Tuesday Feb 28, 2017

Metadata Management in Clinical Trials by Greg Jones

Metadata management in clinical R&D is centered on the concept that each piece of data collected for a clinical trial, as defined by that trial’s protocol, can be managed independently. This process reduces trial time management. It also increases the traceability and auditability needed for regulatory compliance.  

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Thursday Feb 23, 2017

Big Data in Precision Medicine-Lost in Translation--by Jonathan Sheldon, PhD.

Data is the center point of precision medicine. Today huge, ever-expanding amounts of data are available to support precision medicine decisions and research. However, the health industry must solve some very big challenges before the data can help to cure disease by provide insights into the mechanisms that cause it. 

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Thursday Feb 16, 2017

Population Health and Oracle Healthcare Foundation’s Partner Ecosystem by Lesli Adams, MPA

Twenty years ago, the concept of “population health “ was something closer to evidence based medicine. Today, population health is synonymous with a range of US healthcare market subjects including: patient identification, cost analysis, clinical care gaps, precision medicine and early identification, outcomes measures, and EHR Implementations. There are alarge number and variety of population health applications currently available.  The Oracle Health Sciences team looks at population health from the ground up,aggregating a healthcare organization’s data and making it useful multiple times for any question posed, today, tomorrow, a year from now, or five years from now.  [Read More]

Tuesday Feb 14, 2017

Recap: Northern California HIMSS Innovation Conference and Showcase by Rahul Dwivedi

The Northern California HIMSS Innovation Conference and Showcase in Santa Clara this January was very well attended with industry think tank executives and high profile industry leaders. Sessions sponsored by industry leaders such as Intel, Oracle, Salesforce, HealthCatalyst, and others provided leading views on personalized healthcare, innovation, and the impact of artificial intelligence (AI) and machine learning (ML) on precision medicine and on healthcare industry, in general.

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Tuesday Jan 31, 2017

Precision Medicine -- Not Just for Oncology (Part 3) by Summer Kahlon, M.D.

There are a variety of opportunities to apply genetic knowledge toward improved diagnosis and treatment. As sequencing technologies continue to drive scientific discovery, new ways to fix old problems begin to emerge.

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Tuesday Jan 24, 2017

The Future of Value Based Care by Lesli Adams

The current healthcare delivery landscape is changing dramatically. Major regulatory reimbursement models are evolving from fee-for-service to fee-for outcomes and value. This transformation requires health systems to leverage actionable patient outcome and cost analytics, as well as manage several other constraining challenges to address value based contracting, quality measure performance, internal costs, and care team effectiveness. Learn more.


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Wednesday Jan 18, 2017

Interpreting Big, Real World Data – the New Clinical Data Scientist Role by James Streeter

The research world is brimming in terabytes of information. But what does one do with all this data? How can it be optimized to demonstrate breakthrough insights and new patterns in relation to the drug and the disease? These questions pave the way for the introduction of a new research discipline -- data science.

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Tuesday Jan 10, 2017

Precision Medicine -- Not Just for Oncology (Part 1) by Summer Kahlon, M.D.

The increasing use of genetic tumor markers for targeted cancer diagnosis and treatment has generated a common phrase in modern healthcare: precision medicine. The explosion of scientific discovery in oncology offers new hope in identifying and treating rare and deadly cancers. While the term “precision medicine” is relatively new, the concepts are not.

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Tuesday Nov 29, 2016

Real World Data vs.Real World Evidence by Greg Jones

In Clinical R&D, though the terms real world data and real world evidence are used interchangeably, they are not the same.  This post focuses on the role each of these kinds of data can play in providing additional sources of proof for the safety, efficacy, and value of new drugs and therapies.

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Tuesday Nov 22, 2016

Data Quality: A Critical Factor in Risk Assessment

Today, collecting and sharing clinical trial data is easier than ever .But, there still is no guarantee that the resulting data metrics from all systems can identify risk. What is being measured? Are data sources validated? How many sites are involved? Are they all measuring the same things? How accurate is the data? On what was it measured? How often was it measured?  In a given trial how can data metrics pinpoint risk or help researchers make decisions about protocol changes or monitoring of a conditional event?

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Thursday Nov 17, 2016

Precision Medicine: The Gaps Between Its Promise and Today’s Reality by Jonathan Sheldon

Five years ago, precision medicine was one big research experiment utilizing genomics to identify the molecular basis of disease. In the past two years, genetics has started to become a part of clinical care in the form of molecular decision support, a new, important factor that point of care (POC) physicians are using to inform their care decisions.

To advance precision medicine even further, toward its promise of saving many more lives, the health industry needs to identify and correct the “fixable” gaps between good science and clinical application, as well as recognize those gaps that, for good scientific reason, cannot be fixed.

[Read More]

Tuesday Nov 08, 2016

Application of Artificial Intelligence for Clinical Development by Srinivas Karri

Artificial intelligence (AI) is ubiquitous in our daily lives.  Today, one uses a variety of applications that automatically understand what is spoken. They provide near real-time feedback to support decision-making at an unprecedented scale. How can the AI technique of Machine learning help clinical development? Can it accelerate development timelines and reduce costs?

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Tuesday Oct 11, 2016

Clinical Research as a Care Option by James Streeter

Patient centricity is an important trend in clinical trials today. By focusing on what matters to the patient, researchers have reported lowered healthcare costs and better trial results, better trial protocols and greater insights for the research knowledgebase, overall.

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Thursday Oct 06, 2016

From Big Data to Smart Data

In Health Sciences, there 's a lot of data-- big data --being generated. It may be better to label it Smart Data, as the key to using it is to understand how to store, explore, aggregate, analyze, present and act on it.

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Tuesday Oct 04, 2016

When Good Data Goes Bad by Srinivas Karri

This post focuses on the veracity and integrity of clinical trial data, a very important topic. It looks at the consequences of improper data collection and analysis processes and the resulting, dramatic impact their improper management has had on a number of commercial, therapeutic compounds.

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This blog highlights key trends in healthcare and life sciences. It also shares best practices and opinions from Oracle visionaries.

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