Combating diabetes with analytics and AI
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This document discusses how artificial intelligence can help combat diabetes through predictive analytics, lifestyle guidance, and monitoring tools. It outlines conferences held by an analytics company to discuss these topics. Key challenges to adoption include integration issues, data privacy, lack of standards, and gaining acceptance from medical practitioners. However, AI shows promise in predicting glucose levels, personalized education, insulin guidance, and complication monitoring if these obstacles can be overcome.
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Combating diabetes with analytics and AI
- 2. 2022 SERIES OF EVENTS New York JULY (HYBRID) Australia SEPTEMBER (HYBRID) Singapore APRIL (VIRTUAL) Helsinki & North MARCH (VIRTUAL) Paris DECEMBER (HYBRID) London OCTOBER (HYBRID) Hong Kong AUGUST (VIRTUAL) JUNE (VIRTUAL) India MAY (VIRTUAL) APRIL (VIRTUAL) Dubai & Middle East JUNE (VIRTUAL) Check out our API Conferences www.apidays.global Want to talk at one of our conferences? apidays.typeform.com/to/ILJeAaV8
- 3. The Magnitude of the Diabetes Problem
- 4. Diabetes in South Asia • 90 million adults (20-79) are living with diabetes in the IDF South-Asia Region in 2021. This figure is estimated to increase to 113 million by 2030 and 152 million by 2045. • 46 million adults living with diabetes in the IDF South-Asia Region are undiagnosed - 51% of the total number of adults living with diabetes in the region. • 47 million adults in the IDF South-Asia Region have Impaired Glucose Tolerance (IGT), which places them at increased risk of developing type 2 diabetes. This number is projected to reach 59 million by 2030 and 77 million by 2045. • Diabetes was responsible for 747,000 deaths in the IDF South Asia Region in 2021. • USD 10 billion was spent on healthcare for people with diabetes in 2021— the second lowest expenditure of all IDF regions. • 1 in 4 live births in the IDF South Asia Region are affected by hyperglycaemia in pregnancy. Source: International Diabetes Federation (2021)
- 5. What Happens in Diabetes
- 7. Current situation of Diabetes Care The Lancet Diabetes & Endocrinology 2021 9174-188DOI: (10.1016/S2213-8587(20)30381-8)
- 8. Healthcare trends are driving the change to adopting new processes Healthcare trends • Moving from hospital clinic based to community based; its about changing the mindset of patients to empower them to look after their own health • Moving from curing a disease to preventative wellness • Moving from control by a medical Practitioner Empowered Patient • Moving from capital equipment hard wired device in Clinic Wireless / Bluetooth and consumer electronic wearables and cloud based applications.
- 9. Artificial intelligence (AI) offers promise in diabetes care • The purpose is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. • A diverse and complex set of innovative approaches that aim to transform diabetes care are available.
