Medical Marijuana Documentation Practices in Patient Electronic Health Records: Retrospective Observational Study Using Smart Data Elements and a Review of Medical Records
Notice bibliographique
Résumé
BACKGROUND: Medical Marijuana (MMJ) is available in Pennsylvania (PA) and participation in the state-regulated program requires a patient to register and receive certification by an approved physician. There is currently no integration of MMJ certification data in PA into health records that would allow for clinicians to rapidly identify patients that are using MMJ, as there are with other scheduled drugs. This absence of a formal data sharing structure necessitates tools that aid in consistent documentation practices to enable comprehensive patient care. Customized smart data elements (SDEs) were made available to clinicians at an integrated health system, Geisinger, following MMJ legalization in PA. OBJECTIVE: The purpose of this project was to examine and contextualize the use of MMJ SDEs in the Geisinger population. We accomplished this goal by developing a systematic chart review protocol, with the goal of creating a tool that resulted in consistent human data extraction. METHODS: We developed a chart review protocol for extracting MMJ-related information. The protocol was developed between August to December of 2022 and focused on a patient group that received one of several MMJ SDEs between 1/25/2019 and 5/26/2022. Characteristics were first identified on a small pilot sample of patients (n=5), which were then iteratively reviewed to optimize for consistency. Following the pilot, two reviewers were assigned 200 patient charts, selected randomly from the larger cohort, with a third reviewer examining a subsample (n=30) to determine reliability. We then summarized the clinician-level and patient-level features from 156 charts with a table-format SDE that best captured MMJ information. RESULTS: We found the chart review protocol was feasible for those with minimal medical background to complete, with high inter-rater reliability (Kappa = .966 (P<.001), 95% CI (.954 - .978)). MMJ certification was largely documented by nurses and medical assistants (88.5%) and typically within primary care settings (68.6%). The SDE has six pre-set field prompts with heterogeneous documentation completion rates, including certifying conditions (93.6%), product (92.9%), authorized dispensary (87.8%), active ingredient (83.3%), certifying provider (61.5%), and dosage (30.8%). We found pre-set fields were overall well-recorded (76.6% across all fields). Primary diagnostic codes recorded at documentation encounters varied, with the most frequent being routine exams and testing (21.8%), musculoskeletal/nervous conditions (13.5%), and signs and symptoms not classified elsewhere (13.5%). CONCLUSIONS: This method of chart review yields high quality data extraction that can serve as a model for other health record inquiries. Our evaluation showed relatively high completeness of SDE fields, primarily by clinical staff responsible for rooming patients. Additional data captured presents an overview of the conditions under which MMJ is currently being documented. Improving adoption and fidelity of SDE data collection may present a valuable data source for future research on patient MMJ use, treatment efficacy, and outcomes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,075 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».