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1. | EXECUTIVE SUMMARY & CONCLUSIONS |
1.1. | The Scope of Digital Health |
1.2. | Changing Demographics Require Healthcare Reforms |
1.3. | Factors Encouraging the Rise of Digital Health |
1.4. | Enabling Technologies for Digital Health |
1.5. | Effective Use of Resources Enabling Cost Efficiency |
1.6. | The Future of Healthcare is Consumer-Led |
1.7. | Big Tech Players are Moving into Healthcare |
1.8. | Towards a Model of Value-Based Healthcare |
1.9. | Big Pharma is Struggling, Disruption is Inevitable |
1.10. | Consolidating and Collaborating to Survive and Thrive |
1.11. | Regulation of Digital Health: Wanting the Best of Both Worlds |
1.12. | Telehealth and Telemedicine are Poised for Take-Off |
1.13. | The Future for Telehealth and Telemedicine |
1.14. | Remote Patient Monitoring is Changing the Face of Healthcare |
1.15. | The Outlook for Remote Patient Monitoring |
1.16. | Digital Therapeutics - The Next Step for mHealth? |
1.17. | The Outlook for Digital Therapeutics |
1.18. | The Rise of Direct-to-Consumer Genetic Testing |
1.19. | Sensors in Smart Homes: Decentralization of Healthcare |
1.20. | Funding and IPOs |
1.21. | Market Outlook: Wearable Medical Devices |
1.22. | Outlook for Digital Health: Quality, Outcomes, and Value are Key |
2. | INTRODUCTION |
2.1. | The Scope of Digital Health |
2.2. | Digital Health Definitions |
2.3. | Changing Demographics Require Healthcare Reforms |
2.4. | Global Healthcare Spending is Rising |
2.5. | Margins are Being Squeezed in Healthcare |
2.6. | Trouble for Traditional Healthcare? |
2.7. | Mergers and Acquisitions: Vertical Integration |
2.8. | Big Tech Players are Moving into Healthcare |
2.9. | Apple - 2018 Update |
2.10. | Apple - 2019 Update |
2.11. | Amazon |
2.12. | Amazon - Alexa |
2.13. | Alphabet |
2.14. | Microsoft |
2.15. | Tencent |
2.16. | From Fee-for-Service to Value-Based Purchasing |
2.17. | Towards a Model of Value-Based Healthcare |
2.18. | Big Pharma is Struggling |
2.19. | Digital Disruptors & Big Pharma: A Match Made in Heaven? |
2.20. | Big Pharma: Competitions and Digital Hubs |
2.21. | The Future for Pharma |
2.22. | Rising Role of Venture Capital |
2.23. | Funding and IPOs |
2.24. | Mobile Health is Becoming the Norm |
2.25. | Apple Enters the Electronic Health Record Market |
2.26. | The Future of Healthcare is Consumer-Led |
2.27. | Apps are Moving Towards Voice |
2.28. | A Move to Precision/Personalized Medicine |
2.29. | Biosensors are Moving to the Point-of-Care |
2.30. | Consumer-Driven, Patient Centered Healthcare |
2.31. | Global Challenges in Implementing Digital Health |
2.32. | The P4 Healthcare Model |
2.33. | The Emergence of a P4 Healthcare System |
2.34. | Wellness and Prevention |
2.35. | Market Outlook: Wearable Medical Devices |
3. | ENABLING TECHNOLOGIES |
3.1. | Enabling Technologies for Digital Health |
3.2. | IoT |
3.3. | 5G |
3.4. | Access to High Quality Broadband |
3.5. | Artificial Intelligence and Machine Learning |
3.6. | VR, AR and MR |
4. | REGULATIONS AND SECURITY |
4.1. | Digital vs Traditional Healthcare |
4.2. | Medical Device Pathways |
4.3. | Regulation of Digital Therapeutics |
4.4. | FDA Pre-Cert Program |
4.5. | Pre-Cert 1.0 |
4.6. | Digital Tools Not Under FDA Review |
4.7. | Regulation of Direct-to-Consumer Genetic Testing |
4.8. | Genetic Data, Privacy Concerns and a Lack of Trust |
4.9. | Unanswered questions about device security |
4.10. | Security Risks for Medical Devices |
4.11. | The Security of Data is a Critical Issue |
4.12. | National Systems at Risk of Large-Scale Cyber Attacks |
5. | TELEHEALTH & TELEMEDICINE |
5.1. | Defining Telehealth and Telemedicine |
5.2. | Telehealth Encompasses a Range of Services |
5.3. | There are Numerous Types of Telemedicine |
5.4. | The Key Services Models for Telehealth |
5.5. | Use Cases for Telehealth and Telemedicine |
5.6. | Benefits of Telehealth and Telemedicine |
5.7. | Challenges in Telehealth and Telemedicine |
5.8. | Is Telehealth a Cost-Effective Solution? |
5.9. | Telehealth and Telemedicine are Poised for Take-Off |
5.10. | The Growing Network of Care |
5.11. | Doctors Require Better Ways of Communication |
5.12. | Nomadeec |
5.13. | Smartphones Become the Tool for Doctors |
5.14. | Driving the Uptake of Telemedicine |
5.15. | Changes to Reimbursement of Telehealth |
5.16. | Reimbursement of Remote Patient Monitoring |
5.17. | Room to Improve and Mature |
