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Development of Quality Measures and Data Dictionary

Towards National Definitions and Agreed Standards Dr Peter Jones MSc EBHC (Oxon) FACEM With thanks to Dr Alana Harper FACEM and Dr James LeFevre FACEM. Development of Quality Measures and Data Dictionary. MOH Forum, Wellington 5/5/2014. Why Bother?.

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Development of Quality Measures and Data Dictionary

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  1. Towards National Definitions and Agreed Standards Dr Peter Jones MSc EBHC (Oxon) FACEM With thanks to Dr Alana Harper FACEM and Dr James LeFevreFACEM Development of Quality Measures and Data Dictionary • MOH Forum, Wellington 5/5/2014

  2. Why Bother? • Each ED could just decide what to measure and how to measure it • Problems • Time • Resources • Skills • Duplication (x26) • Accuracy • Comparisons • ‘Admission’=3hrs? • To maximise the return for effort in a resource constrained environment 

  3. Aim of this presentation • How to operationalise the MOH suite of quality measures using a real world example • Data definitions / development of a ‘Data Dictionary’ • Data collection for process and clinical outcomes

  4. My QI Background • Auckland City Hospital / ADHB • Morbidity / Mortality 2000-05 • Time to Thrombolysis KPI 2000-05 • ED Ultrasound Credentialing 2000-03 • Procedures Database 2000-05 • AHQAS 2001 • Cardiac Arrest Documentation 2000-2010 • UHCW NHS Trust • Audit Lead 2005-06 • ACEM • Quality Management Subcommittee 2011-current • SOPH • PhD Student: Best measure of ED Overcrowding?

  5. Shorter Stays in ED National Research Project • ADHB/ SOPH Auckland University 2009-current • Health Policy, Effective Practice, Epidemiology, Māori Health, Health Economics, Biostatistics, Clinicians • HRC funded 10-588 • MREC approved MEC 10/06/060 • Multi-stream Mixed-Methods Research • What was done to implement the SSED target? • What effect on markers of care? • Lessons for future health/public service policy • KaupapaMāoriResearch Approach

  6. SSED NRP Stream 2Quality of Care • Quantitative analysis • 13 ‘Quality’ Measures • Process and Outcome • Nationwide (routine flow data) • 4 Case Study Sites(clinical markers) • Richness of information • Target results Q1 2010 • Any difference 2006-08 vs 2010-12? • Adjust for Ethnicity / Age/ Deprivation

  7. SSED NRP Quality • Outcomes • Primary (Nationwide) • ED LOS • Access Block (wait for admission >8hr in ED) • Overcrowding • Hospital LOS • Re-attendance rates within 48 hours of discharge • Re-admission rates within 28 days of discharge

  8. SSED NRP Quality • Outcomes • Secondary • Mortality (N) • hospital inpatients • for ED attenders at 10, 30 and 90 days • Time to treatment in acute asthma (4 sites) • Time to reperfusion for myocardial infarction (1 site) • Time to theatre for fractured neck of femur (1 site) • Time to appendectomy for acute appendicitis (1 site) • Time to antibiotics for severe infections (1 site) • Proportion of patients who leave without being seen (N) • ‘Gaming’ the target (N) • a spike of ED discharges at or near the target time (N) • digit bias in recording time of ED discharge (N) • re-designation of ED patients to ‘stop the clock’ (CS ?N)

  9. SSED NRP Quality Clinical Markers Clinical Quality Markers Selection Literature review / Evidence Search Reference Group Meeting December 2010

  10. SSED NRP Quality Clinical Markers Clinical Quality Markers Selection Critical Appraisal of Quality Indicators (QICA)

  11. SSED NRP Quality Data Required • Two Sources, three types of Information • NZHIS • Clinical diagnosis data from 4 case sites (ICD) • DHBs • Routinely collected process data ED and acute direct inpatient admissions (PIMS) • Hospital Bed Occupancy Census at night (Bed Management System)

