FAQ - Everyone knows the importance of accurate, high quality health data, but what systems, packages or organisations are in place to assess quality?

Answer

Clinical care relies heavily on information about symptoms, diagnosis, treatment and outcomes, increasingly being held electronically in clinical computer systems.  The growing use of computers, plus emphasis on clinical audit and clinical governance, have led to a focus on the quality of data held in clinical systems.
High-quality data should be:

• Complete
• Accurate
• Relevant
• Timely
• Accessible

High-quality data supports:

• improving patient care in the consultation: ensuring availability, legibility, completeness, operation of appropriate warnings, alerts and decision support;
• easier prevention and health promotion: identifying patients with similar health needs;
• better follow up: tracking patients across health care sectors;
• better chronic disease management: calling, recalling and monitoring patients with chronic diseases;
• improving organisation: streamlining processes like cervical cytology recall, and writing referral letters;
• supporting clinical governance: assessing and improving clinical care;
• supporting the PCG/T: enabling effective commissioning and health care planning;
• providing data to the wider NHS: for workload planning, quality of care assessment, health care burden, epidemiology and research.

When assessing data quality, the following should be borne in mind:

• The population considered may not be large enough to smooth random variation.
• Age structure, ethnic mix and social and environmental factors skew prevalence and incidence of most conditions.
• Local knowledge of the context is required to interpret its meaning reliably.

Assessing completeness can be done directly, by looking at prevalence and apparent prevalence, chronic disease monitoring, risk factor and screening recording, and indirectly by age standardisation and examining outliers.

Assessing accuracy can be approached by looking for high prevalence of unusual conditions, missing diagnoses, males with female diseases and vice versa, age-related conditions, and summarisation errors.

In primary care, PRIMIS (Primary Care Information Services) provides feedback on data quality to over 2,000 practices.  The PRIMIS Clinical Advisory Group is also refining the data quality query sets and working on the potential for indices and measures of data quality improvement.

The data quality improvement process, based on feeding back baseline analyses and providing targeted facilitation at problem areas, is starting to show effects, but implementing changes in behaviour is not a rapid process!  Qualitative factors must also be taken into account, increasing use by clinicians of the computer within the consultation, as a clinical tool, streamlining and efficiencies in organisation, increasing use of templates and protocols, improved communication between members of the clinical team, and development of a learning culture.  The educational and culture change aspects are a vital part of this process.

Disclaimer: This FAQ was originally Sheila Teasdale and was amended by Christopher Smith and does not reflect an official endorsement by the HEA or any other organisation.  Any questions or queries should be sent to: enquiries@medev.ac.uk

Last updated: 04 July 2011