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Posts Tagged ‘Health information exchange’


10:15AM 11/13/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

10:15 a.m. Panel Discussion — IT/Big Data

IT/Big Data

The human genome is composed of 6 billion nucleotides (using the genetic alphabet of T, C, G and A). As the cost of sequencing the human genome is decreasing at a rapid rate, it might not be too far into the future that every human being will be sequenced at least once in their lifetime. The sequence data together with the clinical data are going to be used more and more frequently to make clinical decisions. If that is true, we need to have secure methods of storing, retrieving and analyzing all of these data.  Some people argue that this is a tsunami of data that we are not ready to handle. The panel will discuss the types and volumes of data that are being generated and how to deal with it.

IT/Big Data

   Moderator:

Amy Abernethy, M.D.
Chief Medical Officer, Flatiron

Role of Informatics, SW and HW in PM. Big data and Healthcare

How Lab and Clinics can be connected. Oncologist, Hematologist use labs in clinical setting, Role of IT and Technology in the environment of the Clinicians

Compare Stanford Medical Center and Harvard Medical Center and Duke Medical Center — THREE different models in Healthcare data management

Create novel solutions: Capture the voice of the patient for integration of component: Volume, Veracity, Value

Decisions need to be made in short time frame, documentation added after the fact

No system can be perfect in all aspects

Understanding clinical record for conversion into data bases – keeping quality of data collected

Key Topics

Panelists:

Stephen Eck, M.D., Ph.D.
Vice President, Global Head of Oncology Medical Sciences,
Astellas, Inc.

Small data expert, great advantage to small data. Populations data allows for longitudinal studies,

Big Mac Big Data – Big is Good — Is data been collected suitable for what is it used, is it robust, limitations, of what the data analysis mean

Data analysis in Chemical Libraries – now annotated

Diversity data in NOTED by MDs, nuances are very great, Using Medical Records for building Billing Systems

Cases when the data needed is not known or not available — use data that is available — limits the scope of what Valuable solution can be arrived at

In Clinical Trial: needs of researchers, billing clinicians — in one system

Translation of data on disease to data object

Signal to Noise Problem — Thus Big data provided validity and power

 

J. Michael Gaziano, M.D., M.P.H., F.R.C.P.
Scientific Director, Massachusetts Veterans Epidemiology Research
and Information Center (MAVERIC), VA Boston Healthcare System;
Chief Division of Aging, Brigham and Women’s Hospital;
Professor of Medicine, Harvard Medical School

at BWH since 1987 at 75% – push forward the Genomics Agenda, VA system 25% – VA is horizontally data integrated embed research and knowledge — baseline questionnaire 200,000 phenotypes – questionnaire and Genomics data to be integrated, Data hierarchical way to be curated, Simple phenotypes, validate phenotypes, Probability to have susceptibility for actual disease, Genomics Medicine will benefit Clinicians

Data must be of visible quality, collect data via Telephone VA – on Med compliance study, on Ability to tolerate medication

–>>Annotation assisted in building a tool for Neurologist on Alzheimer’s Disease (AlzSWAN knowledge base) (see also Genotator , a Disease-Agnostic Tool for Annotation)

–>>Curation of data is very different than statistical analysis of Clinical Trial Data

–>>Integration of data at VA and at BWH are tow different models of SUCCESSFUL data integration models, accessing the data is also using a different model

–>>Data extraction from the Big data — an issue

–>>Where the answers are in the data, build algorithms that will pick up causes of disease: Alzheimer’s – very difficult to do

–>>system around all stakeholders: investment in connectivity, moving data, individual silo, HR, FIN, Clinical Research

–>>Biobank data and data quality

 

Krishna Yeshwant, M.D.
General Partner, Google Ventures;
Physician, Brigham and Women’s Hospital

Computer Scientist and Medical Student. Were the technology is going?

Messy situation, interaction IT and HC, Boston and Silicon Valley are focusing on Consumers, Google Engineers interested in developing Medical and HC applications — HUGE interest. Application or Wearable – new companies in this space, from Computer Science world to Medicine – Enterprise level – EMR or Consumer level – Wearable — both areas are very active in Silicon Valley

IT stuff in the hospital HARDER that IT in any other environment, great progress in last 5 years, security of data, privacy. Sequencing data cost of big data management with highest security

Constrained data vs non-constrained data

Opportunities for Government cooperation as a Lead needed for standardization of data objects

 

Questions from the Podium:

  • Where is the Truth: do we have all the tools or we don’t for Genomic data usage
  • Question on Interoperability
  • Big Valuable data — vs Big data
  • quality, uniform, large cohort, comprehensive Cancer Centers
  • Volume of data can compensate quality of data
  • Data from Imaging – Quality and interpretation – THREE radiologist will read cancer screening

 

 

 

– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

 

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The Relationship Between “Big Data” and Health Care – Value or Rubbish?

