Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
Lancet Oncol. 2020 Mar; 21(3): 335–337. Published online 2020 Feb 14. doi: 10.1016/S1470-2045(20)30096-6
PMCID: PMC7159000
The National Clinical Research Center for Respiratory Disease and the National Health Commission of the People’s Republic of China collaborated to establish a prospective cohort to monitor COVID-19 cases in China. As on Jan31, 20202007 cases have been collected and analyzed with confirmed COVID-19 infection in these cohorts.
Results: 18 or 1% of COVID-19 cases had a history of cancer (the overall average cancer incidence in the overall China population is 0.29%) {2015 statistics}. It appeared that cancer patients had an observable higher risk of COVID related complications upon hospitalization. However, this was a higher risk compared with the general population. There was no comparison between cancer patients not diagnosed with COVID-19 and an assessment of their risk of infection. Interestingly those who were also cancer survivors showed an increased incidence of COVID related severe complications compared to the no cancer group.
Although this study could have compared the risk within a cancer group, the authors still felt the results warranted precautions when dealing with cancer patients and issued recommendations including:
Postponing of adjuvant chemotherapy or elective surgery for stable cancer should be considered
Stronger personal protection for cancer patients
More intensive surveillance or treatment should be considered when patients with cancer are infected, especially in older patients
Further studies will need to address the risk added by specific types of chemotherapy: cytolytic versus immunotherapy e.g.
Preparedness for COVID-19 in the oncology community in Africa
Africa has a heterogeneity of cultures, economies and disease patterns however fortunately it is one of the last countries to be hit by the COVID-19 pandemic, which allows some time for preparation by the African nations. The authors note that with Africa’s previous experiences with epidemics, namely ebola and cholera, Africa should be prepared for this pandemic.
However, as a result of poor economic discipline, weak health systems, and poor health-seeking behaviors across the continent, outcomes could be dismal. Poverty, low health literacy rates, and cultural practices that negatively affect cancer outcomes will result in poor assimilation of COVID-19 containment strategies in Africa.”
In general African oncologists are following COVID-19 guidelines from other high-income countries, but as this writer acknowledges in previous posts, there was a significant lag from first cases in the United States to the concrete formulation of guidelines for both oncologists and patients with regard to this pandemic. African oncologist are delaying the start of adjuvant therapies and switching more to oral therapies and rethink palliative care.
However the authors still have many more questions than answers, however even among countries that have dealt with this pandemic before Africa (like Italy and US), oncologists across the globe still have not been able to answer questions like: what if my patient develops a fever, what do I do during a period of neutropenia, to their satisfaction or the satisfaction of the patient. These are questions even oncologists who are dealing in COVID hotspots are still trying to answer including what constitutes a necessary surgical procedure? As I have highlighted in recent posts, oncologists in New York have all but shut down all surgical procedures and relying on liquid biopsies taken in the at-home setting. But does Africa have this capability of access to at home liquid biopsy procedures?
In addition, as I had just highlighted in a recent posting, there exists extreme cancer health disparities across the African continent, as well as the COVID responses. In West Africa, COVID-19 protocols are defined at individual institutions. This is more like the American system where even NCI designated centers were left to fashion some of their own guidelines initially, although individual oncologists had banded together to do impromptu meetings to discuss best practices. However this is fine for big institutions, but as in the US, there is a large rural population on the African continent with geographical barriers to these big centers. Elective procedures have been cancelled and small number of patients are seen by day. This remote strategy actually may be well suited for African versus more developed nations, as highlighted in a post I did about mobile health app use in oncology, as this telemedicine strategy is rather new among US oncologists (reference my posts with the Town Hall meetings).
The situation is more complicated in South Africa where they are dealing with an HIV epidemic, where about 8 million are infected with HIV. Oncology services here are still expecting to run at full capacity as the local hospitals deal with the first signs of the COVID outbreak. In Sudan, despite low COVID numbers, cancer centers have developed contingency plans. and are deferring new referrals except for emergency cases. Training sessions for staff have been developed.
For more articles in this online open access journal on Cancer and COVID-19 please see our
Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare
Curator: Stephen J. Williams, Ph.D.
Updated 12/18/2018
In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry. Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.
More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations. This is where blockchain technologies may play an important role.
A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:
The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data. They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.
The review discusses:
Recent achievements in next-generation artificial intelligence
Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
Open source blockchain Exonium and its application for healthcare marketplace
A blockchain-based platform allowing patients to have control of their data and manage access
How advances in deep learning can improve data quality, especially in an era of big data
Advances in Artificial Intelligence
Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:
Prediction of drug properties(2, 3) and toxicities(4)
Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require. Biomedical imaging datasets are an example of use of transfer learning.
One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.
Highly Distributed Storage Systems (HDSS)
The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable. This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.
Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data. Each record in the block contains a timestamp and a link to the previous block in the chain. Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone. This allows a verifiable, secure, and consistent history of a record of events.
Data Privacy and Regulatory Issues
The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain. The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.). The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard. The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:
Issues of Privacy Specific In Use of Blockchain to Distribute Health Data
Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
Blockchain uses a consensus algorithm even though end users may have their own privacy key
Applied cryptography measures and routines are used to decentralize authentication (publicly available)
Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.
YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)
In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.
Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:
References
Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget9, 5665-5690.
Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics13, 2524-2530.
Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research16, 1401-1409.
Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences133, 70-78.
Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging8, 1021-1033.
Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports7, 45938.
Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics14, 3098-3104.
Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel)16.
December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.
The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.
Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.
“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”
O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.
“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”
Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.
“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”
Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.
“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”
As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.
“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”
November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.
Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.
HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.
“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”
The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.
“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”
Accelerate distributes the data to the workforce, making it available to them one business process at a time.
“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”
The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.
It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.
“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”
This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.
Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.
“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”
Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.
“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”
Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.
“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.
“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”
Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.
The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.
“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”
While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.
Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.
“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”
Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.
Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.
Andhra Pradesh, DNA, and blockchain
Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.
With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.
Other Articles in this Open Access Journal on Digital Health include:
Live Conference Coverage Medcity Converge 2018 Philadelphia: Clinical Trials and Mega Health Mergers
Reporter: Stephen J. Williams, PhD
1:30 – 2:15 PM Clinical Trials 2.0
The randomized, controlled clinical trial is the gold standard, but it may be time for a new model. How can patient networks and new technology be leveraged to boost clinical trial recruitment and manage clinical trials more efficiently?
Michele: Medable is creating a digital surrogate biomarker for short term end result for cardiology clinical trials as well as creating a virtual site clinical trial design (independent of geography)
Sameek: OSU is developing RNASeq tests for oncogenic fusions that are actionable
John: ability to use various technologies to conduct telehealth and tele-trials. So why are we talking about Clinical Trials 2.0?
Andrew: We are not meeting many patients needs. The provider also have a workload that prevents from the efficient running of a clinical trial.
Michele: Personalized medicine: what is the framework how we conduct clinical trials in this new paradigm?
Sameek: How do we find those rare patients outside of a health network? A fragmented health system is hurting patient recruitment efforts.
Wout: The Christmas Tree paradigm: collecting data points based on previous studies may lead to unnecessary criteria for patient recruitment
Sameek: OSU has a cancer network (Orion) that has 95% success rate of recruitment. Over Orion network sequencing performed at $10,000 per patient, cost reimbursed through network. Network helps pharma companies find patients and patients to find drugs
Wout: reaching out to different stakeholders
John: what he sees in 2.0 is use of tech. They took 12 clinic business but they integrated these sites and was able to benefit patient experience… this helped in recruitment into trials. Now after a patient is recruited, how 2.0 model works?
