Remote Symptom Monitoring Research Articles

Sociodemographic difference in patients who enroll and decline remote symptom monitoring (RSM).

268 Background: Remote symptom monitoring (RSM) using patient-reported outcomes has been shown to reduce symptom burden and hospitalizations in clinical trials. However, little is known about how willing patients are to participate in remote symptom monitoring in real-world settings, particularly for vulnerable patient populations. This study aims to compare characteristics of cancer patients enrolled vs. patients who declined enrollment into RSM. Methods: This prospective study used data that assessed the characteristics of patients who enrolled vs. patients who declined enrollment into RSM. Inclusion criteria included participants’ age ≥18 with cancer who received chemotherapy, targeted therapy, or immunotherapy at the University of Alabama at Birmingham. Race and ethnicity (Black or African American, White, Asian, other and unknown), sex, cancer type (breast, gastrointestinal [GI], genitourinary [GU], gynecological [GYNX], head and neck, leukemia, lymphoma, melanoma, myeloma and other), urban/rural residence, Area Deprivation Index (ADI), and insurance type (Medicaid, Medicare, none, other and private) were abstracted from electronic medical records (EMR) and PRO platform (Carevive). Descriptive statistics were calculated using frequencies and percentages for categorical variables and medians and interquartile ranges for continuous variables. Differences in enrollment status characteristics were calculated using measures of effect size such as Cramer’s V. Results: Of the 307 patients, two thirds of patients were female (71%); 25% were Black or African American and 66% were White patients; 15% lived in an area of higher disadvantage. For insurance, 46%, 26%, 10%, 8%, and 9% of patients had Private, Medicare, Medicaid, other insurance, and no insurance, respectively. The proportion of patients who declined enrollment was higher for males than females (22% vs. 10%), Black or African American than White (18% vs 13%); and having Medicare than private insurance (22% vs. 10%). Compared to those who enrolled, patients who declined enrollment were more often to be male (V:0.2), Black or African American (V:0.1); and have Medicare insurance (V:0.2). Patients enrolled vs. declined in RSM had similar ADI scores (V:0.01). Conclusions: This study demonstrates that potentially vulnerable patients, including Black patients and those with public insurance, have lower RSM engagement. Future analysis is needed to understand participation barriers and how to better engage diverse populations to ensure optimal healthcare delivery to all patients.

Nursing strategies to improve alert closure for remote symptom monitoring.

421 Background: For successful remote symptom monitoring using patient-reported outcomes, nurses should respond to alerts in a timely fashion. Where clinical trials utilized research staff for alert management, the shift to standard-of-care delivery necessitates that this responsibility be added as a task to an already strained nursing workforce. Little is known about strategies to engage nurses to improve timeliness of alert management. Methods: In this quality improvement initiative, we aimed to improve timeliness of alert closures generated by moderate or severe symptoms within a remote symptom monitoring program. Optimal closure was defined as < 48 hours, which was consistent with institutional requirements for response to patient phone calls. A continuous quality improvement approach, with multiple Plan Do Study Act (PDSA) cycles was conducted. Data was captured from the electronic medical record and PRO platform (Carevive). Descriptive statistics included frequencies and percentages. The proportion of alerts closed each month < 48 hours, 48-72 hours, 3-7 days, and > 7 days were reported overall and by disease team (i.e., major cancer types). Surveys not closed were considered > 7 days. The timing of strategies to improve nursing engagement were documented and evaluated for impact on alert closure. Results: From June 1, 2021-May 31, 2022, 1121 moderate or severe alerts were generated from 234 patients. Disease teams had variable remote symptom monitoring start dates: breast, leukemia, and limited gynecologic (prior to 6/2021); myeloma and gastrointestinal (7/2021); genitourinary (10/2021); head and neck (12/2021); melanoma (2/2022); and Lymphoma (4/2022). In 6/2021, the overall alert closure at < 48 hours, 48-72 hours, 3-7 days, and > 7 days was 57%, 4%, 14%, and 25% respectively (n = 28). To improve alert closures, several key strategies were deployed to improve alert closure times including disease-specific reporting and meetings with nursing leadership (10/2021); identification of a nurse champion, creation of “cheat sheets” to remind nurses how to close alerts, and individualized calls with nurses with open alerts (1/2022), and inclusions of requirement to close alerts in nursing newsletters (2/2022). Overall, alert closure less than 48 hours improved to 61% by 12/2021 (n = 97) and to 69% by 5/2022 (n = 167). Disease group alert closure varied, with higher closure more commonly in teams with greater duration of use, such as breast cancer team with an alert closure of 85% < 48 hours in May 2022. Conclusions: Key nursing engagement strategies improve alert closure for remote symptom monitoring programs implemented in real-world settings.

Trajectory of symptoms reported in remote symptom monitoring over the course of oncology treatment for gynecologic cancers.

