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Related Research Units

Computational Health Informatics Program Research

Research Overview

Jared works translationally bringing digital health projects from research into implementation and enterprise. Through social media, forums and online communities, wearable technologies and mobile devices, there is a growing body of health-related data that can shape our assessment of human illness. Collectively, this data comprises an individual’s ‘digital phenotype’ - unique, unsolicited and real-time information about a person’s health. Jared’s current research focuses on using digital phenotypes for population health surveillance, specifically to identify and analyze specific sub-populations over space and time with the goal of better understanding patient behavior and disease dynamics. Some current research topics include foodborne illness, insomnia, autism, asthma, febrile illness, and patient experience.

Research Background

Jared is a faculty member in the Boston Children’s Hospital Computational Health Informatics Program (Computational Epidemiology Lab), the Director of Informatics in the Innovation Program at Boston Children’s Hospital and an Instructor of Pediatrics at Harvard Medical School. Jared has a diverse background including informatics, computer engineering, biomedical research and computational modeling. In addition to his academic positions, Jared also spends his time as the Chief Technology & Product Officer at Logisticare and Circulation, where he supports the development of a platform for Non-Emergent Medical Transportation. Jared received a BSc from SUNY New Paltz, a PhD in Immunology from Tufts Medical School and a MMSc in Biomedical Informatics from Harvard Medical School.

Selected Publications

  1.        Hswen, Y., Naslund, J.A., Brownstein, J.S. and Hawkins, J.B., 2018. Monitoring online discussions about suicide among Twitter users with schizophrenia: exploratory study. JMIR mental health, 5(4).
  2. Sewalk, K.C., Tuli, G., Hswen, Y., Brownstein, J.S. and Hawkins, J.B., 2018. Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study. Journal of medical Internet research, 20(10).
  3. Hswen, Y., Sewalk, K.C., Alsentzer, E., Tuli, G., Brownstein, J.S. and Hawkins, J.B., 2018. Investigating inequities in hospital care among lesbian, gay, bisexual, and transgender (LGBT) individuals using social media. Social Science & Medicine, 215, pp.92-97.
  4. Hausmann, J.S., Berna, R., Gujral, N., Ayubi, S., Hawkins, J., Brownstein, J.S. and Dedeoglu, F., 2018. Using Smartphone Crowdsourcing to Redefine Normal and Febrile Temperatures in Adults: Results from the Feverprints Study. Journal of general internal medicine, 33(12), pp.2046-2047.
  5. Harris JK, Hinyard L, Beatty K, Hawkins JB, Nsoesie EO, Mansour R, Brownstein JS., 2018. Evaluating the Implementation of a Twitter-Based Foodborne Illness Reporting Tool in the City of St. Louis Department of Health. Int J Environ Res Public Health. 2018 Apr 24;15(5)
  6. Hswen Y, Naslund JA, Brownstein JS, Hawkins JB, 2018. Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media. Psychiatr Q. 2018 Sep;89(3):569-580
  7. Lu FS, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, Hawkins J, Brownstein J, Conidi G, Gunn J, Gray J, Zink A, Santillana M., 2018. Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis. JMIR Public Health Surveill. 2018 Jan 9;4(1):e4.
  8. Hswen, Y., Naslund, J.A., Chandrashekar, P., Siegel, R., Brownstein, J.S. and Hawkins, J.B., 2017. Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia. Psychiatry research, 257, pp.479-484.
  9. Henly, S., Tuli, G., Kluberg, S.A., Hawkins, J.B., Nguyen, Q.C., Anema, A., Maharana, A., Brownstein, J.S. and Nsoesie, E.O., 2017. Disparities in digital reporting of illness: A demographic and socioeconomic assessment. Preventive medicine, 101, pp.18-22.
  10. Hswen, Y., Brownstein, J.S., Liu, J. and Hawkins, J.B., 2017. Use of a digital health application for influenza surveillance in China. American journal of public health, 107(7), pp.1130-1136.
  11. Rasmussen-Torvik, L.J., Almoguera, B., Doheny, K.F., Freimuth, R.R., Gordon, A.S., Hakonarson, H., Hawkins, J.B., Husami, A., Ivacic, L.C., Kullo, I.J. and Linderman, M.D., 2017. Concordance between research sequencing and clinical pharmacogenetic genotyping in the eMERGE-PGx study. The Journal of Molecular Diagnostics, 19(4), pp.561-566.
  12. Harris, J.K., Hawkins, J.B., Nguyen, L., Nsoesie, E.O., Tuli, G., Mansour, R. and Brownstein, J.S., 2017. Using Twitter to Identify and Respond to Food Poisoning: The Food Safety STL Project. Journal of public health management and practice: JPHMP, 23(6), pp.577-580.
  13. Nsoesie, E.O., Flor, L., Hawkins, J., Maharana, A., Skotnes, T., Marinho, F. and Brownstein, J.S., 2016. Social media as a sentinel for disease surveillance: what does sociodemographic status have to do with it?. PLoS currents, 8.
  14. McGough, S.F., Brownstein, J.S., Hawkins, J.B. and Santillana, M., 2017. Forecasting Zika incidence in the 2016 Latin America outbreak combining traditional disease surveillance with search, social media, and news report data. PLoS neglected tropical diseases, 11(1), p.e0005295.
  15. Manzi, S.F., Fusaro, V.A., Chadwick, L., Brownstein, C., Clinton, C., Mandl, K.D., Wolf, W.A. and Hawkins, J.B., 2016. Creating a scalable clinical pharmacogenomics service with automated interpretation and medical record result integration–experience from a pediatric tertiary care facility. Journal of the American Medical Informatics Association, 24(1), pp.74-80.
  16. Hawkins JB, Brownstein JS, Tuli G, Runels T, Broecker K, Nsoesie EO, McIver DJ, Rozenblum R, Wright A, Bourgeois FT, Greaves F. (2015) Measuring patient-perceived quality of care in U.S. hospitals using Twitter. BMJ Qual Saf. 2016 Jun;25(6):404-13.
  17. Hawkins JB*, McIver DJ*, Chunara R, Chatterjee AK, Bhandari A, Fitzgerald TP, Jain SH, Brownstein JS. (2015) Characterizing Sleep Issues Using Twitter. J Med Internet Res. 17(6): e140.
  18. Jain SH, Powers BW, Hawkins JB, Brownstein JS. (2015) The digital phenotype. Nature Biotech. 33(5): 462-3.

