There is a wide range of errors that take can take place in both the clinical and the nonclinical realms of healthcare. There have been a lot of interest in applying Artificial intelligence in these areas to see if it can be leveraged to reduce medical errors. Let’s review these potential opportunities:
Patient identification errors:
This is because of real concern. Every day, hospitals, pharmacies and offices face discrepancies in patient identification. This becomes an even greater issue in the realm of telemedicine. Online identity theft and fake accounts have made it quite challenging for telehealth systems to accurately identify patients as they provide consults to them. AI can play a role in these settings. It can process various sources of data that contribute towards establishing patient identity, such as face recognition, retina scanning, as well as electronic user history and social media profiles, etc. Early pilot products suggest using AI in some of these settings, especially in telemedicine (along with blockchain) can significantly improve patient identification and reduce critical errors.
A lot of research is emerging that shows AI transforming the face of diagnostic medicine. In the realm of pathology and radiology, AI is fueling computer-aided diagnosis and it is propelling the radiogenomics revolution. In the conventional sense, human-based interpretation is being matched by AI/ML driven interpretation, and in some cases exceeded by the latter. This helps in reducing human-based diagnostic errors by automating the process of segmentation, lesion detection, image analysis and interpretation.
AI can streamline the protocoling and other workflow-related processes that can help reduce procedure-related errors in medical practice. This includes issues such as laterality checks, protocol selection, radiation doses, pharmaceutical agent selection, procedure type selection, etc. AI can help in ensuring several quality control checks as well that can also help in reducing procedural errors.
Medical reporting errors:
There is a very high incidence of errors in medical reporting. Radiology and pathology reports can have many errors that can lead to significant issues in medical management. AI-driven bots/systems can streamline the dictation and reporting process so that manual errors are reduced. Feature selection based reporting system, as opposed to completely free text reporting may be useful in terms of reducing errors and when aided by AI, it can be leveraged to generate semiautomate reports to an extent that they limit the scope of making errors and detects them when they occur
Coding & Billing errors:
Similarly, clerical errors such as incorrect coding and indication mismatches leading to billing discrepancy is another drain on the resources. AI can automate the process of coding and billing in a way that no or very limited human involvement is needed while ensuring accurate coding so that no claims are denied and all billing is seamlessly processed. There are a few solutions in the market that are leveraging this technology and it is beginning to show promising results.