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Healthcare conglomeration to access Big Data and lower costs

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Big Data, BioTechnology - Venture Creation, Clinical & Translational, Clinical Diagnostics, Computational Biology/Systems and Bioinformatics, Diagnostics and Lab Tests, Digital HealthCare – biotech & internet joint ventures, Drug Toxicity, Electronic Health Record, HealthCare IT, Healthcare Reform, Intelligent Information Systems, Technology Advance Assessment of, tagged Consolidated data sources, Electronic Medical Record (EMR) on January 13, 2016| Leave a Comment »

Healthcare conglomeration to access Big Data and lower costs

Curator: Larry H. Bernstein, MD, FCAP 

 

 

UPDATED on 3/17/2019

https://www.medpagetoday.com/cardiology/prevention/78202?xid=nl_mpt_SRCardiology_2019-02-25&eun=g99985d0r&utm_source=Sailthru&utm_medium=email&utm_campaign=CardioUpdate_022519&utm_term=NL_Spec_Cardiology_Update_Active

Medicare Advantage plans may be driving up quality of care in terms of preventive treatment for coronary artery disease patients, but that has had little impact on outcomes compared with fee-for-service Medicare, researchers reported in JAMA Cardiology.

 

The expected benefits are not as easily realized as anticipated.   The problem of access to data sources is not as difficult as the content needed for evaluation.

 

Healthcare Big Data Drives a New Round of Collaborations between Hospitals, Health Systems, and Care Management Companies

DARK DAILY   DARK DAILY info@darkreport.com

 

January 13, 2016

Recently-announced partnerships want to use big data to improve patient outcomes and lower costs; clinical laboratory test data will have a major role in these efforts

In the race to use healthcare big data to improve patient outcomes, several companies are using acquisitions and joint ventures to beef up and gain access to bigger pools of data. Pathologists and clinical laboratory managers have an interest in this trend, because medical laboratory test data will be a large proportion of the information that resides in these huge healthcare databases.

For health systems that want to be players in the healthcare big data market, one strategy is to do arisk-sharing venture with third-party care-management companies. This allows the health systems to leverage their extensive amounts of patient data while benefiting from the expertise of their venture partners.

Cardinal Health Acquires 71% Interest in naviHealth

One company that wants to work with hospitals and health systems in these types of arrangements is Cardinal Health. It recently acquired a 71% interest in Nashville-based naviHealth. This company partners with health plans, health systems, physicians, and post-acute providers to manage the entire continuum of post-acute care (PAC), according to a news release on the naviHealth website. NaviHealth’s business model involves sharing the financial risk with its clients and leveraging big data to predict best outcomes and lower costs.

“We created an economic model to take on the entire post-acute-care episode,” declared naviHealth CEO and President Clay Richards in a company news release. “It’s leveraging the technology and analytics to create individual care protocols.”

Click here to see image

“The most basic, and the most important, thing is … they [Cardinal Health] share the same core values as we do, which is to be on the right side of healthcare,” naviHealth CEO Clay Richards told The Tennessean. “It’s about how you deliver better outcomes for patients with lower costs: How do you solve the problems [with growing costs]? That’s what we and Cardinal define as being on the right side of healthcare.” (Caption and image copyright: The Tennessean.)

Provider Investments Signal Continuation of Trend

Cardinal Health intends to combine its ability to reduce costs while providing effective care with naviHealth’s evidence-based, personalized post-acute-care plans. This is one approach to harness the power of big data to improve patient care. One goal is focus this expertise on post-acute care, which is one of Medicare’s quality measures.

Patients and their families often are unsure of what to expect after being discharged. And, according to an article published in Kaiser Health News, a 2013 Institutes of Medicine (IOM) report noted a link between the quality of post-acute care and healthcare spending following the discharge of Medicare patients.

However, maximizing the use of healthcare big data requires the participation of multiple stakeholders. Information scientists, hospital administrators, software developers, insurers, clinicians, and patients themselves must all perform a role in order for big data to reach its full potential. No single sector will be able to bring the benefits of big data to fruition; rather collaboration and partnerships will be necessary.

