| COVID-19 pandemic in the United States S Bergquist, T Otten, N Sarich Health policy and technology 9 (4), 623-638, 2020 | 143 | 2020 |
| Classifying lung cancer severity with ensemble machine learning in health care claims data SL Bergquist, GA Brooks, NL Keating, MB Landrum, S Rose Machine Learning for Healthcare Conference, 25-38, 2017 | 69 | 2017 |
| Accountability across the continuum: the participation of postacute care providers in accountable care organizations CH Colla, VA Lewis, SL Bergquist, SM Shortell Health services research 51 (4), 1595, 2016 | 48 | 2016 |
| Mental health risk adjustment with clinical categories and machine learning A Shrestha, S Bergquist, E Montz, S Rose Health Services Research 53, 3189-3206, 2018 | 34 | 2018 |
| Long-term care partnerships: are they fit for purpose? S Bergquist, J Costa-Font, K Swartz The Journal of the Economics of Ageing 12, 151-158, 2018 | 31 | 2018 |
| Computational health economics for identification of unprofitable health care enrollees S Rose, SL Bergquist, TJ Layton Biostatistics 18 (4), 682-694, 2017 | 24 | 2017 |
| Data transformations to improve the performance of health plan payment methods SL Bergquist, TJ Layton, TG McGuire, S Rose Journal of Health Economics 66, 195-207, 2019 | 15 | 2019 |
| Classifying stage IV lung cancer from health care claims: a comparison of multiple analytic approaches GA Brooks, SL Bergquist, MB Landrum, S Rose, NL Keating JCO clinical cancer informatics 3, 1-19, 2019 | 12 | 2019 |
| Establishing a coalition to pursue Accountable Care in the safety net: A Case study of the FQHC Urban Health Network KE Schoenherr, AD Van Citters, KL Carluzzo, S Bergquist, ES Fisher, ... Health Affairs 32 (3), 587, 2013 | 10 | 2013 |
| Partnership program for long-term care insurance: the right model for addressing uncertainties with the future? S Bergquist, J Costa-Font, K Swartz Ageing & Society 36 (9), 1779-1793, 2016 | 9 | 2016 |
| Hospitalizations reduce health care utilization of household members S Bergquist, M de Vaan Health Services Research 57 (6), 1274-1287, 2022 | 4 | 2022 |
| Comparing risk adjustment estimation methods under data availability constraints M Iommi, S Bergquist, G Fiorentini, F Paolucci Health Economics 31 (7), 1368-1380, 2022 | 3 | 2022 |
| Intervening on the data to improve the performance of health plan payment methods SL Bergquist, TJ Layton, TG McGuire, S Rose National Bureau of Economic Research, 2018 | 3 | 2018 |
| Uncertainty in lung cancer stage for survival estimation via set‐valued classification S Bergquist, GA Brooks, MB Landrum, NL Keating, S Rose Statistics in Medicine 41 (19), 3772-3788, 2022 | 2 | 2022 |
| Sample selection for Medicare risk adjustment due to systematically missing Data SL Bergquist, TG McGuire, TJ Layton, S Rose Health services research 53 (6), 4204-4223, 2018 | 2 | 2018 |
| Classifying lung cancer stage from health care claims with a clinical algorithm or a machine-learning approach. GA Brooks, NL Keating, SL Bergquist, MB Landrum, S Rose Journal of Clinical Oncology 36 (15_suppl), 6589-6589, 2018 | 1 | 2018 |
| The Future of Risk Adjustment in Payment Policy: Putting Goals Before The Data JM McWilliams, S Bergquist, J Wallace, B Diephuis 2022 Health Datapalooza and National Health Policy Conference, 2022 | | 2022 |
| Health Policy and Technology S Bergquist, T Otten, N Sarich Diabetes 7, 13, 2020 | | 2020 |
| Prediction With Systematically Missing Data: Methods for Health Plan Payment and Cancer Stage Classification S Bergquist Harvard University, 2019 | | 2019 |
Gabriel A. BrooksDartmouth Cancer CenterVerified email at hitchcock.org
