Probabilistic techniques for obtaining accurate patient counts in Clinical Data Warehouses

Abstract

Proposal and execution of clinical trials, computation of quality measures and discovery of correlation between medical phenomena are all applications where an accurate count of patients is needed. However, existing sources of this type of patient information, including Clinical Data Warehouses (CDW) may be incomplete or inaccurate. This research explores applying probabilistic techniques, supported by the MayBMS probabilistic database, to obtain accurate patient counts from a clinical data warehouse containing synthetic patient data. We present a synthetic clinical data warehouse (CDW), and populate it with simulated data using a custom patient data generation engine. We then implement, evaluate and compare different techniques for obtaining patients counts. We model billing as a test for the presence of a condition. We compute billing’s sensitivity and specificity both by conducting a “Simulated Expert Review” where a representative sample of records are reviewed and labeled by experts, and by obtaining the ground truth for every record. We compute the posterior probability of a patient having a condition through a “Bayesian Chain”, using Bayes’ Theorem to calculate the probability of a patient having a condition after each visit. The second method is a “one-shot” approach that computes the probability of a patient having a condition based on whether the patient is ever billed for the condition Our results demonstrate the utility of probabilistic approaches, which improve on the accuracy of raw counts. In particular, the simulated review paired with a single application of Bayes’ Theorem produces the best results, with an average error rate of 2.1% compared to 43.7% for the straightforward billing counts. Overall, this research demonstrates that Bayesian probabilistic approaches improve patient counts on simulated patient populations. We believe that total patient counts based on billing data are one of the many possible applications of our Bayesian framework. Use of these probabilistic techniques will enable more accurate patient counts and better results for applications requiring this metric.

Assessing Older Adults’ Perceptions of Sensor Data and Designing Visual Displays for Ambient Environments

“…The use of simulated data in health informatics research is common practice. For example, simulated data have been proposed as a means to facilitate the development and validation of activity monitoring systems for older adults at home(27), have been used to compare different techniques for obtaining patient counts from a clinical data warehouse (28) and have been used to gauge provider preferences for different visualization types (4). This study relied on simulated data to model a fall scenario based on research team expertise and case studies of activity data from prior research (18–21).…”

Patient perceptions on the subject of medical research

“…Therefore, since only imperfect information is available, the clinical judgment and decision making is usually done by comparing the normative model established by the statistical decision theory. 4 …”