Platform as a service (PAAS) and method for ai-driven multi-system communications between healthcare practices

Abstract

An Al-powered platform to automate and streamline laborious manual tasks in healthcare practices. The platform may be programmed to facilitate automated scheduling of patient appointments, referral coordination, insurance verification, and medical record management. By integrating artificial intelligence, healthcare providers could significantly reduce manual data entry, minimize administrative burdens, and improve overall workflow efficiency.

Description

[0001] PLATFORM AS A SERVICE (PAAS) AND METHOD FOR AI-D RIVEN MULTI-SYSTEM COMMUNICATIONS BETWEEN HEALTHCARE PRACTICES

[0002] TECHNICAL FIELD

[0003] The invention relates generally to cloud-based artificial intelligence systems and, more particularly, to a conversational Al platform that autonomously orchestrates interactions between multiple systems or modules used to support respective healthcare practices, said systems including but not limited to:

[0004] • practice management systems (PMS)

[0005] • third-party healthcare SaaS (Software as a Service) tools

[0006] • inventory control systems

[0007] • insurance verification interfaces

[0008] • cross-practice referral networks

[0009] BACKGROUND OF THE INVENTION

[0010] Healthcare practices, particularly dental offices, suffer from numerous inefficiencies that negatively impact both providers and patients. These inefficiencies stem from fragmented workflows, insurance bottlenecks, and referral management challenges. As a result, significant time, resources, and effort are spent on administrative tasks rather than on patient care.

[0011] One of the major inefficiencies in healthcare practices is the fragmentation of workflows. Dental offices typically use multiple software systems to manage different aspectsof patient care, including patient management systems (PMS), radiograph analysis, billing, and insurance verification. Each time a change is made to a patient's file, staff members must manually enter data across multiple systems, leading to redundant work and an increased likelihood of errors. This manual process not only consumes valuable time but also reduces overall efficiency and productivity within the practice.

[0012] Another critical issue faced by healthcare providers is the significant delay in insurance verification. Insurance-related tasks require doctors and administrative staff to spend an excessive amount of time on the phone with insurance carriers to verify patient coverage. Studies indicate that 82% of dental offices experience delays in obtaining insurance verifications over the phone, creating bottlenecks that hinder patient scheduling and treatment planning. These delays can result in postponed procedures, increased administrative costs, and frustration for both patients and providers.

[0013] Additionally, referral inefficiencies pose challenges in coordinating patient care between providers. When a patient requires a referral to a specialist, setting up an appointment with the referred doctor can be a complex and time-consuming process. The referring and referred doctors must communicate to ensure proper scheduling while also accommodating the patient's availability. Further, follow-ups between referring and referred doctors are often not streamlined, leading to gaps in care coordination and potential miscommunication.

[0014] Given these challenges, there is a growing need for an Al-powered platform to automate and streamline key administrative tasks in healthcare practices. Such a platform could facilitate automated scheduling of patient appointments, referral coordination, insuranceverification, and medical record management. By integrating artificial intelligence, healthcare providers could significantly reduce manual data entry, minimize administrative burdens, and improve overall workflow efficiency. The implementation of an Al-driven solution would enhance patient care by allowing healthcare professionals to focus more on clinical tasks rather than administrative duties, ultimately leading to better patient outcomes and a more efficient healthcare system. A platform as described is also desirable in that communications with an insurance company, a referred medical practice, and a radiographic vendor, may occur in real time and simultaneously / autonomously - even while intelligently conversing with a respective patient or even with multiple patients.

[0015] SUMMARY OF THE INVENTION

[0016] A cloud-based Al platform and computer-implemented method according to the present invention autonomously orchestrates interactions between a plurality of cross-platform systems within a predetermined healthcare ecosystem seeking to treat a patient in need of care. For instance, the method may include the following steps:

[0017] accessing a conversant Al agent to converse with the patient in real time regarding scheduling, said Al agent utilizing voice recognition and natural language interfaces;

[0018] accessing the Al agent to communicate with an insurance company in real time so as to verify insurance coverage of the patient;in a case of a patient referral, (1) accessing a cross-platform scheduling interface to communicate with another practice management system (PMS) associated with a referred doctor’s office to coordinate an open appointment time(s) for the patient referral and then (2) prompting the Al agent to converse and confirm the coordinated open appointment time.

