social worker could not change the error in the system so she had to find the nurse to fix it: “And the issue with the system is that the person who entered it has to change it. So I have to track down the nurse to change it” [social worker interview]. Additionally, the social worker also described a time when the patient’s contact information was outdated and she needed to find someone who could fix the problem: “Since I can’t update it as a social worker, I try to call others to update it…I talk to registration, but they say that it’s the nurse’s responsibility, but when I talk to the nurse, they say it’s not their responsibility.” When asked if it was unclear whose responsibility it was to fix the problem, the social worker responded: “Well, it’s clear to them that it’s not their responsibility [laughs]! But it’s not clear whose responsibility it is” [social worker interview]. This highlights how the participants managed the information problem by finding someone who could fix the problem since the EHR system restricted her from being able to fix it. This example also highlights the ambiguity or conflicting opinions around who is accountable for the accuracy and completeness of patient information in an environment where information is collectively managed by a patient-care team. Working around the problem: The participants also found ways to work around the information problem so that they could continue with their task without having to fix the problem in the EHR system. For example: A registration assistant (RA) discusses with another RA how she reads the “comments” field before letting a visitor back to see a patient, since the nurses occasionally write notes in this field about whether a patient can have visitors. However, she also mentions that the field is not always reliable, so they usually call the nurse to double-check [observational field notes]. By calling the nurse, the registration assistant found a way to work around the unreliable “comments” field in order to continue with her task of directing visitors to patient rooms. In addition, the social worker described how the system does not allow her to enter all of her notes about the patient, so she described using a paper-based workaround: “Some of my information about my patient does not get into the patient’s record because I can’t edit [in the system]. And there’s no place for me to put some of my notes about the patient, which could help others, like the next shift. So I usually just write it on the [paper] face sheet or the next shift takes notes during our hand-off.” [social worker interview]. Therefore, these workarounds allowed the ED staff to continue with their work, even though it did not fix the information problem in the system. DISCUSSION The results describe the various types of information problems that ED staff encountered during their work activities, as well as some of the approaches that they took to identify and manage those information problems. Given the highly collaborative nature of hospital work, it is important to consider how these information problems affect the collaboration of patient-care teams. In this section, we discuss how the management of information problems can have a serious impact on collaborative hospital teams including the cascading workflow effects of information problem management and the ambiguous accountability for fixing information problems in collaborative teams.
1851
The Cascading Workflow Effects of Information Problems Patient-care teams are highly collaborative and team members rely on each other to provide accurate and up-to-date information about a patient. EHR systems provide a centralized view of a patient’s history and current status, which creates a shared awareness of that patient across the patient-care team. Therefore, when there is an information problem – wrong, outdated, conflicting, incomplete, or missing information – it does not just affect the one team member who identifies the problem. It affects the entire patient-care team who relies on that information to do their work. Therefore, the way in which hospital staff manage information problems can affect the entire team’s collaborative understanding of a patient. After identifying an information problem, hospital staff may manage the information problem by using a workaround.15 These workarounds are temporary solutions that allow users to adapt technologies or processes in order to minimize interruptions.27 Our study’s results describe how ED staff used workarounds as one way to manage information problems that they encountered during their work. Other researchers have also described how clinicians frequently perform workarounds when the EHR design interferes with their work18,20,30 or when there is a problem with the information in the system (e.g., missing, incomplete, outdated).2,10,11,23 Since the primary concern of patient-care teams is to take care of patients, the staff will find ways to perform their activities by working around EHR design or information problems. Additionally, the user may not go back to the system and fix the known issue.10 Not fixing the problem results in an information problem persisting in the record for an extended period of time. This is common for hospital staff to not immediately fix an information problem in the system because they are responsible for a number of patients and have a variety of urgent tasks.3,16 However, this can cause problems in highly collaborative environments because the effects of workarounds can directly impact other members of the patient-care team. This is especially true if the known information problem is not fixed and other members of the team are not made aware that the information problem exists. Kobayashi et al.10 discuss the “cascading effects” of workarounds where working around one issue can lead to the need for other workarounds. The authors’ discussion of cascading effects describes the impact that one person’s workaround can have on other team members’ work. Similarly, Saleem et al.20 state that the use of EHR workarounds, “introduces the potential for gaps in documentation as well as the unintentional propagation of errors” (p. 662). Therefore, the persistence of an information problem and lack of making others aware of the problem can affect the next team member who uses that information for his/her own work. So, although there may be perfectly valid reasons for hospital staff to enact workarounds to complete their own patient-care tasks, the effects of the workaround on the overall collaborative team should also be considered. For example, our study’s results described how the system restricted a social worker