Description
Under the Affordable Care Act (ACA), Medicare introduced a new coverage of annual preventive care visits in 2011, called the Annual Wellness Visit (AWV), which is available at no cost to beneficiaries.
We previously showed a substantial increase in preventive visit use after this ACA policy change among Medicare fee-for-service (FFS) beneficiaries at the Palo Alto Medical Foundation (PAMF).
In this study, we assess the impact of the expanded ACA coverage on the use of preventive visits (Aim 1) and the use of recommended, evidence-based preventive services (Aim 2) among seniors with multiple comorbid conditions (MCCs) as compared to those without MCCs.
We use electronic health records data from the PAMF between 2006 and 2015. The scope of recommended preventive care is not limited to screening for primary prevention, but also includes the prevention of complications due to existing conditions.
Preventive needs can be partly addressed during routine medical visits, but with short appointments there is little time to address both prevention and existing needs. We expect that for seniors with MCCs, the benefits of preventive visits would be amplified, as each of their existing conditions may require unique management and follow-up, but, despite the potential benefits, seniors with MCCs may be less likely to take advantage of the coverage as they are already overwhelmed by frequent medical appointments.
Sascha Dublin, MD, PhD, Group Health Research Institute, serves as consultant. Thomas Gill, MD, Yale School of Medicine, serves as advisor.
PI of the HCSRN-OAIC AGING Initiative is Jerry Gurwitz, MD, Meyers Primary Care Institute.
We previously showed a substantial increase in preventive visit use after this ACA policy change among Medicare fee-for-service (FFS) beneficiaries at the Palo Alto Medical Foundation (PAMF).
In this study, we assess the impact of the expanded ACA coverage on the use of preventive visits (Aim 1) and the use of recommended, evidence-based preventive services (Aim 2) among seniors with multiple comorbid conditions (MCCs) as compared to those without MCCs.
We use electronic health records data from the PAMF between 2006 and 2015. The scope of recommended preventive care is not limited to screening for primary prevention, but also includes the prevention of complications due to existing conditions.
Preventive needs can be partly addressed during routine medical visits, but with short appointments there is little time to address both prevention and existing needs. We expect that for seniors with MCCs, the benefits of preventive visits would be amplified, as each of their existing conditions may require unique management and follow-up, but, despite the potential benefits, seniors with MCCs may be less likely to take advantage of the coverage as they are already overwhelmed by frequent medical appointments.
Sascha Dublin, MD, PhD, Group Health Research Institute, serves as consultant. Thomas Gill, MD, Yale School of Medicine, serves as advisor.
PI of the HCSRN-OAIC AGING Initiative is Jerry Gurwitz, MD, Meyers Primary Care Institute.
Funder
National Institute on Aging/HCSRN-OAIC AGING Initiative
Related Studies
The Impact of Smoking, Comorbidities, and Race/Ethnicity on COVID-19 Infection and Disease Severity
Using EHR from a large healthcare system in Northern CA, we will study the impact of smoking, comorbidities, race on COVID-19 infection and disease severity.
Investigator: Jiang Li, Ph.D., MPH
COVID-19 Universal Registry for Vital Evaluations (CURVE) Project
To create a COVID-19 Universal Registry for Vital Evaluations (CURVE) database, a semi-real-time registry of Sutter Health patients with COVID-19 infection.
Investigator: Jiang Li, Ph.D., MPH
Statin Use and Risk Prediction of Atherosclerotic Cardiovascular Disease in Minority Subgroups (SURPASS) Study
Overall goal of this study is to address gaps in knowledge about CVD prediction and treatment in understudied racial/ethnic minority populations.
Investigators: External PI, Principal Investigator, Jiang Li, Ph.D., MPH
Pilot Project in Applied Artificial Intelligence: Using Machine Learning to Improve the Safety of Hospital Care
Develop and test predictive model identifying emerging sepsis using artificial intelligence, and translate learnings into improved operational criteria.
Investigator: Sylvia Sudat, PhD
Quantifying 2D and 3D MRI Imaging For Improving MS Diagnosis, Treatment, And Clinical Care
Create a longitudinal proof-of-concept MS research database (MSClinQ) that can support clinical queries, and can be updated with new tx, dx tools, care options
Investigators: J.B. Jones, PhD, MBA, Xiaowei (Sherry) Yan, PhD, MS
Improving Cancer Care
Project will provide information about referrals to palliative care and way to enhance goals of care discussions for patients diagnosed with stage IV cancer.
Investigator: Su-Ying Liang, Ph.D.
Using Mobile Technology and Analytics to Track Outcomes in MS Patients
Develop an app to collect and transform pt-reported outcomes in MS for health care providers to improve care and support shared decision making.
Investigators: J.B. Jones, PhD, MBA, Xiaowei (Sherry) Yan, PhD, MS
Randomized study of EHR Defaults and Using social Comparison feedback to effectively decrease opioid prescription pill burden (REDUCE).
Randomized trial to explore two behavioral economic intervention--changing opioid Rx default and providing social comparison feedback to MDs on opioid pills Rx.
Investigator: Xiaowei (Sherry) Yan, PhD, MS
Major Depressive Disorder
Purpose is to use EHR to understand the management of major depressive disorder in a large healthcare system, to incorporate pt-reported data in care workflow.
Investigator: Kristen M.J. Azar, R.N., BSN, MSN/MPH
Interpretable Deep Learning Model for Longitudinal Electronic Health Record and Applications to Heart Failure Prediction
Develop advanced deep learning model to predict early onset heart failure using EHRs, including structured and unstructured data.
Investigators: Xiaowei (Sherry) Yan, PhD, MS, External PI, Principal Investigator