Data integration in healthcare unlocks many possibilities. Experts can monitor trends in population health, medical records can keep doctors informed of a patient’s information, and individuals can become active participants in their health journey.

I have previously written blogs about these topics and highlighted the importance of data integration in healthcare. 

Yet, numerous challenges impact healthcare data integration, from a lack of standardisation to securing patient data. These challenges are not insurmountable but do require careful planning and execution. By understanding the challenges and addressing them head-on, I believe that we can make healthcare data integration a success.

Data collection systems remain fragmented

Healthcare data often exists across different silos or departments, making it difficult to get a complete picture of a patient’s health. Fragmented systems can lead to issues such as someone getting multiple tests done or repeatedly answering questions about their medical history.

There are many reasons why healthcare data is fragmented. One reason is that many different data sources exist within healthcare. A general practice might receive patient results from the radiology and pathology clinics, but these may arrive at different times and in a different format to other clinics. Another reason is that different organisations often use other software systems, making it difficult to exchange information.

To address these challenges, we must start by breaking down the silos within our healthcare systems. We need data exchange and interoperability standards so that information can flow freely between providers.

Data integration in healthcare lacks standardisation

Data integration is a big challenge for healthcare organisations. They must be able to collect data from disparate sources, including EHRs, clinical decision support systems, and claims databases. Once they have this data, they need to be able to make sense of it and use it to improve patient care.

The lack of standardisation is a significant barrier to healthcare data integration. As hospitals, healthcare practices, and wearable device companies began logging data, they used various data formats, resulting in a great collection of healthcare information that seldom translates between systems. It has become complicated for these systems to communicate with no agreed-upon way to format or exchange healthcare data, leading to errors and duplicate data.

As healthcare data is often unstructured and complex, it becomes difficult to analyse and interpret, meaning that it stands in the way of making decisions and improving patient care. Problems like these exemplify why we need interoperability and standards, such as FHIR, to make healthcare data accessible and understandable to practitioners and their patients.

Adequately securing and anonymising data

Healthcare data is among the most sensitive information that we collect about people. Threat actors can leverage it for identity theft, fraud, or blackmailing someone.

Securing healthcare data is more than securing the computer network within a healthcare practice. If we are to share healthcare data, it must be encrypted and accessible only by those with permission to view it.

In addition, data can help healthcare professionals analyse population health to identify trends. The best way to gain an accurate picture is by anonymising the healthcare data of real people. This way, we get a good idea of common trends but can also see the outliers and rare conditions. However, even this is not proper anonymisation. Given the sheer volume of data online about all of us, it is not difficult to reconstruct the information and associate an anonymous record with a name.

The following is a contrived example, but it demonstrates my point. Let’s say a medical record from a hospital has information about a patient with diabetes, among other conditions. Credit card sales from a nearby pharmacy might show insulin sales corresponding with the date of the person’s visit, and there is a chance someone can associate the two records with some confidence. They could then use the medical record to determine the patient’s other conditions and find their personal details. As I said, it is contrived, but it demonstrates the point.

Manual processes create low-quality data

Inaccurate or incomplete data leads to poor data quality, impacting the decisions made by healthcare professionals and possibly resulting in mistakes.

Poor data quality occurs for several reasons, such as errors when entering data, missing data or failing to validate its accuracy. Data quality becomes a larger challenge when all of these processes take time and there are few people available to do the work. It often falls on doctors and nurses to manage data entry, leading to time-consuming processes and increasing the risk of mistakes.

What we need are processes for reducing manual entry and improving processes, including automation and interoperability between systems and organisations. Having clear and concise documentation describing best practices for using the data sources is also essential and will ensure that the data is of a high quality and reliable.

Healthcare data integration with Fluffy Spider Technologies

We help organisations move toward a future of connected digital healthcare, making existing systems interoperable and modernising infrastructure to unlock the potential of new technologies.

We can help you identify the relevant opportunities to incorporate modern web services and standards for health information exchange, such as HL7 and FHIR, enabling systems to interoperate with other modern health information exchange technologies from the medical software industry and those already implemented by large healthcare providers such as Government health departments.

Visit our Healthcare Integration Services page to learn more about our capabilities and solutions.