We are examining a range of issues related to maternal healthcare in the United States. We are currently interested in both prenatal and postpartum health – better understanding the associated risk factors and how patients engage in care.
Maternal morbidity and mortality is defined by the CDC as physical or psychological conditions that are caused or worsened by pregnancy and result in adverse health outcomes or death. Despite Millenium Development Goals set by the United Nations to reduce maternal mortality around the world, the rate of pregnancy-related deaths in the U.S. has steadily increased from 2000-2014. For Black women, the risk of dying from pregnancy complications is 3.2 times higher than non-Hispanic White women.
The leading causes of pregnancy-related deaths include cardiovascular disease, postpartum infection and hemorrhage. Other postpartum complications, including postpartum depression and anxiety, affect approximately 10% to 20% of all mothers and can be extremely harmful for mother, child and other family members.
In response to these trends, the American College of Obstetrics and Gynecologists (ACOG) revised their guidelines on postpartum care in May 2018. They suggest that women have more frequent, immediate, and individualized attention from healthcare providers following childbirth.
Currently, our team is surveying healthcare providers across the U.S. regarding their opinions and experiences on postpartum care needs in the U.S. These findings will inform policymakers and providers of the gaps between official recommendations and practices, as well as identify potential solutions to overcoming barriers to care. It is our hope that this work will improve postpartum care and the rates of maternal morbidity in the U.S.
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Our team is collaborating with physicians to develop mobile app platforms that use machine learning methods to derive valuable insights about human behavior and improve maternal health. This work will provide users with an individualized, interactive experience on their smartphone.
In the United States, 1 in every 10 infant births is premature, occuring earlier than 37 weeks of gestation. Newborns who are born prematurely are more likely to develop medical complications because their organs are under-developed and to spend time in the neonatal intensive care unit (NICU). In addition to being a stressful and emotionally taxing experience for the parents of premature babies, it is estimated that preterm birth costs $26B to the United States annually.
Key risk factors for preterm birth include: maternal weight gain, smoking, depression and poor attendance at prenatal appointments. Black women are 3-4 times more likely to have an early preterm birth compared to women of other ethnic groups. We designed an app to address various risk factors among vulnerable populations by providing women with feedback on their pregnancy and informing them about their health risks.
The pilot study, published in the Journal of Medical Internet Research mHealth and uHealth, designed to target preterm birth among a particularly difficult-to-reach population, was successful in extending obstetric care,. We are currently improving the precision of our app-based risk models.
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We are developing methods to estimate both the content and structure of decision makers preferences in a variety of domains, including risky options, climate and energy tradeoffs, and difficult medical decisions.
We are developing methods to estimate both the content and structure of decision-makers’ preferences in a variety of domains, including risky options, climate and energy tradeoffs, and difficult medical decisions. As a generalization of traditional preference analysis, the approach can be used to make recommendations for people who know what they want, uncover complex choice rules, and suggest paths toward clarification for those who are uncertain.
From recommender systems used by technology companies to sell products, to revealed preference studies used by researchers to infer quantities like the value of a statistical life, or voting schemes that determine the trajectory of nations, approaches that elicit and infer preferences from the choices people make assume that decision-makers know what they want. That is true if decision-makers can consistently order the available alternatives, yielding transitive preferences, and are not susceptible to subtle but inconsequential changes in how the alternatives are described or made available (framing effects, context effects, reference dependence).
We leverage recent advances in graph matching and non-linear embeddings, combined with pairwise comparison choice data, to cluster decision-makers based on what they want (the content of their preferences) and whether they know what they want (the structure of their preferences). Across three experiments, including classic studies of risky choice and a two attribute study about state-level electricity generation portfolios, we find significant heterogeneity in both the content and structure of decision-maker preferences. Decision-makers most frequently choose in a way consistent with utility maximization, yet some decision-makers make choices consistent with heuristic rules, while others appear to be uncertain about their preferences.
Featured paper: Are preferences for allocating harm rational? (preprint)
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We use analytical approaches to identify critical knowledge about a decision with high uncertainty, as well as mixed methods to identify how the decision makers conceptualize the decision problem.
A mental models approach to developing communications and interventions seeks to close critical gaps between the what people “should” know with current beliefs, understandings, and capabilities. We have applied this approach to varied decision contexts from energy efficiency to contraceptive decision making.
In a 2012 study, our team utilized this approach to analyze how consumers understand implementation of smart grid technologies. Researchers conducted unstructured phone interviews to compile a list of consumer beliefs about smart meters, a sensor that collects accurate data on real-time energy consumption in the home. From this data, a questionnaire was constructed to test consumer beliefs on a larger scale.
The research identified a positive predisposition to the technology. It also identified uncertainty and misconceptions about smart meters. This mental models approach allows researchers and policymakers to pinpoint the gaps in understanding and develop effective communication to correct the erroneous beliefs.
Featured paper: Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters
Other research: A decision science approach for integrating social science in climate and energy solutions
Krishnamurti T, Eggers SL, Fischhoff B. (2008). The impact of over-the-counter availability of Plan B on teens’ contraceptive decision making. Soc Sci Med. 67(4), 618-627.
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Medical Decision Making
Using qualitative interviews, surveys and experiments, we seek to explain how physicians and patients routinely make decisions related to care and healthcare delivery.
In their book Medical Decision Making: A Physician’s Guide, Schwartz and Bergus define medical decision making as the following: “as a normative endeavor, it proposes standards for ideal decision making. As a descriptive endeavor, it seeks to explain how physicians and patients routinely make decisions, and has identified both barriers to, and facilitators of, effective decision making. As a prescriptive endeavor, it seeks to develop tools that can guide physicians, their patients, and health care policymakers to make good decisions in practice.”
We examine medical decision making from both the patient and provider perspectives. Our group conducts work to better understand patients’ information needs and priorities to create more patient-centered decision tools.
Featured paper: A patient-centered approach to informed consent
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Energy and Enivornment
Our team uses a variety of methods to model the beliefs and behaviors related to efficiency, conservation, and environmental health, on both the individual consumer and population level.
New energy technologies affect households in a number of ways, for example through local pollution exposure and impacts on monthly budgets. Technologies also impact communities, nations, and global welfare extending far into the future. Our research team has examined the multidimensional impacts of new energy technologies on individual, community, and social decision-making, summarized in our paper “Integrating social science in climate and energy solutions: A decision science approach” in the journal Nature Climate Change.
As an example, we have conducted several research projects examining household perceptions of smart meter technologies, including perceptions of their risks and benefits, how to design smart meter enabled devices to maximize learning, and how to evaluate those devices to establish their causal impact on energy use.
Another example is how decision-makers perceive risks from extreme events. Global climate change affects not only global temperature, but also the frequency and severity of extreme events like tornadoes and floods. We have conducted projects examining the cues individuals use to determine whether there is a tornado threat, and how individuals accumulate their perception of flood risks over time.
We have also examined the tradeoffs individuals are willing to make with respect to emissions and pollution impacts, as well as resilience in the face of power outages.
Featured paper: Integrating social science in climate and energy solutions
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