Examining the Usefulness of Patient Documentation Forms as a Tool for Community Health Navigators
Notice bibliographique
Résumé
Introduction | Effective documentation of patient encounters may influence Community Health Navigators’ (CHNs) success in providing support to patients as well as provide a data source to examine CHN practices. The ENhancing COMmunity health through Patient navigation, Advocacy, and Social Support (ENCOMPASS) study, based in partnership between the University of Calgary and the Mosaic Primary Care Network (MPCN) is evaluating a CHN program to determine whether CHNs improve outcomes for patients with multiple chronic conditions. CHNs support their patients by helping them navigate the health system, connect to community resources, and access culturally appropriate support. The purpose of this study was to examine the quality and usefulness of CHN-patient documentation forms used in the ENCOMPASS pilot study (i.e., Initial Action Planning Form, Follow-up Action Planning Form, Patient Encounter Form, all implemented on the REDCap platform) and revise the documentation process using co-design with the end user. Methods | An iterative co-design quality improvement process was employed across three phases. First, content analyses were conducted on the Patient Encounter Form notes to examine how CHNs were using the forms and how they were documenting their activities. Second, a survey was distributed to CHNs to gather their perspectives about their experiences with the REDCap platform and the three forms. Third, a working group, consisting of four CHNs, met twice with research team members to discuss barriers to use and opportunities for improvement. Results | The REDCap platform and the three CHN-patient encounter forms did not adequately meet the needs of the CHNs. Content analysis revealed significant variation in how the Patient Encounter Form was utilized and various form sections were not completed as intended. In the survey, CHNs reported that the documentation experience was not satisfactory and the training that they had received to date was insufficient. The CHN working group suggested changes to the interface with the REDCap platform and form structure. Revisions were made based on these suggestions, and approved by the working group. Conclusions | The approved changes to REDCap and the three forms will be implemented and introduced to the CHN team. The research team will develop a patient encounter documentation guidelines document and will provide all members of the CHN team with the opportunity to receive re-training. These changes will be reviewed with the CHNs to continue the iterative quality improvement process. Prior to final implementation, consultation with the Clinical Research Unit administrators on the feasibility of the revisions made to the forms and interface with the REDCap platform will be held. The results of this study have the potential to provide a better overall experience for CHNs in the ENCOMPASS program and enhance their work with patients.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,162 | 0,309 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».