Surgeon and Care Team Network Measures and Timely Breast Cancer Treatment.

Importance: Cancer treatment delay is a recognized marker of worse outcomes. Timely treatment may be associated with physician patient-sharing network characteristics, yet this remains understudied. Objective: To examine the associations of surgeon and care team patient-sharing network measures with breast cancer treatment delay. Design, Setting, and Participants: This cross-sectional study of Medicare claims in a US population-based setting was conducted from 2017 to 2020. Eligible participants included patients with breast cancer who received surgery and the subset who went on to receive adjuvant therapy. Patient-sharing networks were constructed for treating physicians. Data were analyzed from September 2023 to February 2024. Exposures: Surgeon linchpin score (a measure of local uniqueness or scarcity) and care density (a measure of physician team familiarity) were assessed. Surgeons were considered linchpins if their linchpin score was in the top 15%. The care density of a patient's physician team was calculated on preoperative teams for surgically-treated patients and postoperative teams for adjuvant therapy-receiving patients. Main Outcomes and Measures: The primary outcomes were surgical and adjuvant delay, which were defined as greater than 60 days between biopsy and surgery and greater than 60 days between surgery and adjuvant therapy, respectively. Results: The study cohort included 56 433 patients (18 004 aged 70-74 years [31.9%]) who were mostly from urban areas (44 931 patients [79.6%]). Among these patients, 8009 (14.2%) experienced surgical delay. Linchpin surgeon status (locally unique surgeon) was not statistically associated with surgical delay; however, patients with high preoperative care density (ie, high team familiarity) had lower odds of surgical delay compared with those with low preoperative care density (odds ratio [OR], 0.58; 95% CI, 0.53-0.63). Of the 29 458 patients who received adjuvant therapy after surgery, 5700 (19.3%) experienced adjuvant delay. Patients with a linchpin surgeon had greater odds of adjuvant delay compared with those with a nonlinchpin surgeon (OR, 1.30; 95% CI, 1.13-1.49). Compared with those with low postoperative care density, there were lower odds of adjuvant delay for patients with high postoperative care density (OR, 0.77; 95% CI, 0.69-0.87) and medium postoperative care density (OR, 0.85; 95% CI, 0.77-0.94). Conclusions and Relevance: In this cross-sectional study of Medicare claims, network measures capturing physician scarcity and team familiarity were associated with timely treatment. These results may help guide system-level interventions to reduce cancer treatment delays.

The Association Between Oncology Outreach and Timely Treatment for Rural Patients with Breast Cancer: A Claims-Based Approach.

BACKGROUND: Oncology outreach is a common strategy for increasing rural access to cancer care, where traveling oncologists commute across healthcare settings to extend specialized care. Examining the extent to which physician outreach is associated with timely treatment for rural patients is critical for informing outreach strategies. METHODS: We identified a 100% fee-for-service sample of incident breast cancer patients from 2015 to 2020 Medicare claims and apportioned them into surgery and adjuvant therapy cohorts based on treatment history. We defined an outreach visit as the provision of care by a traveling oncologist at a clinic outside of their primary hospital service area. We used hierarchical logistic regression to examine the associations between patient receipt of preoperative care at an outreach visit (preoperative outreach) and > 60-day surgical delay, and patient receipt of postoperative care at an outreach visit (postoperative outreach) and > 60-day adjuvant delay. RESULTS: We identified 30,337 rural-residing patients who received breast cancer surgery, of whom 4071 (13.4%) experienced surgical delay. Among surgical patients, 14,501 received adjuvant therapy, of whom 2943 (20.3%) experienced adjuvant delay. In adjusted analysis, we found that patient receipt of preoperative outreach was associated with reduced odds of surgical delay (odds ratio [OR] 0.75, 95% confidence interval [CI] 0.61-0.91); however, we found no association between patient receipt of postoperative outreach and adjuvant delay (OR 1.04, 95% CI 0.85-1.25). CONCLUSIONS: Our findings indicate that preoperative outreach is protective against surgical delay. The traveling oncologists who enable such outreach may play an integral role in catalyzing the coordination and timeliness of patient-centered care.

Characterizing the Traveling Oncology Workforce and Its Influence on Patient Travel Burden: A Claims-Based Approach.

PURPOSE: Oncology outreach is a common strategy for extending cancer care to rural patients. However, a nationwide characterization of the traveling workforce that enables this outreach is lacking, and the extent to which outreach reduces travel burden for rural patients is unknown. METHODS: This cross-sectional study analyzed a rural (nonurban) subset of a 100% fee-for-service sample of 355,139 Medicare beneficiaries with incident breast, colorectal, and lung cancers. Surgical, medical, and radiation oncologists were linked to patients using Part B claims, and traveling oncologists were identified by observing hospital service area (HSA) transition patterns. We defined oncology outreach as the provision of cancer care by a traveling oncologist outside of their primary HSA. We used hierarchical gamma regression models to examine the separate associations between patient receipt of oncology outreach and one-way patient travel times to chemotherapy, radiotherapy, and surgery. RESULTS: On average, 9,935 of 39,960 oncologists conducted annual outreach, where 57.8% traveled with low frequency (0-1 outreach visits/mo), 21.1% with medium frequency (1-3 outreach visits/mo), and 21.1% with high frequency (>3 outreach visits/mo). Oncologists provided surgery, radiotherapy, and chemotherapy to 51,715, 27,120, and 5,874 rural beneficiaries, respectively, of whom 2.5%, 6.9%, and 3.6% received oncology outreach. Rural patients who received oncology outreach traveled 16% (95% CI, 11 to 21) and 11% (95% CI, 9 to 13) less minutes to chemotherapy and radiotherapy than those who did not receive oncology outreach, corresponding to expected one-way savings of 15.9 (95% CI, 15.5 to 16.4) and 11.9 (95% CI, 11.7 to 12.2) minutes, respectively. CONCLUSION: Our study introduces a novel claims-based approach for tracking the nationwide traveling oncology workforce and supports oncology outreach as an effective means for improving rural access to cancer care.