from editing the patient’s record, so she created a workaround by writing her patient notes on a printed patient report. Although the workaround allowed the social worker to record patient notes for herself, it also prevented any of the other patient-care team members from being able to see her notes in the EHR system. Her notes could help provide a more comprehensive view of the patient’s condition to other members of the clinical team, as seen in prior studies.29 Ambiguous Accountability When Managing Information Problems in Collaborative Teams In hospitals, patient information is co-owned and co-managed by multiple members of the patient-care team. This means that there is a shared responsibility for entering, updating, and maintaining the accuracy of the information. However, members of a collaborative patient-care team may have an ambiguous understanding and, at times, a conflicting opinion about who is responsible for managing the information. This can lead to the persistence of information problems because the team members either assume or believe that someone else is responsible for entering the information or fixing information problems. To help address this issue, collaborative organizations frequently enact formal policies to address accountability. Hospitals create formal policies to outline accountability guidelines for how staff should handle a variety of situations, such as managing information problems. Hospital staff are required to complete training on these policies and comply with the policies during their daily work. However, while some of the formal policies include very specific instructions, others are more general or vague in order to account for variances across different areas of the organization.14 The issue with these general policies is that the staff may have different interpretations of the policies when conducting their daily work. This can lead to an ambiguous understanding or a conflicting opinion about responsibilities, as discussed in the study’s results. Researchers have described how individuals interpret formal policies in their everyday work practices can vary from person-to-person, which causes conflict or tension within
1852
highly collaborative teams.14 Therefore, understanding how well aligned the formal policies are to the everyday work activities of collaborative teams may provide insight into the persistence of information problems. RESEARCH LIMITATIONS A limitation of this study is that the data was collected and analyzed by a single researcher. One researcher in a large ED setting is not able to observe all instances of information problems that occur. Being a single observer required the first author to make choices about who to observe and where to focus when many different activities were occurring simultaneously. However, since the goal of this work was not to present an exhaustive list of information problems, but rather to identify common information problems in the ED and how they were identified and managed by the hospital staff, we believe that we still were able to collect and analyze the data appropriately. CONCLUSION Information problems frequently occur in hospitals and can negatively impact the patient-care workflow and potentially cause harm to patients. This study classifies and describes the types of information problems that ED staff encountered during their work activities. Additionally, the paper addresses a limitation in existing medical informatics research by discussing how collaborative patient-care team members identify and manage these information problems. By better understanding and addressing the challenges of identifying and managing information problems within complex collaborative patient-care teams, hospitals could reduce the negative impact that information problems can have on hospital workflow and the patient-care process. Furthermore, as hospitals transition from paper records to EHR systems, it is important to consider the impact that EHR design has on information problems. EHR systems dynamically display the most recent patient information in a digital format that can be viewed from a variety of distributed locations (e.g., computers, laptops, mobile devices). This differs from paper records that include static information with visual cues indicating who updated the record (e.g., signature, initials, handwriting) and when it was updated (e.g, date, change in ink, new sheet of paper).28 This shift from paper to electronic documentation changes the way that patient-care teams enter, view, and share information and how they do their work. Therefore, given the serious effects that information problems can have on patient care, it is important to more closely examine the design of EHR systems and the impact that they can have on the identification and management of information problems. ACKNOWLEDGEMENTS We would like to thank the hospital’s ED staff for their willingness to participate in this study, as well as the AMIA reviewers for their informative feedback. This work is supported by the U.S. National Science Foundation under grant IIS-1017247. Any opinions, findings, and conclusions or recommendations expressed herein are those of the researchers and do not necessarily reflect the views of the National Science Foundation.
REFERENCES 1. 2.
3.
4.
5. 6. 7.
Abraham J, Kannampallil TG, Reddy MC. Peripheral activities during EMR use in emergency care: A case study. In Proc. of the American Medical Informatics Association (AMIA) Annual Symposium. 2009:1-5. Abramson EL, Patel V, Malhotra S, Pfoh ER, Osorio SN, Cheriff A, et al. Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing. International Journal of Medical Informatics. 2012;81(8):539-548. Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: The nature of patient care information systems-related errors. Journal of the American Medical Informatics Association. 2004;11(2):104-112. Bardram JE, Hansen TR. Why the plan doesn’t hold: A study of situated planning, articulation, and coordination work in a surgical ward. In Proc. of the Computer-Supported Cooperative Work (CSCW) Conference. 2010:331-340. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006;3(2):77101. Dillon TW, Lending D. Will they adopt? Effects of privacy and accuracy. Journal of Computer Information Systems. 2010;50(4):20-29. Embi PJ, Yackel TR, Logan JR, Bowen JL, Cooney TG, Gorman PN. Impacts of computerized physician documentation in a teaching hospital. Perceptions of faculty and resident physicians. Journal of the American Medical Informatics Association. 2004;11(4):300-310.
1853
8. 9.
10.
11.
12. 13. 14. 15. 16.
17. 18.
19.
20.
21. 22. 23. 24.
25. 26. 27. 28.
29. 30.
