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Archive for the ‘HealthCare IT’ Category


Opportunity Mapping of the E-Health Sector prior to COVID19 Outbreak

Authors: Akad Doha, Markman Ofer and Lefkort Jared

 

This paper investigates 30 deals in the fields of digital health and e-health from 2017-2020, specifically observing deal size and other critical information.

Variables:

Target audience – the target audience of the deal purpose

Year – the year in which the deal was conducted

Deal size – deal size in million $USD

Deal business rationale

Platform type

Service type

Deal prioritization

Market

Field

Descriptive Statistics and General Characteristics:

Deals in the field of digital and e-health were targeted towards six groups. This includes patients, {general}, organizations, employees, aging in place, and students. The majority of deals were focused on patients, as is seen in figure 1.

 

Figure 1: Tech Company Deals Organized by Target Population

 

Figure 2: Size of Tech Company Deals from 2017-2019, Organized by Target Population

Figure 2 depicts the size of deals in the digital and e-health fields in $USD between 2017-2019, targeting different populations. Those deals targeting the “general” population, and those targeting patients were observed to have the largest size. In particular, deals focused on patients were found to be significantly larger in 2018 when compared to patient-focused deals in 2017 and 2019.

Figure 3: Size and Specific Market of Digital and E-Health Deals by Target Population

Figure 3 shows that in the technology market, the greatest deal size is observed when targeted towards the general population, or patients. On the other hand, deals in the health services market tend to have the greatest size when targeted towards general customers, employees, and patients.

Figure 4: Rationale for Deals in the Tech Industry

Figure 5: Deal Sizes based on Business Rationale

Deals that introduce a new service for a company represent the largest deals. It is also important to note that deals focused on digital solutions and improvements to existing services were fairly large in size. When examining the relationship between deal size and business rationale, we can see that the largest and majority of deals were focused on company independence, acquiring information, market expansion, the addition of a new service or product, and the expansion of saas (software as a service).

This information has led to the analysis that there is a relationship between business rationale deal size.

 

Figure 6: Number of Deals by Platform Usage

 

While substantial platform usage information was not available for all companies, for those that had data, app and cloud platforms tended to be the dominant platform.

 

Figure 7: Number of Deals by Target Experience Improvement

 

 

 

 

Figure 8: Deal Size by Target Experience Improvement

 

Customer and patient experience where the main interest of deals in 2017 and 2018.

Figure 8 shows that customer and patient experience categories account for the largest deal sizes.

 

Figure 9: Number of Deals by Market Sector

 

Figure 10: Deal Size by Market

Figure 9 depicts the fact that most deals occurred in the health services, technology and analytics markets from 2017 to 2019. Figure 10 shows that clinical, research, and shopping markets have the three largest average deal sizes. Thus, the market in which the deal occurs plays a major role in the size of each deal.

Figure 11: Number of Deals by Field

 

Figure 12: Deal Size by Field

 

The majority of deals observed occurred in the fields of healthcare and internet-based media. The field of the deal is one of the four main contributors to the size of a deal.

If we look at the deal size specified by field, we can see that diabetes care, wearables, life sciences and oncology care have the largest sizes.

 

Figure 13: Average Deal Size ($USD) by Year (2017-2019)

Deals observed in 2018 had the largest size in terms of $USD when compared to those occurring in 2017 and 2019. However, the largest single deal took place in 2019.

Inferential Statistics:

As depicted in the above section, the main factors that affect the size of a deal are the market, business rationale, improvements in targeted user experience, and field of the deal.

A clustering analysis has been performed for years between 2017-2019.

 

Figure 14: Cluster Analysis of Deal Size by Year (2017-2019)

Three different groups were identified through the cluster analysis:

  • Cluster 1 (Red): deals in 2019 and 2018 sizes less than or equal to 1 billion.
  • Cluster 2 (Green): deals between 2017-2019 with sizes of approximately 2 billion or greater.
  • Cluster 3 (Blue): deals in 2017 under 500 million.

 

Figure 15: Cluster Analysis of Deal Size by Market Sector

 

Figure 15 shows that cluster 2 deals (green) in the clinical, health services, and research markets are all sized at approximately 2 billion and greater.

This trend continues amongst the other clusters, as cluster 3 deals (blue) remain at a size of less than half a billion in the health services and analytics markets, and cluster 3 deals (blue) remain at a size of 1 billion or less.

Thus, in general, all markets offer approximately 1 billion and under deals with higher deals only available in clinical, health services, and research markets.

 

Figure 16: Cluster Analysis of Deal Size by Field

 

Figure 16 shows that the cluster 2 deals (2 billion in size) mainly occur in the fields of diabetes care, health wearables, internet-based media, life sciences and oncology care.

There are deals in all fields that are approximately 1 billion and under.

Cheaper deals in blue (below half a billion) are only in healthcare and smartwatches.

 

Figure 17: Cluster Analysis of Deal Size by Business Rationale

Business Rationale: Deals aiming to add new services, increasing company independence and acquiring wider information show deal sizes of approximately 2 billion and above. It is noteworthy that deals whose rationale is to integrate more clients, more experts and provider groups, and analytical solutions are clearly under 0.5 billion $USD.