- 10. Artificial intelligence (AI) offers promise in diabetes care Opportunities in AI powered Diabetes Management AI Assisted Predictive Care • Healthcare can foresee chronic risk development in human by tracking social health determinants • Take preventive measures Unified Healthcare Systems Single digital infrastructure linked to specialized treatment clinics, surgery centres retail clinics for real time monitoring Enhanced Staff & Patient Interaction AI assisted analytical healthcare systems will lead to shorter wait time, enriched staff workflows and reduced burden on healthcare system
- 11. Obstacles and Issues in using disruptive technologies and AI Potential challenges • Development costs and access to data for AI solutions • Integration issues • Reluctance among medical practitioners to adopt AI • Fear of replacing humans • Yet, Medical Insurance driving cost saving adoption • Ethics and Data Privacy and security • Mobile health applications and devices that use AI • Interoperability between consumer and healthcare AI solutions • Data exchange • Need for continuous training by data from clinical studies • Incentives for sharing data on the system for further development and improvement of the system. Nevertheless, • All the parties in the healthcare system, the physicians, the pharmaceutical companies and the patients, have greater incentives to compile and exchange information • Rapid and iterative process of software updates commonly used to improve existing products and services • Attitude and Behaviourial change by People of Influence to adopt innovation
- 12. Obstacles and Issues in using disruptive technologies and AI (cont.) • Medical stakeholder is concerned about risks from false positive and false negative from consumer electronics & empowered patients misinterpreting and relying on the data • Wellness wearables collect data including blood glucose, Blood pressure and heart rates. Where can the patient store personalized Medical records • Some wellness wearables are not regulated compared to FDA approved Medical Devices Practitioner vs Insurance vs Patient
- 13. Obstacles and Issues in using disruptive technologies and AI • We have to build secured network from scratch and maintain them • End-to-end encryption: Data-in-flight and Data-at-rest encryption
- 14. Overcoming Obstacles and Issues Influencing for Innovation Adoption Proactive and managed data mining & analysis with a focus on the governance to address the bias in data. • An attitude and behavior change project, from Director to Medical Partitioner to Patient, • New Design, Development and Deployment process. • Process and skill changes to use the innovation • Training of Practitioners and Patients • Investment justification, risk management, and ethics & governance with Management, Finance & Legal • Data security and privacy • Ethics and Governance
- 15. AI technologies applied to diabetes education mainly focus on 1) diabetes prediction, 2) lifestyle guidance, 3) insulin injection guidance, 4) blood sugar monitoring, self management, and complication monitoring
- 16. How does AI’s predictive algorithm work? A case-study from Medicsen, Spain Medicsen’s Suggin-AI software is driven by a predictive algorithm that, based on a set of data from a person with type 1 diabetes, is able to predict future glucose levels in the next two hours. This software would only give the guaranteed correct data for patients with type 1 diabetes and is intended to give this type more control so that they can anticipate in advance in case their glucose is estimated to be at dangerous levels in the near The data collected and used by the SugginAI are biometric (medical or clinical) and are as follows: • Blood glucose measurements throughout the day (in mg/dL), both those taken manually and those measured with a glucose monitor (CGM). • Amount of carbohydrate eaten at each meal (in grams). This can be both simple and complex carbohydrates. • Amount or dose of insulin injected, either manually or by insulin pump (U). This can be basal or bolus insulin units. • Current exercise intensity and time (METs and min). Thanks to the Medicsen API, SugginAI, patients will be able to keep better control of their chronic disease, knowing at first relevant data of their evolution constantly and foreseeing the next steps to be taken in their daily life.
- 18. Conclusion • The diabetes education and management is an essential means of improving the quality of disease management • The integration of education and management approaches with mobile health and AI technologies has become an unstoppable trend • Current challenges include technological, philosophical, and ethical dilemmas, as well as issues surrounding user data security and privacy, and even legal hurdles. Among these issues, information security is a new challenge for the AI era. • Further efforts will be needed to promote the rapid and effective application of AI to medical fields. • The likes of continuous glucose monitors, wearable technology and smartphone apps empower people with the crucial information to take control of their lives.
- 19. Contact Information Nashya Haider Innotech Consultants Email: nashyahaider@gmail.com Mobile: +65-96113065 • We identify and assess the possibilities for AI in your organisation and build a business case for its implementation. • A strong conceptual understanding of the technologies behind AI such as machine learning, deep learning, neural networks, and algorithms. Bridging the gap across Finance & Admin, Practitioners, Patients and Technology developers • Unique Management Consulting Toolkit: practical Frameworks, Tools & Document Templates required to improve the Capability of your organization
Hinweis der Redaktion
- In Opening slide, enroll people in the script and earn the right. Will craft some ideas on this later. Adding a suggested passion/purpose statement for you and Innotech.
