MétaCan
Menu
Back to cohort
Record W2778817261 · doi:10.5430/jha.v7n1p1

Impact of Computerized Provider Order Entry Systems on hospital staff pharmacist workflow productivity: A three site comparative analysis based on level of CPOE implementation

2017· article· en· W2778817261 on OpenAlexvenueno aff
Benjamin Lewing, Mark Hatfield, Sujit S. Sansgiry

Bibliographic record

VenueJournal of Hospital Administration · 2017
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
FundersAgency for Healthcare Research and Quality
KeywordsOrder entryMedicinePharmacistWorkflowComputerized physician order entryPost-hoc analysisObservational studyClinical pharmacyHealth careMedical emergencyNursingPharmacyComputer scienceInternal medicineDatabase

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.145
GPT teacher head0.496
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hospital AdministrationSame topicElectronic Health Records SystemsFrench-language works237,207