Sameek: since we work with pharma companies, what if we bring in patients from all over the US. how do we continue to take care of them?
Andrew: utilizing a technology is critically important for tele-health to work and for tele-clinical trials to work
Michele: the utilization of tele-health by patients is rather low.
Wout: We are looking for insights into the data. So we are concentrated on collecting the data and not decision trees.
John: What is a barrier to driving Clinical Trial 2.0?
Andrew: The complexity is a barrier to the patient. Need to show the simplicity of this. Need to match trials within a system.
Saleem: Data sharing incentives might not be there or the value not recognized by all players. And it is hard to figure out how to share the data in the most efficient way.
Wout: Key issue when think locally and act globally but healthcare is the inverse of this as there are so many stakeholders but that adoption by all stakeholders take time
Michele: accessibility of healthcare data by patients is revolutionary. The medical training in US does not train doctors in communicating a value of a trial
John: we are in a value-driven economy. You have to give alot to get something in this economy. Final comments?
Saleem: we need fundamental research on the validity of clinical trials 2.0.
Wout: Use tools to mine manually but don’t do everything manually, not underlying tasks
Andrew: Show value to patient
2:20-3:00 PM CONVERGEnce on Steroids: Why Comcast and Independence Blue Cross?
This year has seen a great deal of convergence in health care. One of the most innovative collaborations announced was that of Cable and Media giant Comcast Corporation and health plan Independence Blue Cross. This fireside chat will explore what the joint venture is all about, the backstory of how this unlikely partnership came to be, and what it might mean for our industry.
Moderator:Tom Olenzak, Managing Director Strategic Innovation Portfolio, Independence Blue Cross @IBX Speakers: Marc Siry, VP, Strategic Development, Comcast Michael Vennera, SVP, Chief Information Officer, Independence Blue Cross
Comcast and Independence Blue Cross Blue Shield are teaming together to form an independent health firm to bring various players in healthcare onto a platform to give people a clear path to manage their healthcare. Its not just about a payer and information system but an ecosystem within Philadelphia and over the nation.
Michael: About 2015 at a health innovation conference they came together to produce a demo on how they envision the future of healthcare.
Marc: When we think of a customer we think of the household. So we thought about aggregating services to people in health. How do people interact with their healthcare system?
What are the risks for bringing this vision to reality?
Michael: Key to experience is how to connect consumer to caregiver.
How do we aggregate the data, and present it in a way to consumer where it is actionable?
How do we help the patient to know where to go next?
Marc: Concept of ubiquity, not just the app, nor asking the provider to ask patient to download the app and use it but use our platform to expand it over all forms of media. They did a study with an insurer with metabolic syndrome and people’s viewing habits. So when you can combine the expertise of IBX and the scale of a Comcast platform you can provide great amount of usable data.
Michael: Analytics will be a prime importance of the venture.
Tom: We look at lots of companies that try to pitch technologies but they dont understand healthcare is a human problem not a tech problem. What have you learned?
Marc: Adoption rate of new tech by doctors is very low as they are very busy. Understanding the clinicians workflow is important and how to not disrupt their workflow was humbling for us.
Michael: The speed at which big tech companies can integrate and innovate new technologies is very rapid, something we did not understand. We want to get this off the ground locally but want to take this solution national and globally.
Marc: We are not in competition with local startups but we are looking to work with them to build scale and operability so startups need to show how they can scale up. This joint venture is designed to look at these ideas. However this will take a while before we open up the ecosystem until we can see how they would add value. There are also challenges with small companies working with large organizations.
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#MCConverge
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And at the following handles:
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Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal
10:00-10:45 AM The Davids vs. the Cancer Goliath Part 1
Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.
10,000 cancer patients a month helping patients navigate cancer care with Belong App
Belong Eco system includes all their practitioners and using a trigger based content delivery (posts, articles etc)
most important taking unstructured health data (images, social activity, patient compilance) and converting to structured data
Care+Wear
personally design picc line cover for oncology patients
partners include NBA Major league baseball, Oscar de la Renta,
designs easy access pic line gowns and shirts
OncoPower :Digital Health in a Blockchain Ecosystem
problems associated with patient adherence and developed a product to address this
OncoPower Blockchain: HIPAA compliant using the coin Oncopower security token to incentiavize patients and oncologists to consult with each other or oncologists with tumor boards; this is not an initial coin offering
PolyArum
spinout from UPENN; developing a nanoparticle based radiation therapy; glioblastoma muse model showed great response with gold based nanoparticle and radiation
they see enhanced tumor penetration, and retention of the gold nanoparticles
however most nanoparticles need to be a large size greater than 5 nm to see effect so they used a polymer based particle; see good uptake but excretion past a week so need to re-dose with Au nanoparticles
they are looking for capital and expect to start trials in 2020
Seeker Health
tying to improve the efficiency of clinical trial enrollment
using social networks to find the patients to enroll in clinical trials
steps they use 1) find patients on Facebook, Google, Twitter 2) engage patient screen 3) screening at clinical sites
Seeker Portal is a patient management system: patients referred to a clinical site now can be tracked
11:00- 11:45 AM Breakout: How to Scale Precision Medicine
The potential for precision medicine is real, but is limited by access to patient datasets. How are government entities, hospitals and startups bringing the promise of precision medicine to the masses of oncology patients
Ingo: data is not ordered, only half of patients are tracked in some database, reimbursement a challenge
Eugean: identifying mutations as patients getting more comprehensive genomic coverage, clinical trials are expanding more rapidly as seen in 2018 ASCO
Ingo: general principals related to health outcomes or policy or reimbursement.. human studies are paramount but payers may not allowing for general principals (i.e. an Alk mutation in lung cancer and crizotanib treatment may be covered but maybe not for glioblastoma or another cancer containing similar ALK mutation; payers still depend on clinical trial results)
Andrew: using gene panels and NGS but only want to look for actionable targets; they establish an expert panel which reviews these NGS sequence results to determine actionable mutations
Ankur: they have molecular tumor boards but still if want to prescribe off label and can’t find a clinical trial there is no reimbursement
Andrew: going beyond actionable mutations, although many are doing WES (whole exome sequencing) can we use machine learning to see if there are actionable data from a WES
Ingo: we forget in datasets is that patients have needs today and we need those payment systems and structures today
Eugean: problem is the start from cost (where the cost starts at and was it truly medically necessary)
Norden: there are not enough data sharing to make a decision; an enormous amount of effort to get businesses and technical limitations in data sharing; possibly there are policies needed to be put in place to assimilate datasets and promote collaborations
Ingo: need to take out the middle men between sequencing of patient tumor and treatment decision; middle men are taking out value out of the ‘supply chain’;
Andrew: PATIENTS DON’T OWN their DATA but MOST clinicians agree THEY SHOULD
Ankur: patients are willing to share data but the HIPAA compliance is a barrier
11:50- 12:30 AM Fireside Chat with Michael Pellini, M.D.
Building a Precision Medicine Business from the Ground Up: An Operating and Venture Perspective
Dr. Pellini has spent more than 20 years working on the operating side of four companies, each of which has pushed the boundaries of the standard of care. He will describe his most recent experience at Foundation Medicine, at the forefront of precision medicine, and how that experience can be leveraged on the venture side, where he now evaluates new healthcare technologies.