Other Collaborations and Alliances Target Healthcare Big Data

Two other organizations engaged in a similar collaboration are the Mayo Clinic andOptum360, a revenue management services company that focuses on simplifying and streamlining the revenue cycle process. In a press release, the companies announced that they were partnering to “develop new revenue management services capabilities aimed at improving patient experiences and satisfaction while reducing administrative costs for healthcare providers.” (See Dark Daily, “When It Comes to Mining Healthcare Big Data, Including Medical Laboratory Test Results, Optum Labs Is the Company to Watch,” December 14, 2015.)

In order to accomplish this, Mayo will have to share its revenue cycle management (RCM) data with Optum360, which will use the data to devise improved revenue cycle processes and systems.

“What we’re trying to find out, if we can, is what does healthcare cost, and what of that spend really adds value to a patient’s outcome over time, especially with these high-impact diseases,” stated Mayo Clinic President and CEO John Noseworthy, MD, in a story published by the Star Tribune. He was referencing another big data project Mayo is engaged in with UnitedHealth Group. “Ultimately, we as a country have to figure this out, so people can have access to high-quality care and it doesn’t bankrupt them or the country.”

Click here to see image

Mayo Clinic President and CEO John Noseworthy, MD, believes big data may be the key to transforming healthcare costs by informing clinical decision-making and altering patient outcomes. (Photo copyright: Mayo Clinic.)

Another interesting healthcare big data partnership is the Pittsburgh Health Data Alliance (The Alliance). It involves a collaboration between Carnegie Mellon University (CMU), the University of Pittsburgh (PITT), and the University of Pittsburgh Medical Center (UPMC). The aim of The Alliance is to take raw data from wearable devices, insurance records, medical appointments, as well as other common sources, and develop ways to improve the health of individuals and the wider community.

The common thread among all these collaborative efforts is a desire to improve outcomes while reducing costs. This is the promise of healthcare big data. And no matter which direction the effort takes, clinical laboratories, which generate a vast amount of critical health data, are in a good position to play important roles involving the contribution of lab test data and identifying ways to use healthcare big data projects to improve patient care.
—Dava Stewart

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Biomarker Development

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Big Data, Biochemical pathways, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biomarkers & Medical Diagnostics, Cancer Informatics, Clinical & Translational, Clinical Diagnostics, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Curation, Diagnostics and Lab Tests, Digital HealthCare – biotech & internet joint ventures, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Electronic Health Record, Enzymes and isoenzymes, Epigenetics and Cardiovascular Risks, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, Genomic Testing: Methodology for Diagnosis, Image Processing/Computing, Innovations, Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough, Intelligent Information Systems, International Global Work in Pharmaceutical, Medical and Population Genetics, Metabolism, Microbiologial genetics, Microbiology, Micronutrients, Molecular Genetics & Pharmaceutical, Nutrigenomics, Nutrition and Phytochemistry, Personal Health Applications: Tech Innovations serves HealhCare, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmaceutical Discovery, tagged biomarkers, Institute of Medicine, NBDA, Personalized medicine on November 16, 2015| Leave a Comment »

Biomarker Development

Curator: Larry H. Bernstein, MD, FCAP

 

 

NBDA’s Biomarker R&D Modules

http://nbdabiomarkers.org/

“collaboratively creating the NBDA Standards* required for end-to-end, evidence – based biomarker development to advance precision (personalized) medicine”

http://nbdabiomarkers.org/sites/all/themes/nbda/images/nbda_logo.jpg

http://nbdabiomarkers.org/about/what-we-do/pipeline-overview/assay-development

 

Successful biomarkers should move systematically and seamlessly through specific R&D “modules” – from early discovery to clinical validation. NBDA’s end-to-end systems approach is based on working with experts from all affected multi-sector stakeholder communities to build an in-depth understanding of the existing barriers in each of these “modules” to support decision making at each juncture.  Following extensive “due diligence” the NBDA works with all stakeholders to assemble and/or create the enabling standards (guidelines, best practices, SOPs) needed to support clinically relevant and robust biomarker development.

Mission: Collaboratively creating the NBDA Standards* required for end-to-end, evidence – based biomarker development to advance precision (personalized) medicine.
NBDA Standards include but are not limited to: “official existing standards”, guidelines, principles, standard operating procedures (SOP), and best practices.

https://vimeo.com/83266065

 

“The NBDA’s vision is not to just relegate the current biomarker development processes to history, but also to serve as a working example of what convergence of purpose, scientific knowledge and collaboration can accomplish.”