Using machine learning to forecast symptom changes among subclinical depression patients receiving stepped care or usual care.

BACKGROUND: Subclinical depression (SD) is a mental health disorder characterized by minor depressive symptoms. Most SD patients are treated in the primary practice, but many respond poorly to treatment at the expense of provider resources. Stepped care approaches are appealing for tiering SD care to efficiently allocate scarce resources while jointly optimizing patient outcomes. However, stepped care can be time inefficient, as some persons may respond poorly and be forced to suffer with their symptoms for prolonged periods. Machine learning can offer insight into optimal treatment paths and inform clinical recommendations for incident patients. METHODS: As part of the Step-Dep trial, participants with SD were randomized to receive stepped care (N=96) or usual care (N=140). Machine learning was used to predict changes in depressive symptoms every three months over a year for each treatment group. RESULTS: Tree-based models were effective in predicting PHQ-9 changes among patients who received stepped care (r=0.35-0.46, MAE=0.14-0.17) and usual care (r=0.34-0.49, MAE=0.15-0.18). Patients who received stepped care were more likely to reduce PHQ-9 scores if they had high PHQ-9 but low HADS-A scores at baseline, a low number of chronic illnesses, and an internal locus of control. LIMITATIONS: Models may suffer from potential overfitting due to sample size limitations. CONCLUSION: Our findings demonstrate the promise of machine learning for predicting changes in depressive symptoms for SD patients receiving different treatments. Trained models can intake incident patient information and predict outcomes to inform personalized care.

Telehealth Use Following COVID-19 Within Patient-Sharing Physician Networks at a Rural Comprehensive Cancer Center: Cross-sectional Analysis.

BACKGROUND: In response to the COVID-19 pandemic, cancer centers rapidly adopted telehealth to deliver care remotely. Telehealth will likely remain a model of care for years to come and may not only affect the way oncologists deliver care to their own patients but also the physicians with whom they share patients. OBJECTIVE: This study aimed to examine oncologist characteristics associated with telehealth use and compare patient-sharing networks before and after the COVID-19 pandemic in a rural catchment area with a particular focus on the ties between physicians at the comprehensive cancer center and regional facilities. METHODS: In this retrospective observational study, we obtained deidentified electronic health record data for individuals diagnosed with breast, colorectal, or lung cancer at Dartmouth Health in New Hampshire from 2018-2020. Hierarchical logistic regression was used to identify physician factors associated with telehealth encounters post COVID-19. Patient-sharing networks for each cancer type before and post COVID-19 were characterized with global network measures. Exponential-family random graph models were performed to estimate homophily terms for the likelihood of ties existing between physicians colocated at the hub comprehensive cancer center. RESULTS: Of the 12,559 encounters between patients and oncologists post COVID-19, 1228 (9.8%) were via telehealth. Patient encounters with breast oncologists who practiced at the hub hospital were over twice as likely to occur via telehealth compared to encounters with oncologists who practiced in regional facilities (odds ratio 2.2, 95% CI 1.17-4.15; P=.01). Patient encounters with oncologists who practiced in multiple locations were less likely to occur via telehealth, and this association was statistically significant for lung cancer care (odds ratio 0.26, 95% CI 0.09-0.76; P=.01). We observed an increase in ties between oncologists at the hub hospital and oncologists at regional facilities in the lung cancer network post COVID-19 compared to before COVID-19 (93/318, 29.3%, vs 79/370, 21.6%, respectively), which was also reflected in the lower homophily coefficients post COVID-19 compared to before COVID-19 for physicians being colocated at the hub hospital (estimate: 1.92, 95% CI 1.46-2.51, vs 2.45, 95% CI 1.98-3.02). There were no significant differences observed in breast cancer or colorectal cancer networks. CONCLUSIONS: Telehealth use and associated changes to patient-sharing patterns associated with telehealth varied by cancer type, suggesting disparate approaches for integrating telehealth across clinical groups within this health system. The limited changes to the patient-sharing patterns between oncologists at the hub hospital and regional facilities suggest that telehealth was less likely to create new referral patterns between these types of facilities and rather replace care that would otherwise have been delivered in person. However, this study was limited to the 2 years immediately following the initial outbreak of COVID-19, and longer-term follow-up may uncover delayed effects that were not observed in this study period.