How to Deal with Unstructured Healthcare Data

by | Feb 16, 2022 | Healthcare, Master Data Management

Physician works on medical records and evaluates unstructured healthcare data.

The market size of big data in healthcare is expected to reach $78.03 billion by 2027. However, the ever-increasing volume of processed information, including unstructured healthcare data, makes it challenging to organize and systematize.

Every day, there are many complex tasks associated with inputting, processing, storing, and managing the flow of medical information – but the former approaches to managing medical records are quickly becoming obsolete. Healthcare organizations now need better medical data management systems to take advantage of new, breakthrough trends in healthcare technology and unstructured medical data analytics.

Before learning how to manage unstructured healthcare data, let us first understand healthcare data features and the differences between structured and unstructured data.

Key Takeaways:
  • Medical data, growing exponentially, comprises large amounts of structured and unstructured data.
  • Unstructured data contain important information for data governance in healthcare and healthcare provider data management. 
  • Artificial intelligence and machine data analysis can process, diversify, and structure large amounts of unstructured data in the healthcare system to simplify healthcare data management.

What Are the Features of Healthcare Data?

Healthcare data has become a separate object of interest for tech companies. However, it facilitates the development of the artificial intelligence market due to these features:

  • Diversity: Healthcare data are heterogeneous and collected from various sources: fluorography, computed tomography, ultrasound, laboratory tests, patient complaints, anamneses, etc.
  • Large volumes in health data exchange: Medical information is presented in large data arrays that require appropriate storage space and tools for analysis. The streamlining of all types of data through automated systems will reduce the non-core costs of medical staff for record maintenance, generation of medical statistics, medication accounting, etc.
  • Complexity: Medical documents are complex by their nature for structuring and mathematical description.
  • Importance: Healthcare information is valuable since it helps diagnose and treat illnesses.
  • Sensitivity: Medical data is subject to the opinions and assessments of physicians, physical examinations, and clinical tests.
  • Legal, ethical, and social aspects: Healthcare data consists mainly of patient data. This raises questions about ensuring the confidentiality of personal medical data under current legislation.

Structured Vs. Unstructured Data in Healthcare Services

When it comes to healthcare digitization, existing database management systems store large-scale data in two forms: structured and unstructured. So what is the difference between the two?

Structured Healthcare Data Definition

Structured healthcare data is precise and factual information in the form of letters and numbers, which makes it possible to calculate, measure, and classify.

Examples of structured data are:

  • Databases in healthcare provider data models
  • Spreadsheets like Excel files and Google Docs
  • Electronic health records (EHR) of healthcare providers and insurance companies 
  • Data that contain patient names, birth dates, diagnoses, treatment plans, and procedures
  • Numeric or alphanumeric fields for insurance encoding, ICD-10, and CPT codes

Unstructured Healthcare Data Definition

Unstructured healthcare data is information that cannot be easily managed using predefined data models.

Here are the different sources where healthcare professionals can obtain this data:

The sources of obtaining unstructured healthcare data
Image source: https://www.scnsoft.com/blog/health-data-analytics-overview

Unstructured healthcare data is just as important as structured data, regardless of the difficulty of its categorization. Unfortunately, most medical facilities leave it untouched for extended periods because it is difficult to submit in electronic medical records. Yet, according to official data, the amount of unstructured data accounts for 80% of all healthcare information. 

Typical unstructured medical data represents:

  • Clinical video data generated by new types of medical imaging devices (e.g., endoscope, laparoscope, surgical robot, capsule endoscope, emergency video camera, thoracoscope, x-ray, ultrasound, etc.)
  • Biosignal data recorded in operating rooms or intensive care units and by wearable health monitoring devices
  • Audio data that are verbally or non-verbally generated from pathophysiological data of patients and medical staff for effective communication in clinical practices
  • Telemedicine appointment records

What Are the Benefits of Processing Unstructured Data?

Technological developments fuel the trend of unstructured data management, which can be applied to all health care and treatment areas. The introduction of electronic medical records, digital health tools, and modern technology offers significant benefits for both medical institutions and patients. 

6 real-world applications of unstructured healthcare data analytics
Image source: https://online.maryville.edu/blog/data-analytics-in-healthcare/

Disease Diagnostics

The use of analytical tools for processing unstructured data enables examining the disease, its stage, the course of the disease, and forecasting changes in the patient’s clinical condition when employing various interventions.

Determining Treatment Methods

Systematizing unstructured healthcare data makes it possible to determine the best available treatments by comparing factors such as causes of illness, symptoms, treatment time and cost, and side effects.

Providing Patient Engagement Solutions and Quality Medical Care

Analysis of large medical data sets can reveal new patterns (e.g., about previously unknown aspects of treatment methods or medication use), which will improve the quality of medical care.

Data Quality in Healthcare and Reference Data Management

With the help of automated data processing technology, it is possible to monitor compliance with the guaranteed volume and quality of medical care, taking into account medical and medico-economic standards, monitoring the prescription of medicines, and analysis of compliance of the provided medication with the diagnosis of the patient.

Insurance Fraud Detection

Data categorization allows for the identification of fraud cases, such as billing for non-performed procedures, medication write-offs, unreasonably expensive prescriptions, overbilling for medical services, etc.

5 Steps to Deal with Unstructured Data in Healthcare Systems

Simplifying, collecting, and organizing healthcare data is already a valuable first step for most healthcare providers. However, maximizing the value of big data requires the application of tools capable of structuring information and highlighting the most valuable points.

Video: How Big Data Could Transform the Health Care Industry

All information resources need to be analyzed in terms of their content, storage conditions, and compliance with confidentiality rules. Therefore, there are specific steps for handling unstructured healthcare data:

1. Optimize Data Storage

The first step to data structuring is to analyze all the arrays of available information to the company, their systematization, and distribution into necessary and irrelevant information.

2. Identify Unnecessary Data

After analyzing all unstructured data stored, remove irrelevant data and free up space on the digital storage.

3. Classify Data

This step will assign confidentiality labels to the data, structured into groups, which will facilitate their use in business processes.

4. Keep Data Confidentiality Under Control

Compliance with regulations on personal data protection and internal information security policies is vital to ensure patient confidentiality.

5. Assign Access Levels

Systematization of data, information arrays, and assignment of labels will increase confidentiality by structuring user access to data of different types.

Make the Most of Unstructured Healthcare Data

The Coperor E-MDM platform by Gaine helps healthcare facilities operate more efficiently by applying high-end automated tools and simplifying processes for handling unstructured information, managing core healthcare data, and creating a sustainable and personalized system with a focus on quality healthcare services.

 

We can help you choose the best solution. Contact us today.

 

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