[0019] The platform (which may also be referred to as a Platform as a Service (or PAAS) as Software as a Service (SaaS)) may include a computer system that may include a conversant Al agent which may be referred to in use as “Reena” with the personalized familiarity that users have with Al devices such as Alexa, Siri, Google Assistant, and the like.

[0020] Implementation of invention described in this application, e.g., the application of “Reena” across the many aspects of a healthcare industry ecosystem will have industrywide benefits that are completely disruptive and non-obvious. In other words, the invention recited and disclosed below is an absolute disruption to an industry that has not seen such efficiency literally for decades. Such a disruption is unexpected to say the least.

[0021] The changes, improvements, and efficiencies across the industry will be described as follows, the Al Agent known as Reena acts in multiple ways upon each entity that utilizes it as will be described. First, Katharina streamlines the workflows, reduces chum, reduces expenses, and materially increases profitability and practice value of a healthcare practice that implements the system. Further, any vendor that utilizes this methodology reduces marketing expenses, streamlines distribution, and increases sales. Each dental laboratory improves case communication and quality. Still further other industry practices are improved and, specifically, referral doctors will benefit from referral scheduling of patients as will be described in much greater detail later. Insurance billings are streamlined and the revenuecycle is shortened. And, finally, each healthcare patient is benefited by the entire experience. These benefits are also shown in the accompanying drawings.

[0022] Also shown in the accompanying drawings and described in detail in this document are the “Use Cases” that demonstrate the practical application and usage of the present invention. Specifically, the Use Case drawing shows the simplicity of the voice commands that may be directed to the computer or dedicated electronic device known as “Reena.” With only a minimalistic voice command, the platform according to the present invention may autonomously communicate using voice recognition and Al modules via a phone line with a patient, via the Internet with a referred healthcare practice, insurance company, laboratory, radiologist, billing department, or the like. The Al agent has the capability to communicate with a human being using speech recognition and natural language processing modules as well as with other patient management systems that are also using the Al agent. Exemplary examples of these functions and more will be described below in greater detail.

[0023] Therefore, a general object of this invention is to provide a cloud-based Al platform and computer-implemented method that autonomously orchestrates interactions between a plurality of cross-platform systems within a predetermined healthcare ecosystem seeking to treat a patient.

[0024] Another object of this invention is to provide an Al platform, as aforesaid, that facilitates automated scheduling of patient appointments, referral coordination, insurance verification, communication with other practice providers within a healthcare ecosystem system, and medical record management.Still another object of this invention is to provide an Al platform, as aforesaid, that includes an Al agent capable of communicating simultaneously with a live patient while in real time data and oral communication with Al agents of other healthcare providers associated with the same patient.

[0025] A further object of this invention is to provide an Al platform, as aforesaid, that improves the efficiency of a healthcare practice by an exponential factor.

[0026] Still another object of this invention is to provide an Al platform, as aforesaid, that reduces the number of staff required to operate a healthcare practice.

[0027] Other objects and advantages of the present invention will become apparent from the following description taken in connection with the accompanying drawings, wherein is set forth by way of illustration and example, embodiments of this invention.BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Fig. 1 is a diagram illustrating a computer implemented Al platform and integration of a chosen healthcare practice area according to the present invention;

[0029] Fig. 2 is a block diagram of a system architecture for implementing the platform of Fig.

[0030] 1;

[0031] Fig. 3 is a flowchart illustrating Al agent communications of the integrated system according to the present invention;

[0032] Fig. 4 is a flowchart illustrating an Interactive Voice Recognition (IVR) communication with a Practice Management System (PMS) according to the present invention;

[0033] Fig. 5 is a flowchart illustrating a workflow in the case of a patient or procedure referral process;

[0034] Fig. 6 is a block diagram illustrating how an entire industry ecosystem is improved according to the present invention; and

[0035] ; Fig. 7 is a block diagram illustrating the use of Interactive Voice recognition (IVR) is utilized across the entire industry ecosystem according to the present invention.DESCRIPTION OF THE PREFERRED EMBODIMENT

[0036] A cloud-based Al platform and computer-implemented method according to the present invention will now be described with reference to the accompanying drawings. The system autonomously orchestrates interactions between a plurality of cross-platform systems within a predetermined healthcare ecosystem.

