Here we highlight discussions from the October CLAHRC Research Partners Meeting about the use of data to drive and evaluate improvements in healthcare, which were subsequently presented at the 33rd International scientific meeting on quality and safety in health care in Tokyo.
How does CLAHRC support clinical teams use real-time data for quality improvement?
Measuring for improvement is an essential component of quality improvement (QI) and is advocated by many organisations that use of QI methods to improve healthcare.
Despite this obvious need for data to drive improvement healthcare, information technology systems are rarely enabled to provide this level of real-time data to clinical teams to support their improvement. CLAHRC has developed the Web Improvement Support for Healthcare (WISH) software, which is a toolkit that supports multidisciplinary improvement teams to implement research into practice. The software provides a platform for the continuous collection and analysis of data which is essential when using a quality improvement approach in healthcare.
WISH offers a system which can be designed by the end users, with support from the CLAHRC information team, to design a data collection system that meets the needs of their quality improvement initiative and automatically creates a web application for team members to enter data at regular intervals, and generates progress reports in real-time.
The reports provide run charts that have been analysed using statistical process control and allow qualitative data, in the form of PDSA cycles, to be integrated into the reports. The system is being further developed to integrate other methods that have been applied to quality improvement such as tools that assess sustainability/long term success.
The use of the WISH tool has not only supported teams to successfully undertake QI initiatives but also has provided teams with the data they need to write up and publish their work. Examples include publications describing the design and implementation of a care bundle for the management of chronic obstructive pulmonary disease (COPD) and the implementation of an alcohol-screening tool.
How can we demonstrate that quality improvement initiatives can improve population health?
The prevalence of common mental disorders (CMDs), including anxiety and depression, in the general working age population in England is estimated as 16.2% and whilst there are effective treatments they aren’t always accessed. In 2007 the UK government announced a large-scale health initiative for improving access to psychological therapies (IAPT).
Using a quality improvement approach a local IAPT service developed a social marketing campaign to increase referrals from areas of deprivation. And whilst numbers of referrals increased it wasn’t immediately obvious if referrals had increased from areas of high deprivation and whether the outcomes for these patients had been equitable.
An evaluation was undertaken using geospatial and temporal analyses to attempt to answer these questions. Outcomes were assessed using the Patient Health Questionnaire (PHQ)-9 score, which assesses severity of depression.
Average weekly referrals rose from 17 during the baseline to 43 during social marketing campaign. The geospatial analysis also demonstrated that people from areas of high deprivation had increased referral to this service during this campaign. Analysis of the outcomes showed that patients from areas of high deprivation entered the service with more severe depression (PHQ-9: Mean= 15.47) compared to patients from areas of low (Mean = 13.20) and medium (Mean = 14.44) deprivation. Despite this they achieved similar improvements in clinical outcomes (ΔPHQ9), therefore showed no evidence of differences due to socio-economic status.
Further work was undertaken to identify patient and treatment factors that could predict clinical outcomes using the largest naturalistic mental health intervention dataset in the world. The models assigned a positive or negative clinical outcome to each patient, with an accuracy 69.4% and 79.3% respectively, using five independent pre-treatment variables: initial severity of anxiety and depression, ethnicity, deprivation and gender. The model also identified that the numbers of sessions attended or missed were also important.
This work is now being continued to use statistical modeling to establish a new recovery metric and predict improvement using a larger dataset.