Health on Mobiles

A New Dawn in Healthcare: How AI and LLMs Are Transforming Patient Care – A Story of Hope and Healing

2025-03-11T09:53:00.000-07:00

It was a crisp autumn morning when John, a 45-year-old father of two, first felt the sharp pain in his chest. Like many, he brushed it off as stress or indigestion. But when the pain persisted, his wife, Sarah, insisted he see a doctor. Little did John know that this visit would mark the beginning of a transformative journey—one that would redefine his understanding of healthcare and how technology could seamlessly blend into the patient experience.

John’s story is not unique. Millions of patients navigate the labyrinth of healthcare systems every day, often feeling lost, overwhelmed, and underserved. But John’s experience was different. It was a glimpse into a new era of healthcare delivery, powered by Large Language Models (LLMs), generative AI, and advanced analytics—a world where technology works silently in the background, guiding every step of the care process with precision, empathy, and efficiency.

The First Encounter: A Seamless Entry into Care

When John walked into his physician’s office, he was greeted not just by the receptionist but by an AI-powered assistant embedded in the clinic’s system. The assistant, integrated into the clinic’s electronic health record (EHR), had already analyzed John’s symptoms based on the pre-visit questionnaire he completed online. It flagged his chest pain as a potential cardiac issue and prompted the physician, Dr. Patel, to prioritize John’s case.

As Dr. Patel reviewed John’s history, the AI system provided real-time nudges: “Consider ECG and lipid profile based on patient’s age, symptoms, and family history.” The system also pulled up the latest clinical guidelines for chest pain evaluation, ensuring Dr. Patel adhered to the best practices. John, unaware of the behind-the-scenes orchestration, felt reassured by the thoroughness of the process.

The Diagnosis: Precision and Personalization

John’s ECG revealed abnormalities, and Dr. Patel recommended further tests. Here, the AI system stepped in again, generating a personalized care plan for John. It calculated the most cost-effective diagnostic pathway, factoring in John’s insurance coverage and local facility availability. The system even sent John a text message with the details: “Your next steps: Blood work at LabCorp on Main St. (estimated cost: $75). Appointment scheduled for tomorrow at 10 AM. Click here for directions.”

As John left the clinic, he received a digital guide on his phone: “Understanding Your Chest Pain: What to Expect Next.” The guide, generated by an LLM, was tailored to his health literacy level and included self-help tips, such as dietary changes and stress management techniques, while he awaited his results.

The Treatment Plan: Collaboration and Compliance

John’s tests confirmed early-stage coronary artery disease. Dr. Patel, guided by the AI system, prescribed a combination of medication, lifestyle changes, and follow-up appointments. The system automatically generated a treatment plan, complete with easy-to-understand instructions for John and reminders for Dr. Patel to monitor his progress.

But the real magic happened after John left the clinic. The AI system sent him a series of nudges over the next few weeks: “Time to take your medication,” “Have you logged your meals today?” and “Don’t forget your 30-minute walk!” These prompts were not generic; they were tailored to John’s preferences and progress. For instance, when John skipped logging his meals for two days, the system sent a gentle reminder: “Tracking your meals helps us help you. Need tips on healthy eating? Click here.”

The Aftercare: Continuous Support and Empowerment

John’s journey didn’t end with his diagnosis or treatment. The AI system continued to support him through his recovery. It connected him to a virtual support group of patients with similar conditions, moderated by a healthcare professional. It also provided aftercare instructions, such as warning signs to watch for and when to seek immediate help.

One evening, John experienced mild dizziness. Before he could panic, his phone buzzed with a message: “You reported dizziness. This could be due to medication side effects. Drink water and rest. If symptoms persist, contact your doctor.” The system had analyzed his symptoms in real-time, cross-referenced them with his medical history, and provided actionable advice.

The Stakeholders: A Symphony of Coordination

John’s story isn’t just about him. It’s about the seamless coordination between all stakeholders in his care. The AI system ensured that Dr. Patel was alerted when John’s blood work results were ready, that the pharmacist was informed of his medication regimen, and that Sarah received tips on how to support John’s lifestyle changes. Every intervention was timely, every communication precise, and every action aligned with the highest quality standards.

The Bigger Picture: A Revolution in Healthcare Delivery

John’s experience is a microcosm of what’s possible when AI, LLMs, and generative technologies are integrated into healthcare. These tools don’t replace human expertise; they augment it. They provide physicians with real-time guidance, patients with personalized support, and stakeholders with actionable insights—all while reducing costs and improving outcomes.

Imagine a world where every patient, regardless of their location or socioeconomic status, has access to this level of care. A world where preventable diseases are caught early, chronic conditions are managed effectively, and patients are empowered to take control of their health. This is the promise of AI-driven healthcare—a promise that is already becoming a reality.

A Patient’s Reflection: Hope for the Future

As John reflects on his journey, he feels a sense of gratitude—not just for his recovery but for the system that supported him every step of the way. “It felt like I had a team of experts looking out for me,” he says. “I didn’t have to figure everything out on my own. The technology was there, quietly guiding me, nudging me, and keeping me on track.”

John’s story is a testament to the transformative power of AI in healthcare. It’s a story of hope, healing, and the dawn of a new era—one where technology and humanity work hand in hand to create a healthier, more equitable world.

The question is no longer whether AI will revolutionize healthcare, but how quickly we can embrace this change. For patients like John, the future is already here. And it’s brighter than ever.

Menopause in the Workplace: A Doctor's Perspective on the Unseen Struggles and Untapped Opportunities for Women

2025-03-11T09:50:00.000-07:00

As a doctor and an investor deeply committed to the #menopause sector, I see every day how this natural life stage is anything but “natural” in its impact on women's lives and careers. Menopause is not just a health issue; it’s a workforce issue, a productivity issue, and above all, a deeply personal struggle for millions of women around the globe.

Globally, approximately 48% of women are in the workforce, and the majority of them will experience menopause during their working years. In the UK alone, nearly 80% of menopausal women are employed, representing the fastest-growing demographic in the workplace. Yet, despite their contributions, many of these women face significant challenges.

Menopausal symptoms—like brain fog, insomnia, anxiety, and hot flashes—aren’t just physical inconveniences. They are barriers to professional growth and personal fulfillment. Imagine trying to lead a team, hit tight deadlines, or even just make it through a long meeting while battling debilitating symptoms. Studies show that up to 40% of women miss work days each month due to menopause, and nearly one-third find it hard to cope at all.

This lack of support is pushing women out of the workforce at an alarming rate. In fact, 13% of women have quit their jobs, and 15% have considered doing so due to menopausal challenges. These aren’t just numbers; they are lives interrupted, careers sidelined, and aspirations put on hold.

What’s causing this? A lack of understanding, inadequate workplace policies, and insufficient resources. We need more than awareness campaigns—we need tangible solutions. From accessible healthcare to workplace flexibility and innovative products designed specifically for menopausal women, this is a call to action for change.

If you are building something in the menopause space—be it a groundbreaking product, a technology platform, or a service that supports women through this transition—I want to hear from you.

As someone who has seen the need firsthand and is committed to making a difference, I am actively investing in solutions that empower women to thrive during menopause, not just survive it.Let’s work together to rewrite the story of menopause.

Reach out if you’re creating something impactful—I’m here to help you scale your vision and make a lasting difference.

https://www.linkedin.com/news/story/menopause-support-on-the-rise-6275668/

Would love to hear from Thought leaders actively working in this space.

Clare Glazsher Cliona O'connell James P. Crowley Lisa Boate - Menopause Consultant and Coach Tracy Watts Henpicked: Menopause in the Workplace Kathrin F. Gülsah Wilke my meno Syrona Health OMENA
Vikram Sinai Talaulikar Jacqui Macdonald Sue Hedley Heather Jackson

New Advocacy Tool – Make Your Voice Heard!

2025-03-11T09:47:00.000-07:00

The pandemic-era telehealth flexibilities are at risk of expiring soon unless Congress takes action. This could significantly impact access to virtual care for Medicare beneficiaries. Now is the time for all of us—clinicians, innovators, researchers, academicians, and patients—to step up and make our voices heard.

Thanks to American Telemedicine Association Action, there’s now an easy way to advocate for continued access to telehealth. Their new tool allows you to send personalized letters directly to your U.S. Representatives and Senators in just a few clicks.

📢 **Take action now**: https://lnkd.in/gJumaYs4

A big thank you to Joseph Kvedar for sharing this important update! Let’s spread the word and ensure virtual care remains accessible.

https://ataaction.quorum.us/campaign/111772/

Global Clinical Research: A Complex Puzzle

2025-03-11T09:45:00.000-07:00

🌍 Global Clinical Research: A Complex Puzzle 🧩

One of the biggest challenges in clinical research is its global nature—each region brings unique regulations, cultural nuances, and logistical hurdles. So, what’s shaping the industry in 2024?

🔍 Greenphire’s 2024 Market Trends Survey—featuring insights from nearly 400 sponsors, CROs, and sites worldwide—highlights key priorities and challenges:

🚀 1. Inclusivity, Collaboration & Targeted Care
✅ Bridging healthcare disparities for ethnic minorities & women remains a challenge.
✅ Patient engagement & regulatory reforms are crucial to expanding trial accessibility.

⚖️ 2. Pharmacovigilance & Local Knowledge
✅ Balancing global compliance with local PV expertise is a major hurdle.
✅ A lack of regional knowledge leads to inefficiencies & compliance risks.

