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Statistical analysis and methodological development carried out by Imperial College London on data from the Bristol Royal Infirmary Inquiry and the Shipman Inquiry have led to new monitoring systems in healthcare. Using routinely collected healthcare information, we have highlighted variations in performance and safety, impacting the NHS through direct interventions and/or policy change. For example: (i) findings and recommendations arising from our research for the Bristol Inquiry were reflected in the final inquiry outputs, which highlighted the importance of routinely collected hospital data to be used to undertake the monitoring of a range of healthcare outcomes, (ii) a range of monitoring recommendations have arisen as a direct result of the research on data from the Shipman Inquiry, (iii) analytical tools based on our methodological research are used by managers and clinicians in over two thirds of NHS hospital trusts, (iv) Imperial's monthly mortality alerts to the Care Quality Commission were major triggers leading to the Healthcare Commission investigation into the Mid Staffordshire NHS Trust.
The Variable Life-Adjusted Display (VLAD) is a graphical tool for monitoring clinical outcomes. It has been widely adopted by UK cardiac surgery centres, and has helped a shift in culture towards more open outcome assessment in adult cardiac surgery, which has been credited with reduced mortality rates. VLAD is also being used for a broad range of other clinical outcomes by regulatory bodies worldwide. For example, Queensland Health uses VLAD as a major part of its Patient Safety and Quality Improvement Service to monitor 34 outcomes across 64 public hospitals, and NHS Blood and Transplant uses VLAD to monitor early outcomes of all UK transplants.
Events in the UK NHS have shown the need for a robust understanding of hospital mortality rates.
Surrey's research produced "a unique web-enabled pattern analysis system that is specifically designed to enable clinicians and their teams to view in detail their in-house mortality patterns in the national context" (a).
Launched on a national scale in Ireland in 2013, it has already identified `mortality outliers' and been described as a `game changer' for improving service quality at national level. The tool's impact stems from its ability to translate statistical patterns into a form readily usable by health professionals to improve care quality and sharing best practice.
This case study describes a significant new index used to monitor death rates in hospitals. The Summary Hospital Mortality Index (SHMI) was developed as a direct result of research carried out at the School of Health and Related Research (ScHARR). This was implemented nationally in October 2011 and the SHMI is now the main mortality indicator used by the NHS. Following publication of the high profile Francis Inquiry on Mid Staffordshire in February 2013, set up to investigate excess mortality in the Trust, the Government has used the SHMI to identify and target 8 further hospitals for investigation.
There is growing evidence that official population statistics based on the decennial UK Census are inaccurate at the local authority level, the fundamental administrative unit of the UK. The use of locally-available administrative data sets for counting populations can result in more timely and geographically more flexible data which are more cost-effective to produce than the survey-based Census. Professor Mayhew of City University London has spent the last 13 years conducting research on administrative data and their application to counting populations at local level. This work has focused particularly on linking population estimates to specific applications in health and social care, education and crime. Professor Mayhew developed a methodology that is now used as an alternative to the decennial UK Census by a large number of local councils and health care providers. They have thereby gained access to more accurate, detailed and relevant data which have helped local government officials and communities make better policy decisions and save money. The success of this work has helped to shape thinking on statistics in England, Scotland and Northern Ireland and has contributed to the debate over whether the decennial UK Census should be discontinued.
Patient records underpin the delivery of healthcare. When the recorded data are aggregated, they provide information to support service delivery, audit and research. Research conducted at Swansea University from 2000 to 2011 showed that variations in the structure and content of records across the NHS limit their quality and utility. To address this, the University collaborated with the Royal College of Physicians to develop evidence-based national standards for the structure and content of patient records. First launched in 2008, the standards have been endorsed by numerous statutory bodies and professional organisations, including the Department of Health, NHS England, NHS Litigation Authority, Mid-Staffordshire Inquiry, Care Quality Commission, General Medical Council, Academy of Medical Royal Colleges, and Academy of Medical Sciences.
Since 2008, statistical research at the University of Bristol has significantly influenced policies, practices and tools aimed at evaluating and promoting the quality of institutional and student learning in the education sector in the UK and internationally. These developments have also spread beyond the education sector and influence the inferential methods employed across government and other sectors. The underpinning research develops methodologies and a much-used suite of associated software packages that allows effective inference from complicated data structures, which are not well-modelled using traditional statistical techniques that assume homogeneity across observational units. The ability to analyse complicated data (such as pupil performance measures when measured alongside school, classroom, context and community factors) has resulted in a significant transformation of government and institutional policies and their practices in the UK, and recommendations in Organisation for Economic Co-operation and Development (OECD) policy documents. These techniques for transforming complex data into useful evidence are well-used across the UK civil service, with consequent policy shifts in areas such as higher education admissions and the REF2014 equality and diversity criteria.
Targeted Projection Pursuit (TPP) — developed at Northumbria University — is a novel method for interactive exploration of high-dimension data sets without loss of information. The TPP method performs better than current dimension-reduction methods since it finds projections that best approximate a target view enhanced by certain prior knowledge about the data. "Valley Care" provides a Telecare service to over 5,000 customers as part of Northumbria Healthcare NHS Foundation Trust, and delivers a core service for vulnerable and elderly people (receiving an estimated 129,000 calls per annum) that allows them to live independently and remain in their homes longer. The service informs a wider UK ageing community as part of the NHS Foundation Trust.
Applying our research enabled the managers of Valley Care to establish the volume, type and frequency of calls, identify users at high risk, and to inform the manufacturers of the equipment how to update the database software. This enabled Valley Care managers and staff to analyse the information quickly in order to plan efficiently the work of call operators and social care workers. Our study also provided knowledge about usage patterns of the technology and valuably identified clients at high risk of falls. This is the first time that mathematical and statistical analysis of data sets of this type has been done in the UK and Europe.
As a result of applying the TPP method to its Call Centre multivariate data, Valley Care has been able to transform the quality and efficiency of its service, while operating within the same budget.
In a series of papers from 2003, Gibson (Maxwell Institute) and collaborators developed Bayesian computational methods for fitting stochastic models for epidemic dynamics. These were subsequently applied to the design of control programmes for pathogens of humans and plants. A first application concerns the bacterial infection Clostridium difficile in hospital wards. A stochastic model was developed which was instrumental in designing control measures, rolled out in 2008 across NHS Lothian region, and subsequently adopted across NHS Scotland. Incidence in Lothian reduced by around 65%, saving an estimated £3.5M per annum in treatment and other costs, reducing mortality and improving patient outcomes, with similar impacts elsewhere in Scotland. A second application concerns the spread of epidemics of plant disease in agricultural, horticultural and natural environments. Models developed in collaboration with plant scientists from Cambridge have been exploited by the Department for Environment, Food and Rural Affairs (Defra) and the Forestry Commission under a £25M scheme, initiated in 2009, to control sudden oak death in the UK, and by the United States Department of Agriculture to control sudden oak death in the USA.