An easy guide to understanding healthcare data analytics

In this brave new world, virtually every person generates data. Whether it’s from accessing social media apps on their smartphone or wearing a fitness-tracking device, most of us create information trails that can be recorded, stored and used. This data can be extremely helpful to gain insight into the behaviour or composition of large groups of people, for example. In turn, this can help companies design products that meet certain needs most closely, and proactively fulfil certain customer expectations. Like many industries, the healthcare sector is increasingly moving towards data as the foundation of its decision making.

The field of healthcare data analytics stands at the intersection of technology and healthcare, promising to transform how we manage, deliver and access medical services. In practice, hospital data, medical data and clinical data are already being used to improve inventory management and provide personalized healthcare to patients. However, the potential of healthcare data extends much, much further.

This guide aims to unwrap the complex world of healthcare data analytics for those who work within the healthcare industry and those who don’t.

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What is healthcare data analytics?

At its heart, healthcare data analytics is the uncovering of patterns and insights from raw healthcare data like patient histories, bloodwork and genetic trackers to help healthcare providers determine the best course of treatment.

This field leverages technologies like machine learning and data visualization to enhance medical practices, optimize resource allocation, and drive evidence-based decision making in the healthcare industry. In short, healthcare data analytics seeks to transform vast amounts of raw data into meaningful, actionable knowledge.

How to make healthcare data analytics fit for purpose

There are various subsets of healthcare analytics, each serving a distinct purpose:

Medical data analytics is done on data from the electronic health records, medical imaging, laboratory tests and wearable health devices of individual patients. The practice aims to draw insights into patients’ health status and clinical outcomes to optimize healthcare delivery at the patient level, including diagnosis, treatment planning and monitoring.
Clinical data analytics encompasses the analysis of data collected during clinical care processes, including patient interactions, medical procedures and healthcare interventions. The idea is to spot patterns that could improve clinical workflows.
Hospital data analytics specifically refers to the analysis of data generated within hospital settings, including administrative data, operational metrics and financial performance indicators.

While medical data analytics, clinical data analytics and hospital data analytics all target specific facets of healthcare, they each empower healthcare professionals to make well-informed decisions that can lead to revolutionary improvements in patient care and healthcare management.

Four types of healthcare data analytics explained

When looking at the more technical side of healthcare data analytics, we can discern four fundamental types of analytic techniques:

Descriptive analytics is the initial phase that creates a historical narrative of healthcare events.
Diagnostic analytics goes a little deeper to identify trends and explain them.
Predictive analytics uses past and current data to forecast future events. As such, predictive analytics in healthcare is medicine’s attempt at a crystal ball.
Prescriptive analytics is the final stage. By suggesting actions in response to the predictions made, this analytics process seeks to find a strategy. When done well, it is key to driving informed and data-driven decision making.

Big data in healthcare

By definition, the analysis of any kind of data requires, first and foremost, vast amounts of data. Enter big data, which refers to datasets too large for traditional analytics methods or tools. Big data is often used in the study of human behaviour or interactions, making it the perfect foundation for healthcare data analytics.

The sheer size of these datasets means leveraging big data in healthcare is dependent on advanced technologies like distributed computing, cloud infrastructure and specialized software. Machine learning and data visualization can supplement statistical discoveries and help human operators derive valuable insights.

Applications of big data in healthcare

The potential of big data in healthcare is unlimited, but there are six obvious applications in the healthcare sector:

Early disease detection: By anticipating health deterioration through predictive analytics, medical professionals can intervene proactively.
Faster and more accurate diagnostics: Analysing medical data can lead to quicker treatment decisions and better patient care.
Personalized medicine: Medical data analytics personalizes treatment by considering an individual’s genetic makeup, lifestyle and environmental factors.
Improved operational decisions: By analysing operational data, healthcare organizations can better optimize patient flow, staffing levels and resource allocation within hospitals.
Faster drug development: Healthcare data analytics can help to predict drug interactions and streamline clinical trials.
Oncology research: The benefits of data analytics in cancer research include the accelerated discovery of new treatments and a deeper understanding of cancer biology.
Procurement and supply chain optimization: Simply put, the analysis of supply chain data enables providers to predict demand, optimize inventory levels and reduce waste.

Precision and personalized medicine

Healthcare data analytics plays a pivotal role in advancing precision medicine, a paradigm shift in healthcare that aims to tailor medical treatment and interventions to the individual characteristics of each patient. Running personal patient information like genomic information, clinical records and lifestyle factors through machine learning and other analytics tools can help design treatment strategies that meet the unique needs and makeup of individuals.

Data analytics for precision medicine holds the promise of maximizing efficacy while minimizing adverse effects, ultimately improving patient outcomes.

Challenges of big data in healthcare

None of the above is possible without big data, but it can be difficult to collect and use big data for a variety of reasons:

Data privacy and security: The growing storage and use of health data has already made hospitals and practitioners the targets of cybercriminals and hackers. It is the responsibility of healthcare providers to safeguard this sensitive information by improving their cybersecurity protocols and de-identifying aggregated health data, for example.
Data quality and accuracy: Healthcare data is collected from a number of systems and stored for different purposes. As such, it is diverse. This diversity can result in data silos and inconsistencies, making it difficult to integrate and analyse the datasets. It can also lead to inconsistencies in the accuracy and completeness of datasets.
Unstructured data: This is especially relevant to organizations and providers undergoing the transition to digital records from traditional methods. Paper notes, charts and records can be referred to as unstructured data, and can be very difficult to include into datasets without sophisticated techniques for text mining, natural language processing and image recognition.
Interoperability: Providers are likely to be at different stages of the data analytics adoption process, so collaboration can be tricky. Even if both partners are fully digital, the varying technical infrastructures, security concerns, legal complexities and differing priorities can hinder data exchanges.

Addressing these challenges is no small feat. It will require a commitment from healthcare providers to break down data silos, and the development of tech solutions that not only enhance interoperability but also guarantee the data’s integrity and security.

Promoting interoperability and security

In this context, the safe and reliable development of healthcare data analytics depends on the seamless exchange of data between patients, providers and third parties. International Standards for healthcare data analytics play a crucial role in achieving interoperability across global healthcare systems by providing a common language, shared objectives and monitoring tools.

For healthcare organizations, implementing standards like ISO/HL7 27931 is an effective approach to regulating, managing and handling sensitive data. These steps are crucial for healthcare providers aiming to align with international best practices in data management and patient information security. When implemented effectively, this standard ensures health practitioners have access to information that is accurate, valid, reliable, timely, relevant, legible and complete.

ISO/HL7 27931:2009Data Exchange Standards — An application protocol for electronic data exchange in healthcare environments
ISO/TS 24289:2021Hierarchical file structure specification for secondary storage of health-related information

The future of data-driven healthcare

The potential impact of healthcare data analytics on patient care is monumental, driving a healthcare revolution that is more proactive, personalized and efficient. Innovations such as predictive analytics in healthcare, precision medicine, enhanced disease research and improved drug development – which all stem from big data analytics – would ultimately contribute to a healthier world for all.

While it is already essential to the industry, the importance of healthcare data analytics is set to grow in years to come. Developing this field safely, responsibly and effectively is therefore crucial, but it must be a collective endeavour by all actors in the healthcare space. International Standards can provide the foundation for the seamless, safe and private exchange of data to ensure that this new era of healthcare does not compromise on the very essence of patient care – trust.