Abstract

Objective Electronic health record (EHR) use in ambulatory care can improve safety and quality; however, problems with design, implementation, and poor interface with other systems lead users to develop ‘workarounds’, or behaviors users adopt to overcome perceived limitations in a technical system. We documented workarounds used in independent, community-based primary care practices, and developed a typology of their key features.

Materials and methods Comparative case study of EHR use in seven independent primary care practices. Field researchers spent approximately 1 month in each practice to observe EHR use, conduct patient pathways, and interview clinicians and staff.

Results We observed workarounds addressing a wide range of EHR-related problems, including: user interface issues (eg, insufficient data fields, limited templates), barriers to electronic health information exchange with external organizations, and struggles incorporating new technologies into existing office space. We analyzed the observed workarounds inductively to develop a typology that cuts across specific clinical or administrative processes to highlight the following key formal features of workarounds in general: temporary/routinized, which captures whether the workaround is taken for granted as part of daily workflow or is understood as a short-term solution; avoidable/unavoidable, referring to the extent to which the workaround is within the practice's power to eliminate; and deliberately chosen/unplanned, which differentiates strategically chosen adaptations from less thoughtful workarounds.

Conclusions This workaround typology provides a framework for EHR users to identify and address workarounds in their own practices, and for researchers to examine the effect of different types of EHR workarounds on patient safety, care quality, and efficiency.

Background and significance

Primary care practices have been incentivized to adopt electronic health records (EHRs).1 However, findings on the extent to which EHRs enhance care quality, safety, and efficiency are mixed.2–14 Usage limited to basic documentation features, difficulty incorporating clinical decision support and population health features into clinical work processes,15–17 perceived usability problems, including poor navigation,18,19 crowded or ‘busy’ screens,19 difficulties identifying correct diagnostic and procedure codes,20 laboratory orders disappearing from systems once the laboratory is drawn,21 difficulty displaying progress notes,22 and data interruptions and loss due to low-speed internet connections21 may be part of the reason that EHR usage has not, to date, led to expected improvements in care quality.23–27

EHR implementation also creates significant changes in clinical workflow.28 While workflow redesign may be intentional, more often insufficient time is spent analyzing and adjusting workflow during EHR implementation.28 As a result, unintended changes and blocks in workflow are created, which in turn lead to workarounds. Workarounds are behaviors users adopt to overcome perceived limitations in a technical system.29 Workarounds are often specifically defined as responses to workflow problems.30–34 Increasing attention to workflow redesign has heightened interest in the study of workarounds.35

Some define workarounds as undesirable deviations from the intended use of a technology.33,35–39 However, there is a growing acknowledgement that workarounds are not inherently negative.34,40–47 Workarounds improve workflow34,48 and increase safety when employed to apply an exception to a technologically defined rule correctly, such as skipping the scanning process to get critical medication to a patient immediately.44–45 In addition, workarounds are useful signals of misalignment between work tasks and the health information technology (HIT) system being used, pointing to needed change.37,49,50 Therefore, several studies call for a more complex framing to acknowledge the positive functions and potential dangers of workarounds.37,44

Few studies have developed typologies of HIT workarounds. Those that have focus on workarounds to a single process such as bar code medication administration,2,33,36,46,51–52 clinical decision support,40,53 computerized physician order entry,3–4,34,39,48 nurse vital sign documentation,38 consultation requests,47 and electronic care coordination tools.54 In addition, most earlier studies focus on categorizing the causes of workarounds.30,32 One exception is Flanagan et al,32 who identify broad categories of workarounds that cut across single processes. More research, however, is needed to identify general features of workarounds, particularly those important in distinguishing workarounds that are benign from those that are concerning.

Furthermore, the study by Flanagan et al32 and the majority of studies of EHR workarounds have been conducted in medical groups, hospital systems, and academic medical centers.4,11,32,36,47,55 These settings, which include the Veterans Affairs, Kaiser, and other ‘benchmark’ institutions, differ from smaller practice settings in potentially important ways.8–9,56 These systems use sophisticated custom-built EHRs, employ technology support personnel, and have been using and refining their EHR systems for nearly two decades. Studies of workarounds conducted in these settings may not generalize well to smaller independent primary care practices that rely on commercially available EHRs, and may have less advanced EHR users and limited information technology resources. Our study is unique in that we examine EHR workarounds in a sample of small, independent primary care practices using a range of commercially available EHR systems. Few studies of EHR use and workarounds have addressed this setting where most of the nation's primary care is delivered.8–9,56,57

We used direct observation of small to medium-size privately owned primary care practices in the USA inductively to construct a typology of workarounds that identifies conceptual features of workarounds, including characteristics that distinguish benign or positive workarounds from those that are potentially harmful.

