Economics of COVID-19; Health trends and inequalities; Data analytics, information technology and health care decision-making; Economics of Alzheimer’s Disease and Related Dementias; Dynamics of the health care ecosystem; Medical innovation and the value of health care; Behavior change in health.
2020. Janet Currie. New Modes of Health Care Delivery—Effects on Patients and Providers.
We live in an era of rapid changes in the structures and mechanisms of health care delivery. One striking change has been the development of new types of outpatient facilities including retail clinics, urgent care centers, and ambulatory surgical centers. We aim to examine the impact of these non-traditional care settings on the on the utilization of care and on patient outcomes among the elderly. These new care settings may serve as substitutes or complements for care provided in more traditional settings, such as emergency, inpatient, and primary care office settings. They may therefore provide an opportunity to reduce cost and improve access to care. However, they may also make it more likely that a patient seeks care in a setting that is not optimal for their condition,
which could lead to higher costs and worse outcomes. We will use individual medical histories from longitudinal Medicare claims data to account for underlying differences in patient health status, and we will exploit variation in the closing and opening of facilities in order to study the way that patients are selected into different modes of care, substitution patterns across modes, and impacts on patient outcomes.
A second related objective will be to study the impact of new modes of medical care delivery on traditional providers. At the facility level, does increased competition from new providers change merger and acquisition patterns or the types of services offered? At the individual physician level, can we see impacts in terms of where they practice, or the quality of medical decision making? Because it has data on patients, physicians, and facility
type, the Medicare claims data is well suited for this type of analysis. The project should shed light not only on the specific issue of how new care modes help or harm Medicare patients, but also on the more general question of whether increasing competition in a setting that is still far from perfectly competitive improves welfare.
2020. Kosali Simon. Public Reporting of NH Antipsychotic Use: Changes in the Reporting of Exclusionary Diagnoses?.
In this proposed pilot study, we aim to examine the use of exclusionary diagnoses around the 20-resident threshold for the long-stay APM quality measure to understand whether NHs have been gaming CMS’ public reporting system for APMs. The ultimate goal is to collect preliminary data in order to write a larger NIH funded grant proposal that will include Medicare/Medicaid claims data that document diagnoses outside of the NH (e.g.
physicians and hospitals) and to study the issue of gaming for other quality measures reported on NH Compare. We hope to next link both the intended and unintended effects of this policy on APM access to the health outcomes of NH residents, to better understand how these policies affect health at later life.
2020. Mireille Jacobson, Julie Zissimopoulos. The Role of Medicare's Annual Wellness Visit in the Assessment of Cognitive Health.
Dementia and mild cognitive impairment (MCI) are widely considered under-diagnosed, with some estimates suggesting that between 15 and 40% of dementia cases and an even higher share of MCI cases are undiagnosed (Taylor et al., 2009; Amjad et al., 2018). Clinical diagnosis often occurs late in the disease trajectory, hindering timely treatment of reversible causes of memory loss. Late diagnosis also complicates a patient’s need to
develop clear and consistent medical, legal, and financial plans.
Medicare payment policy for the routine assessment of cognitive impairment in the primary care setting is a potentially potent tool to improve early detection. As part of a new benefit created under the Affordable Care Act (ACA), Medicare now covers an Annual Wellness Visit (AWV) that requires, among other things, an assessment to detect cognitive impairment. Providers are given little guidance on how to do this assessment beyond “direct observation” and, if appropriate, a “brief validated structured cognitive assessment.”1 This assessment is just one of many required components of the AWV, which should include a health risk assessment, an enumeration of current providers and medications, a depression screening, more routine height and weight measurement,
and so on. Importantly, providers bill Medicare for the visit as a whole and are not required to submit documentation of specific details of the visit.
Perhaps not surprisingly, anecdotal evidence suggests that many physicians do not follow the AWV structure. Providers are more inclined to discuss issues such as the management of high blood pressure or vaccinations at the expense of screening for memory loss or depression.2 In addition, most providers, who are reimbursed on a fee-for-service basis and thus get paid irrespective of outcomes, do not face strong incentives to ensure followup
testing is performed if a cognitive test reveals memory deficits.
A few recent studies have analyzed take-up of the AWV benefit (Ganguli et al. 2017), its relationship to use of preventive services and depression screening (Jensen et al. 2015; Pfoh et al. 2015), and its correlation with measures of cognitive care (Fowler et al. 2018; 2019 Alzheimer’s Disease Facts and Figures Special Report on Detection in the Primary Care Setting). Yet, our understanding of whether the AWV has increased assessment for cognitive impairment in the Medicare population; how assessments get done; and what happens if someone shows signs of impaired cognition is extraordinarily limited. The proposed pilot will seed a broader project to fill in the gaps in our understanding of the AWV and the required cognitive assessment component of this visit. The specific aims of the broad project are to:
• Aim 1: Characterize who gets a cognitive assessment in Medicare during an AWV
• Aim 2: Determine why some beneficiaries get screened while others do not
• Aim 3: Describe health care service use after a beneficiary screens positive for cognitive impairment
• Aim 4: Develop and test a nudge to improve take-up and/or targeting of cognitive assessments to appropriate demographic groups
2020. William Evans. Evaluating the Ability of a Senior Companion Program to Improve Senior Health.
This proposal is for a planning grant for a randomized controlled trial that evaluates the effect on health of senior companion services. This study will be a collaboration between researchers at the Wilson Sheehan Lab for Economic Opportunities (LEO) at the University of Notre Dame and a partnering social service agency. The project development team will vet providers interested in conducting a rigorous evaluation and form a research
partnership with the organization best fit for such an evaluation. The search for potential organizations will be conducted by LEO with the assistance of our partner organizations. LEO coordinates with local social service agencies to conduct random assignment experiments of their innovative programs designed to assist those in poverty. LEO has partnered with three national networks (Catholic Charities USA, Lutheran Services of America and Goodwill Industries) and we plan to work with these organization to help vet potential partners. Organizations will be considered fit to participate in research if they meet the following three criteria. First, the partner must currently operate a large and active SCP geared toward low-SES individuals. Second, the organization’s SCP must exhibit an excess demand for services. This can be gauged by the length of the program’s current waitlist.
