Navigating the Data Deluge: How AI is Building Secure & Seamless Healthcare Data Pipelines

Healthcare data is experiencing unprecedented growth because of new sources of information like Electronic Medical Records (EMRs), digitized paper records, wearable data, and telemedicine records. While this data holds great promise, the massive amount of information and its fragmentation pose serious obstacles to optimizing its maximum potential to improve patient care. The healthcare industry must effectively access, share, and synthesize the data to improve patient care.

This is where Artificial Intelligence steps in with its ability to build high-quality healthcare data pipelines that can unlock personalized medicine with more effective and targeted treatments.

From Paper Trail to Digital Deluge

Before the digital age, paper records lacked uniformity and hindered information sharing between doctors and healthcare organizations. Enter Electronic Medical Records (EMRs), which helped organize patient information for quicker access. However, EMRs still had similar challenges because of a lack of standardization and incompatible systems that didn’t share information, a central issue known as the lack of interoperability. Even within hospitals, you could find multiple EMR systems causing problems like incomplete health information and adding training issues.

The Data Explosion

Today, the challenge is magnified because the data is growing exponentially. Healthcare data accounted for 30 percent of the global data volume in 2018; it is expected to increase by 36 percent annually through 2025, outpacing other leading sectors in data generation: manufacturing, financial services, and media & entertainment.

Privacy and Security

Federal regulatory requirements, such as HIPAA, pose another roadblock to easy access to medical records. As the amount of personal health information increases and becomes digitalized, the industry must balance security, privacy, and accessibility to improve healthcare.

AI to the Rescue

Artificial Intelligence and its subsets, Machine Learning (ML) and Natural Language Processing (NLP), can revolutionize healthcare data pipelines if it receives high-quality data:

• AI and Machine Learning tools can improve data accuracy by identifying inconsistencies, filling in missing information, and standardizing the formats.

• Natural Language Processing models can read and digitize hospital records, including advanced autocorrect systems for medical terms and auto-populate for patient information.

The Brighter Future

These advancements pave the way for earlier disease detection, personalized treatment plans, and improved patient outcomes. AI algorithms can detect patterns to predict disease, while computer vision, also part of the AI family, can find anomalies in medical images sooner than human medical specialists. Machine Learning, NLP, and Computer Vision can significantly enhance medical analysis and usher in a new era of personalized medicine, resulting in:

• Early detection, which can prevent diseases from reaching more critical stages.

• Lower healthcare costs because early treatment tends to be less expensive than treating advanced stages.

• Improved survival rates because of early detection.

• Personalized treatment plans because AI algorithms can analyze a patient’s medical history and genetic markers to determine a drug's effectiveness and potential side effects.

• Improved diagnoses, lowering the risk of misdiagnoses because it can detect anomalies that doctors could miss.

• Increased effectiveness in clinical trials because AI can analyze big datasets, which could lead to fast development of drugs

The healthcare industry is entering a brave new world dominated by Artificial Intelligence. The possibilities can save lives and vastly improve the quality of life. However, collaboration between AI and healthcare professionals is paramount. AI will only provide high-quality results if it receives high-quality data. AI can’t be left unattended.

Healthcare companies must develop data governance guidelines to ensure the privacy and quality of the information. The industry needs to guard against biased results, especially regarding race and gender. AI and human expertise must work in tandem to make sure the input will provide you with the best output.