Electronic Health Records (EHR): How to Achieve Healthcare Data Accuracy with Artificial Intelligence

The following is a guest article by Ann Krutsko, Healthcare IT Researcher at ScienceSoft
In healthcare, working with inaccurate patient data is a dangerous game. Electronic Health Records (EHRs) were designed to streamline operations and improve coordination across healthcare systems. And while EHRs have largely benefited the organizations that have adopted them, data accuracy is still a point of concern. Data entry is still fraught with human error. However, innovations in the field in the way of artificial intelligence integrations are making significant strides in enhancing data quality in EHRs. From data validation to real-time alerts, let’s explore how AI is helping to tighten the screws in healthcare data management.
Challenges in Achieving Data Accuracy
Data accuracy in healthcare is literally a matter of life and death. Inaccurate data can put patient health at extreme risk if it leads to misdiagnoses, improper treatment plans, and even fatal medication errors. Yet keeping healthcare data accurate is easier said than done. There are quite a few hurdles that make this a tough task for healthcare systems:

Data Fragmentation – Patient information is often scattered across multiple platforms, hindering the assembly of a complete and accurate medical history.
Human Error – busy healthcare professionals may make mistakes in data entry, diagnosis coding, or interpretation, especially in high-stress environments like emergency rooms.
Lack of Training – Rapid chan...

Source: EMR and HIPAA - March 6, 2024 Category: Information Technology Authors: Guest Author Tags: AI/Machine Learning Analytics/Big Data Health IT Company Healthcare IT AI Integration Ann Krutsko Data Accuracy Data Entry Data Validation EHR Healthcare AI Healthcare Data Real-Time Alerts ScienceSoft Source Type: blogs