  12. SSED NRP QualityData Collection Plan Process Indicators Site Specific Clinical Indicators 7yrs Presentations

  13. SSED NRP Quality Data Dictionary

  14. SSED QualityData Dictionary • Data elements piloted in consultation with analysts from three DHBs • Different names for same process e.g. Episode=Event=Visit=Case • PIMS different • And NZHIS • No time stamped process data • Highlighted inconsistent submission from DHBs

  15. SSED NRP QualityData Dictionary • Then consulted all 20 DHBs • Different PIMS collected data in different ways • Not all data elements currently defined • Some ED & Inpatient systems not integrated • Level of data capture differs • Capture patient event separately • Not all Process measures captured by all DHBs

  16. SSED NRPData Dictionary • Dictionary Facilitated Data Cleansing • Convert different DHB data format to unified set • Identify outliers and validate • Link NZHIS data with DHB data • NNPAC/NMDS Identify duplicates from DHB data • DHB data has elements missing from NNPAC/NMDS

  17. SSED Indicator Selection and Data Dictionary Resources Required • 24 months work • 0.5 FTE Emergency Medicine Specialist • 1 FTE Research Fellow • 1FTE Data Manager • Office / Dedicated desk space • PCs (high spec / dual screen) • Software (reference management / pdf writer / PIMS) • Online Journal Access • Monthly team meetings • Academic and Administrative support • Liaison with NZHIS and all DHB IS departments

  18. SSED NRP Clinical QualityData Collection • List of ICD codes to NZHIS • J45 (0,1,8,9); J46 • Events with that ICD code in each time period for each site • Date / NHI / Demographics • Random sample events • List of NHIs to the sites • Manual collection of data Trained senior clinician data collectors • multiple site visits • Data accuracy / cleaning Site Specific Clinical Indicators

  19. SSED NRP Clinical QualityData Collection Tools • ‘Intelligent’ spreadsheets • Data validation checks / protected formulas

  20. SSED NRP Clinical Quality Data Cleansing Data Accuracy ≈15% independently checked Eligibility 283/297 = 97% Primary outcome 366/387 = 95% Comorbidities 1198/1288 = 93% Data Cleaned Errors corrected where possible No imputation for missing data Time Stamp Data Also Required Cleaning

  21. SSED Data Collection Resources Required • 12 months work • 2-3 sets of records per hour for clinical quality indicators • 0.5 FTE Emergency Medicine Specialist • 1 FTE Research Fellow • 1FTE Data Manager • Office / Dedicated desk space • PCs (high spec / dual screen) • Software (data collection tools) • Clinical records departments • Desk / PC / Laptop

  22. Development of Quality Measures and Data Dictionary: What’s Needed? • MOH • Data definitions / dictionary • IT at each site standardised and audited • Standardised ‘intelligent’ Tools for data collection • Web vs local • DHB • Dedicated resources for quality • Space / Time / Admin / Clinical Records • People (thank you NZMC) • More than lip-service!

  23. Development of Quality Measures and Data Dictionary Towards National Definitions and Agreed Standards Questions / Discussion

  24. SSED NRP Stream 2Quality Method: Clinical Markers Eight conditions identified Represent whole system Pilot 50 sets notes Electronic DEF with logic (reduce errors) Clinically important difference apriori Sample size 90% power, alpha 0.05 (2 tailed)

  25. SSED NRP Stream 2Quality Method: Clinical Markers Sample Size Calculations Not all outcomes could be measured at all sites 10000 sets of notes = not feasible Clinical records departments / our resources Quality Indicator Critical Appraisal Tool 2 Authors independently appraised the indicators Outcome brought to whole team Different views Compromise Desire to measure >1 outcome and reflect whole system 1 outcome all sites; 1 other outcome each site

  26. SSED NRP Stream 2Quality Method: Clinical Markers Asthma (30 min difference time to steroid) Common / relevant / important All ages / Mäori / Pacific Data available / accessible / sample size manageable all sites Acute Myocardial Infarction (15 min difference time to lysis) Evidence strong but practice changed over time:1 site Sepsis (60 min difference time to steroid) Evidence moderate / important / relevant / all ages / Mäori / Pacific Sample size issues + difficult data collection: 1 site Appendectomy (12hr difference time to theatre) Whole system (balance) Fracture NOF (6hr difference time to theatre) Whole system (balance)

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