🌎 3. EU vs. US Clinical Trial Perspectives
✅ Distinct regional regulations, cultures & logistics require tailored approaches.
✅ Sponsors, CROs, & sites must navigate evolving global research landscapes.
The industry is shifting—how is your organization adapting? 💡

ClinicalResearch Pharma ClinicalTrials DrugDevelopment HealthcareInnovation

https://pmlive.com/digital_edition/pme/pharmaceutical-market-europe-february-2025/

Artificial Intelligence Policy and Governance in HEALTHCARE

2022-05-23T07:30:00.001-07:00

Artificial Intelligence has become the new frontier for digital transformation. Several #digitalhealth businesses of today rely on Machine Learning, #AI and other such technologies to make healthcare delivery more efficient and comprehensive.

However, the efficient and responsible use of AI tools is an ongoing discussion and would mean culture, data management, technology shifts in the industry, and required up-grading and training professionals for better coordination.

Hence, with the growing market potential and interest in AI, it is imperative to develop a thoughtthrough regulatory and legal framework on the adoption and use of AI.

I have set forward a hypotheses to design a policy framework for AI technologies in my paper- "𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐢𝐧 𝐇𝐞𝐚𝐥𝐭𝐡 𝐏𝐨𝐥𝐢𝐜𝐲 – 𝐀 𝐆𝐥𝐨𝐛𝐚𝐥 𝐏𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞" last year
https://lnkd.in/emi3XWwa

cited further by "𝐓𝐡𝐞 𝐐𝐮𝐚𝐧𝐝𝐚𝐫𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐢𝐠𝐡𝐭𝐬 𝐭𝐨 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐨𝐟 𝐀𝐈 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐢𝐧 𝐌𝐚𝐥𝐚𝐲𝐬𝐢𝐚"

&
"𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐢𝐧 𝐒𝐦𝐚𝐫𝐭 𝐆𝐫𝐢𝐝 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲: 𝐀 𝐑𝐞𝐯𝐢𝐞𝐰 𝐨𝐟 𝐑𝐞𝐜𝐞𝐧𝐭 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐅𝐮𝐭𝐮𝐫𝐞 𝐏𝐫𝐨𝐬𝐩𝐞𝐜𝐭𝐬" in IEEE journal.

If you are a business in the #digitalhealth space, how are you looking at some of these policy recommendations and recent advancements in Privacy of #AI #machinelearning #digitaltransformation

Join the discussion here>>

Artificial intelligence and Technology for Dentistry

2022-05-23T07:25:00.004-07:00

Have you heard of this company ORCA Dental AI? ORCA combines clinical expertise with machine learning and AI technologies to create diagnostic reports, treatment plan suggestions and smart clinical predictions.

Good to learn about these innovations that benefit every stakeholder in the value chain.
Recently, VideaHealth raised $20M to Expand AI-Based Diagnostics led by Spark Capital including existing investors Zetta Venture Partners. Among its many benefits to dentists and patients are:
– A 31% increase in diagnosis rate for cavities and 26% increase in treatment plan value;
– An increase in case acceptance by patients due to second opinions;
– Automated workflows accelerate treatment planning and charting;

Another company touting AI-powered dental care and practice management is Overjet, which announced a $42.5 million Series B funding round in December months after it had scored $27 million in Series A funding.

Pearl provides AI for dental images to assist in diagnosis. It landed FDA clearance last month, the first to get such a go-ahead for dentistry AI.

The approval paves the way for its use in clinics across the United States.

Pearl offers dentists a product called Second Opinion to aid in the detection of disease in radiography. Second Opinion can identify dozens of conditions to help validate dentist’s findings.

Are you working on a Dental Startup or running one? Let us learn more about you and your USP! Comments Welcome! Join the discussion on - https://www.linkedin.com/posts/ruchidass_machinelearning-ai-dentistry-activity-6924727739694399492-7_3V?utm_source=linkedin_share&utm_medium=member_desktop_web
#machinelearning #ai #dentistry #funding

Big Data Analytics- The Microscope and Telescope for Pharma/CRO

2022-05-23T07:19:00.002-07:00

This article is produced in consultation with Industry experts, and KOLs in India and across the world and especially highlights how “Predictive Analytics algorithms” are poised to provide Useful Analytics to the Pharma industry.

In today’s highly competitive market, it is extremely important for contract research and manufacturers to have access to information that allows them to target the specific segments of the pharmaceutical and biotechnology industry that are looking to outsource the particular services they provide.

Understanding shifts in annual outsourcing budgets and spending can help CROs and CMOs to better position themselves for capturing business, particularly at a time when many – if not all – pharma and biotech companies are looking to cut costs and streamline operations.

As per a recent report by NiceInsights, Analytical Services, Clinical Research, and Clinical Monitoring will be the most demanded services over the next 12 to 18 months. For CROs and CMOs looking to increase partnership opportunities with pharma and biotech companies, one thing is for certain – knowledge is power. Predictive Models for Drug discovery especially Biotherapeutics- The vast majority of pertinent life sciences data is unstructured, residing in the text of scientific papers and journals, Web pages, electronic medical records, e-mails, textual information embedded in structured databases, and product registration documents and filings.

The inability to search text for relevant information has led, no doubt, to countless missed opportunities for faster and less expensive drug discovery and development. Predictive analytics can also be used to model and predict the behaviour of biomolecules, and are increasingly being used in the search for new chemical entities (NCEs) to fill the drug discovery pipelines of the pharmaceutical and biotechnology industries. We define ‘predictive bioanalytics’ as the use of homology-driven methods at the atomic scale that integrates heterogeneous biological data sources to identify multiscale relations between biomolecules as interaction signatures, which can then be used to assess the probability of a compound becoming a drug for a particular indication and or disease.

New data-mining applications – feature expanded analytics, user-friendly interfaces, and powerful algorithms that allow researchers to analyze structured and unstructured data. By leveraging the diversity of available molecular and clinical data, predictive modeling could help identify new potential-candidate molecules with a high probability of being successfully developed into drugs that act on biological targets safely and effectively. Predictive Models for Patient/Subject discovery- Today, a researcher, for example, might use data mining to find clusters of disease subtypes in hope of finding subtypes to focus on that specific target or hopefully enable a more precise treatment course.

Attribute-importance algorithms now help researchers, for instance, select the subset of genes most likely used in discriminating types of cancer. Researchers can use predictive analytics to find factors associated with a disease or predict which patient might respond best to an experimental treatment.

Carefully conducted clinical trials are performed in human volunteers to provide answers to questions such as:

Does a treatment work?

Does it work better than other treatments?

Does it have side effects?

Clinical trials also provide important information on the cost-effectiveness of a treatment, the clinical value of a diagnostic test and how a treatment improves the quality of life. Ever wondered how patients get selected for clinical trials without this advanced algos? Traditionally, physicians have selected trials by manual analysis of patients’ data. The review of resulting selections has shown that they usually do not check all clinical trials and occasionally miss an appropriate trial.

Until now we only have some web systems to address the problem to an extent. To address this problem, the Industry has developed near expert systems that help to select trials for each patient. It prompts a clinician to enter the results of medical tests and uses them to identify appropriate trials. If the available records do not provide enough data, the system suggests additional tests. This is a cumbersome process to find eligible patients and doesn't help reduce any related costs. With Predictive analytics models, Patients are identified to enrol in clinical trials based on more sources—for example, social media—than doctors’ visits. Furthermore, the criteria for including patients in a trial could take significantly more factors (for instance, genetic information) into account to target specific populations, thereby enabling trials that are smaller, shorter, less expensive, and more powerful.

Predictive Models for Performance and Operational Analytics- For a mature CRO, determining the productivity, utilization and profitability of clinical research initiatives can help the CRO team allocates the most effective team members to certain projects. Based on the client which contracts the study, trends such as: Invoice-to-Cash cycle times Propensity of the client to request amendments to the research deliverables Ensure the scheduling of clinical researchers and clinical laboratory space is realistic, based on the nature and volume of projects One can also get a sneak peek to trial site performance capabilities for selecting the best investigators, eliminating non-performing sites and reducing enrolment timelines. With such insights, planning and forecasting clinical enrolment performance, rescuing off-track trials and optimizing contingency plans for those trials becomes easy.

Predictive Models for RISK Monitoring- Traditional monitoring typically allocates resources equally among study sites, regardless of clinical data or the risk to patients.

Routine visits to all clinical sites with 100% SDV (comparing all data points on every case report form to all subjects’ medical records) are common — and are the largest cost driver in clinical trial budgets. Predictive analytics allows for sponsors to assess investigator risk and allocate monitoring resources where they are needed most. Real-time predictive modelling in the form of risk-based monitoring enables a study sponsor to adjust the level of monitoring as risk changes at individual sites.

Predictive Models for Financial Modelling and Cost-effectiveness If you have a rich data asset then using Predictive Analytics you can benchmark cost and visualise data in that context to help sponsors forecast, budget and negotiate the cost for outsourcing clinical trials.

Predictive Models for manufacturing outcomes- Predictive Analytics modules can be used for process Improvement Analysis to Improve Scheduling and Throughput. Take an examples of this biotech company which is the only final stage bio-manufacturing facility in the world.

