Artificial Intelligence (AI) in healthcare has brought transformative changes to the way clinicians, patients, and institutions approach healthcare services. From enhancing and customizing patient care to enabling advancements in precision medicines, streamlining diagnostics, and simplifying and digitizing health records, AI has redefined the healthcare industry in more ways than one.
Nevertheless, a critical issue remains – AI bias in EHR, which, if not addressed at the right time, would result in a compromise of fairness, equity, and the overall effectiveness of healthcare systems.
When AI systems generate systematically prejudiced results because of erroneous assumptions in the machine learning process, it is called AI bias. This can be a result of various factors like flawed algorithms, human prejudices embedded in the data collection and labelling process, and skewed training data.
As AI is increasingly being used to analyze Electronic Health Records (EHRs) for diagnosis support, risk prediction, and treatment recommendations, bias in it can lead to many complications like misdiagnosis, inappropriate treatment procedures, and unequal access to healthcare services which could disproportionately affect marginalized and under-represented groups.
In this post, we shall look at the different types of AI bias in EHR and how to mitigate them:
Why AI Models Show Bias in Clinical Decision Making
Though we can call artificial intelligence (AI) self-evolving, its evolution largely depends on the data it has access to. Hence, we can say that any AI system is only as good as the data it is trained on. Since this data and the AI systems are man-made, they can fall victim to the following biases:
- Training Data Bias: AI models are trained on large volumes of data, and if this data is unrepresentative or contains historical bias, the AI system is very likely to reflect or even amplify this bias.
- Data Labelling Bias: It takes human judgement to label data for AI training, and subjective biases (personal opinions, cultural perspectives, or unintentional assumptions) of the trainer can creep into these datasets.
- Algorithmic Bias: Even with the right datasets, the AI system may produce undesirable results because of the design of the algorithm. Some algorithms may prioritize certain features in ways that are disadvantageous for specific groups.
- Socioeconomic and Environmental Bias: AI systems may not factor in broader social factors like income, education and environment, leading to incomplete or biased assessments.
- Automation Bias: This happens due to over-reliance or blind trust on automated systems, even when the results are questionable. It leads users to accept AI decisions without questioning their viability, especially in high-pressure environments.
- Inclusive Algorithm Design: Begin by developing an all-inclusive AI algorithm by involving healthcare professionals (doctors, nurses, and medical coders), data scientists, ethicists, and representatives from various communities during the designing and testing phase. Also conduct a bias impact assessment during the development stage.
- Diverse and Representative Datasets: Ensure that the training material includes data from diverse sources representing different ages, genders, ethnicities, and socioeconomic backgrounds. Also make sure to continuously update datasets to reflect current and diverse populations.
- Explainable AI in EHR analytics: Always rely on models that can provide clear explanations for their decisions. Conduct regular AI performance audits and make corrections whenever and wherever possible.
- Regulatory Oversight: Ethical development and deployment of AI in healthcare needs to follow strict regulations and standards. Adhere to frameworks like the FDA’s guidelines for AI/ML-based medical devices, while also establishing standards for data privacy, security, and ethical use.
- Continuous Monitoring and Feedback: Systematic monitoring of AI systems and incorporating feedback helps in identifying and correcting biases over time. Make sure to collect feedback from end users to identify and fix problems promptly.
Example: A medical diagnostic AI system trained predominantly on data from a specific demographic like middle-aged whites may show poor results while diagnosing patients from other demographics (say Africans or Asians), which would lead to misdiagnosis or overlooked symptoms or conditions.
Example: If annotators label training data with symptoms or outcomes based on personal biases or stereotypes, the EHR system is likely to project these prejudices and would affect the AI decision-making in healthcare.
Example: An AI system designed to give weightage to cost-saving measures may undervalue treatments that are more effective for minority populations due to systemic economic disparities affecting those groups.
Example: An AI system analyzing the health risks may not consider environmental pollution levels affecting low-income neighborhoods, resulting in underestimation of certain health risks for residents in those areas.
Example: Doctors or underwriters relying on AI recommendations without adequate verification can propagate errors.
Best Practices to Mitigate AI Bias in EHR
The following steps would help in avoiding AI bias in EHR:
Precision healthcare requires focus on personalizing treatments based on data collected from EHRs on a patient’s genetic, environmental, and lifestyle factors. AI has the capabilities to improve personal healthcare by identifying patterns and predicting outcomes with greater accuracy. However, biases in AI models can diminish these advantages, resulting in less effective or even harmful outcomes.
Transform your digital healthcare services by partnering with experts like DeepKnit AI, that can help you establish ethical and regulated practice models. So, if you’re thinking of taking the plunge into the future of unbiased automated healthcare, feel free to consult with us.
Protect Your AI Automation from Bias. Step into Ethical Digital Future.