How Optical Character Recognition (OCR) Technology Optimizes Operations in the Healthcare Industry

Data extraction from patient records, lab reports, insurance claims, and invoices facilitates accurate and quick diagnosis of diseases and streamlines billing and claims processes. However, shortcomings in the traditional data extraction processes are making healthcare leaders explore and invest in reliable data capture technologies. 

For instance, Hakkoda’s 2024 State of Healthcare Data report, surveying 145 CEOs from large healthcare organizations, uncovers these insights: 

• 11.3% of healthcare organizations achieved their financial goals in 2023, and these organizations report an average of 124% ROI on all types of data investments 
• 51% of respondents report that they will modernize their data stack within 2024

Among the well-known data technologies, Optical Character Recognition (OCR) is extensively utilized by healthcare organizations to achieve a high accuracy rate, optimize efficiency, and reduce costs in data extraction processes. This blog discusses the role of OCR in healthcare, its benefits, and use cases, and guides you in choosing the best OCR solution. 

Understanding OCR in Healthcare

In the healthcare industry, Optical Character Recognition (OCR) technology converts information from patient forms and medical records, doctor’s notes, prescription slips, lab results, etc., into machine-readable texts that computers can edit, search, and process further. It distinguishes characters from the background, recognizes texts, and compares them with databases to extract relevant information.  

OCR in the healthcare industry transcends traditional data extraction methods by overcoming several challenges and streamlining healthcare data operations. 

For instance, some of the challenges that healthcare organizations face while extracting data are: 

• Decreased accuracy: Manual data extraction in healthcare is less accurate, leading to surgical, diagnostic, and medication errors and anomalies. These inaccuracies impact patient care and result in heavy financial losses. For example, this research shows that more than 200,000 deaths occur annually due to preventable medical errors.
• Inefficiencies: Manual data extraction demands human resources and is time-consuming and error-prone, leading to inefficiencies and delays. Healthcare providers may be unable to access the data at the right time, hindering timely diagnosis and patient care. 
• Regulatory and security issues: Extracting data manually may pose security challenges, such as patient data theft and leakages. Also, it can be strenuous for employees to adapt to constantly changing regulations and ensure compliance with multiple guidelines. Non-compliance with state, national and international regulations can lead to legal issues and regulatory fines. 

For instance, a physical therapist who co-owned a chiropractic centre with a Doctor of Chiropractic (DC), violated state patient record regulations and the American Physical Therapy Association’s (APTA) Code of Ethics for the Physical Therapist. Inadequate and inaccurate documentation led to the issue and the Board ordered the physical therapist to pay a civil fine of USD 10,000 and the case took more than 6 years to resolve.

In another case, Local Initiative Health Authority for Los Angeles County (LA care) lacked security measures to protect health data and care members were able to view other members' protected health information. The US Department of Health and Human Services Office for Civil Rights ordered LA Care to settle USD 1.3 million for violating HIPAA compliance.

Implementing OCR in their workflows is one way to solve these potential challenges and eliminate inefficiencies. OCR offers numerous benefits for the healthcare industry, which we’ll discuss in the next section.

Benefits of OCR in Healthcare

Let’s take a look at the benefits of healthcare data processing and converting it into structured and actionable information:

1. Increased operational efficiency

One of the most significant benefits of incorporating OCR is enhanced efficiency and productivity. OCR tools capture and process data within 45-60 seconds. They allow batch processing to extract data simultaneously from multiple documents and save time. 

The time saved allows healthcare providers to redirect their focus toward improving operational efficiency and delivering personalized care. 

For instance, vInnovate’s OCR solution helped Vitality process medical records and extract patient history details swiftly. This automated document processing helped Vitality achieve higher productivity and efficiency.

2. Improved data accuracy

Unlike manual data extraction, OCR extracts data with more than 95% accuracy. AI-powered OCR solutions like Docsumo provide 99% accuracy for healthcare institutions. 

Advanced OCR solutions extract data without human intervention, eliminating potential human errors. The extracted data undergoes validations to detect errors and inconsistencies and ensure the highest levels of accuracy.