 

Figure 18: Cluster Analysis of Deal Size by Deal Prioritization

From figure 18 one can observe that deals with higher deal prices tend to focus on customer and patient experience.

Other categories are mixed and do not depict a trend when it comes to the price of deals. However, we can see that most of the cluster 3 deals focus on patient experience.

 

Conclusion:

More Comments, conclusions:

Deals approximately 2 billion and above are featured with:

  1. clinical, health services & research markets
  2. diabetes care, health wearables, internet-based media, life science and oncology care fields
  3. business rationale: adding new services, company independence and acquiring wider information.
  4. Are interested in customer and patient experience.

Deals approximately 1 billion and below are featured with:

  1. in 2018-2019
  2. Analytical, delivery, digital, electronic solutions, expand capability, expand globally, improvement, inelegant platforms and more client’s

Deals under 0.5 billion are featured with:

  1. In 2017 only
  2. Deal offers integrating more clients, more experts and provider groups and analytical solutions.
  3. Patient and employee experience.

 

End Notes

Statistical Methods: Since we are interested in the features of deals in the tech industry between 2017-2019, before doing the clustering several multi-linear models was conducted to decide which model include the best variables to explain deal size looking at different significant measures mainly AIC (r-squared, adj-r and so on).

Additional Clustering Information: in figure 16, Although healthcare exists in all clusters, because of other specific descriptions of the field we still can say that the clusters contributes to the understanding of what fields are best to wrap up a deal.

 

 

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Personalized Medicine, Omics, and Health Disparities in Cancer:  Can Personalized Medicine Help Reduce the Disparity Problem?

Curator: Stephen J. Williams, PhD

In a Science Perspectives article by Timothy Rebbeck, health disparities, specifically cancer disparities existing in the sub-Saharan African (SSA) nations, highlighting the cancer incidence disparities which exist compared with cancer incidence in high income areas of the world [1].  The sub-Saharan African nations display a much higher incidence of prostate, breast, and cervix cancer and these cancers are predicted to double within the next twenty years, according to IARC[2].  Most importantly,

 the histopathologic and demographic features of these tumors differ from those in high-income countries

meaning that the differences seen in incidence may reflect a true health disparity as increases rates in these cancers are not seen in high income countries (HIC).

Most frequent male cancers in SSA include prostate, lung, liver, leukemia, non-Hodgkin’s lymphoma, and Kaposi’s sarcoma (a cancer frequently seen in HIV infected patients [3]).  In SSA women, breast and cervical cancer are the most common and these display higher rates than seen in high income countries.  In fact, liver cancer is seen in SSA females at twice the rate, and in SSA males almost three times the rate as in high income countries.

 

 

 

 

 

 

Reasons for cancer disparity in SSA

Patients with cancer are often diagnosed at a late stage in SSA countries.  This contrasts with patients from high income countries, which have their cancers usually diagnosed at an earlier stage, and with many cancers, like breast[4], ovarian[5, 6], and colon, detecting the tumor in the early stages is critical for a favorable outcome and prognosis[7-10].  In addition, late diagnosis also limits many therapeutic options for the cancer patient and diseases at later stages are much harder to manage, especially with respect to unresponsiveness and/or resistance of many therapies.  In addition, treatments have to be performed in low-resource settings in SSA, and availability of clinical lab work and imaging technologies may be limited.

Molecular differences in SSA versus HIC cancers which may account for disparities

Emerging evidence suggests that there are distinct molecular signatures with SSA tumors with respect to histotype and pathology.  For example Dr. Rebbeck mentions that Nigerian breast cancers were defined by increased mutational signatures associated with deficiency of the homologous recombination DNA repair pathway, pervasive mutations in the tumor suppressor gene TP53, mutations in GATA binding protein 3 (GATA3), and greater mutational burden, compared with breast tumors from African Americans or Caucasians[11].  However more research will be required to understand the etiology and causal factors related to this molecular distinction in mutational spectra.

It is believed that there is a higher rate of hereditary cancers in SSA. And many SSA cancers exhibit the more aggressive phenotype than in other parts of the world.  For example breast tumors in SSA black cases are twice as likely than SSA Caucasian cases to be of the triple negative phenotype, which is generally more aggressive and tougher to detect and treat, as triple negative cancers are HER2 negative and therefore are not a candidate for Herceptin.  Also BRCA1/2 mutations are more frequent in black SSA cases than in Caucasian SSA cases [12, 13].

Initiatives to Combat Health Disparities in SSA

Multiple initiatives are being proposed or in action to bring personalized medicine to the sub-Saharan African nations.  These include:

H3Africa empowers African researchers to be competitive in genomic sciences, establishes and nurtures effective collaborations among African researchers on the African continent, and generates unique data that could be used to improve both African and global health.

There is currently a global effort to apply genomic science and associated technologies to further the understanding of health and disease in diverse populations. These efforts work to identify individuals and populations who are at risk for developing specific diseases, and to better understand underlying genetic and environmental contributions to that risk. Given the large amount of genetic diversity on the African continent, there exists an enormous opportunity to utilize such approaches to benefit African populations and to inform global health.

