Why do we need to connect and combine health data sources?

Why are connectivity and collaboration important?

Standards are everywhere, or maybe not…? What about health care information standards?
Many health care systems have created their own standards to compile, assess and store health data. Problems often arise when health care systems want to compare, connect and combine data that is not standardised.
That’s where interoperability standards come in. Interoperability standards provide a standardised approach to facilitate seamless sharing of information between health information systems.
The power of information (data) lies within every person. The medical breakthroughs of the future will be increasingly defined by our ability to collect, connect, collaborate and understand health-relevant data in vast quantities.

How the benefits of connecting and combining health data from different sources have grown and stimulated the need for health data interoperability standards:

Well over a century ago, health records were created by physicians as a personal reminder about a patient, which they could read when next seeing that patient. These also became a record of evidence of the care a doctor had given if challenged about their professional judgement and treatments. As healthcare has progressively involved a wider range of professionals, such as nurses, physiotherapists, pharmacist, dietitians etc., the records have shifted emphasis from being a personal record by a single clinician to being a tool to support shared care. This has meant that record folders shifted from being the personal property of a doctor 100 years ago to a healthcare organisation’s resource to support its staff in delivering good care to each patient. Healthcare organisations have gradually woken up to the opportunity to examine collections of patient records in order to determine the quality of the care they are giving to patient groups, and to investigate any issues to do with risk or poor health outcomes for some patients.

Nowadays the challenge is how to share access to health record information between organisations. This has become more difficult, and also more important, due to the growing complexity of healthcare and treatments and the widening of the teams that might be supporting the care of any one patient. This might be between a hospital and a general practitioner, or within a health region within which patients might be under multiple specialist centres for different diseases. Since most healthcare organisations have different ICT systems, with different electronic health record databases and different content in them, sharing this information has proved challenging. This need has given rise to standards for how data can be communicated between systems to enable accurate sharing between any two healthcare organisations caring for the same patient.

It is probably only in the last couple of decades that many other health system organisations, such as public health agencies and research organisations, have recognised the value of reusing health data on a significant scale. This is because health data is now mainly collected electronically, and more of it in a well-structured form. There is huge societal value from combining data from multiple care organisations, and potentially other data sources beyond health, to support a wide range of analysis purposes, whether for quality improvement or for clinical research. This has further fueled the need for standards, and accelerated the development of more sophisticated standards to enable data to be shared meaningfully and securely across countries.

The last but not least actor to mention is the patient. Patients have for many years been able to read their health records in some countries, and more recently been offered a patient web portal by their healthcare provider through which they can sometimes order repeat medications, view test results and book appointments. However, disappointingly, it is only in recent years with the growth of mobile phone apps that patients can have on their own mobile devices, sensors which they can wear and other technologies they can utilise. Capturing and integrating this patient created data can give them good use of their own health data ecosystem. These ecosystems increasingly include prevention, wellness and lifestyle information, not only illness related data. There is a growing recognition across all of the actors in healthcare and research that patient generated data provides:

  • a unique, detailed and valuable insight into factors that may cause disease
  • help to manage it better
  • provide very detailed information about how effective their different treatments are.

We are just embarking upon this new reason for promoting standards: for patient and healthy citizen apps and devices, and to connect to these to the more traditional health data systems. We are starting to create a kind of digital citizen, which is the totality of all health and health related information about a person that he or she, and others, can use under agreed rules, for the benefit of all.

Care connectivity stories

In the last 5 years, more scientific data has been generated than in the entire history of mankind. Here are some examples of how data was used to advance health care for patients.

Real life example

‘Big Data’ was used for the early identification of other diseases associated with cancer. There were 17 million new cases of cancer worldwide in 2018 and that number continues to rise. Cancer has a huge impact on patients. It is also a major burden for health care systems across the world. Patients with cancer may be at a higher risk of also developing other diseases. If clinicians knew which other diseases have the highest risk of occurring in patients with each type of cancer, they could prioritise trying to detect that disease early or, if possible, prevent it from occurring.
This research was undertaken to improve medical and research understanding of which additional diseases are most likely to occur in patients with cancer. The work focused on patients with the nine most commonly occurring cancers. The aim was to develop a way of helping clinicians and researchers in the future to discover which other diseases might be associated with cancer in the same patient over time.
After analysing the data, a computer programme was set up to detect diseases occurring in any patient within three years of each other, and to display these disease associations in ways that would help a scientist to determine the strongest disease connections. This collection of disease associations was used to create a visual image that can be rotated or zoomed to enable very detailed inspection. This system is called the Cancer Associations Map Animation (CAMA).
This systems helps clinicians to become aware of other diseases that their cancer patient might be at risk from and early detect or prevent this from occurring.

Case study

Achieving medical device connectivity across a multi-hospital enterprise

Bernoulli One™ is uniquely designed to provide device connectivity and integration across complex healthcare ecosystems, powered by an integrated, scalable, secure, and cost effective web-based platform. As a result, current and complete medical data are available at all times so that doctors can make the best clinical decisions for the patient.


Other standards for quality, monitoring, comparisons etc.

What are (data) standards? (https://www.ncbi.nlm.nih.gov/books/NBK216088/)
Standards go beyond naming and labeling something. Standards in health care also need to encompass many things like:

  • Methods
  • Protocols
  • Terminologies
  • Specifications for the collection:
    • Exchange of data/information
    • Storage of data/information
    • Retrieval of information associated with health care applications, including medical records, medications, radiological images, payment and reimbursement, medical devices and monitoring systems, and administrative processes.

Standardising health care data involves the following:

  • Definition of data elements—determination of the data content to be collected and exchanged.
  • Data interchange formats—standard formats for electronically encoding the data elements (including sequencing and error handling) (Hammond, 2002). Interchange standards can also include document architectures for structuring data elements as they are exchanged and information models that define the relationships among data elements in a message.
  • Terminologies—the medical terms and concepts used to describe, classify, and code the data elements and data expression languages and syntax that describe the relationships among the terms/concepts.
  • Knowledge Representation—standard methods for electronically representing medical literature, clinical guidelines, and the like for decision support.

Interoperability enables safer transitions of care, which leads to better patient outcomes over all. For example, a patient who is on vacation and falls ill may not be able to provide all details of his medical history, which can make all the difference to the doctor charged with his care.