Materials and methods

Sample

We directly observed EHR use in seven primary care practices in the northeastern USA. We selected four practices from a group of 22 that participated in earlier studies.58,59 Selection was based on chart audit data and aimed to ensure a mix of higher and lower performing practices using a quality composite score that combined processes and intermediate outcomes of chronic disease with preventive care measures (eg, percentage of diabetes patients with up-to-date hemoglobin A1c (process of care) or with hemoglobin A1c below recommended levels (intermediate outcome); percentage of eligible patients up to date on mammography (preventive care)). Practices were ranked using this composite score before selection. We selected three additional practices based on recommendations of primary care researchers with HIT expertise who identified clinicians they knew were innovative and efficient EHR users.

Data collection

An experienced researcher spent 10–18 days over approximately 1 month in each practice. Field researchers included two people with doctorates in sociology (AF, JH), and one doctoral student in anthropology (FW). All had previous observation and interviewing experience, and they used an observation guide to ensure comprehensive data collection. Data collection moved through four ‘stages’. The first stage focused on gaining a general understanding of the practice, identifying key work areas and tasks for which the EHR was used (eg, front desk area/check in, clinicians' offices, examination rooms, nurse work stations). Second, researchers shadowed staff members to identify work processes and how the EHR was used to accomplish tasks. This included shadowing clinicians while providing patient care. Third, we conducted ‘patient pathways’ to gain an in-depth understanding of EHR use throughout patient visits.60 This required expanding the field research team and including a physician-researcher. We varied our schedule to observe workflows that might be day or time specific. Observation continued at each site until the field researcher and principal investigator (DJC) agreed that data saturation was achieved. Finally, we conducted one-on-one interviews with key staff to understand their experiences with the EHR.

As shown in table 1, we spent approximately 86 days observing the practices. This yielded nearly 1000 pages of field notes. We conducted 41 interviews, 15 with clinicians and 26 with staff, including front desk staff, scanners, the people handling laboratory data and referrals, office managers, nurses, medical assistants, and people identified as information technology super users in the practice. Interviews lasted between 30 and 60 min, and yielded over 30 h of interview data. We collected 168 documents, including photos of the office, screen shots, and emails with vendors.

Table 1

Data collection summary

  Practice ID 
No of observation sessions, including patient pathways 18 11 14 12 11 10 10 
Total no of practice members observed All All All All All All All 
Total no of interviews 
No of photos and documents collected 47 31 16 22 18 18 16 
Primary field researcher JH FW JH JH JH AF JH 
  Practice ID 
No of observation sessions, including patient pathways 18 11 14 12 11 10 10 
Total no of practice members observed All All All All All All All 
Total no of interviews 
No of photos and documents collected 47 31 16 22 18 18 16 
Primary field researcher JH FW JH JH JH AF JH 

Data management

Data were stored on Health Insurance Portability and Accountability Act (HIPPA)-compliant, password-protected computers. Interview recordings were professionally transcribed. Field notes and interview transcripts were checked for completeness, de-identified, and imported into ATLAS.ti for coding and analysis. The institutional review boards of the University of Medicine and Dentistry of New Jersey–Robert Wood Johnson Medical School and Oregon Health and Science University approved this study.

Analysis

Our multidisciplinary data analysis team included researchers with expertise in primary care, sociology, anthropology, health policy, communications, public health, and business. Using a grounded theory approach,61 we initially met weekly to read data, discuss emerging themes, and develop a preliminary coding scheme. Then, in teams of two, we analyzed and coded the remaining data, meeting regularly to clarify coding questions and discuss new themes. We identified examples of EHR users creating alternative work processes to complete required work tasks, and analyzed these data further, and in collaboration with our consultants, to identify the key features of workarounds.

We incorporated several strategies for assuring trustworthiness of our findings. Reflexivity, the acknowledgement of natural biases, was maintained by requiring researchers to note personal biases during data collection and analysis. Using different methods and including researchers with various perspectives allowed for triangulation, the weaving together of different data gathering techniques, data elements, and investigators. Data saturation was continuously monitored to ensure saturation was reached in the data collection process. Finally, we carefully maintained an audit trail.62–65

Results

The primary care practices we observed were clinician owned, had between one and four clinicians, and were established EHR users. The seven practices used five different commercially available EHR systems and had implemented their EHR between 2 and 10 years before the time of data collection (table 2). While one practice had been using their current EHR for only 1 year, they had previously used a different EHR. All but one practice used paper record-keeping systems before implementing an EHR.