The Human Heredity and Health in Africa (H3Africa) consortium facilitates fundamental research into diseases on the African continent while also developing infrastructure, resources, training, and ethical guidelines to support a sustainable African research enterprise – led by African scientists, for the African people. The initiative consists of 51 African projects that include population-based genomic studies of common, non-communicable disorders such as heart and renal disease, as well as communicable diseases such as tuberculosis. These studies are led by African scientists and use genetic, clinical, and epidemiologic methods to identify hereditary and environmental contributions to health and disease. To establish a foundation for African scientists to continue this essential work into the future work, the consortium also supports many crucial capacity building elements, such as: ethical, legal, and social implications research; training and capacity building for bioinformatics; capacity for biobanking; and coordination and networking.

The World Economic Forum’s Leapfrogging with Precision Medicine project 

This project is part of the World Economic Forum’s Shaping the Future of Health and Healthcare Platform

The Challenge

Advancing precision medicine in a way that is equitable and beneficial to society means ensuring that healthcare systems can adopt the most scientifically and technologically appropriate approaches to a more targeted and personalized way of diagnosing and treating disease. In certain instances, countries or institutions may be able to bypass, or “leapfrog”, legacy systems or approaches that prevail in developed country contexts.

The World Economic Forum’s Leapfrogging with Precision Medicine project will develop a set of tools and case studies demonstrating how a precision medicine approach in countries with greenfield policy spaces can potentially transform their healthcare delivery and outcomes. Policies and governance mechanisms that enable leapfrogging will be iterated and scaled up to other projects.

Successes in personalized genomic research in SSA

As Dr. Rebbeck states:

 Because of the underlying genetic and genomic relationships between Africans and members of the African diaspora (primarily in North America and Europe), knowledge gained from research in SSA can be used to address health disparities that are prevalent in members of the African diaspora.

For example members of the West African heritage and genomic ancestry has been reported to confer the highest genomic risk for prostate cancer in any worldwide population [14].

 

PERSPECTIVEGLOBAL HEALTH

Cancer in sub-Saharan Africa

  1. Timothy R. Rebbeck

See all authors and affiliations

Science  03 Jan 2020:
Vol. 367, Issue 6473, pp. 27-28
DOI: 10.1126/science.aay474

Summary/Abstract

Cancer is an increasing global public health burden. This is especially the case in sub-Saharan Africa (SSA); high rates of cancer—particularly of the prostate, breast, and cervix—characterize cancer in most countries in SSA. The number of these cancers in SSA is predicted to more than double in the next 20 years (1). Both the explanations for these increasing rates and the solutions to address this cancer epidemic require SSA-specific data and approaches. The histopathologic and demographic features of these tumors differ from those in high-income countries (HICs). Basic knowledge of the epidemiology, clinical features, and molecular characteristics of cancers in SSA is needed to build prevention and treatment tools that will address the future cancer burden. The distinct distribution and determinants of cancer in SSA provide an opportunity to generate knowledge about cancer risk factors, genomics, and opportunities for prevention and treatment globally, not only in Africa.

 

References

  1. Rebbeck TR: Cancer in sub-Saharan Africa. Science 2020, 367(6473):27-28.
  2. Parkin DM, Ferlay J, Jemal A, Borok M, Manraj S, N’Da G, Ogunbiyi F, Liu B, Bray F: Cancer in Sub-Saharan Africa: International Agency for Research on Cancer; 2018.
  3. Chinula L, Moses A, Gopal S: HIV-associated malignancies in sub-Saharan Africa: progress, challenges, and opportunities. Current opinion in HIV and AIDS 2017, 12(1):89-95.
  4. Colditz GA: Epidemiology of breast cancer. Findings from the nurses’ health study. Cancer 1993, 71(4 Suppl):1480-1489.
  5. Hamilton TC, Penault-Llorca F, Dauplat J: [Natural history of ovarian adenocarcinomas: from epidemiology to experimentation]. Contracept Fertil Sex 1998, 26(11):800-804.
  6. Garner EI: Advances in the early detection of ovarian carcinoma. J Reprod Med 2005, 50(6):447-453.
  7. Brockbank EC, Harry V, Kolomainen D, Mukhopadhyay D, Sohaib A, Bridges JE, Nobbenhuis MA, Shepherd JH, Ind TE, Barton DP: Laparoscopic staging for apparent early stage ovarian or fallopian tube cancer. First case series from a UK cancer centre and systematic literature review. European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology 2013, 39(8):912-917.
  8. Kolligs FT: Diagnostics and Epidemiology of Colorectal Cancer. Visceral medicine 2016, 32(3):158-164.
  9. Rocken C, Neumann U, Ebert MP: [New approaches to early detection, estimation of prognosis and therapy for malignant tumours of the gastrointestinal tract]. Zeitschrift fur Gastroenterologie 2008, 46(2):216-222.
  10. Srivastava S, Verma M, Henson DE: Biomarkers for early detection of colon cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2001, 7(5):1118-1126.
  11. Pitt JJ, Riester M, Zheng Y, Yoshimatsu TF, Sanni A, Oluwasola O, Veloso A, Labrot E, Wang S, Odetunde A et al: Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features. Nature communications 2018, 9(1):4181.
  12. Zheng Y, Walsh T, Gulsuner S, Casadei S, Lee MK, Ogundiran TO, Ademola A, Falusi AG, Adebamowo CA, Oluwasola AO et al: Inherited Breast Cancer in Nigerian Women. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2018, 36(28):2820-2825.
  13. Rebbeck TR, Friebel TM, Friedman E, Hamann U, Huo D, Kwong A, Olah E, Olopade OI, Solano AR, Teo SH et al: Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations. Human mutation 2018, 39(5):593-620.
  14. Lachance J, Berens AJ, Hansen MEB, Teng AK, Tishkoff SA, Rebbeck TR: Genetic Hitchhiking and Population Bottlenecks Contribute to Prostate Cancer Disparities in Men of African Descent. Cancer research 2018, 78(9):2432-2443.