Roche just bought Foundation Medicine for $2.5 billion. They negotiated over 7 months but aside from critics they felt it was a great deal because it gives them, as a diagnostic venture, the international reach and biotech expertise. Foundation Medicine offered Roche expertise on the diagnostic space including ability to navigate payers and regulatory aspects of the diagnostic business. He feels it benefits all aspects of patient care and the work they do with other companies.
Moderatore: Roche is doing multiple deals to ‘own’ a disease state.
Dr. Pellini: Roche is closing a deal with Flatiron just like how Merck closed deals with genomics companies. He feels best to build the best company on a stand alone basis and provide for patients, then good things will happen. However the problem of achieving scale for Precision Medicine is reimbursement by payers. They still have to keep collecting data and evolving services to suit pharma. They didn’t know if there model would work but when he met with FDA in 2011 they worked with Precision Medicine, said collect the data and we will keep working with you,
However the payers aren’t contributing to the effort. They need to assist some of the young companies that can’t raise the billion dollars needed for all the evidence that payers require. Precision Medicine still have problems, even though they have collected tremendous amounts of data and raised significant money. From the private payer perspective there is no clear roadmap for success.
They recognized that the payers would be difficult but they had a plan but won’t invest in companies that don’t have a plan for getting reimbursement from payers.
Moderator: What is section 32?
Pellini: Their investment arm invests in the spectrum of precision healtcare companies including tech companies. They started with a digital path imaging system that went from looking through a scope and now looking at a monitor with software integrated with medical records. Section 32 has $130 million under management and may go to $400 Million but they want to stay small.
Pellini: we get 4-5 AI pitches a week.
Moderator: Are you interested in companion diagnostics?
Pellini: There may be 24 expected 2018 drug approvals and 35% of them have a companion diagnostic (CDX) with them. however going out ten years 70% may have a CDX associated with them. Payers need to work with companies to figure out how to pay with these CDXs.
10:15AM 11/13/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston
Reporter: Aviva Lev-Ari, PhD, RN
REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com
10:15 a.m. Panel Discussion — IT/Big Data
IT/Big Data
The human genome is composed of 6 billion nucleotides (using the genetic alphabet of T, C, G and A). As the cost of sequencing the human genome is decreasing at a rapid rate, it might not be too far into the future that every human being will be sequenced at least once in their lifetime. The sequence data together with the clinical data are going to be used more and more frequently to make clinical decisions. If that is true, we need to have secure methods of storing, retrieving and analyzing all of these data. Some people argue that this is a tsunami of data that we are not ready to handle. The panel will discuss the types and volumes of data that are being generated and how to deal with it.
Role of Informatics, SW and HW in PM. Big data and Healthcare
How Lab and Clinics can be connected. Oncologist, Hematologist use labs in clinical setting, Role of IT and Technology in the environment of the Clinicians
at BWH since 1987 at 75% – push forward the Genomics Agenda, VA system 25% – VA is horizontally data integrated embed research and knowledge — baseline questionnaire 200,000 phenotypes – questionnaire and Genomics data to be integrated, Data hierarchical way to be curated, Simple phenotypes, validate phenotypes, Probability to have susceptibility for actual disease, Genomics Medicine will benefit Clinicians
Data must be of visible quality, collect data via Telephone VA – on Med compliance study, on Ability to tolerate medication
–>>Curation of data is very different than statistical analysis of Clinical Trial Data
–>>Integration of data at VA and at BWH are tow different models of SUCCESSFUL data integration models, accessing the data is also using a different model
–>>Data extraction from the Big data — an issue
–>>Where the answers are in the data, build algorithms that will pick up causes of disease: Alzheimer’s – very difficult to do
–>>system around all stakeholders: investment in connectivity, moving data, individual silo, HR, FIN, Clinical Research
Computer Scientist and Medical Student. Were the technology is going?
Messy situation, interaction IT and HC, Boston and Silicon Valley are focusing on Consumers, Google Engineers interested in developing Medical and HC applications — HUGE interest. Application or Wearable – new companies in this space, from Computer Science world to Medicine – Enterprise level – EMR or Consumer level – Wearable — both areas are very active in Silicon Valley
IT stuff in the hospital HARDER that IT in any other environment, great progress in last 5 years, security of data, privacy. Sequencing data cost of big data management with highest security
Constrained data vs non-constrained data
Opportunities for Government cooperation as a Lead needed for standardization of data objects
Questions from the Podium:
Where is the Truth: do we have all the tools or we don’t for Genomic data usage
Question on Interoperability
Big Valuable data — vs Big data
quality, uniform, large cohort, comprehensive Cancer Centers
Volume of data can compensate quality of data
Data from Imaging – Quality and interpretation – THREE radiologist will read cancer screening
A recent Science article (Who are the science stars of Twitter?; Sept. 19, 2014) reported the top 50 scientists followed on Twitter. However, the article tended to focus on the use of Twitter as a means to develop popularity, a sort of “Science Kardashian” as they coined it. So the writers at Science developed a “Kardashian Index (K-Index) to determine scientists following and popularity on Twitter.
Now as much buzz Kim Kardashian or a Perez Hilton get on social media, their purpose is solely for entertainment and publicity purposes, the Science sort of fell flat in that it focused mainly on the use of Twitter as a metric for either promotional or public outreach purposes. A notable scientist was mentioned in the article, using Twitter feed to gauge the receptiveness of his presentation. In addition, relying on Twitter for effective public discourse of science is problematic as:
Twitter feeds are rapidly updated and older feeds quickly get buried within the “Twittersphere” =LIMITED EXPOSURE TIMEFRAME
Short feeds may not provide the access to appropriate and understandable scientific information (The Science Communication Trap) which is explained in The Art of Communicating Science: traps, tips and tasks for the modern-day scientist. “The challenge of clearly communicating the intended scientific message to the public is not insurmountable but requires an understanding of what works and what does not work.” – from Heidi Roop, G.-Martinez-Mendez and K. Mills
However, as highlighted below, Twitter, and other social media platforms are being used in creative ways to enhance the research, medical, and bio investment collaborative, beyond a simple news-feed. And the power of Twitter can be attributed to two simple features
Ability to organize – through use of the hashtag (#) and handle (@), Twitter assists in the very important task of organizing, indexing, and ANNOTATING content and conversations. A very great article on Why the Hashtag in Probably the Most Powerful Tool on Twitter by Vanessa Doctor explains how hashtags and # search may be as popular as standard web-based browser search. Thorough annotation is crucial for any curation process, which are usually in the form of database tags or keywords. The use of # and @ allows curators to quickly find, index and relate disparate databases to link annotated information together. The discipline of scientific curation requires annotation to assist in the digital preservation, organization, indexing, and access of data and scientific & medical literature. For a description of scientific curation methodologies please see the following links:
Multiple analytic software packages have been made available to analyze information surrounding Twitter feeds, including Twitter feeds from #chat channels one can set up to cover a meeting, product launch etc.. Some of these tools include:
In a paper entitled Twitter Use at a Family Medicine Conference: Analyzing #STFM13 authors Ranit Mishori, MD, Frendan Levy, MD, and Benjamin Donvan analyzed the public tweets from the 2013 Society of Teachers of Family Medicine (STFM) conference bearing the meeting-specific hashtag #STFM13. Thirteen percent of conference attendees (181 users) used the #STFM13 to share their thoughts on the meeting (1,818 total tweets) showing a desire for social media interaction at conferences but suggesting growth potential in this area. As we have also seen, the heaviest volume of conference-tweets originated from a small number of Twitter users however most tweets were related to session content.
