Achieve Operational Efficiency; Automation in Healthcare

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Ensure Data Solutions In the Healthcare industry Achieve Operational Efficiency; Automation in Healthcare

In the Healthcare industry, physicians should be able to focus on one goal; patient’s health. In reality there is a lot more that goes on in a medical center, administrative tasks like record keeping, insurance authorizations, billing, medical coding and data. All of the tasks involved in these processes can be fully or partly automated or better, now they can be hyperautomation.

Hyperautomation is connecting multiple advanced technologies to achieve an end to end automation process, combining Artificial Intelligence, Machine Learning, Business Process Management System and Analytics to automate more complicated business processes that include human tasks.

This new process allows less people performing the same tasks, improve their existing jobs and therefore the motivation and performance. It can also improve the patients experience, help with patient engagement and retention.

 

The different forms of automation in the healthcare industry are;

1. Automated Scheduling and Managing Appointments

Without automation, scheduling staff must navigate multiple software systems, physician schedules, and insurance portals—all while patients wait on the line. Hyperautomation simplifies the process while drastically reducing data errors along the way. It even limits reliance on human staff through integrated voice- and chat-enabled AI assistants.

 

AUTOMATION IN THE HEALTHCARE

2. Automated Insurance Pre-authorization

Insurance pre-authorization is a labor-intensive process, traditionally requiring knowledgeable employees familiar with the many disparate insurance portals. Trained RPA bots with Artificial Intelligence (AI) capabilities like Computer Vision (CV) and Optical Character Recognition (OCR) can complete pre-authorization through any insurance portal in an instant. This frees staff to focus on customer service.

3. Automated Medical Coding And Billing

Medical coding is an ideal use case for hyperautomation; AI is simply better at keeping up with constant changes to the 70,000+ codes involved in the process than even a highly trained human employee. Eliminate errors and optimize staff utilization with hyperautomation, including AI technologies like Ensure Data Solutions, which can derive accurate coding data that allows physician meet their quality scores.

4. Automated Insurance Claims Processing

Healthcare providers file billions of medical claims each year in the U.S. alone. Robotic process automation handles the rule-based and transactional tasks involved in claims processing rapidly and with total accuracy, while applying hyperautomation for end-to-end processes can tackle any hospital’s claims backlog—allowing staff to transition to more patient-focused tasks.

5. Automated Data Analytics

To manage the data is central to success, but it involves many data-heavy, time-intensive processes—especially for providers with multiple locations. End-to-end digital automation can sew together vastly different tasks—data migration, finances, and even managing hospitalizations cost—into a streamlined process orchestrated by an AI-powered system. With human workers and RPA bots organized through the Ensure platform, this formerly costly and error-prone process can become remarkably accurate and affordable.

At the end the goal is to improve the productivity and efficiency of the business process.   

 

Health Care Quality Data Into Usable Information

The raw scores on most health care quality measures don’t mean much to the average person. The job of report sponsors is to present scores in a way that makes them engaging, easy to understand, and easy to use. This section discusses what you can do to turn data into information that meets the needs of your audience.

Why Good Presentation Matters. Most consumers won’t spend much time looking at a quality report. Your presentation of the information can help keep consumers’ attention and support their understanding of the data.

Generating Scores that Show Differences in Performance. Review approaches for developing valid and reliable scores that are fair to providers and easy for people to understand, compare, and use.

Describing Measures in User-friendly Ways. Learn how to create labels and definitions that translate medical terminology into plain English, and provide other information to help users understand the importance, validity, and limitations of each measure.

Organizing Measures To Reduce Information Overload. Understand how to organize measures and scores into digestible quantities and help people quickly get to the information they want.

Choosing a Point of Comparison. Review the advantages and disadvantages of several approaches that aim to make it as easy as possible to identify high and low performers.

Displaying the Data. Learn about options for vividly displaying your data, including graphs and tables, legends, symbols, and organizing data into “layers” to help people get the level of detail they want.

Taking Advantage of Web Functionalities. Learn how to make your Web-based report easy to search and navigate, and how to help your audience customize their experience of your report so that it meets their needs. (More general guidance on designing quality reports for the Web.

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