Workarounds Emerging From Electronic Health Record System Usage: Consequences for Patient Safety, Effectiveness of Care, and Efficiency of Care
Bibliographic record
Abstract
BACKGROUND: Health care providers resort to informal temporary practices known as workarounds for handling exceptions to normal workflow unintendedly imposed by electronic health record systems (EHRs). Although workarounds may seem favorable at first sight, they are generally suboptimal and may jeopardize patient safety, effectiveness of care, and efficiency of care. OBJECTIVE: Research into the scope and impact of EHR workarounds on patient care processes is scarce. This paper provides insight into the effects of EHR workarounds on organizational workflows and outcomes of care services by identifying EHR workarounds and determining their rationales, scope, and impact on health care providers' workflows, patient safety, effectiveness of care, and efficiency of care. Knowing the rationale of a workaround provides valuable clues about the source of origin of each workaround and how each workaround could most effectively be resolved. Knowing the scope and impact a workaround has on EHR-related safety, effectiveness, and efficiency provides insight into how to address related concerns. METHODS: Direct observations and follow-up semistructured interviews with 31 physicians, 13 nurses, and 3 clerks and qualitative bottom-up coding techniques was used to identify, analyze, and classify EHR workarounds. The research was conducted within 3 specialties and settings at a large university hospital. Rationales were associated with work system components (persons, technology and tools, tasks, organization, and physical environment) of the Systems Engineering Initiative for Patient Safety (SEIPS) framework to reveal their source of origin as well as to determine the scope and the impact of each EHR workaround from a structure-process-outcome perspective. RESULTS: A total of 15 rationales for EHR workarounds were identified of which 5 were associated with persons, 4 with technology and tools, 4 with the organization, and 2 with the tasks. Three of these 15 rationales for EHR workarounds have not been identified in prior research: data migration policy, enforced data entry, and task interference. CONCLUSIONS: EHR workaround rationales associated with different SEIPS work system components demand a different approach to be resolved. Persons-related workarounds may most effectively be resolved through personal training, organization-related workarounds through reviewing organizational policy and regulations, tasks-related workarounds through process redesign, and technology- and tools-related workarounds through EHR redesign efforts. Furthermore, insights gained from knowing a workaround's degree of influence as well as impact on patient safety, effectiveness of care, and efficiency of care can inform design and redesign of EHRs to further align EHR design with work contexts, subsequently leading to better organization and (safe) provision of care. In doing so, a research team in collaboration with all stakeholders could use the SEIPS framework to reflect on the current and potential future configurations of the work system to prevent unfavorable workarounds from occurring and how a redesign of the EHR would impact interactions between the work system components.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".