Holden RJ. Physicians' beliefs about using EMR and CPOE: In pursuit of a contextualized understanding of health IT use behavior. International Journal of Medical Informatics. 2010;79(2):71-86. Horsky J, Allen MB, Wilcox AR, Pollard SE, Neri P, Pallin DJ, et al. Analysis of user behavior in accessing electronic medical record systems in emergency departments. In Proc. of the American Medical Informatics Association (AMIA) Symposium. 2010:311-315. Kobayashi M, Fussell SR, Xiao Y, Seagull FJ. Work coordination, workflow, and workarounds in a medical context. In Proc. of the Association for Computing Machinery CHI Extended Abstracts on Human Factors in Computing Systems. 2005: 1561-1564. Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of computerized physician order entry systems in facilitating medication errors. Journal of the American Medical Informatics Association. 2005;293(10): 1197-1203. Lincoln YS, Guba EG. Naturalistic Inquiry. Newbury Park, CA: Sage Publications; 1985. Maxwell, JA. Qualitative research design: An interactive approach. Thousand Oaks, CA: Sage Publications, Inc.; 2005. Murphy AR, Reddy MC, Xu H. Privacy practices in collaborative environments: A study of emergency department staff. In Proc. of the Computer-Supported Cooperative Work (CSCW) Conference. 2014:269-282. Park SY, Chen Y. Adaptation as design: Learning from an EMR deployment study. In Proc. of the Association for Computing Machinery CHI Conference. 2012:2097-2106. Park SY, Lee SY, Chen Y. The effects of EMR deployment on doctors’ work practices: A qualitative study in the emergency department of a teaching hospital. International Journal of Medical Informatics. 2012;81(3):204217. Paul SA, Reddy MC. Understanding together: Sensemaking in collaborative information seeking. In Proc. of the Computer Supported Cooperative Work (CSCW) Conference. 2010:321-330. Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: A systematic review. Journal of the American Medical Informatics Association. 2005;12(5):505-516. Reddy MC, Shabot MM, Bradner E. (2008). Evaluating collaborative features of critical care systems: a methodological study of information technology in surgical intensive care units. Journal of Biomedical Informatics. 2008;41(3): 479-487. Saleem JJ, Flanagan M, Militello LG, Arbuckle N, Russ AL, Burgo-Black AL, et al. Paper persistence and computer-based workarounds with the electronic health record in primary care. In Proc. of the Human Factors and Ergonomics Society Annual Meeting. 2011;55(1):660-664. Shachak A, Hadas-Dayagi M, Ziv A, Reis S. Primary care physicians' use of an electronic medical record system: a cognitive task analysis. Journal of General Internal Medicine. 2009;24(3):341-348. Siegler EL, Adelman R. Copy and paste: a remediable hazard of electronic health records. The American Journal of Medicine. 2009;122(6):495-496. Sittig DF, Singh H. Defining health information technology-related errors. Archives of Internal Medicine. 2011;171(14):1281-1284. Turchin A, Shubina M, Goldberg S. Unexpected effects of unintended consequences: EMR prescription discrepancies and hemorrhage in patients on warfarin. In Proc. of the American Medical Informatics Association (AMIA) Annual Symposium. 2011:1412-1417. US Department of Health and Human Services. Benefits of EHRs. Retrieved from http://www.healthit.gov/providers-professionals/benefits-electronic-health-records-ehrs. US Department of Health and Human Services. HITECH Act Enforcement Interim Final Rule. Retrieved from http://www.hhs.gov/ocr/privacy/hipaa/administrative/enforcementrule/hitechenforcementifr.html. Vogelsmeier AA, Halbesleben JR, Scott-Cawiezell JR. Technology implementation and workarounds in the nursing home. Journal of the American Medical Informatics Association. 2008;15(1):114-119. Weir CR, Hammond KW, Embi PJ, Efthimiadis EN, Thielke SM, Hedeen AN. An exploration of the impact of computerized patient documentation on clinical collaboration. International Journal of Medical Informatics. 2011;80(8):e62-e71. Zhou X, Ackerman MS, Zheng K. I just don't know why it's gone: maintaining informal information use in inpatient care. In Proc. of the Association for Computing Machinery CHI Conference. 2009:2061-2070. Zhou X, Ackerman M, Zheng K. CPOE workarounds, boundary objects, and assemblages. In Proc. of the SIGCHI Conference on Human Factors in Computing Systems. 2011:3353-3362.
1854
1845
of EHR systems.7 Additionally, researchers also discuss the impact that information problems can have on the workflow of hospital staff. Information Problems Caused by EHR Design It is a challenge to build information systems for the fast-paced and information-intensive environment of hospitals. Researchers describe how EHR systems tend to be overly structured and designed with rigid rules that encourage data standardization (e.g., drop-down menus, text entry restrictions), which can lead to information problems caused by the design of the EHR.3 Koppel et al. 11 discuss how system design can cause problems with medication ordering, including: fragmented displays that prevented a coherent view of patients’ medications, inflexible ordering formats that led to wrong orders, and separation of system functionality that resulted in double dosing or incompatible orders. Abramson et al.2 also describe how a medication ordering system automatically selected the default dosing in medication order forms resulting in inaccurate medication requests. Additionally, Dillion & Lending6 also identify information problems caused by systems preventing users from entering descriptive data into a record and, instead, forcing them to select values from drop-down menus that are not considered intuitive to the user. EHR design can also prevent the entry of important psycho-social information about patients, which nurses argue, “provided continuity of care…[and] a richer picture of the patient’s situation” (p. 2065).29 Furthermore, EHR researchers describe system bugs that cause information problems. This includes EHR systems that truncate data entry fields resulting in lost patient data and EHR systems where two buttons on a screen have the same label but different functionalities, which caused information problems within the system.23 These studies highlight how EHR design can lead to information problems that users must manage. Information Problems Caused by EHR Users Users are also the cause of certain information problems within EHRs. These problems can include information entry errors, delayed information entry, and discrepancies between multiple sources of information. Researchers have often described how users tend to copy-and-paste information from prior notes in the system in order to cut down on their data entry efforts.3,7,22 However, as Embi et al.7 highlight, this can lead to outdated or incorrect information being proliferated throughout the system. Siegler & Adelman22 also discuss how the copy-and-paste function leads to, “reducing the credibility of the recorded findings, clouding clinical thinking, limiting proper coding, and robbing the chart of its narrative flow and function” (p. 495). Other researchers describe how delayed information entry occurs when clinicians are too busy or tightly scheduled to enter patient data