Medical record automation is a growing field in the healthcare industry that has the potential to revolutionize patient data management. With the increasing adoption of electronic health records (EHRs) and other digital tools, healthcare providers are able to automate the process of collecting, storing, and sharing patient data.
However, the integration of EHR systems in healthcare networks has not met initial expectations as they are perceived as inflexible, difficult to use, costly to customize, and built on older technologies.
In this article, we will take a closer look at medical record automation and its features, benefits, and how better technologies could be implemented in healthcare organizations to address these issues and improve the overall effectiveness of EHR systems in healthcare networks.
What is medical record automation?
Medical document automation is the use of technology to automate the creation, management, and distribution of medical documents. This can include
- Electronic health records (EHRs)
- Computerized physician order entry (CPOE) systems,
- Clinical decision support systems.
- Billing and coding documents
The goal of medical document automation is to improve efficiency, accuracy, and accessibility of medical records, while reducing the workload for healthcare professionals.
- In 2021, it was reported that doctors spend an average of 16 hours per week on administrative tasks such as paperwork.
The implementation of more comprehensive medical record automation solutions has been shown to result in significant cost and time savings for healthcare organizations. It is estimated that healthcare organizations can save up to $14 billion by implementing full automation for their administrative processes.1
Which tools are used for medical record automation?
There are plenty of technology solutions available to assist healthcare organizations in addressing the challenges posed by large amounts of digital data. According to our research mostly preferred ones include:
1. Automated charting software
Automated charting software helps to visualize data by automatically pulling relevant data and creating charts. The software uses algorithms to process and analyze the data, and then generates charts that provide insights and information. The process of pulling data and creating charts is the main intersection of automated charting software as the software is designed to streamline the process of visualizing data by automating the process of extracting, organizing, and presenting information in a clear and concise manner. The process of recording patient information is automated by using data from
- Electronic health records
- Laboratory results
- Pharmacy records
This helps to reduce the time healthcare professionals spend on documentation and allows them to spend more time on patient care.
2. Robotic Process Automation (RPA)
This highlights the growing recognition of the potential benefits and efficiency gains that RPA can bring to the healthcare industry. Indeed, today most organizations prefer RPA which utilizes software bots to automate repetitive tasks such as
- Automating Patient Registration and Appointment Scheduling: This includes the automation of inputting patient data, updating patient records and medical history, moving it to patient files, and scheduling relevant appointments in the EHR system. To achieve this, Natural Language Processing (NLP) is utilized to understand and interpret unstructured medical text, such as patient notes and medical reports, and convert it into structured data that can be easily analyzed and used by healthcare organizations.
- Digitizing medical records: Intelligent document processing (IDP) allows for the preparation and integration of various documents, including health records and insurance claims, into a centralized storage system for future use.
- Automating report management: RPA automates reporting for billing payments and submissions. These reports can be used to track payments by provider, insurance carrier, and patient. RPA can be used to monitor payments and bills on a monthly basis, providing insight into the overall industry trends.
- Compliance management: The implementation of RPA enables monitoring and documenting all regulatory processes, keeping them in an organized manner. This allows for quick and easy access during any unexpected or external audits.
3. Machine learning
AI can detect patterns in data to improve efficiency. For example, machine learning software can
- Alert potential errors in patient records
- Predict potential health risks or complications for individual patients, and alert healthcare providers to take preventative measures.
- Improve the accuracy of diagnosis by analyzing large amounts of data from patient records and identifying patterns that might be missed by human analysis alone.
Benefits of medical record automation
Healthcare leaders can leverage medical record automation to achieve the following:
- Streamline administrative tasks and cut costs by automating repetitive and time-consuming manual processes.
- Accelerate processes such as triage by automating routine tasks.
- Improve data accuracy, task consistency, and reporting in clinical settings by minimizing human error and implementing best practices.
- Improves accessibility for patients and healthcare providers in case of a provider change, ensuring continuity of care.
- Boost staff productivity by automating low-level tasks, allowing them to focus on more complex activities.
- Enhance patient care and experience by providing more accurate decisions, reducing costs, and increasing visibility across the entire patient record.
If you have any additional queries regarding medical record automation you can check our in depth article on intelligent automation in healthcare.
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This article was drafted by former AIMultiple industry analyst Kübra İpek.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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