Method for operating a software platform based on artificial intelligence

Abstract

The invention relates to a method for operating an artificial intelligence (AI)-based software platform (SP) for automated, individualized, and interdisciplinary treatment management for humans, animals, and the environment, comprising a system for automated interdisciplinary appointment scheduling that uses AI algorithms to analyze the availability and condition of practitioners, the urgency of treatment, and patient-specific preferences, and dynamically schedules appointments; an interdisciplinary therapy planning component that creates personalized therapy plans based on historical and real-time data and integrates real-time data during treatment; a patient monitoring and complaint management system for continuous health monitoring; and automated treatment reporting for real-time documentation and quality assurance.The platform continuously optimizes treatment quality and efficiency through AI-supported data correlation.

Description

Technical field The invention relates to a method for operating a software platform based on artificial intelligence according to the preamble of claim 1. State of the art The prior art refers to US 8,352,291 B2, which determines a variable medical co-payment based on marginal clinical utility. For this purpose, clinical factors are analyzed to identify a specific clinical condition. An independent panel of physicians describes various medical treatment alternatives for this condition and assesses their marginal clinical utility. The amount of the co-payment varies inversely proportional to the respective clinical utility of the treatment alternatives. Reference is further made to US Patent 2022 / 0189617 A1, which discloses various embodiments of a method for managing healthcare resources. This method includes receiving a selection for a first outcome perspective, calculating impactability scores for this perspective, determining a first sub-area based on these scores, and allocating healthcare resources and costs to this sub-area. The impactability scores are calculated for various sub-areas, including the first sub-area, where the first outcome perspective corresponds to a ratio of healthcare resources to costs. A significant obstacle to interventional and healthcare research is the often stringent data protection regulations. While data protection undoubtedly plays an important role, overly restrictive regulations hinder progress in research and patient-centered care. This prevents crucial data analyses from being conducted to the extent necessary for evidence-based medical advancements. Consequently, the development of early warning systems for patients and healthcare providers, which could, for example, identify risks early on, also falls by the wayside. Another problem lies in the currently dominant approach of the pharmaceutical industry, which often aims to maximize long-term profits. Strategies, as described by management consultancies, rely on the introduction of long-term medications. Drugs such as statins or insulin are examples of therapies that often bind patients for years or even for life, without adequately addressing the underlying causes of their illnesses. Such approaches disregard the economic benefits of sustainable, more complex treatment strategies. More holistic therapies could improve the overall health and employability of the population and reduce healthcare costs in the long run. The willingness of therapists and physicians to improve the quality of care through innovative measures is also limited by professional political barriers. Many of these limitations result from traditional structures and lobbying interests that inhibit changes in the healthcare system and hinder the implementation of new, data-driven approaches. Another obstacle to improved medical research and care lies in the lack of data from essential areas of patients' lives. Information on family structures, environmental factors, the social environment, or even the keeping of pets is not currently systematically collected. Yet these factors could provide valuable insights for personalized and preventive treatment approaches. Legal and technical hurdles currently prevent the comprehensive aggregation and analysis of such data. In countries like South Korea, initial approaches to integrating extensive data sets already exist, but these often involve relaxed data protection regulations. However, shortcomings are evident in the therapeutic, methodological, and social implementation of such systems, meaning that the potential of comprehensive interdisciplinary care is not being fully realized. Genuine progress in intervention research and AI-based treatment management therefore requires a radical reorientation. Only through close collaboration between different disciplines, the targeted use of comprehensive data, and the overcoming of bureaucratic and political hurdles can sustainable solutions be developed that benefit both patients and the healthcare system in the long term. Object of the invention The object of the present invention is to overcome the disadvantages of the prior art. In particular, a method is to be provided which is operated as a universal software platform and thereby collects individualized patient data and makes it available to practitioners as a support tool. Solution to the task The features according to claim 1 lead to the solution of the problem. Advantageous embodiments are described in the dependent claims. The process for operating an AI-based software platform for automated, individualized, and interdisciplinary treatment management for humans, animals, and the environment includes an automated appointment scheduling system. This system uses AI algorithms to analyze the availability and condition of healthcare providers, the urgency of treatment, and patient-specific preferences, and to dynamically schedule appointments. Interdisciplinary in this context means the inclusion of various medical specialties, complementary medicine, traditional therapies, wellness practices, niche therapeutic groups, and self-treatment, taking into account all of the patient's historical, familial, and environmental data. The appointment scheduling system continuously integrates real-time data from various sources, such as wearables, electronic health records, and healthcare provider calendars, to adjust schedules in real time as needed. An interdisciplinary treatment planning component uses machine learning