Other articles on Cancer Health Disparities and Genomics on this Online Open Access Journal Include:

Gender affects the prevalence of the cancer type
The Rutgers Global Health Institute, part of Rutgers Biomedical and Health Sciences, Rutgers University, New Brunswick, New Jersey – A New Venture Designed to Improve Health and Wellness Globally
Breast Cancer Disparities to be Sponsored by NIH: NIH Launches Largest-ever Study of Breast Cancer Genetics in Black Women
War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert
Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk
Ethics Behind Genetic Testing in Breast Cancer: A Webinar by Laura Carfang of survivingbreastcancer.org
Live Notes from @HarvardMed Bioethics: Authors Jerome Groopman, MD & Pamela Hartzband, MD, discuss Your Medical Mind
Testing for Multiple Genetic Mutations via NGS for Patients: Very Strong Family History of Breast & Ovarian Cancer, Diagnosed at Young Ages, & Negative on BRCA Test
Study Finds that Both Women and their Primary Care Physicians Confusion over Ovarian Cancer Symptoms May Lead to Misdiagnosis

 

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Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Reporter: Aviva Lev-Ari, PhD, RN

 

This report is based on an article in Nature Medicine | VOL 25 | December 2019 | 1800–1809 | http://www.nature.com/naturemedicine

Looking forward 25 years: the future of medicine.

Nat Med 25, 1804–1807 (2019) doi:10.1038/s41591-019-0693-y

 

Aviv Regev, PhD

Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.

  • millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
  • In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
  • we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
  • Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers

Feng Zhang, PhD

investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.

  • fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
  • Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
  • 1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
  • refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society

Elizabeth Jaffee, PhD

Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.

  • a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
  • developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.

Jeremy Farrar, OBE FRCP FRS FMedSci

Director, Wellcome Trust.

  • shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
  • building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.

John Nkengasong, PhD

Director, Africa Centres for Disease Control and Prevention.

  • To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
  • Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.

Eric Topol, MD

Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.

  • In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
  • enhanced capabilities to perform functions that are not feasible now.
  • AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
  • the concept of a learning health system will be redefined by AI.

Linda Partridge, PhD

Professor, Max Planck Institute for Biology of Ageing.

  • Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.

Trevor Mundel, MD

President of Global Health, Bill & Melinda Gates Foundation.

  • finding new ways to share clinical data that are as open as possible and as closed as necessary.
  • moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
  • working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
  • deliver on the promise of global health equity.

Josep Tabernero, MD, PhD

Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).

  • genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
  • Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
  • Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
  • AI will help guide the development of individually matched
  • genetic patient screenings
  • the promise of liquid biopsy policing of disease?

Pardis Sabeti, PhD

Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.

  • the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
  • But this will only happen with a commitment from the global community.

Els Toreele, PhD

Executive director, Médecins Sans Frontières Access Campaign

  • we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
  • This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
  • The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

One of the most contagious diseases known to humankind, measles killed an average of 2.6 million people each year before a vaccine was developed, according to the World Health Organization. Widespread vaccination has slashed the death toll. However, lack of access to vaccination and refusal to get vaccinated means measles still infects more than 7 million people and kills more than 100,000 each year worldwide as reported by WHO. The cases are on the rise, tripling in early 2019 and some experience well-known long-term consequences, including brain damage and vision and hearing loss. Previous epidemiological research into immune amnesia suggests that death rates attributed to measles could be even higher, accounting for as much as 50 percent of all childhood mortality.

 

Over the last decade, evidence has mounted that the measles vaccine protects in two ways. It prevents the well-known acute illness with spots and fever and also appears to protect from other infections over the long term by giving general boost to the immune system. The measles virus can impair the body’s immune memory, causing so-called immune amnesia. By protecting against measles infection, the vaccine prevents the body from losing or “forgetting” its immune memory and preserves its resistance to other infections. Researchers showed that the measles virus wipes out 11% to 73% of the different antibodies that protect against viral and bacterial strains a person was previously immune to like from influenza to herpes virus to bacteria that cause pneumonia and skin infections.

 

This study at Harvard Medical School and their collaborators is the first to measure the immune damage caused by the virus and underscores the value of preventing measles infection through vaccination. The discovery that measles depletes people’s antibody repertoires, partially obliterating immune memory to most previously encountered pathogens, supports the immune amnesia hypothesis. It was found that those who survive measles gradually regain their previous immunity to other viruses and bacteria as they get re-exposed to them. But because this process may take months to years, people remain vulnerable in the meantime to serious complications of those infections and thus booster shots of routine vaccines may be required.