270 Background: Patients now have the ability to utilize electronic patient reported outcomes (ePROs) for remote symptom monitoring (RSM). This analysis seeks to better understand trajectory of reported symptoms during treatment for patients with gynecologic cancer participating in RSM. Methods: We approached patients with gynecological cancer initiating treatment at the Mitchell Cancer Institute (MCI) between 7/1/21-4/30/2022. Patients were eligible if they were starting chemotherapy, targeted therapy, or immunotherapy for a new cancer. Patients seeking a second opinion were excluded. Enrolled patients received symptom survey (PRO-CTCAE questions) via text or email once per week. Initially, only severe alerts were forwarded to the clinical care team; moderate alerts were forwarded to clinical teams once they were comfortable with alert management. Patients completed symptom assessments for 24 weeks or until withdrawal. Patient age at enrollment, race, sex, cancer type, cancer stage, and PROs were abstracted from electronic health records and the PRO platform (Carevive). Descriptive statistics were calculated using frequencies and percentages for categorical variables and median and interquartile ranges (IQR) for continuous variables. Results: A total of 60 female patients with gynecological cancer were enrolled; 33% were Black or African American and 67% were White; median age was 61 years (IQR 53-68). Seventy-eight percent (47/60) of patients reported 379 symptoms with at least one moderate or severe alert during this time period; 32% considered moderate and 68% considered severe. Overall, the most frequently reported symptom was pain (29%). At baseline (week 0), 14% and 41% of 56 patients reported moderate symptoms and severe symptoms, respectively. Symptom burden decreased over time with 4% and 7% of 27 patients who completed a survey at 12 weeks reporting moderate and severe symptoms. Specific symptom trajectories followed similar patterns. Conclusions: In our sample, patients reported the majority of symptoms during the first three months of treatment. Symptom trajectory decreased with time, suggesting symptoms are being effectively monitored and addressed by the clinical teams engaging in RSM. Future research is needed to understand if symptom improvement translates to increased quality of life, decreased hospitalizations, and increased survival for patients, as well as lessen the burden of call volume on the clinical team.

Evaluating nurses' time to response by severity and cancer stage in a remote symptom monitoring program.

341 Background: Remote symptom monitoring (RSM) using electronic patient reported outcomes (ePROs) allow for patients with cancer to communicate symptoms to their clinical team between clinic visits. Prior randomized control trials of RSM focused on advanced cancer, and less data are available for patient with early stage cancers. The University of Alabama at Birmingham (UAB) implemented RSM for early stage (I-III) and advanced stage (IV) patients on active treatment. This study evaluates nurses’ real-world response time to alerts by varying severity and by patients cancer stages. Methods: This study included women with stage I-IV breast cancer who received care at UAB from October 2020 through May 2022. The program was first implemented in the breast clinic allowing for larger patient numbers with early and advanced stage breast cancer. A composite score for symptom severity is automatically calculated in the Carevive® platform for moderate, severe, or worsening symptoms using patient responses for frequency, severity, and interference. The nurse receives an alert if a symptom is moderate or severe. Surveys with at least one severe alert were categorized as severe and response time was categorized as optimal if the survey was closed within 48 hours (goal time for phone message follow-up). Odds ratios (OR), predicted probabilities, and 95% confidence intervals (CI) were estimated using a patient nested logistic regression evaluating time to response comparing surveys with at least one severe alert notification to those with no severe, adjusting for age at enrollment, race, cancer stage, provider who closed the surveys, and quarter from study start and date. An interaction between severity and cancer stage was evaluated. Results: Of 137 patients included in this study, 64% were White; 86% were diagnosed with early-stage breast cancer. The median age at diagnosis was 54 (27-79). Of 802 surveys included, 38% reported at least one severe symptom and 70% had an optimal response time. Similar results were seen when stratified by early vs. advanced stage with 39% and 38% reporting at least one severe alert and 68% and 71% an optimal response time, respectively. In our adjusted analysis, when compared with surveys that had no severe alerts, surveys with at least one severe alert had similar odds of having an optimal response time (OR, 1.29; 95%CI, 0.88, 1.89). No significant interaction between severity and stage was observed on the odds of optimal response time. Conclusions: Response times to alerts were similar regardless of the severity of the alert and cancer stage, suggesting alert management is incorporated into routine workflows and not prioritized based on disease or alert severity. Additional research is needed to understand factors contributing to non-optimal response times.

Identification of target population in the implementation of navigator-delivered home ePRO for patients with cancer receiving treatment.

