Impact of Computerized Provider Order Entry Systems on hospital staff pharmacist workflow productivity: A three site comparative analysis based on level of CPOE implementation
Bibliographic record
Abstract
Objective: Computerized Provider Order Entry (CPOE) is a system that enables physicians to send medication orders electronically rather than physically writing out the order. CPOE can reduce handwriting and transcription related medication errors and has been a major implementation goal for health systems. The objective of this study was to quantify and examine differences seen in the workflow of pharmacists at hospitals, with different levels of CPOE implementation.Methods: An observational, prospective time and motion study was conducted among three hospitals within the same health system: one classified as a non-CPOE system, one as short-term CPOE, and one as long-term CPOE. Pharmacists were observed in one-hour blocks, in which a data instrument was used to record 38 different tasks, which were grouped into four activities: clinical, distributive, administrative, and miscellaneous. The distributive category was further divided into three sub-categories. The average time associated with performing activities across the three hospitals was compared by descriptive and comparative analyses using ANOVAs and the post-hoc Tukey’s range test.Results: A total of 252 hours were collected and 235 met the inclusion criteria. The significant differences in time spent on task categories among hospitals were as follows: Non-CPOE vs. short term CPOE vs. long-term CPOE (mean ± SD in min/h) clinical tasks: (6.55 ± 6.40) vs. (4.95 ± 4.15) vs. (3.79 ± 4.91), respectively, (p < .05); order entry tasks: (29.62 ± 11.24) vs. (17.44 ± 10.73) vs. (10.27 ± 8.88) respectively, (p < .05); order verification tasks: (0.88 ± 1.77) vs. (13.93 ± 8.50) vs. (16.60 ± 9.63) respectively, (p < .05); other distributive tasks: (13.60 ± 10.04) vs. (15.86 ± 8.38) vs. (19.66 ± 8.42) respectively, (p < .05); and miscellaneous: (3.78 ± 4.64) vs. (1.54 ± 3.20) vs. (2.23 ± 3.51) respectively, (p < .05).Conclusions: The presence of a CPOE system could affect pharmacists’ workflow and time allotment on different types of pharmacy activities. Further, the time spent on certain activities was associated with the amount of time the CPOE system was implemented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".