The ability of healthcare providers and medical researchers to share data is critical to managing both individual and population health. Rich data goes far beyond the patient information stored in electronic medical records (EMRs) to include structured and unstructured data either residing in other databases or being generated in real time.

Perhaps never before has sharing rich data been so important. By doing so, healthcare providers and public officials can better prepare for patients, exchange best treatment practices, and manage room capacity and medical device inventory. And ultimately, this can lead to better care.

I addressed the need for the healthcare industry making the leap to rich data sharing through digital transformation in a November 2019 blog post. This data typically has been trapped in siloes, but digital transformation in healthcare is setting data free, enabling innovation and actionable insights through advanced analytics and the use of artificial intelligence (AI) and machine learning (ML).

A hospital trying to project how many patients it can expect could apply ML and AI algorithms to rich, aggregated datasets that could include weather data and trends, mobile tracking data, population education data and more. Analysing and learning from these vast streams of data help determine where to allocate clinical, support and outreach resources.

Aggregated healthcare data use cases

Adding real-time and predictive data to the historical data found in EMRs allows healthcare providers to access the information they need at the point of care and plan for the future. This is invaluable for ensuring that clinical protocols are followed. For example, hospitals could use data to determine whether people who meet the criteria for virus testing every 4 weeks are being tested. If they aren’t, alerts can be sent to staff to book follow-ups.

To fight disease, you need metrics that help you understand it. Key performance indicators include the number of people impacted, that number aggregated by age, and which individuals have any other disease that would put them in a high-risk group. Having a map of all these people can give providers information in real time to quickly conduct interventions and treatments.

The long-term payoff of rich data and smart technologies to healthcare providers is in predictive data. Imagine how useful it would be to have data that could predict overcrowding in health facilities, or the length of stay of a certain hospital patient, or the risk of sickness among particular cohorts of people or sections within a city, such as an area where many elderly people with diabetes live.

This can be accomplished by using historical rich data to run simulations about the impact of various educational and preventive initiatives. Historical data contains so much hidden information, which is why this data must be freed from the siloes and combined with structured and unstructured data aggregated from multiple sources.

Sharing rich data is essential to the future care and treatment goals of healthcare organizations. Instead of investing millions and millions of dollars in updating EMRs, hospitals should invest in leveraging the data we’ve already created in new ways.