into the system directly after seeing the patient, which leads to information in the EHR being outdated or incomplete for extended periods of time.16 This negatively affects any other patient-care team member who needs access to updated, accurate patient records. As Ash et al.3 describe, delayed information entry could result in the patient being given the same medication twice by another member of the patient-care team who relies on the patient record for information. Furthermore, information discrepancies are another issue that can occur during the use of EHR systems. Turchin, Shubina, & Goldberg24 discuss how EHR users encountered situations where medication information provided in the system’s structured fields (e.g., medication name, dosage) contradicted information found in the free-text description field. Therefore, these studies highlight how information problems can also be caused by the users themselves. Impact of Information Problems on Workflow Within a hospital, a number of clinical and non-clinical staff members must work together to gather, document, and share information in order to provide effective patient care. This workflow of activities is highly collaborative, complex, and prone to interruptions. This is especially true in fast-paced hospital environments like the emergency department where patients require immediate attention. However, it is important that whatever can negatively affect the hospital workflow is minimized in order to reduce impacts to the patient-care process. Medical informatics researchers have studied how information problems can have a negative impact on the hospital workflow. Researchers discuss how the design of EHR systems can increase the time it takes to perform workflow activities. Holden8 discusses how issues with information accessibility (e.g., system login, system response time) can negatively impact the workflow of physicians. Additionally, Shachak et al.21 describe how there are communication interruptions when physicians try to navigate EHR systems to find or enter patient information while also talking with their patients. This can lead to inaccurate information being entered into the system. Other researchers also describe the additional time and effort spent finding and updating information in multiple systems or on different screens.1,9 The time and effort that users often do not have.
1846
The existing medical informatics literature on information problems describes what causes information problems and the impacts that information problems can have on workflow. In this paper, we seek to extend this understanding to include how patient-care teams identify and manage the information problems that they encounter during their daily work. METHODS Research Site We conducted this study in the emergency department of a large teaching hospital in northeastern United States. The ED has approximately 55,000 visits per year. The first author conducted 54 hours of observations and 4 hours of semi-structured interviews with 7 clinical and non-clinical staff in the ED. We observed approximately 85 ED staff members (Table 1). Table 1. Observed ED Staff ED Staff Role
Number Observed
Nurses (including Charge Nurses) Registration Assistants Physicians Residents Emergency Medical Technicians (EMTs) ED Technicians Transporters ED Volunteers Chaplains (spiritual advisors) Sanitation/Cleaning Care Coordinators Social Workers Maintenance Pharmacists TOTAL
18 14 12 10 6 4 4 4 3 3 2 2 2 1 85 TOTA
The ED staff communicated and received information using a variety of sources, including: verbal communication, cell phones, pagers, desktop computers, laptops, computers-on-wheels (COWs), paper documents, white boards, and mounted electronic screens that displayed the “tracking board” (i.e. a chart of ED rooms with patient information and the assigned ED staff). The ED staff primarily used an electronic health record (EHR) system to document patient information. This system included the patients’ medical records, laboratory and medication orders, laboratory results, clinical team narratives, registration information, and tracking board. One group of users, registration assistants, also used an Admissions Discharge and Transfer (ADT) system that was integrated with the EHR to document information when patients first arrived in the ED. This ADT system created the patients’ unique identification number and included their name, date of birth, social security number, zip code, and complaint (i.e. symptoms, why they were there). Data Collection In this study, we used qualitative data collection methods including observations and semi-structured interviews. We used this methodological approach because qualitative research includes an immersion into a field site, which allows the researcher to observe the naturalistic processes and activities of the participants.12 Our ED observations resulted in detailed descriptions of participants’ behaviors and interactions related to information problems. These problems were situated within the context of the participants’ busy ED setting and everyday work activities.13 These qualitative methods have also been used in other informatics studies for a similar purpose.3,4,19 The first author conducted 54 hours of observations in two ED areas that had the largest amount of communication and information exchange among the staff: the registration area and the main nurses’ station. We also observed the ED staff in hallways and at smaller nurses’ stations. Detailed field notes were taken about the workflow,
1847
communication, collaboration, and technology used by both clinical and non-clinical staff. During the observations, the first author also had short, informal discussions with participants in order to clarify assumptions or gain additional information about specific situations. The field notes were then transcribed into an electronic document for analysis (Table 2). The first author also conducted 7 semi-structured interviews in order to better understand the ED staff’s roles and responsibilities, as well as their use and perceptions of the EHR system. The first author interviewed 1 physician, 1 registered nurse, 3 registration assistants, 1 care coordinator, and 1 social worker. The interview data was then transcribed into an electronic document for analysis (Table 2). Table 2. Data Collection Method, Participants, and Data Method
Focus
Hours
Participants
Transcribed Data
Observations
ED Workflow
54 hours
85 staff
175 pages
Semi-structured Interviews
ED Staff
4 hours
7 staff
28 pages
Data Analysis The transcriptions resulted in 203 pages of data. This data was analyzed by the first author using Braun & Clarke’s5 six-phase thematic analysis approach (Table 3). This approach facilitates the process of becoming familiar with the data, systematically identifying codes and themes, and then defining and naming the common themes found across the entire data set. The analysis resulted in three main themes, as described in the results section. Table 3. Braun & Clarke Six-Phase Thematic Analysis Approach Phase (1) Familiarizing ourselves with the data
Description Transcribe the notes taken during observations/interviews and read through the transcriptions to ensure a general understanding of the data.