The company is struggling to meet rapidly increasing customer demand. Repeated unanticipated production delays and starvation at critical parts of the operation were causing not only late and missed deliveries, but the expiration of batches of product at a cost of approximately $1 million per batch.

Predictive analytics can be deployed in such a facility to uncover the root cause(s) of the unanticipated delays creating the late and missed deliveries; project such incompetence in advance; Run “what if” scenarios to see if additional capacity from the new facility would be required; the facility could produce two more lots per month; better way to do long-term expansion planning and predict when and where more line capacity would be required in future.

Whether you're a renowned ACO, Medicare Shared Savings Program, risk-bearing provider or health plan collaborating with providers in risk-based contracts, you need to understand the risk profile and experiences of your risk-based populations. One can only achieve this by combining, clinical, financial and predictive analytics with robust technology and expert data management services across multiple players. You can read my last post on Predictive Analytics for Better Patient outcomes here>>

Use Cases:

Cell-Based Assays for Drug Target Discovery: Lessons from Transcriptomic Studies With Human Lymphoblastoid Cell Lines Noam Shomron, Ph.D., Associate Professor, Head, Functional Genomics Laboratory, Faculty of Medicine, Tel-Aviv University Genome-wide pharmacogenomic studies for developing targeted therapies offer the advantage of hypothesis-free search for tentative drug response biomarkers (efficacy and safety). However, they require large patient cohorts and are therefore very costly. This talk presents our experience with an alternative approach, based on genome-wide transcriptomic profiling of a panel of human lymphoblastoid cell lines (LCLs) representing unrelated healthy donors.

Using Big Data Analytics in the Cloud to Improve Vaccine Yields Jerry Megaro, Director, Manufacturing Advanced Analytics and Innovation, Merck Craig Sutherland, Executive Director, Technology & Data Science, Life Sciences and Health Practice, Booz Allen Hamilton Here we present a proof-of-concept experiment where big data tools and techniques were applied to integrate and analyze 12 years worth of vaccine manufacturing data from 16 data sources to identify characteristics that influence yield. Additionally, the talk will describe how we leveraged shared data lake platform services operated within an Amazon Web Services Virtual Private Cloud (VPC) and scaled up Elastic Compute Cloud (EC2) services on-demand to support the analysis effort.

From Big Data to Smart Data: Using Quantitative Systems Pharmacology for De-risking R&D Projects in CNS R&D Hugo Geerts, Ph.D., CSO, Computational Neuropharmacology, In Silico Biosciences Despite heavy investment in CNS R&D and increased available information in multiple –omics databases, the clinical failure rate remains above 90%. The ‘reductionist’ trap that focuses on very detailed bits and pieces of data makes it a huge challenge to generate actionable knowledge in an integrative and useful way. We propose Quantitative Systems Pharmacology as an example of a completely new generation of deep analytics approaches that can be a possible powerful and valuable tool for knowledge generation in the context of going from ‘Big Data to Smart Data’. The use of computer-based mechanistic modeling based upon the physiology of (human) brain networks, functional imaging of genetics and pharmacology of drug-receptor interaction and parametrized with clinical data as a common language allows integrating a wide diversity of information into an actionable platform that can be interrogated at different levels. We will show examples in Alzheimer’s disease and schizophrenia where this approach could have made a substantial impact on clinical trial success rate. Similar to other ‘engineering’ industries, the platform can also be developed also as a knowledge repository that conserves and expand ‘tacit’ corporate scientific knowledge.”

An Integrative Platform for Discovery of Drugs and Small Chemicals Associated With Autism Spectrum Disorder Adam Brown, Ph.D. Student, Biological and Biomedical Sciences, Harvard University Repositioning approved drug molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. To aid in this effort, several large databases have been compiled in an attempt to aggregate gene-level information about drug molecules (e.g. Drugbank, the Comparative Toxicogenomics Database, and the Connectivity Map, among others). However, no pipelines exist to comprehensively query of gene expression databases for associations between drugs and diseases. To address these issues, we developed a generalized Kolmogorov-Smirnov enrichment test-based methodology for identifying compounds that modulate genes derived from expert-curated and publicly available datasets. We demonstrated our approach by correctly identifying two out of three frontline prostate cancer therapies from a database of over 7,000 drug and non-drug compounds. We predict a list of eight drugs that modulate genes that are also perturbed in autism spectrum disorder (ASD), seven of which target genes that have previously been implicated in ASD. Finally, we show that our method significantly enriches for drugs from a database containing both drug and non-drug compounds, demonstrating its utility as a hypothesis-generating tool for drug repositioning studies.

The Next Generation of Systems and Network Biology to Support Gene Variant and Expression Co-Analysis Chris Willis, Ph.D., Solution Scientist, Discovery and Translational Sciences, Thomson Reuters To date, drugs have only explored targeting 10% of the coding human genome. Systems biology approaches aim to increase these numbers through understanding molecular disruptions at the pathway level in disease. This talk will discuss the challenges associated with analyzing the exponential growth in biomedical research knowledge and multi-omics data. Furthermore, this talk will demonstrate the use of Thomson Reuters MetaCoreTM platform to analyze gene variant and transcriptomic data simultaneously. Lastly, the development of bioinformatic algorithms and the value of taking a combination knowledge/data driven approach to data analysis will highlight the roadmap for MetaCoreTM.

References

Market segmentation. BusinessDictionary.com. WebFinance, Inc.http://www.businessdictionary.com/definition/market-segmentation.html. Accessed December 29, 2014. Spending Trends in the CMO and CRO Market PharmPro, January 2012- NiceInsight BigData Analytics and solutions conference, October 2014, Boston Promodel- Formulation and Filling - Scheduling and Throughput Analysis Higher Quality at Lower Cost: The Benefits of Portfolio-Wide Predictive Analytics in Clinical Trial Monitoring Aug 26, 2013 By Brian Slizgi, Robert Franco, Mark Hronec, Applied Clinical trials How big data can revolutionize pharmaceutical R&D April 2013 | byJamie Cattell, Sastry Chilukuri, and Michael Levy Analyzing Big Data: the path to competitive advantage- Geoconnexion Predictive Analysis Is Data Mining’s Future- BioITWorld Mining for Information Gold in Unstructured Claims Data By Steve Holcomb Big Data and Pharmaceutical R&D: The Value Equation By Tim Carruthers on February 27, 2014 2:51 PM http://www.fda.gov/Drugs/DevelopmentApprovalProcess/ucm284077.htm Citations 1 www.sims.berkeley.edu/research/projects/how-much-info/ 2 www.nlm.nih.gov/pubs/factsheets/medline.html 3 Berry, M.J.A. and Linhoff, G. Data Mining Techniques for Marketing, Sales and Customer Support. Wiley, 1997. 4 Berger, A. and Berger, C.R. “Data mining as a tool for research and knowledge development in nursing.” CINMay/June 2004. 5 Stephens, S. and Tamayo, P. “Supervised and unsupervised data mining techniques for life sciences.” Curr Drug Disc June 2003. 6 Agosta, L. “The future of data mining -- predictive analytics.” DM Rev August 2004. 7 Blumberg, R. and Atre, S. “The problem with unstructured data.” DM Rev February 2003. 8 Alonso, O. and Ford, R. Text Mining with Oracle Text. January 2004 (PDF at http://www.oracle.com/ Image credits: Business Insights- Drug Discovery- http://www.business-insights.com/drug-discovery

Data revolution in Healthcare CES 2020 Highlights

2020-01-17T09:40:00.000-08:00

CES 2020 Highlights
From #Samsung NEON to Impossible pork! Check out the coolest and weirdest new fitness gadgets at CES 2020
https://lnkd.in/fmwRFNb #digitalhealthCES
Big shout out to the Influencers.
#ces2020 #digitalhealth #publichealth #healthtechnology

International Finance Corporation, World Bank & TechEmerge innovations in Health

2020-01-17T09:36:00.003-08:00

If you have been following my posts, you know that in 2016 IFC launched a unique health innovations acceleration program called "TechEmerge". It is a World Bank initiative to Accelerate the Adoption of Health Technology Innovations in Emerging Markets. The first program took place in India, Brazil and will be launched later this year in East Africa.

I worked as a consultant and Advisor to the program to shortlist innovations, review pilot proposals, bring hospitals onboard and launch pilot projects. I also held webinars and mentoring sessions with startups to help them understand business models, market opportunities and challenges. You can see some info sessions on this link: https://www.slideshare.net/health2dev/techemerge-webinar-understanding-the-basics-healthtech-in-india

IOT in Healthcare #digitalhealth

2020-01-17T09:30:00.004-08:00

The healthcare industry is in a unique position to take advantage of the digital revolution, since healthcare’s goals align naturally with the promises of digital transformation to better leverage data, improve customer-centric services and reduce costs. However, pursuing these promises requires balancing efforts across complex and competing demands, and contending with seismic industry and technology shifts. The Internet of Things is redefining healthcare as we know it. We’re moving on to a whole new level when it comes to the way that apps, devices, and people interact when delivering healthcare solutions. IoT has given us a fresh outlook as new tools that accommodate an integrated healthcare network, subsequently, the care that is provided is of a higher standard. The use of IoT in healthcare allows for the automation of processes that have previously taken time; these processes previously allowed human error. For example, nowadays many hospitals use connected devices to control the airflow and temperature in operating theatres. What is your IOT Story?

https://www.linkedin.com/posts/drruchidass_healthtechnology-digitalhealthces-digitalhealth-activity-6622136583472676864-KTxy

HealthCursor Consulting Group at Arab Health 2020

2020-01-17T09:28:00.003-08:00

I am traveling to Dubai for Arab Health, Dubai, UAE between 27th Jan to 30th Jan 2020. In case you would like to meet, please drop me a line with a brief agenda. Looking forward to meeting companies interested in #digitaltransformation #digitalhealth #healthtechnology #publichealth #iotsolutions #healthcareinnovations

https://www.linkedin.com/posts/drruchidass_digitaltransformation-digitalhealth-healthtechnology-activity-6623965144126984192-TXrj

Deep Learning for Cancer

2019-06-27T04:28:00.000-07:00

Image Credit: MIT

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks.MIT’s Computer Science and Artificial Intelligence Lab has developed a new deep learning-based AI prediction model that can anticipate the development of breast cancer up to five years in advance. Researchers working on the product also recognized that other similar projects have often had inherent bias because they were based overwhelmingly on white patient populations, and specifically designed their own model so that it is informed by “more equitable” data that ensures it’s “equally accurate for white and black women.”

