Using routinely collected GP data for quality improvement research

1
Using routinely collected GP data for quality
improvement research

• Presented to
• BCS HI LSE Specialist Groups
• Presented by
• Simon de Lusignan
• slusigna_at_sgul.ac.uk

2
About me

• GP in Guildford
• 11,500 patient practice
• 6.5 Whole time equivalent GPs
• Computerised since 1988
• Senior Lecturer, St. Georges
• Primary Care Informatics (PCI) research group
• Using routinely collected data for quality
  improvement research
• Electronic libraries
• Computer in the consultation
• Telemonitoring
• Chair PCI WG of EFMI
• Developing a BSc in BMI

3
Presentation in two parts

• Part 1 - Practical uses of data for quality
  improvement
• The PCDQ Educational intervention
• Examples from heart disease, diabetes renal
  disease
• Part 2 the informatics issues need for
  transparent processing
• End Black Box data processing
• Informatician in every project using routinely
  collected data
• Further reading
• de Lusignan S, van Weel C. The use of routinely
  collected computer data for research in primary
  care opportunities and challenges. Family
  Practice 200623(2)253-63.

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Part 1Data for quality improvementPCDQ
Primary Care Data Quality
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Part 1 Experiential learning PCDQ Primary
Care Data Quality

• Introduction
• International consensus Computerised medical
  records (CMR)
• pre-requisite for quality safety
• Educational interventions are an appropriate
  change strategy
• Ten years learning
• Methods
• Educational framework
• Technical method
• Results
• Examples from a range of disease programmes
• AF, raised cholesterol in heart disease,
  identifying undetected CKD
• Discussion
• Principles of about how to raise data quality
  standards

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Introduction (1) Central place of PC CMR

• CMR are essential for quality safety
• International consensus as to the unique and
  central place of the primary care record

• References
• IOM report Crossing the quality chasm. 2001
• IOM report To err is human building a safer
  health system. 2000
• de Lusignan S, Teasdale S, Little D. et al.,
  Comprehensive computerised primary care records
  are a pre-requisite Inform PC 200412(4)255-62

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Introduction (2) Education as change strategy

• Education is an appropriate change strategy
• Multi-faceted to meet different learning styles
• Incorporate the theory of diffusion of
  innovation
• Include academic detailing of the evidence-base

• References
• Davis D. Clinical practice guidelines and the
  translation of knowledge the science of
  continuing medical education. CMAJ.
  2000163(10)1278-9.
• NHS Centre for Reviews and Dissemination, Getting
  evidence into practice. 1999, 5(1).
• Rogers E. Diffusion of Innovations, 4th ed. New
  York, Free Press, 1995.
• Berwick D. Disseminating innovations in health
  care. JAMA 2003289(15)1969-75.
• Soumerai SB, Avorn J. Principles of educational
  outreach ('academic detailing') to improve
  dinical decision making. JAMA 1990263(4)549-56.

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Introduction (3)Ten years of working with PC
Data

• Literature review
• Features of effective data quality initiatives
• Educational interventions
• Non judgemental
• Minimum distortion of coding process
• Objective - Quality improvement Implementing
  EBM

• References
• de Lusignan S, Teasdale S. The features of an
  effective primary care data quality programme.
  In Ed, Bryant J. Current Perspectives in
  Healthcare Computing, 2004. Proceedings of HC
  2004. Swindon British Computer Society, 2004
  95-102.
• de Lusignan S, Hague N, Brown A, Majeed A. An
  educational intervention to improve data
  recording in the management of ischaemic heart
  disease in primary care. Journal of Public
  Health 200428(1)34-7.

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Method

• Educational framework
• Technical process

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Method Educational process

• Support local lead
• Relate data to local population
• E.g. Age-sex profile
• Work in areas where EBM
• Engage clinicians
• Data Quality workshops on educational ½ days
• Comparative feedback to practices
• Lists of patients requiring interventions (left
  in practice)
• Reflect on process PCDQ Forum

• References
• de Lusignan S. Hague N. The primary care data
  quality programme. Bandolier.

