Insert name of presentation on Master Slide Developing the measurement of harm 25 th November 2010

Global Trigger Tool Standardised measurement of harm in acute hospitals. Wales is the only country to use it as a national measure. Purpose –Track harm –Characterise harm

Developments Research Primary Care Further developments Support

Measuring harm and informing quality improvement longitudinally in NHS Wales Four year programme funded by the National Institute for Health Research Led by Cardiff University in collaboration with Imperial College Aims to build on the progress NHS Wales has made in the global adoption of the Global Trigger Tool Phase 1: (Years 1 and 2) will assess the validity of the GTT for widespread use across the NHS Phase 2 aims to concentrate NHS GTT resources and the research teams resources around a single tool

Automated Universal Trigger Tool Graham Copeland Consultant Surgeon

UK global Trigger Tool  Adult care –32 variables –5 modules  General care  Surgical care  Intensive care  Medication care  Laboratory tests

Usual technique  Review random case notes –50 initially –Then 20 per month  Identify trigger events  Assess causation

Problems  Very labour intensive  Only a tiny sample of the whole –Credibility with clinicians  Random selection –Can be few triggers –Difficult to show improvements over time  Not all triggers result in harm

Avoiding these problems  Assess ALL patients admitted –Increase credibility with clinicians –Allows comparison over time  Prioritise selection of cases for review –Maximises potential improvements –Focuses review on harm

Automated data sources  Hospital episode statistic (HES) –Demographic data –Admission Source –Diagnostic codes –Procedure codes –ITU data –Outcome data  Mortality  Re-admission

Automated data sources  Pathology system  Radiology system  Bed management system  Theatre management system

Universal Trigger Tool HES Surrogates  Manual comparison of 10,000 random cases over a 2 year period –Coding vs manual review  Manual comparison of all mortalities over a 3 year period (120 cases/month) –Coding vs manual review  Correlation between trigger and best fit to code combinations  95% accuracy of trigger detection for the more complex trigger events  Only harm events produce automated trigger

Universal Trigger Tool HES Surrogates  Some direct mapping codes  Some triggers require multiple codes Complications – 147 codes  Some triggers require complex code combination –Failure to respond to deterioration in EWS- combination of 3-5 codes  Some have no code but are still part of the HES dataset

Universal Trigger Tool HES Surrogates  Some are easy (Direct single code maps) –HyponatraemiaE87.1 –HypernatraemiaE87.0 –HypokalaemiaE87.6 –HyperkalaemiaE87.5 –Wound infection T81.4

Universal Trigger Tool HES Surrogates  Some require multiple single codes –Complications 147 –Hypotension and cardiac arrest 8 –Sudden cessation of treatment 7  Some require combinations –Nosocomial pneumonia  5 Code sets + Y95X

Universal Trigger Tool HES Surrogates  Complex combinations –Elective admission + procedure code + myocardial infarction code = Post-op MI –Admission source + episode identifiers and critical care flow dates gives HDU/ITU information

Universal Trigger Tool HES Surrogates  Potential problems with HES –iLAB suggested 40% inaccuracy –Takes first man standing  However accuracy in surgery 95%  Automated system takes –‘Last man standing approach’ –Radically decreases inaccuracy

PS  If you think I’m intending to reveal all the CRAB code THINK AGAIN !!! THINK AGAIN !!!

Universal Trigger Tool HES Surrogates  For single relationship codes 99%  For multiple codes relationship 99%  For more complex relationships 95%  Some areas may potentially over-assess –Orthopaedic prosthetic complications –Orthopaedic prosthetic infections –Pressure ulcers/MRSA/C difficleCausation  Some areas may potentially under-assess –Falls Harm

SOFTWARE  CRAB trigger tool module  CRAB Clinical Informatics –c-ci.definingdesign.co.uk –c-ci.definingdesign.co.uk  Internalised system –Real time monitoring once coded –Produces automated exception report for non-coded –28 day delay to allow readmission –Automated reports

Triggers  Triggers recorded –10% of admissions –Water muddied by day cases  Large volume in surgery, endoscopy  Very few trigger events (<0.2%)  Unless converted to overnight or readmitted  Excluding day cases –Trigger events in 31%

Triggers  Consultant specific data –Patients with triggers31.2% –Patients with 3 or more3.7% –Average number of triggers per admission0.5% –Range  Excluding the 1 patient 100%’s  4.4 to 51.1%  2 lowest orthopaedic consultants

Triggers  Commonest –General medicine37%  Elderly care26-52%  Respiratory25-42%  Gastrointestinal30-44%  Other high groups –Obstetrics/gynaecology

Module types  General care 71.3%  Lab test20.3%  Surgical care6.9%  Intensive care0.3%  Medication 1.3%

General care module % of total in group  Readmission54%  Complication20%  EWS/Cardiac arrest/shock14.2%  Transfer to higher level4%  Decubiti3.5%  DVT/PE2.3%  Fall 1.4%

Lab test module (% of total in group)  Haematological25.7%  Urea/electrolyte34.6%  Hypoglycaemia6.4%  Troponin16.1%  C.difficile/MRSA2.2%  Wound infection4.2%  Nosocomial pneumonia3.6%  Blood culture7.1%

Variation of numbers of patients with trigger events > Trigger events

Variation of numbers of patients with trigger events (Mortality) > Triggers events

Effect of mortality on trigger events  85% patients who die have at least 1 trigger event  34% have 3 or more  5% have 6 or more  5 patients had 10!

Trends (myths and facts)  No sessional variation –General care module –Lab test module  Medication –Highest Jan to May –Lowest in the summer

Trends (myths and facts)  Intensive care –Small numbers –Large variations –Related to occupancy levels  Surgical care –Peak July/ August –Lowest Winter

Change technique  Automated e mail to all consultants –Monthly trigger report –Performance in speciality (%)  Automated management report –Performance overall and in all modules –Variation over time –Highlights for  Cquin  LIPS

Change technique  Mortality review group –Review all deaths >4 triggers  Divisional governance groups –Review all patients >3 triggers –Review variations in individuals –Automated data sheets  Overseen by Benchmarking Group  Report to Clinical Governance Committee and Board

CONCLUSION  Automated Trigger tool analysis possible  Accurate and reproducible  Can allow focused review  Can monitor changes over time  Perhaps the time has come to revisit the methodology  Could increase clinician engagement

Developments Research Primary Care Further developments Support Automate?

Any Questions?