The Importance of Data Quality
The Importance of Data Quality
Reliable information, and the quality of the underlying data that supports it, is fundamental to any NHS trust’s ability to deliver effective treatment of patients.
Furthermore, access to data that is accurate, valid, reliable, timely, relevant, complete, unambiguous and unique is crucial in supporting all levels of patient care, management processes, clinical governance, service agreements, remuneration, accountability and future healthcare planning.
Data quality is a perception or assessment of data’s fitness to serve its purpose in a given context. It should be:
1) Accurate – recorded data should be factually correct for the intended purposes
2) Valid – data should be recorded, coded and used in compliance with relevant and agreed standards
3) Reliable – data should reflect stable and consistent data collection processes across collection points and over time
4) Timely – data should be captured as near as possible to the data source and as quickly as possible after the event or activity, and it must be available for the intended use within a reasonable time period
5) Relevant – data captured should be relevant to the purposes for which it is to be used and should be regularly reviewed to ensure data collection requirements are being met
6) Complete – all of the relevant data should be captured, with no omissions
7) Unambiguous – recorded data should have only a single valid interpretation
8) Unique – data should not be unnecessarily duplicated, either in whole or in part.
Generally, NHS trust managers appreciate the value of good-quality data, and the majority of trusts have detailed Data Quality Policies in place to support this. However, such attitudes and their real-world application are often very different. This can be for a variety of reasons. The principal causes include user error often due to operational pressures, poor staff training, weak Standard Operating Procedures (SOPs), lack of data quality engagement by clinicians, and transformational change.
Good data quality is not an optional extra; it is a fundamental consideration for any health and social care organisation on all levels. Patient information is not a fixed state; its very nature means that data supporting it is continually being referenced, updated and shared across different environments.
The fact that data is drawn from so many sources, even different computer and laboratory systems within and external to a trust, adds real risk of duplication and data corruption. Each author of the source data will naturally have their own style and way of entering data.
Over time, hundreds of different sources will have contributed to a single patient’s set of records.Consequently, there will never be a time when all is in equilibrium.
When faced with finite or real-term shrinking budgets, data quality can play a significant role in helping support the option shown on the bottom row: Improve productivity. Removal of or reduction in data errors through better SOPs and more accurate coding will inevitably deliver cost savings and higher-quality patient care through improved productivity.
Failing to maintain high levels of data quality or to improve poor data has significant financial implications and a direct bearing on the following:
- A) Financial penalties
- B) Decreased revenues / loss of income
- C) Increased operating costs
- D) Remedial costs
- E) Future planning
- F) Damages and legal costs
However, Stalis’ Data Quality Service can help to ensure that information is as accurate, valid, reliable, timely, relevant and complete as possible at any given time. The features and benefits are listed here;
- Daily insight into the quality of data
- Ability to adjust and add/remove KPI’s accordingly to actual needs
- Tried and tested methodology and tools
- Explicit areas for focus and improvement
- Highly automated to minimise user intervention
- Practical guidance provided on how to improve data
- Delivered by experienced consultants with many years of experience
- Vendor neutral tools and processes
- Maintaining the level of data quality
- Increased confidence in your data
- Clinical decisions based on accurate, good quality data
- Improved patient safety and outcomes
- Minimise adverse impact to revenues
- Unlocks data for future migration and transformation
- Improvement to methods of data input within your organisation
- Minimise risks in EPR/PAS implementation
- Maximise efficiency of new EPR/PAS functionality
For more details on our Data Quality Service and to download our full white paper please visit our website