(2) Generating initial codes
Label segments of data in a systematic way across all of the data.
(3) Searching for themes
Review individual codes and identify preliminary themes.
(4) Reviewing themes
Review preliminary themes to ensure that they make sense across the entire data set.
(5) Defining and naming themes
Continuously refine each theme, identify a specific name for each theme, and define the boundaries of the theme.
(6) Producing the report
Present themes with interesting examples from the data that illustrate the individual themes.
RESULTS Through our data analysis, we identified three main findings: the types of information problems encountered by ED staff, how the staff identified the information problems, and how the staff managed the information problems. Types of Information Problems We identified the following information problems in the ED: wrong, outdated, conflicting, incomplete, and missing information (Table 4). These information problems identified in the ED are similar to information problems described by other researchers.3,11,16,29
1848
Table 4. Types of Information Problems in the ED Information Problem Type
Description
Example from Data
Wrong
Information is not accurate.
“For example, if you put allergies in the wrong spot, they’re put in the problems list instead of the allergies list – which happens – [and it led to] another physician making patient care decisions off of the wrong information.” [physician interview]
Outdated
Information is no longer accurate and has not been updated.
“We had a patient who was in here because of abuse injuries and the boyfriend was listed as the emergency contact from a previous visit. The boyfriend was called, but the boyfriend was the abuser. So this created a potentially dangerous situation for the patient and the hospital because the information was not updated.” [social worker interview]
Conflicting
Information that should be the same but it is not the same.
In the check-out area, a physician tells the registration assistants that his patient’s insurance had been stolen. He says that while scanning the patient’s registration information he realized something was wrong because the record said the patient’s race was listed as African American and the man was Caucasian. The patient then confirmed that the [EHR] record did not have his correct social security number. [observational field notes]
Incomplete
Only part of the information is provided.
“Some of my information about my patient does not get into the patient’s record because I can’t edit [in the system]. And there’s no place for me to put some of my notes about the patient, which could help others, like the next shift.” [social worker interview]
Missing
Information is not available.
In the registration area, a doctor is talking to an EMT and the family of his patient. He says that the patient is refusing to tell him anything about his medical conditions, medications, and what happened in his accident. The doctor says there is no existing record for this patient and he needs any information they can provide so he can proceed with his patient care plan. [observational field notes]
These information problems occurred quite frequently in the ED. Even though participants discussed the importance of ensuring information accuracy in their work, they also stated that certain problems, such as data entry errors or incompleteness, are often an inherent aspect of the highly dynamic hospital environment. A participant described this: "There’s always the possibility of errors, that’s just the nature of entering information. And you only know what you know. Sometimes you don’t have the whole story" [social worker interview]. Identification of Information Problems The ED staff recognized the information problems when the problems interfered with their ability to do work, and they were subsequently identified as information problems. Any of the information problems described in the previous section could have persisted within the EHR system if no one had identified the problem. Therefore, it is important to understand how ED staff identifies information problems because the information problem must first be identified before it can be managed or fixed by the team. The ED staff identified information problems in two ways: comparing the EHR system’s information to other information sources, and trying to make sense of information that did not “look right.” Comparing Sources: The ED staff frequently identified information problems when they were comparing the EHR system information to other sources of information. These other sources included other ED staff members, patients, visitors, or paper records. For example:
1849
A registration assistant identifies that a patient’s first and last names are switched in the system while she is talking with a visitor who requested to see the patient [observational field notes]. The registration assistant was comparing the EHR system to the information that the visitor was verbally providing about the patient when she identified this as an information problem. Additionally, a social worker identified an information problem when comparing the information in the EHR system to a patient’s statement: "The other problem with the system is people checking the wrong information. So the nurses will ask [patients] questions, like “has anyone ever forced you to have sex against your will?” One time a nurse checked the wrong box for that question, then I am called in [through a notification in the EHR system] to talk to the patient about it. When I addressed them about the sensitive topic, the patient became irate saying that they never said that and demanding that their record be changed” [social worker interview]. Furthermore, the participants also identified information problems when comparing the EHR information to something they were physically seeing. For instance, the care coordinator identified an information problem when she noticed that the patient’s pill color was not the same color that the patient normally received: “I had a patient who was elderly that had an INR of 16 and it should have been 3.5 at most. I went in to talk to the patient and she said, “the color changed” in reference to her pills. So I looked in the records and found that the drug she was taking was 5 times the amount she should have been taking. So [comparing the pill to] the record in the system helped me identify that there was an issue” [care coordinator interview]. Sensemaking: Participants also described identifying information problems when they encountered information that did not look right, which then caused them to think about why that information did not look right – or to make sense of the information. Sensemaking is “the process of encoding information into external representations to answer task-specific questions” (p. 322).17 When the ED staff tried to make sense of information that did not look right, they were questioning the accuracy of information based on their previous experiences, medical knowledge, or other training or skills that they have. The staff described this as a “gut instinct” or simply that the information just “didn’t look right.” The following two examples illustrate the identification of information problems while making ED staff was trying to make sense of the information: The care coordinator described a time when the EHR system showed that the diabetes patient was using a glucometer, but noticed that her “[glucose] numbers were off.” The care coordinator stated: “A lot of times I have a gut instinct, you know? It didn’t look right. So I talked to the patient and she said, ‘I had a glucometer but my son stepped on it’ so she wasn’t using one at home, even though her record said she was [using a glucometer]” [care coordinator interview]. A registration assistant (RA) questions another RA about something she had entered into the EHR system. The second RA said she could not read her handwriting from her notes when she was transcribing the information into the system, so the information in the system may be wrong. The first RA said: “Yeah, it just didn’t look right. So I had to check with you” [observational field notes]. In both of these examples, the social worker and the RA identified the information problem by making sense of the information and discussing it with a patient or other ED staff member. Management of Information Problems After the ED staff identified information problems, they then had to respond to the problem. The participants managed information problems in one of three ways: by fixing the problem, finding the right person to fix the problem, or finding a way to work around the problem. Fixing the problem: When the participants identified an information problem and knew how to correct the problem, they typically just fixed the issue in the EHR system themselves. This is exemplified in the following two scenarios: In the check-out area, a physician tells the registration assistants that his patient’s insurance had been stolen. He says that while scanning the patient’s registration information he realized something was wrong because the record said the patient’s race was listed as African American and the man was Caucasian. The patient then confirmed that the [EHR] record did not have his correct social security number. The physician says he then asked the patient for his correct information and entered it into the EHR system so that the patient could be billed correctly for the visit” [observational field notes].
