Harvard Medical School

Faculty researchers bring drug safety and effectiveness studies to enhanced levels with IBM PureData System for Analytics

Published on 14-Nov-2011

Validated on 18 Nov 2013

"We as a society cannot afford to be told, ‘I can’t run your algorithms… because our technology can’t support it.’ People need and want to know the information that our studies generate. We can’t afford not to carry out these studies. It’s too important to get these answers for drug developers, regulators, physicians, policy makers, payers and most importantly, for our patients." - Dr. Schneeweiss, Director for Drug Evaluation and Outcomes Research, Brigham and Women’s Hospital

Customer:
Harvard Medical School

Industry:
Education

Deployment country:
United States

Solution:
PureData System for Analytics (powered by Netezza technology), PureSystems, Big Data, Data Warehouse, Smarter Computing

Overview

The Harvard Medical School® Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital in Boston, Massachusetts, is a globally recognized leader in drug safety and effectiveness research. Created in 1998, the division is led by Dr. Jerry Avorn, Professor of Medicine at Harvard.

Business need:
The lab at Brigham and Women’s Hospital was looking to find a platform for computational pharmacoepidemiologic analytics that would address rapidly emerging trends.

Solution:
IBM PureData System for Analytics, powered by Netezza technology

Benefits:
By utilizing PureData System for Analytics in their pharmacoepidemiology research studies, the Harvard Medical School division will be able to: · Increase the speed of computationally-intense analysis of claims data · Accelerate testing of new, more sophisticated algorithms · Facilitate automation of continuous drug safety and effectiveness monitoring

Case Study

Introduction

The Harvard Medical School® Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital in Boston, Massachusetts, is a globally recognized leader in drug safety and effectiveness research. Created in 1998, the division is led by Dr. Jerry Avorn, Professor of Medicine at Harvard. The division is part of Brigham and Women’s Hospital’s Department of Medicine, and performs advanced analytics on patient health claims data. Through computationally intensive ‘big data’ analytics, this research brings to light insights into how drugs compare to each other in terms of safety and effectiveness.

The charter of the Division of Pharmacoepidemiology and Pharmacoeconomics is to:

· Study the use of medications in very large populations in order to determine which drugs are most likely to effectively treat patients’ medical conditions, and which are more likely to cause adverse events.
· Develop advanced, data-intensive methods in epidemiology, biostatistics and informatics to detect and evaluate important early signals of potential drug safety problems, as well as to compare the therapeutic results of alternative treatment strategies.
· Define patterns of physicians’ prescription and patients’ use and compliance of medications.
· Analyze the influence of health system factors (policies, coverage and reimbursement differences) on the quality of medication use.
· Design, implement, and test innovative programs to improve the appropriateness of medications prescribed by physicians and used by patients.

The challenge
The nature of Harvard’s pharmacoepidemiology research has been highly complex and computationally intensive since its inception. Today, however, the sheer volume of information available — and the increasing detail in which the information is recorded — has dramatically increased the analytic and computational challenges.

The expansion of data — both in its depth and breadth — has also presented opportunities for more complex and robust drug safety and effectiveness studies. Dr. Avorn and his colleague, Dr. Sebastian Schneeweiss, Director for Drug Evaluation and Outcomes Research, in their 2009 editorial “Managing Drug-Risk Information-What to Do with All Those New Numbers” (New England Journal of Medicine, July 27, 2009), reveal some of the diverse challenges facing epidemiological research in the current era. The challenges include applying new, rigorous analytic techniques to enhance the levels of confidence in observed drug safety results, as well as challenges in organizational politics, patient privacy and administration. This diversity is precisely why the research group includes experts in fields such as specialty medicine, health policy, sociology, law and more.

With access to the markedly larger amounts of patient data, the more traditional ‘reactive’ approaches to adverse drug events can no longer suffice. Examining data after the fact to confirm a suspected drug safety problem will always be a crucial element of safety research. Powerful computation simultaneously opens up avenues for identifying issues after a drug has entered the market, but before it is in widespread use. The Harvard team envisioned the potential of proactive research techniques using very large databases. This way, they could identify “signals” of risk associated with the particular drugs themselves or with combinations of drugs.

The lab at Brigham and Women’s Hospital was looking to find a platform for computational pharmacoepidemiologic analytics that would address these trends. Specifically, Dr. Jeremy Rassen, Assistant Professor of Medicine at Harvard, identified certain needs as critical:

Rapid execution of computationally intense analysis of structured medical claims data.
Capabilities for parallelized in-database analytics.
Accelerated development and testing of new, more
complex algorithms.

Facilitated automation of continuous drug safety event monitoring.
Capabilities for complex analytic SQL queries.
Simplified database administration.
Ability to work with semi-structured electronic healthcare record databases.
Security features for the continued protection of patient privacy.

