Telehealth and Health Care Contact Days Among Patients With Advanced Cancer After COVID-19.

Introduction

Many cancer treatments, despite offering only modest survival benefits, require substantial time investments, including hours in clinics, infusion centers, and waiting rooms, as well as managing logistics such as transportation, insurance, and prescriptions. Patients often need emergency department visits and inpatient hospitalization for treatment and disease complications. 1 Patients with advanced cancer require frequent and intensive health care use, primarily driven by complex treatments and poor prognoses. Studies have consistently shown that patients with advanced cancer experience high rates of hospitalizations and complications. 2 , 3 Management of advanced cancer, including chemotherapy, immunotherapy, or targeted therapies, necessitate frequent visits, infusions, and close monitoring. The disease itself often leads to complications, symptom exacerbations, and an increased need for acute care, resulting in frequent hospitalizations. 4 , 5

Time toxicity, ie, the burden of time spent interacting with the health care system, is gaining prominence as an important consideration in cancer care beyond traditional assessments of treatment efficacy, especially in individuals with limited life expectancy. 6 Prior research has focused mostly on in-person interactions, such as inpatient stays and outpatient services, and was conducted before the COVID-19 pandemic before the widespread use of telehealth. The COVID-19 pandemic brought rapid changes to health care delivery, including a surge in telehealth use. 7 , 8 Given this shift, it is crucial to expand the definition of time toxicity to include telehealth to fully reflect modern patient experiences. In this study, we evaluated how time toxicity changed during the pandemic and modified the traditional measure to include both in-person and telehealth interactions. Our aim was to examine the association of the COVID-19 pandemic with health care contact days among patients with advanced cancer.

Methods

Data Collection

This retrospective cohort study included adult patients (aged ≥18 years) at Kaiser Permanente Northern California (KPNC) with advanced (stage 4) cancer who died within 1 year of diagnosis between January 1, 2015, and June 30, 2022. Limiting follow-up to 1 year ensured a uniform observation period and enabled comparison across COVID-19 periods. The cancer cohort was identified using the National Cancer Care Registry at KPNC. The KPNC Institutional Review Board approved the study with an exemption of informed consent for study participants given its minimal risk and being a data-only study. Results adhered to the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline. The data are not publicly available due to institutional polices and patient confidentiality.

Patients were required to have had at least 6 months of continuous health plan membership before cancer diagnosis and uninterrupted coverage from diagnosis to death to ensure continuity of care and complete capture of health care use. Solid tumor types included thoracic, upper gastrointestinal, genitourinary, lower gastrointestinal, gynecologic, head and neck, breast, melanoma, and others (endocrine, bone, soft tissue, non–melanoma skin, and other rare cancers). Hematologic cancer encompassed acute and chronic leukemia and lymphoma. Multiple myeloma was excluded due to typically longer survival. Patients were excluded if they had multiple stage 4 cancers, defined as additional primary tumors at the same or different site within 10 years prior to diagnosis, or if they had no health care use record after diagnosis.

Demographic characteristics included age at diagnosis, sex, race and ethnicity , preferred language, need for an interpreter, body mass index, and comorbidities to calculate the Elixhauser Comorbidity Index score. Race and ethnicity were self-reported in electronic health records using predefined institutional categories. Mutually exclusive categories were used, including Asian or Pacific Islander, Black, Hispanic, White, multiracial, other, or unknown. Individuals identifying as Hispanic were classified as Hispanic only. The multiracial category included patients who self-identified as more than 1 race. The other category included Alaska Native or American Indian. The multiracial, other, and unknown race and ethnicity categories were collapsed due to small patient numbers. Race and ethnicity were queried separately and combined for analysis. These variables were assessed to examine potential disparities in health care use and time toxicity. Socioeconomic data were obtained from census-linked records to calculate the neighborhood deprivation index.

