Related Experiment Video
Updated: Feb 2, 2026

A Method to Study Adaptation to Left-Right Reversed Audition
Published on: October 29, 2018
Integrating Population-Wide Laboratory Testing Data with Audit and Feedback Reports for Ontario Physicians
Michael A Campitelli1, Matthew Kumar2, Anna Greenberg3
1A staff scientist in the Strategic Partnerships and External Services department at ICES. He can be contacted at michael.campitelli@ices.on.ca.
Abstract:
Audit and feedback reports, distributed by Health Quality Ontario to consenting primary care physicians, provide doctors with a confidential summary of how they manage patients with diabetes; these reports currently lack clinical information. We examined the feasibility of linking the Ontario Laboratories Information System (OLIS), a large provincial database of laboratory test results, with the existing provincial audit and feedback reporting structure to integrate measures of glycemic and cholesterol control among patients with diabetes. We found that we could ascertain glycated hemoglobin (69.9%) and low-density lipoprotein cholesterol (64.1%) test results in the previous year for most patients and that there was wide variation among physicians in the proportion of patients who exceeded clinical thresholds for these measures. Our study highlights the potential value of reporting more clinically rich information to physicians to improve diabetes care and management and demonstrates the feasibility of using OLIS data at the population level to enhance ongoing research and quality improvement.
More Related Videos
11:21Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
08:22IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae
Published on: January 15, 2020
Related Concept Videos
Data Reporting and Recording
Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
Self-Report Tests of Personality
Analysis of Population Pharmacokinetic Data
Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...