Archive for category health outcome models

Big Ideas for Big Data Analytics in Real-World Evidence: Insights from Dr. Rich Gliklich, CEO of OM1



Rich Gliklich, MD, CEO of OM1Dr. Patti Peeples, CEO of HealthEconomics.Com, sat down with Dr. Rich Gliklich, CEO of OM1, to discuss how big data analytics are changing the face of real world evidence (RWE). The mission of this Cambridge, Massachusetts digital health company is forward-thinking, like its leader: to solve the problem of determining and understanding the true results of healthcare and offer a more complete view of patient outcomes. By bringing together multiple data sources at the individual patient level to construct patient journeys, OM1 rapidly measures and forecasts patient outcomes, combining expertise in clinical research and informatics with big data technology and advanced data science to reinvent how real world evidence is generated and used.

HealthEcoOM1logonomics.Com asked Dr. Gliklich to shares his views on major transformational changes in evidence development and how these will drive a more personalized delivery of healthcare in the future.

[HE.Com] With the influx of big data, how do you think the pharmaceutical industry’s approach to real-world evidence should evolve? Are patient registries still relevant?

[RG]  Healthcare data is growing at an astounding rate. This creates both challenges and opportunities.  Pharma is being asked to demonstrate value to more and more stakeholders. We define Value as:  Value = Outcomes/Cost, and thus, Pharma will increasingly seek deeper clinical information and true outcomes to help demonstrate value and justify their prices.

To get to this critical evidence, they will need to turn to highly specialized capabilities and data for particular conditions, and they will need these data regularly updated, because the dynamics in the market change. For most organizations, it will be more cost-effective and timely to subscribe to sources that track and analyze these changes, than to build out the infrastructrure and acquire the data independently, such as from clinical trials and prospective patient registries.

The goals of patient registries are still relevant—from understanding the natural history of a disease to meeting a post market safety commitment.  But, big data plus advanced technologies creates alternative opportunities to meet those goals across many therapeutic categories and conditions, and to do so with very large patient numbers (orders of magnitude more patients vs registries), and at a fraction of the cost. data technology graphic

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[HE.Com] Are there disease or therapeutic areas where using big data, as you’ve described, may be more effective? What types/level of disease and patient level data could we get to that is different than what we have access to today with traditional registries and trials?   

[RG] Conditions where comprehensive patient journeys can be readily captured through linking different data sources will be most amenable to using big data.  There are a number of conditions where this is the case, from airway to cardiovascular and immunologic diseases to name a few. And, the number of such conditions with comprehensive patient journeys using disparate data linking is growing every day. Beyond traditional clinical and laboratory data available through registries and trials, we are now able to collect more and more patient-generated data, cost information, socioeconomic data, mortality, and so on. In addition to a more thorough understanding of the patient experience, these approaches also have the advantage of collecting information on all patients. This removes some of the enrollment biases we see in both registries and trials. The graphic below demonstrates the large sample size that can be attained through linked data compared with the sample size from a typical prospective registry.

Linked Big Data in Healthcare

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[HE.Com] Recently STAT News reported that Actemra® (tocilizumab) was responsible for hundreds of deaths and that the risks for life threatening complications were as high or higher than competing products. Could you comment on this finding, and is there a better way to look at this situation, using a big data perspective?

The STAT report was based on the FDA Adverse Event Reporting System.  The problem with those systems is that they are based on voluntary reporting and there is no denominator to actually determine the incidence of the events. This creates potential bias in reporting, since clinicians who are aware of a potential problem with a drug are then more likely to report events associated with it. Using big data, we can have a true denominator where the number of exposed patients and the number of events are both known, and these are derived from a combination of data sources.

In response to the STAT report, OM1 rapidly analyzed 120,000 patients on DMARDS (disease-modifying antirheumatic drugs) over the past 12 months to assess these findings in a more systematic and controlled analysis using big data. We did not find the same difference in event rates when patients carefully adjusted for comorbidities. Since the results are not yet published, I cannot go into details, but it demonstrates the power of using big data to respond rapidly to safety and other signals that may come up from time to time.

[HE.Com] How will payer needs for RWE evolve over the next 5 years, and what are the opportunities for service providers (pharma, researchers, data analytics companies) to address these future needs? 