351 Background: One key challenge of practice transformation activities, such as remote symptom monitoring (RSM) using electronic patient reported outcomes (ePROs), is identification of patients starting treatment. In real-world settings, reliance on referrals is likely to miss patients. We describe the difficulties encountered in patient identification and the subsequent changes implemented in protocol to remediate this. Methods: We conducted two PDSA cycles focused on identification and engagement of patients for RSM at the Mitchel Cancer Institute (MCI). Target patient capture was > 75%. Modifications to the patient identification process were documented. Schedules of physicians participating in the RSM program were reviewed from 6/2021 – 5/2022 to identify eligible patients. Patients were considered eligible if they were starting chemotherapy, targeted therapy, or immunotherapy. Patients seeking a second opinion were excluded. Patient demographics, cancer type, cancer stage, and PROs were abstracted from electronic health records and the PRO platform (Carevive). Initial clinic roll-out was conducted in gynecologic oncology, with expansion to breast and thoracic oncology in 10/2021 and 3/2022, respectively. The proportion of eligible patients approached per month was reported.Results: In the first PDSA cycle, the eligibility criteria was defined. Although clinical trials included advanced disease, non-clinical staff screening expressed concern about determining advanced vs. early-stage disease. Thus, inclusion criteria was broadened to include all patients starting treatments. From 6/2021 –8/2021, navigators identified patients by screening patients who presented for chemo-education visits. The navigation team approached 23 patients during this period. However, this process didn’t identify all eligible patients as not all patients beginning treatment received chemo-education visits. In PDSA Cycle 2, the process for new patient contact from initial call for appointment through treatment was reviewed. The implementation team screened all patients in a physician’s schedule a week prior to the office visit as well as on the day of visit. This updated process identified all eligible patients starting either intravenous or oral chemotherapy. The recruitment process was modified to screen the physician schedules rather than chemo educator visits. From 9/2022-5/22, the proportion of eligible patients identified and approached remained high at 100%. This methodological screening process helped the navigation team identify all eligible patients in an efficient manner and they reported comfort in expanding to additional disease teams. Conclusions: Systematic screening of physician schedules can be successfully leveraged for patient identification and reduce time spent manually screening for eligible patients by non-clinical navigators. Clinical trial information: NCT04809740.

Adaptation of remote symptom monitoring using electronic patient-reported outcomes for implementation in real-world settings.

272 Background: Despite evidence of clinical benefits, widespread implementation of remote symptom monitoring has been limited. We describe a process of adapting a remote symptom monitoring intervention developed in a research setting to a real-world clinical setting at two cancer centers. Methods: This formative evaluation assessed core components and adaptations to improve acceptability and fit of remote symptom monitoring using Stirman’s Framework for Modifications and Adaptations. Implementation outcomes were evaluated in pilot studies at the two cancer centers testing technology (Phase I) and workflow (Phase II and III) using electronic health data; qualitative evaluation with semi-structured interviews of clinical team members; and capture of field notes from clinical teams and administrators regarding barriers and recommended adaptations for future implementation. Results: Core components of remote symptom monitoring included electronic delivery of surveys with actionable symptoms, patient education on the intervention, a system to monitor survey compliance in real-time, the capacity to generate alerts, training nurses to manage alerts, and identification of personnel responsible for managing symptoms. In the pilot studies, while most patients completed > 50% of expected surveys, adaptations were identified to address barriers related to workflow challenges, patient and clinician access to technology, digital health literacy, survey fatigue, alert fatigue, and data visibility. Conclusions: Using an implementation science approach, we facilitated adaptation of remote symptom monitoring interventions from the research setting to clinical practice and identified key areas to promote effective uptake and sustainability.

Telehealth closing racial/ethnic disparities? Increased adherence with remote symptom monitoring using patient-reported outcomes (PROs) among Black and Hispanic patients with telemedicine compared to in-person visits.

392 Background: Adherence with remote PROs in the real-world, non-clinical trial setting remains varied in pts with cancer. The rapid widespread telehealth into routine healthcare delivery raises concern for disparate impact on race/ethnic minorities. In this study, we sought to understand the impact of telehealth via electronic health record (EHR) on pt adherence with PRO-based remote symptom monitoring implemented through that same EHR portal. Methods: We queried a prospective institutional database of all PROs remotely delivered to our pts bw 1/1/18 - 12/31/21. Pts were divided into 2 time cohorts (pre-pandemic 1/1/18 to 3/31/20, mid-pandemic 4/1/20 to 12/31/21), their self-identified race/ethnicity, and age cohorts (AYA 15-39y, midage 40-64y, older adults 65y+). Then, one representative global symptom burden PRO measure used across different clinics was analyzed for completion in a representative month over 4 years, 2 pre- (Oct ’18, ‘19) and 2 mid-pandemic (Oct ’20, ‘21). We calculated descriptive statistics and compared (t-test, ANOVA) between time, race/ethnicity and age cohorts and independent variables. Results: Over 4 years, 93,875 unique patients received 1+ EHR-based remote PROs within their routine cancer care. Remote EHR-based PRO response rate almost doubled from pre-pandemic (35%, 12011 responders of 34742 pts) to mid-pandemic time (67%, 39812 of 59200 pts, p < 0.00001). Analysis of one global symptom burden PRO widely used at our institution pre- and during pandemic showed the response rates rising (56.7% pre-pandemic vs 84.9% mid-pandemic, p 0.0004) with the sharp rise traced to 2nd and 3rd quarters of 2020 (response rate, 42.4% vs 85%, p < 0.001), with the transition to telehealth. Within the representative 4m period, 2738 pts (1075 pts in 2 pre- & 1663 in 2 mid-pandemic months) were sent that PRO – median age 60y, range 17-94y; 290 AYA 15-39y, 1444 midage 40-64y, 1004 older adults 65y+. PRO response rate increased (52% pre- to 81% mid-pandemic) w more responding without reminders from clinic team (13% pre-pandemic to 79% mid, p < 0.001). Older adults saw the highest increases in PRO response rates up by 35% vs AYA up by 21% & midage up 27%, p 0.012). Response rate without clinic reminders increased significantly (AYA, by 71%, midage by 78%, senior by 61%, p < 0.00001). When further analyzing by visit type during pandemic, the increased PRO response rates are driven by telehealth where in-person PRO completion decreased by 19% (pre-pandemic 52%, during 33%) while pts who had an upcoming virtual visit had 94% PRO response rate (p < 0.00001). Conclusions: Pt adherence w PRO-based remote symptom monitoring increased during the pandemic due to telehealth with the greatest gains seen in racial/ethinic minorities, inc. Black and Hispanic patients with cancer.

