Jerome Carter, MD  November 9, 2015

Part One: Workarounds, Disruptions, and Electronic Health Records
HITECH EHR incentives have been successful in driving EHR adoption.  However, as more hospitals and practices have embraced HIT, the number of complaints of poor usability, workflow disruptions, and decreased productivity have grown (1,2,3,4,5).   As a result, EHR systems have been one of the most important factors in bringing discussions of clinical workflow to the forefront.  Of course, this does not mean that inefficient workflows did not exist prior to EHR systems,  only that EHR systems provided sufficient contrast with known processes so that the differences became obvious.  

 Every clinical organization has policies and procedures that guide work activities.  Strict adherence is rarely enforced, which gives those charged with carrying out said polices/procedures significant leeway in determining how they are done.   If the form is filled out correctly, no one sweats the details.   Electronic systems change this dynamic — they are supposed to “help”.  As it turns out, the degree to which they help or hurt varies considerably.  Some processes are more efficient, some less.   Published studies on workarounds provide valuable information on how processes are affected by the presence of electronic systems (6,7). 

Flanagan et al. looked at the workarounds present at three institutions (inpatient and outpatient systems EHR systems) (6).  They state:

Workarounds to EHR for reasons of efficiency, awareness, and memory were consistent across all three benchmark institutions. Efficiency workarounds included actions that health professionals perceived to make their work go more quickly. Efficiency was cited as a reason for using both paper-based and computer-based workarounds, though the specific workarounds were different.

Friedman et al. studied seven independent primary care practices (7).   They found a similar pattern of adaptations to the presence of EHR systems.  Each group of authors used different strategies to organize their findings, making them difficult to compare directly.  However, the gist of their findings is easy to grasp.    I found Friedman’s topology more compelling for future use, so abridged descriptions of their findings are listed below.

We categorized observed workarounds into ‘ideal types’  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.


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.


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. 


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.


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…

While research workarounds reported in these papers is in reaction to EHR systems, its value goes beyond understanding EHR-related changes.  Ultimately, the attention paid to common processes may prove to be more valuable.   Why?  EHR systems are designed to be patient information repositories, not clinical care assistants.  As a result, supporting clinical work is seen as a data availability problem, not a process support problem. The underlying assumption is that providing data is the same thing as supporting processes.  Workaround research demonstrates just how wrong this assumption is.   Workarounds are workflow issues.  Every workaround is an alternate path to the same goal.   

Workflow consist of a series of steps and each step consumes or produces information, uses resources, and is performed by someone or something.  If both groups of authors had rendered their findings in a formal process language (e.g., YAWL, BPMN, Colored Petri Nets) using acknowledged workflow patterns, their findings would have been easier to compare and possibly apply (say for software design). 

As with workarounds, usability research has also exploded with EHR adoption.  The most obvious usability issue with EHRs—they are designed to provide data, not support processes — remains under-appreciated. Thankfully,  this is slowly changing.   

TURF defines the following usability concepts (8):

Useful - A system is useful if it supports the work domain where the users accomplish the goals for their work, independent of how the system is implemented.


Usable - A system is usable if it is easy to learn, easy to use, and error-tolerant.


Learnability -  Number of trials to reach a certain performance level,  number of items that need to be memorized, number of sequences of steps that need to be memorized


Efficiency - Time on task, task steps, task success, mental effort


Error Prevention and Recovery - Error occurrence rate, error recovery rate


Satisfying - A system is satisfying to use if the users have good subjective impression of how useful, usable, and likable the system is.


Note  the emphasis on work support within a specific domain and the centrality of tasks.  Clearly, usability has a process dimension. Consequently, it makes no sense to talk about usability without discussing processes.  EHR systems with poor usability result in workarounds.  However, EHR systems are only one cause of workarounds.  As anyone who has worked in a large bureaucratic organization knows, workarounds for paper-based processes happen all the time. 

