Automation, Analytics and AI- the next for RPM program that can result in 6 figure additional revenue for HCPs! 

Automation, Analytics and AI- the next for RPM program that can result in 6 figure additional revenue for HCPs! Avakash Dekavadiya RPM Program- the New Normal for Chronic Disease Management and a Potential to generate an additional 6 figures revenue for HCPs! Post COVID-19, the need for Remote Patient Monitoring (RPM) has increased exponentially in the US and the RPM program has been adopted by both patients and providers. With the growing sophistication of the RPM solutions and greater clarity from CMS on RPM reimbursements, it is evident Remote Patient Monitoring will become the new normal of managing chronic conditions.   Along with its clinical decision making, continuous monitoring and remote telemedicine capabilities the program also brings the potential to generate 6 figures additional revenue to the HCPs!  CMS RPM Reimbursement Rules 2024: One Device reimbursement bill per month One practitioner reimbursement per month Patients with Established patient relationship status eligible for RPM programs Minimum 16 days of data records essential using RPM devices and platform If you RPM program adheres to the above rules, below are the latest reimbursement rates with CPT Code: 99453: Initial setup and education, with an average reimbursement rate of $19.65 99454: Data transmission, with an average reimbursement rate of $46.50 99457: Management services for the initial 20 minutes, with an average reimbursement rate of $48.14 99458: Management services for each additional 20 minutes, with an average reimbursement rate of $38.64 99091: Collecting and analyzing physiologic data, with an average reimbursement rate of $52.71 RPM and 6 Figures Additional Revenue Generation: There are majorly 3 different parties involved in the RPM program that can benefit from RPM reimbursements that are RPM device manufacturers, RPM solution providers and Physicians/Hospitals. If we roughly calculate the reimbursement amount considering the 16-day rule, each RPM patient who gets enrolled in the program can generate an additional reimbursement revenue of $1374.5 per annum! If a hospital manages to enroll 100 patients in the RPM program, it can effectively generate 6 figures of additional revenue per annum from the program. Also, these are not just hypothetical scenarios. Many such Hospitals and RPM solutions providers have a successful RPM program. But to generate this additional revenue with stability, there are certain essential functionalities and digital experiences that RPM solution providers must integrate into their platform to make it successful and future proof. Techno-functional essentials to ensure the success of the RPM program and RPM solutions: Integration and Interoperability: The MedTech industry is heading towards the new scale of data interoperability and right from data ingestion to data processing and sharing, RPM platforms will need a certain standardization of the data to ensure hassle free and seamless data interoperability. Consumer-Driven User Experience and Accessibility: While the use case of RPM is purely medicinal, patients or users that are enrolling to the platform will also have to generate the data for 16 days at least for RPM platforms and providers to claim the reimbursements. This requires a certain level of experience design because while patients value the health benefits and outcomes of RPM program, they might refrain from using it if the workflows and system navigation becomes a hassle for them. Automation, Analytics and AI: One of the major reasons why Physicians or Providers shy away from RPM programs is because of the manual operational burdens of the platform and the lack of ease for providers to access the required information. While RPM program brings the additional revenue, RPM platform will have to bring the required automation for seamless onboarding, deliver the required outcome analytics to help with the clinical decision making and use AI to deliver clinical intelligence and operational excellence to the providers. We engineer the next for RPM platforms! Bring the best of integrations, intelligence and interoperability to your RPM program with AIMDek’s Innovation Engineering: To ensure RPM has the essential data intelligence, seamless device and health systems integrations, AIMDek offers Data-as-a-Service accelerator to the RPM and Medical Device Manufacturers! From standardized data ingestion to Change Data Capture based data processing automation, we have designed a perfect Interoperability solution for RPM platforms and RPM devices. Our integration solution can make customer onboarding seamless and data transmissions automated for various purposes and business models. To understand more about our Integration and Innovation engineering solutions, send us an email at marketing@aimdek.com and talk to our SMEs today! skip render: ucaddon_next_prev_post

