Data as medicine tech revolutionises healthcare insights

GenAI can help the healthcare sector enhance data quality, enabling real-time analysis and generating synthetic data for improved patient care

Data, the new oil of the digital age, is not just a resource but a powerful tool that provides insights into customer behaviour, market trends, and performance. It empowers organisations to make informed decisions, discover new opportunities, and enhance their processes. High-quality data is the key to unlocking potential and gaining a competitive edge in this digital era.

Modern digital tools, powered by data, unlock immense value. Cloud computing provides the necessary storage and processing power, while advanced analytics techniques, including artificial intelligence and machine learning, reveal patterns and insights. Data visualisation simplifies complex information into easy-to-understand visuals. These technologies empower businesses, inspire them to make data-driven decisions and gain a competitive edge.

Despite technological advancements in data collection and storage, data quality remains a significant issue. Inconsistent formats, missing information, and errors make data unreliable and difficult to analyse. Data is often stored in disparate locations, complicating the understanding and integration process. This hinders organisations from extracting valuable insights and making informed decisions.

Generative AI (GenAI), with its model capabilities of large language models (LLM), is a pivotal solution to many of these data quality challenges. Organisations can fully exploit their data potential by improving data accuracy and usability. GenAI is a significant advancement in machine learning, enabling systems to analyse and generate data. Its adoption is not just a choice but a necessity, especially for industries like healthcare, which face numerous key challenges. GenAI is the linchpin to unlocking the full potential of data in healthcare and beyond.

The difficulty of real-time data processing makes it challenging to update records across multiple systems and analyse data to make patient care decisions.

#1

Data Quality Challenges

Inconsistent patient records and manual data entry errors are not just data quality issues but significant challenges that can have life-threatening consequences. For instance, a patient’s allergy information might be missing in one hospital’s system but present in another, leading to dangerous medication errors. The healthcare industry, in particular, faces significant challenges due to inconsistent and incorrect patient data, such as misspelt names, different date formats, and incomplete medical histories. The urgency for a solution to these challenges cannot be overstated.

GenAI technology, particularly using LLMs, offers several powerful capabilities to address these challenges. Here are some strategies to implement in this space.

•  Automated data cleaning: GenAI can automatically detect and correct dataset errors. This includes fixing typos, filling in missing values, and standardising data formats, ensuring higher data quality.

• Advanced data integration: LLMs can merge data from different sources by understanding the context and structure of diverse datasets. This facilitates the creation of a unified view of the data, even when it comes from various formats and systems.

•  Enhanced data standardisation: These models can enforce consistent standards across all data entries. They can automatically convert different units of measurement, formats, and terminologies into a single, standardised format.

• Data enrichment: GenAI can augment existing data by generating additional context or filling in missing information. For instance, it can infer missing demographic details from existing customer profiles, enriching the dataset.

•  Processing unstructured data: LLMs excel at processing and organising unstructured data, such as text and images. They can extract critical information from emails, documents, and social media posts and transform it into structured data for analysis.

•  Semantic understanding: These models can understand the meaning and relationships within data. This allows for more sophisticated data queries and insights, as the AI can interpret the nuances and context of the data.

With robust privacy measures and the integration of Explainable AI, the sector can ensure that AI-generated insights are secure and trustworthy.

#2

Real-Time Data Processing Challenges

Healthcare providers struggle to process and analyse real-time patient monitoring data from ICUs and wearable devices. This difficulty in real-time processing makes it challenging to immediately update patient records across multiple systems and analyse data for immediate patient care decisions. Delayed processing of this data could lead to missing early warning signs of patient deterioration.

GenAI technologies offer several capabilities, and it is important to follow a strategic approach to address the inherent challenges.

• Real-time data integration and normalisation: GenAI can integrate and normalise data from various sources in real-time. It quickly processes and standardises data from different devices and systems, ensuring consistency across all platforms. Learning patterns and structures of data from various sources can help harmonise data formats on the fly.

• Pattern recognition and prediction: GenAI excels at recognising patterns and making predictions. It analyses real-time data streams to identify subtle patterns. The GenAI generates predictive models that continuously update based on incoming data.

• Automated decision support: GenAI provides real-time decision support to healthcare providers. It analyses incoming patient data, compares it with vast medical knowledge, and generates suggestions for immediate care decisions.

#3

Scarce Real Data Challenges

Healthcare institutions often lack access to comprehensive historical data, particularly for rare diseases and new health conditions like COVID-19. This scarcity limits the data available for clinical trials, long-term health studies, and effective treatment development. For instance, there may be insufficient data on the long-term effects of new treatments, making it hard to assess their efficacy and safety. Healthcare organisations can adopt multiple strategies to address this challenge.

• Synthetic data generation: GenAI can create synthetic data that mirrors real-world data patterns without exposing actual patient information. This synthetic data can supplement limited datasets, providing researchers with more comprehensive information for clinical trials and studies.

• Data augmentation: GenAI can augment existing datasets by creating variations and expansions of current data points. By adding realistic variations to existing data, GenAI can expand the dataset, providing more scenarios and outcomes to analyse. This helps better understand and predict treatments’ long-term effects, even with limited initial data.

• Predictive modelling and simulations: GenAI can build predictive models and run simulations to predict outcomes based on limited data. These models can simulate various treatment scenarios and predict long-term effects, helping assess new treatments’ efficacy and safety. This is particularly useful when real-world data is scarce.

GenAI is a powerful technology for addressing critical data challenges across various industries, from data quality and processing real-time information to overcoming data scarcity in niche areas. With robust privacy measures and the integration of Explainable AI, the sector can ensure that AI-generated insights are secure and trustworthy, paving the way for more effective, data-driven decision-making in any industry.

Healthcare institutions often lack access to comprehensive historical data, particularly for rare diseases and new health conditions like COVID-19.

It is time for the healthcare sector to embrace GenAI confidently, drive innovation, improve efficiency, and unlock new opportunities in their data-driven patient-care journey. 

By Naveen Krishnamoorthy

The author is the Director of Engineering Management at Ascendion.

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