The solution
IBM® partnered with the lab and installed IBM PureData System for Analytics, powered by Netezza® technology, which is built to make advanced analytics on data simpler, faster and more accessible.

The following are features of IBM PureData System for Analytics:

Designed specifically for running complex analytics on very large data volumes, at orders of magnitude faster than competing solutions.
Simple to maintain – The appliance architecturally integrates database, server and storage into a single, easy to manage system that requires little on-going maintenance.
Simple to deploy – As a purpose-built product, the PureData System for Analytics comes pre-tuned with the optimal architecture for fast, advanced analytics, making it ready to go with built-in expertise.
Simple to try – The test drive makes it easy to experience the high-performance data warehouse appliances on-site, with real data.
Unmatched in value – Offers faster time-to-value and lower total cost of ownership than competing products in the industry.

PureData System for Analytics delivers the proven performance, value and simplicity organizations need to dive deep into their growing data. Customers are able to easily and cost effectively scale their business intelligence and analytical infrastructure, to leverage deeper insights from growing data volumes, throughout their organization.

The results
According to Dr. Rassen: “The IBM PureData System for Analytics put us ahead of the curve. With almost no system administration or index-building, we quickly saw orders of magnitude performance improvement. It also allowed us to conduct basic analytic processing at two to three times previous speeds, with no change in code. Furthermore, it enabled one of our novel algorithms, the high-dimensional propensity scoring, to run 20-30 times faster than in our previous relational database environment. The appliance also gave us the ability to explore previously inconceivable new research avenues, with the availability of highly parallel data access and analysis.”

As a result, Dr. Rassen’s team has been able to:

Load data into the database more quickly.
Run all previously developed SQL and SAS code with minimal changes.
Know that their efforts to migrate certain SAS analytics and processes to the open-source R language would be easy.
Dramatically increase query response.

The research team noted that one of the very significant benefits of the technology was its simplicity of use. Dr. Rassen remarked that the system was situated in the data center, installed, and up and running in less than 48 hours. In addition, the team required no outside administration time, and system maintenance was handled by the researchers and analysts themselves. Other systems typically require weeks of set-up and tuning, not to mention frequent or constant administration.

The impact of the analytics
The stakes are high. In the US alone, half of the population is taking one or more prescription drugs. Nearly four billion prescriptions are being filled annually. US prescription drug sales are well over $300 billion per year, and according to IMS Health, global sales are expected to reach $1.1 trillion by 2014. Additionally, the Kaiser Foundation is reporting that prescription drugs account for well over 10 percent of the total US health expenditure (over $2.5 trillion). These figures highlight the positive impact that Harvard’s advanced research can have on more rapid identification of unsafe and less effective drugs. The absence of more rapid risk awareness can be devastating to patients and continuously crippling to healthcare costs. Without steadily advancing complex analytics and tremendous computing power, the exceptional potential of Harvard’s vision in pharmacoepidemiology research could face delays that global healthcare cannot afford.

The Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital has developed research relationships with several Fortune 500 companies to perform intervention studies on their medication benefit programs offered to employees. The lab is also a critical drug safety and effectiveness research training destination for many doctoral and post-doctoral students from all over the world. Division faculty have produced well over 300 notable papers on drug safety, drug costs and health policy. The quality and amount of accessible patient data for the group’s research is critical. The lab calls a major portion of what they do “High Dimensional” Pharmacoepidemiology. It seeks to take advantage of all the high-dimensional data space available in longitudinal insurance claims data, registries and electronic healthcare record databases. This will improve the validity of the research in the frequent cases where running a randomized trial is not possible or feasible.

Recognizing that the safety and effectiveness of new and existing prescription drugs plays an ever-increasing role in the pursuit of improved healthcare worldwide, the research team designs, develops and executes studies to provide more reliable, actionable data that can:

Bring greater clarity to issues of benefits versus risks.
Evaluate the impact of prescription drug expenditures.
Understand how medications are prescribed by doctors and used by their patients.
Create action plans to help patients improve adherence
to medications.

Develop methods that optimize the use of prescribed drugs.
Identify drug safety issues for specific subsets of the population.
Assist public sector governing bodies in their decision
making processes.

Provide physicians with new insights when prescribing medications.
Create a system of active drug safety surveillance, enabling proactive risk intervention.
Help pharmaceutical companies bring safe, effective new drugs to market more smoothly.

The Harvard Medical School division has already achieved major accomplishments, one of which is developing new methodologies for computer-intensive analysis of very large datasets to identify drug risks. Another is identifying important “confounders” that can distort the results of non-randomized studies – these confounders can skew results and make incorrect findings appear robust. A third methodology seeks to assess the cost effectiveness of particular medications and drug-use strategies. As meaningful as these developments are, Dr. Schneeweiss envisions continuous advances in the analysis of ever-growing patient databases with even richer data. These advances will help automate and steadily improve society’s ability to monitor drug safety and effectiveness.The dramatic expansion of available patient data, and the ever-increased complexity of questions that need to be asked in more enriched studies, mean traditional methods have simply become too slow. With the research group’s new algorithms and goals of drug safety automation, the computational demands on processing technology have increased exponentially but are now being met with PureData System for Analytics.