[0037] As will be discussed later in more detail, the present invention will utilize various subsets of artificial intelligence such as machine learning and natural language processing. More particularly, machine learning (ML) is a subset of Al that enables systems to learn from data and improve performance without explicit programming. Machine Learning (ML)) is a subset of Al that enables systems to learn from data and improve performance without explicit programming instructions. Further, another subset of Al that may be utilized in the present invention is Natural Language Processing (NLP). NLP refers to the ability of an Al system to understand, interpret, and generate human language. Both of these subsets may be utilized in making the Al agent conversant with humans as well as other Al agents, as will be described in more detail later.

[0038] The concepts of machine learning and natural language processing are closely related and include considerations of large language models (LLM). More particularly, a Large Language Model (LLM) is a specialized subset of Machine Learning (ML) that falls under Natural Language Processing (NLP). To summarize these subsets of artificial intelligence that may be utilized in the present invention and which may be called using respective APIs:1. Machine Learning (ML): This is a branch of Al that enables systems to learn patterns from data and make predictions without being explicitly programmed.

[0039] 2. Deep Learning (DL): A subset of ML that uses artificial neural networks, particularly deep neural networks, to model complex patterns and relationships in data.

[0040] 3. Natural Language Processing (NLP): A specialized field within ML that focuses on enabling computers to understand, interpret, and generate human language.

[0041] 4. Large Language Model (LLM): A subcategory of NLP that consists of deep learning models trained on massive datasets to generate human-like text. Examples include GPT (like ChatGPT), BERT, and T5.

[0042] With consideration regarding how artificial intelligence modules can make the Al agent conversant with a human person as well as with another Al agent, more understanding of large language models is helpful as outlined below:

[0043] • Use of Deep Learning: LLMs employ transformer architectures, such as the Transformer neural network, to process and generate language.

[0044] • Pretrained on Massive Datasets: These models learn language patterns by training on large corpora of text data.

[0045] • Fine-Tuning for Specific Tasks: LLMs can be adapted for tasks like translation, summarization, and conversational Al.• Generative Capabilities: They can produce coherent, contextually relevant text responses.

[0046] The Al platform according to the present invention serves as a communication and connection platform to other “Reena’s” or systems within the ecosystem that the practice selects. Communication is transferred by human voice or to conversant Al and back or system to system via API as needed. The computing device itself need not be intelligent on its own. The artificial intelligence agent known as “Reena” controls all actions initiated by a user, preferably through a back-and-forth exchange of voice recognition and natural language processing paradigms. The vast majority of “Reena” lives in the “cloud.” The device serves as the platform for input and output. The computing device by which the platform is created may include a a controller or processor, a non-volatile memory, and input and output devices such as a mouse. The computing device, of course, is in data communication with the Internet to facilitate communications across associated platforms also connected to the Internet.

[0047] Preferably, the system is generated using open-source API technology from vendors which the office chooses to use, (for instance, radiograph analysis, payment systems, Practice Management Systems (PMS), inventory control, pharmacies, vendors, specialists and others) Reena connects to the function and issues the commands needed for the action to be executed in a secure manner. Reena can be configured for any environment that is specialized, recognizing scientific or procedural jargon specific to the functions it needs to commit actions in a specific environment. It is important to note that Reena is technology neutral. That means that it can connect to, for instance, any number of Practice Management Systems in the market as long as they have an API interface engine. An API, which stands for "ApplicationProgramming Interface," is a set of rules or protocols that allows different software applications to communicate with each other, exchanging data and functionality by acting as a middle ground between them; essentially, it's a way for one program to request and receive information from another program.

[0048] Since Reena is located (at least partially) in the “cloud” the latest version is always running. In the event that the device needs to be updated, Reena can initiate an update routine located on the device. The agent also continuously checks for malfunctions with every point of contact in its universe. This may include the device itself or the connections between third parties.

[0049] For instance, SaaS programs often break the API’ s when they update their software. In the event that Reena recognizes this, it can communicate with the vendor and take whatever action is required to remedy the situation. In this event, Reena will communicate with the practice’s designated staff to report the issue when it occurs and when it is resolved.

