Description
This longitudinal study investigates the impact of Lean on: (1) efficiency, measured as inputs relative to traditional fee-for-service (FFS) outputs (e.g., RVU production) and non-FFS provider work effort (e.g., medical advice via e-messaging); (2) system affordability, indicated by use of medical resources with attention to clinical quality; and (3) patient experience reflected in satisfaction scores, access to services, and timely movement through office visits.
Co-Investigator(s)
Funder
Agency for Healthcare Research and Quality
Research Topics
Related Studies
COVID-19 Impacts on Cancer Care Management, Patient Experience and Care Costs
Project goals: To understand impacts of COVID-19 on cancer care management (detection, diagnosis, treatment), pt-reported experience, and cancer care costs.
Investigators: Cheryl Stults, Ph.D., Monique de Bruin, M.D., MPH, Su-Ying Liang, Ph.D.
Stupski Serious Illness Program Evaluation
Evaluate palliative care projects at seven healthcare organizations in the SF Bay Area.
Investigator: Sylvia Sudat, PhD
Impact of COVID-19 on Cancer Care Management
Study will enhance understanding of cancer care management due to COVID-19.
Investigators: Su-Ying Liang, Ph.D., Monique de Bruin, M.D., MPH
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