NBDA Workshop VII – “COLLABORATIVELY BUILDING A FOUNDATION FOR FDA BIOMARKER QUALIFICATION”
NBDA Workshop VII   December 14-15, 2015   Washington Court Hotel, Washington, DC

The upcoming meeting was preceded by an NBDA workshop held on December 1-2, 2014, “The Promising but Elusive Surrogate Endpoint:  What Will It Take?” where we explored in-depth with FDA leadership and experts in the field the current status and future vison for achieving success in surrogate endpoint development.  Through panels and workgroups, the attendees extended their efforts to pursue the FDA’s biomarker qualification pathway through the creation of sequential contexts of use models to support qualification of drug development tools – and ultimately surrogate endpoints.

Although the biomarker (drug development tools) qualification pathway (http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentTools…) represents an opportunity to increase the value of predictive biomarkers, animal models, and clinical outcomes across the drug (and biologics) development continuum, there are myriad challenges.  In that regard, the lack of evidentiary standards to support contexts of use-specific biomarkers emerged from the prior NBDA workshop as the major barrier to achieving the promise of biomarker qualification.  It also became clear that overall, the communities do not understand the biomarker qualification process; nor do they fully appreciate that it is up to the stakeholders in the field (academia, non-profit foundations, pharmaceutical and biotechnology companies, and patient advocate organizations) to develop these evidentiary standards.

This NBDA workshop will feature a unique approach to address these problems.  Over the past two years, the NBDA has worked with experts in selected disease areas to develop specific case studies that feature a systematic approach to identifying the evidentiary standards needed for sequential contexts of use for specific biomarkers to drive biomarker qualification.   These constructs, and accompanying whitepapers are now the focus of collaborative discussions with FDA experts.

The upcoming meeting will feature in-depth panel discussions of 3-4 of these cases, including the case leader, additional technical contributors, and a number of FDA experts.  Each of the panels will analyze their respective case for strengths and weaknesses – including suggestions for making the biomarker qualification path for the specific biomarker more transparent and efficient. In addition, the discussions will highlight the problem of poor reproducibility of biomarker discovery results, and its impact on the qualification process.

 

Health Care in the Digital Age

Mobile, big data, the Internet of Things and social media are leading a revolution that is transforming opportunities in health care and research. Extraordinary advancements in mobile technology and connectivity have provided the foundation needed to dramatically change the way health care is practiced today and research is done tomorrow. While we are still in the early innings of using mobile technology in the delivery of health care, evidence supporting its potential to impact the delivery of better health care, lower costs and improve patient outcomes is apparent. Mobile technology for health care, or mHealth, can empower doctors to more effectively engage their patients and provide secure information on demand, anytime and anywhere. Patients demand safety, speed and security from their providers. What are the technologies that are allowing this transformation to take place?

 

https://youtu.be/WeXEa2cL3oA    Monday, April 27, 2015  Milken Institute

Moderator

---

Michael Milken, Chairman, Milken Institute

 

Speakers

---

Anna Barker, Fellow, FasterCures, a Center of the Milken Institute; Professor and Director, Transformative Healthcare Networks, and Co-Director, Complex Adaptive Systems Network, Arizona State University

Atul Butte, Director, Institute of Computational Health Sciences, University of California, San Francisco

John Chen, Executive Chairman and CEO, BlackBerry

Victor Dzau, President, Institute of Medicine, National Academy of Sciences; Chancellor Emeritus, Duke University

Patrick Soon-Shiong, Chairman and CEO, NantWorks, LLC

 

Mobile, big data, the Internet of Things and social media are leading a revolution that is transforming opportunities in health care and research. Extraordinary advancements in mobile technology and connectivity have provided the foundation needed to dramatically change the way health care is practiced today and research is done tomorrow. While we are still in the early innings of using mobile technology in the delivery of health care, evidence supporting its potential to impact the delivery of better health care, lower costs and improve patient outcomes is apparent. Mobile technology for health care, or mHealth, can empower doctors to more effectively engage their patients and provide secure information on demand, anytime and anywhere. Patients demand safety, speed and security from their providers. What are the technologies that are allowing this transformation to take place?

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Best Big Data?

