News
IPL help Audit Commission analyse 250m NHS records for 'payment by
results'
9 June 2008
IPL, the Bath-based software and systems company, is assisting the
Audit Commission with the development of advanced data assurance
techniques and statistical analysis tools to support the Department of
Health’s Payment by Results (PbR) funding regime.
These tools benchmark clinical outcomes data allowing stakeholders to
compare relative performance of multiple NHS trusts for each type of
procedure. The programme has resulted in the largest and most complex
dataset ever compiled by the Commission.
Data is the lifeblood of most organisations and nowhere more so than
within the NHS. Approximately 12 million hospital admissions take place
in England each year, accounting for over 50 million bed-days across 172
Acute trusts. The accuracy of the data recorded for each hospital
admission is imperative to ensure the smooth operation of the health
service. Not only does it assist the planning and resourcing of future
healthcare, but under the Payment by Results (PbR) system it also
directly affects the way trusts are reimbursed for the patient care they
provide, through a transparent, rules-based costing system with payments
linked to a national tariff.
The Department of Health asked the Audit Commission to develop a data
assurance framework for PbR to support the accuracy of inpatient data
underpinning the new financial regime. A key element of the framework
would be a programme of audits focused on clinical coding, a term used
to describe the process of translating a patient's medical case notes
into a set of internationally recognised codes. The translation relies
heavily on the skills of a team of clinical coders and is therefore a
potential source of error in patient data.
Following an initial pilot study, a senior IPL consultant was brought
in under IPL’s supply contract with OGCbuying.solutions to assist the
Audit Commission with putting together a formal benchmarking process for
the data assurance framework. The goal was to define and develop a
robust methodology that would help target the clinical coding audits at
those areas within each trust where data quality was most likely to be
an issue.
The consultant worked closely with the Audit Commission's statistical
experts and those of the Information Centre for Health and Social Care
for a period of several months. During this time, the consultant helped
identify suitable data feeds, develop a robust set of performance
indicators and create a rigorous statistical process for identifying
outliers. This involved acquiring a detailed understanding of health
data and translating highly complex statistical theories into a set of
clear, unambiguous business requirements.
Once the requirements analysis was complete, the consultant provided
additional project management expertise, leading a team of IPL
developers to produce a data warehouse and associated tools to support
the benchmarking process.
Six months after IPL began work, the Audit Commission's programme of
clinical coding audits commenced, driven by a data warehouse containing
over 250 million records. This represents the biggest and most complex
dataset ever compiled by the Commission.
Furthermore, the benchmarking methodology, with its 22 performance
indicators and use of funnel plot analysis has now gained considerable
respect throughout the health community and is widely recognised for its
depth and rigour.
Four months later, the scope of the work was extended with the launch
of the National Benchmarker, an online tool to allow NHS organisations
to access the PbR dataset and associated analyses directly. This will
encourage self-monitoring and help trusts identify issues independently
of the clinical coding audits.
In the future, the work of the Audit Commission will help drive
further improvements in data quality through the analysis of outpatient
data — an even larger dataset.
IPL has continued to provide invaluable expertise throughout and as
the second year of the audit programme approaches, the original
consultant is helping to refine the methodology to ensure it remains at
the cutting edge of health data analysis.
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