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Abstract Early forecasts give people in a storm’s path time to prepare. Less is known about the cost to society when forecasts are incorrect. In this observational study, we examine over 700,000 births in the path of Hurricane Irene and find exposure was associated with impaired birth outcomes. Additional warning time was associated with decreased preterm birth rates for women who experienced intense storm exposures documenting a benefit of avoiding a type II forecasting error. A larger share of this at-risk population experienced a type I forecasting error where severe physical storm impacts were anticipated but not experienced. Disaster anticipation disrupted healthcare services by delaying and canceling prenatal care, which may contribute to storm-impacted birth outcomes. Recognizing storm damages depend on human responses to predicted storm paths is critical to supporting the next generation’s developmental potential with judicious forecasts that ensure public warning systems mitigate rather than exacerbate climate damages.
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Practitioner Attitudes towards an Early Warning System: From Professional Distraction to Relational Support
As the general population ages and life expectancy increases in the United States, demand for virtual health care is also on the rise. Undoubtedly, the next several decades will see a rise in automated patient care and the widespread use of data-driven warning systems, trends which have already accelerated in the wake of Covid-19. Thus, understanding how traditionally trained healthcare practitioners respond to predictive analytics, like early warning systems, is vital for their successful implementation in the future. Our findings both reinforce and also extend previous research on tele-ICU’s and early warning systems, offering important insights into the relational and organizational dynamics that underlie their implementation. Overall, healthcare workers are more willing to accept the use of technology in their workplace when they see tangible benefits to themselves. Administrators who were removed from everyday interactions with algorithmic systems were highly skeptical of their utility.
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- Award ID(s):
- 2026607
- PAR ID:
- 10327800
- Date Published:
- Journal Name:
- IEEE International Professional Communication Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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