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Big Data, BioIT: BioInformatics, Computational Biology/Systems and Bioinformatics, Curation, Digital HealthCare – biotech & internet joint ventures, Intelligent Information Systems, tagged Big data, Google on November 16, 2015| Leave a Comment »

Best Big Data?

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

 

What’s The Big Data?

Google’s RankBrain Outranks the Best Brains in the Industry

Bloomberg recently broke the news that Google is “turning its lucrative Web search over to AI machines.” Google revealed to the reporter that for the past few months, a very large fraction of the millions of search queries Google responds to every second have been “interpreted by an artificial intelligence system, nicknamed RankBrain.”

The company that has tried hard to automate its mission to organize the world’s information was happy to report that its machines have again triumphed over humans. When Google search engineers “were asked to eyeball some pages and guess which they thought Google’s search engine technology would rank on top,” RankBrain had an 80% success rate compared to “the humans [who] guessed correctly 70 percent of the time.”

There you have it. Google’s AI machine RankBrain, after only a few months on the job, already outranks the best brains in the industry, the elite engineers that Google typically hires.

Or maybe not. Is RankBrain really “smarter than your average engineer” and already “living up to its AI hype,” as the Bloomberg article informs us, or is this all just, well, hype?

Desperate to find out how far our future machine overlords are already ahead of the best and the brightest (certainly not “average”), I asked Google to shed more light on the test, e.g., how do they determine the “success rate”?

“That test was fairly informal, but it was some of our top search engineers looking at search queries and potential search results and guessing which would be favored by users. (We don’t have more detail to share on how that’s determined; our evaluations are a pretty complex process).”

I guess both RankBrain and Google search engineers were given possible search results to a given query and RankBrain outperformed humans in guessing which are the “better” results, according to some undisclosed criteria.

I don’t know about you, but my TinyBrain is still confused. Wouldn’t Google search engine, with or without RankBrain, outperform any human being, including the smartest people on earth, in terms of “guessing” which search results “would be favored by users”? Haven’t they been mining the entire corpus of human knowledge for more than fifteen years and, by definition, have produced a search engine that “understands” relevance more than any individual human being?

The key to the competition, I guess, is that the “search queries” used in it were not just any search queries but complex queries containing words that have different meaning in different context. It’s the kind of queries that will stump most human beings and it’s quite surprising that Google engineers scored 70% on search queries that presumably require deep domain knowledge in all human endeavors, in addition to search expertise.

The only example of a complex query given in the Bloomberg article is “What’s the title of the consumer at the highest level of a food chain?” The word “consumer” in this context is a scientific term for something that consumes food and the label (the “title”) at highest level of the food chain is “predator.”

This explanation comes from search guru Danny Sullivan who has come to the rescue of perplexed humans like me, providing a detailed RankBrain FAQ, up to the limits imposed by Google’s legitimate reluctance to fully share its secrets. Sullivan: “From emailing with Google, I gather RankBrain is mainly used as a way to interpret the searches that people submit to find pages that might not have the exact words that were searched for.”

Sullivan points out that a lot of work done by humans is behind Google’s outstanding search results (e.g., creating a synonym list or a database with connections between “entities”—places, people, ideas, objects, etc.). But Google needs now to respond to some 450 million new queries per day, queries that have never been entered before into its search engine.

RankBrain “can see patterns between seemingly unconnected complex searches to understand how they’re actually similar to each other,” writes Sullivan. In addition, “RankBrain might be able to better summarize what a page is about than Google’s existing systems have done.”

Finding out the “unknown unknowns,” discovering previously unknown (to humans) links between words and concepts is the marriage of search technology with the hottest trend in big data analysis—deep learning. The real news about RankBrain is that it is the first time Google applied deep learning, the latest incarnation of “neural networks” and a specific type of machine learning, to its most prized asset—its search engine.

Google has been doing machine learning since its inception. The first published paper listed in the AI and  machine learning section of its research page is from 2001, and, to use just one example, Gmail is so good at detecting spam because of machine learning). But Goggle hasn’t applied machine learning to search. That there has been internal opposition to doing so we learn from a summary of a 2008 conversation between Anand Rajaraman and Peter Norvig, co-author of the most popular AI textbook and leader of Google search R&D since 2001. Here’s the most relevant excerpt:

The big surprise is that Google still uses the manually-crafted formula for its search results. They haven’t cut over to the machine learned model yet. Peter suggests two reasons for this. The first is hubris: the human experts who created the algorithm believe they can do better than a machine-learned model. The second reason is more interesting. Google’s search team worries that machine-learned models may be susceptible to catastrophic errors on searches that look very different from the training data. They believe the manually crafted model is less susceptible to such catastrophic errors on unforeseen query types.