Table 2

Practice characteristics

Characteristic Practice ID 
Ownership Physician Physician Four physician partners Physician Physician Physician and spouse Physician 
No of clinicians 
Location Suburb Urban Suburb Suburb Suburb Suburb Suburb 
Years in business 31–35 26–30 1–10 26–30 21–25 1–10 1–10 
EHR system Practice partners Practice partners eClinical works Praxis eClinical works eMDs Allscripts 
Years since Implemented 10 10 
First EHR system No Yes Yes No Yes Yes Yes 
Transitioned from paper to EHR Yes Yes Yes Yes Yes No Yes 
Characteristic Practice ID 
Ownership Physician Physician Four physician partners Physician Physician Physician and spouse Physician 
No of clinicians 
Location Suburb Urban Suburb Suburb Suburb Suburb Suburb 
Years in business 31–35 26–30 1–10 26–30 21–25 1–10 1–10 
EHR system Practice partners Practice partners eClinical works Praxis eClinical works eMDs Allscripts 
Years since Implemented 10 10 
First EHR system No Yes Yes No Yes Yes Yes 
Transitioned from paper to EHR Yes Yes Yes Yes Yes No Yes 

EHR, electronic health record.

Common examples of problems that users worked around included insufficient or inconveniently located structured data fields for commonly collected clinical information, disease-specific templates that made it difficult to capture the complexities of primary care visits, and the receipt of paper test results that needed to be integrated into the EHR. Common workarounds for these problems included: double documentation, scanning, using paper, limited use of EHR in examination rooms, manual verification of electronic data, ignoring or disabling EHR functions, staggering EHR access, and manually entering data.

We categorized observed workarounds into ‘ideal types’66 along three dimensions: temporary/routinized; avoidable/unavoidable; and deliberately chosen/unplanned (see supplementary appendix 1, available online only, which summarizes each category of the typology, including key features and examples).

Temporary versus routinized workarounds

Temporary workarounds are short-term solutions to a time-delimited problem and typically arise from transitory situations or unusual events, such as implementing a new EHR system, malfunctions following version upgrades, power outages, or work process disruptions necessitated by learning new systems. For example, practice 4 had recently implemented a new EHR. When scheduling a patient visit, a front desk person noted: ‘There's no history in this record. Let me see if I can cut and paste it from (their prior EHR system)… we scanned a lot of those old charts into this system.’ She successfully located the patient's ‘medical history’ and copied and pasted it into the current system. Once this information was incorporated, the workaround (searching for the information in a second system) was no longer needed.

Routinized workarounds become part of the regular workflow. Unlike temporary workarounds, EHR users incorporate routinized workarounds into everyday work processes. For instance, we observed users documenting the same information in more than one system as a long-established and accepted part of daily workflow. In practices 1 and 4 the use of a stand-alone e-prescribing system requiring double data entry was a standard part of everyday work. When clinicians e-prescribed medications they manually transferred medication information into the EHR.

‘A patient sends me an email […] asking for a prescription refill. I would then have to go to [the EHR] to look at the patient's notes and things like that to make sure that it's appropriate to give that prescription. Then I have to go back to [the e-prescribing system] and send that prescription. Then I have to go back to [the EHR] to document that I sent that prescription.’ (physician, practice 4)

Across the practices, a common routinized workaround was scanning paper documents and attaching the resulting PDF files to patients' electronic records. These documents included radiology and consultation reports, insurance documents, hospital documents, and all other paper-based clinical information.

One important characteristic of this typology is its ability to acknowledge the potentially shifting nature of workarounds. A temporary workaround may become routinized if it becomes a taken-for-granted and regular aspect of workflow, and discussion of eliminating the workaround ceases. For example, in practices 1 and 2 we observed staggered work schedules as a workaround to manage a limited number of EHR user licenses. While there may have initially been plans to purchase more licenses, in both cases the practice of staggered schedules was routine, with no discussion of ending this workaround at the time of our observations. Shifting between categories of workarounds is less likely, although still possible, among other categories of the typology. For example, an unplanned adaptation could later be adopted systematically.

Avoidable versus unavoidable workarounds

Avoidable workarounds address problems users could solve permanently (making them also temporary workarounds), but do not because of a lack of time, motivation, or money. For example, in practice 3 a staff member looked up missing information about a patient's last tetanus shot in their previous billing system, but then did not enter the information into the EHR. As a result, the same information will have to be tracked down again if needed in the future. The workarounds that manifest as a result of having an insufficient number of user licenses are also arguably avoidable workarounds, but the cost of user licenses poses a barrier to addressing this problem.

In contrast, unavoidable workarounds result from work processes that are externally constrained. For example, referrals typically had to be processed in a stand-alone web-based system provided by insurance companies; practices also documented and tracked these referrals for themselves, often in a paper-based log. In addition, nursing homes frequently did not accept electronic signatures, and practices worked around this problem by printing, signing and faxing documents to the nursing home, and then scanning the documents into their EHR. Design features of the EHR also created unavoidable workarounds. For instance, a registered nurse from practice 2 noted that there was no field to enter the patient's race or ethnicity in their EHR, despite the fact that they are required to capture and report this information. She explained that she enters this information as free text into the ‘progress notes’. One consequence of this workaround is that the information is harder to find and, as it is not structured data, cannot be easily used to run reports.

