Current Projects
physician networks
Breast cancer patient-sharing network at Dartmouth-Hitchcock Health
Patient-sharing physician networks offer a novel lens through which to study cancer care delivery, and there is growing evidence that patient-sharing physician networks are related to cost of care, care quality, and patient outcomes. Using national Medicare claims data, we create cancer physician networks to study how cancer care is organized. We employ cutting-edge social network methods to characterize cancer care coordination, physician network centrality, and peer influence, and their impacts on patient outcomes. We develop novel network measures that consider attributes such as physician speciality to identify physicians who are vital in their local networks. For more details on this project, see the Linchpin score page.
Related publications
Liu Y-C, Schmidt RO, Kapadia NS, Phillips JD, Moen EL. Disparities in access to multidisciplinary cancer consultations and treatment for early-stage non-small cell lung cancer patients: A SEER-Medicare analysis. IJORBP. 2024, in press.
Moen EL, Schmidt RO, Onega T, Brooks GA, O’Malley AJ. Association between a network-based physician linchpin score and cancer patient mortality: A SEER-Medicare analysis. J Natl Cancer Inst. 2024; 116(2):230-8.
Moen EL, Brooks GA, O’Malley AJ, Schaefer A, Carlos HA, Onega T. Use of a novel network-based linchpin score to characterize accessibility to the oncology physician workforce in the United States. JAMA Network Open. 2022;5(12):e2245995.
Nemesure MD, Schwedhelm TM, Sacerdote S, O’Malley AJ, Rozema LR, Moen EL. A measure of local uniqueness to identify linchpins in a social network with node attributes. Applied Net Sci. 2021 Aug; 6(56).
Moen EL, Bynum JP. Evaluation of physician network-based measures of care coordination using Medicare patient-reported experience measures. JGIM. 2019 Nov; 34 (11): 2482-9.
Moen EL, Kapadia NS, O’Malley AJ, Onega T. Evaluating breast cancer care coordination at a rural National Cancer Institute Comprehensive Cancer Center using network analysis and geospatial methods. Cancer Epidemiol Biomarkers Prev. 2019; 28(3):455-461.
Oncology physician workforce
Cornelius et al 2024.
We have developed claims-based approaches for characterizing the oncology physician workforce. In addition to the network-based measures described above, we have characterized physicians who travel between clinical settings to extend cancer care in rural areas. We are also interested in studying movement (oncologist relocation) and the impact workforce instability has on cancer care teams.
Related publications
Scodari BT, Schaefer AP, Kapadia NS, Brooks GA, O’Malley AJ, Moen EL. The association between oncology outreach and timely treatment for rural patients with breast cancer. Annals of Surgical Oncology. 2024 Jul;31(7):4349-4360.
Scodari BT, Schaefer AP, Kapadia NS, O’Malley AJ, Brooks GA, Tosteson ANA, Onega T, Wang C, Wang F, Moen EL. Characterizing the traveling oncology workforce and its influence on patient travel burden: A claims-based approach. JCO Oncol Tract. 2024 Feb: Online ahead of print.
Cornelius SL, Shaefer AP, Wong SL, Moen EL. Comparison of US oncologist rurality by practice setting and patients served. JAMA Netw Open. 2024 Jan;7(1):e2350504.
Adoption of cancer precision medicine
Oncotype DX adoption among cancer physicians treating a nationwide Medicare breast cancer patient cohort
When an effective innovation becomes available, it is important that patients who will benefit from the innovation have access to it as soon as possible. The identification of barriers to access to care is the first step towards improvement. Using local, regional, and national data sources, we aim to identify characteristics of physicians associated with early-, late-, and non-adoption of novel cancer tests and treatments. Physicians are potentially amenable to intervention, and if we can identify the characteristics of the late or non-adopters, we can create tailored opportunities for education and improve care for their patients.
Related publications
Zipkin R, Schaefer A, Chamberlin M, Onega T, O’Malley AJ, Moen EL. Surgeon and medical oncologist peer network effects on the uptake of the 21-gene recurrence score assay. Cancer Med. 2021. Epub ahead of print.
Schwedhelm TM, Rees JR, Onega T, Zipkin RJ, Schaefer A, Celaya MO, Moen EL. Patient and physician factors associated with Oncotype DX and adjuvant chemotherapy utilization for breast cancer patients in New Hampshire, 2010-2016. BMC Cancer. 2020 Sep 3;20(1):847.
PATIENT SIMILARITY NETWORKS
Similarity network of patients based on comorbidities at the time of non-small cell lung cancer diagnosis.
Clustering similar patients based on distances between various features of data is an emerging topic in precision medicine. Patient similarity networks represent a new model for clustering patients based on heterogeneous data by converting any data type into a similarity network and defining a similarity measure. Patient similarity networks thus offer a novel data-driven approach for integrating sociodemographic, clinical, and genomic information to uncover patterns and influences on disease risk and progression.
Related abstracts
“Predicting node-negative non-small cell lung cancer outcomes through survival modeling”. Lightning talk presented at the Northeast Regional IDeA Conference by Zoe Chen. August 2021 (Virtual).
Moen EL, Chen Z, Schaefer A, Bhimani A, O’Malley AJ, Dragnev K. “Characterizing non-small cell lung cancer patients with similarity networks: A CancerLinQ Discovery Analysis”. ASCO Annual Meeting abstract. June 2022.
Learn more about our research below
Dr. Moen discusses her research for Discovery @ Dartmouth