1850
A registration assistant identifies that a patient’s first and last names are switched in the system while she is talking with a visitor who requested to see the patient. She then asks the visitor additional questions about the patient (date of birth, home address) to verify the patient’s identity and asks other registration assistants about the patient’s correct name. The registration assistant realizes that the first and last names of the patient are mistakenly switched, so she fixes the information error in the system [observational field notes]. Finding someone to fix the problem: There were other times where the participant who identified the information problem did not have the knowledge or the editing rights to fix the problem in the EHR system. For instance, in the previous example, a social worker realized that a nurse had checked the wrong box in the system after the patient verified that the system was incorrect. However, the social worker could not change the error in the system so she had to find the nurse to fix it: “And the issue with the system is that the person who entered it has to change it. So I have to track down the nurse to change it” [social worker interview]. Additionally, the social worker also described a time when the patient’s contact information was outdated and she needed to find someone who could fix the problem: “Since I can’t update it as a social worker, I try to call others to update it…I talk to registration, but they say that it’s the nurse’s responsibility, but when I talk to the nurse, they say it’s not their responsibility.” When asked if it was unclear whose responsibility it was to fix the problem, the social worker responded: “Well, it’s clear to them that it’s not their responsibility [laughs]! But it’s not clear whose responsibility it is” [social worker interview]. This highlights how the participants managed the information problem by finding someone who could fix the problem since the EHR system restricted her from being able to fix it. This example also highlights the ambiguity or conflicting opinions around who is accountable for the accuracy and completeness of patient information in an environment where information is collectively managed by a patient-care team. Working around the problem: The participants also found ways to work around the information problem so that they could continue with their task without having to fix the problem in the EHR system. For example: A registration assistant (RA) discusses with another RA how she reads the “comments” field before letting a visitor back to see a patient, since the nurses occasionally write notes in this field about whether a patient can have visitors. However, she also mentions that the field is not always reliable, so they usually call the nurse to double-check [observational field notes]. By calling the nurse, the registration assistant found a way to work around the unreliable “comments” field in order to continue with her task of directing visitors to patient rooms. In addition, the social worker described how the system does not allow her to enter all of her notes about the patient, so she described using a paper-based workaround: “Some of my information about my patient does not get into the patient’s record because I can’t edit [in the system]. And there’s no place for me to put some of my notes about the patient, which could help others, like the next shift. So I usually just write it on the [paper] face sheet or the next shift takes notes during our hand-off.” [social worker interview]. Therefore, these workarounds allowed the ED staff to continue with their work, even though it did not fix the information problem in the system. DISCUSSION The results describe the various types of information problems that ED staff encountered during their work activities, as well as some of the approaches that they took to identify and manage those information problems. Given the highly collaborative nature of hospital work, it is important to consider how these information problems affect the collaboration of patient-care teams. In this section, we discuss how the management of information problems can have a serious impact on collaborative hospital teams including the cascading workflow effects of information problem management and the ambiguous accountability for fixing information problems in collaborative teams.