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MobileODT creates smart colposcopy and visual assessment solutions for women's health clinicians at the point of care.EVA COLPO is a portable, Internet-connected, and FDA-cleared colposcope that is as simple to use as a mobile phone. EVA combines advanced hardware with integrated software to serve a variety of clinical needs.Read more on my blog https://www.linkedin.com/pulse/deep-learning-cancer-detection-dr-ruchi-dass #colposcopy #cervical #cancer #digitalhealth #women #cancer #mhealth https://www.mobileodt.com

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That’s key, MIT notes in a blog post, because black women are more than 42 percent more likely than white women to die from breast cancer, and one contributing factor could be that they aren’t as well-served by current early detection techniques. MIT says that its work in developing this technique was aimed specifically at making the assessment of health risks of this nature more accurate for minorities, who are often not well represented in development of deep learning models. The issue of algorithmic bias is a focus of a lot of industry research and even newer products forthcoming from technology companies working on deploying AI in the field.

This MIT tool, which is trained on mammograms and patient outcomes (eventual development of cancer being the key one) from over 60,000 patients (with over 90,000 mammograms total) from the Massachusetts General Hospital, starts from the data and uses deep learning to identify patters that would not be apparent or even observable by human clinicians. Because it’s not based on existing assumptions or received knowledge about risk factors, which are at best a suggestive framework, the results have so far shown to be far more accurate, especially at predictive, pre-diagnosis discovery.

“Neural network” models of AI process signals by sending them through a network of nodes analogous to neurons. Signals pass from node to node along links, analogs of the synaptic junctions between neurons. “Learning” improves the outcome by adjusting the weights that amplify or damp the signals each link carries. Nodes are typically arranged in a series of layers that are roughly analogous to different processing centers in the cortex. Today's computers can handle “deep-learning” networks with dozens of layers. Image credit: Lucy Reading-Ikkanda (artist).

AI Algorithms from some of the most innovative companies across the world are now available for $1/scan and are targetted towards developing countries to improve diagnosis and save time.
Worldwide, the cancer of the cervix (lower portion of the uterus) is the fourth most common cancer. It is also one of the most common causes of deaths due to cancer in women.

Most of my patients that participated in my public health project had either dementia, Alzheimer's or were frail and sometimes immobile. They would forget their surroundings, spouse name and even getting a regular medical checkup was a challenge. These women, when asked to go for cervical cancer diagnosis, opted out and never showed up. Most of these tests are widely available but are uncomfortable and invasive. Patients are also not keen to go for them unless indicated.

1 in 5 cancer patients across the world experience delay in diagnosis and, it holds true for cervical cancer as well. Cervical cancer is diagnosed more frequently at more advanced stages.

The human papillomavirus (HPV) infection is responsible for 90 percent cases. However, all women infected with this virus will not develop cervical cancer. Of the 150-300 known strains of HPV, 15 are classified as high risk for causing cervical cancer. Other risk factors include a weak immune system as due to HIV infection, malnutrition, having sex from an early age, multiple sex partners, multiple pregnancies, and smoking. Oral contraceptive pill use for a long time has been associated with increased risk of cervical cancer.

Training models (Deep Learning) is just the first of many steps in translating exciting research into a real product. A pathologist’s report after reviewing a patient’s biological tissue samples is often the gold standard in the diagnosis of many diseases. For cancer, in particular, a pathologist’s diagnosis has a profound impact on a patient’s therapy. The reviewing of pathology slides is a very complex task, requiring years of training to gain the expertise and experience to do well. Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient, which can lead to misdiagnoses.

Studying and reviewing all the biological tissues visible on a slide is done by a Pathologist. There can be multiple slides per patient, and at 40X magnification each is more than 10+ gigapixels when digitized. There is a lot of data to cover, and often time is limited.

To address these issues of limited time and diagnostic variability, we are evaluating ways deep learning methods like automated detection algorithm can be applied to digital pathology, helping improve diagnosis and also complement pathologists’ workflow.

In various instances and research on the incidence of cervical cancer and ineffective diagnosis, the following results were found. Patients were either:

1) Adequately screened with normal results

2) Inadequately tested with normal results

3) Unscreened

4) Low-grade abnormality

5) High-grade abnormality

The microstructure of normal tissue is uniform but as the disease progresses the tissue microstructure becomes complex and different. Based on this correlation, Scientists have created a novel light scattering-based method to identify these unique microstructures for detecting cancer progression.

The morphology of healthy and precancerous cervical tissue sites are entirely different, and light that gets scattered from these tissues varies accordingly. It is difficult to evaluate with naked eyes the subtle differences in the scattered light characteristics of the normal and precancerous tissue. The AI (Artificial intelligence) identifies precancerous tissue, and also the stage of progression in minutes.

Cervical cancer diagnoses and deaths are predicted to rise among women over the age of 50. However, deaths from the disease among the young who have been vaccinated are likely to be almost eradicated thanks to school vaccination programs for HPV.

One such company to mention that we worked with at TechEmerge is MobileODT. MobileODT turns mobile technologies into intelligent visual diagnostic tools that enable any health provider, anywhere in the world, to conduct visual inspections at the level of an expert practitioner.

AI and deep learning methods give a promising start to discover the unknown, but there is a lot that still needs to be done. AI Algorithms from some of the most innovative companies across the world are now available for $1/scan and are targetted towards developing countries to improve diagnosis and save time.

Recommended reads:

https://www.digitaltrends.com/cool-tech/screening-ai-cervical-cancer/
https://www.omicsonline.org/open-access/novel-benchmark-database-of-digitized-and-calibrated-cervical-cells-forartificial-intelligence-based-screening-of-cervical-cancer-ccoa-1000105.php?aid=68453
https://www.cdc.gov/cancer/cervical/statistics/index.htm
https://www.mobileodt.com
https://www.pnas.org/content/116/4/1074
http://news.mit.edu/2019/using-ai-predict-breast-cancer-and-personalize-care-0507

Healthcare Transformation with #AI power

2019-06-27T04:06:00.002-07:00

Artificial intelligence’s #AI transformative power is reverberating across many industries, but in one healthcare its impact promises to be truly life-changing.

The total public and private sector investment in healthcare AI are stunning: All told, it is expected to reach $6.6 billion by 2021, according to some estimates. Even more staggering, Accenture predicts that the top AI applications may result in annual savings of $150 billion by 2026.

In theory, artificial intelligence and machine learning (AI/ML) can be applied to nearly every process in healthcare. In practice, however, entrepreneurs, enterprise leaders, and investors need to discriminate between incremental improvements and the 10X improvements that will transform the industry.

In developing markets as well #AI driven companies are gaining attention and VC. Companies like Mfine has raised more than $24 million and has around 200 staff in Bengaluru and Hyderabad. But #AI faces several hurdles as well. When patient files are faxed, emailed as unreadable PDFs or set as images of handwritten notes, it will be quite difficult for AI to extract useful information. However, Apple and other big tech companies have the advantage here as they are familiar with onboarding a large network of partners including healthcare providers and #EHR vendors.

Just over 10% of all digital health VC dollars have gone to AI/machine learning companies since 2011—(last week’s deals were no exception). But not all use cases are getting equal attention from AI entrepreneurs and investors. Recently, Rock Health dug some numbers and found that drug discovery and population health management top the charts. It is hard to determine however which areas of healthcare will be first to see sizable shifts from AI integration.

Seventy-seven percent of healthcare executives reported that their organizations are accelerating investments in big data analytics and artificial intelligence (AI), citing disruptive forces and industry competitors as major motivators for increased spending, a cross-industry report from NewVantage Partners revealed.

However, nearly 80 percent of data analytics leaders said their organizations still struggle with big data analytics and AI adoption, with 92.5 percent naming cultural and organizational resistance as major barriers.

Adding to the investments in AI, The Russian Direct Investment Fund (RDIF) agreed to invest 100 million RUB in Oncobox, a company developing solutions for cancer diagnosis based on artificial intelligence (AI). Led by CEO Andrew Garazha, Oncobox is currently developing a digital platform for performing molecular diagnostics of cancers.