Dr. Schneeweiss is fervent about the prospects of enhanced automation and more complex, large database studies that will be pursued with the PureData System for Analytics. His vision of a system that is continuously “learning” by incorporating data from ongoing drug safety and effectiveness research results is a “game changer” for the future of how prescription drugs are managed and developed.

Dr. Schneeweiss fully expects that the ongoing system will be able to generate reliable, early warning signals of adverse drug effects that have previously taken months to years to discover.

The benefits
By utilizing IBM PureData System for Analytics in their pharmacoepidemiology research studies, the Harvard Medical School division will be able to:

Increase the speed of computationally-intense analysis of claims data.
Accelerate testing of new, more sophisticated algorithms.
Facilitate automation of continuous drug safety and
effectiveness monitoring.

Enable research studies on larger databases.
Operate with little to no outside database administration.
Make it possible to work on multiple databases at the same time.
Add the ability to store and analyze electronic health record databases.
Achieve higher levels of research result-confidence.
Continue the division’s protection of patient privacy.
Facilitate the development of an automated ‘learning’
healthcare system.

Identify drug risk ‘signals’ far more quickly than previous
systems could.

Provide drug developers and regulators with superior large-scale, post-market data.
Facilitate in-depth, active surveillance for post-market drug safety.
Provide extensive data to assist with public sector health decisions.
Offer physicians more detailed data for their prescription
decision process.

Create tools to improve adherence to beneficial medications.
Present patients with more reliable information about their
prescribed drugs.

The team
The Harvard team’s drug safety and effectiveness research mission takes a uniquely inclusive approach by providing a deep interdisciplinary environment of experts for its studies. The division brings together clinicians from a wide-range of backgrounds including cardiology, geriatrics, primary care, rheumatology and nephrology; with experts in the quantitative sciences of biostatistics, epidemiology and complex analytics. Professionals in other pertinent disciplines such as health policy, law, decision analysis and the social sciences are also included. In doing so, they set their research focus sharply on the ultimate care of the patient. A small sample of publications from the Harvard faculty-researchers in the Division of Pharmacoepidemiology:


Risk of death and hospital admission for major medical events after initiation of psychotropic medications in older adults admitted to nursing homes.
Huybrechts K.F., Rothman K.J., Silliman R.A., Brookhart M.A., Schneeweiss S.
CMAJ. 2011 Apr 19; 183(7): E411-9

Anticonvulsant medications and the risk of suicide, attempted suicide, or violent death.
Patorno E., Bohn R.L., Wahl P.M., Avorn J., Patrick A.R., Liu J., Schneeweiss S.
JAMA 2010; 303: 1401-9

High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.
Schneeweiss S., Rassen J.R., Glynn R.J., Avorn J., Mogun H.,
Brookhart M.A.
Epidemiology 2009; 20: 512–22

Privacy-maintaining propensity score-based pooling of multiple databases applied to a study of biologic agents.
Rassen J.A., Solomon D.H., Curtis J., Harrington L., Schneeweiss S.
Med Care 2010; 48: S83-9

Variation in the risk of suicide attempts and completed suicides by antidepressant agent in adults: A propensity score-adjusted analysis of 9 years of data.
Schneeweiss S., Patrick A.R., Solomon D.H., Metha J., Dormuth C., Miller M., Lee J., Wang P.S.
Arch Gen Psychiatry 2010; 67: 497-506

Post marketing studies of drug safety.
Schneeweiss S., Avorn J.
BMJ. 2011 Feb 8; 342: d342. doi: 10.1136/bmj.d342

The epidemiology of prescriptions abandoned at the pharmacy.
Shrank W.H., Choudhry N.K., Fischer M.A., Avorn J., Powell M., Schneeweiss S., Liberman J.N., Dollear T., Brennan T.A., Brookhart M.A.
Ann Intern Med. 2010 Nov 16; 153(10): 633-40

Cardiovascular outcomes and mortality in patients using clopidogrel with proton pump inhibitors after percutaneous coronary intervention.
Rassen J.A., Choudhry N., Avorn J., Schneeweiss S.
Circulation 2009; 120: 2322-9

About IBM PureData System for Analytics
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For more information
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ibm.com/software/data/puredata/analytics.

Client quotes are based on the Netezza 1000 product. The Netezza 1000 has been upgraded and replaced by IBM PureData System for Analytics, powered by Netezza technology.

Products and services used

IBM products and services that were used in this case study.

Software:
PureData System for Analytics (powered by Netezza technology)

Legal Information

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