Adherence to Patient-Reported Symptom Monitoring and Subsequent Clinical Interventions for Patients With Multiple Myeloma in Outpatient Care: Longitudinal Observational Study

Implementing Systematic Patient-Reported Measures for Chronic Conditions Through the Naveta Value-Based Telemedicine Initiative: Observational Retrospective Multicenter Study

BACKGROUND

Patient-reported outcome and experience measures can play a critical role in providing patient-centered and value-based health care to a growing population of patients who are chronically ill. Value-based telemedicine platforms such as the Naveta initiative may facilitate the effective integration of these tools into health care systems.

OBJECTIVE

This study aims to evaluate the response rate to electronic patient-reported outcome measures (ePROMs) and electronic patient-reported experience measures (ePREMs) among patients participating in the Naveta telemedicine initiative and its correlations with sociodemographic and clinical characteristics, as well as the evolution of the response rates over time.

METHODS

Between January 1, 2021, and June 30, 2023, a total of 53,364 ePREMs and ePROMs for 20 chronic conditions were administered through the Naveta-Phemium platform. Descriptive statistics were used to summarize continuous and categorical variables. Differences in response rates within each sociodemographic variable were analyzed using logistic regression models, with significance assessed via chi-square and post hoc Tukey tests. Two-way ANOVA was used to examine the interaction between time interval and disease type on response rate evolution.

RESULTS

A total of 3372 patients with severe chronic diseases from 64 public hospitals in Spain participated in the Naveta health questionnaire project. The overall response rate to ePROMs and ePREMs during the first 2.5 years of the Naveta initiative was 46.12% (24,704/53,364), with a baseline rate of 53.33% (7198/13,496). Several sociodemographic factors correlated with lower response rates, including male gender, older age, lower education level, frequent alcohol use, being a student, and not being physically active. There were also significant variations in response rates among different types of chronic conditions (P<.001), with the highest rates being for respiratory (433/606, 71.5%), oncologic (200/319, 62.7%), digestive (2247/3601, 62.4%), and rheumatic diseases (7506/12,982, 57.82%) and the lowest being for HIV infection (7473/22,695, 32.93%). During the first 6 months of follow-up, the response rates decreased in all disease types, except in the case of the group of patients with oncologic disease, among whom the response rate increased up to 100% (6/6). Subsequently, the overall response rate approached baseline levels.

CONCLUSIONS

Recognizing the influence of sociodemographic factors on response rates is critical to identifying barriers to participation in telemonitoring programs and ensuring inclusiveness in patient-centered health care practices. The observed decline in response rates at follow-up may be due to survey fatigue, highlighting the need for strategies to mitigate this effect. In addition, the variation in response rates across chronic conditions emphasizes the importance of tailoring telemonitoring approaches to specific patient populations.

Patient-reported symptom monitoring: using (big) data to improve supportive care at the macro-, meso-, and micro-levels

PURPOSE

This paper aims to provide a comprehensive understanding of the need for continued development of symptom monitoring (SM) implementation, utilization, and data usage at the macro-, meso-, and micro-levels.

METHODS

Discussions from a patient-reported SM workshop at the MASCC/ISSO 2022 annual meeting were analyzed using a macro-meso-micro analytical framework of cancer care delivery. The workshop categories "initiation and implementation, barriers to adoption and utilization, and data usage" were integrated for each level.

RESULTS

At the macro-level, policy development could encourage data sharing and international collaboration, including the exchange of SM methods, supportive care models, and self-management modules. At the meso-level, institutions should adjust clinical workflow and service delivery and promote a thorough technical and clinical integration of SM. At the micro-level, SM should be individualized, with timely feedback for patients, and should foster trust and understanding of AI decision support tools amongst clinicians to improve supportive care.

CONCLUSIONS

The workshop reached a consensus among international experts on providing guidance on SM implementation, utilization, and (big) data usage pathways in cancer survivors across the cancer continuum and on macro-meso-micro levels.