However, as the authors note, although it is easy to measure common metrics such as number of tweets and retweets, determining quality of engagement from tweets would be important for gauging the value of Twitter-based social-media coverage of medical conferences.
Thea authors compared their results with similar analytics generated by the HealthCare Hashtag Project, a project and database of medically-related hashtag use, coordinated and maintained by the company Symplur. Symplur’s database includes medical and scientific conference Twitter coverage but also Twitter usuage related to patient care. In this case the database was used to compare meeting tweets and hashtag use with the 2012 STFM conference.
These are some of the published journal articles that have employed Symplur (www.symplur.com) data in their research of Twitter usage in medical conferences.
Here, at Leaders in Pharmaceutical Business Intelligence (LBPI) we have integrated our web site, Twitter handle (@pharma_BI), and meeting specific hashtags, with a unique methodology, to monitor and measure meeting participant engagement for various international meetings (please see our Press Coverage section of our site for more information). These meetings included the 2nd Annual Sachs Associates Cancer BioInvestment & Partnering Forum and the 14th Annual Sachs Associates Global Forum.
B. Twitter Usage for Patient Care and Engagement
Although the desire of patients to use and interact with their physicians over social media is increasing, along with increasing health-related social media platforms and applications, there are certain obstacles to patient-health provider social media interaction, including lack of regulatory framework as well as database and security issues. Some of the successes and issues of social media and healthcare are discussed in the post Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.
Networking opportunities for learning disability nurses.
Abdulla, S., Marsden, D., Wilson, S., & Parker, M. (2013). Networking opportunities for learning disability nurses: Samuel Abdulla and colleagues explain why social media offer professionals new opportunities for information sharing, discussion and peer support. Learning Disability Practice, 16(5), 30-32.
Using Twitter for professional knowledge.
Kraft, M. A. (2013). Using Twitter for professional knowledge. Journal of the European Association for Health Information and Libraries, 9(4), 10.
There have been concerns with using Twitter and social media to monitor for adverse events. For example FDA funded a study where a team of researchers from Harvard Medical School and other academic centers examined more than 60,000 tweets, of which 4,401 were manually categorized as resembling adverse events and compared with the FDA pharmacovigilance databases. Problems associated with such social media strategy were inability to obtain extra, needed information from patients and difficulty in separating the relevant Tweets from irrelevant chatter. The UK has launched a similar program called WEB-RADR to determine if monitoring #drug_reaction could be useful for monitoring adverse events. Many researchers have found the adverse-event related tweets “noisy” due to varied language but had noticed many people do understand some principles of causation including when adverse event subsides after discontinuing the drug.
However Dr. Clark Freifeld, Ph.D., from Boston University and founder of the startup Epidemico, feels his company has the algorithms that can separate out the true adverse events from the junk. According to their web site, their algorithm has high accuracy when compared to the FDA database. Dr. Freifeld admits that Twitter use for pharmacovigilance purposes is probably a starting point for further follow-up, as each patient needs to fill out the four-page forms required for data entry into the FDA database.
Course: Information 290. Analyzing Big Data with Twitter
School of Information
UC Berkeley
Lecture 1: August 23, 2012
Course description:
How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered.
This is a hands-on project course in which students are expected to form teams to complete intensive programming and analytics projects using the real-world example of Twitter data and code bases. Engineers from Twitter will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus). Project topics include building on existing infrastructure tools, building Twitter apps, and analyzing Twitter data. Access to data will be provided.
Other posts on this site on USE OF SOCIAL MEDIA AND TWITTER IN HEALTHCARE and Conference Coverage include:
Track 9 Pharmaceutical R&D Informatics: Collaboration, Data Science and Biologics @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA
Reporter: Aviva Lev-Ari, PhD, RN
April 30, 2014
Big Data and Data Science in R&D and Translational Research
10:50 Chairperson’s Remarks
Ralph Haffner, Local Area Head, Research Informatics, F. Hoffmann-La Roche AG
11:00 Can Data Science Save Pharmaceutical R&D?
Jason M. Johnson, Ph.D., Associate Vice President,
Scientific Informatics & Early Development and Discovery Sciences IT, Merck
Although both premises – that the viability of pharmaceutical R&D is mortally threatened and that modern “data science” is a relevant superhero – are
suspect, it is clear that R&D productivity is progressively declining and many areas of R&D suboptimally use data in decision-making. We will discuss
some barriers to our overdue information revolution, and our strategy for overcoming them.
11:30 Enabling Data Science in Externalized Pharmaceutical R&D
Sándor Szalma, Ph.D., Head, External Innovation, R&D IT,
Janssen Research & Development, LLC
Pharmaceutical companies have historically been involved in many external partnerships. With recent proliferation of hosted solutions and the availability
of cost-effective, massive high-performance computing resources there is an opportunity and a requirement now to enable collaborative data science. We
discuss our experience in implementing robust solutions and pre-competitive approaches to further these goals.
12:00 pm Co-Presentation: Sponsored by
Collaborative Waveform Analytics: How New Approaches in Machine Learning and Enterprise Analytics will Extend Expert Knowledge and Improve Safety Assessment
Tim Carruthers, CEO, Neural ID
Scott Weiss, Director, Product Strategy, IDBS
Neural ID’s Intelligent Waveform Service (IWS) delivers the only enterprise biosignal analysis solution combining machine learning with human expertise. A collaborative platform supporting all phases of research and development, IWS addresses a significant unmet need, delivering scalable analytics and a single interoperable data format to transform productivity in life sciences. By enabling analysis from BioBook (IDBS) to original biosignals, IWS enables users of BioBook to evaluate cardio safety assessment across the R&D lifecycle.
12:15 Building a Life Sciences Data
Sponsored by
Lake: A Useful Approach to Big Data
Ben Szekely, Director & Founding Engineer,
Cambridge Semantics
The promise of Big Data is in its ability to give us technology that can cope with overwhelming volume and variety of information that pervades R&D informatics. But the challenges are in practical use of disconnected and poorly described data. We will discuss: Linking Big Data from diverse sources for easy understanding and reuse; Building R&D informatics applications on top of a Life Sciences Data Lake; and Applications of a Data Lake in Pharma.
12:40 Luncheon Presentation I:
Sponsored by
Chemical Data Visualization in Spotfire
Matthew Stahl, Ph.D., Senior Vice President,
OpenEye Scientific Software
Spotfire deftly facilitates the analysis and interrogation of data sets. Domain specific data, such as chemistry, presents a set of challenges that general data analysis tools have difficulty addressing directly. Fortunately, Spotfire is an extensible platform that can be augmented with domain specific abilities. Spotfire has been augmented to naturally handle cheminformatics and chemical data visualization through the integration of OpenEye toolkits. The OpenEye chemistry extensions for Spotfire will be presented.
1:10 Luncheon Presentation II
1:50 Chairperson’s Remarks
Yuriy Gankin, Ph.D., Co. Founder and CSO, GGA Software Services
1:55 Enable Translational Science by Integrating Data across the R&D Organization
Christian Gossens, Ph.D., Global Head, pRED Development Informatics Team,
pRED Informatics, F. Hoffmann-La Roche Ltd.
Multi-national pharmaceutical companies face an amazingly complex information management environment. The presentation will show that
a systematic system landscaping approach is an effective tool to build a sustainable integrated data environment. Data integration is not mainly about
technology, but the use and implementation of it.