[0050] In a critical aspect, there are many practical use cases that illustrate the practicality of the present invention and that prove that the present invention includes “something more” than a mere abstract idea or abstract usage of artificial intelligence models or paradigms. In a critical distinction and in many of the following use cases, it is multiple iterations of Al agents (i.e., multiple Reena’ s) that are interacting automatically, in real-time, and silently amongst one another as opposed to an Al agent merely responding to a human asking a question. Multiple such use cases are profiled below:Use case 1: Setup:

[0051] The system, i.e., the platform, is placed in a dental office in sufficient numbers to interact with the humans in the office. It has been configured via human or Al Agent, to integrate securely with the practice’s technology vendors, inventory control systems, other professional office practice management systems in which the practice is in a referral or partnership agreement and the local PMS. This may include advanced set up of passwords, access codes, or the like.

[0052] A configuration file is then created to create the ecosystem to this instance of Reena. That file is then uploaded to the platform. In use, the system autonomously locates the related systems and then automatically and securely integrates with all systems named in the configuration file, vastly simplifying the integration process. Practically speaking, this may include calls to respective APIs which enable intelligent communications between systems within the respective ecosystem or practice area.

[0053] For instance, upon receiving the credentials for the local PMS, the conversant Al agent referred to as Reena connects to all schedules, billing information, patient records and practice financial systems, among others. More particularly, consider if the practice needs a specific piece of equipment for a planned procedure, for instance, an implant. Reena then automatically checks with the practice’s inventory control system (which may or may not be a third-party technology) to ensure that the needed implant is in stock. If not, Reena connects to the implant vendor in real time and orders the necessary implant to be delivered prior to the scheduled procedure.

[0054] Use case 2: RadiographsIn a dental office, the device is installed where needed to interact with the humans in the office using secured Wi-Fi or Bluetooth networking technology and voice recognition technology native to Reena.

[0055] A patient is in the operatory. A provider asks Reena to pull up the radiographs for the patient and highlight any areas of concern on the local display, most likely a smart television. Reena automatically connects it real-time and without specific prompting or instruction (other than requesting Reena to pull up respective radiographs for the patient) via API to the third-party system the practice has selected to analyze radiographs, connects to the local PMS, finds the radiographs requested and displays them as dictated by the radiograph analysis engine where requested.

[0056] Use Case 3: a Referral

[0057] The dentist decides that the patient needs to be referred to an oral surgeon to whom he regularly refers. The oral surgeon has an instance of Reena running in his practice.

[0058] The dentist describes the nature of the issue he is referring. The dentist’s Reena then automatically and in real-time reaches out via internet to the oral surgeon’s instance of Reena and queries it for a few available times.

[0059] The surgeon’s Reena analyzes the surgeon’s schedule, looking for possible appointment times that are long enough for the procedure, when any needed staff is required, and returns the information to the dentist’s instance of Reena.

[0060] Reena then announces the options, the patient can then select in real time and while still in the dentist’s chair, the appointment that best works for them. Once the appointment is booked, the dentist’s Reena automatically sends the patient record, imaging and the dentist’snotes to the surgeon’s Reena, who then connects the information to the new patient record that it has created for the patient. The dentist’s records where, appropriate, indicate the facts of the case as they stand. This may also automatically prompt obtaining respective radiographs and verification of insurance coverage and the procedures described above. Both instances of Reena then communicate according to the requirements dictated by the practice, for instance, a text message confirming the time and location of the surgeon’s office to the patient.

[0061] Further, if the patient wants to know any information, for instance, the cost, then Reena may query the appropriate systems and deliver the appropriate response. The surgeon’s Reena already knows if the patient has valid insurance because that is transmitted with the new patient file. If the patient agrees, the appointment is made at the oral surgeon. Once the patient receives the treatment required by the oral surgeon, the surgeon’s Reena autonomously communicates the necessary information to its counterpart in the dentist’s office and schedules a follow up in the same manner that it made the appointment at the oral surgeon’s office and sends a message via email or text to all concerned parties with the information they need to know.

[0062] One result is a more satisfied patient who doesn’t have to spend time going back and forth trying to get appointments, provide information or other clerical issues. The patient shows up at the oral surgeon as scheduled, receives the necessary treatment and receives the date and time for the follow ups as required. All of this occurs seamlessly and with minimal doctor or staff involvement

[0063] Another result is that staff time is dramatically reduced as Reena has taken care of appointments, referral communication, payment information, and inventory control. Thismakes the practice more profitable and may result in lower cost care, and more time for the dentist, which may result in better patient outcomes.