 

VirScan detects antiviral and antibacterial antibodies in the blood that result from current or past encounters with viruses and bacteria, giving an overall snapshot of the immune system. Researchers gathered blood samples from unvaccinated children during a 2013 measles outbreak in the Netherlands and used VirScan to measure antibodies before and two months after infection in 77 children who’d contracted the disease. The researchers also compared the measurements to those of 115 uninfected children and adults. Researchers found a striking drop in antibodies from other pathogens in the measles-infected children that clearly suggested a direct effect on the immune system resembling measles-induced immune amnesia.

 

Further tests revealed that severe measles infection reduced people’s overall immunity more than mild infection. This could be particularly problematic for certain categories of children and adults, the researchers said. The present study observed the effects in previously healthy children only. But, measles is known to hit malnourished children much harder, the degree of immune amnesia and its effects could be even more severe in less healthy populations. Inoculation with the MMR (measles, mumps, rubella) vaccine did not impair children’s overall immunity. The results align with decades of research. Ensuring widespread vaccination against measles would not only help prevent the expected 120,000 deaths that will be directly attributed to measles this year alone, but could also avert potentially hundreds of thousands of additional deaths attributable to the lasting damage to the immune system.

 

References:

 

https://hms.harvard.edu/news/inside-immune-amnesia?utm_source=Silverpop

 

https://science.sciencemag.org/content/366/6465/599

 

www.who.int/immunization/newsroom/measles-data-2019/en/

 

https://www.ncbi.nlm.nih.gov/pubmed/20636817

 

https://www.ncbi.nlm.nih.gov/pubmed/27157064

 

https://www.ncbi.nlm.nih.gov/pubmed/30797735

 

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Artificial Intelligence and Cardiovascular Disease

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.

 

The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.

 

Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.

 

In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.

 

One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.

 

Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.

 

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.

 

The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.

 

Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.

 

References:

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.bhf.org.uk/informationsupport/heart-matters-magazine/research/artificial-intelligence

 

https://www.medicaldevice-network.com/news/heart-attack-artificial-intelligence/

 

https://www.nature.com/articles/s41569-019-0158-5

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711980/

 

www.j-pcs.org/article.asp

http://www.onlinejacc.org/content/71/23/2668

http://www.scielo.br/pdf/ijcs/v30n3/2359-4802-ijcs-30-03-0187.pdf

 

https://www.escardio.org/The-ESC/Press-Office/Press-releases/How-artificial-intelligence-is-tackling-heart-disease-Find-out-at-ICNC-2019

 

https://clinicaltrials.gov/ct2/show/NCT03877614

 

https://www.europeanpharmaceuticalreview.com/news/82870/artificial-intelligence-ai-heart-disease/

 

https://www.frontiersin.org/research-topics/10067/current-and-future-role-of-artificial-intelligence-in-cardiac-imaging

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.sciencedaily.com/releases/2019/05/190513104505.htm

 

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Real Time @BIOConvention #BIO2019:#Bitcoin Your Data! From Trusted Pharma Silos to Trustless Community-Owned Blockchain-Based Precision Medicine Data Trials

Reporter: Stephen J Williams, PhD @StephenJWillia2
Speakers

As care for lifestyle-driven chronic diseases expands in scope, prevention and recovery are becoming the new areas of focus. Building a precision medicine foundation that will promote ownership of individuals’ health data and allow for sharing and trading of this data could prove a great blockchain.

At its core, blockchain may offer the potential of a shared platform that decentralizes healthcare interactions ensuring access control, authenticity and integrity, while presenting the industry with radical possibilities for value-based care and reimbursement models. Panelists will explore these new discoveries as well as look to answer lingering questions, such as: are we off to a “trustless” information model underpinned by Bitcoin cryptocurrency, where no central authority validates the transactions in the ledger, and anyone whose computers can do the required math can join to mine and add blocks to your data? Would smart contracts begin to incentivize “rational” behaviors where consumers respond in a manner that makes their data interesting?

Moderator:  Cybersecurity is extremely important in the minds of healthcare CEOs.  CEO of Kaiser Permenente has listed this as one of main concerns for his company.

Sanjeey of Singularity: There are Very few companies in this space.  Singularity have collected thousands of patient data.  They wanted to do predictive health care, where a patient will know beforehand what health problems and issues to expect.  Created a program called Virtual Assistant. As data is dynamic, the goal was to provide Virtual Assistant to everyone.

Benefits of blockchain: secure, simple to update, decentralized data; patient can control their own data, who sees it and monetize it.

Nebular Genetics: Company was founded by Dr. George Church, who had pioneered the next generation sequencing (NGS) methodology.  The company goal is to make genomics available to all but this currently is not the case as NGS is not being used as frequently.

The problem is a data problem:

  • data not organized
  • data too parsed
  • data not accessible

Blockchain may be able to alleviate the accessibiltiy problem.  Pharma is very interested in the data but expensive to collect.  In addition many companies just do large scale but low depth sequencing.  For example 23andme (which had recently made a big deal with Lilly for data) only sequences about 1% of genome.