models to create individualized treatment plans. It analyzes historical and current patient data, including medical, social, and economic information, to generate predictable and precise treatment recommendations. These recommendations take into account real-time data collected during and after treatment from sensors, wearables, and treatment devices, and dynamically adapt the therapy to changes. It also utilizes real-time data from the healthcare provider and their team to enable, for example, medication recommendations, nutritional advice based on genetic and epigenetic data, or behavioral adjustments. If recommendations are not followed, this is documented and analyzed as part of a risk management process.The platform promotes a holistic and self-optimizing treatment strategy that integrates interdisciplinary dependencies between fields such as medicine, dentistry, psychology and physiotherapy. An automated patient monitoring and complaint management system uses, among other things, natural language processing to analyze patient feedback from surveys, chatbots, or voice recordings. It continuously collects real-time data from wearables, apps, and sensors to monitor patients' health status between appointments. AI-powered algorithms, such as anomaly detection and predictive analytics, identify potential health risks early and send corresponding alerts to patients and healthcare providers. Automated measures to improve treatment quality and patient satisfaction, such as adjusting treatment plans or appointments, are also initiated. Another element of the platform is automated treatment reporting, which uses AI-supported analysis to generate detailed reports on therapy progress, practitioner performance, and patient outcomes. These reports support quality assurance through measures such as benchmarking and error analysis, contributing to the dynamic improvement of treatment standards. The platform also provides data for the continuing education and motivation of practitioners. The constantly growing volume and diversity of data is evaluated in real time by AI and compared with scientific data, resulting in new, evidence-based insights that can be established as dynamic treatment standards. The platform also contributes to economic value creation by reducing expenditures for pension funds and the healthcare system, raising public awareness of health issues, and opening up new personalized healthcare markets. It promotes motivation and quality improvement in medical care and increases national economic productivity by reducing absenteeism and presenteeism. The process involves integrating interdisciplinary, real-time data from wearables, medical devices, and lifestyle apps for the continuous monitoring and dynamic adjustment of treatment plans. Physiological parameters such as heart rate, blood pressure, sleep quality, and activity levels are continuously recorded, while real-time data from treatment devices, such as cameras in operating lights or sensors in medical instruments, are collected during treatment. Additionally, data from smart home devices, such as smart scales or air quality sensors, as well as from nutrition and fitness apps, are integrated. This data enables a real-time analysis of the patient's health status and recovery, leading to timely adjustments to treatment plans, behavioral tips, nutritional recommendations, and medications. Any recommendations that are not followed are incorporated into a risk management system to guide and document appropriate measures.The principle here is that the availability of extensive and diverse data further improves the quality of therapy optimization. An AI-powered early warning system uses predictive analytics, such as time series analysis and anomaly detection, to identify potential health risks early on. It identifies risk factors, including deteriorating health status or compliance issues, and sends automated alerts to patients and healthcare providers as soon as critical thresholds are exceeded. The system suggests preventive measures, including medication adjustments or lifestyle recommendations, to minimize potential health risks. To support patients, an interactive information component is available that uses natural language processing to generate personalized health information and therapy instructions in real time. It offers chatbot functionality for questions and problems during therapy and integrates gamified elements such as reward systems to encourage patients to adhere to their therapy goals. The practitioner monitoring and quality assurance system captures real-time data from the practitioner during treatment, such as stress levels and heart rate, and correlates this with patient data. This allows for the evaluation of treatment quality and the automatic generation of feedback and training recommendations for practitioners to continuously improve their performance. Virtual training simulations support this continuing education in a personalized and redundant manner. A dynamic correlation component analyzes medical, social, and economic data in real time to reduce noise and bias. AI algorithms such as clustering and pattern recognition identify new relationships between the various data sources. These insights inform the optimization of treatment recommendations, enabling more precise predictions of treatment success and supporting the development of new treatment methods. The platform thus offers continuous improvement in treatment quality through data-driven decisions based on comprehensive and interdisciplinary information. The AI-supported software platform incorporates an interdisciplinary treatment management system that operates across medical disciplines and integrates complementary medicine, traditional therapies, wellness, and self-treatment. All of the patient's historical, familial, and environmental data are consolidated. A central interface enables the integration of interdisciplinary data, including medical diagnoses, treatment histories, and laboratory results from electronic health records and imaging studies. Video and sensor data from treatment devices, such as cameras in operating lights or medical instruments, are analyzed using AI.Simultaneously, social data, such as information on living conditions, dietary habits, and psychosocial stressors, are collected from patient feedback, wearables, and lifestyle apps, and economic data, such as treatment costs, work capacity, absenteeism, and presenteeism, are incorporated from insurance