Measurement is essential to monitoring success and helps guide your team towards your specific intervention goal. Measurement also tells us what’s working and what’s not, and provides evidence to inspire other healthcare providers to improve the quality of patient safety.
The measurement methodology and recommendations regarding sampling size referenced in this GSK, is based on The Model for Improvement and is designed to accelerate the pace of improvement using the PDSA cycle; a "trial and learn" approach to improvement based on the scientific method. Langley, G., Nolan, K., Nolan, T., Norman, C., Provost, L. The Improvement Guide: A Practical Approach to Enhancing Organizational Performance. San Francisco, Second Edition, CA. Jossey-Bass Publishers. 2009.
It is not intended to provide the same rigor that might be applied in a research study, but rather offers an efficient way to help a team understand how a system is performing. When choosing a sample size for your intervention, it is important to consider the purposes and uses of the data and to acknowledge when reporting that the findings are based on an "x" sample as determined by the team.
The scope or scale (amount of sampling, testing, or time required) of a test should be decided according to:
- The team's degree of belief that the change will result in improvement
- The risks from a failed test
- Readiness of those who will have to make the change
Provost, Lloyd P; Murray, Sandra (2011-08-26). The Health Care Data Guide: Learning from Data for Improvement (Kindle Locations 1906-1909). Wiley. Kindle Edition.
Please refer to the
Improvement Frameworks GSK (2015) for additional information.
Hand Hygiene Measures
HH 1 - Volume of Alcohol Based Hand Rub Used for the Area being Monitored||Increase baseline||Process|
|HH 2 - Volume of Hand Hygiene Soap Used for the Area being Monitored||Increase baseline||Process|
|HH 3 - Percent Appropriate Hand Hygiene Practice by Health Care Workers (HCW)||80%||Process|
|HH 4 - Percent Availability of Hand Hygiene Products at Bed Spaces or Patient Areas being Monitored - Bundle Compliance||95%||Process|
Infection Prevention and Control Measures
IPAC 1 - Number of Gowns Used for the Area being Monitored
IPAC 2 - Number of Boxes of Gloves Used for the Area being Monitored
IPAC 3 - Percentage of Eligible Patient Admissions Screened for MRSA per Month||90%||Process|
IPAC 4 - Percentage of Eligible Patient Admissions Screened for VRE per Month||90%||Process|
IPAC 5 - Percent Appropriate Environmental Cleaning Practice
IPAC 6 - Reduction in Mean Time to Placement on Contact Precautions
IPAC 7 - Reduction in Mean Time from Lab Notification to Placement on Contact Precautions
IPAC 8 - Incidence of HAI-MRSA Clinical Isolates per 1000 Patient Days
IPAC 9 - Incidence of HAI-VRE Clinical Isolates per 1000 Patient Days
IPAC 10 - Incidence of HAI-CDAD Positive Toxin Assay per 1000 Patient Days
Safer Healthcare Now! has two types of measures for each of the interventions: process measures and outcome measures. Some interventions also have balancing and information measures. Below are examples of each.
Outcome measures – These answer whether the team is achieving what it is trying to accomplish and articulate the picture of success. For example, if the team wants to reduce falls, it should measure the number of falls.
Process measures – Processes that directly affect the outcome are measured to ensure that all key changes are being implemented to impact the outcome measure. The timely delivery of prophylactic antibiotics to reduce surgical site infection is one example.
Balancing measures – These determine whether improvements in one part of the system were made at the expense of other processes in other parts of the system. For example, in a project to reduce the average length of stay for a group of patients, the team should also monitor the percentage of readmissions within 30 days for the same group.
Information measures – These collect general details relative to the intervention.
Please note that the Patient Safety Metrics system is no longer accepting data.