Supportive oncology care at home after discharge for patients with advanced cancer.

295 Background: Patients with advanced cancer often experience frequent and prolonged hospitalizations, and the transition from hospital to home represents a critical period for these individuals, as they prefer to maximize time at home and avoid readmissions. We sought to demonstrate the feasibility and acceptability of a Supportive Oncology Care at Home intervention to address the post-discharge needs of recently hospitalized patients with advanced cancer. Methods: We conducted a single-arm pilot trial at Massachusetts General Hospital (MGH). We enrolled English-speaking adults with advanced solid tumors experiencing their second or later unplanned hospitalization, who were being discharged home without hospice services and residing within a 50 mile radius of MGH. The three-week intervention consisted of: 1) hospital in the home care model for proactive symptom assessment and management, including clinician visits to assess patients, draw labs, administer intravenous medications and hydration, and ensure optimal symptom management; 2) remote monitoring of daily patient-reported symptoms, vital signs, and body weight; and 3) structured communication with the oncology team. The primary endpoint of the study was feasibility, defined as ≥60% of approached and eligible patients enrolling and ≥60% of participants completing daily symptom assessments. After intervention completion, patients rated the helpfulness and convenience of the intervention and symptom monitoring technology. Results: From 12/2021-6/2022, we enrolled 40 out of 66 approached patients (60.6% enrollment rate). Enrolled patients (median age = 58.5 years, 50% female, 75% white, 68% married, 50% gastrointestinal cancers) completed 93.8% of daily symptom assessments. 12 patients (30%) did not complete the intervention due to withdrawal (5), hospice transfer (4), or death (3). Among enrolled patients, 20.0% were enrolled in hospice and 15.4% died at 30 days after hospital discharge. In exit interviews, 100% and 75% rated the intervention and symptom monitoring as helpful, respectively. 83% of patients found the in-home monitoring technology convenient. Conclusions: We found that a three-week Supportive Oncology Care at Home intervention is a feasible approach to providing post-discharge care for high acuity, seriously ill hospitalized patients with advanced cancer. These patients also found the intervention highly acceptable. Future studies will test the efficacy of the intervention for reducing hospital readmissions, improving symptom management and quality of life, and increasing days spent at home near the end of life. Clinical trial information: NCT04637035.

Patient (pt) experience with a smartphone application (app) and biosensor for remote symptom monitoring during systemic cancer therapy: Qualitative findings from initial testing.

443 Background: Up to half of pts with cancer receiving systemic therapy have unplanned emergency department or hospital admission during treatment. ML41539 is a prospective observational study of an app to collect electronic patient-reported outcomes (ePROs) and a wearable biosensor (Samsung Galaxy Watch3) to evaluate the use of a remote monitoring system to improve symptom management and reduce unplanned admissions (ISRCTN25569053). Here, we report the initial feasibility and usability results for the ePRO app and biosensor. Methods: The study includes a vanguard phase (n = 32) to obtain early feedback on pt experience of the app and biosensor. After 2 weeks of continuous biosensor monitoring and daily ePRO reporting, 10 pts participated in semi-structured telephone interviews conducted by trained study staff. Open-ended questions addressed usability and technology-driven facilitators and barriers. Pts were also asked about ease of use and biosensor comfort on a 10-point Likert scale (higher scores reflected better usability and comfort). Transcripts were summarized using a FITTE-adapted framework and coded in ATLAS.ti 22.0.11.0. Coded data were summarized and consolidated into matrices to identify areas for improvement in future study phases. Results: Participants were approached until the target of 10 completed interviews was met. Respondents resembled the overall study population with regards to gender, race, ethnicity, age, and adherence to wearing the sensor and completing daily ePROs. For ease of use, the average rating among participants was 9 for the app (range 8.5-10) and 9.5 for the biosensor (range 7-10). Biosensor comfort was rated 7 on average (range 3-9). The most common words used to describe the app were “easy,” “simple,” and “straightforward.” Sixty percent of pts said that the watch needed to be charged too often and that their experience could be improved by offering more sensor functions and allowing pts to view and track their ePRO responses and biometric data. Sixty percent of pts also supported using these technologies to monitor symptoms during cancer therapy, stating that it was a good idea or useful, especially if it prevented hospitalization or increased communication with their care team. Conclusions: Overall, pts reported positive experiences wearing the sensor and using the ePRO app to report their symptoms during the 2-week observation period. Ease of use was high for both the app and biosensor. A majority reported dissatisfaction with the battery life of the biosensor. Most stated that they would prefer more opportunities to interact directly with the devices and to monitor their own symptoms by viewing collected ePRO and biometric data. Building this type of functionality into the app and sensor for future study phases could help pts engage more with the devices and potentially improve adherence. Clinical trial information: ISRCTN25569053.