systems. This information is correlated in real time to create a holistic picture of the patient's health status and life situation. An AI-powered data analysis component uses machine learning algorithms such as deep learning, clustering, and pattern recognition to identify relationships between different data sources. Noise and bias are reduced through continuous algorithm calibration. This analysis leads to precise and personalized therapy recommendations tailored to the patient's specific needs. A dynamic correlation component analyzes historical and current data to identify long-term patterns in health outcomes. It considers interdisciplinary dependencies between medical specialties, social services, and economic factors to develop holistic treatment strategies. New correlations are identified and used to continuously improve therapy recommendations and maximize the chances of success. The platform includes a quality assurance component that uses blockchain technology to ensure data integrity and prevent manipulation and unauthorized access. Automatically established feedback loops between patients, healthcare providers, and the platform continuously improve data quality and treatment outcomes. Furthermore, continuing education programs based on the analyzed data are provided, specifically designed to enhance healthcare providers' skills. An interdisciplinary treatment planning component combines medical, social, and economic factors to create individualized treatment plans and adapt them in real time to new data and insights. Preventive measures such as lifestyle changes and psychosocial support are also suggested to minimize long-term health risks and sustainably improve treatment quality. The process comprises an AI-powered software platform with a macroeconomic analysis component that assesses the efficiency of therapies in relation to macroeconomic factors. These factors include the reduction of absenteeism and presenteeism, increased productivity, and lower treatment costs. The component also analyzes the long-term societal impact of the therapies, such as promoting social prosperity and reducing healthcare costs in old age. The data collected informs the optimization of interdisciplinary treatment recommendations that maximize macroeconomic benefits. This platform is of interest to government and economic institutions, insurance companies, and privately held companies that focus on improving their citizens' voluntary participation in healthcare.The platform does not represent individual interests such as those of professional organizations, trade unions or the pharmaceutical industry. An integrated cost optimization component reduces treatment costs through precise, individualized therapy recommendations that avoid unnecessary treatments and medications. Simultaneously, it continuously monitors and improves healthcare efficiency. In addition, a productivity enhancement component assesses the impact of therapies on patients' ability to work and prioritizes recommendations that increase productivity and prevent long-term disability. This component helps promote patient satisfaction and motivation and maximize the economic benefits of therapies. The platform also includes a sustainability component that evaluates long-term treatment success and preventive measures. It prioritizes sustainable treatments, such as lifestyle changes, over short-term solutions and continuously monitors the sustainability of therapies to improve them dynamically and immediately. An AI-powered forecasting component analyzes future trends and costs in healthcare. The therapy recommendations based on these forecasts are optimized to achieve the greatest possible economic benefit. Furthermore, the component examines the long-term societal impacts of the therapies, such as promoting general prosperity and reducing healthcare costs in old age. A prioritization component ranks treatment recommendations according to their economic benefit. Sustainable treatment methods, such as preventive measures and lifestyle changes, are given preference over long-term medications. This component focuses on the long-term effects of therapies and prioritizes the sustainable improvement in patients' health as well as the societal benefit. The process also includes an AI-supported, interdisciplinary therapy optimization component that generates sustainable therapy recommendations based on medical, social, and economic data, avoids long-term medications, and prioritizes alternative treatment methods such as lifestyle changes or physiotherapy. The effectiveness of therapies is continuously monitored and adjusted to ensure long-term health optimization. An economic analysis component evaluates therapy efficiency in terms of factors such as reducing absenteeism and presenteeism, increasing productivity, and lowering treatment costs. It also analyzes the long-term societal impact of therapies, for example, regarding increased societal prosperity and reduced healthcare costs in old age.This data is incorporated into the optimization of therapy recommendations in order to maximize the economic benefit. Furthermore, the platform includes an AI-powered simulation environment that provides virtual scenarios for the development and optimization of therapies, materials, and technologies. It tests the efficacy of treatments, drugs, and medical devices in silico and complements or replaces clinical trials to accelerate the development process and reduce costs. A prioritization component ensures that therapy recommendations are favored based on their economic benefits, prioritizing sustainable treatment methods such as preventive measures and lifestyle changes over long-term medications. This component focuses on the long-term impact of therapies on health and society. Furthermore, the platform includes an AI-powered material and technology recommendation component that suggests optimal materials and technologies for therapy based on analyzed data. It monitors and improves the efficacy and sustainability of these materials and develops new solutions based on the specific needs of patients and society. Finally, an ethics and bias monitoring component ensures that the AI ​​algorithms are continuously reviewed for ethical considerations and potential biases. This ensures that therapy recommendations are fair, unbiased, and in line with societal values, while maintaining the transparency and traceability of AI decisions. The process comprises a comprehensive AI-powered platform that