Talking of the big wigs, Facebook, Apple, Microsoft, Google, Amazon (FAMGA) are the leading acquirers of artificial intelligence startups (CB Insights Report). Google is leading the #healthcare space. In the last seven years, Google contributed to 16 cybersecurity startups, including CrowdStrike, and 14 healthcare startups. Security and data management is where Google is investing most heavily with a primary focus on the healthcare and financial industries.

Health-care investment can be unpredictable, requiring big bets on new technologies, a handful of which can yield marketable products, while many others go down in flames. Pharma companies are increasingly turning to artificial intelligence and machine learning to speed the costly process of discovering and developing new drugs. Benevolent AI Ltd., based in London, is using the approach to discover treatments for brain cancers, Parkinson’s disease, and other disorders, and just entered a collaboration with AstraZeneca Plc to research treatments for lung and kidney illnesses. The company was valued at just over $2 billion last year when it completed a financing round with investors including Woodford, who first invested in 2014, according to his website.

Read more on how #Google plans to use #AI to reinvest $3 Trillion #healthcare industry. #digitalhealth #artificialintelligence

https://www.cbinsights.com/research/report/google-strategy-healthcare/

Glaucoma detection in a Paediatric Setting

2019-06-17T01:02:00.000-07:00

Has your child suffered from an eye problem lately? It can be #glaucoma !

Many of the devices currently available for IOP measurement require cooperation from the subject so that accurate and repeatable reading can be obtained. Dr. Sirisha Senthil, MS, FRCS heading the VST Centre for Glaucoma care at LV Prasad Eye Institute mentioned that younger children who cannot cooperate for Goldmann applanation tonometer are tested with Icare tonometer in their Hospital.

#ophthalmology #digitalhealth #eyes #child #medical #medical #innovation #diagnostics #LVPEI

https://www.linkedin.com/pulse/glaucoma-detection-paediatric-setting-dr-ruchi-dass/?published=t

For a vast majority of the population glaucoma in children is unheard of. It is a gradual process, and since there is almost no indication of loss of vision, many people do not realize they have it. Timely diagnosis of Glaucoma is hence, a challenge. When it comes to children, the challenge is even more significant. Children often cannot do a reliable visual field test. They are less cooperative and hence doctors, rely more heavily on pressure measurements in identifying and managing Glaucoma in children than in adults, which isn't very comfortable for children.

Here is the story of Rachna. Rachna recently relocated to Bengaluru with her family and her 4-year-old son Vihaan. Within a few months, Vihaan complained pain in eyes with sensitivity to light. Rachna ignored it for a while but then started noticing that the child has become irritable, is not eating well and is complaining to discomfort in eyes often. She went in to see an ophthalmologist. It was a stressful day mentions Rachna. An eye examination isn't comfortable with children. In spite of encouragement by the mother and requesting cooperation, Vihaan wasn't cooperative.

"The doctor was kind to us and wanted to measure the intraocular pressure, but the moment he tried to measure IOP with applanation tonometer, Vihaan started crying. It looks like the doctor was trying to touch his pupil and Vihaan got so scared that he almost ran away. It was a nightmare, and we had to reschedule the appointment that day,", added Rachna.

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Many of the devices currently available for IOP measurement require cooperation from the subject so that accurate and repeatable reading can be obtained. Dr. Sirisha Senthil, MS, FRCS heading the VST Centre for Glaucoma care at LV Prasad Eye Institute mentioned that younger children who cannot cooperate for Goldmann applanation tonometer are tested with Icare tonometer in their Hospital. "We use this as a routine tonometer in pediatric clinics including pediatric glaucoma, cornea, retina and general pediatric clinics" added Dr. Senthil.

Glaucoma, says the WHO, is the second most common cause of blindness in the world, and the fourth most common in India — mainly because there's not enough awareness about the condition. Increased Intraocular pressure (IOP) can damage the optic nerve situated at the back of the eye. Initially, patients will suffer from defects in the field of vision and then progress to complete blindness.

An IOP measurement is an essential component in the assessment of a child suspected of having Glaucoma. Something as simple as automated visual field testing to measure peripheral vision isn't useful until a child reaches age 7 or 8. The decision to treat a child with drugs or proceed to surgery is dependent on whether the IOP is acceptable in a particular situation. Although examination under anesthesia enables a careful analysis of most clinical parameters, it is associated with potential adverse events from an allergy to death. There is also a significant financial implication involved in anesthetizing a child. The use of advanced Icare tonometers lowers the frequency of exams under general anesthesia. Anesthesia is costly, exposes the child to well-known risks and can sometimes falsely alter the awake pressure readings.

The standard Goldmann applanation tonometry is commonly used for measuring IOP among the adult population, but it has limitations. It is not portable, requires the patient to sit upright, and takes more time due to the anesthetic drops & Fluorescein. This means that it is often not the most practical choice for both pediatric and adult patients. Icare tonometer is easier and faster to use, especially for children making Icare the ideal tonometer for all IOP measuring applications.

Figure 3: Dr. Sirisha Senthil, MS, FRCS Head VST Centre for Glaucoma care at LV Prasad Eye Institute
On asking why Icare tonometry is superior, Dr. Senthil mentioned, "In several cases like scarred corneas, IOP would have been recorded subjectively by finger tension rather than qualifying with Icare. Finger tension method is not very comfortable with children. Icare is a superior technology as it helps to check scarred corneas and other corneal pathologies, over the worn contact lens in eyes as well."

Icare has been working with leading eye hospitals in India and is present in more than 85 countries. At LV Prasad Eye Institute, we collected feedback on the use of Icare tonometer and its quality, efficiency, running costs, service support and they scored Icare tonometer "High" on quality, value for money and efficiency. Icare is recommended for excellent service support and low running costs, which is a common perception for the best quality product available in India.

Icare has been innovating its product line continually. The latest model is the ic200 tonometer which was introduced in the European and Indian markets in late 2018. Icare ic200 is fully portable, requires no anesthesia, and allows measuring the patient not only in sitting, standing, and lateral recumbent positions but also in half-sitting and supine positions. As young children can be less cooperative, unfinished readings are also helpful, and ic200 can save incomplete measurements and store IOP results with time and date. The readings can be transferred to the computer or printed using a wireless Bluetooth printer.

Icare is working with the L.V. Prasad Eye Institute (LVPEI), India. LVPEI is an institution of repute for eye care and has become the first in the world to perform over 2,000 corneal transplants in a year. It performed 2,043 corneal transplants in 2016-17, setting the record for the highest ever corneal transplant surgeries in the world by a single center. LVPEI believes in its vision of providing equitable and efficient eye care to all sections of society.

For more information on the Icare tonometers please visit https://www.icaretonometer.com or contact info@icarefinland.com.

Data analytics for cell and gene therapy

2019-03-06T09:36:00.000-08:00

Cell and gene therapies are becoming more and more popular because of encouraging clinical results worldwide. Major pharma manufacturing companies have invested in the concept's commercialization worldwide. Recently, we read about Takeda’s license for commercialization
of Aloficel (developed by TiGenix), Celgene’s acquisition of Juno Therapeutics or Gilead’s acquisition of Kite Pharma.

As this sector grows further, there is hope that more and more complex therapies will enter the market leading to a consequent increase in the number of treated patient population. This will put further pressure on manufacturing and R&D leading to larger adoption of QbD (Quality by design) principles. The data volume will increase significantly and that is where the concept of big data analytics will kick in.

Data is important- it guides the manufacturing operations; allows proper monitoring and control to ensure quality and assures efficiency, production quality, and regulatory compliance. Below are listed the possible use cases for use of predictive models of data analytics in the pharma and life sciences industry.

Predictive Models for manufacturing outcomes- Predictive Analytics modules can be used for process Improvement Analysis to Improve Scheduling and Throughput. Take an example of this biotech company which is the only final stage bio-manufacturing facility in the world. The company is struggling to meet rapidly increasing customer demand. Repeated unanticipated production delays and starvation at critical parts of the operation were causing not only late and missed deliveries, but the expiration of batches of product at a cost of approximately $1 million per batch. Predictive analytics can be deployed in such a facility to uncover the root cause(s) of the unanticipated delays creating the late and missed deliveries; project such incompetence in advance; Run “what if” scenarios to see if additional capacity from the new facility would be required; the facility could produce two more lots per month; better way to do long-term expansion planning and predict when and where more line capacity would be required in future.

Predictive Models for RISK Monitoring- Traditional monitoring typically allocates resources equally among study sites, regardless of clinical data or the risk to patients. Routine visits to all clinical sites with 100% SDV (comparing all data points on every case report form to all subjects’ medical records) are common — and are the largest cost driver in clinical trial budgets. Predictive analytics allows sponsors to assess investigator risk and allocate monitoring resources where they are needed most. Real-time predictive modeling in the form of risk-based monitoring enables a study sponsor to adjust the level of monitoring as risk changes at individual sites.

Predictive Models for Financial Modelling and Cost-effectiveness

If you have a rich data asset then using Predictive Analytics you can benchmark cost and visualize data in that context to help sponsors forecast, budget and negotiate the cost for outsourcing clinical trials. Predictive Models for Performance and Operational Analytics- For a mature CRO, determining the productivity, utilization, and profitability of clinical research initiatives can help the CRO team allocates the most effective team members to certain projects. Based on the client which contracts the study, trends such as:

Invoice-to-Cash cycle times
The propensity of the client to request amendments to the research deliverables
Ensure the scheduling of clinical researchers and clinical laboratory space is realistic, based on the nature and volume of projects. One can also get a sneak peek to trial site performance capabilities for selecting the best investigators, eliminating non-performing sites and reducing enrolment timelines. With such insights, planning and forecasting clinical enrolment performance, rescuing off-track trials and optimizing contingency plans for those trials becomes easy.