As payers move towards value-based care, they need a deeper understanding of clinical outcomes beyond what is available in their own claims data sets. While the goal is to understand the clinical outcome, meaning the patient and provider-relevant impact of the treatment on the patient, the actual results today are tabulated from what transactions were billed for by the doctor or hospital.  As a result, these outcomes have generally focused on easily measured billing items such as hospitalizations, adverse event rates or complications of procedures.

But data derived from these sources lacks clinical depth and nuance. Critical questions are unanswered when using billing data for outcomes assessment. How serious was the hospitalization?  Did the patient return to normal functioning and well-being?  Did the underlying disease process improve or worsen as result of the treatment?  None of this is actually measurable in claims data. These shortcomings are leading payers and pharma to seek more clinically-focused RWE, and each will do it for their own purposes.  Payers will also increasingly partner with pharma to support value-based assessments and outcomes based contracts.

[HE.Com] How will patients’ use of big data evolve? Are there case studies of patient use that will become the norm for other disease states in the future?

Both patients, and providers on behalf of patients, will use big data to personalize healthcare.  Big data analytics that are focused on outcomes will support better decision-making for patients. As a result, patients will have the opportunity to better understand their own specific risks and benefits with respect to different therapies, as well as their likelihood to achieve both positive and negative outcomes. Let me give you a generalized example. In the future, a patient with rheumatoid arthritis will be able to know that for patients like them, 80% improved with drug A vs. 40% with drug B and these results were derived from broad big data rather than limited clinical trials. In this scenario, a typical patient will have much more information to make informed choices about their own or their loved one’s healthcare.

[HE.Com] What regulatory hurdles or opportunities do you see as they relate to big data, RWE, or related evidence dissemination?

Regulatory interest in big data and RWE is at an all time high as recent regulations such as the 21st Century Cures Act are driving regulators to consider RWE as a potential replacement for other forms of evidence generation around and post-approval.  In Section 505F of the Cures Act, it states, “The Secretary shall establish a program to evaluate the potential use of real world evidence (1) to help support approval of a new indication under section 505(e); and, (2) to help support or satisfy post-approval study requirements.” The program is to be implemented “within 2 years” and ‘real world evidence’ is defined as data “from sources other than clinical trials”. At the same time, some of these new paths to use RWE for these purposes are untested and there will certainly be some growing pains. Examples of such hurdles are seen in the work we do with outcomes-based contracting where issues regarding measurement outside of the label and Medicaid best price continue to create barriers.

As researchers, we are doing our part to facilitate better informed decisions for individual impact, using our intelligent data cloud to transform population data into precision health. And we’re trying to get deeper into clinical and patient data, more quickly. But there is much work to be done.

[HE.Com] Thank you, Rich. For more information on the work of Dr. Gliklich and colleagues in the area of RWE, view the recent on-demand webinars by clicking on the links below. 


Click the image to request access to the webinar.


Click the image to request access to the webinar.

Rich Gliklich, MD, CEO of OM1About Rich Gliklich, MD

Rich is the CEO of OM1, a healthcare technology company focused on understanding the patient journey and the true results of healthcare. Since 2014, Rich has been an XIR at General Catalyst, where he supports businesses in the healthcare industry. Prior to joining General Catalyst, Rich was President of the Outcome division of Quintiles, the largest provider of biopharmaceutical development and commercial outsourcing services, and he also served on its Executive Committee through its 2013 IPO.   Prior to Quintiles, Rich was Founder, CEO and Chairman of Outcome, a health information and services company that served more than 2,500 healthcare organizations and a majority of the global top 30 life sciences companies.  Rich led Outcome from its start as a spin-off from his Harvard affiliated research laboratory in 1998 through its acquisition by Quintiles in October 2011.  In addition to his experience as an entrepreneur and executive, Dr. Gliklich is well known in the areas of registries, outcomes and analytics.  He is senior editor of the landmark publication by the U.S. Agency for Healthcare Research and Quality (AHRQ) handbook “Registries for Evaluating Patient Outcomes: A User’s Guide” and the PI for the Outcomes Measures Framework, which focuses on standardization of outcomes measurement.  Rich has led several key national and international efforts focused on evaluating the safety, effectiveness, value and quality of healthcare.  Rich also holds several patents for both health outcomes systems and medical devices. Rich is a graduate of Yale University and Harvard Medical School and a former Charles A. Dana Scholar at the University of Pennsylvania. Rich is also a surgeon and the Leffenfeld Professor at Harvard Medical School.


