Raina Merchant Named National Academy of Medicine Emerging Leader
Penn Medicine's Amol Navathe Appointed to MedPAC
Are Fewer Physicians and Self-Service Health Care the Answer?
Machine Learning 102 Report - Learning the Future at LDI/IBI Workshop
Four Innovation Teams Re-Imagine Parts of Penn Medicine
The Effect of Predictive Analytics-Driven Interventions on Healthcare Utilization
Among high-risk Medicare Advantage members with congestive heart failure, a proactive outreach program driven by a claims-based predictive algorithm reduced the likelihood of an emergency department (ED) or specialist visit in one year by 20% and 21%, respectively. The average number of visits dropped as well, with a 40% reduction in the volume of ED visits and a 27% reduction in the volume of cardiology visits after the first year.
Rise of the Machines in Health Care?
Report from a Penn Machine Learning Workshop for HSR Researchers
Online Ratings of the Patient Experience: Emergency Departments Versus Urgent Care Centers
Anish K. Agarwal, Kevin Mahoney, Amy L. Lanza, Elissa V. Klinger,...
ABSTRACT [FROM JOURNAL]
Study Objective: Individuals increasingly use online rating platforms to rate and review hospitals. We seek to describe and compare publicly available online review content and ratings of emergency departments (EDs) and urgent care centers.
Methods: We analyzed Yelp reviews of EDs and urgent care centers to
...Popular Science Spotlights Penn Medicine Nudge Unit Chief
Comparison of Pharmacy Claims and Electronic Pill Bottles for Measurement of Medication Adherence Among Myocardial Infarction Patients
Shivan J. Mehta, David A. Asch, Andrea B. Troxel, Raymond Lim, Jennifer Lewey, Wenli Wang, Jingsan Zhu, Laurie Norton, Noora Marcus, ...
ABSTRACT [FROM JOURNAL]
Background: Medication adherence after myocardial infarction remains low. Pharmacy claims have typically been used to measure medication adherence, but electronic pill bottles may offer additional information.
Objective: The main objectives of this study were to compare the association of adherence measured by prescription claims and remote monitoring technologies with cardiovascular events.
Research Design: This study was a secondary analysis of a remote monitoring intervention to increase...
A Randomized Controlled Trial of Opt-in Versus Opt-Out Colorectal Cancer Screening Outreach
Shivan J. Mehta, Tanya Khan, Carmen Guerra, Catherine Reitz, Timothy McAuliffe, ...
ABSTRACT [FROM JOURNAL]
Objectives: Colorectal cancer (CRC) screening uptake is suboptimal, despite national efforts to increase screening rates. Behavioral economic approaches such as changing defaults may increase participation. We compare response rates to opt-in or opt-out messaging in mailed fecal immunochemical test (FIT) outreach.
Methods: This is a two-arm randomized controlled trial among 314 patients aged 50–74 years who had at least two primary care visits in the 2...
Advances in Alzheimer's Imaging are Changing the Experience of Alzheimer's Disease
Shana D. Stites, Richard Milne, and Jason Karlawish
ABSTRACT [FROM JOURNAL]
Neuroimaging is advancing a new definition of Alzheimer's disease (AD). Using imaging biomarkers, clinicians may begin to diagnose the disease by identifying pathology and neurodegeneration in either cognitively impaired or unimpaired adults. This “biomarker-based” diagnosis may allow clinicians novel opportunities to use interventions that either delay the onset or slow the progression of cognitive decline, but it will also bring novel challenges. How will changing the definition of AD from a clinical to a biomarker construct change the experience of...
Nurse Generated EHR Data Supports Post-Acute Care Referral Decision Making: Development and Validation of a Two-step Algorithm
Kathryn H. Bowles, Sarah J. Ratcliffe, Mary D. Naylor, ...
ABSTRACT [FROM JOURNAL]
Objective: Build and validate a clinical decision support (CDS) algorithm for discharge decisions regarding referral for post-acute care (PAC) and to what site of care.
Materials and Methods: Case studies derived from EHR data were judged by 171 interdisciplinary experts and prediction models were generated.
Results: A two-step algorithm emerged with area under the curve (AUC) in validation of 91.5% (yes/no refer) and AUC 89.7% (where to refer).
Discussion: CDS for...
Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay
Gary E. Weissman, Rebecca Hubbard, Lyle H. Ungar, Michael O. Harhay, Casey S. Greene, Blanca E. Himes, ...
Abstract [from journal]
Objectives: Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, random forest, and elastic net regression models, with and without unstructured clinical text data, to predict a binary composite outcome of in-hospital death or ICU length of stay greater than or equal to 7 days using data from the first 48 hours of hospitalization.
Design...