Finally, the organization must be willing to use a lottery to allocate SCP services in place of a waitlist. In our experience, partnering with an organization that meets these three criteria will ensure a successful rigorous study. During this planning stage, our research team will also design and program two surveys that will be used during the full evaluation. The first is the intake survey that will be used during the enrollment process and the
second is the follow-up survey that will accompany the evaluation.
The ultimate objective of this project is to provide precise estimates of the impact of SCP programs on seniors’ health status and independent living. For the purposes of this study, independent living and health status will be measured using both self-reported surveys and administrative data on emergency department and in-patient admissions. The randomized controlled trial (RCT) that will provide senior companions to a sample of around 200 predominantly low-SES homebound seniors. An additional 200 seniors will comprise the control group. This study pool will be recruited from the partnering organization’s current waitlist. Following informed consent protocols appropriate to the study, clients choosing to participate will enter a lottery to determine their placement in the treatment versus control group. which will determine if they will receive a study senior companion. Ideally,
the partnering organization would currently operate a lengthy wait list (e.g., a two-year wait to enter the program); this way, clients selected to the control group can expect a waiting period as long as clients who choose not to participate in the study. LEO has extensive experience working with social service providers. They have 30 active/completed projects in 25 different locations across the country. Their service partners have included social service organizations, county and city governments, and juvenile justice centers.
2019. Adler-Milstein, Handel, Kolstad. Electronic Medical Records, Provider Behavior and Health Outcomes among the Elderly.
also with Malmendier and Obermeyer
2019. Aparna Soni. Pharmaceutical Access, Functional Outcomes and Implications for Caregiver.
2019. J. Abaluck, P. Hull, A. Starc. Differential Mortality in Medicare Advantag.
2019. R. Abramitzky, H. Williams, D. Fetter. End-of-Life Care in U.S. History.
2019. T. Gross, T. Layton, D. Prinz. Liquidity and Healthcare Consumption.
2018. David Silver. Early Life Origins of Intergenerational Mobilit.
2018. Jessica Van Parys. Does Internet Access Affect Provider Choice and Health Outcomes of Medicare Beneficiaries.
2018. Leila Agha. Provider Organizations and Care Coordination: Effects on Utilization, Quality and Outcome.
2018. Molly Schnell, E. Meara, N. Morden. Understanding Opioid Prescribing Practice.
2017. Leila Agha. Assessing the Medicare Hospital Readmissions Reduction Program.
2017. David Cutler, Adriana Lleras-Muney. Economic Growth, Pollution, and Health Improvements.
2017. Amitabh Chandra. Asymmetric Information in Health Care: Evidence from Physicians Who Are Now Patients.
2017. Amitabh Chandra, Doug Staiger, Maurice Dalton. Predicting the Impact of Hospital Closures on Patient Outcomes.
2017. Bruce Weinberg. Measuring the Impact of Scientific Research on Health.
2017. Jeffrey Clemens, Joshua Gottlieb. Financial Incentives and Long-Run Health Sector Capacity.
2017. Nicole Maestas, David Grabowski, Brian McGarry. Improving Decision-Making by Older Adults in the Medicare Part D Program.
2017. Timothy Layton. The Effect of Medicaid Managed Care on the Health of Aging Individuals with Disabilities.
2017. Amanda Kowalski. Risk Factors and Breast Cancer Screening.
2017. Katherine Baicker, Z. Obermeyer. Assessing the Overuse and Underuse of Diagnostic Testing.
2017. M. Notowidigdo, J. Graves,T. Gross. Medicaid Expansion, Hospital Choice and Health Outcomes: Evidence from a Near-Census of Hospitalizations.
2017. Joseph Doyle, P.Gulur, M.Jacobsen. Pain Management for Opioid Tolerant Patients: A Randomized Controlled Trial.
2016. Nikhil Agarwal. Effects of Medicare Reimbursement Rates on Quality of Dialysis Care and Patient Outcomes.
2016. Zack Cooper. All-Payer Measures of Health Spending and the Link among Spending, Health Care Utilization and Health Outcomes.
2016. Marcella Alsan. How do Preferences Over Doctors Vary by Patient Gender and Race?.
2016. L. Katz, J. Kling, L. Sanbonmatsu. Using Data from Moving to Opportunity to Investigate Early Influences on Educational Outcomes.
2015. David Chan. The Impact of Local Coverage Determinations on Costs and Patient Outcomes.
2014. David Cutler. ACOs and Decision-Making in Hospitals.
2014. Sita Slavov, John Shoven & D. Wise. Using Firm Benefits Data to Understand Labor Force Decision-Making.
2014. Katherine Baicker. The Use of Financial Incentives and "Nudges" to Improve Behavior.
2014. Amanda Kowalski. The Long Term Impact of Health Insurance Expansions.
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