We all have personal work habits that help us to accomplish complex tasks. We use colors, notes, and specific sequences to remind us of important steps or to check a specific data item. Processes that are repeated frequently usually give rise to personal workflows that are optimized, consciously and unconsciously, over the years. Software often interferes with personal workflows, which drives users crazy and leads to workarounds.
Unfortunately, software systems are designed for the average user and are often difficult to fine-tune to satisfy personal work habits. For those interested in EHR design, this raises an important and intriguing question: How much user dissatisfaction with EHR systems is due to disruptions in personal workflows?
Two papers explore personal workflow disruption in a way that I find particularly useful from a software design standpoint. The first article, Exploring the Persistence of Paper with the Electronic Health Record , published in 2009, identifies 11 categories of workarounds that occur with EHR systems. The methods and results of the initial study appear below.
Methods: We conducted semi-structured interviews with 20 key-informants in a large Veterans Affairs Medical Center (VAMC), with a fully implemented EHR, to understand the use of paper-based alternatives. Participants included clinicians, administrators, and IT specialists across several service areas in the medical center.
Results: We found 11 distinct categories of paper-based workarounds to the use of the EHR. Paper use related to the following: (1) efficiency; (2) knowledge/skill/ease of use; (3) memory; (4) sensorimotor preferences; (5) awareness; (6) task specificity; (7) task complexity; (8) data organization; (9) longitudinal data processes; (10) trust; and (11) security. We define each of these and provide examples that demonstrate how these categories promoted paper use in spite of a fully implemented EHR.
The workaround categories were later used to study EHR usage at three major healthcare sites. The results of the second study, described in Paper- and Computer-based Workarounds to Electronic Health Record Use at Three Benchmark Institutions, found that many of the workarounds were present at two or more institutions. Workaround categories appear in the table below.
|Observed across three institutions||Definition|
|Observed across two institutions|
Looking at the data from the second study, two things standout. First, the observed workarounds appear to fit into two categories. The first group encompasses those workarounds that address cognitive needs (memory and awareness) of the type that everyone uses to keep tabs on what is going on and what needs to be done. Typically, the workaround involved paper notes being used as a cue or reminder for a task.
The second group of workarounds seems to be more directly related to the EHR system’s design. For example, the knowledge/skill/ease of use category is directly tied to the difficulty in developing expertise in using the system. This is reflected in the need for more training.
Task complexity workarounds imply a failure by system designers to grasp the nuances involved in completing a task. Examples of this oversight might be assuming that ordering processes are essentially the same across specialties, and thereby failing to see how neonatal or oncology orders might require specialized order processes, as the authors noted. No correct path problems, likewise, represent a failure to capture a complete set of requirements for all the processes the EHR was intended to support. That EHR systems cause workflow headaches and result in users resorting to workarounds is old news. The question is how to address these problems as part of the EHR design process.
Obviously, the designers of these systems did the best they could at the time. By looking at workarounds that exist across different institutions and systems, it becomes possible to uncover process-based cognitive needs that users are unaware of until something like an EHR system changes their routine. It can be very difficult for software designers to capture these needs using typical design methods such as interviews and use cases. I have experienced this problem with software written for my own use. More than a few times I’ve had to change a design I was quite satisfied with until I actually began to use the software.
So where to begin? In the post, The EHR as an Object Worthy of Study, I argued for a greater focus on EHR system design research. Currently, the bulk of this research occurs in the private sector, greatly limiting knowledge sharing and the vetting of design choices and technical improvements. We have a plethora of informatics standards, but none that focus on the details of EHR design.
The recent focus on usability might help to improve this situation. However, the usability push does not (at least currently) encompass basic research on database schema, data structures, workflow notation, or algorithms specific to clinical systems. Fundamental definitions and concepts that are essential for building good systems are lacking. There are no standard, objective definitions of EHR data quality (1) or units of clinical workflow (2, 3). Without standard definitions, it is difficult to build good systems, analyze EHR designs, or compare research findings across HIT projects.
I think it is safe to say that no one knows how to build an ideal EHR system or even state, at the design level, exactly what an ideal EHR system is. Experience has shown that EHR systems are not simply front-ends to databases nor merely a mélange of functions and features. I am not advocating that ONC or a standards group mandate a design standard for EHR systems. No, my point is that just as there are professions dedicated to designing bridges, airplanes, and power grids, EHR systems, which in their own way are just as complex, require similar rigor and formal methods in their development. Until these facts are acknowledged and EHR design becomes a more active research domain with its own dedicated cadre of professionals, articles detailing the latest ways that EHRs are frustrating users will continue to appear.
- Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013 Jan 1;20(1):144-51.
- Zheng K, Guo MH, Hanauer DA. Using the time and motion method to study clinical work processes and workflow: methodological inconsistencies and a call for standardized research. J Am Med Inform Assoc. 2011 Sep-Oct;18(5):704-10.
- Unertl KM, Novak LL, Johnson KB, Lorenzi NM. Traversing the many paths of workflow research: developing a conceptual framework of workflow terminology through a systematic literature review. J Am Med Inform Assoc. 2010 May-Jun;17(3):265-73.