Author: Alan Fleischman, MBA      E-mail: a.fleischman@verizon.net

A blog (pathcareblog.com) entitled Why Big Data for Healthcare is Rubbish

http://pathcareblog.com/why-big-data-for-healthcare-is-rubbish/?goback=%2Eanb_1839273_*2_*1_*1_*1_*1_*1 takes direct aim at a recent report by the McKinsey Global Institute (Big Data: The Next Frontier for Innovation, Competition, and Productivity) http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation that projects substantial quantitative and qualitative benefits from implementing Big Data initiatives in health care.  Pathcare essentially states that McKinsey and Big Data ignore the two major stakeholders in healthcare – doctors and patients: “The study does not cite a single interview with a primary care physician or even a CEO of a healthcare organization that might support or validate their theories about big data value for healthcare. This is shoddy research, no matter how well packaged.” http://pathcareblog.com/why-big-data-for-healthcare-is-rubbish/?goback=%2Eanb_1839273_*2_*1_*1_*1_*1_*1

An article in Businessweek (The Health-Care Industry Turns to Big Data by Jordan Robertson, May 17, 2012) http://www.businessweek.com/articles/2012-05-17/the-health-care-industry-turns-to-big-data quotes benefits experienced by New York-Presbyterian Hospital from several data initiatives – including reducing “the rate of potentially fatal blood clots by about a third”, according to surgeon Nicholas Morrissey.  Morrisey is also working to develop a big data driven system to assess risk factors on new patients in the emergency room and the admission wards.  Along with hospitals, NSF and NIH have launched an initiative on Big Data to accelerate progress in biomedical research.

This article will not attempt to defend the research methodology utilized by McKinsey or the magnitude of the benefits projected, but it will defend the premise that medicine must improve its processes and procedures. Information systems are essential to this improvement and large amounts of data will need to be exchanged, integrated, and analyzed as a result. Evidence based medicine, effectiveness research, and performance assessments require the analysis of large amounts of data.  Like it or not, medicine is an industry with massive amounts of data, whether it is clinical, administrative, performance, or business.  Medicine can no longer function as a guild where senior craftsmen dispense tricks of the trade to apprentices and society grins and bears the results in terms of lives impacted and national treasure dispensed.  What is truly alarming to this author is the fact that healthcare has been so slow to adopt methods that have been proven effective in other industries – even low-tech methods.  This may explain the positive reception given to the use of simple checklists that have been advocated by the Institute for Healthcare Improvement and A. Gawandi in his book The Checklist Manifesto. http://gawande.com/the-checklist-manifesto  Checklists have been used in the airline industry since its inception.  Other industries have already demonstrated the benefits of Big Data over a substantial time frame – including finance, transportation, manufacturing, and retail. To be sure, I do not believe that Big Data is a cure-all for what ails medicine, nor do I believe that McKinsey advocated that viewpoint in its study.  However, it is one component on the road to improving a chaotic system.

The eye opening report by the Institute of Medicine on Medical Errors (To Err is Human: Building a Safer Health System, November 1999) http://www.iom.edu/~/media/Files/Report%20Files/1999/To-Err-is-Human/To%20Err%20is%20Human%201999%20%20report%20brief.pdf

estimated that as many as 98000 people die in hospitals each year as a result of preventable medical errors.  The costs in addition to loss of life are estimated to range from $17 billion to $29 billion each year.  One of the major conclusions from the Institute’s study was that faulty systems, processes, or conditions lead people to make mistakes or fail to prevent them.  The report clearly stated a need to address medicine from a systems perspective to decrease the alarming rate of medical errors.  A number of prominent physicians and healthcare organizations have advocated other approaches to improve the provision of healthcare – including changes to the basic organization of how primary care is dispensed (ACO, PCMH),  http://www.pcpcc.net/guide/better_to_best how hospitals fit into the provision of care, and how information systems can be utilized to improve both safety/quality and productivity /effectiveness.

Due to the impact of healthcare costs on our society and the slow rate of change in the industry, government policy makers have also been forced to take a more active role.  Thomas Lee and James Mongan of Partners HealthCare System in their book Chaos and Organization in Health Care http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11875 strongly advocate for this role and the importance of improving the healthcare information infrastructure.    In 2009 Congress passed the HITECH Act http://www.pwwemslaw.com/content.aspx?id=540 providing nearly $30 billion to address barrier to health IT adoption, $14.6 billion of which went to encourage adoption of electronic medical records.  Other funds were focused on developing Health Information Exchanges (HIE)  http://searchhealthit.techtarget.com/definition/Health-information-exchange-HIE toward the goal of making patient information available across all care delivery settings.  Bitton, Flier, and Jha (Health Information Technology in the Era of Care Delivery, To What End? JAMA,June 27,2012 – Vol 307,No. 24, P2593)

http://jama.jamanetwork.com/article.aspx?articleid=1199162 argue that the debate over whether health information and technology will save money and improve care is anachronistic.  They state flatly that information technology will be used in health care.  “Health IT is inevitable.  The question now is how best to do it”.

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