One of our key aims is to research how best to use administrative databases to inform how the NHS is performing both overall but also at the level of hospital, surgeon and general practice. Challenges that we tackle include how to best process these complex data sets, indicator development, risk adjustment, statistical monitoring and feeding back the results to NHS stakeholders.
Tools to investigate quality
The most successful tool developed by the Unit currently in use by the NHS today is our mortality alert system. The usefulness of this tool was noted in the Mid Staffordshire Inquiry.
Developed in England, Quality Investigator began under the name Real Time Monitoring (RTM) and was distributed successfully throughout the Netherlands. Today, every hospital in the state of Victoria, Australia uses it. This is a web-based information tool that compares patient outcomes – specifically, in-hospital mortality, unplanned readmission, long length of stay and recorded patient safety incidents such as infections – across hospitals. Hospitals use QI to compare their outcomes with those at peer hospitals or the national or regional average in order to identify areas for potential improvement. The tool is interactive, meaning hospitals can select their own patient groups of interest and run comparisons by e.g. age, day of the week and hospital site to help understand where their potential problems lie.
The Dr Foster Unit at Imperial College devised the methodology for this tool; Dr Foster Intelligence (DFI) devised the web-based front end and provides the customer support for it.
Risk adjustment for administrative data using comorbidity measures and machine learning
To compare hospitals fairly, we need to measure and adjust for patient factors such as age and comorbidities using the most appropriate statistical models. These increasingly use administrative information, but this relies on clinical coding to record diagnosis and operation details, which is known to vary between hospitals. In this project we built statistical models for several key indicators of quality of care using English NHS administrative data, with a focus on comorbidities.
- To derive the most suitable comorbidity index for the NHS
- To compare logistic regression with machine learning methods for risk adjustment for mortality, readmission and unplanned reoperation.
- Bottle A, Sanders RD, Mozid A, Aylin Pet al., 2013, Provider profiling models for acute coronary syndrome mortality using administrative data, INTERNATIONAL JOURNAL OF CARDIOLOGY, Vol: 168, Pages: 338-343, ISSN: 0167-5273 PubMed
- Sharabiani MTA, Aylin P, Bottle A, 2012, Systematic Review of Comorbidity Indices for Administrative Data, Medical Care, Vol: 50, Pages: 1109-1118, ISSN: 0025-7079 PubMed
- Gaudoin R, Montana G, Jones S, Aylin P, Bottle A. Classifier calibration using splined empirical probabilities in clinical risk prediction. Health Care Manag Sci 2015;18(2):156-65. PubMed
- Bottle A, Gaudoin R, Goudie R, Jones S, Aylin P. Can valid and practical risk-prediction or casemix adjustment models, including adjustment for comorbidity, be generated from English hospital administrative data (Hospital Episode Statistics)? A national observational study. Health Serv Delivery Res 2014;2(40). PubMed
- Bottle A, Aylin P, Bell D. Effect of the readmission primary diagnosis and time interval in heart failure patients: analysis of English administrative data. Eur J Heart Fail 2014;16(8):846-53. PubMed
- Bottle A, Aylin P. Comorbidity scores for administrative data benefited from adaptation to local coding and diagnostic practices. J Clin Epidemiol 2011;64(12):1426-33. PubMed
- Bottle A, Sanders RD, Mozid A, Aylin P. Provider profiling models for acute coronary syndrome mortality using administrative data. Int J Cardiol 2013;168(1):338-43. PubMed
Volume-outcome relations for colorectal, upper GI and urological surgery
With our longstanding surgical collaborators at Imperial NHS Trust and other hospitals, we have assessed the extent to which the number of operations that a surgeon or hospital performs each year is associated with the outcomes for their patients. This work informs the growing calls for the NHS and many other healthcare systems in the developed world to centralise services.