Application of a Palm OS Patient Monitoring Tool in an Infectious Diseases Consult Service
Bibliographic record
Abstract
ABSTRACT Personal digital assistants (PDAs) and their application software must be evaluated to determine their suitability to either an individual’s or a department’s clinical services. In an infectious diseases clinical pharmacy service, accuracy, efficiency, and organization of documentation are required to ensure effective provision of pharmaceutical care to patients. The features and capabilities of PDAs can play an important role for the infectious diseases pharmacist. One of the objectives of this project was to assess the feasibility of incorporating an electronic patient monitoring form into infectious diseases pharmacy services. In 2002, a pharmacy resident, working with the clinical coordinator for infectious diseases, developed an electronic patient monitoring form for the infectious diseases consult service. The monitoring form was pilot-tested during the pharmacy resident’s 5-week clinical rotation. During that period, a paperless system was used to track and monitor patients referred to the service. The electronic monitoring form reduced the pharmacist’s clinical workload by consolidating patient information in one medium. Furthermore, data collected from monitored patients were directly synchronized and stored in a Microsoft Access database, which allowed for more efficient data retrieval and analysis. The development and implementation of a suitable electronic patient monitoring form for the infectious diseases consult service provided assurance of the benefits of a PDA in a specialized area of clinical practice by facilitating clinical monitoring and data collection activities. RESUME Les assistants numeriques personnels (ANP), ou PDA en anglais, et leurs logiciels doivent etre evalues afin de determiner s’ils conviennent ou non a une personne ou aux services cliniques d’un departement. Dans un service de pharmacie clinique en infectiologie, la precision, l’efficacite et l’organisation de la documentation sont necessaires a la prestation efficace des soins pharmaceutiques aux patients. Les fonctions et les capacites d’un ANP peuvent jouer un role important pour le pharmacien specialise en infectiologie. L’un des objectifs de ce projet etait d’evaluer la possibilite d’incorporer un formulaire electronique de suivi des patients dans les services de pharmacie en infectiologie. En 2002, un resident en pharmacie, en collaboration avec le coordonnateur du service de pharmacie clinique en infectiologie, ont mis au point un formulaire electronique de suivi des patients pour le service de consultation en infectiologie. Le formulaire de suivi a fait l’objet d’un essai pilote durant le stage de cinq semaines du resident. Au cours de cette periode, un systeme zero-papier a ete utilise pour retracer et faire le suivi des patients adresses au service. Le formulaire electronique de suivi a reduit la charge de travail clinique du pharmacien en consolidant l’information sur le patient dans un seul et meme outil. De plus, les donnees recueillies sur les patients suivis ont ete directement synchronisees et mises en memoire dans une base de donnees Access de Microsoft, ce qui a permis l’extraction et l’analyse plus efficaces des donnees. L’elaboration et la mise sur pied d’un formulaire electronique de suivi des patients approprie dans un service de consultation en infectiologie a confirme les avantages de l’utilisation d’un ANP dans un milieu de pratique clinique specialise, en facilitant les activites de suivi clinique et de collecte des donnees.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".