Electronic screening improves efficiency in clinical trial recruitment.

This study evaluated the performance of an electronic screening (E-screening) method and used it to recruit patients for the NIH sponsored ACCORD trial. Out of the 193 E-screened patients, 125 met the age criterion ("age>or=40"). For all of these 125 patients, the performance of E-screening was compared with investigator review. E-screening achieved a negative predictive accuracy of 100% (95% CI: 98-100%), a positive predictive accuracy of 13% (95% CI: 6-13%), a sensitivity of 100% (95% CI: 45-100%), and a specificity of 84% (95% CI: 82-84%). The method maximized the use of a patient database query (i.e., excluded ineligible patients with a 100% accuracy and automatically assembled patient information to facilitate manual review of only patients who were classified as "potentially eligible" by E-screening) and significantly reduced the screening burden associated with the ACCORD trial.

Probing genetic overlap among complex human phenotypes.

Geneticists and epidemiologists often observe that certain hereditary disorders cooccur in individual patients significantly more (or significantly less) frequently than expected, suggesting there is a genetic variation that predisposes its bearer to multiple disorders, or that protects against some disorders while predisposing to others. We suggest that, by using a large number of phenotypic observations about multiple disorders and an appropriate statistical model, we can infer genetic overlaps between phenotypes. Our proof-of-concept analysis of 1.5 million patient records and 161 disorders indicates that disease phenotypes form a highly connected network of strong pairwise correlations. Our modeling approach, under appropriate assumptions, allows us to estimate from these correlations the size of putative genetic overlaps. For example, we suggest that autism, bipolar disorder, and schizophrenia share significant genetic overlaps. Our disease network hypothesis can be immediately exploited in the design of genetic mapping approaches that involve joint linkage or association analyses of multiple seemingly disparate phenotypes.

Architectural design of a data warehouse to support operational and analytical queries across disparate clinical databases.

As the clinical data warehouse of the New York Presbyterian Hospital has evolved innovative methods of integrating new data sources and providing more effective and efficient data reporting and analysis need to be explored. We designed and implemented a new clinical data warehouse architecture to handle the integration of disparate clinical databases in the institution. By examining the way downstream systems are populated and streamlining the way data is stored we create a virtual clinical data warehouse that is adaptable to future needs of the organization.

Automated real-time detection and notification of positive infection cases.

Infection control in the healthcare setting is an essential component for patient safety and quality of care. To assist with daily infection control functions, we have implemented an alert in the Vigilens Health Monitor (a clinical decision support system at our institution) for real-time detection and notification of positive infection cases in both inpatient and outpatient settings.

EpiPortal: an electronic decision support system for infection control.

A comprehensive, electronic hospital epidemiology decision support system serves diverse users but its primary user is the infection control professional (ICP). Utilizing off-the-shelf components and accepted standards enables the system to be open, vendor-independent and ICP-controlled. Its development can flexibly respond to the evolving nature of infection control practice.