[0064] Use Case 4: Autonomous Cross-selling Opportunities

[0065] Staying with the dental ecosystem, a patient calls the dentist’s office. The phone is answered by Reena’s built-in receptionist. Reena identifies the caller ID as an existing patient and answers the phone with a personal greeting “Hello Mrs. Jones, how can we help today?”

[0066] The patient wants to schedule an appointment. Reena recognizes it as an emergency and follows the practice’s guidelines for emergency care as set forth in the configuration file or some other avenue.

[0067] Reena then looks for cross-sell opportunities — automatically accessing the caller’s patient record simultaneously with conversing with the caller. For instance, the patient record indicates that it is time for a cleaning. It then suggests that the patient may want to schedule a cleaning while on the phone and take appropriate action as dictated by the patient and the office policies.

[0068] During the conversation, Reena asks if the patient’s insurance information is correct. If it is, Reena checks the PMS to ensure that the practice takes the insurance (as described above), this action requires Reena to utilize a respective API to contact the insurance company to confirm that the patient is qualified and attempts to verify coverage. In its current state, dental insurance companies rarely allow API access to this kind of information. An individual must call the insurance company and speak to a customer service representative. There are often lengthy wait times, wasting large blocks of time for thedental office’s staff. Reena dials the number and navigates the system to get to the appropriate source of information. If it is a human that answers, Reena can speak to the human on the other end of the phone in a human voice (using its IVR and Al technologies), verify the information and record it in the PMS. No staff time is required. Again, this action is handled simultaneously while conversing with the caller.

[0069] This use case can save thousands of hours over the course of a year and free up nonrevenue producing staff. This allows the practice to streamline its headcount or redeploy the staff to more productive issues. In short, the present invention allows multitasking across multiple platforms and even with simultaneous actual live patient communications.

[0070] Efficiency is money in the present invention brings an efficiency improvement to respective healthcare industries at a historic, unexpected, and probably exponential level.

[0071] As described briefly above, traditional interactive voice recognition technology may be used along with Natural Language Processing

[0072] It is understood that while certain forms of this invention have been illustrated and described, it is not limited thereto except insofar as such limitations are included in the following claims and allowable functional equivalents thereof.

Claims

CLAIMSWhat is claimed is:

1. A computer-implemented method using artificial intelligence for autonomously orchestrating communications between a plurality of cross-platform systems within a predetermined healthcare ecosystem for treating a patient in need of care, said method comprising:accessing a conversant Al agent to converse with the patient in real time regarding scheduling, said Al agent utilizing voice recognition and natural language interfaces;accessing the Al agent to communicate with an insurance company in real time so as to verify insurance coverage of the patient; and,in a case of a patient referral, (1) accessing a cross-platform scheduling interface to connect with another practice management system (PMS) associated with a referred doctor’s office, (2) causing the Al agent to converse with another Al agent associated with the referred doctor’s office so as to coordinate an open appointment time(s) for the patient referral, and then (3) prompting the Al agent to converse and confirm the coordinated open appointment time to the patient.

2. The method as in claim 1, wherein said conversant Al agent is named “Reena”.

3. The method as in claim 1, further comprising:using another cross-platform interface for communicating with another practice management system (PMS); andprompting the Al agent to request a HIPAA-compliant patient record.

4. A cloud-based Al platform that autonomously orchestrates interactions between a plurality of cross-platform systems within a predetermined healthcare ecosystem, said platform comprising:a computing device having a processor in data communication with the Internet; a memory in said computing device configured to store program instructions and data;wherein said processor is configured to execute artificial intelligence program code stored in said memory and to access a conversant Al agent;wherein said processor is configured to autonomously activate said Al agent to converse with a patient in real-time via the Internet or telephone regarding scheduling, said Al agent accessing voice recognition and natural language interfaces in order to be conversant5. The Al platform as in claim 4, wherein said Al agent includes a first portion of programming stored in said memory and a second portion of programming stored remotely from the computing device (i.e., “in the cloud”).

6. The Al platform as in claim 4, wherein said artificial intelligence program code includes a plurality of calls to application programming interfaces (APIs) each configured to communicate data pertinent to the patient.

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