Remote Symptom Monitoring of Patients With Cancer Undergoing Radiation Therapy.

The goal of the study was to develop and test an automated short message service (SMS) and web service platform using CareSignal for remote symptom monitoring in a diverse population of patients with cancer. Twenty-eight patients with cancer undergoing radiotherapy were recruited at the start of their treatment regimen. Patients received a weekly SMS symptom survey to assess the severity of the side effects they experienced from treatment. An assessment of patient perceptions of the system in terms of patient-provider communication, feasibility, and overall satisfaction was conducted, finding overall good compliance in a sick patient population and patient willingness to engage with the software in the future.

Systematic review: Wearable remote monitoring to detect nonalcohol/nonnicotine-related substance use disorder symptoms.

Substance use disorders (SUDs) are chronic relapsing diseases characterized by significant morbidity and mortality. Phenomenologically, patients with SUDs present with a repeating cycle of intoxication, withdrawal, and craving, significantly impacting their diagnosis and treatment. There is a need for better identification and monitoring of these disease states. Remote monitoring chronic illness with wearable devices offers a passive, unobtrusive, constant physiological data assessment. We evaluate the current evidence base for remote monitoring of nonalcohol, nonnicotine SUDs. We performed a systematic, comprehensive literature review and screened 1942 papers. We found 15 studies that focused mainly on the intoxication stage of SUD. These studies used wearable sensors measuring several physiological parameters (ECG, HR, O2 , Accelerometer, EDA, temperature) and implemented study-specific algorithms to evaluate the data. Studies were extracted, organized, and analyzed based on the three SUD disease states. The sample sizes were relatively small, focused primarily on the intoxication stage, had low monitoring compliance, and required significant computational power preventing "real-time" results. Cardiovascular data was the most consistently valuable data in the predictive algorithms. This review demonstrates that there is currently insufficient evidence to support remote monitoring of SUDs through wearable devices. This is the first systematic review to show the available data on wearable remote monitoring of SUD symptoms in each stage of the disease cycle. This clinically relevant approach demonstrates what we know and do not know about the remote monitoring of SUDs within disease states.

Supportive Oncology Care at Home Intervention for Patients With Pancreatic Cancer.

We sought to determine the feasibility of delivering a Supportive Oncology Care at Home intervention among patients with pancreatic cancer. We prospectively enrolled patients with pancreatic cancer from a parent trial of neoadjuvant fluorouracil, leucovorin, oxaliplatin, and irinotecan (FOLFIRINOX). The intervention entailed (1) remote monitoring of patient-reported symptoms, vital signs, and body weight; (2) a hospital-at-home care model; and (3) structured communication with the oncology team. We defined the intervention as feasible if ≥ 60% of patients enrolled in the study and ≥ 60% completed the daily assessments within the first 2-weeks of enrollment. We determined rates of treatment delays, urgent clinic visits, emergency department visits, and hospitalizations among those who did (n = 20) and did not (n = 24) receive Supportive Oncology Care at Home from the parent trial. From January 2019 to September 2020, we enrolled 80.8% (21/26) of potentially eligible patients. One patient became ineligible following consent because of moving out of state, resulting in 20 participants (median age = 67 years). In the first 2 weeks of enrollment, 65.0% of participants completed all daily assessments. Overall, patients reported 96.1% of daily symptoms, 96.1% of daily vital signs, and 92.5% of weekly body weights. Patients receiving the intervention had lower rates of treatment delays (55.0% v 75.0%), urgent clinic visits (10.0% v 25.0%), and emergency department visits/hospitalizations (45.0% v 62.5%) compared with those not receiving the intervention from the same parent trial. Findings demonstrate the feasibility and acceptability of a Supportive Oncology Care at Home intervention. Future work will investigate the efficacy of this intervention for decreasing health care use and improving patient outcomes.

Telehealth Interventions in Head and Neck Cancer Patients: A Systematic Review.

Head and neck cancer (HNC) and its treatments often result in adverse effects that impair a patient's quality of life. Although intensive rehabilitative strategies can be used, their applicability can be limited due to patient-specific and socioeconomic barriers. Telehealth interventions represent a possible novel approach to increase access to these services and improve posttreatment quality of life in the HNC population. The objective of this systematic review was to identify studies investigating telemedicine-based interventions for HNC patients to determine whether there is a consensus concerning the cost-effectiveness, clinical utility, and accessibility of this model for rehabilitation. PubMed, EMBASE, Web of Science, and CINAHL were used to identify literature without time limit for publication. A critical appraisal of individual sources was conducted by 2 reviewers. Sixteen studies met inclusion criteria. Studies related to telehealth interventions in the HNC population are limited. Salient themes included feasibility of telehealth as an intervention, effects on self-management and knowledge, impact on quality of life, physical and psychiatric symptoms, and cost. Although the current literature presents promising data, indicating that telehealth interventions may be both effective and cost-efficient in the management of HNC patients, more research is needed to definitively elucidate their role in management. Telehealth interventions are valuable for clinicians as an alternative to expand access to care across the cancer continuum, to strengthen patients' knowledge and consequently their self-management, and to provide continuity of services as well as for remote monitoring of symptoms and response to treatment.