- In 2021, Approximately 537 million adults (20-79 years) are living with diabetes. The total number of people living with diabetes is projected to rise to 643 million by 2030 and 783 million by 2045. 3 in 4 adults with diabetes live in low- and middle-income countries Almost 1 in 2 (240 million) adults living with diabetes are undiagnosed Diabetes caused 6.7 million deaths; Diabetes is one of the leading causes of death in the world Diabetes caused at least USD 966 billion dollars in health expenditure – 9% of total spending on adults More than 1.2 million children and adolescents (0-19 years) are living with type 1 diabetes 1 in 6 live births (21 million) are affected by diabetes during pregnancy 541 million adults are at increased risk of developing type 2 diabetes
- 2 main types Type 1 diabetes (lack of insulin) and Type 2 diabetes (ineffective use of insulin)
- Remote glucose management during the COVID-19 pandemic Real-time CGM or flash glucose monitoring data are transmitted via Bluetooth from the sensor to a receiver or smartphone. From a smartphone, sensor glucose data can be transferred to the cloud (via cellular signal or WiFi) and from there to real-time followers (health-care providers and telemetry) as well as to dashboard software (eg, LibreView or Dexcom CLARITY) for comprehensive assessment of multiple patients. Comprehensive glucose reports can be scanned and uploaded to the chart. Until more data on the reliability of inpatient CGM are available, a hybrid approach is recommended. Data can be documented and validated in the EHR (eg, sensor values are within 20% of POC glucose values for blood glucose concentrations >100 mg/dL).. POC=point-of-care. Already many devices including Continuous Glucose Monitoring (CGM) which currently are medical devices approved by agencies like FDA. Next generation of SMART watches will have glucose monitoring. Yet there is a big technology and process difference between medical devices and consumer electronic wearables.
- The Healthcare system, is already overloaded and with the growth of Diabetes and associated heart and kidney disease, it will be overloaded even more. Hence developed countries are moving to prevention versus treatment; and heatlhcare is moving from hospitals to home.
- Many of these new AI powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. Some, are medical devices and others wellness solutions.
- Data sourcing and collection, limitations of the data and the governance required to address the bias in data. Process and skill changes required to use the technology themselves Training and certification of health and non-health participants Data security and privacy so that there is no private and confidential data leakage How can we resolve these challenges? That is the call to action for API developers and software developers – TAKE ACTION NOW
- . Doctors can use this system in conjunction with patients’ blood sugar diaries to provide individualized feedback and recommendations via real-time online communication.
- This is the Call to Action for the API audience is to join the game now! To build a connected Virtual Ecosystem for Diabetes Care. This is what success looks like in an ideal situation: Its preventative rather than Reactive care; There is high rate of Successful patient outcomes; The devices and wearables are accessible to all socio-economic levels and geographies within mid to lower income countries. Developers can leverage this platform to create novel applications with CGM data, including estimated glucose values, events, statistics, and more. The API is RESTful and utilizes the OAuth 2.0 standard for authentication, allowing individuals to securely authorize their CGM data for use in third party applications. For Dexcom users, the Dexcom API enables the creation of an app ecosystem, thereby supporting choice in diabetes management. Data produced by a Dexcom CGM is an invaluable tool for people with diabetes and their care teams, and third-party applications can help users benefit from this resource in entirely new ways. Take a look at the Gallery to see some examples of third-party applications using the Dexcom API. Of course, privacy is paramount at Dexcom, and granting access to user data is voluntary; users own their data and can revoke access at any time. Personal relationships are critical to advancing prediabetes strategies and diabetes management. Personal relationships – between physician and patient, among patients and other care team clinicians, and among patients in the context of group visits, online communities, and peer support – are critical to advancing prediabetes strategies and diabetes care management.
- Patients appear eager to use technology for disease management. Just as consumers rely on mobile apps and online communication to manage other areas of their life, they are getting on board with connected health (cHealth) to manage their diabetes care. Connected health is technology-enabled, integrated care delivery that allows for remote communication, diagnosis, treatment, and monitoring. An important goal of an effective, patient-centered cHealth approach is to improve digital connectivity between providers and patients to allow individuals to access the care they need anytime and anywhere.