If you or someone you know would like to be featured in the HealthEconomics.Com CEO Profiles Series, contact Dr. Patti Peeples, CEO of HealthEconomics.Com. 

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Good, Bad, or Indifferent on Drug Pricing: A Value Benchmark Approach for Newly Launched Drugs in the United States.

swarali tadwalker

Swarali Tadwalker, MPH

Hardly a day goes by without a new article being published in the mainstream press or a medical journal discussing drug pricing.  The cost of drugs is a hot potato topic at medical meetings, pharma conference rooms, government hearings, and dinner tables.  And this topic seems to generate high emotions and dogmatic views, typically resulting in a call for more data, reasoned approaches, and better insight.

For most newly launched drugs, the relationship of a drug’s price to its true value is critically important, yet persistently elusive.  We are all asking:  “Are they worth the price?”  Value-based pricing is one avenue to answer this question, but in so doing, must take into consideration the perspectives of various stakeholders: manufacturers, payers, patients, providers, and society.

In a bold effort to facilitate value-based drug pricing decision-making from a nationwide perspective, the non-profit Institute for Clinical and Economic Review (ICER) announced on July 21, 2015 the Emerging Therapy Assessment and Pricing (ETAP) program with the aim to provide a reliable source for information on drug cost-effectiveness to different stakeholders in the United States (US). The press release on the launch of ETAP can be viewed here.  Bolstered by a $5.2 million grant from a Houston philanthropist, ICER plans to double its staff and increase the production of up to 20 benchmark reports on the value of newly launched drugs in the United States.  The two major goals for ETAP are:

  1. Authoritative assessment and price benchmark reports; and,
  2. Public engagement with all stakeholders to enhance legitimacy, dissemination and impact.


HealthEconomics.Com’s President, Dr. Patti Peeples, conducted an in-depth interview in July 2015 with two driving forces of ETAP, Dr. Steve Pearson, President and Dr. Dan Ollendorf, Chief Review Officer of ICER, to explore the goals behind ETAP and the implications for this new benchmark approach to drug pricing.
 Pearson Steven D. Pearson, MD, MSc, FRCP is an internist and health services researcher who has a long history of academic work on comparative effectiveness research (CER) and bioethics.
 ollendorf1 Daniel A. Ollendorf, PhD is a multi-decade researcher focused on comparative effectiveness systematic reviews and health technology assessment (HTA) process.

Patti Peeples, RPh, PhD [PP]: Let’s start by hearing an overview of ETAP program and a description of the catalyst for the ETAP initiative. What can you tell us about ETAP?

Steve Pearson [SP]: In broad strokes, the Emerging Therapy Assessment and Pricing or ETAP program is meant icer logoto expand what ICER has been doing for several years, but to expand in a way that would really enhance its impact for greater usefulness for different decision makers.

First, let’s look at what ICER does:  ICER has traditionally focused on full systematic review of the comparative clinical effectiveness of emerging therapies, and this is typically integrated with a cost-effectiveness analysis (CEA) as well as an analysis of potential budget impact of shifting care to new treatment. More recently, ICER has been developing what we call a value-based price benchmark in which we basically anchor a price range to a new therapy along with the degree to which it improves patients’ health.

In addition, ICER looks at short-term affordability to better understand both the long-term and short-term value.  The idea here is to give insight into whether a drug will be brought into the healthcare system at a price and payment mechanism or fashion that can prove affordable in the long term.

Our new program – ETAP – is an expansion of ICER.  We had an early test drive of ETAP with our work on the Hepatitis C drugs, the results of which received a lot of notice, as well as use by insurers and others. ETAP will allow us to ramp up this activity so that we plan to have an ICER report available at or near the time of FDA approval for all significant new specialty drugs. Our target is to produce between 15-20 reports in the first two years.

[PP]: Steve, when you say that you are looking at both short-term as well as the long-term value, can you elaborate on those time horizons and also describe from whose perspective you are looking at this?

[SP]: Sure. For the long-term perspective in our reports we include cost-effectiveness analyses.  These analyses come from simulation models based on data from published literature and publicly available databases (e.g., from the US government), or manufacturer- or payer-provided data.  We typically take either a life-time or a very long-term horizon in our cost-effectiveness analyses so that we really capture the important downstream effect, both clinical and economic, on the entire healthcare system.

