Collection of Health Data in Scotland

1. Collection of Health Data in Scotland What do we have and what is missing? Jim Chalmers Information Services Division, NHS National Services Scotland

2. Scotland • Population 5 million • Mainly urban • concentrated in central belt • Large and sparsely populated rural areas • More deprived than UK average • especially around Glasgow (West) • National Health Service administration separate from England and Wales • NHS collects data relating to use of health services • General Register Office for Scotland (GROS) collects civil registration data – provides downloads to NHS for record linkage.

3. Sources of Information • Civil Registrations • Births • Stillbirths • Marriages • Deaths.

4. Sources of Information (2) • Hospital data • patient based • difference between Scotland and the rest (SMR series) • Aggregate • Community data • Child Health Systems – Pre-school, SIRS, School, Special Needs.

5. Sources of Information (3) • Special systems • infectious disease notifications • abortion notifications • laboratory statistics • cervical smears.

6. Sources of Information (4) • Special studies • Census • Surveys • General Household Survey • Scottish Health Survey • Lifestyle surveys • Infant Feeding Survey • Scottish Schools Adolescent Lifestyle and Substance Use Survey (SALSUS).

7. Limited information in Scotland • Primary care • Sample morbidity • Prescribing • Quality and Outcomes Framework (April 2004) • Community services • Details of inpatient stays • Case Mix • Opportunity to adapt clinical systems • Outpatient details • A&E.

8. QOF – prevalence of hypothyroidism

9. Top five diagnoses in primary care with rates per thousand population

10. Collecting hospital discharge data • All Inpatients and Day Cases • since mid-1960s • One million records per year • Standard form (Scottish Morbidity Record) • submitted electronically • Completed by clerical staff • case notes • discharge letters

11. Clinical Staff Medical record Clerical Staff Patient Administration System SMR Coding Staff ISD

12. Clinical observation Clinician writes onto paper Coder (clerical staff) reads paper document and decides on codes Coders complete SMR form SMR forms collated centrally Data manipulated Data interpreted for simple analyses Data further manipulated for complex analyses (eg linkage) Accuracy Detail Training and time Transcription Organisation Skill Interpretation Complexity. Summary of process Potential Problems

13. Strategies for improving data quality (1) • Making things easier for coding staff • Teaching doctors to write clearly and structure notes! • Difficult • Some progress once doctors understand and value the whole process • Automatic validation and accreditation • Will not prevent all errors

14. Strategies for improving data quality (2) • Ensure coders use national definitions and standards • Centrally supplied training • Data dictionary • Support/advice • Quality assurance visits

15. Quality Assurance Visits • Small team • Hospital selected • Random sample of SMR data • Compared with case notes • Completeness • Accuracy

16. QA results for SMR01 • Example – major teaching hospital 2002 • 338 records sampled • Administrative data • 3% error in postcode • 7% error in consultant identity • 12% error in waiting list date

17. QA - Accuracy of clinical data

18. Strategies for improving data quality (3) • Use the data • Locally • Audit • Regionally • Planning services • Nationally • Planning • Surveillance • Research

19. Using the data • Convince users that quality is “fit for purpose” • QA results • Demonstrate ways in which the data can help clinicians • Clinical Governance • General Medical Council • Clinical audit • Produce interesting research • Manipulate data • Record linkage

20. Record linkage • Episode data into patient pathway • Does not rely on unique identifier • ‘Probability matching’ copes with errors in data

21. Linked Scottish Morbidity Record Database • Established in Scotland since 1981 • Hospital stays linked together and linked to Registrar General death certificate data • Includes deaths in and out of hospital • Allows “patient journey” to be studied

22. Value of record linkage • Allows affordable cohort studies • Whole population • No bias • Long term follow-up • Assuming • Reasonable data quality • Minimal emigration from Scotland

23. Myocardial Infarction – Overall deaths and influence of gender • Women previously noted to have worse survival, even after adjustment for age • Suspicion of poorer treatment • But studies were of hospitalised patients • First events • 1986-1995 • 201,114 patients

24. Myocardial Infarction – Overall deaths and influence of gender • Men have higher risk of dying before reaching hospital • Women have higher risk of dying after reaching hospital • Overall, risks much the same

25. Pregnancy Cohort Studies

26. Feedback to individual clinicians • Access to data online • Numbers of admissions for various diagnoses • Numbers of particular types of operation • Lengths of stay • Comparison with other similar specialists • “Drill down” to individual patients

