Healthcare Quality Management: Conversing with ChatGPT about the Role of AI

As I am attending the Quality Management Conference as part of #ArabHealth2023, there are discussions about data-driven decision-making in healthcare. The amount of data and the growth in the volume of data available for healthcare leaders is huge and growing exponentially. As a result, artificial intelligence (AI) is the next part of that discussion as a tool to analyze the data that is too big for human analysts to assess effectively and timely.

As someone active in healthcare operations and human resource management, this conversation has made me very curious about the topic of #ChatGPT and other artificial intelligence tools being used to analyze and summarize big data.

So, I queried ChatGPT to ask it about artificial intelligence and its value to healthcare organizations managing big data. Here is the response.

Artificial intelligence (AI) can help healthcare organizations to manage and extract meaningful insights from big data in several ways:

1. Predictive modeling: AI algorithms can analyze large amounts of healthcare data to predict patient outcomes, identify high-risk patients, and support personalized treatment plans.

2. Diagnostic imaging: AI algorithms can analyze medical images such as X-rays, CT scans, and MRI to assist with diagnosis and treatment planning.

3. Natural Language Processing (NLP): AI algorithms can be used to extract and categorize information from electronic health records, improving data quality and reducing the time required to access and analyze data.

4. Fraud detection: AI algorithms can help healthcare organizations detect fraudulent activities in real-time, by analyzing large amounts of claims data.

5. Clinical trial optimization: AI can help with the design and execution of clinical trials by predicting patient response to treatments, reducing trial duration and costs.

Overall, AI can significantly enhance the ability of healthcare organizations to process and analyze large amounts of data, leading to improved patient outcomes and reduced costs.

Clearly, we need the judgment, management, and leadership from human beings to lead these efforts, interpret the AI output, and train the AI for future use and refinement.

The next question that arose for me was, can we trust the results from AI algorithms? Of course, as you could imagine, I asked ChatGPT.

Trust in the results from AI algorithms can depend on several factors, including:

1. Data quality: The accuracy of AI algorithms depends on the quality and representativeness of the data they are trained on. Poor quality data can lead to biased or inaccurate results.

2. Model transparency: The lack of transparency in some AI algorithms can make it difficult to understand why a particular result was generated, making it challenging to assess the accuracy and trustworthiness of the output.

3. Bias: AI algorithms can perpetuate existing biases in the data they are trained on, leading to discriminatory or unfair results.

4. Model validation: It's important to validate AI algorithms with independent data sets and to assess their performance regularly to ensure they are still producing accurate and trustworthy results.

5. Human oversight: AI algorithms should be designed and used in conjunction with human expertise, to provide a second check on results and ensure that their limitations are understood.

By taking these factors into account, it is possible to increase the trustworthiness of AI algorithms and ensure that their results are used responsibly and ethically.

My next question was what are the risks associated with the use of AI in healthcare. and ChatGPT told me:

There are several risks associated with the use of AI in healthcare, including:

1. Bias: AI algorithms can perpetuate existing biases in the data they are trained on, leading to discriminatory or unfair results.

2. Data privacy: The use of large amounts of sensitive personal health information (PHI) in AI algorithms raises concerns about data privacy and security.

3. Model transparency: The lack of transparency in some AI algorithms can make it difficult to understand why a particular result was generated, potentially leading to incorrect diagnoses or treatment decisions.

4. Job loss: The automation of certain tasks through AI can lead to job loss and economic disruption for healthcare workers.

5. Dependence: Overreliance on AI algorithms in healthcare decision-making can reduce the role of human expertise and lead to decreased critical thinking and diagnostic skills.

6. Misuse: AI algorithms can be misused if they are not designed or used appropriately, leading to incorrect diagnoses or treatment decisions.

It is important to carefully consider these risks and take steps to mitigate them when using AI in healthcare, to ensure that the technology is used responsibly and ethically.

My future work in this area will be to conduct a literature review on these topics to see how they compare to what ChatGPT told me! stay tuned. So, trust but verify!

The critical point to make here is that digital advancement and innovation allow us to expand the power and precision of data analytics. We are able to create remarkable results in patient outcomes, efficiency gains, and other progress in the healthcare space. And our role will matter more significantly (not less) because our judgment, experience, leadership, and ethics will be more essential than before.

Pam Jackson, PhD CPHQ is an organizational and behavioral economist who builds the skills and value of human capital in workplaces around the world. Based in Dubai, UAE, and serving clients in such countries as Kuwait, Oman, Saudi Arabia, and Qatar, Dr. Pam also reaches the USA, UK, and Europe to provide corporate training, coaching, and consulting to improve workplace culture, operations, and quality management, employee experience and to ensure high-performing teams. Contact her team here.