Solving problems related to EHR use requires paying attention to processes and workflows; this much is now clear.  Clinical work —diagnosing a disease, writing a prescription, assessing hearing,  performing a patient intake — consists of processes, and processes can be analyzed, modeled and, to some degree, optimized.   Further, given the importance of processes in clinical care, the ability to analyze and model processes should be considered an essential skill for those charged with improving clinical systems—clinicians, informaticists, human factors specialists, software developers,  and others. 

Basic definitions
Articles about the effects of EHR systems on clinical processes have been written by a wide variety of researchers with different backgrounds and ways of describing workflows.  As might be expected, there is no common set of rules, guidelines, or modeling approaches that are shared by all.  Progress, whether in the form of better EHR systems or care quality/safety, requires greater attention to processes and a shared conceptualization of what processes are and how to document and model workflows.  To date, the most detailed and formal research on processes and workflows has been done within the academic IT and business communities.

Starting with graph theory, moving on to Petri nets, workflow patterns and finally business process management (BPM), our colleagues in IT and business have provided workflow tools that have a basis in mathematics, can model workflows at arbitrary levels of detail, are adaptable to any domain, and can be used to create production quality software.   In other words, there is no need to reinvent the wheel.  The only things missing are specific definitions for clinical workflow concepts.  With this in mind, I offer the following clinical workflow definitions, which will be used on  Clinical Workflow Center, and are strongly suggested for all article submissions in order to maintain consistency. 

Clinical Work - Activities  performed to assess, change, or maintain the health of a patient.

Clinical Process - A specific clinical work activity undertaken by one or more clinical professionals. Each clinical  process has a specific start point, end point, and an expected clinical outcome.

Information Metabolism - The movement of information in and out of clinical processes.  

Clinical Workflow Analysis - Methods/tools/techniques used to deconstruct processes in order to determine their exact steps, control-flow, participants, resources, and information metabolism.

Clinical  Workflow - The directed series of steps comprising a clinical process that 1) are performed by people or equipment/computers and consume, transform, and/or produce information. (Note that  patient outcomes count as information.)

Clinical Workflow Model - A human-readable, visual representation of a clinical workflow that can be executed by workflow technology.

 Workflow models are process representations.  The level of detail captured in a workflow is determined by its intended use.   Flowcharts may be sufficient for a general discussion, while software design or workflow technology applications require significantly more detail.  

In Part Two, we will look at specific types of clinical workflow disruptions.  

  1. Hill RG Jr, Sears LM, Melanson SW. 4000 clicks: a productivity analysis of electronic medical records in a community hospital ED. Am J Emerg Med. 2013 Nov;31(11):1591-4. 
  2. Zahabi M, Kaber DB, Swangnetr M. Usability and Safety in Electronic Medical Records Interface Design: A Review of Recent Literature and Guideline Formulation. Hum Factors. 2015 Aug;57(5):805-34. 
  3. Middleton B, Bloomrosen M, Dente MA, Hashmat B, Koppel R, Overhage JM, Payne TH,Rosenbloom ST, Weaver C, Zhang J; American Medical Informatics Association. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc. 2013 Jun;20(e1):e2-8. 
  4. Benda NC, Meadors ML, Zachary Hettinger A, Ratwani RM. Emergency Physician Task Switching Increases With the Introduction of a Commercial Electronic Health  Record. Ann Emerg Med 2015 Sep 10. [E]
  5. Improving Care: Priorities to Improve Electronic Health Record Usability. American Medical Association. 2014
  6. Flanagan ME, Saleem JJ, Millitello LG, Russ AL, Doebbeling BN. Paper- and computer-based workarounds to electronic health record use at three benchmark institutions. J Am Med Inform Assoc. 2013 Jun;20(e1):e59-66. 
  7. Friedman A, Crosson JC, Howard J, Clark EC, Pellerano M, Karsh BT, Crabtree B, Jaén CR, Cohen DJ. A typology of electronic health record workarounds in small-to-medium size primary care practices. J Am Med Inform Assoc. 2014 Feb;21(e1):e78-83 
  8. Zhang J, Walji MF. TURF: toward a unified framework of EHR usability. J Biomed Inform. 2011 Dec;44(6):1056-67.