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Method Eight-step technical processMore on
this later.

• Design phase define dataset / audit criteria
• Appraising data entry issues
• Define the data extraction method
• MIQUEST
• Anonymised data for central analysis
• Data with patient identifiers left in practices
• Migration of the data into the data repository
• Integration of the data with other data sources
• Data cleaning
• Data processing
• Data analysis

• References
• van Vlymen J, de Lusignan S, Hague N, Chan T,
  Dzregah B. Ensuring the quality of aggregated
  general practice data lessons from the Primary
  Care Data Quality Programme (PCDQ). Studies in
  Health Technologies and Informatics
  20051161010-15.

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Defining a dataset

• References
• Thiru, K, de Lusignan, S, Sullivan, F, Brew, S,
  Cooper, A. Three steps to data quality.
  Informatics in Primary Care 2003 11(2)95-102.

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Results

• Population denominator
• Examples from a range of clinical areas

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Age-sex pyramid
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Rule of halves for patients with IHDHalf of
people with IHD cholesterol gt5mmol/l, and only
half on a statin
de Lusignan S, Dzregah B, Hague N, Chan T.
Cholesterol management in patients with
ischaemic heart disease an audit-based
appraisal of progress towards clinical targets
in primary care. British Journal of Cardiology
200310223-8.
16
Undiagnosed CKD5 of people have CKD and over
90 undiagnosed(Chi-square test plt0.001)
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Stroke risk in malesTreatment shows no
significant targeting of treatment at those at
highest risk
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Inter-practice variation
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Change in risk factor recording in maturity
onset diabetes
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Trends in diabetic therapy 1994 -2001 in
maturity onset diabetes
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Trends in risk factor control (UK national
guidance .targets www.nice.org.uk ) more
obesity and progress with HbA1c control
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Improvement in the recording of HbA1c in 80
practices
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Cholesterol management in IHDInter-practice
variation in cholesterol measurement management
Practice number
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Complexity of inter-practice variation
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References

• de Lusignan S, Dzregah B, Hague N, Chan T.
  Cholesterol management in patients with ischaemic
  heart disease an audit-based appraisal of
  progress towards clinical targets in primary
  care. British Journal of Cardiology
  200310223-8.
• de Lusignan S, Hague N, Yates C, Harvey M. A
  case study from a Sussex Primary Care Group
  improving secondary prevention in coronary heart
  disease using an educational intervention.
  British Journal of Cardiology 20029(6)362-368.
• de Lusignan S. Sismaniidis C, Carey IM, de Wilde
  S, Richards N, Cooke D. Trends in the prevalence
  and management of type2 diabetes 1994 - 2001.
  BMC Family Practice. 20056(1)13

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Discussion
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Features of PCDQ programme feedback.
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Discussion

• Our approach is non-judgemental
• Feedback and discussion about quality as recorded
  in CMR
• Is it data quality
• Is it quality of care
• Inter-practice variation is also an effective
  motivator
• Is this approach generaliseable?
• We cant ignore the magnificent response of UK
  GPs to financial incentives

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Part 2The informatics issues focus on
longitudinal study of routine data
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Part 2 The informatics !

• Introduction
• Growing volumes of accessible primary care data
  increasingly used for quality improvement
  research
• Objective
• to develop an open method for describing the
  informatics
• Features of quality data
• What is data quality?
• Unique identifiers denominators
• What need to be defined about data processing
  storage
• Discussion

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Introduction

• Routinely collected clinical data is used
  increasingly for
• Quality improvement
• Clinical Audit
• Health Service Planning
• Research
• Need to move from black box to open processing
  methods

References 1. de Lusignan S, van Weel C. The use
of routinely collected computer data for research
in primary care opportunities and challenges.
Fam Pract. 2006 Apr23(2)253-63. 2 de Lusignan
S, Hague N, van Vlymen J, Kumarapeli P. Routinely
collected general practice data are complex but
with systematic processing can be used for
quality improvement and research. Accepted for
publication Informatics in primary care
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Objective