Deliberately chosen versus unplanned workarounds

The workarounds we observed often increased time spent on a task, adding steps to everyday work routines or duplicating work effort. In a subset of cases, however, deliberately chosen workarounds increased efficiency or enhanced patient care. In deliberately chosen workarounds, one or more practice members make an explicit, self-reflexive decision about the best way to work around a technical limitation. For instance, one doctor in practice 3 decided not to use the computer in the examination room for routine visits because she could be more efficient without it. In another case, the physician-owner of practice 6 paid an outside vendor to pull data from the EHR to produce a paper point-of-care document for each patient that summarized immunizations, screenings, and current problems, and prompted the clinicians and medical assistants to take action when preventive care was not up to date. They used this single-page document as a clinical decision support tool (eg, an action list) and summary for patients of current preventive and chronic disease care. This report was a workaround for an EHR system that did not technically fit the information and decision-making needs of this practice.

Deliberately chosen workarounds were unusual; most workarounds were unplanned and less efficient. For instance, several practices employed lengthy, multi-step processes to track laboratory results:

‘The phlebotomist scrolls through the list of patient names and IDs that the EHR has matched with the lab orders. This list always contains more names on it than there are results for the day. Today there were 19 completed lab results, but the EHR populated about 55 matching names. The phlebotomist goes through the list to find the 19 correct patients. This step is carried out to have the lab results attached to the individual patient record in the EHR.’ (field notes, practice 2)

Furthermore, in almost every practice, clinicians and staff had to work around the layout of the examination rooms in order to use the EHR while talking to patients. In all but one practice, clinicians and clinical support staff did one of the following: sit facing the patient with the laptop on their lap; rest the computer on a surface that was too low for them to work at comfortably; place the laptop on a small counter with a sink (the computer was either perched precariously on the edge of the counter or placed on the far side of the sink requiring the user to stretch over the sink to type). These were not planned uses, but workarounds to accommodate the existing physical environment after EHR implementation.

Co-occurrence of different types of workarounds

Although up to this point we have discussed the three ideal types separately, workarounds can be categorized on multiple dimensions of our typology simultaneously. Both avoidable and unavoidable workarounds, for example, can be part of the accepted daily routine (routinized). Manual referral tracking (unavoidable) is a routinized workaround in the practices we observed. Using paper to document notes during a power outage (also unavoidable) is a temporary workaround; when power returns, this workaround is no longer necessary. In addition, deliberately chosen workarounds can be temporary (eg, a carefully defined protocol for transferring patient information from paper charts to the EHR following implementation), or they can be routinized (eg, a uniform method to indicate a patient's race or ethnicity in the chart in the absence of a dedicated electronic field). However, not all categories of workarounds are equally likely to co-occur. For example, workarounds that are unavoidable are more likely to be unplanned; that is, when a workaround is dictated externally it is less likely to be strategically chosen by the practice to improve workflow or patient care.

Discussion

To date, there have been few systematic attempts to categorize workarounds in general, rather than focusing on specific processes (eg, computerized physician order entry) and to differentiate problematic from necessary deviations from the intended use of EHR technology. To address this gap, we inductively developed a three-dimensional typology of workarounds: temporary/routinized; avoidable/unavoidable; and deliberately chosen/unplanned. Our typology begins the important work of separating workarounds that are benign or even beneficial from those that are more concerning by drawing attention to three key questions: Is the workaround avoidable? Is the workaround temporary or has it become incorporated into the normal workflow? Was the workaround explicitly chosen as an improvement or was it unplanned?

Temporary workarounds, in particular those due to transitions in medical recording keeping systems and system outages, are not likely to be eliminated, but other kinds of workarounds are avoidable with better planning. Poor integration of HIT is a known source of technical failure21 that results from choices made when purchasing an EHR. While we acknowledge that expense is a factor, particularly among small and medium-size independent practices, many avoidable workarounds could be eliminated with more and better-positioned computers, additional user licenses, use of an integrated EHR rather than several stand-alone systems, and professional redesign and renovation of examination rooms. Although not the purpose of this study, our examination of workarounds thus highlights some of the hidden costs associated with purchasing an EHR that practices should carefully consider.

Some workarounds careful planning cannot mitigate. For instance, practices lack the infrastructure to exchange information electronically with other organizations, such as hospitals, nursing homes, laboratory corporations, and diagnostic facilities, with whom they must communicate to deliver patient care. This limitation is the source of a range of workarounds (eg, scanning, double-documentation), and constitutes a major barrier to optimizing EHR use in the primary care setting. Without local, state and federal leadership, the infrastructure for communicating across these organizations will probably remain inadequate.57,60 Because these problems are largely outside of practice control, the associated workarounds are unavoidable until the technical and organizational barriers to information exchange are addressed. Unavoidable workarounds highlight the importance of taking a ‘socio-technical systems’ approach to the study of workarounds, attending to the ways workarounds involve people, technology, context, and other organizational factors, including the arrangement of physical space in the office.32,34,47,51 This approach avoids simplistic understandings that lay blame on either the user or the software design, acknowledging that the causes of workarounds are often complex and multiple, and also include work processes, the physical environment, practice policies, and training.

