When it comes to healthcare, clinical research is vitally important. Through research, we can find the answer to tough questions, and refine the way healthcare professionals work. We can discover new ways to treat patients, and establish which of these treatments are most effective.

But in what is an increasingly digital world, managing the plethora of available health data should be a key part of any research effort.

Health data management involves how to best house and use digital health data, ranging from electronic medical records through to hand-written medical notes. But what constitutes good health data management?

To answer this question, we’ll follow the journey of Dr Jade, a clinical data science researcher who’s facing challenges with data needed to develop a localised readmission risk model – and show how a modern data platform can address these challenges.

Meet Dr Jade

Applying predictive modelling to identify patients with high risk of hospital readmission, and applying effective interventions to mitigate that risk requires well-managed health data.

In creating a localised readmission risk model for this, Dr Jade’s primary responsibilities include enabling data-driven clinical research approaches, protecting the privacy of the data, and validating the reliability and credibility of research results.

Her research to create this model commences with conversations with local clinicians to understand the factors that contribute to hospital readmissions, and conducting a detailed literature review on causes of readmissions, globally.

The next step is to analyse clinical data collected from a range of sources, including (but not limited to) Electronic Health Records (EHR), health registries, clinical trials, genetic information, wearable data, and care management databases.

While this may sound straight-forward, Dr Jade’s challenge is a big one: she needs to get access to relevant data; collect, structure and process it; and when that’s completed, she has to know what to look for in the data itself.

Accessing and standardising quality clinical data

Having proper access to clinical data is important for research. While the level of health data being generated has skyrocketed in recent years, much of this is held in ‘siloed’ systems that aren’t interoperable (able to exchange and use health information from one another to benefit research).

In the United States alone for example, approximately 1.2 billion clinical documents are produced each year – but there is no single repository of information to rely on. For researchers like Dr Jade, access to data beyond that contained in their own health system is required to achieve the best research results.

Currently, Dr Jade will have to make multiple queries to pull data from various sources. Assembling this unstructured data into structured cohorts, forming a standardised set of data from data types such as medications, lab tests and procedures can be particularly time consuming.

Different types and sources of data

Dr Jade will also need to consider different types of data. Social determinants of health – which are non-medical factors that impact a person’s health – play a significant role in the health status of an individual.

Requesting information from another health system on current readmission risk rates for certain conditions is also something Dr Jade will need to do. In some cases, these health systems won’t be sufficiently resourced to facilitate this.

Patient-generated data (such as data generated by medical devices), and genomic data are also important types of data Dr Jade may wish to analyse, but currently there’s limited flexibility for ad hoc data sets and new data types such as those outlined above to be added to her research programme.

In short, the sheer volume and scope of data Dr Jade needs to assess is overwhelming. Without the right healthcare technology and processes in place, it’s difficult to validate models and scale-up adoption of these models into regular use.

A new way to empower data-driven clinical research

Given the significant roadblocks Dr Jade faces, it’s clear that a solution is needed to remove current barriers and allow data to be accessed and used in research. Modern data platforms are one such solution.

Modern data platforms possess the capability to address fragmented health data; aggregate and curate disparate sources of health data; and add new data types as they’re created. They provide a single point of access for research, such as that which Dr Jade is undertaking.

Research shows that effective readmission interventions which are poorly targeted can reduce hospital readmission rates by over 28%. Imagine the potential of using a modern data platform, with machine learning capability, to improve targeting?

Health systems can reasonably expect an even greater reduction in the rate of hospital readmissions. This will not only save researchers like Dr Jade time, effort, and cost and making sure the right data is in the right hands at the right time.

Want to learn more?

Read more about data management, the potential that modern data platforms offer clinical healthcare research, and Dr Jade’s journey in our latest whitepaper, A modern data platform: Making clinical research more effective.