1851
The Cascading Workflow Effects of Information Problems Patient-care teams are highly collaborative and team members rely on each other to provide accurate and up-to-date information about a patient. EHR systems provide a centralized view of a patient’s history and current status, which creates a shared awareness of that patient across the patient-care team. Therefore, when there is an information problem – wrong, outdated, conflicting, incomplete, or missing information – it does not just affect the one team member who identifies the problem. It affects the entire patient-care team who relies on that information to do their work. Therefore, the way in which hospital staff manage information problems can affect the entire team’s collaborative understanding of a patient. After identifying an information problem, hospital staff may manage the information problem by using a workaround.15 These workarounds are temporary solutions that allow users to adapt technologies or processes in order to minimize interruptions.27 Our study’s results describe how ED staff used workarounds as one way to manage information problems that they encountered during their work. Other researchers have also described how clinicians frequently perform workarounds when the EHR design interferes with their work18,20,30 or when there is a problem with the information in the system (e.g., missing, incomplete, outdated).2,10,11,23 Since the primary concern of patient-care teams is to take care of patients, the staff will find ways to perform their activities by working around EHR design or information problems. Additionally, the user may not go back to the system and fix the known issue.10 Not fixing the problem results in an information problem persisting in the record for an extended period of time. This is common for hospital staff to not immediately fix an information problem in the system because they are responsible for a number of patients and have a variety of urgent tasks.3,16 However, this can cause problems in highly collaborative environments because the effects of workarounds can directly impact other members of the patient-care team. This is especially true if the known information problem is not fixed and other members of the team are not made aware that the information problem exists. Kobayashi et al.10 discuss the “cascading effects” of workarounds where working around one issue can lead to the need for other workarounds. The authors’ discussion of cascading effects describes the impact that one person’s workaround can have on other team members’ work. Similarly, Saleem et al.20 state that the use of EHR workarounds, “introduces the potential for gaps in documentation as well as the unintentional propagation of errors” (p. 662). Therefore, the persistence of an information problem and lack of making others aware of the problem can affect the next team member who uses that information for his/her own work. So, although there may be perfectly valid reasons for hospital staff to enact workarounds to complete their own patient-care tasks, the effects of the workaround on the overall collaborative team should also be considered. For example, our study’s results described how the system restricted a social worker from editing the patient’s record, so she created a workaround by writing her patient notes on a printed patient report. Although the workaround allowed the social worker to record patient notes for herself, it also prevented any of the other patient-care team members from being able to see her notes in the EHR system. Her notes could help provide a more comprehensive view of the patient’s condition to other members of the clinical team, as seen in prior studies.29 Ambiguous Accountability When Managing Information Problems in Collaborative Teams In hospitals, patient information is co-owned and co-managed by multiple members of the patient-care team. This means that there is a shared responsibility for entering, updating, and maintaining the accuracy of the information. However, members of a collaborative patient-care team may have an ambiguous understanding and, at times, a conflicting opinion about who is responsible for managing the information. This can lead to the persistence of information problems because the team members either assume or believe that someone else is responsible for entering the information or fixing information problems. To help address this issue, collaborative organizations frequently enact formal policies to address accountability. Hospitals create formal policies to outline accountability guidelines for how staff should handle a variety of situations, such as managing information problems. Hospital staff are required to complete training on these policies and comply with the policies during their daily work. However, while some of the formal policies include very specific instructions, others are more general or vague in order to account for variances across different areas of the organization.14 The issue with these general policies is that the staff may have different interpretations of the policies when conducting their daily work. This can lead to an ambiguous understanding or a conflicting opinion about responsibilities, as discussed in the study’s results. Researchers have described how individuals interpret formal policies in their everyday work practices can vary from person-to-person, which causes conflict or tension within
1852
highly collaborative teams.14 Therefore, understanding how well aligned the formal policies are to the everyday work activities of collaborative teams may provide insight into the persistence of information problems. RESEARCH LIMITATIONS A limitation of this study is that the data was collected and analyzed by a single researcher. One researcher in a large ED setting is not able to observe all instances of information problems that occur. Being a single observer required the first author to make choices about who to observe and where to focus when many different activities were occurring simultaneously. However, since the goal of this work was not to present an exhaustive list of information problems, but rather to identify common information problems in the ED and how they were identified and managed by the hospital staff, we believe that we still were able to collect and analyze the data appropriately. CONCLUSION Information problems frequently occur in hospitals and can negatively impact the patient-care workflow and potentially cause harm to patients. This study classifies and describes the types of information problems that ED staff encountered during their work activities. Additionally, the paper addresses a limitation in existing medical informatics research by discussing how collaborative patient-care team members identify and manage these information problems. By better understanding and addressing the challenges of identifying and managing information problems within complex collaborative patient-care teams, hospitals could reduce the negative impact that information problems can have on hospital workflow and the patient-care process. Furthermore, as hospitals transition from paper records to EHR systems, it is important to consider the impact that EHR design has on information problems. EHR systems dynamically display the most recent patient information in a digital format that can be viewed from a variety of distributed locations (e.g., computers, laptops, mobile devices). This differs from paper records that include static information with visual cues indicating who updated the record (e.g., signature, initials, handwriting) and when it was updated (e.g, date, change in ink, new sheet of paper).28 This shift from paper to electronic documentation changes the way that patient-care teams enter, view, and share information and how they do their work. Therefore, given the serious effects that information problems can have on patient care, it is important to more closely examine the design of EHR systems and the impact that they can have on the identification and management of information problems. ACKNOWLEDGEMENTS We would like to thank the hospital’s ED staff for their willingness to participate in this study, as well as the AMIA reviewers for their informative feedback. This work is supported by the U.S. National Science Foundation under grant IIS-1017247. Any opinions, findings, and conclusions or recommendations expressed herein are those of the researchers and do not necessarily reflect the views of the National Science Foundation.
REFERENCES 1. 2.
3.
4.
5. 6. 7.