Practices were conscious that certain EHR usage behaviors were temporary workarounds to compensate for recognized system limitations. Practices were less aware of routinized workarounds, such as scanning and attaching documents, which were treated as a necessary part of working with EHR. Reframing scanning as a routinized workaround reminds us of the underlying and unaddressed problems with the electronic exchange of patient information as well as the implications of this workaround (eg, reduced utility of data ‘trapped’ in scanned documents, unavailable information due to scanning backlogs). Routinization of workarounds is particularly problematic, as inefficient work processes become integrated into normal workflow without the assessment of whether they are positive or negative.28 Not all workarounds are equally concerning. A small subset of deliberately chosen workarounds increased efficiency or enhanced patient care capabilities, and highlighted that the impact of workarounds on safety, efficiency, and quality of care depends on the type of workaround employed and its reason for use.

This study contributes to research on workarounds in a number of important ways. First, we extend the study of EHR workarounds to include a range of commercially available EHRs, and examine EHR use and workarounds in an understudied setting—smaller, independent primary care practices. Second, we examine EHR workarounds broadly; we did not focus on a single process or clinical task, but looked across functional areas to identify EHR-related workarounds. Third, while previous categorization schemes look almost exclusively at the causes of workarounds, we identify the conceptual features of workarounds, and establish a typology for distinguishing beneficial from problematic workarounds.

Despite its strengths, this study has several limitations. First, some may question whether or not our findings generalize to other settings. There is evidence that practices in other regions of the country, and of different sizes and ownership structures, have similar experiences and struggles with EHR use.67–73 Second, it is possible that we did not capture all of the workarounds practices employed. However, the wide range of workarounds we observed produced a robust typology that can be used to classify workarounds that we may not have seen during observation. Finally, the possibility of creating a Hawthorne effect, which is slight in primary care research,74 was mitigated by prolonged engagement and data triangulation.

Conclusion

The workaround typology we developed provides a framework that practices can use to identify and eliminate workarounds that are not serving them well. With the growing use of EHR in primary care settings, categorizing user-generated workarounds is an important first step toward creating an awareness of workarounds, understanding the implications of these behaviors in practice, and eliminating unwanted or unsuccessful workarounds. In addition, our typology invites future researchers to examine the types of workarounds that are associated with better outcomes (eg, efficiency, safety, and quality), or that are more dangerous or disruptive, the prevalence of specific types or combinations of workarounds, and variations in how workarounds manifest across various healthcare settings and types of providers (eg, physicians, mid-level providers). Furthermore, this work is of importance to policy-makers. After more than a decade of EHR implementation in the USA, there are still problems with EHRs—highlighted by the modifiable workarounds we identify—that could be avoided by systematic education and professional training, and influencing EHR vendors to improve EHR design so products support the information and cognitive needs of primary care.

Acknowledgements

The authors would like to thank Fatima Williams for assistance with data collection.

Contributors

All authors meet the three criteria for authorship defined in the uniform requirements for manuscripts submitted to biomedical journals. AF: Data collection and analysis, drafted the article, final approval. JCC: Data analysis, substantive revisions, final approval. JH: Data collection and analysis, substantive revisions, final approval. ECC: Data collection and analysis, substantive revisions, final approval. MP: Data collection and analysis, substantive revisions, final approval. BTK: Interpretation of data and substantive revisions, approval of an earlier draft of the manuscript; however, BTK died during the revisions and could not approve the final version. BC: Data analysis, substantive revisions, final approval. CRJ: Interpretation of data, substantive revisions, final approval. DJC: Conception, data collection and analysis, substantive revisions, final approval.

Funding

This work was supported by National Heart, Lung, and Blood Institute grant number 1R21HL092046.

Competing interests

None.

Ethics approval

This study was approved by the institutional review boards of the University of Medicine and Dentistry of New Jersey–Robert Wood Johnson Medical School and Oregon Health and Science University.

Provenance and peer review

Not commissioned; externally peer reviewed.

References

1
42 CFR Parts 412, 413, 422
.
Medicare and Medicaid programs, electronic health record incentive program, final rule
.
Federal Register: Rules and Regulations
 
2010
:
75
:
44313
588
.
2
Patterson
ES
Rogers
ML
Chapman
RJ
et al
.
Compliance with intended use of bar code medication administration in acute and long-term care: an observational study
.
Hum Factors
 
2006
;
48
:
15
22
.
3
Koppel
R
Metlay
JP
Cohen
A
et al
.
Role of computerized physician order entry systems in facilitating medication errors
.
JAMA
 
2005
;
293
:
1197
203
.
4
Schoen
C
Osborn
R
Huynh
PT
et al
.
On the front lines of care: primary care doctors' office systems, experiences, and views in seven countries
.
Health Aff
 