A Remote Digital Monitoring Platform to Assess Cognitive and Motor Symptoms in Huntington Disease: Cross-sectional Validation Study.

BackgroundRemote monitoring of Huntington disease (HD) signs and symptoms using digital technologies may enhance early clinical diagnosis and tracking of disease progression, guide treatment decisions, and monitor response to disease-modifying agents. Several recent studies in neurodegenerative diseases have demonstrated the feasibility of digital symptom monitoring.ObjectiveThe aim of this study was to evaluate a novel smartwatch- and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD.MethodsThis analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts—the HD Natural History Study (NCT03664804) and the Digital-HD study. Across these studies, controls (n=20) and individuals with premanifest (n=20) or manifest (n=179) HD completed 6 motor and 2 cognitive tests at home and in the clinic.ResultsParticipants in the open-label extension study, the HD Natural History Study, and the Digital-HD study completed 89.95% (1164/1294), 72.01% (2025/2812), and 68.98% (1454/2108) of the active tests, respectively. All sensor-based features showed good to excellent test-retest reliability (intraclass correlation coefficient 0.89-0.98) and generally low quality control failure rates. Good overall convergent validity of sensor-derived features to Unified HD Rating Scale outcomes and good overall known-groups validity among controls, premanifest, and manifest participants were observed. Among participants with manifest HD, the digital cognitive tests demonstrated the strongest correlations with analogous in-clinic tests (Pearson correlation coefficient 0.79-0.90).ConclusionsThese results show the potential of the HD Digital Monitoring Platform to provide reliable, valid, continuous remote monitoring of HD symptoms, facilitating the evaluation of novel treatments and enhanced clinical monitoring and care for individuals with HD.

Remote symptom monitoring (RSM) during treatment for metastatic prostate cancer (mPC) in older men: Feasibility and efficacy.

12056 Background: Emerging data support multiple benefits of RSM during chemotherapy to improve outcomes. These studies do not focus on older adults and do not include non-chemotherapy strategies. mPC represents a major burden in older men. Although both chemotherapy and androgen receptor axis-targeted therapies (ARATs) prolong survival, toxicities are substantial and increased in older men. Understanding the feasibility of RSM and key symptoms experienced by men with mPC on treatment is crucial to designing appropriate supportive care interventions. We aimed to assess RSM feasibility and understand key symptoms during treatment with chemotherapy or an ARAT among older men. Methods: Older adults aged 65+ starting chemotherapy, an ARAT, or Radium-223 for mPC were enrolled in a prospective observational multicentre study. Participants completed the Edmonton Symptom Assessment Scale (ESAS) on weekdays online or by phone. Weekly detailed questionnaires assessed mood, anxiety, fatigue, insomnia, and pain. Notifications were sent to the clinical oncology team with severe symptoms (ESAS 7 or higher). Study duration was the first treatment cycle (̃3-4 weeks). Feasibility data were analyzed descriptively. Linear mixed effects models examined symptoms over time and by cohort. Clinician responses were assessed descriptively. Results: A total of 90 men were included (mean age 76.5y, 48% ARAT, 38% chemotherapy, and 14% Radium-223, 42% frail by Vulnerable Elders Survey-13 cutoff of 3+). Approximately half the patients preferred phone-based RSM. Patients provided RSM responses in 1,874 of approximately 2,000 (94%) instances. In the combined cohort, the most common symptoms of moderate to severe intensity (ESAS 4 or higher) occurring at least once were poor well-being (66%), fatigue (62%), reduced appetite (56%), insomnia (54%), and pain (46%). Symptom patterns were similar between chemotherapy and ARAT groups. Moderate to severe symptoms were more common and lasted longer among frail than non-frail men. Symptoms tended to remain stable or improve over the course of 3-4 weeks of RSM. 89% of participants were satisfied or very satisfied with RSM, although daily reporting was reported by several as burdensome. 45% had severe symptoms from RSM leading to informing the oncology care team, 79% of whom were followed up by a nurse or physician, and 12% of treatments were modified. Conclusions: RSM is feasible, acceptable to older adults, and identifies clinically relevant symptoms, but accommodation needs to be made for phone and the optimal frequency of RSM needs to be established. Poor well-being, fatigue, reduced appetite, and insomnia occurred in over half of participants. Longer-term follow up will be important.

Association between telehealth and adherence with patient-reported outcomes (PRO)-based remote symptom monitoring among adolescent/young adults (AYA), middle age, and older adults with cancer.

