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Maximizing Efficiency: RPA with AI in Healthcare 

ai in healthcare

The goal of healthcare is to provide patients with the best possible care and experience. Unfortunately, time consuming tasks, such as paperwork and scheduling, often get in the way of meeting this goal.

The good news is that there is a better way of approaching these tasks. Using technologies such as Robotic Process Automation (RPA) coupled with ever-advancing Artificial Intelligence (AI), can help staff transfer written documents to digital records, respond to patient’s questions, build care plans, and schedule medical appointments. By employing these technologies, healthcare staff have much more time to focus on the patient.

Let’s dive into some sample use cases, about how processes can be automated to improve accuracy, staff job satisfaction, and, most importantly, the patient experience and ongoing care.

Automating the transfer of form data to an EHR

Transferring data from paper form fills to an EHR system through manual data entry is not only time consuming, but fraught with the potential for error. Intelligent Document Processing (IDP) applications can be trained to extract required data from an unstructured document, (such as a handwritten medical form), after the form is scanned and digitized. RPA “bots” can then take the data and rout it to the appropriate system. This not only saves time but increases accuracy, ultimately improving patient satisfaction and providing cost savings as technology can intake information faster and with less errors.  RPA and AI are both powerful technologies on their own, but when combined the outcome is enhanced and more valuable to your organization.

In addition, as IDP with AI consumes form data, it can be categorized for integration to multiple systems without the need to manually re-key. This integration enables your organization to produce real-time reports of diverse patient data, such as age demographics, geographic distribution, medical conditions, and prescribed medication.

Think of the benefits this automation can provide – comprehensive data that easily provides reporting, so staff have information at their fingertips allowing them to maximize their time to focus on the patient.

The combination of RPA and IDP with AI generates comprehensive insights, providing healthcare providers with actionable information for improved decision-making and patient care management.

Providing Automated Patient Queries with Natural Language Processing (NLP)

Once you have integrated vital, accurate patient information using IDP and RPA, wouldn’t it be great for AI to use that data to provide answers to queries within your organization – and with your patients as well? Welcome to Natural Language Processing (NLP) using AI.

Even if you think you are unfamiliar with the term Natural Language Processing (NLP), you most certainly have experienced this at least once in your life, if not every day. Chatbots, language software for translations, and voice assistance (Siri, Alexa, Google, etc.) are using NLP to provide answers to your queries.

NLP is a step beyond understanding how to retrieve data from documents; it interprets how data concepts are related based on key words and phrases. And NLP can operate in many different languages. This allows a bot to respond and converse with patients, delivering appropriate answers to patient queries, in their native language. With in-depth Machine Learning training, chatbots can receive requests and retrieve related information to deliver personalized experiences. As NLP improves with training, providers can offer next level patient care helping to redefine how health care is delivered.

Generative AI to the Rescue!

To rehash, IDP will help you retrieve data from forms and documents. NLP will assist in answering questions or queries from patients or internal staff without human intervention. However, there is still another AI process which can help! Adding Generative AI to your internal processes is a game changer. A recent article by McKinsey and Company states that Generative AI can help healthcare organizations realize $1 trillion of improvement potential.

Generative AI is artificial intelligence that can generate new datasets and insights into your patient’s medical needs. This can be used to enhance aspects of medical care plans. Here are a few examples for you to consider:

  • Generate Medical Reports: Using AI technology, machines can generate medical reports by analysing both organized and unstructured information, such, as patient backgrounds, test findings and notes from doctors.
  • Medical Billing and Coding: AI technologies aid in accurately coding medical procedures and diagnoses based on clinical documentation, while RPA automates the billing process by generating invoices, submitting claims, and processing payments.
  • Medical Appointment Scheduling: AI technology can help in managing appointment schedules by analysing patient availability, doctors’ schedules and available clinical resources thus automating the booking process across various systems and channels.
  • AI-powered chatbots: These bots can engage with patients to provide information about appointments, medication reminders, and general health inquiries. RPA can handle repetitive tasks like sending follow-up emails or SMS notifications.
  • Personalized Treatment Plans: Generative AI analyses patient data, such as medical history and current patient health, to generate more effective care plans without the need for a slowdown of manual research.
  • Clinical Decision Support: The accuracy of care plans which AI can produce may be considered more thoroughly researched, as Generative AI can access and process vast datasets quickly. Using patient data in combination with clinical guidelines, medical text and literature produces plans with greater accuracy and speed, allowing healthcare organizations to communicate care plans to patients much more quickly.   
  • Medical Imaging: Generating representational medical images is a massive benefit of Generative AI. Generative AI is highly beneficial in medical imaging. The AI enhances images based on patient information and historical data using vast datasets, helping identify and classify medical conditions. Nevertheless, it’s essential to understand that AI works to aid doctors in their work, not serve as a replacement. Doctors can use these AI-generated images to assist in pinpointing medical issues and developing treatment strategies to improve patient care and treatment results.

Using AI with Image Analysis

Healthcare professionals significantly benefit from leveraging artificial intelligence for image analysis. Using AI for image analysis can assist doctors by recognizing patterns for early disease detection, which provides greater insight in diagnosing medical conditions and improves confidence in diagnosis assessment. Reducing the potential for misjudgment is essential in any medical diagnosis – especially in cases where immediate medical intervention is needed.

How do all these AI-driven processes flow together?

Now let us consider how all these usage examples tie together to advance patient care in healthcare organizations. We have all been through a situation where the receptionist hands you a pen and form to fill out while you wait for the staff to call your name to begin your appointment. You spend time filling out the form, providing your name, contact info, and a summary of your medical history and current medications.

Consider it like this:  A patient visits an organization for medical needs.

  1. A staff member at the facility will scan the forms, kicking off the IDP process, which will add your form data to a structured digital environment. The patient information now exists within your EHR and contributes to your defined datasets.
  2. Image analysis provides a level of support for doctors in determining medical diagnoses. AI will deeply review any images taken during the visit and, using the vast datasets, will recognize nuances and similarities to provide a clear understanding of any potential medical issues the doctor can then review and deliver to the patient for ongoing care.
  3. Once the appointment or visit is complete, Generative AI will assist the medical provider and patient. Based on the results from the visit, Generative AI will process unique patient data in the realm of existing rules-based datasets, to quickly develop a care plan, avoiding any delay that can negatively impact the patient. The care plans can be reviewed by a healthcare staffer or doctor and delivered to the patient.
  4. If the patient has questions about their medical needs, natural language processing steps up to the plate, as the AI engine is trained to recognize medical terms and reach vast datasets to match queries. If guidance from a doctor still needs to be fully understood, the patient can reach out to the facility in many ways, including phone calls, emails, and chats on your website. In all instances, the power of NLP will quickly take care of these follow-up inquiries to guide patients.

Conclusion: RPA with AI for Good!

As mentioned earlier in this article, the goal of every healthcare organization is to provide exceptional healthcare to drive positive patient outcomes and strong patient experiences. By leveraging RPA and AI, your organization will be able to succeed in this goal and instill confidence in patients to feel good about their care plan moving forward and that they made the right decision to visit your organization. Since automation freed up time previously dedicated to paperwork, staff can now focus on their number one priority – the patient.

The power of AI is in your corner to assist in improving process flow, so that healthcare organizations can realize their goal of providing exceptional patient care.

About SphereGen

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SphereGen is a unique solutions provider that specializes in cloud-based applications, Intelligent Automation, and Extended Reality (AR/VR/MR). We offer full-stack custom application development to help customers employ innovative technology to solve business problems.

Learn more about what we do in RPA: https://www.spheregen.com/robotic-process-automation

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