Product Engagement Score evaluation for Noona Patient Engagement Platform
Bibliographic record
Abstract
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.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".