IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v26y2023i4d10.1007_s10729-023-09645-4.html
   My bibliography  Save this article

Leveraging the E-commerce footprint for the surveillance of healthcare utilization

Author

Listed:
  • Manuel Hermosilla

    (Johns Hopkins University)

  • Jian Ni

    (Pamplin College of Business, Virginia Tech)

  • Haizhong Wang

    (Sun Yat-sen University)

  • Jin Zhang

    (Jinan University)

Abstract

The utilization of healthcare services serves as a barometer for current and future health outcomes. Even in countries with modern healthcare IT infrastructure, however, fragmentation and interoperability issues hinder the (short-term) monitoring of utilization, forcing policymakers to rely on secondary data sources, such as surveys. This deficiency may be particularly problematic during public health crises, when ensuring proper and timely access to healthcare acquires special importance. We show that, in specific contexts, online pharmacies’ digital footprint data may contain a strong signal of healthcare utilization. As such, online pharmacy data may enable utilization surveillance, i.e., the monitoring of short-term changes in utilization levels in the population. Our analysis takes advantage of the scenario created by the first wave of the Covid-19 pandemic in Mainland China, where the virus’ spread lead to pervasive and deep reductions of healthcare service utilization. Relying on a large sample of online pharmacy transactions with full national coverage, we first detect variation that is strongly consistent with utilization reductions across geographies and over time. We then validate our claims by contrasting online pharmacy variation against credit-card transactions for medical services. Using machine learning methods, we show that incorporating online pharmacy data into the models significantly improves the accuracy of utilization surveillance estimates.

