Trial Information

Technology for Optimizing Population Care in a Resource-limited Environment

In prior NIH-funded research, the investigators have demonstrated the efficacy of an
IT-based population management system to improve breast cancer screening (NCI R21 CA121908).
The investigators will expand our current IT platform from this single function (breast
cancer screening) to a package of cancer prevention actions (breast, cervical, and
colorectal cancer screening) and examine the added benefit of population-level preventive
cancer care that is directed by specific clinician knowledge of individual patient needs.
Moreover, rather than compare our system to currently sub-optimal "usual care" practice, our
goal is to test whether the impact of our intervention exceeds the current state-of-the-art
of IT-based population management. Therefore, control group practices will receive augmented
standard care defined as a population-level reminder system with automated patient contacts.

In augmented standard care control practices, the investigators will implement a system that
includes: 1) a population-based perspective to identify all eligible patients overdue for
screening, 2) an automated, centralized process to contact selected patients by letter, 3) a
result management system that automatically tracks test scheduling and completion, 4) a
web-based, easily accessible tool allowing practice personnel to contact patients not
completing testing, and 5) use of patient navigators for high risk patients not responding
to initial outreach. In the control arm, the process of escalating the reminder intervention
from a letter, to contact by phone call, to a patient navigator, will occur in a standard
algorithmic fashion without provider input. While not yet the standard of care nationwide,
prior studies have proven the efficacy of such an approach. In intervention practices, the
investigators will enhance augmented standard care by implementing a novel system that will
enable physicians and clinical population managers to individualize care for each patient in
their panel using tools to classify and organize patients by their clinical attributes. The
investigators hypothesize that this personalized identification of patients by both their
clinical outcome and clinical process risk status will improve the efficacy and efficiency
of resource allocation decisions. The key additions to the health IT system for intervention
practices will be: 1) a clinical systems IT platform to organize and present clinical data
for each clinician's patient panel, 2) an accessible Web-based tool allowing clinicians
(physicians and clinical population managers) to view, organize, and investigate their
patient panels, and 3) a simple process where the clinician can make a tailored screening
decision and designate the method of clinical intervention based upon the patient's risk
profile.