Clinical and Education Workload Measurements Using Personal Digital Assistant–Based Software
Bibliographic record
Abstract
OBJECTIVE: There are no accepted and practical measures of the relative clinical and educational activities of pediatricians who work in an academic health science center. Such measures are necessary for justification of existing and future human resource plans and evaluation of the activities and performance of physicians. The limited literature on the measurement of physician workload usually focuses on a specific subspecialty group and does not account for such issues as indirect patient care, such as telephone calls or e-mail consultations; variables that affect the delivery of clinical care, including patient acuity and complexity; and the presence of students during the patient care activities. After completing a pilot study that assessed the educational workload of faculty members, we adapted existing personal digital assistant technology and software to document clinical and educational activities. METHODS: Twenty full-time physicians from 4 subspecialty pediatric divisions participated in a 2-week evaluation project in May through June 2005. Clinical activities, with and without trainees, and educational activities were collected with the use of personal digital assistants. Software allowed an individualized division-specific drop-down menu. Information that was collected included clinical (location of activity, diagnosis, and time requirement) and educational activities. After completion of a 2-week data collection period, each physician was asked to complete a 5-question evaluation form. RESULTS: The project was completed successfully with capture of additional clinical and educational activities. A 5-question evaluation form was completed by 70% of the participants at the end of the 2-week data collection. Data on clinical and educational activities were analyzed qualitatively and graphed. CONCLUSIONS: This method of workload data collection added significant information in capturing activities that are not measured in traditional workload evaluations for either clinical activities, such as e-mail, telephone, and patient information review, or educational endeavors, including mentoring and educational lectures and presentations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".