There are two types of genome sequencing companies

  1.  large scale and low depth – like 23andme
  2. smaller scale but higher depth – like DECODE and some of the EU EXOME sequencing efforts like the 1000 Project

Simply Vital Health: Harnesses blockchain to combat ineffeciencies in hospital records. They tackle the costs after acute care so increase the value based care.  Most of healthcare is concentrated on the top earners and little is concentrated on the majority less affluent and poor.  On addressing HIPAA compliance issues: they decided to work with HIPAA and comply but will wait for this industry to catch up so the industry as a whole can lobby to affect policy change required for blockchain technology to work efficiently in this arena.  They will only work with known vendors: VERY Important to know where the data is kept and who are controlling the servers you are using.  With other blockchain like Etherium or Bitcoin, the servers are anonymous.

Encrypgen: generates new blockchain for genomic data and NGS companies.

 

Please follow LIVE on TWITTER using the following @ handles and # hashtags:

@Handles

@pharma_BI

@AVIVA1950

@BIOConvention

# Hashtags

#BIO2019 (official meeting hashtag)

#blockchain
#bitcoin
#clinicaltrials

 

 

 

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Real Time Coverage @BIOConvention #BIO2019: Chat with @FDA Commissioner, & Challenges in Biotech & Gene Therapy June 4 Philadelphia

Reporter: Stephen J. Williams, PhD @StephenJWillia2

 

  • taking patient concerns and voices from anecdotal to data driven system
  • talked about patient accrual hearing patient voice not only in ease of access but reporting toxicities
  • at FDA he wants to remove barriers to trial access and accrual; also talk earlier to co’s on how they should conduct a trial

Digital tech

  • software as medical device
  • regulatory path is mixed like next gen sequencing
  • wearables are concern for FDA (they need to recruit scientists who know this tech

Opioids

  • must address the crisis but in a way that does not harm cancer pain patients
  • smaller pain packs “blister packs” would be good idea

Clinical trial modernization

  • for Alzheimers disease problem is science
  • for diabetes problem is regulatory
  • different diseases calls for different trial design
  • have regulatory problems with rare diseases as can’t form control or placebo group, inhumane. for example ras tumors trials for MEK inhibitors were narrowly focused on certain ras mutants
Realizing the Promise of Gene Therapies for Patients Around the World

103ABC, Level 100

Speakers
Lots of promise, timeline is progressing faster but we need more education on use of the gene therapy
Regulatory issues: Cell and directly delivered gene based therapies have been now approved. Some challenges will be the ultrarare disease trials and how we address manufacturing issues.  Manufacturing is a big issue at CBER and scalability.  If we want to have global impact of these products we need to address the manufacturing issues
 of scalability.
Pfizer – clinical grade and scale is important.
Aventis – he knew manufacturing of biologics however gene therapy manufacturing has its separate issues and is more complicated especially for regulatory purposes for clinical grade as well as scalability.  Strategic decision: focusing on the QC on manufacturing was so important.  Had a major issue in manufacturing had to shut down and redesign the system.
Albert:  Manufacturing is the most important topic even to the investors.  Investors were really conservative especially seeing early problems but when academic centers figured out good efficacy then they investors felt better and market has exploded.  Now you can see investment into preclinical and startups but still want mature companies to focus on manufacturing.  About $10 billion investment in last 4 years.

How Early is Too Early? Valuing and De-Risking Preclinical Opportunities

109AB, Level 100

Speakers
Valuing early-stage opportunities is challenging. Modeling will often provide a false sense of accuracy but relying on comparable transactions is more art than science. With a long lead time to launch, even the most robust estimates can ultimately prove inaccurate. This interactive panel will feature venture capital investors and senior pharma and biotech executives who lead early-stage transactions as they discuss their approaches to valuing opportunities, and offer key learnings from both successful and not-so-successful experiences.
Dr. Schoenbeck, Pfizer:
  • global network of liaisons who are a dedicated team to research potential global startup partners or investments.  Pfizer has a separate team to evaluate academic laboratories.  In Most cases Pfizer does not initiate contact.  It is important to initiate the first discussion with them in order to get noticed.  Could be just a short chat or discussion on what their needs are for their portfolio.

Question: How early is too early?

Luc Marengere, TVM:  His company has early stage focus, on 1st in class molecules.  The sweet spot for their investment is a candidate selected compound, which should be 12-18 months from IND.  They will want to bring to phase II in less than 4 years for $15-17 million.  Their development model is bad for academic labs.  During this process free to talk to other partners.

Dr. Chaudhary, Biogen:  Never too early to initiate a conversation and sometimes that conversation has lasted 3+ years before a decision.  They like build to buy models, will do convertible note deals, candidate compound selection should be entering in GLP/Tox phase (sweet spot)

Merck: have MRL Venture Fund for pre series A funding.  Also reiterated it is never too early to have that initial discussion.  It will not put you in a throw away bin.  They will have suggestions and never like to throw out good ideas.

Michael Hostetler: Set expectations carefully ; data should be validated by a CRO.  If have a platform, they will look at the team first to see if strong then will look at the platform to see how robust it is.

