5 Ways To Improve Healthcare Data Accuracy With Automation

The healthcare industry thrives on data. However, inaccurate or incomplete data can have serious consequences, impacting everything from patient diagnoses and treatment plans to resource allocation and operational efficiency.

This blog will highlight the increasing need for reliable data and show how automation addresses this challenge while ensuring data security and improving data quality. We’ll also touch on integrating automation with existing systems. Let's look at 5 ways to improve healthcare data accuracy with automation.

Why Healthcare Data Accuracy Is So Important

Accurate healthcare data is the cornerstone of effective patient care. It influences clinical decisions, patient safety, and overall healthcare quality. Inaccurate or incomplete data can lead to misdiagnoses, inappropriate treatments, and increased healthcare costs.

Impact on Patient Safety

Errors in patient data, such as incorrect medication histories or allergy information, can result in adverse drug events and other medical errors. The World Health Organization estimates that medication errors alone cause at least one death every day and injure approximately 1.3 million people annually in the United States.

Clinical Decision-Making

Healthcare providers rely on precise data to make informed decisions regarding diagnoses and treatment plans. Inaccurate data can compromise these decisions, leading to ineffective or harmful interventions. For example, a study found that predictive modeling of electronic health record data achieved 70–72% accuracy in predicting individualized treatment responses, highlighting the importance of data quality in clinical outcomes.

Operational Efficiency

High-quality data streamlines healthcare operations by reducing redundancies and minimizing errors. Conversely, poor data quality can lead to inefficiencies, such as duplicate testing and extended hospital stays, which increase operational costs and strain resources.

Regulatory Compliance

Accurate data is essential for compliance with healthcare regulations and standards. Inaccurate reporting can result in legal penalties and damage to an organization's reputation. Implementing robust data quality measures helps healthcare organizations meet regulatory requirements and maintain trust with patients and stakeholders.

5 Ways To Improve Healthcare Data Accuracy With Automation

Here are 5 ways to improve healthcare data accuracy with automation you can implement in your medical organization:

1. Automated Data Entry and Validation

Manual data entry is prone to errors like typos, missed fields, or inconsistent formatting. Automating this process with tools like electronic health records (EHRs) drastically reduces mistakes. A 2021 report shows most hospitals and physicians have adopted EHR systems, demonstrating a move toward digital data management.

Combining automated data entry with validation checks ensures only clean, consistent data enters the system. This fixes immediate errors and prevents future ones, improving patient demographics and health insurance information accuracy.

Editor's Note: Many of the errors that occur during patient intake or eligibility verification are due to data entry mistakes. Many companies in the healthcare industry, like Dignity Health, are turning to Magical to help with these errors.

Magical is an automation and productivity tool that eliminates repetitive typing tasks like patient registration and transferring patient data between systems. You can set up automations with Magical to autofill forms, thereby reducing errors.  

2. Robotic Process Automation for Repetitive Tasks

Many administrative healthcare tasks, like prior authorization or claims processing, involve repetition. Robotic process automation (RPA) can handle these tedious chores. RPA bots reduce human error, improve speed and efficiency, and free staff to focus on quality care.

Reallocating staff to more demanding duties reduces burnout among medical professionals. A study in Mayo Clinic Proceedings highlighted the risk of burnout among medical professionals, with many planning to reduce work hours. RPA helps alleviate this by streamlining workflows and automating time-consuming tasks.

Automation also cuts healthcare expenses. A CAQH report showed a 12% increase in healthcare spending in a single year despite fewer medical procedures, partially due to external challenges. RPA has saved trillions across industries since 2016, according to an Accenture report, demonstrating its cost-effectiveness.

3. Natural Language Processing for Unstructured Data

Much healthcare data, like doctor's notes or medical reports, is unstructured. Natural language processing (NLP) extracts key information from this unstructured text and converts it into a standardized format. This makes all vital insights and observations usable.

Medical practitioners often struggle with the volume of information they manage. NLP helps them quickly sort and analyze data, improving patient care by making proper use of patient and treatment information.

NLP enhances data usage patterns across healthcare organizations, improves resource allocation, and enables effective treatments with high-quality data. This results in more informed decisions and accurate diagnoses, which can positively impact patient outcomes.

4. Machine Learning for Predictive Modeling and Anomaly Detection

Machine learning algorithms analyze past healthcare data to predict future patient needs, supply chain issues, or staffing levels. Predictive modeling allows healthcare providers to be proactive. Machine learning excels at identifying anomalies, which can indicate data inaccuracies or inconsistencies.

Implementing machine learning improves data exchange and helps resolve data quality issues. This allows healthcare organizations to address problems quickly and prevent them from escalating. Ultimately, machine learning contributes to more informed decisions about patient care, resource allocation, and overall healthcare management.

5. Data Integration and Interoperability

Many healthcare systems operate in silos, with patient data needing to travel across multiple platforms. Data automation tools enhance data integration and interoperability. This smoother, quicker data exchange improves efficiency and information sharing while preventing information silos.

Integrating data effectively across exchanges between healthcare providers significantly improves patient outcomes by preventing information loss. This reduces redundant tests and streamlines treatment processes by ensuring all relevant patient data is readily available.

Integrating processes helps healthcare facilities using multiple patient record databases. It leads to fewer data omissions, ensures compliance, and supports the implementation of automated billing procedures for insurance organizations. These tools also implement data governance and data quality management across platforms.

Final Thoughts

These 5 ways to improve healthcare data accuracy with automation mark a significant advancement in patient information security. By implementing data automation for accurate and reliable data, we can better promote patient outcomes by ensuring patient care with improved data exchange information and more informed decision-making by medical professionals.

If you're part of a healthcare admin team and want to know how to manage patient data and other administrative tasks more efficiently, try Magical. Magical is used at more than 50,000 companies like United Healthcare, Optum, and Nuance to save 7 hours a week on their repetitive tasks.