1513 Background: PRO-based remote symptom monitoring favorably impacts quality of life, healthcare utilization, and overall survival in patients (pts) with cancer. However remote PRO completion rates outside of a clinical trial remained widely varied. With the wide adoption of telehealth in cancer care during the pandemic, telehealth’s impact on health behaviors such adherence w remote PROs is not fully characterized. To that end, we investigated PRO completion patterns in routine cancer care, pre- and during the pandemic. Methods: We queried a prospectively maintained institutional database of all PROs remotely delivered to pts at our institution from 1/1/18 to 12/31/21. Pts were divided into 2 time cohorts (“pre-pandemic” 1/1/18 to 3/31/20, “during pandemic” 4/1/20 to 12/31/21) and 3 age cohorts (AYA 15-39y, midage 40-64y, older adults 65y+). We calculated descriptive statistics and compared (t-test, ANOVA) between time and age cohorts and independent variables. Results: Overall 93,875 unique patients over 4 years received 1+ remote PROs as a part of their routine cancer care. PRO response rate increased from 35% prepandemic (12011 of 34742 pts responding) to 67% during pandemic (p <0.00001). To understand patient-level response patterns, we selected one representative global health PRO tool used widely across clinics in our institution and analyzed completion in a representative month over 4 years, 2 before (Oct ’18, ‘19) and 2 mid-pandemic (Oct ’20, ‘21). Overall 2738 pts (median age 60y, range 17-94y; 290 AYA 15-39y, 1444 midage 40-64y, 1004 older adults 65y+) were sent 3249 PROs during these 4m, 1378 PROs to 1075 pts in 2 pre-pandemic months & 1871 to 1663 pts in 2 mid-pandemic months. Overall, PRO response rate increased from 52% pre-pandemic to 81% during, non-responders dropping from 48% to 19%, and response rate without any reminder from the team increasing from 13% pre-pandemic to 79% during. Across all 3 age cohorts, overall PRO response rates increased (AYA up 21%, midage up 27%, seniors up 35%, p 0.012), PRO non-response rate decreased (AYA by 21%, midage by 27%, seniors by 35%, p 0.01), and PRO response rate without reminders from clinic team increased significantly (AYA, by 71%, midage by 78%, senior by 61%, p <0.00001). When further analyzing by visit type during pandemic, the improvements in overall PRO response rates are driven almost exclusively by telehealth where in-person PRO completion decreased by 19% (pre-pandemic 52%, during 33%) while pts who had an upcoming virtual visit had 94% PRO response rate (p < 0.00001). Conclusions: Substantially higher adherence with PRO-based remote symptom monitoring was seen during the pandemic with virtual visits accounting substantially for this broad adherence and the highest increases seen in older adults, highlighting the implications of telehealth on cancer care.

Remote symptom monitoring after hospital discharge.

1517 Background: Strategies to improve transitions from the hospital to home for patients with cancer are considered an important component of quality, patient-centered care in oncology. CMS evaluates cancer hospital performance based on the 30-day unplanned hospital readmission rate, and this measure has been endorsed by the National Quality Forum. Nationally, the 30-day readmission rate for oncology patients ranges from 19%-27%. These readmissions come at high psychosocial, physical, and financial costs for patients and caregivers. A remote monitoring intervention that includes frequent contacts with the patient is likely to be effective in improving this transition. Methods: We evaluated the feasibility, acceptability, and perceived value of a mobile health intervention to monitor and manage symptoms of adult medical and surgical oncology patients discharged from an NCI-designated cancer center to home. Patients were monitored for 10 days, which is the median time to readmission for an oncology patient. The technology supporting the program included: 1) a patient portal enabling daily electronic patient-reported outcomes assessments; 2) a pulse oximeter to provide data on blood oxygen level and heart rate; 3) alerts for concerning symptoms; 4) an application to allow staff to review and trend symptom data; 5) a secure platform to support communications and televisits between staff and patients; 6) an advanced feedback report to provide just-in-time patient symptom education. Feasibility and acceptability were evaluated through engagement (goal: > 50% response rate) and symptom alerts and perceived value was measured through a patient engagement survey that included a net promoter score (how likely the patient is to recommend the program to similar patients; goal > 0.7). Results: Between September 27, 2020 to December 31, 2021, the program enrolled 1,091 medical oncology (median age: 63 years, 55% female) and 4,222 surgical oncology patients (median age: 63 years,55% female). Of those enrolled, 65% of medical and 74% of surgical oncology patients participated in home remote monitoring by self-reporting symptom data. This resulted in 2,869 completed symptom assessment from medical and 16,009 completed assessments from surgical patients. Sixty-three percent of medical oncology assessments resulted in a yellow (moderate) or red (severe) symptom alert compared with 26% for surgical oncology patients. Pain was the predominant symptom generating red alerts for medical oncology patients (17%). Fifty-two percent of patients completed the engagement survey, and the net promoter score was 0.82. Conclusions: A remote monitoring program after discharge was feasible, acceptable, and perceived to be of value by oncology patients discharged from a cancer center. Surgical and medical patients have similar response rates but differ in symptom burden. Future work will evaluate the value of a remote symptom monitoring platform in decreasing readmissions.

A randomized study to measure and enhance the health-related quality of life in patients with cancer receiving immune checkpoint modulators (ME-Q).