utilizes blockchain integration or encryption software to securely and transparently store patient and healthcare provider data. This storage is complemented by a predictive analytics component that identifies future health risks and optimizes preventive measures based on historical and real-time data. To promote therapy compliance, a gamification component is integrated, employing game-like elements and personalized incentives. An augmented reality component provides real-time information to support healthcare providers during therapy, particularly in complex medical procedures such as surgical interventions. Furthermore, an emotional AI component analyzes the emotional state of both patients and healthcare providers to adjust therapy accordingly and improve treatment quality. The platform utilizes IoT integration to collect data from wearables and smart home devices and analyze it in real time to dynamically adapt treatment plans. An ethics and bias monitoring component continuously monitors the AI ​​algorithms for ethical considerations and potential biases to ensure that treatment recommendations are fair and transparent. An AI-powered simulation environment provides virtual training scenarios for practitioners to enhance their skills and knowledge, and simulates complex treatment scenarios to evaluate the effectiveness of treatment methods and optimize therapy recommendations. Furthermore, the platform includes a materials and technology recommendation component that suggests optimal materials and technologies for therapy and continuously monitors and improves their effectiveness and sustainability. An economic forecasting component analyzes future trends and costs in healthcare and uses these insights to optimize therapy recommendations that maximize economic benefits. A patient-provider matching component optimally matches patients and providers based on their needs, preferences, and skills, thereby improving satisfaction and therapy outcomes.Finally, the AI-powered interdisciplinary therapy recommendation engine provides individual, dynamic therapy recommendations that are continuously adapted to the latest data and findings to increase treatment quality and efficiency and optimize therapy outcomes in real time. The process includes an AI-powered development and optimization component that automatically generates new therapies, materials, and technologies based on the analysis of interdisciplinary data from medical, social, and economic fields. These developments are tested in silico to evaluate efficacy and safety and to complement or replace clinical trials. The platform continuously suggests improvements tailored to the specific needs of patients and society. A virtual simulation environment simulates complex treatment scenarios to assess the effectiveness of interdisciplinary treatment methods, medications, and medical devices. It also predicts the impact on patient health and society to optimize treatment recommendations and drive further development. In addition, a material and technology recommendation component is integrated, which suggests optimal materials and technologies for therapy based on the analyzed data and continuously monitors and improves their effectiveness and sustainability. New materials and technologies are developed based on the specific needs of patients and society. An AI-powered innovation component identifies new therapeutic approaches and treatment methods based on the collected data and tests them in virtual environments to verify their effectiveness and safety. These test results are used to further optimize therapy recommendations and drive development forward. The quality assurance component ensures that developments are continuously reviewed for effectiveness, safety, and sustainability. Feedback loops between patients, healthcare providers, and the platform are established to continuously improve the quality of the developments and communicate this information to all stakeholders in real time, thus guaranteeing consistent and high-quality treatment. An ethics and bias monitoring component continuously monitors the AI ​​algorithms for ethical considerations and potential biases, ensuring that developments are fair, unbiased, and in line with societal values, and guaranteeing the transparency and traceability of AI decisions. The invention solves the problem of automated, AI-supported, and interdisciplinary treatment management through an innovative software platform for humans, animals, and the environment (SP). This platform comprises numerous functions and technologies that sustainably improve treatment efficiency and quality while simultaneously offering economic benefits. A key component of the SP (Social Practice) is automated, interdisciplinary, and dynamically personalized treatment management. This covers the entire personalized treatment process during and between therapy sessions. The platform considers medical, pharmacological, complementary, and traditional therapeutic approaches, as well as related areas such as wellness and self-treatment. Additionally, historical data, family and environmental information, including data on the patient's pet ownership, are integrated. Key features include automated and personalized appointment scheduling. This is done dynamically based on the patient's individual health data, as well as the availability and condition of the healthcare provider. The AI ​​takes into account factors such as urgency, patient preferences, and the optimal sequence of different treatment steps. In addition, the platform supports AI-assisted therapy planning by analyzing historical, current, and social data. This enables the creation of highly precise, individualized therapy recommendations based on the highest probability of success. Another key focus is automated patient reporting and monitoring. Patient data is continuously collected, analyzed, and categorized. Complaints and feedback are directly integrated into the platform's self-learning processes to continuously improve treatment quality. Simultaneously, automated treatment reports are generated, containing detailed information on therapy progress, materials used, and outcomes. This data supports quality assurance and enables faster evaluation and optimization of treatment procedures, as well as more precise prediction of treatment success. The platform also offers features for the automated development of materials, techniques, and treatment methods. Complex, individualized data can be used in