A Beginners Guide to Artificial Intelligence

A Beginners Guide to Artificial Intelligence Avakash Dekavadiya  Artificial Intelligence has been around since a long time, and has been in the buzz due to its extraordinary applications. The term AI was first proposed by John McCarthy in 1956, at the time when the IT industry hadn’t taken the revolution as it is in the 21st century, so the concepts were only limited to the theories. Today, we find the AI being used in almost every successful product which is directly or indirectly communicating with the user.  AI is strongly used in almost every industries some of them are as follows : Social Networks Social Networks Digital Media Transportationc E-Commerce Finance Any leading product in above industries uses AI services. AI powered products have given the users the ability to use the products with ease and give the friendliness in the service. In today’s competitive market users have many options for products in the same industry, things are getting changed rapidly due to increase of data and users, no user will waste time in manually setting the app’s requirements, the product itself has to adapt to the user’s perspective. And like this all sort of requirements are fulfilled by AI. Branches of AI To understand the AI in deep, we need to go through the sub levels or the branches of AI. As we have already gone through the meaning of AI, let us go through the branches of AI . Machine Learning The science of making the software learn by itself from the provided data is known as Machine Learning. ML is the branch or can be said as application of AI. It is a type of application in which it provides the system to improve and learn from the data provided. With every iteration the system is smarter from the previous iteration. Knowledge is the base for every ML application because the system keeps on improving from the knowledge provided. The more the knowledge the more accurate the result will be. It is next to impossible to ignore the Machine Learning applications in today’s tech generation, some of the applications which are widely used in our day to day life are as follows. Virtual Assistants : In the world of Information Technology, all the people are connected to smartphone and every smartphone no matter the manufacturer, are having the services of Virtual Assistants, Android users have Google Assistant, iPhone users have Siri, Amazon also provides the Virtual assistant named Alexa. All these Virtual Assistants are the result of Machine Learning. Now the role of Machine Learning in this Virtual Assistant is to gather the information asked by the user based on his/her previous queries and information. Virtual assistant also triggers any app from the device, for example if any command given to Assistant like “Make a Phone call to John”, then the virtual assistant will open the voice call app and will dial to John. Social Media applications : Social Media users are increasing day by day, with increase in the users, there is an increase in the data. Managing every user manually is quite impossible so many features are powered by Machine Learning which helps the users to use the applications and get the best result. Some of the Social Media examples are as follows : Facebook has one of the most popular features which is Friends Suggestion. The user will get the suggestion of the friends based on the profile visits, mutual friends and some common city or work place shared by users. This feature takes the huge advantage of the Machine Learning. One of the widely used social networking services is Twitter, it is hard to ignore the influence of this platform in today’s generation. Twitter shows the suggestion of people to follow to the users based on some similar following. Face recognition is expanding in almost every photo sharing application, one of the prime services given by Facebook is to detect the person and the things get visible in the picture, is again the example of Machine Learning. For this the software of face recognition should have the base knowledge of the person visible in the picture, based on this knowledge the software will identify the user. World’s most successful video sharing platform YouTube makes takes the most advantage of Machine Learning concepts, the recommended videos shown below the video is always related to the current playing video. Natural Language Processing Natural Language Processing is again one of the most important areas of Artificial Intelligence. The main goal if NLP is to process the human language and transform it into the computer understandable language. NLP becomes the bridge between human and the computer. The data which is used or processed by the computer is in linear form, and the data exchanged between Humans is in non linear or unstructured form, which is not understandable by computers. Current tech generation gives users the ability to talk with the computers, this feature is only possible because of NLP. Let us go through the step by step mechanism of NLP : Sentence Segmentation : The process of breaking down the paragraph into the sentence and sentence into the meaningful words. These words will have some meaning which will create the links between the words. Words Tokenization : In the first step the paragraph is broken down into the sentences and now in this step the sentences are further broken down into the words. These words will have separate meanings. Deriving the meaning : When the words and tokens are separated from the sentence, now it’s time to analyze each word separately and know whether it’s a noun, verb, adjective or any other parts of speech. This step will help us in understanding the meaning of the words and will also make the sentence more sensible. Text Lemmatization : The process of finding the similar concept from two different sentences is known as Text Lemmatization. Let us go through the below example :  India is democratic country.