TPS6603 Background: Existing data demonstrate the impact of Immune checkpoint modulators (ICMs) on Health-Related Quality of Life (HRQOL), but this was from studies that used HRQOL tools developed for patients treated with chemotherapy and/or radiation. To address this gap, we developed a toxicity subscale to cover important immune-related adverse events (irAEs) to combine this subscale with the generic FACT-G to measure HRQOL in patients treated with ICMs, the FACT-ICM. This trial proposes to validate the use of the FACT-ICM as a tool for HRQOL measurement and simultaneously to assess the benefit of remote symptom monitoring and management using a new electronic platform developed specifically for patients receiving ICMs. Methods: Participants will be randomized (1:1) before starting their standard of care ICM therapy to either usual care or the monitoring arm. The ME-Q platform uses a web-based application to enable remote monitoring of patient symptoms and HRQOL, allowing all participants enrolled in both the usual care or monitoring arms to complete the FACT-ICM and other questionnaires (DART, Health Resource Utilization, Decision regret scale, post-study system usability questionnaire) at set time points. Patients on the monitoring arm will have their electronic responses sent automatically, and new or significant worsening symptoms of clinical concern will generate an alert to the advanced practicing nurse (APN) responsible for replying to the patient and acting on responses. Via phone or a video teleconference, the nurse will perform a targeted assessment and provide standardized clinical advice and interventions based on well-established clinical algorithms and international guidelines for the management of irAEs. The APN will inform the patient's clinical team about the patient's responses and obtain the treating team's input and agreement on a proposed treatment plan. Patients enrolled in the usual care arm will respond to the questionnaires; however, their responses are not transmitted to their clinical team. The primary objective will be to validate the PRO tool for HRQOL measurement, and the secondary objective is to improve HRQOL in cancer patients receiving ICMs by remote monitoring and symptom management using the ME-Q electronic platform. We estimate that a total sample size of 266 patients (divided into two arms) achieves 90% power to detect an effect size of 0.4 in mean score changes (from baseline to 4 months) between arms using a two-sample t-test with a two-sided significance level of 0.05. Enrolment is to be completed in 2 years.

Oral Oncolytics: Using Remote Technology to Improve Access, Operational Efficiency, and Satisfaction.

Oncology care management via oral oncolytic therapy requires frequent laboratory monitoring for potential toxicities. A lag in these processes can result in treatment delays and care team dissatisfaction. A nurse-led quality improvement project was implemented to streamline processes, clearly define job tasks, and introduce a remote patient-reported symptom monitoring application to improve patient safety, access, operational productivity, and care team satisfaction. Project results included eliminating paper fax distribution, a 97% decrease in time required to process faxed remote laboratory results, and a 78% reduction in mouse clicks to complete laboratory orders and patient correspondence.

EPRO-based digital symptom monitoring in a community oncology practice to reduce emergency room and inpatient utilization.

1508 Background: We have previously reported on the successful implementation of an electronic patient-reported outcomes (ePRO)-based symptom monitoring tool in a community oncology practice. Basch et al reported that use of such a tool in the academic trial setting reduced ER visits and hospitalizations. We have examined the impact of the tool on ER and inpatient utilization in this real world patient population. Methods: Highlands Oncology Group (HOG) is a 21 physician oncology group located in Northwest Arkansas. Beginning in June 2020, HOG offered patients receiving parenteral cancer therapy enrollment onto Expain, an EMR-integrated ePRO system which enables remote symptom monitoring during therapy. EMR data were linked with the Arkansas State Health Alliance for Records Exchange (SHARE), the state’s Health Information Exchange (HIE), to obtain ER visits/hospitalization data. All patients at HOG treated between September 30, 2020 and November 30, 2021 were included in this analysis. Clinical and demographic characteristics were compared in patients who enrolled on Expain versus those who did not, and corresponding p-values were calculated using Mann-Whitney and Chi-square tests. Crude rates for ER visits / hospitalizations were calculated as the total number of events per total person-time. Results: There were 855 patients enrolled on the ePRO system. Concurrently, in the same practice, 1773 patients were treated but not enrolled. Reasons for non-enrollment included patient’s choice to not participate and patient not yet offered enrollment due to rolling enrollment. The non-ePRO cohort was slightly older (66.7 vs 63.3 yrs, p <.001), more commonly male (47.3% vs 39.3%, p <.001) and less likely to be White (85.3% vs 89.4%, p = 0.003). The cohorts were comparable with respect to cancer site distribution and included a diverse and representative distribution of common malignancies receiving systemic therapy in a community practice. The proportion of patients with metastatic disease was comparable (ePRO 52.9% vs non-ePRO 51.6%, p = 0.55). Health resource utilization rates were lower for patients in the ePRO cohort: ER visits: 1.72 vs 2.34 per 100 patient-months, rate ratio and 95% CI = 0.74 (0.60, 0.92), p-value = 0.005; hospitalizations: 4.76 vs 5.41 per 100 patient-months, rate ratio and 95% CI = 0.87 (0.77, 0.99), p-value = 0.04. Conclusions: Our findings confirm the substantial benefits of using an ePRO tool in reducing health care resource utilization, and extend the initial findings of previous publications in the academic, clinical trial setting to the real world setting. This observational data is subject to confounding factors and we are evaluating the robustness using various methods to address non-comparability of the cohorts. We are further examining the benefits in specific patient subsets and attempting to correlate these benefits with improved survival.