3. Cost reduction

Investing in an OCR solution saves costs by automating several repetitive tasks, such as data entry, reducing the need for manual labor. Moreover, accurate data ensures error-free billing and claims processing, reducing potential financial losses for healthcare organizations. 

For example, Access Healthcare automated Explanation of Benefits (EoB) document processing using echopay’s OCR engines. The result? It reduced operational costs by 50% for Access Healthcare.

4. Enhanced patient care

The inaugural AMIA 25×5 Task Force TrendBurden 2024 survey of 1253 healthcare professionals across 49 states and the District of Columbia reveals shocking facts about how excessive documentation impacts patient care: 

• 77.42% of respondents reported finishing work later than expected or working at home due to excessive documentation tasks

• Nearly 74.38% of respondents believe that documentation tasks impede patient care

Incorporating AI-powered OCR technology in healthcare document processing workflows automatically extracts relevant data from patient documents and enters them into other systems for reference. Automating repetitive documentation tasks using OCR streamlines healthcare operations, helping improve treatment outcomes.

For instance, TidalHealth’s clinicians were spending more time extracting relevant drug data despite their investment in various drug informational databases. IBM’s Micromedex streamlined this process, helping clinicians reduce time spent per clinical search to less than 1 minute on average. This AI-powered data extraction improved efficiency and patient care for TidalHealth. 

5. Compliance and security

AI-powered OCR solutions automate data extraction, validation, and compliance audits, ensuring compliance with regulations and laws such as HIPAA, GDPR, The HITECH Act, CCPA, and the 21st Century Cures Act. 

OCR solutions offer standardized security features such as access controls, cloud storage, and encryption to safeguard sensitive patient information against unauthorized access. This prevents fraud and legal issues that may result in hefty fines. 

6. Data accessibility 

OCR solutions provide a unified view of patient data across different systems by capturing data from vital patient and health insurance forms, converting them into digital documents, and storing them in a centralized location. 

This significantly enhances data visibility and accessibility, ensuring every healthcare provider can access the right data. 

7. Improved employee retention

Investing in technologies, such as OCR solutions for data extraction and validation reduces employees’ burden, enabling them to focus on high-value tasks. The result would be a more productive, engaged, and happier workforce. 

8 Common OCR Use Cases in Healthcare

Healthcare facilities generate vast amounts of data daily. Here are the OCR use cases that can be implemented in a variety of ways to manage data more efficiently and effectively:

1. Patient record management

Managing patient records is critical to delivering accurate care, complying with standard regulations, and ensuring security. For example, in Florida, hospitals must retain patient records for nearly seven years before disposing of them. 

The OCR platform collects relevant data such as patient names, addresses, phone numbers, diagnoses, genetic issues, recommended medicines, and treatment protocols from patient forms, reports, and prescriptions. Collected data can be entered into other medical software systems for storage, maintaining patient records, and ensuring compliance. 

2. Medical billing

Medical billing is a challenging and tedious process that demands accuracy and knowledge about insurance policies and medical codes. You can train the OCR model to extract data from patient documents and create detailed records of patient's demographics, disease diagnoses, prescribed medicines, and procedures performed for billing and medical coding purposes. 

Accurate and swift bill data extraction using an OCR solution optimizes the medical billing process. 

3. Insurance claim processing

Health insurance agencies process health insurance forms at scale daily, and OCR can swiftly extract accurate data such as: 

• Patient details: Patient name, address, date of birth, insurance policy number, and gender details
• Provider details: Healthcare provider's name, qualification, hospital location, specialization, and treatment facilities
• Industry-recognized codes: ICD and CPT codes to know about diagnoses and medical treatment administered
• Financial details: Income, total bill amount, deductibles, co-pays, and reimbursement requested

Efficient data extraction with the help of OCR accelerates healthcare claims processing, reducing claim rejections and ensuring faster reimbursements to healthcare providers. 

4. Prescription management

Prescription and medication management is critical to maintaining consistency in healthcare treatments and preventing potential mistakes. OCR helps to achieve this by scanning prescription sheets and extracting medication names, dosages, and other protocols.

