Why we must make data integrate a priority in healthcare

"Without data integrity, healthcare is blind; with it, we can anticipate and prevent." Sven Jungmann

In healthcare, data is our vision; without it, we're blind. But raw data isn't enough. If it's incomplete, inaccurate or not maintained with integrity, it's like trying to read a map in the dark. Without data integrity, we can't see the patterns or predict the potential pitfalls ahead. We're forced to navigate in a fog, relying on guesswork rather than insight.

During my time working in hospitals, I saw first-hand the critical role that accurate, reliable data plays. When a patient's medical records are incomplete or scattered across disparate systems, clinicians spend valuable time piecing together their health history like a jigsaw puzzle. They often miss important clues, leading to redundant tests, misdiagnoses or delayed treatment.

Data integrity means that all information is accurate, consistent and complete. It's essential for identifying patterns that may signal future health problems. For example, if a patient regularly presents to the emergency department with high blood pressure and elevated cholesterol, but no formal diagnosis is made, this is a red flag for cardiovascular disease. If clinicians only see isolated episodes without an integrated health history, they may miss this crucial trend. But with a comprehensive and structured record, healthcare providers can identify such patterns early and intervene before a crisis develops.

In Germany, the electronic patient record (ePA) was introduced to standardise data across systems, but its uptake remains limited, with many patients and practitioners unaware of or sceptical about its benefits. Part of the problem is the lack of an integrated, user-friendly platform that healthcare professionals can trust. Many doctors are still forced to rely on fax machines and phone calls, making data retrieval a cumbersome process.

A digital record with high data integrity can also help prevent unnecessary treatments and reduce medical errors. Algorithms can automatically flag potential drug interactions or alert doctors to specific allergies. But this only works if the data is accurate and complete. Inconsistent entries or missing information can lead to false positives or, worse, missed alerts that put patients at risk.

Moreover, data integrity extends beyond treatment to public health. High-quality data can identify emerging trends that could predict health crises. Early detection of infection clusters or the spread of chronic diseases can help public health officials allocate resources and develop targeted interventions.

To achieve this vision, healthcare organisations must prioritise data integrity through rigorous standards of collection and verification. Staff training, robust electronic health record (EHR) systems, and clear guidelines for data entry are critical steps. A digital 'health twin', where each patient's comprehensive record is fully integrated, standardised and accessible to all relevant providers, is key.

Ultimately, data integrity isn't just about efficiency or convenience - it's about saving lives. Without it, we're blind to the health challenges our patients face now and in the future. With it, we can see clearly enough to anticipate, intervene and prevent the worst outcomes.

I've written extensively on this topic in my book, "Wie gesund wollen wir sein?", published by Penguin RandomHouse. It explores how data and digital health can transform our healthcare system for the better. Here's the link: https://amzn.to/3JRpGLz