As artificial intelligence (AI) usage across all fields of medicine has increased, players across the spectrum, from patients to providers to payors, are using AI to automate their workflows. AI-based models use training data sets to form trends and patterns of behavior that they can then utilize in real-world applications, from analyzing population data to predict patient risk levels to analyzing genetic sequences to determine the likelihood of a patient developing a disease.
AI models in healthcare come with a particular set of challenges. These models are developed using historical data to make decisions and assessments in situations where they receive new patient data and must figure out how to handle it. In the case of medical AI documentation, AI models can automate and make the entire note-taking process more efficient, ensuring that a doctor’s notes from a patient visit are transcribed and stored in EHRs correctly so that the provider can be reimbursed correctly and facilitate a successful patient interaction. However, these models also offer risk: patient data security and privacy in medical AI documentation remains one of the paramount concerns that healthcare stakeholders must address as the AI revolution sweeps through healthcare.
Risks and Considerations
While improving medical documentation is one of the key workflows that AI can automate, it comes with a unique set of risks and considerations that stakeholders must analyze as this technology is implemented across the industry. Given the complexity of modern data sets, there exists the risk of patient leakage, which is when data might spill and be compromised to potential hackers, competitors or patients. This can occur in a number of ways, from human error (accidentally sending information to the wrong person) or cyber attacks. While AI has emerged as a force for good, it has also empowered hackers to devise solutions that can steal sensitive data from healthcare corporations. Data hacking is one of the greatest threats to AI, and hospitals and providers need to ensure that their cybersecurity protocols are up to standard in order to survive.
As AI tools are given more responsibility in a clinical setting, questions will also arise over their accuracy and quality. An increasingly prominent technology is AI scribes, which transcribe a doctor’s notes and enter them into an electronic health record (EHR). Inaccurate note-taking may lead to disputes with payors and providers that could ultimately lead to negative outcomes.
For example, a doctor using a medical scribe might have used the scribe’s functionality to store notes in their EHR. However, if the scribe mistakenly stores or passes along incorrect information, patients could be severely affected, either receiving wrong medications and treatments or even seeing their confidential information fall into the wrong hands. Given that these AI documentation technologies fundamentally exist to facilitate improved patient outcomes, these inaccuracy issues may render them catastrophic and ultimately useless.
Developers must build AI models with safeguards to ensure patient documentation is transcribed and transmitted correctly. As technology continually improves, providers can ensure that there is still a human check over the entire process by reading over notes periodically or manually confirming that EHRs are updated correctly. Furthermore, providers and developers must adhere to protocols and privacy regulations to ensure that data is only being shared with the right parties and is completely desensitized when being used or shared. Adherence to HIPAA regulations combined with fundamentally sound technology solutions will ensure the accuracy and quality of information and protect patient data in medical AI documentation from cyberattacks or hackers.
Patient Comfort
Patients may also feel uncomfortable with their personal information being stored or used by AI for administrative purposes or even machine learning capabilities. Companies and healthcare stakeholders must ensure that AI models do not use confidential PHI to advance their capabilities. Furthermore, technologies that use these AI models must follow all required compliance and security protocols to ensure that data is handled safely and does not fall into the wrong hands.
Providers and doctors must also work with patients to ensure they feel a sense of safety and control over who handles their data. While medical AI documentation aims to improve providers’ lives, the ultimate goal of these technologies is to facilitate improved patient outcomes. If patients are uncomfortable with the sharing and handling of their data, AI technologies must be designed to exclude these patients’ data if necessary. Furthermore, doctors and nurses should discuss these issues with patients to ensure that they understand who is handling their data and the risk of potential leaks.
What's Next?
Ultimately, AI is here to stay, and its applications in the medical field are dramatic and transformative. As the industry embraces AI and its multifaceted applications, it must also ensure that it mitigates its risks. Medical documentation is one of the most obvious use cases for AI; a tedious, human task that can be automated and save physicians hours of administrative work. However, medical AI documentation must address security and privacy challenges to ensure patient comfort as this technology is simultaneously implemented and improved upon. Healthcare is a human industry, and that human element should always be prioritized. We must find a correct balance between AI and doctors in order to allow for a successful patient experience.