AI has permeated almost every aspect of our lives; smart driving cars, digital assistants, and connected homes have increased in popularity and come a long way from the early days. For example, when Apple launched Siri, the digital assistant became the source of many jokes due it misunderstanding voice commands. Such interfaces have vastly improved today, with many digital assistants easily understanding our requests.

AI has become central to workplaces as well; a study by KPMG and the University of Queensland found that 40% of Australians are comfortable using AI at work. So, it makes sense that AI in healthcare would become a reality too. 

However, when dealing with a topic as sensitive as healthcare data, where errors or misunderstandings create adverse effects on patients, we need to be careful with the type of data leveraged. The data we use to train AI models is as important as the algorithms. This data’s quality, accuracy, and comprehensiveness can profoundly impact the outcomes AI produces. To ensure AI’s safe and effective application in healthcare, we must scrutinise and optimise the quality of the data we leverage.

So, why is high-quality data crucial for unlocking AI’s potential in healthcare?

The costs of low-quality data in AI models

In AI, the saying ‘garbage in, garbage out’ indicates that flawed input data means we cannot trust the output data. Whether incomplete, inaccurate or irrelevant, low-quality data introduces noise and error into the system, negatively impacting the AI’s ability to make precise predictions or recommendations.

Faulty AI-driven diagnoses or predictions due to low-quality data could lead to patient harm, unnecessary treatments, and poor resource allocation. Beyond direct patient impacts, data quality issues can give rise to significant bias in predictions. For example, if data improperly represents specific demographics, the AI model can disadvantage these groups.

Correcting and retraining AI models built on low-quality data comes with substantial monetary and time costs. The process involves identifying the source of the issue, rectifying the data quality problem, and then retraining the AI model. In a sector like healthcare, where swift decision-making can be critical, these delays are costly and can impact care delivery.

Strategies for improving data quality for AI in healthcare

We need reliable and accurate data sources to unlock AI’s potential in healthcare. Data needs to come from trustworthy sources using a diverse and comprehensive dataset. The dataset should encompass a broad spectrum of patient demographics and health conditions to prevent any inherent bias. 

Additionally, it’s beneficial to establish a regular auditing process to verify data integrity over time. By prioritising these strategies, we can substantially improve data quality, making AI in healthcare more effective and reliable.

Collecting healthcare data

Disconnected systems alone aren’t sufficient for educating AI models effectively. Siloed data limits the scope and diversity of the information available for the AI to learn from. As a result, the AI might miss patterns that it otherwise would have identified. Therefore, an integrated approach to data collection is crucial to leverage the full potential of AI in healthcare.

For AI models to be optimally effective, they require data from many sources, such as electronic medical records, imaging data, laboratory results, and even information from wearable health devices. Each data source provides a different perspective on patient health, creating a multifaceted view that enables AI models to generate more accurate and comprehensive insights.

Preprocessing data for AI

Preprocessing includes data cleaning, normalisation, and transformation. Data cleaning involves handling missing values, resolving inconsistencies, and removing outliers or irrelevant information. Normalisation is the process of standardising data, ensuring that no particular feature disproportionately influences the model. Transformation converts categorical data into a format that the AI model understands.

By ensuring that the data fed into the model is clean, standardised, and appropriately formatted, preprocessing mitigates the risk of providing low-quality or misleading data to the AI model. So, preprocessing enhances the model’s ability to learn effectively and generate accurate predictions or recommendations.

Quality control standards

Quality control is another element that we cannot overlook when leveraging AI in healthcare. Given the high stakes involved in healthcare decisions, it’s paramount that the data driving these decisions is of the highest possible quality. Quality control standards enforce this, ensuring the data’s accuracy and reliability and fostering trust in the insights or recommendations generated by AI in healthcare.

Maintaining data quality involves standard practices such as data validation. This involves cross-checking data against set criteria or a trusted source to verify its accuracy and consistency. 

Regulatory bodies will be critical in setting and enforcing these quality control standards. Their oversight ensures uniformity in data quality across different AI applications in healthcare, making it possible to compare and evaluate models objectively. Additionally, we will need these regulatory bodies to ensure that the data used does not violate patient privacy.

Once we have these in place, what benefits will AI bring?

AI can considerably enhance predictive analytics and disease diagnostics in healthcare. By learning from vast and diverse datasets, AI models can identify patterns and make predictions that may be beyond the capabilities of traditional methods. For example, practitioners can diagnose diseases at earlier stages and start preventative measures or treatment planning earlier.

Beyond diagnostics and predictions, AI has the potential to personalise healthcare delivery and improve the patient experience. By analysing individual health data, AI can help tailor treatments to each patient’s unique needs and circumstances. For example, AI might suggest medication dosages, tailored lifestyle advice, or predictive health monitoring that allows people to manage health conditions proactively. This personalised approach leads to more effective care, improving health outcomes and patients’ engagement with their healthcare.


High-quality data is the key to unlocking AI’s potential in healthcare. The data provided to AI models will influence the accuracy and reliability of AI in healthcare, from sourcing and collecting data to preprocessing and quality control. Low-quality data can lead to poor output and high costs for healthcare organisations and patients, while high-quality data can enhance predictive analytics and personalise healthcare delivery.

Fluffy Spider Technologies bridges the gaps in healthcare data

Our goal is to help companies build a future of connected digital healthcare by making existing systems interoperable and modernising infrastructure to unlock the potential of new technologies.

We can collaborate with you to identify relevant opportunities using modern web services and standards for health information exchange, like HL7 and FHIR (Fast Healthcare Interoperability Resources). We enable interoperability with the technology used by the medical software industry, including those used by large healthcare providers such as Government health departments.

You can visit our Healthcare Integration Services page for more information.

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