virtual simulations to test and optimize new developments, potentially eliminating the need for clinical trials in many cases. Another key aspect is the integration of real-time data. Wearables such as the Apple Watch, Floxo devices, Aura Rings, and other IoT devices continuously record vital parameters like heart rate, sleep quality, and stress levels. This data can be collected both during treatment and in the periods between sessions. The simultaneous collection and analysis of patient and practitioner data is particularly innovative. Cameras and sensors can document treatment and provide information on practitioner performance and the treatment environment, enabling new approaches to quality assurance and practitioner training. This data also contributes to the development of personalized early warning systems that can identify potential health risks early and initiate appropriate measures. Interdisciplinary data integration is another key feature of the platform. The SP correlates medical, social, and economic data to obtain a comprehensive and holistic picture of the patient's health. Social data such as dietary habits, emotional resilience, and work capacity complement the medical information, while economic data allows conclusions to be drawn about the efficiency and sustainability of the therapy. This broad data foundation enables the platform to reduce noise and bias, identify dynamic correlations, and generate precise, interdisciplinary therapy recommendations. Long-term improvements in treatment quality are achieved through the continuous analysis of large datasets. AI algorithms detect systematic biases and random fluctuations and dynamically adjust therapy recommendations. This ensures evidence-based adaptation of treatment standards and enables the continuous growth of medical knowledge. The platform functions as a learning system that supports both practitioners and patients through virtual training programs and motivating workshops. Practitioners can receive individualized training using virtual scenarios in VR environments, which enhances the quality and motivation of their daily work. The platform creates not only medical but also economic benefits. Efficient and sustainable treatments reduce sick leave (absenteeism) and improve patient productivity (reducing presenteeism). Healthier patients are more productive, which has a positive impact on the economy. At the same time, precise therapy recommendations avoid unnecessary medications and procedures, significantly reducing healthcare costs. The platform can also reduce the costs of caring for chronically ill patients. Cost savings extend beyond materials and procedures to include efficiency gains through optimized workflows. Furthermore, the platform's incentive system leads to better compensation for healthcare providers, increasing the attractiveness of the profession and promoting long-term treatment quality. Another advantage of the platform is the automated development of new treatment techniques, materials, and pharmaceutical products. Based on the comprehensive data collected, the SP continuously suggests improvements and new developments. These can be verified through virtual testing, which significantly accelerates the innovation cycle and reduces the need for clinical trials. The platform also integrates a variety of innovative technologies. These include blockchain-based systems for the secure and transparent storage of patient data, predictive analytics for risk identification, gamification elements for patient motivation, augmented reality to support healthcare providers, and emotional AI for analyzing the emotional state of those involved. IoT systems and GDPR-compliant data processing ensure adherence to the highest data protection standards. Additionally, APIs and interfaces can be used for secure data exchange between different systems. The platform also protects its source code and other critical components through patents and copyrights. In an exemplary application, such as a dental procedure, the platform demonstrates its comprehensive capabilities. It offers automated treatment management, real-time data integration, interdisciplinary data analysis, and early warning systems that not only improve the treatment itself but also the patient's long-term health. The SP thus enhances the efficiency, quality, and sustainability of healthcare and makes a crucial contribution to optimizing the healthcare system. The process includes a personalized, interdisciplinary therapy planning component that uses AI algorithms to create individualized treatment plans and continuously integrates real-time data from various sources, such as wearables, treatment devices, and patient feedback, to dynamically adapt the therapy. Preventive measures are suggested to minimize long-term health risks through lifestyle changes or psychosocial support. In addition, an AI-supported, interdisciplinary quality assurance system is included, which evaluates treatment quality through continuous analysis of therapy progress, patient outcomes, and practitioner performance. Automatically established feedback loops between patients, practitioners, and the platform contribute to the continuous optimization of therapy results.Redundant training programs based on the analyzed data support both practitioners and patients through targeted training that is regularly updated to keep pace with evolving technologies. These programs also incorporate gamification elements to increase participant motivation. An automated early warning system uses predictive analytics, such as time series analysis and anomaly detection, to identify health risks early on. It automatically generates alerts to patients and healthcare providers when critical thresholds, such as abnormal vital signs or insufficient therapy compliance, are exceeded. The system suggests preventive measures, such as medication adjustments and lifestyle changes, to minimize potential risks. A preventive measures component complements this functionality by identifying specific risk factors and providing targeted health promotion recommendations focused on longevity. The platform also includes an interactive patient information component that uses natural language processing to generate personalized health information and therapy instructions in real time. Chatbot functionalities are available to support patients during therapy. Gamification elements, such as reward systems for achieving therapy goals, encourage patients to adhere to