Clinical characteristics included cancer type and stage, treatment received, (ie, surgery, radiation, chemotherapy, hormone therapy), and date of death. Patients were categorized into pre–COVID-19 and post–COVID-19 groups as illustrated in eFigure 1 in Supplement 1 . Pre–COVID-19 patients were those diagnosed and who died between January 1, 2015, and February 29, 2020; post–COVID-19 patients were diagnosed between March 1, 2020, and June 30, 2022, with follow-through June 2023. March 1, 2020, marked the beginning of the month when California issued a statewide shelter-in-place order, representing a clear inflection point in health care delivery. To avoid cohort overlap, patients diagnosed before March 1, 2020, but who received care after that date were excluded. The date of death was the actual recorded death, not hospice enrollment.

Measure of Time Toxicity

The primary outcome was time toxicity, characterized by the number of health care contact days. 9 Each patient interaction with the health care system, whether a brief laboratory visit or same-day surgery, was counted as 1 contact day. Contact days included outpatient clinician and treatment visits, outpatient services (imaging and laboratory tests), institutional stays (emergency department visits, acute inpatient care, nonacute institutional stays), and telehealth encounters (telephone and videoconferencing visits).

Time toxicity was expressed as the percentage of health care contact days over total survival days. To visualize trends, we standardized time from diagnosis to death and plotted mean weekly contact days (total contact days divided by 7) using a spline, stratified by total, in-person, and telehealth encounters. 9

We calculated the percentage of contact days for each category (eg, in-person visit, telehealth) and subcategory (eg, outpatient visit, outpatient service) contributing to the total contact days. Categories and subcategories did not overlap. Types of health care use were prioritized as (1) emergency department/acute inpatient, (2) nonacute institutional stay, (3) outpatient visit, (4) outpatient service, and (5) telehealth to reflect the acuity of encounters and ensure a distinct contribution to total contact days.

Statistical Analysis

Descriptive statistics were used to summarize demographics and clinical characteristics. Continuous variables are presented as means (SDs) or medians (IQRs). Categorical variables are presented as proportions. Group differences were assessed using t tests for normally distributed variables, Wilcoxon signed rank tests for skewed variables, and χ 2 tests for categorical variables.

To evaluate factors associated with contact days, we used multivariable negative binomial regression for each contact type (eg, total, in-person, telehealth), with survival days as an offset. Models were adjusted for age at diagnosis, sex, race and ethnicity, neighborhood deprivation index, Elixhauser score, treatment during the first 6 months (ie, surgery, radiation, chemotherapy, hormone therapy), and hospice enrollment. Adjusted prevalence ratios (APRs) and 95% CIs are reported. The threshold for statistical significance was a 2-tailed P < .05. The data analyses were performed using SAS, version 9.4 (SAS Institute Inc) and R, version 4.3.1 (R Foundation for Statistical Computing).

Results

Demographics

The study included a total of 9643 patients (median [IQR] age at diagnosis, 74 [66-81] years; 4524 female [46.9%] and 5119 male [53.1%]; 1248 identifying as Asian or Pacific Islander [12.9%], 762 as Black [7.9%], 1183 as Hispanic [12.3%], 6221 as White [64.5%], and 229 as multiracial or other or unknown race and ethnicity [2.4%]), with 6558 in the pre–COVID-19 group and 3085 in the post–COVID-19 group ( Table 1 ; Figure 1 ). The median Elixhauser comorbidity score was 4.0 (IQR, 2.0-6.0).

