The integration of artificial intelligence (AI) in healthcare is accelerating at a tremendous pace. However, the efficient utilization of AI in healthcare largely depends on the quality of the data. According to studies, 80% of healthcare data exists in unstructured formats, making it challenging for AI algorithms or large language models to extract meaningful insights.
The phrase "garbage in, garbage out" aptly describes this situation. To truly harness the capabilities of generative AI in healthcare, it's essential to address and overcome the challenges related to data quality and to maintain clean data.
The unique challenge of preparing healthcare data for AI
When we talk about AI data preparation for healthcare, it's a two-phase process—training the AI models and then implementing these trained models for useful insights. One of the major hurdles in using healthcare data for training AI models is the consistency of data quality and accuracy. Healthcare data from different care settings lacks standardized formats and accuracy, which often results in data misinterpretation or loss of valuable insights.
Challenges with medical data quality
Moreover, medical or lab data usually contains inaccuracies, incomplete information, and lacks validity. These data quality issues can mislead the AI models into perceiving patterns that don't actually exist, which can further lead to inaccurate or misleading results. Therefore, it's crucial to understand and address these pitfalls while preparing data for machine learning models.
A common problem during AI data preparation arises from semi-structured and unstructured data. With 80% of healthcare data existing in unstructured formats, like clinical notes, there is a need to map that raw information to industry standards.
Because of these challenges, it's crucial for healthcare organizations to put in place certain tools or processes for assessing, cleaning, and standardizing their data before utilizing it for AI technologies. Clinical terminology tools that codify clinical notes to industry standards can help improve the data quality going into AI models.
The risks of poor quality data in AI training
The successful integration of AI in healthcare largely depends on the quality of the data. Training AI models with unclean or messy data can lead to several complications such as a decrease in accuracy and the inclusion of bias. Insufficient or overly simplified data related to minority populations can cause bias to be built into the model, which may lead to wrong assumptions and poor recommendations.
Core elements for data quality in AI implementation
Maintaining data quality is crucial when it comes to preparing data for AI models. Six core elements that organizations should focus on when implementing AI tools are accuracy, validity, data integrity, completeness, consistency, and timeliness. By ensuring these qualities, healthcare organizations can prepare their data for AI in an efficient way, with minimal error.
The role of data governance in AI success
A strong data governance process including aligning data and validating codes to an industry standard is essential for maintaining data quality for AI models. It can help in distinguishing between good and bad data.
For example, it's important to verify lab results against appropriate codes to avoid incorrect codes getting into the system. We have found in one data set that the data quality was as low as 30% accurate as it contained invalid codes and incorrect codes for labs. Normalizing and mapping lab data to LOINC can help in consolidating information from multiple sources and authors, thereby ensuring the accuracy of data.
The clinical terminologies used to standardize healthcare data like LOINC, ICD-10, CPT, and SNOMED, release code set updates 600+ times a year. Having a single source of truth for clinical terminology is key to ensure that the data used to train AI models is correct. Continuous assessment of data used for training the model helps in identifying gaps or bias within the data.
The path to effective AI in healthcare
Healthcare data can present challenges due to its complexity, so creating a process where the data is properly assessed and cleaned is crucial. AI's potential in healthcare is vast, but the basics of data quality must not be overlooked as they determine the success of AI platforms. Through effective data governance and normalization practices, healthcare organizations can maximize AI capabilities and ensure the most accurate outputs for the betterment of patient care. Health Language Data Solutions can help ensure your healthcare data is prepared to power your AI tools. Speak to an expert today to help understand your data quality.