2006
;
25
:
w555
71
.
5
Harrison
MI
Koppel
R
Bar-Lev
S
.
Unintended consequences of information technologies in health care: an interactive sociotechnical analysis
.
J Am Med Inform Assoc
 
2007
;
14
:
542
9
.
6
Ash
J
Sittig
D
Dykstra
R
et al
.
Categorizing the unintended sociotechnical consequences of computerized provider order entry
.
Int J Med Inform
 
2007
;
76
:
S21
7
.
7
Ash
JS
Sittig
DF
Seshadri
V
et al
.
Adding insight: a qualitative cross-site study of physician order entry
.
Stud Health Technol Inform
 
2004
;
107
:
1013
17
.
8
Chaudhry
B
Wang
J
Wu
S
et al
.
Systematic review: impact of health information technology on quality, efficiency, and costs of medical care
.
Ann Intern Med
 
2006
;
144
:
742
52
.
9
DesRoches
CM
Campbell
EG
Rao
SR
et al
.
Electronic health records in ambulatory care—a national survey of physicians
.
N Engl J Med
 
2008
;
359
:
50
60
.
10
Edmondson
AC
.
Learning from failure in health care: frequent opportunities, pervasive barriers
.
Qual Saf Health Care
 
2004
;
13
(Suppl. 2)
:
ii3
9
.
11
Garrido
T
Jamieson
L
Zhou
Y
et al
.
Effect of electronic health records in ambulatory care: retrospective, serial, cross sectional study
.
BMJ
 
2005
;
330
:
581
.
12
Karsh
BT
Weinger
MB
Abbott
PA
et al
.
Health information technology: fallacies and sober realities
.
J Am Med Inform Assoc
 
2010
;
17
:
617
23
.
13
Sittig
DF
Singh
H
.
Defining health information technology-related errors: new developments since to err is human
.
Arch Intern Med
 
2011
;
171
:
1281
4
.
14
Carayon
P
Hundt
AS
Karsh
B
et al
.
Work system design for patient safety: the SEIPS model
.
Qual Saf Health Care
 
2006
;
15
(Suppl. I)
:
i50
8
.
15
Simon
SR
Soran
CS
Kaushal
R
et al
.
Physicians' use of key functions in electronic health records from 2005 to 2007: a statewide survey
.
J Am Med Inform Assoc
 
2009
;
16
:
465
70
.
16
Fernandopulle
R
Patel
N
.
How the electronic health record did not measure up to the demands of our medical home practice
.
Health Aff
 
2010
;
29
:
622
8
.
17
Nutting
PA
Crabtree
BF
Stewart
EE
et al
.
Effect of facilitation on practice outcomes in the National Demonstration Project model of the patient-centered medical home
.
Ann Fam Med
 
2010
;
8
(Suppl. 1)
:
S33
44
;
S92
.
18
Miller
RH
Sim
I
.
Physicians' use of electronic medical records: barriers and solutions
.
Health Aff
 
2004
;
23
:
116
26
.
19
Rose
AF
Schnipper
JL
Park
ER
et al
.
Using qualitative studies to improve the usability of an EMR
.
J Biomed Inform
 
2005
;
38
:
51
60
.
20
Bostrom
AC
Schafer
P
Dontje
K
et al
.
Electronic health record: implementation across the Michigan Academic Consortium
.
Comput Inform Nurs
 
2006
;
24
:
44
52
.
21
Christensen
T
Grimsmo
A
.
Instant availability of patient records, but diminished availability of patient information: a multi-method study of GP's use of electronic patient records
.
BMC Med Inform Decis Mak
 
2008
;
8
:
12
.
22
Gamm
LD
Barsukiewicz
CK
Dansky
KH
et al
.
Pre- and post- control model research on end-users' satisfaction with an electronic medical record: preliminary results
.
Proc AMIA Symp
 
1998
:
225
9
.
23
Crosson
JC
Ohman-Strickland
PA
Cohen
DJ
et al
.
Typical electronic health record use in primary care practices and the quality of diabetes care
.
Ann Fam Med
 
2012
;
10
:
221
7
.
24
Crosson
JC
Ohman-Strickland
PA
Hahn
KA
et al
.
Electronic medical records and diabetes quality of care: results from a sample of family medicine practices
.
Ann Fam Med
 
2007
;
5
:
209
15
.
25
Linder
JA
Ma
J
Bates
DW
et al
.
Electronic health record use and the quality of ambulatory care in the United States
.
Arch Intern Med
 
2007
;
167
:
1400
5
.
26
Zhou
L
Soran
CS
Jenter
CA
et al
.
The relationship between electronic health record use and quality of care over time
.
J Am Med Inform Assoc
 