Sociodemographic differences were observed between the pre–COVID-19 and post–COVID-19 groups. The proportion of White patients decreased from 4341 of 6558 (66.2%) before COVID-19 to 1880 of 3085 (60.9%) after COVID-19, while the proportion of Asian or Pacific Islander patients increased from 793 (12.1%) to 455 (14.7%) and Hispanic patients from 748 (11.4%) to 435 (14.1%), respectively. Most patients spoke English (8886 of 9643 [92.1%]), but the proportion needing an interpreter increased from 2.9% in the pre–COVID-19 group (191 of 6558) to 5.1% in the post–COVID-19 group (158 of 3085). Neighborhood deprivation index scores showed that both ends of the deprivation spectrum increased slightly in the post–COVID-19 group, with quartile 1 (least deprived) increasing from 1273 of 6558 patients (19.4%) to 730 of 3085 patients (23.7%) and quartile 4 (most deprived) increasing from 1305 patients (19.9%) to 636 patients (20.6%) ( P < .001 for all).

Clinical Characteristics

The distribution of cancer types among all 9643 patients showed that thoracic (3193 [33.1%]) and upper gastrointestinal ( 2819 [29.2%]) were the most common ( Table 1 ). Less common cancers included melanoma (114 patients [1.2%]) and breast (262 patients [2.7%]). Regarding treatment type, 3416 patients (35.4%) received chemotherapy, 1697 (17.6%) radiation therapy, 1166 (12.1%) surgery, and 580 (6.0%) hormone therapy. A total of 6790 patients (70.4%) enrolled in hospice, with a median time from enrollment to death of 15.0 days (IQR, 6.0-38.0 days).

Survival and Time Toxicity

Most patients (6181 of 9643 [64.1%]) died within 6 months, with a median survival of 89 days (IQR, 38-175 days). Overall, patients spent a median of 32.5% (IQR, 21.1%-50.0%) of their time in contact with the health care system ( Table 2 ). Total contact days were higher after COVID-19 (median [IQR], 34.3% [22.9%-51.9%] vs 31.7% [20.3%-50.0%] before COVID-19; APR, 1.04 [95% CI, 1.02-1.07]), while in-person contact days declined (median [IQR], 26.0% [15.8%-42.3%] vs 27.6% [16.6%-44.7%], respectively; APR, 0.93 [95% CI, 0.91-0.96]). Figure 1 shows the mean weekly health care contact (total and in-person) over the time from diagnosis to death. Total contact included both in-person and telehealth, whereas in-person excluded telehealth. Both followed a flattened U-shaped curve, peaking early after diagnosis and again near death, with a wider gap between total and in-person contact in the post–COVID-19 group, likely reflecting increased telehealth.

When stratified by type of health care use, post–COVID-19 compared with pre–COVID-19 patients had significantly more emergency department/acute inpatient days (median [IQR], 8.9% [2.8%-23.7%] vs 7.6% [2.4%-21.1%], respectively; APR, 1.09 [95% CI, 1.04-1.15]), outpatient services (median [IQR], 4.4% [1.5%-7.1%] vs 3.1% [0.4%-5.8%]; APR, 1.28 [95% CI, 1.24-1.33]), and telehealth (median [IQR], 6.0% [3.3%-9.5%] vs 2.7% [0.9%-5.2%], respectively; APR, 2.01 [95% CI, 1.94-2.07]) Table 2 ). Meanwhile, outpatient visit contact days decreased (median [IQR], 5.9% [1.6%-10.7%] vs 8.7% [3.5%-14.6%], respectively; APR, 0.71 [95% CI, 0.69-0.74]). Figure 2 depicts the percentage of health care contact days in both cohorts stratified by type of interaction (ie, institutional stays, outpatient visits, outpatient services, and telehealth). Institutional stays showed U-shaped curves in both periods, with peaks immediately after diagnosis and near the end of life. Outpatient visits declined across the timeline, with more variability in the post–COVID-19 group. Outpatient services and telehealth remained relatively flat lines, with both higher in the post–COVID-19 group and declining near death.

Similar trends appeared in the proportion of each type contributing to total contact days. In the post–COVID-19 group, in-person visits decreased from a median of 90% (IQR, 80%-96%) to 80% (IQR, 68%-88%), telehealth rose from a median of 10% (IQR, 4%-20%) to 20% (IQR, 12%-32%), outpatient visits decreased from a median of 50% (IQR, 24%-70%) to 40% (IQR, 16%-58%), and outpatient services increased from a median of 12% (IQR, 2%-20%) to 14% (IQR, 6%-24%) ( Table 3 ).

