Long Term Trends in Referrals for Radiology Diagnostics

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Long Term Trends in Referrals for Radiology Diagnostics Dr David Parker Consultant Radiologist [email protected] John Collins Head of Systems & Information Management [email protected] NISCHR Knowledge Transfer Workshop 6 th May 2014 1

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Long Term Trends in Referrals for Radiology Diagnostics by Dr David Parker , Consultant Radiologist & John Collins, Head of Systems & Information Management from Betsi Cadwaladr University Health Board. Presented at "Using Research Evidence to Improve Health and Social Care". A NISCHR AHSC Workshop to Explore Strategies in Knowledge Transfer. 6th May 2014 – Cardiff

Transcript of Long Term Trends in Referrals for Radiology Diagnostics

Page 1: Long Term Trends in Referrals for Radiology Diagnostics

Long Term Trends in Referrals for Radiology

DiagnosticsDr David Parker

Consultant [email protected]

John CollinsHead of Systems & Information Management

[email protected]

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Before 1950

1878: Crookes Tube1895: Roentgen Rays1896: Becquerel - Radioactivity1897: Marie Curie – Radium1897: Braun – Oscilloscope1904: Fleming Valve1913: Coolidge – X-ray Tube1926: Baird’s Televisor1934: Telefunken CRT TV

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Late 1950’s

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Development of Ultrasound

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Diasonograph (Early 1960’s)

Howry’s Sonascope (Life Magazine 1954)

Early Use of Ultrasound

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1960’s

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Transluminal Treatment of Arteriosclerotic Obstruction: Description of a new technique and a preliminary report of it’s application

Charles T Dotter M.D., Melvin P Judkins M.D. Circulation 1964

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1970’s

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1980’s

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Medical Radiology Workforce

Source: RCR Workforce Census 2011

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Radiology Equipment Replacement

Source: RCR Good Practice Guide for Clinical Radiologists 2012

Equipment Type Age

Ultrasound 5 yrs

Nuclear Medicine 7 yrs

Interventional 7 yrs

Computed Tomography

7 yrs

MRI 7 yrs

X-ray 10 yrs

Technology lifecycle

imposes a natural

frequency of innovation

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Data & Knowledge Transfer

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Wisdom

Intelligence

Knowledge

Information

Data

Tuomi I (1999) Data is more than knowledge

Implications of the reversed knowledge hierarchy for knowledge management and organisational memory.

Proceedings of the 32nd Hawaii International Conference on System Science

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Normal Variation?

Key Range0 to 0.49%0.5 to 0.951 to 1.49%1.5 to 1.9%2 to 2.49%2.5 to 2.9%

Data Shows % of population attending for MRI

Is variation ‘normal’?

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Data Collection

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• Data extracts from RadIS 2 RIS (Radiology Information System)

• Monthly activity totals for specific exam code groups and referral streams

• Identified codes that reflected a narrow group of clinical indications for referral

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A&E Referrals for Skull X-ray

Oct Aug Jun Apr Feb Dec Oct Aug Jun Apr Feb Dec Oct Aug Jun Apr Feb Dec Oct Aug Jun Apr Feb Dec Oct Aug1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013

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50

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Site 1Site 2Site 3

Nice Head Injury Clinical Guideline, June 2003

Nice Head Injury Clinical Guideline, Sept 2007

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CT Referrals for Investigation of Suspected Pulmonary Embolism

Jun DecJun Dec Jun Dec Dec Jun Dec Jun Jun Dec Jun Dec Jun Dec Jun Dec Jun Dec Jun Dec Jun2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

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BTS Guidelines for the management of suspected acute PE, June 2003

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Referrals for IVU

Sep Jun Mar Dec Sep Jun Mar Dec Sep Jun Mar Dec Sep Jun Mar Dec Sep Jun Mar Dec Sep Jun Mar Dec Sep Jun1995 1997 1999 2001 2003 2005 2007 2009 2011 2013

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Where Are We Now?

• Data rich / information poor

• Poorly structured / coded data sources

• Retrospective view of practice

• Target focussed NHS

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What Have We Learned?

• Changes in practice are often not associated with issuing of clinical evidence

• Technology / Capacity are not the primary drivers or constraints

• Human factors seem to have a significant role to play

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What Are The Barriers?

Is the evidence good enough?• NICE Head Injury 2003 vs 2007• NICE CG131 (Nov 2011) vs SIGGAR Trial

(Feb 2013)

Do we focus too much on the individual right to practice the ‘art’ of medicine?• Accountability = autonomy• Reluctant to get involved in ‘management’Doctors Championing Change, Dr Jack Silversin 2013

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What Would Google Do?

Big Data• Relationships between data sources• Improve data quality

Timeliness• Provide near ‘real time’ access to data• Encourage experimentation through PDSA

cycles

Relevance• Ensure efforts are aligned to corporate

objectives• Shorten feedback loops

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Prerequisites for Innovation?

Leadership

Teamwork

Intelligence