Suggested Citation

  • Manuel Hermosilla & Jian Ni & Haizhong Wang & Jin Zhang, 2023. "Leveraging the E-commerce footprint for the surveillance of healthcare utilization," Health Care Management Science, Springer, vol. 26(4), pages 604-625, December.
    • Handle: RePEc:kap:hcarem:v:26:y:2023:i:4:d:10.1007_s10729-023-09645-4
    DOI: 10.1007/s10729-023-09645-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-023-09645-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10729-023-09645-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lei Qin & Qiang Sun & Yidan Wang & Ke-Fei Wu & Mingchih Chen & Ben-Chang Shia & Szu-Yuan Wu, 2020. "Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index," IJERPH, MDPI, vol. 17(7), pages 1-14, March.
    2. Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2018. "Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change," NBER Working Papers 24952, National Bureau of Economic Research, Inc.
    3. Hessam Bavafa & Anne Canamucio & Steven C. Marcus & Christian Terwiesch & Rachel M. Werner, 2022. "Capacity Rationing in Primary Care: Provider Availability Shocks and Channel Diversion," Management Science, INFORMS, vol. 68(4), pages 2842-2859, April.
    4. Govind, Rahul & Chatterjee, Rabikar & Mittal, Vikas, 2008. "Timely access to health care: Customer-focused resource allocation in a hospital network," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 294-300.
    5. Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2018. "Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 77-82, May.
    6. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    7. Miller, Amalia R. & Tucker, Catherine, 2014. "Health information exchange, system size and information silos," Journal of Health Economics, Elsevier, vol. 33(C), pages 28-42.
    8. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    9. Jeffrey M. Wooldridge, 2001. "Applications of Generalized Method of Moments Estimation," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 87-100, Fall.
    10. Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher Ré & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
    11. Parag A. Pathak & Tayfun Sönmez & M. Utku Unver & M. Bumin Yenmez, 2020. "Leaving No Ethical Value Behind: Triage Protocol Design for Pandemic Rationing," NBER Working Papers 26951, National Bureau of Economic Research, Inc.
    12. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    13. Corwin Rhyan & Ani Turner & George Miller, 2020. "Tracking the U.S. health sector: the impact of the COVID-19 pandemic," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 55(4), pages 267-278, October.
    14. Chang, H.-J. & Huang, N. & Lee, C.-H. & Hsu, Y.-J. & Hsieh, C.-J. & Chou, Y.-J., 2004. "The Impact of the SARS Epidemic on the Utilization of Medical Services: SARS and the Fear of SARS," American Journal of Public Health, American Public Health Association, vol. 94(4), pages 562-564.
    15. Y. Wang, 2006. "Price competition in the chinese pharmaceutical market," International Journal of Health Economics and Management, Springer, vol. 6(2), pages 119-129, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahlfeldt, Gabriel M. & Barr, Jason, 2022. "Viewing urban spatial history from tall buildings," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    2. Morgan Ubeda, 2020. "Local Amenities, Commuting Costs and Income Disparities Within Cities," Working Papers halshs-03082448, HAL.
    3. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2020. "Estimating Sleep and Work Hours from Alternative Data by Segmented Functional Classification Analysis, SFCA," SoDa Laboratories Working Paper Series 2020-04, Monash University, SoDa Laboratories.
    4. Breithaupt, Patrick & Kesler, Reinhold & Niebel, Thomas & Rammer, Christian, 2020. "Intangible capital indicators based on web scraping of social media," ZEW Discussion Papers 20-046, ZEW - Leibniz Centre for European Economic Research.
    5. Taixia Shen & Chao Wang, 2021. "Big Data Technology Applications and the Right to Health in China during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(14), pages 1-15, July.
    6. Zeynep Ertem & Dorrie Raymond & Lauren Ancel Meyers, 2018. "Optimal multi-source forecasting of seasonal influenza," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-16, September.
    7. Jose L Herrera & Ravi Srinivasan & John S Brownstein & Alison P Galvani & Lauren Ancel Meyers, 2016. "Disease Surveillance on Complex Social Networks," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-16, July.
    8. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    9. Klaus Ackermann & Simon D. Angus & Paul A. Raschky, 2020. "Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)," Papers 2010.08102, arXiv.org.
    10. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
    11. Imryoung Jeong & Hyunjoo Yang, 2021. "Using maps to predict economic activity," Papers 2112.13850, arXiv.org, revised Apr 2022.
    12. Valentina Lorenzoni & Gianni Andreozzi & Andrea Bazzani & Virginia Casigliani & Salvatore Pirri & Lara Tavoschi & Giuseppe Turchetti, 2022. "How Italy Tweeted about COVID-19: Detecting Reactions to the Pandemic from Social Media," IJERPH, MDPI, vol. 19(13), pages 1-14, June.
    13. Kristian Behrens & Julien Martin & Florian Mayneris, 2021. "Analyse de la gentrification urbaine dans l'agglomération de Montréal et regard particulier sur les secteurs traversés par la ligne rose," CIRANO Project Reports 2020rp-36, CIRANO.
    14. Yunmi Park & Minju Kim & Kijin Seong, 2021. "Happy neighborhoods: Investigating neighborhood conditions and sentiments of a shrinking city with Twitter data," Growth and Change, Wiley Blackwell, vol. 52(1), pages 539-566, March.
    15. Fe, Hao & Sanfelice, Viviane, 2022. "How bad is crime for business? Evidence from consumer behavior," Journal of Urban Economics, Elsevier, vol. 129(C).
    16. Glaeser, Edward L. & Luca, Michael & Moszkowski, Erica, 2023. "Gentrification and retail churn: Theory and evidence," Regional Science and Urban Economics, Elsevier, vol. 100(C).
    17. Konstantinos N. Konstantakis & Despoina Paraskeuopoulou & Panayotis G. Michaelides & Efthymios G. Tsionas, 2021. "Bank deposits and Google searches in a crisis economy: Bayesian non‐linear evidence for Greece (2009–2015)," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5408-5424, October.
    18. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2017. "The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations," Papers 1701.05632, arXiv.org.
    19. Indaco, Agustín, 2020. "From twitter to GDP: Estimating economic activity from social media," Regional Science and Urban Economics, Elsevier, vol. 85(C).
    20. Kristian Behrens & Brahim Boualam & Julien Martin & Florian Mayneris, 2024. "Gentrification and Pioneer Businesses," The Review of Economics and Statistics, MIT Press, vol. 106(1), pages 119-132, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:hcarem:v:26:y:2023:i:4:d:10.1007_s10729-023-09645-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.