5. Document digitization

OCR can automate document digitization by extracting vital patient data from multiple documents and converting them into a standardized digital format within 45-60 seconds versus manual data extraction that can take days and weeks. 

6. Medical image analysis 

AI-powered OCR solutions are predominantly used in the medical image analysis process, and they can effectively analyze Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI), mammograms, brain scans, and X-rays. It uses computer vision and deep learning algorithms to recognize the images and detect the slightest abnormalities, such as lesions and bone thinning. 

For example, AI-enabled OCR solutions with machine learning algorithms can help with cancer prevention, management, and early diagnosis. You can train the model with malignant and benign tumor images and shuffle them periodically to verify if it can detect cancer growth accurately. 

These trained models can potentially analyze lung images to identify early signs of lesions and assist pathologists in detecting lung cancer at an early stage. Moreover, they can also analyze real-time surgical images to help surgeons differentiate healthy cells and make accurate surgical decisions. 

A note of caution: This is more of a future-focused use case of AI-powered OCR in healthcare and has not been proven yet. 

7. Inventory management 

OCR optimizes hospital inventory management processes and ensures healthcare providers have adequate resources for their practice. It recognizes barcodes, invoices, and medicine labels to extract details such as stock levels and expiration dates. 

OCR solutions with ML and NLP algorithms can effectively analyze the extracted and historical data, patient behaviors, and environmental conditions to identify trends and patterns and anticipate future demands. Such analysis can prevent stockouts and overstocking and streamline inventory processes.

Implementing OCR in Healthcare Operations 

1. Assessing business needs

The first step to implementing OCR into the workflow begins by assessing your healthcare organization’s data requirements and needs. 

• Do you want just to process healthcare documents and capture vital data such as a patient's name, address, disease, and history? 
• Are you looking for an OCR solution with ML and NLP algorithms to eliminate the need for human intervention? 
• Can your employees create templates for changing variations in insurance papers, bank statements, and lab reports? 

The answers to these questions would help you decide between a template-based and AI-powered OCR solution. Regardless of the data extraction needs, OCR integrated with AI can help you automate healthcare document processing workflows, which may not be possible with standalone OCR solutions. 

2. Choosing the right OCR solution

Here are some critical features and factors that you should consider when evaluating the platform’s capabilities:

• Adaptability:  Insurance documents, patient forms, lab reports, and Medicaid coverage forms differ highly in their structures and have multiple variations. The OCR software must effortlessly adapt to different formats, layouts, and templates and extract data accurately. 
• Versatility: Does the OCR solution extract key-value pairs and line items from documents? While patient forms and reports contain key-value pairs, insurance papers and bank statements have complex tables that require smart table extraction algorithms. Depending on the document, the OCR must employ feature recognition, image identification, table detection and segmentation algorithms, and neural networks to capture accurate line items and key-value pairs. 
• User interface: An intuitive and easy-to-use interface maximizes user adoption and efficiency. With a minimal learning curve, non-technical healthcare workers can easily adopt the tool for data extraction.
• Bulk processing: Processing huge volumes of documents simultaneously saves time and improves efficiency. But there's a catch. The OCR platform must be able to process multiple documents without compromising speed and accuracy. 
• Preprocessing: How does the OCR solution ensure accuracy in poor quality and distorted images of patients' documents? An OCR solution that can automatically perform advanced pre-processing techniques such as contrast and density adjustments, deskewing, denoising, and binarization significantly improves accuracy and prevents errors.  
• Accuracy rate: Choose an OCR platform that offers a 95-99% accuracy rate to avoid errors and inconsistencies. 
• Security and compliance: Does the OCR software provide enterprise-grade security features to safeguard sensitive patient data against data breaches? The software must offer role-based access, encryption, and two-factor authentication to prevent unauthorized access and data theft. Lastly, it should comply with healthcare regulations to avoid fines and legal repercussions. 
• Integration with existing systems: Integrating the extracted data with your healthcare CRMs and ERPs can help transfer data without errors.

