How data quality impacts healthcare efficiency and patient outcomes

Our survey found that healthcare professionals, including Chief Data Officers, Clinical Data Analysts, and Healthcare IT Managers, rated their confidence in data quality at an average of 7.08 out of 10. This partial confidence suggests significant room for improvement. Poor data quality can lead to missed opportunities and incorrect treatments whilst strong data governance protocols can help elevate this confidence, enabling more precise, life-enhancing care.

Operational consequences of poor data quality

We asked respondents to identify their main concerns related to poor data quality, the three main challenges they flagged were:

• Duplicated administrative efforts: 74% mentioned duplicated administrative work due to data inconsistencies, which places a burden on healthcare staff, consuming valuable time that could be better spent on direct patient support.
• Incorrect address details: 46% noted address inaccuracies as a frequent challenge, complicating outreach efforts and impacting billing, patient satisfaction, and resource allocation.
• Missed appointments: 42% of respondents cited that outdated or incorrect patient information resulted in missed appointments, disrupting continuity of care and impacting operational efficiency.

The challenge of predicting outcomes

In the age of data-driven insights, predictive analytics play a vital role in enhancing patient care and streamlining healthcare delivery. However, 46% of the respondents reported difficulties in identifying patterns or predicting outcomes due to data limitations. Incomplete or inaccurate data hinders the utility of predictive models, obstructing early detection of conditions, patient risk assessments, and resource allocation planning.

Data silos and interoperability

The survey highlighted the persistence of data silos across healthcare organisations, with 17% of respondents indicating that data could not be easily identified and shared between systems. This lack of interoperability can lead to fragmented patient records and delays in care, especially when different departments or locations are unable to access a unified view of patient information.

Breaking down these silos through effective data governance and integration strategies is essential. By enhancing data interoperability, healthcare organisations can facilitate seamless information sharing, which not only improves patient experiences but also streamlines workflows, ensuring accurate information is available when it’s needed most.

Ensuring data validity for confident decision-making

Another significant finding was that 22% of respondents expressed doubts about the validity of their data for reporting and decision-making. High-quality data is crucial for informed choices, from patient care to organisational planning. Without it, healthcare providers risk suboptimal decisions impacting patient outcomes and operations.

The importance of compliance

Data quality in healthcare is essential for regulatory compliance. However, over 10% of respondents indicated that their data quality was insufficient to meet regulatory requirements, with challenges in tracking data collection and processing compliance. NHS^[1] highlights the critical role of standardised data systems to improve care coordination, reduce errors, and enable continuity across patient interactions and settings. The push for high-quality, reliable data within the NHS underscores how critical these frameworks are to meeting regulatory standards and delivering improved patient care outcomes in an evolving healthcare landscape.

Conclusion: The future of data in healthcare

Data quality and governance are more than technical necessities; they are foundational to the future of healthcare. Our survey findings demonstrate the importance of addressing common challenges in data management:

• Issues like missed appointments (42%), incorrect address details (46%), and duplicated administrative efforts (74%) illustrate the operational costs of poor data quality.
• Predictive analytics and data integration remain limited, with 46% unable to identify patterns effectively and 17% reporting challenges in sharing data across systems.
• Validity issues, noted by 22% of respondents, and compliance challenges affecting 10% underscore the need for reliable data to ensure effective decision-making and regulatory adherence.

To address these challenges, healthcare organisations must prioritise investments in data quality initiatives and governance frameworks. By doing so, they can enhance patient care, improve operational efficiency, and ensure compliance with confidence.