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Data quality

The DQMI is a quarterly publication about data quality in the NHS, which provides data submitters with timely and transparent information.

Current Data Quality Maturity Index (DQMI)

The DQMI is a quarterly publication about data quality in the NHS, which provides data submitters with timely and transparent information.

Links to all of the DQMI publications, the associated methodology documents and the interactive report can be found below:

February 2018

DQMI-8 (2017-18_Q2) [694kb]

DQMI-8_(2017-18_Q2) [694kb](Opens in a new window)

DQMI_8_CSV [2Mb](Opens in a new window)

Power BI Interactive Report

DQMI Power BI User Guide

Current Comorbidity Diagnostic Persistence

Diagnostic codes for conditions that do not remit should not be lost from patient records over time - this "diagnostic persistence" is a measure of data quality. The below factsheet highlights the proportion of patient episodes that include, or omit, a code for one of five clinical diagnoses where there is reason to expect that a code should continue to be recorded. We reviewed diagnostic codes for all episodes of hospital care over a three month period, and then looked back to compare whether a relevant code was previously recorded.

February 2018

DQMI Comorbidity Outputs 17-18 Q2 Jul to Sep v1.0 Final [859kb](Opens in a new window)

February Diagnostic Persistence Infographic_FINAL v0.1 [518kb](Opens in a new window)

Note: The Analysis is to show diagnostic coding for conditions that do not remit should not be lost from patient records over time - this "diagnostic persistence" is a measure of data quality, it is not to show poor diagnosis or treatment of conditions.

Historic Comorbidity Diagnostic Persistence

November 2017

DQMI Comorbidity Outputs Q1 Apr to Jun 17-18 v1.0 Final [209kb](Opens in a new window)

November Diagnostic Persistence Infographic_FINAL [519kb](Opens in a new window)

August 2017

Comorbidities analysis raw data [166kb](Opens in a new window) 

Comorbidities analysis Infographic [517kb](Opens in a new window)

Historic DQMI Publications

November 2017

There are missing records for a number of providers (predominantly independent sector) in the CDS (APC, A&E, OP) data. This was caused by a change introduced by SUS+ in August which resulted in these missing records.

DQMI-7_(2017-18_Q1) [689kb](Opens in a new window)

DQMI-7 Methodology [1Mb](Opens in a new window)

DQMI 7 CSV [1Mb](Opens in a new window)

Power BI Interactive report

DQMI Power BI User Guide

Note: The Analysis is to show diagnostic coding for conditions that do not remit should not be lost from patient records over time - this "diagnostic persistence" is a measure of data quality, it is not to show poor diagnosis or treatment of conditions.

August 2017

DQMI-6 (2016-17 Q4) [730kb](Opens in a new window) covers data for the period of January - March 2017.

DQMI-6 Methodology [977kb](Opens in a new window) 

 DQMI-6 (2016-17 Q4) [730kb](Opens in a new window)

Power BI Interactive report

DQMI Power BI User Guide

May 2017

DQMI-5 (2016-17 Q3) [723kb](Opens in a new window) covers data for the period of October - December 2016. 

DQMI-5 Methodology [1018kb](Opens in a new window)

DQMI-5 CSV file [1Mb](Opens in a new window) CSV reissued on 24/05/2017 due to duplicates in the original file.

Power BI Interactive report

Power BI Report user guide

February 2017

DQMI-4 (2016-17 Q2) [643kb](Opens in a new window) covers data for the period of July - September 2016

DQMI-4 (2016-17 Q2) [643kb](Opens in a new window) 

DQMI-4 Methodology [1Mb](Opens in a new window)

DQMI-4 CSV [1Mb](Opens in a new window)

Power BI interactive report

Power BI Report User Guide

November 2016

DQMI-3 (2016-17 Q1) [683kb](Opens in a new window)  covers data for the period of April - June 2016

DQMI-3 Methodology [1Mb](Opens in a new window)

DQMI-3 CSV [1Mb](Opens in a new window)

Power BI interactive report

Power BI Report User Guide

August 2016

DQMI-2 (2015-16 Q4) covers data for the period of January - March 2016

DQ_maturity_index_introduction_and_methodology__15_8_16

DQMI-2 CSV file

Power BI interactive report

May 2016

DQMI-1 (2015 Annual) covers data for the period of January - December 2015

DQMI-1 methodology document

 

The Health and Social Care Act 2012 (section 266) states that our statutory data quality role is to assess the extent to which the data we collect meets defined national standards and to publish the results of the assessments. In addition, we may give advice or guidance on data quality relating to the collection, analysis, publication or other dissemination of data and information.

Our Data Quality Assurance Strategy 2015-2020 [47kb](Opens in a new window) sets out how we will fulfil these responsibilities.

DQA Steering Group

Data Quality Assurance (DQA) activity is governed by the DQA Steering Group who meet every two months to provide overall strategic direction for DQA. The group is made up of internal members, as well as external representatives from Care Quality Commission, Department of Health, NHS England and NHS Improvement. We are working to ensure that the provider community are also represented at the DQA Steering Group. The group's Terms of Reference are available for reference, and any queries about the group should be directed to DQAteam@nhs.net.

DQA Provider Forum

A DQA Provider Forum meets quarterly with representatives from 20 provider organisations from around the country. The forum is supported by a national Provider E-Forum enabling further engagement from over 100 active members.

DQA User Forum

The DQA User Forum meets quarterly with representatives from a number of Clinical Commissioning Groups (CCGs), Commissioning Support Units (CSUs), NHS England and the Care Quality Commission (CQC). The Forum is in its early stages but has already seen some very positive actions come out of the first meeting to proactively support the improvement of data quality.

Performance Evidence Delivery Framework

NHS Digital recommends the use of a supportive Performance evidence delivery framework (version 2) designed to help data providers to improve their level of data quality by enhancing their own local processes. Aspects of the framework have been used successfully in an Acute Trust environment, leading to an improved understanding of the importance of data quality alongside an improvement in the quality of data itself.

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