Predictive Models for Patient/Subject discovery- Today, a researcher, for example, might use data mining to find clusters of disease subtypes in hope of finding subtypes to focus on that specific target or hopefully enable a more precise treatment course. Attribute-importance algorithms now help researchers, for instance, select the subset of genes most likely used in discriminating types of cancer. Researchers can use predictive analytics to find factors associated with a disease or predict which patient might respond best to an experimental treatment.

Carefully conducted clinical trials are performed in human volunteers to provide answers to questions such as:

Does this treatment work?
Does it work better than other treatments?
Does it have side effects?

Clinical trials also provide important information on the cost-effectiveness of treatment, the clinical value of a diagnostic test and how a treatment improves quality of life. Ever wondered how patients get selected for clinical trials without these advanced algorithms? Traditionally, physicians have selected trials by manual analysis of patients’ data. The review of resulting selections has shown that they usually do not check all clinical trials and occasionally miss an appropriate trial.

Until now we only have some web systems to address the problem to an extent. To address this problem, Industry has developed near expert systems that help to select trials for each patient. It prompts a clinician to enter the results of medical tests and uses them to identify appropriate trials. If the available records do not provide enough data, the system suggests additional tests. This is a cumbersome process to find eligible patients and doesn't help reduce any related costs.

With Predictive analytics models, Patients are identified to enroll in clinical trials based on more sources—for example, social media—than doctors’ visits. Furthermore, the criteria for including patients in a trial could take significantly more factors (for instance, genetic information) into account to target specific populations, thereby enabling trials that are smaller, shorter, less expensive, and more powerful.

References:

Csaszar E, Kirouac DC, Yu M et al. Rapid expansion of human hematopoietic stem cells by automated control
of inhibitory feedback signaling. Cell Stem Cell 2012; 10(2), 218–229.

Food and Drug Administration. FDA Data Integrity and Compliance With CGMP – Guidance for Industry. 2016.

Geris L, Lambrechts T, Carlier A, Papantoniou I. The future is digital: In silico tissue engineering. Curr. Opin.
Biomed. Eng. 2018; 6, 92–98.

Streamlining data management & process analytics for the manufacturing of cell & gene therapies. 2018
Sébastien de Bournonville, Toon Lambrechts, Thomas Pinna, Ioannis Papantoniou & Jean-Marie Aerts, bioinsights.

Stanton D. Lonza: CAR-T Manufacturing Glitch an Industry Problem, not Just Novartis’s BioProcess
International 2018; [Online] www.bioprocessintl.com/bioprocess-insider/ therapeutic-class/lonza-car-t-manufacturing-
glitch-an-industry-problem- not-just-novartiss/.

Viazzi S, Lambrechts T, Schrooten J, Papantoniou I, Aerts JM. Real-time characterization of the harvesting process
for adherent mesenchymal stem cell cultures based on on-line imaging and model-based monitoring. Biosyst. Eng. 2015; 138, 104–113.

"Big Data for Health" Initiative by Bloomberg, Dhaka Bangladesh

2018-10-16T01:19:00.000-07:00

Join me in #Dhaka, Bangladesh on 5th and 6th November at the #International Conference on #Big #Data for #Public #Health, a two-day event that will advance and identify promising Big Data applications that could significantly advance health outcomes for the people of Bangladesh. Partner @Bloomberg Philanthropies @D4H - Data for Health Access to Information #Ministry of Health and Family Welfare (MOHFW), #Bangladesh #dhaka #digitalhealth #innovation #bigdata

Effective #decision-making about #public #health policies and programs require robust data and rigorous analysis. However, the nature of both “data” and “analysis” are being fundamentally transformed. The explosive growth of smart devices, remote sensing/imaging technologies, and social media are generating streams of electronic data that are voluminous, varied, and high velocity. Deriving #insights from these #emerging sources (generally referred to as “Big Data”) often require #data #mining techniques that are significantly different from the #epidemiological #analysis that has been the traditional focus of public health training. Therefore, to stay at the forefront of #evidencebased decision-making, #publichealth authorities will need to master these new data sources and new analytic techniques.

Big Data to improve Patient outcomes

2018-10-16T01:13:00.000-07:00

Join us HealthCursor Consulting Group on 1st November 2018 in Mumbai, India as we talk about the use of #Big #Data to Improve #Patient #Outcomes.

Event Partner: @Abbott

#Data plays a vital role in a society like India that has a huge population outside healthcare and where resources are limited. The opportunity for big data in India is fairly diverse and consists of providers, health insurance, researchers, pharma and medical device R&D. HealthXL is bringing thought leaders to discuss how we are currently leveraging data and analytics in India, how it can reduce health care costs and the opportunity at hand for the future. #innovation #bigdata #healthcare #analytics 

https://www.healthxl.com/events/mumbai-2018

HEALTHXL EVENT BRIEF:

This event will allow attendees to understand first-hand the cost-efficient, innovation paradigm at work in India – innovation solutions that can be exported to other markets throughout the world.

Abbott in India (primarily EPD) has consistently invested in creating digital tools with the objective of shifting the paradigm in doctor detailing and patient experience. Abbott uses tools such as Augmented Reality, Virtual Reality using Google Cardboard and Image Recognition to enrich medical detailing across therapy areas.

Topics
Non-communicable Diseases: Chronic Disease Management in India’s Digital Era

India has been quite successful in containing communicable, maternal, neonatal, and nutritional diseases. The contribution of NCDs to death and disability in India continues to grow and affect not just health, but also the economy. We want to highlight what initiatives are being taken up in the prevention and management of NCDs from primary care to tertiary care, and also highlight the role of technology in the digitally strengthened India.

Patient centricity: How India is making the patient it's most important customer

How are various stakeholders utilizing patient data more effectively for better R&D, diagnosis, treatment, and monitoring? How is India enhancing patient engagement and experience to make them active participants in all care related decisions?

Big Data to Improve Patient Outcomes

Data plays a vital role in a society like India that has a huge population outside healthcare and where resources are limited. The opportunity for big data in India is fairly diverse and consists of providers, health insurance, researchers, pharma and medical device R&D. We bring thought leaders to discuss how we are currently leveraging data and analytics in India, how it can reduce health care costs and the opportunity at hand for the future.

Dubai Healthcare Transformation #artificialintelligence

2018-10-11T23:47:00.000-07:00

The ability of computer systems to assume tasks for humans has improved efficiency in virtually every industry, from manufacturing to transportation. Now hospitals are getting into the game, deploying AI to take on challenges from diagnosing patients more quickly in the emergency room and streamlining communication between doctors to lessening the risk of complications so patients can go home sooner – and avoid being readmitted.

Take a look at how Dubai is trying to create their own version of Patient-centric healthcare, by enhancing the very foundation of healthcare infrastructure and delivery in the city. They are willing to incorporate to adopt, try and harness the power of #artificial #intelligence.

4 major highlights where Health authorities in the city have gathered much attention include:

1. EMR rollout

In 2017, Dubai Health Authority completes the final phase of Salama electronic medical records system roll-out. Dubai Health Authority continues its progress towards a fully integrated electronic patient records system. In the second phase of the Salama project, 30 million more transactions have been migrated to the EMR system. DHA health facilities added to the system were: Nad Al Sheba, Al Mankhool, Al Badaa, Al Lusaily, Al Safa and Al Zabeel PHC, Dubai Hospital, Nad Al Hamar, Family Gathering Clinic and Dubai Diabetes Centre.

2. Robot Pharmacies

After launching a smart pharmacy at Dubai’s Rashid Hospital earlier this year, the Dubai Health Authority (DHA) has unveiled a robot to prescribe and dispense medication at Dubai Hospital. According to the DHA, the robot system can store up to 35,000 medicines and dispense about 12 prescriptions in less than a minute. The robot dispenses prescribed medication by using a barcode system, minimizing any human error. This smart pharmacy system follows the DHA’s adoption of a smart system to monitor pharmaceuticals in storage to ensure they are kept in the right conditions, including temperature.

3. Medical check up

Smart Kiosks The smart mobile booth will allow employees at government entities to conduct medical tests and check-ups that include blood pressure, blood glucose, body mass index (BMI), heart rate and blood oxygen levels. This kiosk/booth also offers a telemedicine feature that will allow patients to communicate with a specialized DHA doctor after the results are out for consultation. The smart mobile booth initiative is part of DHA’s Dubai Future Accelerators Projects and was kicked off at the Road and Transport Authority (RTA) headquarters, one of the four governmental bodies that will host the smart consultation booth.

4. Artificial Intelligence/ Patient centric healthcare

DHA has signed a memorandum of understanding (MoU) with four international companies to adopt their healthcare innovations and technologies at the recent fourth cycle of the Dubai Future Accelerators programme. The companies that signed the MoU with the DHA were @Babylon, @Bodyo, @Admetsys Corporation, and @HINT Neuro.

Babylon- Babylon is a subscription health service provider that enables users to have virtual consultations with doctors and health care professionals via text and video messaging through its mobile application. The service also allows users to receive drug prescriptions, referrals to health specialists, and book health exams with nearby facilities. Babylon has created a chatbot that lets you talk with an AI that can interpret symptoms and tell a person what the problem is. It is not an official medical diagnosis, however.

