Time to event - track unlimited events in relation to time and targets
In every medical center events occur which, if tracked and analyzed for outcomes in relation to other events in time, are indicators of care quality and the quality of operational practices. CLG is unique in its ability to track and analyze time-related events such as time in range and time to survival. When combined with event collections which include demographics, cohorts can be studied over time for factors such as relative admission rates, length of stay, lab outcomes, effectiveness of medications, percent of time patients spend in/out of targeted ranges, and the impact of out-of-system medical visits on the rate of hospitalization.
Reusable research objects - recapitulate research studies
Among the issues plaguing research-oriented medical centers are the arguments over research results owing to the lack of clarity concerning the assumptions and methods which drove the study. CLG significantly reduces these issues. The tool creates reusable research objects. These objects allow transparent and self-documenting recapitulation of research methods; they effectively automate the documentation of methods. All the criteria are generated for recapitulation and regeneration of results.
Compose and manage complex logical operations in relation to time
A major CLG differentiator is its strong and flexible User Interface for composing and managing complex, nested conditions in time. Using the Event Condition Editor, it is possible to easily specify conditions such as the intersection of mortality and readmissions with or without particular lab tests and/or medications over a duration of time and/or from a specific index date.
Guide inferences using statistically sound confidence levels
Quality Improvement decisions driven by studies are often constrained by small sample sizes. Built-in statistics provide users with epidemiologic insight (models, p-values, confidence limits) for high-level inference. CLG brings research grade methods to the QI worker and the clinical manager.
Filter and pivot data at will
In CLG, patient groups and appended analytic data can be viewed in aggregate in the form of a data cube or pivot. CLG produces these structures on-demand and provides users with an interactive viewer to allow them to slice, dice, and filter data in both grid and chart formats. The Cube Analysis module automatically creates summary data from sorts and counts, organizing list data into pivot data. As a result, groups of clinics, physicians, procedures, medications, diagnoses, etc. can be created and tracked; patient and clinician populations, diagnoses, procedures, medications, and test results relative to normal limits or other goals and standards can be changed and analyzed.
With CLG a cohort or a query can quickly and repeatedly be changed in order to examine the impact of those changes on the results. This allows the investigator to generate new questions that probe more deeply and quickly lead to answers.
Build multi-dimensional queries on the fly; create drag-and-drop visual rules, end- user reports, and custom analytics
CLG empowers users to build complex queries and evaluate complex conditions on the fly. It eliminates the need to compete for the availability of IT resources in order to create new functionality and to view information from multiple vantage points. Easier and more timely access to the data supports the creation of a data-driven culture.
Plugs easily into any IT environment
CLG is built on an open architecture, exposing Web Services, component-based, and designed for integration with legacy systems and environments.