3. Estimated costs 

Compare prices and consider the costs associated with implementing the OCR, including direct costs such as software licensing fees, license renewal, hardware requirements, support, and configuration, and indirect costs such as training employees to help them use the tool. 

The cost also depends on the volume of documents. If there’s a fixed volume, the fixed pricing model makes sense, whereas the pay-as-you-go works best when the number of documents processed per month is not fixed. 

4. Training and support for users

Train employees to adopt the OCR tool by leveraging free trials given by the vendor. Sign up for a free trial of the chosen OCR solution, and process several healthcare documents for free. 

User manuals and onboarding support provided by the OCR vendor to educate employees about the features and their uses are helpful. 

5. Continuous monitoring and optimization

Train the OCR platform with a sample dataset and evaluate its performance in terms of speed and accuracy. 

For example, ingest 50 patient forms into the OCR tool and let it extract key-value pairs automatically. After extraction, review the data manually to detect errors. In cases of errors, making corrections manually trains the model to yield a 99% accuracy rate from the next batch onwards. 

Future Trends of Automation in Healthcare Document Management

Healthcare organizations using outdated systems for manual data extraction face inefficiencies, errors, and increased costs. Automation drives efficiency, and here are some trends expected to reshape automation in healthcare data management: 

• Intelligent Document Processing (IDP) 

Intelligent Document Processing technology integrates OCR with AI to automate routine data extraction tasks. Healthcare document management systems based on IDP are emerging as a transformative solution, helping healthcare organizations automatically extract data from structured and unstructured patient documents. 

The platform adapts to variations in the templates, layouts, and structures using machine learning (ML) algorithms and requires no templates. Once the patient document is ingested into the IDP platform, the software automatically sorts, classifies, routes, extracts, and validates data without human intervention. This significantly reduces manual effort, saving time and costs for healthcare organizations. 

• Blockchain 

The adoption of blockchain technologies in healthcare data management is constantly growing as it provides several advantages, including traceability, interoperability, data integrity, and security. Once a healthcare document is stored on the distributed ledger, it cannot be altered or deleted, ensuring the integrity of patient data and transparency. 

• Cloud computing 

Healthcare document management systems (DMS) incorporate cloud infrastructure because of its scalability and accessibility. Healthcare providers can access patient records from any device and location, and it stores growing volumes of documents, enhancing accessibility and safe storage. 

• Enhancing security 

Apart from basic requirements such as passwords, encryption, and role-based access, healthcare DMSs enhance security through various methods. As the industry predominantly stores sensitive data, stringent measures are driving the way toward security. 

Factors such as one-time passwords, security keys, automated redaction, biometric scans, disk encryption, granular access control lists, and classifying documents based on their sensitivity effectively help prevent data leakages and ensure security. 

• Telehealth integrations

Telehealth consultations are growing continuously as people look to access quality healthcare services from the comfort of their homes. Integrating patient data with telehealth platforms in such situations is critical to efficiently access patient data. Therefore, healthcare DMs are offering seamless integrations with telehealth software solutions. 

Healthcare enterprises that adopt growing technologies can gain a competitive edge in several ways. These trends in healthcare document management automation enhance document security and compliance and improve an organization's efficiency.

Transforming Healthcare Operations with OCR

Capturing data from healthcare documents such as insurance claims, patient records, and diagnostic reports using OCR technology reduces errors, costs, and time and improves efficiency and productivity. 

Docsumo, an AI-powered OCR solution, can automatically extract and validate data from healthcare documents using Excel-like formulas, ensuring over 99% accuracy rate. ML and NLP algorithms in Docsumo help identify patterns and trends and forecast the demand for health and wellness products.

Docsumo is GDPR- and HIPAA-compliant and offers cloud storage to help you ensure compliance and high security. You can process multiple healthcare documents simultaneously and extract data in 30-60 seconds. The result is a 60-70% reduction in operational costs and improved efficiency by 10X. 

‍Sign up on Docsumo to automate healthcare data extraction and improve efficiency by 10X.