To illustrate trends beyond the pre– and post–COVID-19 divide, eFigure 2 in Supplement 1 shows annual health care use patterns from 2015 to 2023. Telehealth rose sharply in 2020, from 3.8% in 2019 to a peak of 7.0% in 2020, then declined slightly. Institutional care also increased starting in 2020.

Discussion

This cohort study is the first to our knowledge to include telehealth in time toxicity evaluations and to identify a postpandemic increase in total contact days (31.7%-34.3%), coinciding with expanded telehealth and pandemic-related care shifts. In-person and outpatient contact declined while telehealth increased, suggesting broad changes in care delivery. Our findings highlight the importance of including telehealth in time toxicity, aligning with studies showing its growing role during the pandemic. 10 , 11 The durability of telehealth may reflect positive patient and clinician perceptions, reduced financial toxicity, and potential environmental benefits. 12 , 13 , 14 , 15 , 16

The rise in emergency department visits, hospitalizations, and outpatient services after COVID-19 may reflect delays in care, greater complexity in cancer treatment, reliance on imaging and laboratory tests, or shifts in health care–seeking behavior rather than a direct effect of telehealth. A pre–COVID-19 systematic review and meta-analysis found that telehealth significantly reduced hospitalizations, suggesting that the increased inpatient use observed in our study may be more attributable to pandemic-related disruptions than to telehealth itself. 17 Incorporating telehealth into time toxicity remains essential to capture modern health care practices, though telehealth may inadvertently increase time toxicity when in-person care might be better for treating early complications. Telehealth-driven tools (eg, patient-reported outcomes, navigation) may help with improving communication, identifying early warning signs, and reducing unnecessary high-acuity care. 18 , 19 Our findings support including telehealth in future time toxicity assessments to better understand how care models impact patients’ daily lives in the modern era. 20 , 21

In this representative cancer population within an integrated health system, we observed a flattened U-shaped curve in health care use, with contact days peaking after diagnosis (suggesting a peak of diagnostic procedures and treatment early in diagnosis) and near death (suggesting increased interventions and hospitalizations as the disease progresses). This finding contrasts with previous studies showing steeper U-shaped curves, even for patients with short survival. 22 , 23 , 24 , 25 , 26 The unique trajectory in our study may have been influenced by KPNC’s integrated care model and robust end-of-life practices, including palliative care integration. A recent KPNC study showed that palliative care enrollment in advanced cancer was significantly associated with improved end-of-life outcomes, reducing hospitalizations and increasing hospice enrollment in the last 30 days of life, which may mitigate extreme health care use peaks. 27 Identifying high-contact periods may help clinicians to set expectations and develop strategies to minimize time toxicity. Despite rising use of telehealth, our findings reveal persistently high time toxicity, with nearly one-third of time from diagnosis to death spent with the health care system, and institutional care remaining at nearly 30%. This finding highlights the ongoing need to address time toxicity burden through patient-centered communication and targeted interventions to optimize time in the last year of life.

Recent studies have emphasized the importance of measuring time toxicity, particularly in the context of advanced cancer, in which treatment decisions are complex and time is limited. Incorporating time toxicity metrics into care decisions may align treatment plans with patient preferences, balancing quality and length of life. A systematic review highlighted that older patients often prioritize quality of life, while younger patients may pursue aggressive care, decisions driven by baseline quality of life and expectation about life after treatment. 28 A recent study showed that while some cancer treatments offer small extensions in survival, they often come at the cost of considerable time spent in medical care, including travel, treatment, and follow-up. 9 Gupta et al 29 found that patients receiving cetuximab for advanced colorectal cancer experienced significantly more health care contact days than those receiving supportive care alone, despite modest survival benefits. Another recent study revealed that nearly one-half of patients with advanced cancer spontaneously cited time burden as a decisive factor when considering treatments with marginal benefits. 30 Collectively, these data support integrating time toxicity into oncology care to inform patient-centered decision making.

