Examining the Costs and Utilization of Care for Those Over 65 Years Old from a Nursing-Economic Perspective

PI: Diana Bowser, Professor & Associate Dean for Research and Integrated Science, Connell School of Nursing

Collaborator(s):
Kit Baum, Professor, Economics Department and Michael Grubb, Associate Professor, Economics Department

Project Brief:
Medicare spending in the United States has increased substantially over the last 10 years. Health care costs are especially burdensome for older Americans, who report finding it increasingly difficult to afford necessary treatments, medications, and preventive care. These rising costs can lead to forgone care, exacerbated health issues, and reduced the quality of life for older individuals, especially those who need nursing home care. Additionally, recent reports show that the US government is overpaying the Medicare program in the order of $88-$90 billion annually. This project will bring to Boston College a large database that contains Medicare claims from the Centers for Medicare and Medicaid Services, with data on beneficiary enrolment information, demographics, individual health conditions and health insurance plans characteristics for individual in two plans: Medicare Advantage and Traditional Medicare. The analysis will propose methods to better understand the drivers of costs and utilization of services in these populations. We will create an interdisciplinary team leveraging cost/benefit analysis skills from the Economics Department and the clinical knowledge and health perspectives from the Nursing School to facilitate continued collaboration and future research projects to tackle the growing issue of healthcare costs in the U.S.

Computer-assisted Design of New Hybrid Superconductors for Energy-efficient Technology

PI: Fazel Tafti, Associate Professor, Physics Department

Collaborator(s):
James Morken, Professor, Chemistry Department and Jan Engelbrecht, Professor, Physics Department

Project Brief:
Superconductivity is an enigmatic phase of matter in which all resistance to electrical current is lost. Below a critical temperature, the electrons in the superconductor form a coherent fluid with two fascinating properties. First, it conducts electricity without loss, i.e. the electrical resistance becomes zero. Second, it expels any magnetic field from the interior of the material. Thus, superconductors have several energy-related applications including (1) electrical transmission without loss, (2) energy storage in superconducting rings, and (3) public transportation via levitating trains. In this SI-GECS project, an interdisciplinary team of three PIs will work together to create a new class of superconductors based on organic matter. We will first synthesize layered inorganic superconductors and then intercalated them with organic molecules. The goal will be to enhance the critical temperature of the layered superconductors after intercalation with organic molecules. We will also develop a machine learning to help predicting future hybrid superconductors.

Our team consists of Fazel Tafti (experimental physicist experienced in the synthesis of inorganic superconductors), James Morken (organic chemist with expertise in the synthesis of chiral organic molecules), and Jan Engelbrecht (theoretical physicist with experience in developing machine learning tools to find new superconductors).

Melanin Nanoparticle - Infrared Light Cancer Therapy

PI: Michael J. Naughton, Professor, Physics Department

Collaborator(s):
Thomas Seyfried, Professor, Biology Department and Krzysztof Kempa, Professor, Physics Department

Project Brief:
Photothermal therapy (PTT) uses light to target cancer by local heating of a light-absorbing medium proximate to or internalized by cells and malignant tumors. It uses a photosensitizer molecule that, upon absorption of light, locally heats up that attack cancer cells in their environs, leading to hyperthermia and initiating apoptosis. Clinically, PTT uses light in the UV to visible range but, due to strong absorbance by blood and tissue, can only address cancer in parts of the body the light can reach, such as skin, eyes, mouth, and esophagus. A number of current technical difficulties can be overcome by incorporating of strongly infrared-absorbing melanin nanoparticles (mNP), which are biologically benign and strongly optically absorbing in the visible and IR regions. A collaboration between Michael Naughton in Physics, Kris Kempa in Physics and Tom Seyfried in Biology is using mNPs in a PTT targeting three cell lines known to exhibit strong macrophagic character. The project will use NIR light, which both penetrates deeper than visible and is absorbed more strongly in melanin compared to competing matter such as blood and tissue. Results of this study could lead to the development of a therapy for control of metastatic stages of cancer.

Promoting Breastfeeding: A Qualitative and Data Science Approach to Better Understand Triple Feeding

PI: Lindsey Camp, Assistant Professor, Connell School of Nursing

Collaborator(s):
Emily Prud'hommeaux, Gianinno Family Sesquicentennial Assistant Professor, Computer Science Department

Project Brief:
Exclusive breastfeeding has been associated with numerous, well-documented health benefits for infants and mothers. While over 83% of newborns in the US receive at least some breastmilk, rates of exclusive and extended breastfeeding lag behind national goals. Two of the most commonly reported barriers to meeting 6 and 12 month breastfeeding recommendations are perceived low milk supply and inadequate infant weight gain. One protocol to address these issues is a practice known as “triple-feeding” in which mothers 1) feed the infant at the breast, 2) pump or hand express remaining breastmilk, and 3) feed the infant expressed milk or formula. Repeated 8 – 12 times per day, this protocol has been described in grey literature as exhausting, yet little evidence is available in scientific literature to assess its impact on mothers’ postpartum experience. Thus, using qualitative methods and natural language processing, this study aims to 1) explore mothers’ lived experience while triple feeding, 2) identify sources of information that influence triple feeding recommendations and implementation, and 3) examine messaging shared about triple feeding on social media. Study results may help providers better understand the context in which this protocol is being implemented and inform interventions to support mothers while triple feeding.