2:25 The Role of Collaboration in Enabling Great Science in the Digital Age: The BARD Data Science Case Study
Andrea DeSouza, Director, Informatics & Data Analysis,
Broad Institute
BARD (BioAssay Research Database) is a new, public web portal that uses a standard representation and common language for organizing chemical biology data. In this talk, I describe how data professionals and scientists collaborated to develop BARD, organize the NIH Molecular Libraries Program data, and create a new standard for bioassay data exchange.
May 1. 2014
BIG DATA AND DATA SCIENCE IN R&D AND TRANSLATIONAL RESEARCH
10:30 Chairperson’s Opening Remarks
John Koch, Director, Scientific Information Architecture & Search, Merck
10:35 The Role of a Data Scientist in Drug Discovery and Development
A major challenge in drug discovery and development is finding all the relevant data, information, and knowledge to ensure informed, evidencebased
decisions in drug projects, including meaningful correlations between preclinical observations and clinical outcomes. This presentation will describe
where and how data scientists can support pharma R&D.
11:05 Designing and Building a Data Sciences Capability to Support R&D and Corporate Big Data Needs
Shoibal Datta, Ph.D., Director, Data Sciences, Biogen Idec
To achieve Biogen Idec’s strategic goals, we have built a cross-disciplinary team to focus on key areas of interest and the required capabilities. To provide
a reusable set of IT services we have broken down our platform to focus on the Ingestion, Digestion, Extraction and Analysis of data. In this presentation, we will outline how we brought focus and prioritization to our data sciences needs, our data sciences architecture, lessons learned and our future direction.
11:35 Data Experts: Improving Sponsored by
Translational Drug-Development Efficiency
Jamie MacPherson, Ph.D., Consultant, Tessella
We report on a novel approach to translational informatics support: embedding Data Experts’ within drug-project teams. Data experts combine first-line
informatics support and Business Analysis. They help teams exploit data sources that are diverse in type, scale and quality; analyse user-requirements and prototype potential software solutions. We then explore scaling this approach from a specific drug development team to all.
Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.
A report on how gamification mobile applications, like CyberDoctor’s PatientPartner, may improve patient adherence to oral chemotherapy.
(includes interviews with CyberDoctor’s CEO Akhila Satish and various oncologists)
Writer/Curator: Stephen J. Williams, Ph.D.
UPDATE 5/15/2019
Please see below for an UPDATE on this post including results from the poll conducted here on the value of a gamification strategy for oral chemotherapy patient adherence as well as a paper describing a well designed development of an application specifically to address this clinical problem.
Studies have pointed to a growing need to monitor and improve medical adherence, especially with outpatient prescription drugs across many diseases, including cancer.
The trend to develop oral chemotherapies, so patients can take their medications in the convenience of their home, has introduced produced a unique problem concerning cancer patient-medication adherence. Traditionally, chemotherapies were administered by a parental (for example intravenous) route by clinic staff, however, as noted by Jennifer M Gangloff in her article Troubling Trend: Medication Adherence:
with the trend of cancer patients taking their oral medication at home, the burden of adherence has shifted from clinicians to the patients and their families.
A few highlights from Jennifer Gangloff’s article highlight the degree and scope of the problem:
There is a wide range of adherence for oral chemo– as low as 16% up to 100% adherence rates have been seen in multiple studies
High cost in lives and money: estimates in US of 125,000 deaths and $300 billion in healthcare costs due to nonadherence to oral anticancer medications
Factors not related to the patient can contribute to nonadherence including lack of information provided by the healthcare system and socioeconomic factors
Numerous methods to improve adherence issues (hospital informative seminars, talking pill bottles, reminder phone calls etc.) have met with mixed results.
More strikingly, patient adherence rates can drastically decline over treatment, with one study showing an adherence rate drop from 87% to 50% over 4 years of adjuvant tamoxifen therapy.
Tackling The Oral Chemotherapy-Patient Adherence Problem
Documented factors leading to non-adherence to oral oncology medications include
Patient feels better so stop taking the drug
Patient feels worse so stops taking the drug
Confusing and complicated dosing regimen
Inability to afford medications
Poor provider-patient relationships
Adverse effects of medication
Cognitive impairment (“chemo fog”; mental impairment due to chemotherapy
Inadequate education/instruction of discharge
There are many examples of each reason why a patient stopped taking medication. One patient was prescribed capecitabine for her metastatic breast cancer and, upon feeling nausea, started to use antacids, which precipitated toxicities as a result of increased plasma levels of capecitabine.
This review also documented the difficulties in accurately measuring patient adherence including:
Inaccuracy of self-reporting
Lack of applicability of external measurements such as pill counts
Hawthorne effect: i.e. patient pill documentation reminds them to take next dose
The group suggests that using MTM programs, especially telephony systems involving oncology nurses and pharmacists and utilizing:
Therapy support (dosing reminders)
Education
Side effect management
may be a cost-efficient methodology to improve medical adherence.
Although nurses are important intermediary educating patients about their oral chemotherapies, it does not appear that solely relying on nurses to monitor patient adherence will be sufficient, as indicated in a survey-based Japanese study.
Survey results indicated that 90% of nurses reported asking patients on oral chemotherapy about emergency contacts, side effects, and family/friend support. Nurses also provided patients with education materials on their assigned medication.
However, less than one-third of nurses asked if their patients felt confident about managing their oral chemotherapy.
“Nurses were less likely to ask adherence-related questions of patients with refilled prescriptions than of new patients,” the researchers wrote. “Regarding unused doses of anticancer agents, 35.5% of nurses reported that they did not confirm the number of unused doses when patients had refilled prescriptions.”
From the Roswell Park Cancer Institute blog post Making Mobile Health Work
US physicians are recognizing the need for the adoption of mobile in their practice but choice of apps and mobile strategies must be carefully examined before implementation. In addition, most physicians are using mobile communications as a free-complementary service and these physicians are not being reimbursed for their time.
Some companies are providing their own oncology-related mobile app services:
San Francisco, August 13, 2013 – CollabRx, Inc. (NASDAQ: CLRX), a healthcare information technology company focused on informing clinical decision making in molecular medicine, today announced a multi-year agreement with Everyday Health’s MedPage Today. The forthcoming app, which will target oncologists and pathologists, will focus on the molecular aspects of laboratory testing and therapy development. Over time, the expectation is that this app will serve as a comprehensive point of care resource for physicians and patients to obtain highly credible, expert-vetted and dynamically updated information to guide cancer treatment planning.
The McKesson Foundation’s Mobilizing for Health initiative
has awarded a grant to Partners HealthCare’s Center for Connected Health to develop a mobile health program that uses a smartphone application to help patients with cancer adhere to oral chemotherapy treatments and monitor their symptoms, FierceMobileHealthcare reports.
CancerNet announces mobile application (from cancer.net)
The report suggests that there are too many apps either offering information, suggesting behavior/lifestyle changes, or measuring compliance data but little evidence to suggest any of these are working the way they intended. The article suggests the plethora of apps may just be adding to the confusion.
MyCyberDoctor™, a True Gamification App, Shows Great Results in Improving Diabetics Medical Adherence and Health Outcome
Most of the mobile health apps discussed above, would be classified as tracking apps, because the applications simply record a patient’s actions, whether filling a prescription, interacting with a doctor, nurse, pharmacist, or going to a website to gain information. However, as discussed before, there is no hard evidence this is really impacting health outcomes.
Another type of application, termed gamification apps, rely on role-playing by the patient to affect patient learning and ultimately behavior.
An interested twist on this method was designed by Akhila Satish, CEO and developer of CyberDoctor and a complementary application PatientPartner.