their treatment plans. A dynamic, interdisciplinary standardization component automatically updates treatment standards based on analyzed data and therapy outcomes. These standards are continuously adapted to new scientific findings and technologies and communicated to practitioners and patients in real time to ensure consistent, high-quality treatment. The ongoing evaluation of extensive datasets by AI enables the development of new, evidence-based insights that are immediately integrated into the platform's dynamic standards. The practitioner monitoring and motivation system captures real-time practitioner data during treatment, such as stress levels and heart rate, and correlates this with patient data to assess treatment quality. Based on this data, feedback and training recommendations are automatically generated to promote continuous improvement in practitioner performance. Additionally, the platform provides individualized training programs, tailored to the specific needs of practitioners through virtual training simulations, ensuring their long-term motivation and the quality of their work. The present invention comprises an AI-based, interdisciplinary software platform for humans, animals, and the environment, enabling fully or partially automated treatment management. This platform pursues several central objectives aimed at sustainably improving healthcare, increasing efficiency, and optimizing costs. A key objective of the platform is the automated management of the entire treatment process, including the time between individual interventions. This management encompasses all relevant phases, from interdisciplinary appointment scheduling and the complex collection and processing of medical and non-medical data to therapy planning, the generation of recommendations, and complaint management. Furthermore, the platform enables comprehensive reporting and monitoring, providing both patients and healthcare providers with continuously updated information. The automated management and analysis of these processes reduces the workload for healthcare professionals, improves efficiency, and enables data-driven decisions in real time. Another key component is the integration of real-time data. Traditionally, treatment processes are documented before or after therapy, which offers only limited insight into the actual progress. In contrast, the platform enables continuous real-time monitoring. Wearables such as the Apple Watch, Floxo devices, Aura Rings, or cameras, as well as specialized apps, continuously provide data on the patients' health status. This data is collected and analyzed during and between treatments to develop comprehensive early warning systems with personalized alerts. A further innovation is the simultaneous, real-time evaluation of data from both the patient and the practitioner. This includes the recording of physiological parameters from both parties and real-time documentation of the treatment process, for example, via cameras.Especially in invasive and non-invasive interventions—such as in surgery, dentistry, manual therapy, or psychotherapy—this function offers a significant enhancement of quality assurance. The platform thus creates new opportunities for documentation, individualized training and motivation of practitioners, and the collection of new, correlated data for research. The platform aims to achieve comprehensive interdisciplinary data integration. It combines medical, social, and economic information to systematically reduce noise and bias. This data fusion forms the basis for precise, individualized therapy recommendations. Simultaneously, it creates a redundant learning environment for both patients and healthcare providers. Continuing education programs based on the insights gained from this data can be used to motivate healthcare providers and support patients in managing their health. Another key objective of the invention is to improve the quality of treatment. Dynamic data acquisition from various sources and processes enables the use of automated processes and data-driven decisions to optimize treatment. Precise, evidence-based therapy recommendations allow for predictable and individualized treatment approaches. Patients can access personalized healthcare through continuous monitoring and real-time interactions. Behavioral changes and therapy adjustments are dynamically adapted to the patient's life circumstances and lifestyle, promoting the long-term effectiveness and sustainability of the treatment. Furthermore, the platform pursues a macroeconomic benefit. By increasing treatment efficiency and quality, it contributes to reducing healthcare costs. At the same time, it boosts productivity by reducing absenteeism (illness-related absence from work) and presenteeism (working despite reduced capacity). This approach contrasts with the often profit-maximizing strategy of the pharmaceutical industry, which frequently aims for long-term patient retention. Instead, the platform promotes sustainable and holistic health improvement that focuses on prevention and personalized interventions. Another innovative aspect of the platform is its ability to automatically or semi-automatically develop treatment approaches, materials, and pharmaceutical products. Based on comprehensively collected and correlated data from various processes, the platform can continuously identify new optimization opportunities. Virtual tests and simulations make it possible to improve or even develop new treatment techniques and therapies without necessarily requiring extensive clinical trials in every case. This accelerates the innovation cycle and supports the evidence-based development of medical standards. Overall, this interdisciplinary software platform represents a milestone in modern healthcare. It combines technological innovations with a holistic approach to improving treatment quality, promoting sustainability, and creating long-term social and economic benefits. The present invention relates to an AI-based, interdisciplinary software platform for humans, animals, and the environment, enabling semi- or fully automated, data-driven treatment management across disciplines and beyond the boundaries of medicine. With regard to interventional research, this software covers the entire treatment process. It encompasses all steps from appointment scheduling and therapy planning to automated patient survey and complaint management, as well as real-time, interdisciplinary treatment reporting accessible to both