All noted that you should be completely honest at this phase.  Do not overstate your results or data or overhype your compound(s).  Show them everything and don’t have a bias toward compounds you think are the best in your portfolio.  Sometimes the least developed are the ones they are interested in.  Also one firm may reject you however you may fit in others portfolios better so have a broad range of conversations with multiple players.

 

 

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Verily kicked off Project Baseline in April 2017, with a health study geared to gather health data from 10,000 people over four years – Partnership with Big Pharma on Clinical Trials announced on 5/21/2019

 

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 5/22/2019

On Tuesday morning, Verily, Alphabet’s unit focused on life sciences, announced that it had formed alliances with Novartis, Sanofi, Otsuka, and Pfizer to work on clinical trials. What are those drug giants getting out of the deal? STAT sat down with Scarlet Shore, who leads Verily’s project Baseline, to learn about the company’s vision for the clinical trial of the future. The conversation took place at CNBC’s “Healthy Returns” conference, where the partnerships were unveiled.

SOURCE

https://www.statnews.com/2019/05/21/four-of-the-worlds-largest-drug-companies-are-teaming-with-verily-here-is-what-they-get/?utm_source=STAT+Newsletters&utm_campaign=1630aad75d-Readout_COPY_03&utm_medium=email&utm_term=0_8cab1d7961-1630aad75d-150237109

Novartis, Otsuka, Pfizer, Sanofi join Verily’s Project Baseline

“Evidence generation through research is the backbone of improving health outcomes. We need to be inclusive and encourage diversity in research to truly understand health and disease, and to provide meaningful insights about new medicines, medical devices and digital health solutions,” said Jessica Mega, M.D., Verily’s chief medical and scientific officer, in the statement. “Novartis, Otsuka, Pfizer and Sanofi have been early adopters of advanced technology and digital tools to improve clinical research operations, and together we’re taking another step towards making research accessible and generating evidence to inform better treatments and care.”
Jessica Mega, M.D., Verily’s chief medical and scientific officer, in the statement. “Novartis, Otsuka, Pfizer and Sanofi have been early adopters of advanced technology and digital tools to improve clinical research operations, and together we’re taking another step towards making research accessible and generating evidence to inform better treatments and care.”

 

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Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals


Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Digital Therapeutics (DTx) have been defined by the Digital Therapeutics Alliance (DTA) as “delivering evidence based therapeutic interventions to patients, that are driven by software to prevent, manage or treat a medical disorder or disease”. They might come in the form of a smart phone or computer tablet app, or some form of a cloud-based service connected to a wearable device. DTx tend to fall into three groups. Firstly, developers and mental health researchers have built digital solutions which typically provide a form of software delivered Cognitive-Behaviour Therapies (CBT) that help patients change behaviours and develop coping strategies around their condition. Secondly there are the group of Digital Therapeutics which target lifestyle issues, such as diet, exercise and stress, that are associated with chronic conditions, and work by offering personalized support for goal setting and target achievement. Lastly, DTx can be designed to work in combination with existing medication or treatments, helping patients manage their therapies and focus on ensuring the therapy delivers the best outcomes possible.

 

Pharmaceutical companies are clearly trying to understand what DTx will mean for them. They want to analyze whether it will be a threat or opportunity to their business. For a long time, they have been providing additional support services to patients who take relatively expensive drugs for chronic conditions. A nurse-led service might provide visits and telephone support to diabetics for example who self-inject insulin therapies. But DTx will help broaden the scope of support services because they can be delivered cost-effectively, and importantly have the ability to capture real-world evidence on patient outcomes. They will no-longer be reserved for the most expensive drugs or therapies but could apply to a whole range of common treatments to boost their efficacy. Faced with the arrival of Digital Therapeutics either replacing drugs, or playing an important role alongside therapies, pharmaceutical firms have three options. They can either ignore DTx and focus on developing drug therapies as they have done; they can partner with a growing number of DTx companies to develop software and services complimenting their drugs; or they can start to build their own Digital Therapeutics to work with their products.

 

Digital Therapeutics will have knock-on effects in health industries, which may be as great as the introduction of therapeutic apps and services themselves. Together with connected health monitoring devices, DTx will offer a near constant stream of data about an individuals’ behavior, real world context around factors affecting their treatment in their everyday lives and emotional and physiological data such as blood pressure and blood sugar levels. Analysis of the resulting data will help create support services tailored to each patient. But who stores and analyses this data is an important question. Strong data governance will be paramount to maintaining trust, and the highly regulated pharmaceutical industry may not be best-placed to handle individual patient data. Meanwhile, the health sector (payers and healthcare providers) is becoming more focused on patient outcomes, and payment for value not volume. The future will say whether pharmaceutical firms enhance the effectiveness of drugs with DTx, or in some cases replace drugs with DTx.

