Annotation Services

Unleashing the Power of Data Annotation in Electronic Health Record Management

In today’s era of digital healthcare, electronic health record (EHRs) management is of paramount importance. EHRs contain a wealth of valuable information, ranging from patient demographics to clinical notes, test results and treatment histories. However, the sheer volume and complexity of EHR data pose significant challenges for healthcare providers and researchers who are constantly seeking to extract meaningful insights from the data. This is where data annotation fits right in.

Data annotation involves the informative and meaningful labelling or tagging of raw data to make it understandable and usable by machines. Given the size of the data, one of the key challenges in EHR management is the need for accurate and consistent data annotation. In the context of EHRs, data annotation services perform a vital part in making sure that EHR data is properly structured, categorised and interpreted.

The role of data annotation services

Data annotation services use advanced technologies such as machine learning (ML) and natural language processing (NLP) to annotate EHR data efficiently and accurately. It involves extracting and encoding medical information such as concepts, events, entities and relationships in image, video, text and audio files. All of this structured data is then utilised to create data solutions that guide clinical decision-making.

Data annotation services help healthcare providers and researchers realise the complete capabilities of their EHRs by:

• Boosting data quality: High-quality data is of utmost importance in EHR management. Data annotation services ensure that EHR data is accurately labelled and categorised, enhancing its overall quality and reliability. Unannotated data can be incomplete, irrelevant or misleading, all of which can impact patient care negatively.
• Enhancing data accessibility: By annotating EHR data, healthcare providers can easily search, retrieve and analyse relevant information, thereby improving decision-making and patient care. Data annotation simplifies data and makes it manageable and easier to comprehend. This feature ensures that diverse and complex data can be shared seamlessly across various relevant systems.
• Facilitating research: Data annotation services enable researchers to quickly identify and extract valuable insights from EHR data, accelerating the pace of medical research and innovation. Annotated data can help AI models perform well with diagnostics, recognise anomalies and identify disease trends and patterns.
• Supporting machine learning models: Annotated EHR data is essential for training and validating ML models used in healthcare, such as predictive analytics and clinical decision support systems.
• Upgrading patient care: Annotated EHRs can help healthcare providers deliver better care because of the availability of every patient’s unique health history, lifestyle habits and genetic predisposition. The information also makes it possible to practise patient care with a proactive approach.  
• Ensuring regulatory compliance: EHRs clearly contain sensitive information that must be protected and managed in compliance with healthcare regulations. Accurate data annotation ensures that patient data is correctly handled and stored.

Globally, the data annotation tools market in the healthcare sector was valued at USD 129.9 million in 2022. It is expected to expand at a CAGR of 27.5 per cent by 2030. AI, ML, the Internet of Things (IoT), Robotic Process Automation (RPA), and other technological advances are driving the market and encouraging healthcare companies to work with medical data annotation companies to enhance their models' performance.

Applications of data annotation tools

The areas that benefit the most from data annotation tools include:

Digital radiology: X-rays, MRIs, CAT scans and other radiological images are scanned for early detection of anomalies such as cardiovascular abnormalities, neurological conditions, detection of some common cancers, thoracic complications and musculoskeletal problems.

Drug development: High-quality annotated data can help accelerate the development of certain drugs.

Robotic procedures: In both robotic surgery and endoscopy, AI algorithms look at large datasets of relevant images and help detect and classify any existing abnormalities. Robotic systems guide instruments through the patient’s body for accurate diagnosis.

Virtual assistants: Data annotation experts provide organisations with structured datasets needed to train conversational AI. The algorithms empower virtual assistants to help patients with medication reminders, monitor status, identify diseases, schedule appointments and much more.

Implementing data annotation in EHR management

To leverage the power of data annotation in EHR management, healthcare providers and organisations can follow a few key steps:

1. Define annotation goals: Clearly define the objectives of data annotation, such as boosting data quality and searchability, or supporting research initiatives.
2. Select appropriate annotation tools: Choose annotation tools that are suitable for the specific types of EHR data being annotated, such as text, images, video or structured data.
3. Establish annotation guidelines: Create precise and thorough annotation guidelines to maintain uniformity and precision during the labelling procedure.
4. Train annotators: Provide training to annotators to familiarise them with the annotation guidelines and ensure that they can accurately label EHR data. Experience, expertise, the right skillset as well as continuous monitoring are all essential for accurate data annotation. 
5. Iterate and improve: Continuously review and refine the annotation process based on feedback and insights gained from the annotated data.

Shaping the future of EHR management

Data annotation is clearly a powerful tool for unlocking the full potential of EHRs in healthcare. As the field of healthcare continues to evolve, data annotation will play an increasingly important role in shaping the future of EHR management and healthcare delivery. Its potential cannot be ignored.

How can Infosys BPM help?

The Data Annotation Services of Infosys BPM help client teams build training data, at scale, for AI. We use a platform along with a human-in-the-loop service model, which harnesses the power of automation (software) and the power of human intelligence to produce high-quality training datasets. Such datasets can then be used for AI evaluation and training.