patients and practitioners. The possibility of complete process automation is taken into account. The platform integrates real-time data from diverse sources in everyday life, such as wearables like the Apple Watch, Floxo devices, Aura rings, Foodwatch systems, or future technologies.This data serves to ensure comprehensive and continuous monitoring of the patients' health status. A key advantage of the platform lies in the correlation of interdisciplinary data from medical, dental, social, and economic sources. This data is analyzed both in real time and serially. Advanced algorithms reduce data noise and minimize biases, such as those arising from incomplete data collection. This significantly contributes to quality assurance and enables the generation of precise, predictable, and individualized treatment recommendations. These recommendations are based on probabilities of treatment success and lead to a significant improvement in treatment outcomes. The platform also functions as a redundant learning environment, continuously providing insights for both patients and practitioners.Personalized advice and recommendations can help patients optimize their health behavior, while data-driven insights can improve healthcare providers' decision-making processes. This establishes early warning systems that identify health risks early and support preventative measures. The invention aims to sustainably maximize treatment quality and efficiency. Simultaneously, long-term cost reduction is achieved through the optimization of resources and processes. This not only has positive effects on individual patients but also leads to increasing economic benefits. By reducing preventable illnesses and complications, the burden on the healthcare system can be lowered and the population's ability to work can be increased. This approach differs fundamentally from conventional pharmaceutical industry strategies, which often aim to maximize profits through long-term medication. Such medications, like the use of statins or insulin, often lead to patients becoming permanently dependent on medication without addressing the underlying causes of their illnesses. The software platform, instead, focuses on sustainable health improvements for people, animals, and the environment. It considers not only medical but also social and environmental factors to enable comprehensive health promotion. This not only improves the well-being of individual patients but also contributes to social and economic prosperity. This integrative approach aligns with the principles of economic efficiency and sustainable development, as promoted by consulting firms such as Roland Berger. The platform offers significant benefits for various stakeholders in the healthcare sector. Communities, governments, non-profit organizations, insurance companies, and businesses can all profit from its dynamic quality assurance and improvement capabilities. Simultaneously, the interdisciplinary networking and management of treatments further minimizes bias and noise. This leads to sustainably improved care and lays the foundation for a learning, future-oriented healthcare system that optimizes both preventive and curative measures. The platform contributes to addressing the challenges of a modern healthcare system and ensuring the long-term quality of life for all involved. QUOTES INCLUDED IN THE DESCRIPTION This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature US 8 352 291 B2

[0002] US 2022 / 0189617 A1

[0003]

Claims

Method for operating an artificial intelligence (AI)-based software platform (SP) for automated, individualized, and interdisciplinary treatment management for humans, animals, and the environment, comprising a system for automated interdisciplinary appointment scheduling that uses AI algorithms to analyze the availability and condition of practitioners as well as the urgency of treatment and patient-specific preferences and dynamically plans appointments; an interdisciplinary therapy planning component that creates personalized therapy plans based on historical and real-time data and integrates real-time data during treatment; a patient monitoring and complaint management system for continuous health monitoring; and automated treatment reporting for real-time documentation and quality assurance, whereby the platform continuously optimizes treatment quality and efficiency through AI-supported data correlation. The method according to claim 1, characterized in that it includes an integration of interdisciplinary real-time data from wearables, medical devices and lifestyle apps for continuous monitoring and dynamic adaptation of therapy plans, a risk management component for recording and analyzing compliance with behavioral recommendations, an AI-supported early warning system for identifying health risks using predictive analytics and anomaly detection, an interactive patient information component with natural language processing and chatbot functionalities for providing personalized health information, and a practitioner monitoring and quality assurance system for real-time correlation of patient and practitioner data for optimizing treatment quality and continuous training. A method according to claim 1 or 2, characterized in that an interface for integrating interdisciplinary data is established, which captures medical data such as diagnoses, treatment courses and laboratory results from electronic patient records and imaging procedures, analyzes video and sensor data from treatment devices using AI, integrates social data such as living conditions and dietary habits from patient feedback and wearables, includes economic data such as treatment costs and ability to work from insurance systems, correlates this data in real time to create a comprehensive health profile, includes an AI-supported data analysis component with machine learning algorithms for recognizing correlations and reducing noise and bias, generates precise therapy recommendations, and includes a dynamic correlation component for analyzing long-term patterns and interdisciplinary dependencies.continuously improves the accuracy of therapy recommendations, offers a quality assurance component to ensure data integrity using blockchain technology and to automatically optimize data quality and therapy results, and provides redundant training programs for practitioners based on the analyzed data. A method according to one of the preceding claims, characterized in that a personalized interdisciplinary therapy