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.
Integrating laboratory data into physician reports can improve diabetes care. Linking the Ontario Laboratories Information System (OLIS) with audit reports enhances clinical information for better patient management.
Area of Science:
- Health Services Research
- Clinical Informatics
- Population Health Management
Background:
- Audit and feedback reports are provided to primary care physicians in Ontario to summarize patient management.
- Current reports lack crucial clinical information, limiting their utility for quality improvement in diabetes care.
Purpose of the Study:
- To assess the feasibility of linking the Ontario Laboratories Information System (OLIS) with existing audit and feedback reports.
- To integrate key clinical measures of glycemic and cholesterol control for patients with diabetes.
Main Methods:
- Examined the linkage between the OLIS database and provincial audit and feedback reporting.
- Analyzed the ascertainment of glycated hemoglobin and low-density lipoprotein cholesterol test results from the previous year.
Main Results:
- Successfully ascertained glycated hemoglobin (69.9%) and low-density lipoprotein cholesterol (64.1%) test results for a majority of patients.
- Observed significant variation among physicians in the proportion of patients exceeding clinical thresholds for these measures.
Conclusions:
- Linking OLIS data to audit reports is feasible and enhances clinical information for physicians.
- This integration has the potential to improve diabetes care and management through data-driven insights.
- The approach supports population-level research and quality improvement initiatives in diabetes care.
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
01:24Data Reporting and Recording
01:11Testing 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...
01:22Self-Report Tests of Personality
01:12Analysis of Population Pharmacokinetic Data
01:24Testing 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...
01:21Testing 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;...