2009
;
6
:
457
64
.
27
Stead
WW
Lin
HS
.eds
Computational technology for effective health care: immediate steps and strategic directions
 .
Washington, DC
:
National Academies Press
,
2009
.
28
Carayon
P
Karsh
B-T
Cartmill
RS
et al
.
Incorporating health information technology into workflow redesign—summary report
. (
Prepared by the Center for Quality and Productivity Improvement, University of Wisconsin-Madison, under Contract No. HHS 290-2008-10036c). AHRQ Publication No. 10-0098-EF
.
Rockville, MD
:
Agency for Healthcare Research and Quality
,
October
2010
.
29
Cresswell
K
Worth
A
Sheikh
A
.
Integration of a nationally procured electronic health record system into user work practices
.
BMC Med Inform Decis Mak
 
2012
;
12
:
15
.
30
Halbesleben
JRB
Wakefield
DS
Wakefield
BJ
.
Workarounds in healthcare settings: literature review and research agenda
.
Health Care Manage Rev
 
2008
;
33
:
2
12
.
31
Halbesleben
JRB
Rathert
C
Bennett
S
.
Measuring nursing workarounds: tests of the reliability and validity of a tool
.
J Nurs Adm
 
2013
;
43
:
50
5
.
32
Flanagan
ME
Saleem
JJ
Millitello
LG
et al
.
Paper- and computer-based workarounds to electronic health record use at three benchmark institutions
.
J Am Med Inf Assoc
 
2013
:
20:
e59
e66
.
33
Rack
L
Dudjak
L
Wolf
G
.
Study of nurse workarounds in a hospital using bar code medication administration
.
J Nurs Care Qual
 
2012
;
27
:
232
9
.
34
Niazkhani
Z
Pirnejad
H
van der Sijs
H
et al
.
Evaluating the medication process in the context of CPOE use: the significance of working around the system
.
Int J Med Inform
 
2011
;
80
:
490
506
.
35
Rathert
C
Williams
E
Lawrence
E
et al
.
Emotional exhaustion and workarounds in acute care: cross sectional tests of a theoretical framework
.
Int J Nurs Stud
 
2012
;
49
:
969
77
.
36
Carayon
PP
Wetterneck
TBMD
Hundt
ASP
et al
.
Evaluation of nurse interaction with bar code medication administration technology in the work environment
.
J Patient Saf
 
2007
;
3
:
34
42
.
37
Ferneley
EH
Sobreperez
P
.
Resist, comply or workaround? An examination of different facets of user engagement with information systems
.
Eur J Inf Syst
 
2006
;
15
:
345
56
.
38
Yeung
M
Lapinsky
S
Granton
J
et al
.
Examining nursing vital signs documentation workflow: barriers and opportunities in general internal medicine units
.
J Clin Nurs
 
2012
;
21
:
975
82
.
39
Grossman
J
Cross
D
Boukus
E
et al
.
Transmitting and processing electronic prescriptions: experiences of physician practices and pharmacies
.
J Am Med Inform Assoc
 
2012
;
19
:
353
9
.
40
Karsh
B
.
Clinical practice improvement and redesign: How change in workflow can be supported by clinical decision support
 .
AHRQ Publication
2009
;
No. 09-0054-EF
.
41
Chan
BC
Perkins
D
Wan
Q
et al
.
Finding common ground? Evaluating an intervention to improve teamwork among primary health-care professionals
.
Int J Qual Health Care
 
2010
;
22
:
519
24
.
42
Karsh
BT
Holden
RJ
Alper
SJ
et al
.
A human factors engineering paradigm for patient safety: designing to support the performance of the healthcare professional
.
Qual Saf Health Care
 
2006
;
15
(Suppl.1)
:
i59
65
.
43
Holden
RJ
.
Cognitive performance-altering effects of electronic medical records: an application of the human factors paradigm for patient safety
.
Cogn Technol Work
 
2010
;
13
:
11
29
.
44
Alper
SJ
Karsh
B-T
.
A systematic review of safety violations in industry
.
Accid Anal Prev
 
2009
;
41
:
739
54
.
45
Hollnagel
E
.
The design of fault tolerant systems—prevention is better than cure
.
Reliab Eng Syst Saf
 
1992
;
36
:
231
7
.
46
Halbesleben
JRB
Savage
GT
Wakefield
DS
et al
.
Rework and workarounds in nurse medication administration process: Implications for work processes and patient safety
.
Health Care Manage Rev
 
2010
;
35
:
124
33
.
47
Saleem
JJ
Russ
AL
Neddo
A
et al
.
Paper persistence, workarounds, and communication breakdowns in computerized consultant management
.
Int J Med Inform
 
2011
;
80
:
466
79
.
48
van der Sijs
H
Rootjes
I
Aarts
J
.
The shift in workarounds upon implementation of computerized physician order entry
. In:
Moen
A
et al
.
User centered networked health care
 .
European Federation for Medical Informatics
,
2011
:
290
4
.
49
Spear
SJ
Schmidhofer
M
.
Ambiguity and workarounds as contributors to medical error
.
Ann Int Med
 