5.18. | The Next-Generation of Telemedicine |
5.19. | Can AI Replace Your Doctor? |
5.20. | Babylon Health |
5.21. | TytoCare |
5.22. | American Well |
5.23. | Key American Well Partnerships |
5.24. | The Future for Telehealth and Telemedicine |
5.25. | The Future for Telehealth and Telemedicine (cont.) |
6. | REMOTE PATIENT MONITORING |
6.1. | Remote Patient Monitoring: Measurements and Applications |
6.2. | Components of a Remote Monitoring System |
6.3. | Remote Patient Monitoring is Changing the Face of Healthcare |
6.4. | Remote Patient Monitoring in Hospitals and the Home |
6.5. | Remote Patient Monitoring Solutions in the Home |
6.6. | Omron |
6.7. | Owlet |
6.8. | toSense |
6.9. | Biotricity |
6.10. | Sony |
6.11. | Remote Patient Monitoring Solutions in Hospitals |
6.12. | Evolution of the Stethoscope into the Digital Realm |
6.13. | Digital Stethoscopes Enable Decentralized Healthcare |
6.14. | The Benefits of Remote Patient Monitoring for Payers |
6.15. | UnitedHealthcare Motion: US |
6.16. | Momentum Multiple: South Africa |
6.17. | Vitality: UK |
6.18. | Vitality and Apple Watch |
6.19. | Fitbit |
6.20. | Elder Care |
6.21. | Elder Care: Fall Detection |
6.22. | Elder Care: Medical Adherence |
6.23. | Is Remote Patient Monitoring Really Helpful? |
6.24. | Skin Patches Emerging as a Key Form Factor |
6.25. | Contactless/Invisible RPM |
6.26. | Xandar Kardian |
6.27. | The Outlook for Remote Patient Monitoring |
7. | DIGITAL THERAPEUTICS |
7.1. | Digital Therapeutics: App-Based Healthcare |
7.2. | Digital Therapeutics - The Next Step for mHealth? |
7.3. | The Rationale Behind Digital Therapeutics (DTx) |
7.4. | Digital Therapeutics for Chronic Conditions Poised for Success |
7.5. | Tracking and Monitoring Adherence |
7.6. | Proteus Digital Health |
7.7. | Propeller Health |
7.8. | Mental Health is a Key Focus for DTx |
7.9. | Pear Therapeutics |
7.10. | Carrot |
7.11. | Akili Interactive |
7.12. | Insurers are Investing in Digital Therapeutics |
7.13. | Difficulties in Realising the Potential of Digital Therapeutics |
7.14. | Digital Therapeutics Alliance |
7.15. | Diabetes Partnerships are Proliferating |
7.16. | The Outlook for Digital Therapeutics |
8. | CASE STUDY: DIABETES MANAGEMENT |
8.1. | Diabetes is an Early Adopter of Digital Healthcare Initiatives |
8.2. | The cost of diabetes |
8.3. | Managing side effects accounts for 90% of the total cost of diabetes |
8.4. | Diabetes management device roadmap: Summary |
8.5. | Strategy comparison amongst the largest players |
8.6. | New directions with glucometers: Connectivity |
8.7. | The case for CGM |
8.8. | CGM: Overview of key players |
8.9. | Skin patches are the form factor of choice |
8.10. | Smarter insulin delivery informing decisions |
8.11. | Smart Pen Platform Preventing Missed Doses |
8.12. | Smart insulin delivery device manufacturers |
8.13. | Diabetes apps |
8.14. | Growing ecosystem via acquisitions and partnerships |
8.15. | Roche & mySugr |
8.16. | Lilly & Rimidi, Lilly & Livongo |
8.17. | Blue Mesa Health & Merck |
8.18. | Glooko-Novo Nordisk in Diabetes Care |
8.19. | Other case studies: Digital diabetes management |
8.20. | BlueStar |
8.21. | Voluntis |
8.22. | DIABNEXT |
8.23. | Better Therapeutics |
9. | CONSUMER GENETIC TESTING |
9.1. | The Central Dogma: DNA, RNA and Proteins |
9.2. | What is Direct-to-Consumer Genetic Testing? |
9.3. | Genetic Variations: What Are We Testing For? |
9.4. | The Rise of Direct-to-Consumer Genetic Testing |
9.5. | Monetizing Genomic Testing |
9.6. | The Emergence of Genomics Analysis Companies |
9.7. | Not All Data is Created Equal |
9.8. | AI Driven Genomics and Drug Development |
9.9. | Alexa-Powered AI Genomics Platform |
9.10. | Using Genomics to Diagnose NHS Patients |
9.11. | AncestryDNA |
9.12. | 23andme |
9.13. | Foundation Medicine |
9.14. | Atlas Biomed Group |
9.15. | Orig3n |
10. | SMART HOME AS A CARER |
10.1. | Smart Home as a Carer Becomes Increasingly Important with Ageing Populations |
10.2. | Sensors in Smart Homes: Decentralization of Healthcare |
10.3. | Bringing Healthcare into the Home by Fitting Sensors |
10.4. | Medical Asset Tracking Allows for More Productive Refilling of Medicines and Vital Equipment in the Home |
10.5. | Philips Smart Home as a Carer Ecosystem is Able to Alert Carers in Case of an Emergency |
10.6. | Home Asthma Care |
10.7. | Dr Alexa |
10.8. | Health Information at Home Through Voice Technology |