Publications

  1. Comparison of longitudinal trends in self-reported symptoms and COVID-19 case activity in Ontario, Canada. PLoS One. 2022; 17(1):e0262447. View Abstract
  2. The Federal Menu Labeling Law and Twitter Discussions about Calories in the United States: An Interrupted Time-Series Analysis. Int J Environ Res Public Health. 2021 10 14; 18(20). View Abstract
  3. A 10-Year Social Media Analysis Exploring Hospital Online Support of Black Lives Matter and the Black Community. JAMA Netw Open. 2021 10 01; 4(10):e2126714. View Abstract
  4. Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis. SSM Popul Health. 2021 Sep; 15:100851. View Abstract
  5. The effect of seasonal respiratory virus transmission on syndromic surveillance for COVID-19 in Ontario, Canada. Lancet Infect Dis. 2021 05; 21(5):593-594. View Abstract
  6. Association of "#covid19" Versus "#chinesevirus" With Anti-Asian Sentiments on Twitter: March 9-23, 2020. Am J Public Health. 2021 05; 111(5):956-964. View Abstract
  7. Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study. Lancet Digit Health. 2021 03; 3(3):e148-e157. View Abstract
  8. Data curation during a pandemic and lessons learned from COVID-19. Nat Comput Sci. 2021 Jan; 1(1):9-10. View Abstract
  9. Web and phone-based COVID-19 syndromic surveillance in Canada: A cross-sectional study. PLoS One. 2020; 15(10):e0239886. View Abstract
  10. Mask Wearing and Control of SARS-CoV-2 Transmission in the United States. medRxiv. 2020 Sep 01. View Abstract
  11. Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter. J Med Internet Res. 2020 08 21; 22(8):e17048. View Abstract
  12. Investigation of Geographic and Macrolevel Variations in LGBTQ Patient Experiences: Longitudinal Social Media Analysis. J Med Internet Res. 2020 07 31; 22(7):e17087. View Abstract
  13. Use of social media to assess the impact of equitable state policies on LGBTQ patient experiences: An exploratory study. Healthc (Amst). 2020 Jun; 8(2):100410. View Abstract
  14. Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study. JMIR Mhealth Uhealth. 2019 02 12; 7(2):e12264. View Abstract
  15. Feasibility of using social media to monitor outdoor air pollution in London, England. Prev Med. 2019 04; 121:86-93. View Abstract
  16. Monitoring Online Discussions About Suicide Among Twitter Users With Schizophrenia: Exploratory Study. JMIR Ment Health. 2018 Dec 13; 5(4):e11483. View Abstract
  17. Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study. J Med Internet Res. 2018 10 12; 20(10):e10043. View Abstract
  18. Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media. Psychiatr Q. 2018 09; 89(3):569-580. View Abstract
  19. Investigating inequities in hospital care among lesbian, gay, bisexual, and transgender (LGBT) individuals using social media. Soc Sci Med. 2018 10; 215:92-97. View Abstract
  20. Evaluating the Implementation of a Twitter-Based Foodborne Illness Reporting Tool in the City of St. Louis Department of Health. Int J Environ Res Public Health. 2018 04 24; 15(5). View Abstract
  21. Using Twitter to Identify and Respond to Food Poisoning: The Food Safety STL Project. J Public Health Manag Pract. 2017 Nov/Dec; 23(6):577-580. View Abstract
  22. Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia. Psychiatry Res. 2017 11; 257:479-484. View Abstract
  23. Use of a Digital Health Application for Influenza Surveillance in China. Am J Public Health. 2017 07; 107(7):1130-1136. View Abstract
  24. Disparities in digital reporting of illness: A demographic and socioeconomic assessment. Prev Med. 2017 Aug; 101:18-22. View Abstract
  25. Concordance between Research Sequencing and Clinical Pharmacogenetic Genotyping in the eMERGE-PGx Study. J Mol Diagn. 2017 07; 19(4):561-566. View Abstract
  26. Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. PLoS Negl Trop Dis. 2017 01; 11(1):e0005295. View Abstract
  27. Creating a scalable clinical pharmacogenomics service with automated interpretation and medical record result integration - experience from a pediatric tertiary care facility. J Am Med Inform Assoc. 2017 01; 24(1):74-80. View Abstract
  28. Scalable and cost-effective NGS genotyping in the cloud. BMC Med Genomics. 2015 Oct 15; 8:64. View Abstract
  29. Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Qual Saf. 2016 06; 25(6):404-13. View Abstract
  30. Characterizing Sleep Issues Using Twitter. J Med Internet Res. 2015 Jun 08; 17(6):e140. View Abstract
  31. The digital phenotype. Nat Biotechnol. 2015 May; 33(5):462-3. View Abstract

Contact Jared Hawkins

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