Time toxicity is not experienced equally, as shown in prior literature. Factors such as socioeconomic status, sex, and health care access are associated with time burdens. 1 Future research should explore whether increases in emergency department visits, hospitalizations, and ancillary care reflect broader disparities. Additionally, incorporating patient-defined goals and preferences might mitigate the burden of time toxicity. Documentation of goals of care in relation to time spent could be a first step in empowering patients to make informed decisions. 31 Patient preferences for telehealth, particularly for sensitive discussions, should inform communication, balanced with in-person follow-up to prevent avoidable acute care. 14 Future research should integrate time toxicity into clinical trials, particularly for high-intensity therapies such as chemotherapy. Establishing time toxicity as a universally accepted measure might enhance the evaluation of treatment burdens and promote patient-centered care, particularly in advanced cancer with limited life expectancy.

Limitations

This study has several limitations to consider. Distinguishing cancer-specific contact days from those of comorbidities remains challenging, potentially influencing time toxicity assessments. Certain types of care, such as nonacute institutional stays (eg, rehabilitation centers), home health, and unscheduled triage calls, were excluded, which possibly led to an underestimation of time toxicity. Although nearly two-thirds of patients were enrolled in hospice, we did not include hospice in the calculation of time toxicity, given its focus on comfort and lack of consistent daily clinician visit data. The study did not differentiate between good (eg, follow-up for effective symptom management) vs bad (eg, prolonged hospitalization with little benefit) contact days, an inherently subjective distinction. While telehealth may reduce travel and waiting times, some patients may find it less effective than in-person visits (eg, for addressing complex symptoms). Similarly, hospitalizations may relieve symptoms or cause greater distress, depending on the context. We dichotomized data into before and after the COVID-19 pandemic for analytic clarity, though this may oversimplify the data. We included the annual trends (eFigure 2 in Supplement 1 ) to show the pandemic’s inflection point amid broader patterns.

We acknowledge that baseline characteristics differed somewhat between groups, ie, the post–COVID-19 group had higher proportions of racial and ethnic minority patients, a greater neighborhood deprivation index score, and more upper gastrointestinal cancers, which may have influenced health care use. In future work, we plan to stratify analyses by cancer type. We also could not determine reasons for hospitalizations, which may have stemmed from cancer, comorbidities, or COVID-19 infection. Although we observed statistically significant changes in contact days, their clinical significance may vary by patient goals and care context. Finally, the study was conducted within an integrated health care system, which may limit generalizability. However, KPNC populations have been shown to be representative of general community populations. 32 , 33

Conclusions

In this cohort study of patients with advanced cancer, time toxicity remained high after the onset of the COVID-19 pandemic. Although telehealth use increased, it was accompanied by higher rates of emergency department visits and hospitalizations. Patients still spent approximately one-third of their remaining time in contact with the health care system. These findings support including both in-person and telehealth into time toxicity measures, reflecting persistent changes in health care delivery. Efforts to refine these measures, integrate patient goals, address disparities, and establish time toxicity as a standard outcome may improve patient-centered care and support informed decision making for patients with advanced cancer.

eFigure 1. CONSORT Diagram: Patient Selection Criteria

eFigure 2. Yearly Trends in Health Care Contact Types Among Patients With Advanced Cancer (2015-2023)

Data Sharing Statement

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Associated Data

Supplementary Materials

eFigure 1. CONSORT Diagram: Patient Selection Criteria

eFigure 2. Yearly Trends in Health Care Contact Types Among Patients With Advanced Cancer (2015-2023)

Data Sharing Statement