2005
;
142
:
627-W-128
.
50
Vestal
K
.
Lessons learned. Nursing and the art of the workaround
. Nurse Lead
 
2008
;
6
:
8
9
.
51
Koppel
R
Wetterneck
T
Telles
JL
et al
.
Workarounds to barcode medication administration systems: their occurrences, causes, and threats to patient safety
.
J Am Med Inform Assoc
 
2008
;
15
:
408
23
.
52
Holden
RJ
Rodriguez
AJ
Faye
H
et al
.
Automation and adaptation: nurses' problem-solving behavior following the implementation of bar coded medication administration technology
.
Cogn Technol Work
 
2013
:
15:283–96
.
53
Hysong
SJ
Sawhney
MK
Wilson
L
et al
.
Provider management strategies of abnormal test result alerts: a cognitive task analysis
.
J Am Med Inform Assoc
 
2010
;
17
:
71
7
.
54
Smith
PC
Araya-Guerra
R
Bublitz
C
et al
.
Missing clinical information during primary care visits
.
JAMA
 
2005
;
293
:
565
71
.
55
Saleem
JJ
Russ
AL
Justice
CF
et al
.
Exploring the persistence of paper with the electronic health reacord
.
Int J Med Inf
 
2009
;
78
:
618
28
.
56
O'Malley
AS
Grossman
JM
Cohen
GR
et al
.
Are electronic medical records helpful for care coordination? Experiences of physician practices
.
J Gen Intern Med
 
2010
;
25
:
177
85
.
57
Institute of Medicine (US)
.
Committee on Patient Safety and Health Information Technology
.
Health IT and patient safety: building safer systems for better care
 .
Washington, DC
:
National Academies Press
,
2012
.
58
Balasubramanian
BA
Chase
SM
Nutting
PA
et al
.
Using learning teams for reflective adaptation (ULTRA): insights from a team-based change management strategy in primary care
.
Ann Fam Med
 
2010
;
8
:
425
32
.
59
Ferrante
JM
Ohman-Strickland
P
Hahn
KA
et al
.
Self-report versus medical records for assessing cancer-preventive services delivery
Cancer Epidemiol Biomarkers Prev
 
2008
;
17
:
2987
94
.
60
Pommerenke
FA
Dietrich
A
.
Improving and maintaining preventive services. Part 1: applying the patient path model
.
J Fam Pract
 
1992
;
34
:
86
91
.
61
Strauss
A
Corbin
J
.
Basics of qualitative research: grounded theory procedures and techniques
 .
Newbury Park, CA
:
Sage
,
1990
.
62
Cohen
DJ
Crabtree
BF
.
Evaluative criteria for qualitative research in health care: controversies and recommendations
.
Ann Fam Med
 
2008
;
6
:
331
9
.
63
Malterud
K
.
Qualitative research: standards, challenges, and guidelines
.
Lancet
 
2001
;
358
:
483
8
.
64
Patton
MQ
.
Enhancing the quality and credibility of qualitative analysis
.
Health Serv Res
 
1999
;
34
:
1189
208
.
65
Lincoln
YS
Guba
EG
.
Naturalistic inquiry
 .
Newbury Park, CA
:
Sage
,
1985
.
66
Weber
M
.
The methodology of the social sciences
 .
Shils
EA
Finch
HA
(ed.
and trans.
).
New York
:
Free Press
,
[1904] 1949
.
67
Beasley
JW
Wetterneck
T
Temte
J
et al
.
Information chaos in primary care: implications for physician performance and patient safety
.
J Am Board Fam Med
 
2011
;
24
:
745
51
.
68
Crosson
JC
Schueth
AJ
Isaacson
N
et al
.
Early adopters of electronic prescribing struggle to make meaningful use of formulary checks and medication history documentation
.
J Am Board Fam Med
 
2012
;
25
:
24
32
.
69
Coordinator
.
Health IT.gov. (http://www.healthit.gov/
) (accessed 9 Mar 2012).
70
Wickens
CD
.
Multiple resources and mental workload
.
Hum Factors
 
2008
;
50
:
449
55
.
71
Altmann
EM
Gray
WD
.
Forgetting to remember: the functional relationship of decay and interference
.
Psychol Sci
 
2002
;
13
:
27
33
.
72
Nemeth
C
Cook
R
.
Hiding in plain sight: what Koppel et al. tell us about healthcare IT
.
J Biomed Inform
 
2005
;
38
:
262
3
.
73
Lapin
J
Beasley
J
Smith
P
et al
.
Proactive risk assessment of primary care of the elderly
.
ARHQ Conference
.
Bethesda, MD
,
September 7–10, 2008
.
74
Goodwin
MA
.
Using direct observation in primary care research—the Hawthorne effect: defining the nature and impact of the presence of researcher observers on patients and physicians in community family practices
  [
doctoral dissertation
] Case Western Reserve University,
2003
.
Professor Karsh died on 18 August 2012.

Comments

0 Comments