A health application from Babylon will use the latest AI technology to deliver 24/7 video links for patients to consultants around the world. The app is already being used by 2.2 million patients worldwide and will be available in Dubai this summer. Around June 2018, Healthcare start-up Babylon Health said that its #artificial #intelligence(AI) was able to diagnose medical conditions as accurately as a doctor.

Bodyo- Bodyo helps communities better take care of themselves to prevent serious illnesses through free regular health assessments. Bodyo health pods are easily accessible around local communities, empowering transformation by giving you the platform to capture key health vitals in controlling diets & nutrition, monitoring sleep patterns, or just to get fit.

Bodyo health analysis pods will be rolled out in government buildings so that staff can monitor their health and spot any problems that may need a hospital check-up. With BodyO's free app one can monitor progress, set milestones, achieve goals and know yourself better.

Admetsys- Admetsys has developed a first-of-its-kind artificial pancreas for hospital and surgical care, leveraging adaptive learning algorithms and counterbalancing treatment of insulin and glucose. The company that I met and evaluated during TechEmerge Innovation program via IFC in India uses AI to monitor and regulate patients’ blood sugar levels.

Admetsys will help the city to improve the standard of care and control costs at the same time. Better glucose control in the hospital reduces mortality, reduces a range of complications and reduces a patient’s overall length of stay. Admetsys stresses on #precisionmedicine via a learning algorithm that adapts the model to the specific patient’s metabolism, to administer a correct and healthy dose of insulin and dextrose when required.

Other Admetsys innovations to be introduced in Dubai are a neuro headband to detect strokes, and flow cell sensors that alert doctors to sudden drops in the vital signs of intensive care patients. The Admetsys technology is due to be rolled out at Dubai Hospital and Rashid Hospital.

HINT Neuro- Founded in 2016 to help the millions of people who suffer from strokes every year. HiNT stands for Healthcare Innovation in NeuroTechnology and we are developing a wearable point-of-care monitoring device that detects when patients at high-risk are having a stroke. The device is a comfortable, wearable headband that will alert patients, caregivers, and emergency services in the case of a stroke.

The DHA will also roll out the Health Care and Innovative New Technology (HINT) neuro headband, designed to detect strokes in patients before they happen. This technology was originally designed for use by high-risk patients in the home, but working with doctors in Dubai has opened up new avenues for medical technology company.

As part of their innovation and AI strategy, the DHA also plans to train more than 120 of its employees in project management, programme management, portfolio management and risk management.

The #Dubai #Health #Authority (DHA) is already using Artificial Intelligence (AI) to sort out all chest X-ray scans required for mandatory medical fitness for residency purposes. This is streamlining the system, cutting down on processing time and providing a more efficient and consolidated system for issuing results.

The DHA signed an agreement with Agfa IT Health Care Systems for validation of the first radiology AI algorithm in the UAE. This is part of Dubai Health Strategy 2021 of using AI and smart systems to streamline health care in the emirate. Dubai government plans to further improve healthcare quality through automation The healthcare industry in Dubai is set to grow exponentially, with demand for world-class medical service currently outpacing supply.

To meet the growing demand on the local and regional level, the Dubai Health Authority outlined a strategy that uses artificial intelligence (AI) and robotics to largely automate the process, in addition to identifying key public-private partnerships and contributing with weighty healthcare investments. Burgeoning healthcare market swells in the billions - According to Alpen Capital, the UAE healthcare market is expected to grow 12.7%, to nearly US$20BN (AED 71.56BN) by 2020. The UAE leads in the top 20 countries in the world with US$1,200 per capita spend on healthcare (AED 4,400), which is indicative of residents’ trust in local medical establishments to deliver the same (if not better) medical treatment in the UAE as in Europe or the USA.

To satisfy medical seekers on the local and international level, the Dubai government is supporting the growth in the medical industry with investments and ambitious initiatives. The Dubai Health Authority (DHA) introduced Dubai Health Strategy 2021, which focuses on technology and automation, in particular with AI and robotics.

With medical tourism already on the rise (and predicted to reach US$708M / AED 2.6BN by 2020), Dubai is blazing a trail in both innovative care and top-tier health facilities for medical seekers in the MENA. But in order to meet the increasing demand for better healthcare, there are gaps in the market that the DHA has identified in order to move forward, namely: investment in R&D and training, government regulation, and service delivery.

The DHA outlined clear initiatives in order to tackle these challenges, tailoring solutions for each vertical to programmes under the larger umbrella strategy. With the legal framework and foreign partnerships in place, and AI already making strides in local medical circles, it won’t be long before Dubai will secure its position as a global leader in health technology. We will witness how some of these efforts contribute to better healthcare delivery in the future as they become a part of the ongoing smart city, smart government programs in the country.

For now, some of these efforts are encouraging and are providing ample opportunities for innovative companies from across the world to make an impact in the healthcare industry in the region.

If you are an innovative company working in the field or Women health (mainly cancers), mental health or other cancer related detection and cure, feel free to get in touch with me. I am working on several projects in the region and outside where these innovations can become a part of the overall offering.

Credits:

https://www.visitdubai.com/en/business-in-dubai/why-dubai/news-and-insights/artificial-intelligence-in-healthcare

https://www.computerweekly.com/news/450424753/Second-phase-of-electronic-medical-record-system-goes-live-in-Dubai

https://www.thenational.ae/uae/exclusive-full-body-health-scanners-chosen-for-uae-trial-to-aid-early-diagnosis-1.717937

https://www.usnews.com/news/healthcare-of-tomorrow/articles/2017-10-31/hospitals-utilize-artificial-intelligence-to-treat-patients

https://www.computerweekly.com/news/450430353/Dubai-Health-Authority-completes-final-phase-of-Salama-electronic-medical-records-system-roll-out

https://www.computerweekly.com/news/450422969/Dubai-Health-Authority-unveils-smart-medical-check-initiative

https://www.thenational.ae/uae/exclusive-full-body-health-scanners-chosen-for-uae-trial-to-aid-early-diagnosis-1.717937

PREPARE for Impact- Artificial Intelligence Promises a New Paradigm for Healthcare

2018-06-09T00:32:00.000-07:00

From interpreting lab tests, simplifying in-vitro fertilization, and personalizing cancer care to monitoring surgical video in real-time or aiding in the diagnosis of pneumothorax, the creativity and ingenuity evident in the flourishing AI research community is both astounding and heartening.

#artificialintelligence #healthcare #WMIF #WorldMedicalinnovationforum

The central dogma of the Information Technology requirement in medicine is software that can efficiently ‘read •• mine •• understand’ medical data; such as patient diagnostic information/ health data, radiography images, clinical trial data, and drug combination therapy data etc.
The health care system contains not only the simple text records but also complex data ranging from graphs from the diagnosis labs to images from the radiology instruments.
What if we could use image and text analytics to produce a computerized radiologist assistant which could quickly filter to clinicians/radiologists for decision support? What if computers can combine imaging data along with the rest of clinical patient data to summaries of the patients’ conditions, coincidental diagnosis, automatic triage and a second opinion, as well as statistical comparisons to similar patients?

Read more on https://www.linkedin.com/feed/update/urn:li:activity:6411085374394859520

Happy New Year and 2017 Highlights @HealthCursor Consulting Group

2018-01-02T12:57:00.000-08:00

HAPPY NEW YEAR 2018 to all my friends, colleagues and followers. As we bid adieu to the year #2017, here is the snapshot of our work that make us proud @ HealthCursor Consulting Group. Looking forward to more of such fulfilling and magical moments in #2018.

Hopefully we will get to work with you this year and continue to make a difference in #Digitalhealth

AI and Deep learning for Cervical Cancer

2017-12-25T03:47:00.000-08:00

Worldwide, the cancer of the cervix (lower portion of the uterus) is the fourth most common cancer. It is also one of the most common causes of deaths due to cancer in women.

Most of my patients that participated in my public health project had wither dementia, Alzheimer's or were frail and sometimes immobile. They would forget their surroundings, spouse name and even getting a regular medical checkup was a challenge. These women, when asked to go for cervical cancer diagnosis, opted out and never showed up. Most of these tests are widely available but are uncomfortable and invasive. Patients are also not keen to go for them unless indicated.

1 in 5 cancer patients across the world experience delay in diagnosis and, it holds true for cervical cancer as well. Cervical cancer is diagnosed more frequently at more advanced stages.

The human papillomavirus (HPV) infection is responsible for 90 percent cases. However, all women infected with this virus will not develop cervical cancer. Of the 150-300 known strains of HPV, 15 are classified as high risk for causing cervical cancer. Other risk factors include a weak immune system as due to HIV infection, malnutrition, having sex from an early age, multiple sex partners, multiple pregnancies, and smoking. Oral contraceptive pill use for a long time has been associated with increased risk of cervical cancer.

Training models (Deep Learning) is just the first of many steps in translating exciting research into a real product. A pathologist’s report after reviewing a patient’s biological tissue samples is often the gold standard in the diagnosis of many diseases. For cancer, in particular, a pathologist’s diagnosis has a profound impact on a patient’s therapy. The reviewing of pathology slides is a very complex task, requiring years of training to gain the expertise and experience to do well. Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient, which can lead to misdiagnoses.