Abraham J, Kannampallil TG, Reddy MC. Peripheral activities during EMR use in emergency care: A case study. In Proc. of the American Medical Informatics Association (AMIA) Annual Symposium. 2009:1-5. Abramson EL, Patel V, Malhotra S, Pfoh ER, Osorio SN, Cheriff A, et al. Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing. International Journal of Medical Informatics. 2012;81(8):539-548. Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: The nature of patient care information systems-related errors. Journal of the American Medical Informatics Association. 2004;11(2):104-112. Bardram JE, Hansen TR. Why the plan doesn’t hold: A study of situated planning, articulation, and coordination work in a surgical ward. In Proc. of the Computer-Supported Cooperative Work (CSCW) Conference. 2010:331-340. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006;3(2):77101. Dillon TW, Lending D. Will they adopt? Effects of privacy and accuracy. Journal of Computer Information Systems. 2010;50(4):20-29. Embi PJ, Yackel TR, Logan JR, Bowen JL, Cooney TG, Gorman PN. Impacts of computerized physician documentation in a teaching hospital. Perceptions of faculty and resident physicians. Journal of the American Medical Informatics Association. 2004;11(4):300-310.
1853
8. 9.
10.
11.
12. 13. 14. 15. 16.
17. 18.
19.
20.
21. 22. 23. 24.
25. 26. 27. 28.
29. 30.
Holden RJ. Physicians' beliefs about using EMR and CPOE: In pursuit of a contextualized understanding of health IT use behavior. International Journal of Medical Informatics. 2010;79(2):71-86. Horsky J, Allen MB, Wilcox AR, Pollard SE, Neri P, Pallin DJ, et al. Analysis of user behavior in accessing electronic medical record systems in emergency departments. In Proc. of the American Medical Informatics Association (AMIA) Symposium. 2010:311-315. Kobayashi M, Fussell SR, Xiao Y, Seagull FJ. Work coordination, workflow, and workarounds in a medical context. In Proc. of the Association for Computing Machinery CHI Extended Abstracts on Human Factors in Computing Systems. 2005: 1561-1564. Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of computerized physician order entry systems in facilitating medication errors. Journal of the American Medical Informatics Association. 2005;293(10): 1197-1203. Lincoln YS, Guba EG. Naturalistic Inquiry. Newbury Park, CA: Sage Publications; 1985. Maxwell, JA. Qualitative research design: An interactive approach. Thousand Oaks, CA: Sage Publications, Inc.; 2005. Murphy AR, Reddy MC, Xu H. Privacy practices in collaborative environments: A study of emergency department staff. In Proc. of the Computer-Supported Cooperative Work (CSCW) Conference. 2014:269-282. Park SY, Chen Y. Adaptation as design: Learning from an EMR deployment study. In Proc. of the Association for Computing Machinery CHI Conference. 2012:2097-2106. Park SY, Lee SY, Chen Y. The effects of EMR deployment on doctors’ work practices: A qualitative study in the emergency department of a teaching hospital. International Journal of Medical Informatics. 2012;81(3):204217. Paul SA, Reddy MC. Understanding together: Sensemaking in collaborative information seeking. In Proc. of the Computer Supported Cooperative Work (CSCW) Conference. 2010:321-330. Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: A systematic review. Journal of the American Medical Informatics Association. 2005;12(5):505-516. Reddy MC, Shabot MM, Bradner E. (2008). Evaluating collaborative features of critical care systems: a methodological study of information technology in surgical intensive care units. Journal of Biomedical Informatics. 2008;41(3): 479-487. Saleem JJ, Flanagan M, Militello LG, Arbuckle N, Russ AL, Burgo-Black AL, et al. Paper persistence and computer-based workarounds with the electronic health record in primary care. In Proc. of the Human Factors and Ergonomics Society Annual Meeting. 2011;55(1):660-664. Shachak A, Hadas-Dayagi M, Ziv A, Reis S. Primary care physicians' use of an electronic medical record system: a cognitive task analysis. Journal of General Internal Medicine. 2009;24(3):341-348. Siegler EL, Adelman R. Copy and paste: a remediable hazard of electronic health records. The American Journal of Medicine. 2009;122(6):495-496. Sittig DF, Singh H. Defining health information technology-related errors. Archives of Internal Medicine. 2011;171(14):1281-1284. Turchin A, Shubina M, Goldberg S. Unexpected effects of unintended consequences: EMR prescription discrepancies and hemorrhage in patients on warfarin. In Proc. of the American Medical Informatics Association (AMIA) Annual Symposium. 2011:1412-1417. US Department of Health and Human Services. Benefits of EHRs. Retrieved from http://www.healthit.gov/providers-professionals/benefits-electronic-health-records-ehrs. US Department of Health and Human Services. HITECH Act Enforcement Interim Final Rule. Retrieved from http://www.hhs.gov/ocr/privacy/hipaa/administrative/enforcementrule/hitechenforcementifr.html. Vogelsmeier AA, Halbesleben JR, Scott-Cawiezell JR. Technology implementation and workarounds in the nursing home. Journal of the American Medical Informatics Association. 2008;15(1):114-119. Weir CR, Hammond KW, Embi PJ, Efthimiadis EN, Thielke SM, Hedeen AN. An exploration of the impact of computerized patient documentation on clinical collaboration. International Journal of Medical Informatics. 2011;80(8):e62-e71. Zhou X, Ackerman MS, Zheng K. I just don't know why it's gone: maintaining informal information use in inpatient care. In Proc. of the Association for Computing Machinery CHI Conference. 2009:2061-2070. Zhou X, Ackerman M, Zheng K. CPOE workarounds, boundary objects, and assemblages. In Proc. of the SIGCHI Conference on Human Factors in Computing Systems. 2011:3353-3362.
1854
