AI in health care: opportunities, threats
The application of AI represents a promising example of leveraging technology to improve efficiency, accuracy and compliance in health care. As AI continues to evolve, we can expect to see further innovations in health care.
The most viable example of using AI in health care is the application of natural language processing, or NLP, and machine learning algorithms to improve clinical documentation and coding processes. Traditionally, clinical documentation and coding involve manually transcribing patient entries into electronic health records and assigning appropriate diagnosis and procedure codes for billing and insurance reimbursement processes.
It can be very time-consuming, error-prone and subject to variability among health care providers.
To address these challenges, providers are not only investing in digital transformation but also increasingly adopting AI-driven solutions to automate clinical documentation and coding tasks. These solutions use NLP to extract relevant information from unstructured and even handwritten clinical notes, such as patient histories, physical examinations and treatment plans. Then, machine learning algorithms help analyze data to suggest appropriate diagnosis codes, procedure codes and billing information.
AI algorithms can accurately identify and extract relevant clinical information from unstructured text, reducing errors and inconsistencies in clinical documentation and coding.
Hence, by automating clinical documentation and coding processes, AI-powered solutions can significantly improve accuracy and efficiency. It will also help streamline the workflow of various stakeholders.
By automating manual documentation and coding tasks, AI solutions can free up health care workers’ time to focus on patient care activities and reduce administrative burdens.
AI-powered solutions can help providers automate their processes and optimize revenue cycle management by ensuring accurate and timely billing, reducing claim denials and maximizing reimbursement for services rendered.
It also helps them with regulatory compliance by automatically matching up with coding standards and various documentation guidelines.
That is why health care providers are ambitiously putting their dollars into AI solutions.
Even if there are many advantages to integrating AI into health care practice, it also brings several negative consequences and challenges.
Addressing these challenges requires collaboration among stakeholders, including health care providers, developers, policymakers and, most importantly, patients.
Governments must also establish ethical standards and regulatory frameworks for the responsible use of AI in health care to protect patients. Transparent and accountable AI systems, robust data protection measures and ongoing monitoring and governance are essential to mitigate risks and ensure the safe and effective use of AI in health care.
Particularly, a lack of transparency can undermine trust in AI-powered health care solutions among patients and health care professionals. It is yet quite challenging to expect accountability when AI systems make numerous errors and produce unexpected outcomes. Regulators must outsmart AI systems.
However, the rapid advancement of AI technology in health care has consistently outpaced the speed of development of regulatory frameworks and ethical guidelines.
There are still many ethical dilemmas around patient consent, data ownership, transparency of algorithms and the responsible deployment of AI in critical clinical decision-making. Clear and comprehensive regulations and ethical guidelines are much needed to ensure the responsible and ethical use of AI in the medical field.
AI algorithms could inadvertently perpetuate or amplify biases present in the data used to train them. It could lead to disparities in health care outcomes based on factors such as race, ethnicity, gender or socio-economic status.
Biased AI systems can exacerbate existing inequalities in health care and lead to unjust treatment of certain patient populations.
AI algorithms, like any technology, are not infallible and can make errors, leading to misdiagnosis or incorrect treatment recommendations. Relying too much on AI-driven diagnosis without human intervention can result in harm to patients and undermine trust in AI-powered health care solutions. AI systems rely on large amounts of sensitive patient data, raising concerns about privacy breaches, unauthorized access and data misuse. Inadequate data protection measures and cybersecurity vulnerabilities can, therefore, pose a significant risk to patients.
AI algorithms may operate as dashcams to deal with these issues. However, it would be even more difficult to understand how AI engines arrive at their decisions. If these risks such as bias, misdiagnosis, privacy risks and lack of transparency are well taken care of with further advancement of technology and governance, AI would certainly fill the gap in health care.
Governments must establish ethical standards and
regulatory frameworks for the responsible use of AI in health care.