As reported here, the PatientPartner application was used in the first IRB-approved mhealth clinical-trial to see if the gamification app could improve medical adherence and outcomes in diabetic patients. PatientPartner is a story-driven game in changing health behavior and biomarkers (blood glucose levels in this trial). In the clinical trial, 100 non-adherent patients with diabetes played the PatientPartner game for 15 minutes. Results were amazing, as the trial demonstrated an increase in patient adherence, with only 15 minutes of game playing.
Results from the study
Patients with diabetes who used PatientPartner showed significant improvement in three key areas – medication, diet, and exercise:
Medication adherence increased by 37%, from 58% to 95% – equivalent to three additional days of medication adherence per week.
Diet adherence increased by 24% – equivalent to two days of additional adherence a week.
Exercise adherence increased by 14% – equivalent to one additional day of adherence per week.
HbA1c (a blood sugar measure) decreased from 10.7% to 9.7%.
As mentioned in the article:
The unique, universal, non-disease specific approach allows PatientPartner to be effective in improving adherence in all patient populations.
PatientPartner is available in the iTunes store and works on the iPhone and iPod Touch. For information on PatientPartner, visit www.mypatientpartner.com.
Ms. Satish, who was named one of the top female CEO’s at the Health Conference, gratuitously offered to answer a few questions for Leaders in Pharmaceutical Business Intelligence (LPBI) on the feasibility of using such a game (role-playing) application to improve medical adherence in the oncology field.
LPBI: The results you had obtained with patient-compliance in the area of diabetes are compelling and the clinical trial well-designed. In the oncology field, due to the increase in use of oral chemotherapeutics, patient-compliance has become a huge issue. Other than diabetes, are there plans for MyCyberDoctor and PatientPartner to be used in other therapeutic areas to assist with patient-compliance and patient-physician relations?
Ms. Satish: Absolutely! We tested the application in diabetes because we wanted to measure adherence from an objective blood marker (hbA1c). However, the method behind PatientPartner- teaching patients how to make healthy choices- is universal and applicable across therapeutic areas.
LPBI: Recently, there have been a plethora of apps developed which claim to impact patient-compliance and provide information. Some of these apps have been niche (for example only providing prescription information but tied to pharmacy records and company databases). Your app seems to be the only one with robust clinical data behind it and approaches from a different angle, namely adjusting behavior using a gamefying experience and teaching the patient the importance of compliance. How do you feel this approach geared more toward patient education sets PatientPartner apart from other compliance-based apps?
Ms. Satish: PatientPartner really focuses on the how of patient decision making, rather than the specifics of each decision that is made. It’s a unique approach, and part of the reason PatientPartner works so effectively with such a short initial intervention! We are able to achieve more with less “app” time as a result of this method.
LPBI: There have been multiple studies attempting to correlate patient adherence, decision-making, and health outcome to socioeconomic status. In some circumstances there is a socioeconomic correlation while other cases such as patient-decision to undergo genetic testing or compliance to breast cancer treatment in rural areas, level of patient education may play a bigger role. Do you have data from your diabetes trial which would suggest any differences in patient adherence, outcome to any socioeconomic status? Do you feel use of PatientPartner would break any socioeconomic barriers to full patient adherence?
Ms. Satish: Within our trial, we had several different clinical sites. This helped us test the product out in a broad, socioeconomically diverse population. It is our hope that with a tool as easy to scale and use as PatientPartner we have the opportunity to see the product used widely, even in populations that are traditionally harder to reach.
LPBI: There has been a big push for the development of individual, personalized physician networks which use the internet as the primary point of contact between a primary physician and the patient. Individuals may sign up to these networks bypassing the traditional insurance-based networks. How would your application assist in these types of personalized networks?
Ms. Satish: PatientPartner can easily be plugged into any existing framework of communication between patient and provider. We facilitate patient awareness, engagement and accountability- all of which are important regardless of the network structure.
LBPI: Thank you Akhila!
A debate has begun about regulating mobile health applications, and although will be another post, I would just like to summarize a nice article in May, 2014 Oncology Times by Sarah Digiulo “Mobile Health Apps: Should They be Regulated?
In general, in the US there are HIPAA regulations about the dissemination of health related information between a patient and physician. Most of the concerns are related to personal health information made public in an open-access platform such as Twitter or Facebook.
In addition, according to Dr. Don Dizon M.D., Director of the Oncology Sexual Health Clinic at Massachusetts General Hospital, it may be more difficult to design applications directed against a vast, complex disease like cancer with its multiple subtypes than for diabetes.
Mobile Health Applications on Rise in Developing World: Worldwide Opportunity
According to International Telecommunication Union (ITU) statistics, world-wide mobile phone use has expanded tremendously in the past 5 years, reaching almost 6 billion subscriptions. By the end of this year it is estimated that over 95% of the world’s population will have access to mobile phones/devices, including smartphones.
This presents a tremendous and cost-effective opportunity in developing countries, and especially rural areas, for physicians to reach patients using mHealth platforms.
Drs. Clara Aranda-Jan Neo Mohutsiwa and Svetla Loukanova had conducted a systematic review of the literature on mHealth projects conducted in Africa[1] to assess the reliability of mobile phone and applications to assist in patient-physician relationships and health outcomes. The authors reviewed forty four studies on mHealth projects in Africa, determining their:
strengths
weaknesses
opportunities
threats
to patient outcomes using these mHealth projects. In general, the authors found that mHealth projects were beneficial for health-related outcomes and their success related to
accessibility
acceptance and low-cost
adaptation to local culture
government involvement
while threats to such projects could include
lack of funding
unreliable infrastructure
unclear healthcare system responsibilities
Dr.Sreedhar Tirunagari, an oncologist in India, agrees that mHealth, especially gamification applications could greatly foster better patient education and adherencealthough he notes that mHealth applications are not really used in India and may not be of much use for those oncology patients living in rural areas, as cell phone use is not as prevalent as in the bigger inner cities such as Delhi and Calcutta.
1) do you see a use for such apps which either track drug compliance or use gamification systems to teach patients the importance of continuing their full schedule of drug therapy
2) do you feel patient- drug compliance issues in the oncology practice is due to lack of information available to the patient or issues related to drug side effects?
“I think that Apps could help in this setting, we are in
Informatics era but..
The main question is that chronic patients are special ones.
Cancer patients have to deal with prognosis, even in therapies
with curative intent such as aromatase inhibitors are potent
Drugs that can cure; only in the future the patients know.
But meanwhile he or she has to deal with side-effects every day. A PC can help but suffer this symptoms…it. Is a real problem believe me!”
“The main app is his/her doctor”
I would like to invite all oncologists to answer the poll question ABOVE about the use of such gamification apps, like PatientPartner, for improving medical adherence to oral chemotherapy.
UPDATE 5/15/2019
The results of the above poll, although limited, revealed some interesting insights. Although only five oncologists answered the poll whether they felt gamification applications could help with oral chemotherapy patient adherence, all agreed it would be worthwhile to develop apps based on gamification to assist in the outpatient setting. In addition, one oncologist felt that the success of mobile patient adherence application would depend on the type of cancer. None of the oncologist who answered the survey thought that gamification apps would have no positive effect on patient adherence to their chemotherapy. With this in light, a recent paper by Joel Fishbein of University of Colorado and Joseph Greer from Massachusetts General Hospital, describes the development of a mobile application, in clinical trial, to promote patient adherence to their oral chemotherapy.
