What are the pitfalls of using data that EFM teams should protect against? 

Data-driven decision-making will be able to save lives when the data is of a high quality, analysed, and the systems are working. But how do we get them to a high standard? And what are the pitfalls that EFM teams need to be aware of when setting up, managing, and using data?

Sodexo’s Lead Data Scientist gives his insights into the core four areas where data pitfalls are likely to occur:  

• Data Quality & Integrity  
• Data Interpretation  
• Data Security & Management  
• Organisational Data Dynamics

As an EFM leader, this will help guide the questions you could ask yourself about your team’s data management.

Read on to see the recommendations for EFM teams.

What should you look out for when it comes to data quality and integrity?

Data-quality pitfalls

High-quality data forms the foundation of any robust decision-making process. If acquiring it isn't consistent or replicable, it can lead to unreliable insights. Therefore, it’s crucial to consider how your EFM team acquires its data. 

Challenges typically faced by NHS Trusts in acquiring quality data:

• Legacy systems: Older systems can hinder data extraction, causing information gaps. In practice, your team may have difficulty retrieving historical data, face incompatibility issues with modern tools and lose data during upgrades.
• Misplaced data: Storing data in the wrong locations can cause retrieval issues. You may have problems like data being stored in the wrong folders, information on local machines rather than servers or inconsistent file-naming conventions.
• Manual processes: Reliance on tools like Excel and even paper-based systems or manual entries can introduce errors and inconsistencies. You may see things like transcription errors by mistyping or even miscalculations in Excel due to formula errors.
• Non-repeatable processes: A non-replicable data acquisition process refers to a method of collecting data that cannot be consistently repeated to yield the same results, so you’ll only get that data once. For example, with patient surveys, this could look like surveys capturing only a subset of patients present on a specific day or feedback swayed by external factors like being captured straight after a meal, at discharge or even after they have left.

You could ask yourself:

• What are the primary causes of our data quality challenges?  
• How might we transition from older systems to more modern, data-centric platforms?  
• Are we addressing our data challenges in manageable segments?  
• How are we recognising and celebrating early successes to maintain momentum?  
• Have we established Standard Operating Procedures (SOPs) to guide consistent data acquisition? 

Are we bridging the data quality gap with data lakes?

One solution to overcome data quality gaps is the creation of a data lake, a unified hub that allows for efficient data storage and analysis. Sodexo, for instance, employs this strategy for our clients, ensuring a more integrated approach to data management. 

Therefore, you could ask:

• Are we leveraging the right tools and systems for effective data integration?  
• How are we addressing the challenge of data silos in our organisation?  
• How are we ensuring data security while integrating data from diverse sources?

What problems may you face with data interpretation?

The pitfalls when analysing data for insight

Biases can skew interpretations and lead to misguided conclusions from your data. Misinterpreted data can lead to incorrect strategies and wasted resources.

Look out for two types of bias: 

• Confirmation Bias: Favouring data that aligns with pre-existing beliefs. This can show itself in ways such as:

1. Ignoring patient feedback that contradicts established practices 
2. Overvaluing positive performance metrics while downplaying negative one 
3. Prioritising data that supports a preferred strategy, even if it's flawed

•  Selective Bias: Highlighting only a subset of data, potentially missing the bigger picture. You may experience this if your team:

1. Showcases positive patient outcomes, ignoring areas needing improvement 
2. Focuses on one department's feedback while neglecting others 
3. Emphasises data from senior staff while overlooking inputs from ground-level employees

• Misalignment with NHS reporting standards: Not adhering to central NHS data interpretation guidelines. This can look like: 

1. Misinterpreting data due to not following NHS standards  
2. Making decisions based on non-standardised data interpretations  
3. Struggling with comparisons due to lack of like-for-like data 

You could ask yourself: 

• Are we aware of the common biases that can affect our data interpretation?  
• How are we ensuring diverse perspectives in our data analysis to counteract biases?  
• Are we engaging the right stakeholders from the outset to ensure a holistic view of our data? 

What pitfalls might you face with data security and management?

The pitfalls within data security & compliance

With non-clinical management tasks that impact people’s health at stake, the challenges in ensuring data security and compliance are huge. Overlooking this can lead to breaches, loss of trust, potential legal ramifications, and compromised patient care.

Common hurdles include:  

• Incorrect data access for stakeholders: You may find that incorrect stakeholders can access sensitive information. In practice, this could look like a junior staff member accessing sensitive data, an admin viewing financial data unrelated to their role or even a clinician accessing non-clinical management data.  
• Falling behind the changing data regulations: Your team may have this challenge if you encounter things like overlooking a recent GDPR update, missing a new NHS data handling guideline or not updating consent forms in line with new regulations.  
• Low protection against evolving cyber threats: Your team may face this critical challenge if you experience situations such as outdated software leading to a data breach, not patching a known vulnerability in time, or using unsupported legacy systems.  
• Human Error: You may encounter unintentional breaches due to oversight or lack of training. In practice, this may look like accidental data deletion by an untrained staff member, sharing sensitive data via unsecured channels or failing to log out, leaving data exposed. 

You could ask yourself:

• How frequently are our data security protocols reviewed and updated?  
• Are we up to date with the latest regulations and compliance requirements?  
• How are we training our staff to prevent unintentional breaches and ensure data integrity? 

How can you avert team dysfunction around data? 

The pitfalls within data culture & communication

 Without the right data culture, your EFM team risks fragmented efforts, incorrect priorities, and overlooked opportunities.  

Common pitfalls include: 

• Lack of shared responsibility: This can lead to a fragmented use of data. Within your team, it can look like team members disregarding digital initiatives, misaligned goals causing conflicts and ignoring feedback on digital strategies.  
• Siloed teams: If your teams don’t work together on data, they may develop tunnel vision, missing critical data insights. In practice, you may experience things like technical teams overlooking commercial implications, non-clinical teams not using technical tools effectively and missed opportunities due to a lack of diverse expertise.  
• Presentations that don’t resonate: One-size-fits-all data presentations can mislead stakeholders. You may experience situations like senior leaders overwhelmed with granular details, stakeholders glossing over crucial insights and misunderstandings due to complex data visuals. 

You could ask yourself:

• How are we fostering a culture where digital responsibility is shared across the board?  
• Are multidisciplinary teams in place to ensure diverse perspectives in decision-making?  
• How are NEDs equipped to provide effective challenge and support in technology and data?  
• How are we tailoring our communication to cater to different stakeholders? 

Do you need support for your tech skills integration?

As you can see, data is set to play a pivotal role in shaping the future of healthcare in the UK. From ensuring data integrity and quality to fostering a robust data culture, your journey towards data-driven decision-making is nuanced yet critical.  

However, we understand your path to reaching the full potential of data is challenging. Your EFM teams must be vigilant against pitfalls such as data silos, biases in interpretation, and security vulnerabilities.  

By addressing these challenges head-on and fostering a culture of continuous learning and collaboration, your teams can pave the way for a more efficient and patient-centric NHS. However, you may need support to achieve your aims.  

The Government Sourcing Playbook suggests outsourcing could be a way for the NHS to meet its long-term aims³.  

"Organisations, such as Sodexo, have skills, resources, scale, and we need to use those", according to Daly⁴. 

With outsourcing support from Sodexo, you could create a data-driven EFM department that allows for efficient data storage and analysis and is future-proofed to better handle upcoming challenges.