Product Engagement Score evaluation for Noona Patient Engagement Platform
Notice bibliographique
Résumé
In the field of oncology, there is increasing evidence that real-time patient-provider communication of symptoms through Patient Engagement Platforms (PEP) like Noona has been showing an impact on a range of outcomes including improved treatment adherence, better symptom management, and overall enhanced well-being. Noona is a cloud-based, multifeatured PEP that facilitates real-time communication of patient-reported cancer treatment symptoms as well as clinical care team (CCT) recommendations. PEP success requires consistent and sustained use, which can be quantified using patient engagement metrics to explicate site-level app use and, potentially, cross-site benchmarking. However, such use has yet to be investigated. The purpose of this exploratory study is to find out the insights to indicate potential drivers and barriers for patient engagement with Noona by looking into the Product Engagement Score (PES) metric and its components. \nNoona users receiving surgery, systemic therapy, and/or radiotherapy from July to December 2023 (n=31,396) at clinics in the USA, Canada, and Europe (n=10) were included. Patient interactions with Noona were passively collected with PES comprised of three components: Adoption (use of ten specified features), Stickiness (percentage of users within a month that comes back weekly), and Growth (ratio between new and/or reengaged patients and nonusers within a calendar month). Data was interpreted with the help of semi-structured interviews with the Customer Success Manager (CSM) overseeing a particular clinic and further analyzed employing the thematic analysis method. \nThe study showed that PES can serve as a valuable indicator of user engagement with the tool. In the oncology setting, PES has special utility by offering real-time insights into patient usage, thereby streamlining the feedback loop without adding extra steps for patients, ultimately facilitating platform improvement and reducing patient burden. The results revealed the main themes impacting patients’ engagement with the tool – CCT use and perception of Noona, features driving higher adoption and stickiness, expansions and trainings provided to CCT. Adoption was generally consistent across time but varied across sites because of differential feature utilization. Stickiness was higher for sites that utilize features stimulating regular review (e.g., appointments, education, questionnaires, lab results). Growth fluctuated within and across sites because of clinic operations (e.g., expansion to additional clinic locations, conducted trainings for CCT). Generally, lower growth was associated with holidays (November-December in the USA and Canada). PES fluctuated across sites and time. \nPatient engagement metrics facilitate understanding of site-level PEP use, uptake, and longitudinal trends. However, benchmarking is limited by Noona’s bespoke customization, which maximizes CCT resources and workflows, and other site-level differences.
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 | 0,009 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».