27. Feedback to hospitals • Clinical Outcome Indicators • Data standardised by local demography and deprivation • Publicly available • Comparisons of such things as • Readmission within 28 days of selected pelvic and abdominal operations • Survival in first 30 days following acute MI

28. Emergency readmissions within 28 days of selected pelvic/abdominal surgery

29. Survival in first 30 days following acute MI

30. History – NHS data collection in Scotland relating to maternity and children (1) • Sixties - Investment in collection of computerised data on hospital discharge • Based on hospital records staff • Seventies - Maternity data (SMR2) • Eighties - Neonatal data (SMR11) - Stillbirth and Infant Death Survey - Record linkage • basis for congenital anomaly recording.

31. History – NHS data collection in Scotland relating to maternity and children(2) • Nineties - SMR11 for sick babies only • Decline in quality of congenital anomaly recording - Development of Child Health Surveillance systems • Now - Development of web-based data collection (Scottish Birth Record), A&E, Screening • Soon - Electronic Health Record.

32. Perinatal mortality comparisons, rates per 1000

33. Methods of collection • Specific systems • Registrations, notifications etc • Side effect of administrative systems • Patient administration systems • Prescribing data.

34. Abortion data Hospital (SMR01) Maternity and Infant Information Route Map conception early pregnancy late pregnancy delivery postnatal care next conception early miscarriage miscarriage & abortion Neonatal (first month) Postneonatal (<1 year) childhood Limited information Maternity (SMR02) Maternity (SMR02) stillbirth neonatal death death Scottish Birth Record General Register Office (Stillbirth and Neonatal Death Survey)

35. Structure of SMR02 (1) • Personal/Demographic data • Episode and facility • Proposals for delivery • Previous pregnancies • Caesarean sections, miscarriages etc • Current pregnancy • Gestation, smoking etc • Drugs and alcohol.

36. Structure of SMR02 (2) • Record of labour • Episiotomy, Induction, type of delivery etc. • Diagnostic • ICD10 • Baby record • Apgar, measurements, feeding etc.

37. Collecting SMR02 (maternity) data • Developed from SMR1 • All Inpatients and Day Cases • since mid-1960s • c. One million records per year • Started mid-1970s, well-established in early 1980s • c. 100,000 records per year • 50,000 relating to labour • Submitted electronically • Completed by clerical staff • case notes • discharge letters.

38. Childhood Route Map Postneonatal Adulthood Well Ill Screening (Blood spot Hearing) Immunisation Pre-school Surveillance School surveillance Special needs Primary care Death Hospital OP, IP, A&E Child Health Surveillance PTI SMR01 SMR04 SMR00 New A&E system GROS New systems Surveys etc.

39. Current data sources on children - universal • Immunisation • SIRS • Hospital • SMR02 – (healthy babies); SMR01 – inpatients; SMR00 – outpatients; SBR • GROS • Birth, stillbirth, death – augmented by stillbirth and neonatal death enquiry • Dental • Registrations and treatments.

40. Primary Immunisation Uptake Rates, 1 year (2 for MMR)

41. Proportion of children drinking once per month, by age – SHS 1998 & 2003

42. Gaps • Lack of data • Missing data types • Screening systems • Mental health • Incomplete coverage • Child Health Systems • Primary care data • Dental data.

43. Where are we now? Pregnancy Screening Newborn Screening Social Work SBNND SMR02 Local Neonatal Systems Abortions Local Maternity Systems School Surveillance SMR11/SBR Education SIRS Patient Administration Systems Special Needs Pre-school Surveillance CHI/UPI Perinatal transport

44. Where are we now? • Plethora of systems • National systems • Data collection eg SMR02 • Widespread systems • Administrative eg PASs • Local systems • Clinical eg neonatal intensive care • Some combined: clinical/administrative/data • Child Health Surveillance, Scottish Birth Record.

45. Need for a change in philosophy • Promote integration and timely data collection • Present systems based on • Institutions • Episodes • Should be based on • Individuals • Problems • Activities.

46. The future • Electronic patient records • Clinical functionality • Encourage use by clinicians • Data available as a by-product • Issues • Expense • Complexity • Completeness • Coding

47. Combined Clinical/Administrative/Data systems • Accuracy • Completeness • Timeliness • Trustworthiness But • Clinical data must be easy to collect • Must give some immediate advantages.