 

Digital Therapeutics have the potential to change what the pharmaceutical industry sells: rather than a drug it will sell a package of drugs and digital services. But they will also alter who the industry sells to. Pharmaceutical firms have traditionally marketed drugs to doctors, pharmacists and other health professionals, based on the efficacy of a specific product. Soon it could be paid on the outcome of a bundle of digital therapies, medicines and services with a closer connection to both providers and patients. Apart from a notable few, most pharmaceutical firms have taken a cautious approach towards Digital Therapeutics. Now, it is to be observed that how the pharmaceutical companies use DTx to their benefit as well as for the benefit of the general population.

 

References:

 

https://eloqua.eyeforpharma.com/LP=23674?utm_campaign=EFP%2007MAR19%20EFP%20Database&utm_medium=email&utm_source=Eloqua&elqTrackId=73e21ae550de49ccabbf65fce72faea0&elq=818d76a54d894491b031fa8d1cc8d05c&elqaid=43259&elqat=1&elqCampaignId=24564

 

https://www.s3connectedhealth.com/resources/white-papers/digital-therapeutics-pharmas-threat-or-opportunity/

 

http://www.pharmatimes.com/web_exclusives/digital_therapeutics_will_transform_pharma_and_healthcare_industries_in_2019._heres_how._1273671

 

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/exploring-the-potential-of-digital-therapeutics

 

https://player.fm/series/digital-health-today-2404448/s9-081-scaling-digital-therapeutics-the-opportunities-and-challenges

 

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Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?

Reporter: Stephen J. Williams, Ph.D.

 

Boston Healthcare sponsored a Webinar recently entitled ” Role of Informatics in Precision Medicine: Implications for Innovators”.  The webinar focused on the different informatic needs along the Oncology Care value chain from drug discovery through clinicians, C-suite executives and payers. The presentation, by Joseph Ferrara and Mark Girardi, discussed the specific informatics needs and deficiencies experienced by all players in oncology care and how innovators in this space could create value. The final part of the webinar discussed artificial intelligence and the role in cancer informatics.

 

Below is the mp4 video and audio for this webinar.  Notes on each of the slides with a few representative slides are also given below:

Please click below for the mp4 of the webinar:

 

 


  • worldwide oncology related care to increase by 40% in 2020
  • big movement to participatory care: moving decision making to the patient. Need for information
  • cost components focused on clinical action
  • use informatics before clinical stage might add value to cost chain

 

 

 

 

Key unmet needs from perspectives of different players in oncology care where informatics may help in decision making

 

 

 

  1.   Needs of Clinicians

– informatic needs for clinical enrollment

– informatic needs for obtaining drug access/newer therapies

2.  Needs of C-suite/health system executives

– informatic needs to help focus of quality of care

– informatic needs to determine health outcomes/metrics

3.  Needs of Payers

– informatic needs to determine quality metrics and managing costs

– informatics needs to form guidelines

– informatics needs to determine if biomarkers are used consistently and properly

– population level data analytics

 

 

 

 

 

 

 

 

 

 

 

 

What are the kind of value innovations that tech entrepreneurs need to create in this space? Two areas/problems need to be solved.

  • innovations in data depth and breadth
  • need to aggregate information to inform intervention

Different players in value chains have different data needs

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Data Depth: Cumulative Understanding of disease

Data Depth: Cumulative number of oncology transactions

  • technology innovators rely on LEGACY businesses (those that already have technology) and these LEGACY businesses either have data breath or data depth BUT NOT BOTH; (IS THIS WHERE THE GREATEST VALUE CAN BE INNOVATED?)
  • NEED to provide ACTIONABLE as well as PHENOTYPIC/GENOTYPIC DATA
  • data depth more important in clinical setting as it drives solutions and cost effective interventions.  For example Foundation Medicine, who supplies genotypic/phenotypic data for patient samples supplies high data depth
  • technologies are moving to data support
  • evidence will need to be tied to umbrella value propositions
  • Informatic solutions will have to prove outcome benefit

 

 

 

 

 

How will Machine Learning be involved in the healthcare value chain?

  • increased emphasis on real time datasets – CONSTANT UPDATES NEED TO OCCUR. THIS IS NOT HAPPENING BUT VALUED BY MANY PLAYERS IN THIS SPACE
  • Interoperability of DATABASES Important!  Many Players in this space don’t understand the complexities integrating these datasets

Other Articles on this topic of healthcare informatics, value based oncology, and healthcare IT on this OPEN ACCESS JOURNAL include:

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Broad Institute launches Merkin Institute for Transformative Technologies in Healthcare

HealthCare focused AI Startups from the 100 Companies Leading the Way in A.I. Globally

Paradoxical Findings in HealthCare Delivery and Outcomes: Economics in MEDICINE – Original Research by Anupam “Bapu” Jena, the Ruth L. Newhouse Associate Professor of Health Care Policy at HMS

Google & Digital Healthcare Technology

Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

The Future of Precision Cancer Medicine, Inaugural Symposium, MIT Center for Precision Cancer Medicine, December 13, 2018, 8AM-6PM, 50 Memorial Drive, Cambridge, MA

Live Conference Coverage @Medcity Converge 2018 Philadelphia: Oncology Value Based Care and Patient Management

2016 BioIT World: Track 5 – April 5 – 7, 2016 Bioinformatics Computational Resources and Tools to Turn Big Data into Smart Data

The Need for an Informatics Solution in Translational Medicine

 

 

 

 

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