Using Game Theory to Make AI Safe Enough to Entrust With Our Health and Infrastructure

PI: Carl McTague, Assistant Professor of the Practice, Computer Science Department

Collaborator(s):
Mehmet Ekmekci, Professor, Economics Department

Project Brief:
AI’s are accelerating progress on health, energy, and the environment, promising to make our world a safer, healthier, more stable and just place.

But can we trust these AIs? As they become more capable, more complex, it becomes more difficult to ensure that they are trustworthy, that their behavior in new situations will be aligned with human interests.

Thus, as AI’s become more capable, there is a parallel risk that they could make our world less safe, less healthy, less stable, less just.

We wish to develop fundamental new approaches to this problem using game theory.

Currently scientists produce monolithic AI’s designed to solve a given problem. It’s like relying on a single human expert.

Our approach is inspired by what humans do in this situation: get a second opinion. A decision maker may lack the expertise of her advisers, but by hearing them debate, she may be able to recognize which is correct.

The essential thing is to ensure that the advisors choose not to collude. If we can incentivize AI’s not to collude against humans, then even if they become smarter than us, we may be able to trust them, because they would be scrutinizing each other.

Climate Simulations to Serve Local Community Planning: Modeling Climate Change in the Neponset River Watershed (Massachusetts)

PI: Amin Mohebbi, Associate Professor of Practice, Engineering Department

Collaborator(s):
Yi Ming, Institute Professor of Climate Science and Society & Professor, Department of Earth and Environmental Sciences

Project Brief:
Our project is designed to unravel the complexities of climate change impacts within the Neponset River Watershed, employing sophisticated methods tailored specifically to this study domain. Through the integration of advanced simulation techniques and an innovative hindcast approach, we aim to generate crucial regional information that will significantly contribute to impactful climate research in the Neponset River Watershed.

At the core of our methodology is the utilization of regional weather/climate models such as Weather Research and Forecasting (WRF) for 'time-slice' simulations. This approach involves leveraging boundary conditions generated by the Coupled Model Intercomparison Project (CMIP6) model ensemble. Executed at resolutions of a few kilometers within the unique context of the Neponset River Watershed, this method will produce actionable climate information valuable for community-level climate mitigation and adaptation.

What sets our methodology apart is its departure from traditional statistical downscaling methods. By enforcing physical consistency among climate variables, we overcome limitations identified in the Greater Boston Research Advisory Group (GBRAG) report, offering a substantial improvement in accuracy and reliability for climate impact assessments tailored for the Neponset River Watershed.

To deepen our insights into model performance and its implications for high-impact weather events within the Neponset River Watershed, we propose a series of hindcast simulations by revisiting major historical events such as Nor’easters or 'bomb-cyclones' that occurred in January and March 2018. These simulations will be perturbed with anomalies induced by climate change in so-called storyline simulations, providing an understanding of how the same extreme events may unfold differently in an altered climate within the Neponset River Watershed.

Our project goes beyond mere scientific investigation, aiming to impact the Neponset community directly. Using extensive storyline simulations, we aim to effectively communicate climate risks, emphasizing the tangible implications of climate change on specific extreme weather events in the Neponset River Watershed. By incorporating cutting-edge simulation techniques and innovative methodologies in this study, our project stands at the forefront of climate impact research, significantly contributing to our collective understanding of the complex interactions between climate change and regional weather patterns in this crucial and unique geographical context.

Harnessing Solar Energy For Sustainable Ocean-based CO2-to-fuel Conversion

PI: Xingchen Tony Wang, Assistant Professor, Earth and Environment Sciences

Collaborator(s):
Jier Huang, Associate Professor, Schiller Institute and Chemistry

Project Brief:
The ocean has absorbed approximately 35% of the cumulative CO2 emissions since the industrial revolution. Various methods have been proposed to capture CO2 from the ocean, thereby enabling theocean to absorb additional CO2 from the atmosphere. One of the most sustainable approaches for storing CO2 captured from seawater is to convert it into fuels using solar energy. This proposal aims to investigate the feasibility of ocean-based CO2-to-fuel conversion technology through a collaborative effort between ocean scientist Dr. Xingchen Tony Wang and materials and physical chemist Dr. Jier Huang. We plan to construct a laboratory prototype, which links an oceanic CO2 capture system to a CO2 conversion system. In this prototype, our objective is to process up to 500 liters of seawater and convert the extracted CO2 into CO and other fuels. After one year of exploratory research with this seedgrant, we aim to gather preliminary data to support an application for an external grant.