planning component, which creates individual therapy plans based on AI algorithms, continuously integrates real-time data from various sources to dynamically adapt the therapy and suggests preventive measures to minimize long-term health risks; an AI-supported interdisciplinary quality assurance system for evaluating treatment quality through continuous analysis of therapy progress, patient outcomes and practitioner performance, which establishes feedback loops between patients, practitioners and the platform and provides redundant training programs for practitioners and patients; an automated early warning system that uses predictive analytics for the early identification of risk factors.automatically generates warnings and suggests preventive measures to avoid potential health risks; an interactive patient information component that uses natural language processing to generate personalized health information, offers chatbot functionalities to support patients and integrates gamification elements; a dynamic standardization system that automatically updates interdisciplinary treatment standards based on analyzed data and continuously adapts them to new scientific findings and technologies; and a practitioner monitoring and motivation system that collects real-time practitioner data during treatment, correlates it with patient data, assesses treatment quality, generates feedback and training recommendations, and provides individualized training programs to improve performance. A method according to one of the preceding claims, characterized in that an economic analysis component is established for evaluating therapy efficiency with regard to economic factors, wherein economic factors include the reduction of absenteeism and presenteeism, the increase in productivity and the reduction of treatment costs, a cost optimization component for reducing treatment costs through precise, interdisciplinary and individualized therapy recommendations that avoid unnecessary treatments and monitor and improve the efficiency of the healthcare system, a productivity enhancement component for evaluating and prioritizing therapy recommendations that increase patients' ability to work and avoid long-term incapacity for work, and a sustainability component for evaluating and promoting long-term therapy success and preventive measures.It features an AI-supported forecasting component for analyzing and predicting future trends and costs in healthcare, as well as a prioritization component for the preferred selection of sustainable and preventive therapy recommendations based on their economic benefits. A method according to any of the preceding claims, characterized in that an AI-supported interdisciplinary therapy optimization component is established which generates sustainable therapy recommendations based on analyzed medical, social, and economic data, avoids long-term medications, and continuously monitors and adjusts the effectiveness of therapies; an economic analysis component for evaluating therapy efficiency with regard to factors such as reducing absenteeism and presenteeism, increasing productivity, and lowering treatment costs; an AI-supported simulation environment for the virtual development and optimization of therapies, materials, and technologies, which performs computer-aided tests to supplement or replace clinical trials; and a prioritization component for favoring sustainable treatment methods over long-term medications based on their economic benefit.an AI-supported material and technology recommendation component for selecting and continuously improving optimal materials and technologies, as well as for developing new solutions based on specific patient and societal needs; and an ethics and bias monitoring component for monitoring the AI ​​algorithms for ethical aspects and biases, ensuring transparency, fairness, and societal values ​​in the therapy recommendations. A method according to one of the preceding claims, characterized in that a blockchain integration or encryption software integration is established for the secure and transparent storage of patient and practitioner data, which includes a predictive analytics component for identifying future health risks and optimizing preventive measures based on historical and real-time data, a gamification component for promoting therapy compliance through game-like elements and personalized incentives, an augmented reality component to support practitioners with real-time information and visualizations to improve treatment quality, an emotional AI component for analyzing the emotional state of patients and practitioners, and an IoT integration for continuous monitoring of health status through data from wearables and smart home devices.an ethics and bias monitoring component to monitor AI algorithms for ethical aspects and distortions, an AI-supported simulation environment for developing and evaluating complex therapy scenarios, a material and technology recommendation component for selecting and optimizing therapy technologies, an economic forecasting component for predicting trends and costs in healthcare, a patient-provider matching component for optimal interdisciplinary assignment and continuous adaptation, and a therapy recommendation engine for generating, adapting, and monitoring individualized therapy recommendations in real time. A method according to one of the preceding claims, characterized in that an AI-supported development and optimization component is established for the automatic development and improvement of new therapeutic procedures, which evaluates materials and technologies based on interdisciplinary medical, social, and economic data and has a virtual simulation environment for evaluating and predicting the efficacy and effects of interdisciplinary treatment methods and for optimizing therapy recommendations, wherein a material and technology recommendation component is provided for selection, including the monitoring and further development of optimal therapy technologies, an AI-supported innovation component for identifying and virtually testing new therapeutic approaches, and a quality assurance component for continuously verifying efficacy.The development demonstrates safety and sustainability with real-time communication to practitioners and patients, as well as an ethics and bias monitoring component to monitor AI algorithms for ethical aspects and potential distortions to ensure fairness, transparency, and social conformity of the developments.