Why is data quality important in healthcare?

NHS trusts, local authorities, and private healthcare providers will have some sort of data services team – whether dedicated or as part of an IT department – that will be responsible for healthcare data.

Data quality in healthcare applies these six principles to medical information, so IT technicians make sure the healthcare information systems work and can record and present the data; a vital function for doctors, nurses, and other staff.

The importance of data quality in healthcare stems from how data can improve patient care. In NHS England’s own words, “Consistent, timely and accurate data improves patient care and decision making”.

The opposite can be said to be true as well: the combined impact of problems ultimately leads to poor care, poor care planning, and poor policy decisions. Poor data means inappropriate care for a patient, which can have a knock-on effect. The obvious impact is that a patient’s health would be damaged, but there’s also the impact of wasted time and expense. Giving a patient the wrong treatment requires further treatment to correct the mistake. This can take valuable time and resources away from other patients, limiting the scope of an organisation’s ability to care.

Healthcare professionals need to have confidence in the information provided to them. Better quality data empowers doctors and nurses, and this gives them confidence in their decision making. By feeling supported there is also a boost in morale and job satisfaction for healthcare professionals, so an organisation loses fewer staff to stress or burnout, which in turn brings down turnover and boosts productivity: a win-win situation.

Timeliness is one of the six data quality characteristics, and by ensuring the most up-to-date information a clinician can properly diagnose a patient and create a suitable care plan, thus minimising their stress and maximising their chances of recovery or improving their wellbeing as a whole. This is the importance of quality data in healthcare.

The six characteristics for data quality also double up as the challenges for data quality in healthcare. Accuracy is often the most common one to cause a problem. Healthcare demand is continuing to grow, with a deficit of clinical staff in the industry. This means staff are overworked and risk losing focus on repetitive tasks like data input. This can result in silly mistakes, like a patient’s height being 6.2cm not 6.2 feet.

Things like this are often easy mistakes to fix, but it does impact the patient experience, and damaging that confidence can impact on their trust of you as a care provider. For the NHS, this means complications down the line when care isn’t administered at an early stage, and for private organisations this simply could mean a loss of business.

It also requires human input to query and amend, hindering any automation within an information system. This lack of accountability is also a challenge. Human error is unfortunate but large chunks of healthcare are still based on the manual input of details. Repeat failings within information systems will inevitably lead to dissatisfaction and hesitance to use these systems, and even distrust.

Data drift is a further problem. This is when data changes or evolves over time, moving from the initial understanding of information or trends. Things such as increased life expectancy and the demand for surgery or seasonal flu are examples of this drift. The same goes for new medical breakthroughs and new understanding, which in turn forces re-evaluation of past recorded data. This means revisiting a lot of old information.

Improving data quality in healthcare requires a comprehensive investigation of the six characteristics of data quality that we mentioned at the start of this guide.

1. Accuracy issues stem from human error. This is either down to a lack of skill regarding data input, or because of the high levels of workload and stress experienced by clinicians.
   
   
2. Having complete records is important. If a record isn’t complete, why not? Instances must be properly evaluated to make sure that gaps are only present if truly necessary.
   
   
3. Continued and consistent record keeping is a huge benefit for data quality. This helps build trends and from that there can be pattern recognition, which feeds back into the bigger picture of a local, regional, or national care plan and policies associated.
   
   
4. Validity is about ensuring the data collected meets legal standards, as well as the organisation’s own.
   
   
5. Some might argue uniqueness falls under accuracy but ensuring no duplication or overlap from data sets helps avoid any skewed results and trends. This also applies to patient records. Two records with different information could waste a clinician’s time or worse, lead to inappropriate treatment.
   
   
6. Timeliness is punctuality. A healthcare provider can check whether those providing data are experiencing difficulties and delays, and flag that as an issue for management to resolve.

Software solutions are quickly becoming the go-to answer for how to improve data quality in healthcare. By implementing a computer program or application, clinicians and administrative staff are finding their workloads are either streamlined or even simplified to reduce their burdens.

A lot of internet guides about data quality refer to “integrated data analytics”, which is a fancy way of saying you add information, you make it so people can read it, and you make it so people can share or access it when they need it. The process is important though. By using high quality software, a lot of the challenges for data quality go away.

Healthcare software such as Rio Cloud from The Access Group allows a central storage point of patient or client information, and it can provide the integrated data analytics an organisation needs. It’s easy to learn and flexible, allowing you to add more data or more staff to the system as needed – as well as having the security to protect data from the outside world and to limit users to access only the data they need.

For clinicians, this tackles the issue of accuracy. By reducing the skill required for the data input and having all the correct data fields, stress is reduced and so too are errors in data recording. This saves time in future for staff so that they are not correcting the error, but also prevents any incorrect care provision. Also by being easier to do there’s less likelihood of there being delays to the provision of data.

The ease of use and the clear display of a record makes it easy to see whether or not a record is complete, as well as who has provided data. This path of accountability matters, and will help enforce consistency and adherence to an organisation or trust’s rules but also it will reassure staff that data input is worth their time and system can be trusted.

Being able to work from anywhere improves the timeliness of updating records. Healthcare professionals can update information during an appointment so up-to-date information is available immediately. This also mitigates the risk of forgetting or losing information that has been written down elsewhere, making patient records more accurate.

Beyond this there should be proper communication with a data quality team or IT department. This will help push back on healthcare staff when data appears to be problematic. Computing errors are their problem still, but channels of discussion can help uncover areas of difficulty for staff or improvement for data capturing – information that could even be of benefit to a software provider like The Access Group in tailoring or improving the computer programming.