Quality Improvement Program

SNHN has a strong commitment to data quality in primary care.  We offer all our practices access to a data extraction tool, the CATPlus suite of products and training to ensure practices can effectively analyse their data using the tool. There are four components to CATPlus which are set out below:

  • CAT4 – understand your patient population
  • Cleansing CAT – create lists of patients with missing information
  • Topbar – be alerted to missing information at point of care
  • PATCAT – data aggregation tool used the PHN level

Using a data extraction tool allows a practice to analyse their own data and enables maintenance of and building of disease registers, ensures you are meeting RACGP accreditation standards and facilitates data cleansing in practice software.  Practice staff will have a better understanding of the demographics of patients along with disease prevalence of the patients attending the practice.  Understanding the practices patient population is essential to improve patient care and outcomes and maximise practice return on investment by ensuring MBS item numbers are claimed correctly for the care provided.

SNHN provides support for all practices to embed Quality Improvement (QI) strategies in their day to day workflow.  The Primary Care Advancement Team (PCAT) can help with analysis of your data and to identify areas for QI.  An important aspect of quality improvement is goal setting and activities associated with each goal.  We provide practices with quarterly data reports which measure quality improvement and guiding the direction of your QI activities.

QI needs to be approached in small steps, and we will offer assistances to do this by guiding practices in the Model for Improvement (MFI).  The MFI consists of completing Plan, Do, Study, Act or PDSA cycles.

Engaging in quality improvement activities is an opportunity for the practices’ GPs and other staff members to collaborate as a team to consider quality improvement. Quality improvement can relate to many areas of a practice and achieving improvements will require the collaborative efforts of the practice team as a whole.

Not every change is an improvement, but by making small changes you can test ideas on a smaller scale before implementing bigger changes to meet the desired goal/ s.

Resources

Link to QI guide here.

Link to PDSA sheet here.

Practice Incentive Payment Quality Improvement (PIP QI)

Medical Director Data Cleansing Guide

Best Practice Data Cleansing Guide

 

With the upcoming changes to the current PIP, to the new PIP QI, it is important to understand and get a head start on the QI process.  As part of the PIP QI practices you may be required to have and be using a data extraction tool, however, it remains unclear will be required.

More information on the CATPlus Suite of tools can be found here.

Frequently Asked Questions

How does the de-identified data extraction work?

A de-identified data file can be created using the ‘de-identify dataset’ tool in CAT4. This is the only data file that can be exported and taken off-site. What the de-identification process does, is remove patient identifiers including name, date of birth, address and replace it with a Statistical Linkage Key (SLK). The SLK is created when the extract is collected at the practice level and is encrypted before uploaded to PAT CAT*.  Once de-identified, a patient’s SLK is not re-traceable. The SLK system is also used by the Australian Bureau of Statistics that enables two or more records belonging to the same individual to be brought together.

More information here.

More information on CAT4 data de-identification can be found here on the PenCS Website: * PAT CAT is a web based solution that aggregates de-identified General Practice data and displays the information through a comprehensive collection of graphs, charts and reports.

Who owns the data?

Pen CS (software company) does not hold or own patient data. Pen CS provides the tools that allow organisations to work with their data. In the QI program, data is held or owned by:

  • the General Practice or Health Service in an identifiable format for analysis and reporting which supports activities such as accreditation and quality improvement;
  • the Primary Health Network (PHN), in agreement with the Practice, in a de-identifiable format which supports population health and service delivery planning;

Pen CS Data Governance Policy – available upon request.

What will SNHN do with my practice’s data?

SNHN abides by its policies including SNHN Information Privacy Policy and SNHN Population Heath Analysis Policy. These polices outline how the data can be used:

  • to assist SNHN and its employees to fulfil its duty which includes the making of data reports
  • to provide information to SNHN health gap analysis in the region
  • to comply with SNHN corporate reporting requirements

SNHN Information Privacy Policy can be found on the website.

https://sydneynorthhealthnetwork.org.au/privacy-policy/

Will the data be shared with a third body?
  • SNHN will not provide your data to third parties with commercial interests
  • SNHN will not publish information with practice names or provider names without written consent.
How is the QI program funded?

Sydney North Health Network (SNHN) is one of 31 Federal Government funded PHNs across Australia, that have been established with the key objectives of increasing the efficiency and effectiveness of medical services for patients. SNHN QI program aims to collaborate with practices to improve patients care through data reporting and quality improvement activities and prepare General Practices for the introduction of new PIP QI incentive, My Health Record and possibly the Patient Centred Medical Home.

QI program and Accreditation

RACGP standards for General Practice (5th Edition) and SNHN Quality Improvement Program

The purpose of this document is to demonstrate how practices can meet areas of the 5th edition by participating SNHN QI program.

View document here.

Quality improvement isn’t a formal concept of knowledge. Businesses in most sectors are driven to focus on the quality of their products so as not to lose market share. The economic mechanisms are not the same in healthcare, and the use of data for improving systems and processes so that health outcomes are better is not a natural task for healthcare professionals. Probably the most famous example and exponent of data-informed quality improvement was Florence Nightingale. Nightingale was a statistician before she became a nurse. She used her deep understanding of data to identify the real cause of problems (often flying in the face of prevailing knowledge) and measuring the impact of changes. She found columns of figures more enlivening than a novel! This quick video is a good overview of Nightingale’s work and impact, click here.

In general, we can’t improve what we can’t measure. But it’s very important the data doesn’t become the driver. It should inform, stimulate conversations, generate surprises, confirm assumptions, show trends etc.

Improvement cannot be done by one person, it needs to be a team effort to make the changes that will lead to better patient outcomes, improved system performance and better professional development. Without quality improvement healthcare will not realise its full potential unless change making becomes an intrinsic part of everyone’s job, every day.