But we are quite sensitive to the fact that sometimes there are drugs that are relatively cost-effective in the long-term, but if these drugs are being used to treat a very large patient population and if a large number of patients shift to the new treatment in the short-term, it can really create affordability problem for patients and insurers. So, we have a methodology that links together both the long-term cost-effectiveness and the short-term budget impact over a two-year horizon to calculate our value-based price benchmark.


ICER Rating System Overview

The ICER Integrated Evidence Rating™ combines a rating for comparative clinical effectiveness and a rating for comparative value. The clinical effectiveness rating arises from a joint judgment of the level of confidence provided by the body of evidence and the magnitude of the net health benefit — the overall balance between benefits and harms. This method for rating the clinical effectiveness is modeled on the “Evidence- Based Medicine matrix” developed by a multi-stakeholder group convened by America’s Health Insurance Plans. More here.


[PP]: Steve and Dan, what thresholds do you use to determine cost-effectiveness and how are these data presented to stakeholders for evaluation and feedback?

[SP]: Good question. So, in some of our reviews in the past we provide, where possible, cost per Quality-Adjusted Life Year (QALY) data. We recognize that the cost per QALY metric is not very intuitive or applicable for many decision makers here in the United States.  Therefore, we also present costs for different kinds of intermediate clinical outcomes that may be more readily understandable or actionable.

But as far as CEA thresholds, we don’t use one single bright line in the sand in deciding “cost-effective or not”.  Importantly, our organization, ICER, is not the ultimate decision-maker on cost-effectiveness; we simply provide the information to decision-makers and facilitate a forum for discussion.  In addition, we typically model our results around the World Health Organization’s general approach to cost-effectiveness linked to Gross Domestic Product (GDP) per capita. Moreover, we integrate our considerations with elements of value that might lie outside the traditional cost for clinical outcomes framework.

These data are presented in reports and at public meetings comprised of independent panels of doctors and public representatives. Our expanded ETAP initiative will include working with external academic health economists as part of a virtual network to help us with our work. We think that this is a great opportunity to enhance the impact of much of the great work being done in health economics both in America and elsewhere.

[DO]: It is important to convey that this approach is intended to be collaborative with all stakeholders, including the pharmaceutical industry.  All parties are recognizing that trends are changing.  There’s been a significant change – even as recently as the last couple of years –  and we believe that this is one effort to try to have a clear and transparent way to recognize and reward drug and medical innovation.

[PP]: There are several currently available drugs that have garnered substantial publicity based on their price and clinical benefits (or lack thereof).  Will ETAP focus only on new drugs just being launched or will they also look at drugs already on the market?

[SP]: Yes, we most likely will look at existing drugs already on the market. Our grant funding specifies a focus on developing the infrastructure to be able to capture the incoming new drugs. But if there are drugs for which the evidence around their effectiveness in an applicable patient population or their price seems to be raising a lot of questions, we very much intend to have the potential to look at those drugs as well.

[PP]: What is your mechanism for public comment and review of your comparative effectiveness reports?  Will your results be “vetted” by interested stakeholders? 

[SP]: We have a comprehensive public comment process (see an example here). We have had and will continue to have multiple avenues for manufacturers, payers, patient groups, or anyone who wishes to comment on the scope of our reviews. They’ll get a chance to comment on the first draft and then we have public meetings where they can make public comments as well. We anticipate and welcome input from anybody on our processes as we go through them.  Our website also has an option to sign up for regular updates on our programs and activities, including calls for public comment on recently-released draft reports.

ICER’s existing core public programs, the New England Comparative Effectiveness Public Advisory Council (CEPAC) and the California Technology Assessment Forum (CTAF), offer simulcasts of their events.


The Institute for Clinical and Economic Review is launching a membership program in 2015 to allow a small, influential group of leaders from insurers, pharmacy benefit management firms, health technology assessment groups, and life science companies to work together, over time, to shape the future of evidence and coverage policy in the U.S. Learn more about membership benefits here.


For more information on ICER and ETAP, visit

Click for a larger view.

Click for a larger view.


In an infographic by Institute for Clinical and Economic Review, effects of high drug pricing were vividly illustrated. While many new therapies did not offer any new benefits from existing medications, in 2014, as many as 576,000 people had over $50,000 yearly medication costs with about a 60% increase. Further, high prices have led many patients to skip their dose or cut their dose.




Written by Swarali Tadwalker, MPH

Guest Blogger and Research Intern at HealthEconomics.Com

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