10.9. | Digital Health Apps Using Amazon Alexa |
10.10. | 3rings |
10.11. | Artificial intelligence in health care diagnostics: state-of-the-art and competitive landscape |
10.12. | The rise of biomedical data |
10.13. | Measures in deep learning(1): sensitivity and specificity |
10.14. | Measures in deep learning(2): Area Under Curve (AUC) or area under curve of receiver operating characteristics (AUCROC). |
10.15. | Measures in deep learning(3): Reproducibility |
10.16. | F1-Score |
10.17. | When AUC is not a good measure of the algorithm success? |
11. | HOW AI HAS HELPED IN DIAGNOSTICS |
11.1. | 1) Skin disease: Dermoscopic melanoma recognition (2018) |
11.2. | Dermoscopic melanoma recognition and its challenges |
11.3. | SkinVision: a Netherland based firm to understand risk factors for skin cancer |
11.4. | Haut AI: Using machine learning to predict effect of everything in lifestyle on skin health |
11.5. | 2) Diagnosing diabetic retinopathy and diabetic macular edema from fundus photographs (2019) |
11.6. | Diabetic retinopathy: features of severity and learning structure |
11.7. | IDx: A company with $50M of funding with AI based diabetic retinopathy as the first focus |
11.8. | 3) Google DeepMind's AI can detect over 50 sight-threatening eye conditions (2018) |
11.9. | DeepMind: automating triage based on OCT images |
11.10. | DeepMind: automating triage based on OCT images |
11.11. | DeepMind: Deep learning-based mind that knows about eye diseases |
11.12. | 4) Coronary Heart disease diagnosis (2018) |
11.13. | 5) Determination of ejection fraction from echocardiograms (2019) |
11.14. | Left ventricular ejection fraction assessment by deep learning procedure |
11.15. | Caption health (Bay Labs): Deep learning on echo cardiogram creation and interpretation |
11.16. | 6) Detection and quantification of breast densities via mammography (2018) |
11.17. | Breast cancer screening via mammograms and pathology slides |
11.18. | Reproducible breast cancer screening: Densitas, Kheiron Medical, and Therapixel |
11.19. | 7) Detection of stroke, brain bleeds, and other conditions from computerized axial tomography (2018) |
11.20. | Deep learning facilitates finding the rout cause of intracranial hemorrhage |
11.21. | Viz ai: Brain scan deep-learning-based analysis (a physician diagnostic assistant) |
11.22. | Brainomix: Brain imaging interpretation using deep learning |
11.23. | 8) Deep learning of lung cancer histopathological images is able to identify cancer cells, determine their type, and predict what somatic mutations are present in the tumor (2018) |
11.24. | Lung cancer detection made easier |
11.25. | Optellum: early lung cancer detection |
11.26. | 9) Facial image recognition to identify rare genetic disorders and to guide molecular diagnoses (2019) |
11.27. | Facial image and genetic disorder: FDNA approach vs NIH (US national institute of health) approach |
11.28. | 10) AI based time series analysis: AI applied to electrocardiograms can detect and classify arrhythmias (2019) |
11.29. | Cardiologist-Level Arrhythmia Detection by applying AI electrocardiograms: Stanford machine learning group (2017) |
11.30. | 11) AI based time series analysis: Detect atrial fibrillation (2018) |
11.31. | Current health (Snap40): a band for determining who is in health risk...how does this fit? |
11.32. | Cardiogram: accurately detecting various heart conditions with wearables |
11.33. | 12) AI based time series analysis: Detect cardiac contractile dysfunction(2019) |
11.34. | 12) AI based time series analysis: Detect blood chemistries linked to cardiac rhythm abnormalities |
11.35. | Cardiologs : A French based start up to help recognize abnormal heart conditions through deep learning |
11.36. | 14) AI based time series analysis: Detecting functional DNA sequence elements that are indicative of gene splicing (2014) |
11.37. | Deep genomics: Cell biology manipulation as intended, powered by AI |
11.38. | Electronic health records |
11.39. | Natural language processing to facilitate useful healthcare record taking and big data interpretation |
11.40. | Ubiquity of Electronic Health Records (EHRs) and learning from them |
11.41. | Information extraction from EHRs and EHR representation learning |
11.42. | Outcome Prediction, Computational Phenotyping, and Clinical Data De-identification from EHRs by deep learning |
Slides | 259 |
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