Mobile Applications to Promote Adherence to Oral Chemotherapy and Symptom Management: A Protocol for Design and Development
Oral chemotherapy is increasingly used in place of traditional intravenous chemotherapy to treat patients with cancer. While oral chemotherapy includes benefits such as ease of administration, convenience, and minimization of invasive infusions, patients receive less oversight, support, and symptom monitoring from clinicians. Additionally, adherence is a well-documented challenge for patients with cancer prescribed oral chemotherapy regimens. With the ever-growing presence of smartphones and potential for efficacious behavioral intervention technology, we created a mobile health intervention for medication and symptom management.
OBJECTIVE:
The objective of this study was to develop and evaluate the usability and acceptability of a smartphone app to support adherence to oral chemotherapy and symptom management in patients with cancer.
METHODS:
We used a 5-step development model to create a comprehensive mobile app with theoretically informed content. The research and technical development team worked together to develop and iteratively test the app. In addition to the research team, key stakeholders including patients and family members, oncology clinicians, health care representatives, and practice administrators contributed to the content refinement of the intervention. Patient and family members also participated in alpha and beta testing of the final prototype to assess usability and acceptability before we began the randomized controlled trial.
RESULTS:
We incorporated app components based on the stakeholder feedback we received in focus groups and alpha and beta testing. App components included medication reminders, self-reporting of medication adherence and symptoms, an education library including nutritional information, Fitbit integration, social networking resources, and individually tailored symptom management feedback. We are conducting a randomized controlled trial to determine the effectiveness of the app in improving adherence to oral chemotherapy, quality of life, and burden of symptoms and side effects. At every stage in this trial, we are engaging stakeholders to solicit feedback on our progress and next steps.
CONCLUSIONS:
To our knowledge, we are the first to describe the development of an app designed for people taking oral chemotherapy. The app addresses many concerns with oral chemotherapy, such as medication adherence and symptom management. Soliciting feedback from stakeholders with broad perspectives and expertise ensured that the app was acceptable and potentially beneficial for patients, caregivers, and clinicians. In our development process, we instantiated 7 of the 8 best practices proposed in a recent review of mobile health app development. Our process demonstrated the importance of effective communication between research groups and technical teams, as well as meticulous planning of technical specifications before development begins. Future efforts should consider incorporating other proven strategies in software, such as gamification, to bolster the impact of mobile health apps. Forthcoming results from our randomized controlled trial will provide key data on the effectiveness of this app in improving medication adherence and symptom management.
In this paper, Fishbein et al. describe the methodology of the developoment of a mobile application to promote oral chemotherapy adherence. This mobile app intervention was named CORA or ChemOtheRapyAssistant.
Of the approximately 325,000 health related apps on the market (as of 2017), the US Food and Drug Administration (FDA) have only reviewed approximately 20 per year and as of 2016 cleared only about 36 health related apps.
According to industry estimates, 500 million smartphone users worldwide will be using a health care application by 2015, and by 2018, 50 percent of the more than 3.4 billion smartphone and tablet users will have downloaded mobile health applications. However, there is not much scientific literature providing a framework for design and creation of quality health related mobile applications.
Methods
The investigators separated the app development into two phases: Phase 1 consisted of the mobile application development process and initial results of alpha and beta testing to determine acceptability among the major stakeholders including patients, caregivers, oncologists, nurses, pharmacists, pharmacologists, health payers, and patient advocates. Phase 1 methodology and results were the main focus of this paper. Phase 2 consists of an ongoing clinical trial to determine efficacy and reliability of the application in a larger number of patients at different treatment sites and among differing tumor types.
The 5 step development process in phase 1 consisted of identifying features, content, and functionality of a mobile app in an iterative process, including expert collaboration and theoretical framework to guide initial development.
There were two distinct teams: a research team and a technical team. The multidisciplinary research team consisted of the principal investigator, co-investigators (experts in oncology, psychology and psychiatry), a project director, and 3 research assistants.
The technical team consisted of programmers and project managers at Partners HealthCare Connected Health. Stakeholders served as expert consultants including oncologists, health care representatives, practice administrators, patients, and family members (care givers). All were given questionaires (HIPAA compliant) and all involved in alpha and beta testing of the product.
There were 5 steps in the development process
Implementing a theoretical framework: Patients and their family caregivers now bear the primary responsibility for their medical adherence especially to oral chemotherapy which is now more frequently administered in the home setting not in the clinical setting. Four factors were identified as the most important barriers to oral chemotherapy adherence: complexity of medication regimes, symptom burden, poor self-management of side effects, and low clinical support. These four factors were integral in the design of the mobile app and made up a conceptual framework in its design.
Conducting Initial Focus Group Interviews with key stakeholders: Stakeholders were taken from within and outside the local community. In all 32 stakeholders served as study collaborators including 8 patient/families, 8 oncologists/clinicians, 8 cancer practice administrators, and 8 representatives of the health system, community, and overall society. The goal of these focus groups were to obtain feedback on the proposed study and design included perceived importance of monitoring of adherence to oral chemotherapy, barriers to communication between patients and oncology teams regarding side effects and medication adherence, potential role of mobile apps to address barriers of quality of cancer care, potential feasibility, acceptability, and usage and feedback on the overall study design.
Creation of Wireframes (like storyboards or page designs) and Collecting Initial Feedback: The research and design team, in conjunction with stakeholder input, created content wireframes, or screen blueprints) to provide a visual guide as to what the app would look like. These wireframes also served as basis for what the patient interviews would look like on the application. A total of 10 MGH (Massachusetts General Hospital) patients (6 female, 4 male) and most with higher education (BS or higher) participated in the interviews and design of wireframes. Eight MGH clinicians participated in this phase of wireframe design.
Developing, Programming, and Refining the App: CORA was designed to be supported by PHP/MySQL databases and run on LAMP hosts (Linux, Apache, MySQL, Perl/PHP/Python) and fully HIPAA compliant. Alpha testing was conducted with various stakeholders and the app refined by the development team (technical team) after feedback.
Final beta testing and App prototype for clinical trial: The research team considered the first 5 participants enrolled in the subsequent clinical trial for finalization of the app prototype.
There were 7 updated versions of the app during the initial clinical trial phase and 4 updates addressed technical issues related to smartphone operating system upgrades.
Finally, the investigators list a few limitations in their design and study of this application. First the patient population was homogenous as all were from an academic hospital setting. Second most of the patients were of Caucasian ethnic background and most were highly educated, all of which may introduce study bias. In addition, CORA was available on smartphone and tablet only, so a larger patient population who either have no access to these devices or are not technically savvy may experience issues related to this limitation.
In addition other articles on this site related to Mobile Health applications and Health Outcomes include
Aranda-Jan CB, Mohutsiwa-Dibe N, Loukanova S: Systematic review on what works, what does not work and why of implementation of mobile health (mHealth) projects in Africa. BMC public health 2014, 14:188.