• To define the informatics

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Features of quality data

• Defining Data Quality
• Unique identitifiers
• Defined process of data extraction storage

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Defining data quality

• Evolving definitions
• Completeness accuracy (Pringle et al. BJGP
  1995)
• Currency (Williams, Methods 2003)
• Sensitivity positive predictive value (Thiru
  et al., BMJ 2003)
• Data Quality Probe (Brown Warmington IPC
  2003)
• Fit for purpose (PCI WG EFMI, 2005)

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Unique IDs

• Linkage of data
• Interoperability of systems
• Follow-up / traceability of individuals
• Population denominator ghosts.
• England Wales - NHS number
• Scotland - CHI number
• Our system
• MIQUEST unique ID for one practice
• compound with study number
• unique ID for practice
• Convert to non-case sensitive ASCII format

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Processing data

• Appreciation of data entry issues contemporary
  perspective of system users
• Defined stages of data processing applications
  used at each stage, quality controls
• Archive coding systems and the look-up tables
  used to infer meaning or rubrics
• The queries used to extract the data
• A metadata system to ensure traceability of each
  cell of data
• The ethical constraints that apply to the
  dataset.

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(1) Data entry issues contemporary
perspective of users

• COPD and Bronchitis codes are easily confused
• Recoding half of the practice asthmatics from a
  diagnosis to history of code

Ref Faulconer ER, de Lusignan S. An eight-step
method for assessing diagnostic data quality
COPD as an exemplar. Inform Prim Care.
200412(4)243-54.
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(2) Defined stages of data processing

• We have defined eight discrete steps in data
  processing
• (1) Design of queries, piloting,
• (2) Data entry, (already dealt with)
• (3) Extraction,
• (4) Migration, unique IDs essential
• (5) Integration,
• (6) Cleaning,
• (7) Processing, and
• (8) Analysis

Ref van Vlymen J, de Lusignan S, Hague N, Chan
T, Dzregah B. Ensuring the Quality of Aggregated
General Practice Data Lessons from the
Primary Care Data Quality Programme (PCDQ). Stud
Health Technol Inform. 20051161010-5.
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(3) Archive coding systems.

• Coding systems are constantly evolving
• In general coding systems are becoming larger
  more complex
• You can go from many to few but not from few to
  many
• We archive Clinical codes look-up engine used
• e.g. NHS Triset Browser
• Each relevant version
• E.g. 4 and 5-Byte Read Codes Drug Dictionary,
  Proprietary codes

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Example of look-up engine
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(4) The query library

• Re-issued by date
• Query set for each clinical programme
• e.g. C1, C2, C3 Cardiac programme
• Query set for each extraction type
• e.g. E4, E5, G4, G5 (E for EMIS, G for Generic)
• Defined look-up tables rubrics for queries

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The query library
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The C2 queries
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The C2 EMIS 5-Byte set
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(5) Metadata system

• Follows data from query set to analysis
• Preserves original data
• Derived variables clearly identified
• Associated dates numerics labelled
• Rules for units used
• Look-up table used to define variable names

van Vlymen J, de Lusignan S. A system of metadata
to control the process of query, aggregating,
cleaning and analysing large datasets of primary
care data. Inform Prim Care. 200513(4)281-91.
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Source data metadata structure
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Linking elementsQuery libraryQuery Core
Clinical Concept Read code
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Core clinical concept (CCC)
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Automation
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(6) Ethics

• The Ethical constrains on any dataset are
  indexed in the query library

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Summary
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Summary

• Data quality is best defined in terms of
• Fitness for purpose - What purpose when?
• Transparent methods of data processing allow
  audit of results
• Understanding data entry issues / context is
  essential
• Metadata can help control processing
• Careful curation of data may allow its use
  beyond the timescale of the original study
• Lots can be done to improve data quality in
  primary care

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• Thanks for listening
• Thanks to my colleagues collaborators
• Simon de Lusignan
• Tel [phone]
• Fax [phone]
• Email slusigna_at_sgul.ac.uk
• Web www.gpinformatics.org
• www.sgul.ac.uk/informatics/