This was written three years after Microsoft has applied machine learning to its search technology. But now, Google got over its hubris. 450 million unforeseen query types per day are probably too much for “manually crafted models” and google has decided that a “deep learning” system such as RankBrain provides good enough protection against “catastrophic errors.”

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Better bioinformatics

Posted in Clinical & Translational, Computational Biology/Systems and Bioinformatics, Curation, Diagnostics and Lab Tests, Digital HealthCare – biotech & internet joint ventures, Electronic Health Record, tagged Big data, Data management, data storage, EMR on November 9, 2015| Leave a Comment »

Better bioinformatics

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Big data in biomedicine: 4 big questions

Eric Bender  Nature Nov 2015; S19, 527.     http://dx.doi.org:/10.1038/527S19a

http://www.nature.com/nature/journal/v527/n7576_supp/full/527S19a.html

Gathering and understanding the deluge of biomedical research and health data poses huge challenges. But this work is rapidly changing the face of medicine.

 

http://www.nature.com/nature/journal/v527/n7576_supp/images/527S19a-g1.jpg

 

1. How can long-term access to biomedical data that are vital for research be improved?

Why it matters
Data storage may be getting cheaper, particularly in cloud computing, but the total costs of maintaining biomedical data are too high and climbing rapidly. Current models for handling these tasks are only stopgaps.

Next steps
Researchers, funders and others need to analyse data usage and look at alternative models, such as ‘data commons’, for providing access to curated data in the long term. Funders also need to incorporate resources for doing this.

Quote
“Our mission is to use data science to foster an open digital ecosystem that will accelerate efficient, cost-effective biomedical research to enhance health, lengthen life and reduce illness and disability.” Philip Bourne, US National Institutes of Health.

 

2. How can the barriers to using clinical trial results and patients’ health records for research be lowered?

Why it matters
‘De-identified’ data from clinical trials and patients’ medical records offer opportunities for research, but the legal and technical obstacles are immense. Clinical study data are rarely shared, and medical records are walled off by privacy and security regulations and by legal concerns.

Next steps
Patient advocates are lobbying for access to their own health data, including genomic information. The European Medicines Agency is publishing clinical reports submitted as part of drug applications. And initiatives such as CancerLinQ are gathering de-identified patient data.

Quote
“There’s a lot of genetic information that no one understands yet, so is it okay or safe or right to put that in the hands of a patient? The flip side is: it’s my information — if I want it, I should get it.”Megan O’Boyle, Phelan-McDermid Syndrome Foundation.

 

3. How can knowledge from big data be brought into point-of-care health-care delivery?

Why it matters
Delivering precision medicine will immensely broaden the scope of electronic health records. This massive shift in health care will be complicated by the introduction of new therapies, requiring ongoing education for clinicians who need detailed information to make clinical decisions.

Next steps
Health systems are trying to bring up-to-date treatments to clinics and build ‘health-care learning systems’ that integrate with electronic health records. For instance, the CancerLinQ project provides recommendations for patients with cancer whose treatment is hard to optimize.

Quote
“Developing a standard interface for innovators to access the information in electronic health records will connect the point of care to big data and the full power of the web, spawning an ‘app store’ for health.” Kenneth Mandl, Harvard Medical School.

 

4. Can academia create better career tracks for bioinformaticians?

Why it matters
The lack of attractive career paths in bioinformatics has led to a shortage of scientists that have both strong statistical skills and biological understanding. The loss of data scientists to other fields is slowing the pace of medical advances.

Next steps
Research institutions will take steps, including setting up formal career tracks, to reward bioinformaticians who take on multidisciplinary collaborations. Funders will find ways to better evaluate contributions from bioinformaticians.

Quote
“Perhaps the most promising product of big data, that labs will be able to explore countless and unimagined hypotheses, will be stymied if we lack the bioinformaticians that can make this happen.” Jeffrey Chang, University of Texas.

 

Eric Bender is a freelance science writer based in Newton, Massachusetts.

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