PLENARY KEYNOTE PRESENTATIONS: THURSDAY, MAY 1 | 8:00 – 10:00 AM @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA
Reporter: Aviva Lev-Ari, PhD, RN
Keynote Introduction: Sponsored by Fred Lee, M.D., MPH, Director, Healthcare Strategy and Business Development, Oracle Health Sciences
Heather Dewey-Hagborg
Artist, Ph.D. Student, Rensselaer Polytechnic Institute
Heather Dewey-Hagborg is an interdisciplinary artist, programmer and educator who explores art as research and public inquiry. She recreates identity from strands of human hair in an entirely different way. Collecting hairs she finds in random public places – bathrooms, libraries, and subway seats – she uses a battery of newly developing technologies to create physical, life-sized portraits of the owners of these hairs. Her fixation with a single hair leads her to controversial art projects and the study of genetics. Traversing media ranging from algorithms to DNA, her work seeks to question fundamental assumptions underpinning perceptions of human nature, technology and the environment. Examining culture through the lens of information, Heather creates situations and objects embodying concepts, probes for reflection and discussion. Her work has been featured in print, television, radio, and online. Heather has a BA in Information Arts from Bennington College and a Masters degree from the Interactive Telecommunications Program at Tisch School of the Arts, New York University. She is currently a Ph.D. student in Electronic Arts at Rensselaer Polytechnic Institute.
Yaniv Erlich, Ph.D.
Principal Investigator and Whitehead Fellow, Whitehead Institute for Biomedical Research
Dr. Yaniv Erlich is Andria and Paul Heafy Family Fellow and Principal Investigator at the Whitehead Institute for Biomedical Research. He received a bachelor’s degree from Tel-Aviv University, Israel and a PhD from the Watson School of Biological Sciences at Cold Spring Harbor Laboratory in 2010. Dr. Erlich’s research interests are computational human genetics. Dr. Erlich is the recipient of the Burroughs Wellcome Career Award (2013), Harold M. Weintraub award (2010), the IEEE/ACM-CS HPC award (2008), and he was selected as one of 2010 Tomorrow’s PIs team of Genome Technology.
Isaac Samuel Kohane, M.D., Ph.D.
Henderson Professor of Health Sciences and Technology, Children’s Hospital and Harvard Medical School;
Director, Countway Library of Medicine; Director, i2b2 National Center for Biomedical Computing;
Co-Director, HMS Center for Biomedical Informatics
Isaac Kohane, MD, PhD, co-directs the Center for Biomedical Informatics at Harvard Medical School. He applies computational techniques, whole genome analysis, and functional genomics to study human diseases through the developmental lens, and particularly through the use of animal model systems. Kohane has led the use of whole healthcare systems, notably in the i2b2 project, as “living laboratories” to drive discovery research in disease genomics (with a focus on autism) and pharmacovigilance
(including providing evidence for the cardiovascular risk of hypoglycemic agents which ultimately contributed to “black box”ing by the FDA) and comparative effectiveness with software and methods adopted in over 84 academic health centers internationally. Dr. Kohane has published over 200 papers in the medical literature and authored a widely used book on Microarrays for an Integrative Genomics. He has been elected to multiple honor societies including the American Society for Clinical Investigation, the American College of Medical Informatics, and the Institute of Medicine. He leads a doctoral program in genomics and bioinformatics within the Division of Medical Science at Harvard University. He is also an occasionally practicing pediatric endocrinologist.
Track 5 Next-Gen Sequencing Informatics: Advances in Analysis and Interpretation of NGS Data @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA
Reporter: Aviva Lev-Ari, PhD, RN
NGS Bioinformatics Marketplace: Emerging Trends and Predictions
10:50 Chairperson’s Remarks
Narges Baniasadi, Ph.D., Founder & CEO, Bina Technologies, Inc.
11:00 Global Next-Generation Sequencing Informatics Markets: Inflated Expectations in an Emerging Market
Greg Caressi, Senior Vice President, Healthcare and Life Sciences, Frost & Sullivan
This presentation evaluates the global next-generation sequencing (NGS) informatics markets from 2012 to 2018. Learn key market drivers and restraints,
key highlights for many of the leading NGS informatics services providers and vendors, revenue forecasts, and the important trends and predictions that
affect market growth.
Organizational Approaches to NGS Informatics
11:30 High-Performance Databases to Manage and Analyze NGS Data
Joseph Szustakowski, Ph.D., Head, Bioinformatics, Biomarker Development,
Novartis Institutes for Biomedical Research
The size, scale, and complexity of NGS data sets call for new data management and analysis strategies. High-performance database systems
combine the advantages of both established and cutting edge technologies. We are using high performance database systems to manage and analyze NGS, clinical, pathway, and phenotypic data with great success. We will describe our approach and concrete success stories that demonstrate its efficiency and effectiveness.
12:00 pm Taming Big Science Data Growth with Converged Infrastructure
Aaron D. Gardner, Senior Scientific Consultant,
BioTeam, Inc.
Many of the largest NGS sites have identified IO bottlenecks as their number one concern in growing their infrastructure to support current and projected
data growth rates. In this talk Aaron D. Gardner, Senior Scientific Consultant, BioTeam, Inc. will share real-world strategies and implementation details
for building converged storage infrastructure to support the performance, scalability and collaborative requirements of today’s NGS workflows.
12:15 Next Generation Sequencing: Workflow Overview from a High-Performance Computing Point of View
Carlos P. Sosa, Ph.D., Applications Engineer, HPC Lead,
Cray, Inc.
Next Generation Sequencing (NGS) allows for the analysis of genetic material with unprecedented speed and efficiency. NGS increasingly shifts the burden
from chemistry done in a laboratory to a string manipulation problem, well suited to High- Performance Computing. We explore the impact of the NGS
workflow in the design of IT infrastructures. We also present Cray’s most recent solutions for NGS workflow.
SOSA in REAL TIME
Bioinformatics and BIG DATA – NGS @ CRAY i 2014
I/O moving, storage data – UNIFIED solution by Cray
Data access
Fast Access
Storage
manage high performance computinf; NGS work flow, multiple human genomes 61 then 240 sequentiallt, with high performance in 51 hours, 140 genomes in simultaneous
Architecture @Cray for Genomics
sequensors
Galaxy
servers for analysis
workstation: Illumina, galaxy, CRAY does the integration of 3rd party SW using a workflow LEVERAGING the network, the fastest in the World, network useding NPI for scaling and i/O
Compute blades, reserves formI?O nodes, the Fastest interconnet in the industry
scale of capacity and capability, link interconnect in the file System: lustre
optimization of bottle neck: capability, capacity, file structure for super fast I/O
12:40 Luncheon Presentation I
Erasing the Data Analysis Bottleneck with BaseSpace
Jordan Stockton, Ph.D., Marketing Director,
Enterprise Informatics, Illumina, Inc.
Since the inception of next generation sequencing, great attention has been paid to challenges such as storage, alignment, and variant calling. We believe
that this narrow focus has distracted many biologists from higher-level scientific goals, and that simplifying this process will expedite the discovery
process in the field of applied genomics. In this talk we will show that applications in BaseSpace can empower a new class of researcher to go from
sample to answer quickly, and can allow software developers to make their tools accessible to a vast and receptive audience.
1:10 Luncheon Presentation II: Sponsored by
The Empowered Genome Community: First Insights from Shareable Joint Interpretation of Personal Genomes for Research
Nathan Pearson, Ph.D. Principal Genome Scientist,
QIAGEN
Genome sequencing is becoming prevalent however understanding each genome requires comparing many genomes. We launched the Empowered Genome Community, consisting of people from programs such as the Personal Genome Project (PGP) and Illumina’s Understand Your Genome. Using Ingenuity Variant Analysis, members have identified proof of principle insights on a common complex disease (here,myopia) derived by open collaborative analysis of PGP genomes.
Pearson in REAL TIME
One Genome vs. population of Genomes
IF one Genome:
ancestry
family health
less about drug and mirrors
health is complex
CHallenges
1. mine genome
2. what all genome swill do for Humanity not what my genome can do for me
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