The Latest in Predictive Analytics
Summary: Stroke is the second most frequent cause of death in the world. Assessing patient risk of stroke can help deliver the right interventions at the right time to improve patient health outcomes. In this paper, we proposed and validated a risk model predictive of stroke in the future 1 year period across all ages, all payors, and all disease groups in Maine.
Summary: Understanding the future healthcare service utilization cost of patients allows for better resource allocation. The aim of this study is to develop a risk stratification model for the future six month healthcare resource utilization for patients of the state of Maine.
Development, Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in Maine Health Information Exchange
Abstract: Identifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups.
Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study
Abstract: The increasing rate of health care expenditures in the United States has placed a significant burden on the nation’s economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation.
NLP based congestive heart failure case finding: A prospective analysis on statewide electronic medical records
In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process both structured and unstructured electronic medical records (EMR) to allow complementary and cost-efficient identification of CHF patients.
Real-Time Web-Based Assessment of Total Population Risk of Future Emergency Department Utilization: Statewide Prospective Active Case Finding Study
An easily accessible real-time Web-based utility to assess patient risks of future emergency department (ED) visits can help the health care provider guide the allocation of resources to better manage higher-risk patient populations and thereby reduce unnecessary use of EDs.
Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization.
Although AKI is common among hospitalized children, comprehensive epidemiologic data are lacking. This study characterizes pediatric AKI across the United States and identifies AKI risk factors using high-content/high-throughput analytic techniques.