Studying and reviewing all the biological tissues visible on a slide is done by a Pathologist. There can be multiple slides per patient, and at 40X magnification each is more than 10+ gigapixels when digitized. There is a lot of data to cover, and often time is limited.

To address these issues of limited time and diagnostic variability, we are evaluating ways deep learning methods like automated detection algorithm can be applied to digital pathology, helping improve diagnosis and also complement pathologists’ workflow.

In various instances and research on the incidence of cervical cancer and ineffective diagnosis, the following results were found. Patients were either:

1) Adequately screened with normal results
2) Inadequately tested with normal results
3) Unscreened
4) Low-grade abnormality
5) High-grade abnormality

The microstructure of normal tissue is uniform but as the disease progresses the tissue microstructure becomes complex and different. Based on this correlation, Scientists have created a novel light scattering-based method to identify these unique microstructures for detecting cancer progression.

The morphology of healthy and precancerous cervical tissue sites are entirely different, and light that gets scattered from these tissues varies accordingly. It is difficult to evaluate with naked eyes the subtle differences in the scattered light characteristics of the normal and precancerous tissue. The AI (Artificial intelligence) identifies precancerous tissue, and also the stage of progression in minutes.

One such company to mention that we worked with at TechEmerge is MobileODT. MobileODT turns mobile technologies into intelligent visual diagnostic tools that enable any health provider, anywhere in the world, to conduct visual inspections at the level of an expert practitioner.

Cervical cancer diagnoses and deaths are predicted to rise among women over the age of 50. However, deaths from the disease among the young who have been vaccinated are likely to be almost eradicated thanks to school vaccination programs for HPV.

AI and deep learning methods give a promising start to discover the unknown, but there is a lot that still needs to be done. AI Algorithms from some of the most innovative companies across the world are now available for $1/scan and are targetted towards developing countries to improve diagnosis and save time.

New services launched- Developing innovative Medical and Healthcare content

2017-12-14T00:13:00.000-08:00

After completing over a decade in Healthcare consulting, HealthCursor is now providing Medical content services to all its clients across the globe. When we talk about medical and healthcare content, having a subject matter expert who understands the recent trends and the industry well makes a difference. Well written articles not only help set the right image for your brand and also portray scientificness and authenticity of your work in the healthcare industry.

Healthcursor’s certified writer’s team comprises of professional experts with years of experience in the healthcare industry across the world in areas of DigitalHealth, healthcare IT, wellness, Fitness, Disease management, etc. Coupled with the company’s unparalleled expertise in marketing and SEO, we bring the best results for your brand. With our team, you’re giving your business a competitive edge that you won’t get with any other writing service available.

HCG will invest its resources and time to acquaint us with your product, your target market, content needs and marketing plan and it shows in our results. Our writing team is prepared to work within your schedule and your vision to produce quality content that meets your needs. Whether it’s online or print marketing materials you need, you’ll be provided with informative and engaging health content written to your exact specifications.

All of our content is 100% unique and original, and we can work within the confines of any marketing or SEO campaign – even if you’re working with another company. We provide custom content for any writing project, including:

Patient education materials
Medical/healthcare articles
Company Newsletters
Flyers, brochures, and posters/Print or online advertisements
Scientific Journals/ Whitepaper/ Speacilised content (Oncology, Clinical trials)
Press releases
Healthcare Business Blogs (IT solutions, Insurance, Market trends, Brand communication)
Podcasts
Surveys (User/Consumer, TG)
Healthcare Magazines, Tabloids etc.

We also provide content on subscription like Health tips, Health videos (Diseases, Anatomy, Drug interactions), Educational content etc. By leveraging our experience in healthcare, digital and print marketing, and SEO, we’re able to provide content that converts better and brings you more business. To know more contact Laura Mich on Laura.m@healthcursor.com with your needs. Every need is different and we provide the best solution for your company needs.

Innovations on the Front Lines, Advances in Point of Care Technologies for Rural Settings

2017-10-11T01:04:00.001-07:00

Will be speaking at Health 2.0 India Conference 2017 at Hyderabad on 10th Nov. https://lnkd.in/fKSfY7A #healthcare Health 2.0 Innovations on the Front Lines, Advances in Point of Care Technologies for Rural Settings 12:00pm - 1:00pm Nov 10th 2017

Affordable point of care technologies are an integral part of an effective healthcare system, but continue to be a critical unmet need for billions of rural consumers in emerging markets like India. The role and impact of point of care technologies will be transformational over the next 10 years. This panel is designed for physicians, clinicians, hospital administrators, technologists and all healthcare and medical professionals who must learn about cutting-edge technology and how it will improving the business and service of patients.

Genetic sequencing and Exciting Predictions in store

2017-08-26T10:09:00.001-07:00

Genome, Chromosome, DNA all refer to our Genetic material and make us unique. DNA or Deoxyribo Nucleic Acid are molecules that are building entities of the genetic material present in an organism- a plant, a dog or a human being. These DNA molecules form long chains and then termed as a chromosome. A human chromosome may have 30 million DNA units in a chain. The Genome is referred to all of these chromosomes being together, i.e., all the genetic material in an organism.

1 in every 5 healthy adults may unwittingly carry alterations in disease susceptibility genes- Medical bulletin news - 5 hours ago

Genome analysis or sequencing relates to finding out the order of DNA nucleotides in a genome-i.e. makeup of an organism's DNA. A Human DNA is made up of over 3 billion of these genetic letters.

Just like in astrology, predictions are made about career, life and health based on birth date, time, location (Lat. and Long.), Lagna, Dasha, Nakshatra, similarly using the whole genomic sequencing we can understand the heritability and predisposition of certain diseases, drug and treatment effectiveness, etc. Different genes are associated with diseases and hence knowing more about your genes and gene variants can help us study the associated disorders and their predisposition better.

I was excited about this new science and was a witness to some significant research in this area. My family used Mapmygenome for our Genome sequencing. As it is still a new concept for Indians, mapmygenome provided full support regarding briefing with regards to the process, the significance of genomic sequencing and expected results. Mapmygenome has some real reputable names in their clientele already. The sample collection was relatively straightforward. We were asked to provide a buccal swab that took ten minutes only. It was collected in a test tube and was sent to the lab for DNA extraction and further processing. During the next 10 days, I received timely updates on my email and phone throughout the process when the sample was received and was going through the process of extraction and wet lab process and also when counseling was scheduled. Mapmygenome genetic counselors counseled us individually for over an hour helping us understand our reports better. Very exciting a process- must say! Worth investing and the counseling was the best part.

My sister recently got diagnosed with idiopathic dermatitis herpetiformis and anemia, and the doctors were not able to concur on the diagnosis. She kept visiting Gynecologists for hormonal imbalance, Dermatologists, and Gastroenterologists regarding her food intolerances but was treated symptomatically. Her situation was getting worse, and she decided to get in touch with the genetic counselor again along with the doctor the genetic counselor suggested on request. Based on genetic linkage (HLA DQ2 or HLA DQ8) now she is diagnosed with coeliac diseases and since last eight months, she is maintaining a gluten free diet. Her symptoms have subsided, and quality of life has significantly improved :) I am more hopeful now because if Genetic testing can give you the lowdown on you or your family's chances of developing celiac disease, it can do much more in the areas of prevention and personalized healthcare.

Knowing of a particular genetic predisposition could be a powerful tool for preventative medicine. Depending on the disease, specific drugs or behavioral modifications like diet or exercise could be prescribed to their patients early on hence reducing and managing the impact of those diseases later in life. With the recent advancement in computing, and popularity of cloud there is a realistic hope that sooner we will be able to tease out these elusive genetic markers with biologists who can shed light on what those genes do.

Blockchain, Predictive Analytics and Healthcare

2017-02-07T04:38:00.000-08:00

Episode-of-care payment and comprehensive care payment systems can help providers prevent health problems; avoid the occurrence of acute episodes among individuals who have health conditions; prevent poor outcomes during major acute episodes, such as infections, complications, and hospital readmissions; and reduce the costs of successful treatment.

Using cryptography to keep exchanges secure, blockchain provides a decentralised database, or “digital ledger”, of transactions that everyone on the network can see. This network is essentially a chain of computers that must all approve an exchange before it can be verified and recorded.

Learn more about the use of Blockchain in healthcare here:

Blockchain, Predictive Analytics and Healthcare from Dr. Ruchi Dass

It is distributed. Means it is de- centralised. Nobody is an owner.

It is public. Everyone can see it.

Things that have happened are time stamped, recorded and coded.

It is persistent. As far as everyone is participating, it is for forever.

SECURITY AND PRIVACY are built into the system as well. (On the internet the problem was that TCPIP is not built in for identity or encryption.

Smart contracts are built in- which means once a transaction is made automatically the laws of the contract are applied and executed. Value, data and money can be moved now.

For example, if everyone in a conference room has a spreadsheet of no. of shirts they own and they share it. The data miners after every 10 seconds will find out the new shirts added, shorts deleted, ownership of shirts changed, etc..

The miners will then publish this to everyone holding a ledger who participated in the blockchain. This is called block of work chained to the initial block and hence blockchain- that is why it is persistent, and you get a running, consistent account of things that is time stamped, verified by everyone, virtually un-hackable because no one can reverse it.

And this is the reason why for Bitcoins, financial services it is an important technology because this is why you wait for 3 days for your cheque to get cleared. Right!