Posts Tagged big data

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

profiles-c-suite

 

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.

he.com-big data technology graphic

Click this image to see a larger version.

[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

Click this image to see a larger version.

 

[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. 

OUTCOMES-AS-KEY

Click the image to request access to the webinar.

BIG-DATA

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. 

, , , ,

No Comments

Industry Groups Challenge Readiness of ICER’s Value Framework

Hdecisionsealthcare is all about making decisions.

Who to treat? When to treat? How to treat? How long to treat? How much to spend to treat? The list goes on and on.

Fortunately, great strides have been made in healthcare decision analysis, but not without controversy.

On the plus side, decision analysis can provide objectivity and potentially improve the quality of the final choice.  On the downside, decision analysis is fraught with challenges, including methodological, process, outcomes, perspective, data sources, and how to present the data insights, to name a few.  ICER Logo

In this blog, we’ll discuss a few of the challenges of systematic decision approaches in the context of a topic that’s been in the news of late, pharmaceutical Value Assessment Frameworks. Specifically, we will focus on a relative newcomer on the block: the Institute for Clinical and Economic Review‘s (ICER) Value Assessment Framework and explore some areas of needed improvement. While doing so, we will also compare two ways of presenting these insights: Long-form content and a visual format called PepperSlice.

What’s the beef with ICER’s Value Assessment Framework?

For more than a year, ICER has used its value assessment framework to guide its evidence reports on new drugs and other interventions. Pharma industry and patient advocacy networks have reacted strongly (and by strongly, we mean mostly negatively) to the ICER framework. One of the most common responses to ICER is that their (and other) frameworks squash pharmaceutical innovation and prevent some patients from receiving needed care.

In addition, major industry groups have challenged the readiness of the ICER framework, questioning its appropriateness in several areas. To their credit, ICER has requested stakeholder comments in preparation for a 2017 revision of the framework.

The NPC (National Pharmaceutical Council), PhRMA (biopharma trade group), and AMCP (the Academy of Managed Care Pharmacy) have significant concerns about ICER’s methodology. In particular, they take issue with the following areas:

Real-world evidence.

NPC says “ICER [should] have a clear process for managing the evolution of evidence, especially in the case of emerging therapies…. [T]hese reviews will continue to be relied upon by other stakeholders even after additional data (e.g., real-world evidence) emerge.”

And AMCP suggests real-world evidence and patient-reported outcomes should be “re-examined to further enhance the utility and relevance of the value assessment framework.”

Budget impact.

ICER should not confuse budget impact with value. “Budget impact assessments — which are measures of resource use, not of value — should remain completely separate from value assessments,” says NPC.

And this from PhRMA: ICER should suspend “the use of budget impact estimates until more sound methods are developed and validated.”

Economic model transparency.

The information provided is “not sufficient to enable reviewers to reproduce the results and provide meaningful, real-time input. Full transparency — down to the equation level — is needed to enable reproducible results and support fully informed stakeholder collaboration.” NPC asks that ICER release the model to all stakeholders.

QALY.

PhRMA asks for “Adjustment of the cost-effectiveness component of the framework to reflect the inherent and widely recognized limitations in traditional quality adjusted life years-based cost-effectiveness analysis (CEA), including capturing a wider range of benefits in CEA and presenting a range of care value estimates based on sound assumptions and varied approaches.”

Other responses to the ICER Value Assessment Framework include:

NPC: To guide future development, NPC published a set of Guiding Practices for Patient-Centered Value Assessment. Dan Leonard recently recapped NPC’s viewpoint on how frameworks should be developed.

PhRMA: Four specific recommendations are offered, intended to move ICER in a more “methodologically rigorous, patient-centered direction”. They request significantly more transparency into how it works with stakeholders. And they offer specific advice on How to Get Value Assessment Frameworks Right.

AMCP: The managed care and specialty pharmacists’ group expresses concern that the current framework “lacks a process for incorporating real-world evidence (RWE) and patient reported outcomes (PROs) into the catalog of evidence that informs the underlying economic models. [Doing so would] better represent the patient experience.”

ICER Value Framework Version 2.0.

In October, ICER convened a broad group of stakeholders to inform its planned update. Invitees included people from pharma, academia, payers, patient advocates, and trade groups. A revised framework will be posted for additional comments next month; ICER’s 2.0 version will likely become final in early 2017.

What’s next for value frameworks?

ICER is only one of several frameworks gaining traction in healthcare. Stakeholders are weighing in. To help establish best practices going forward, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) has launched an initiative on US value assessment frameworks; more than 250 people attended the kickoff stakeholder conference. A task force is preparing a policy white paper on the appropriate definition and use of value assessment frameworks, expected Q1 2017. ISPOR is the sole funder of the effort.

Moving From Data Output to Data Insights.

Much of the data derived from decision analysis is challenging to present to decision-makers.  As an industry, we all need to get better at communicating complex evidence – simply. One tool, called PepperSlice, may help.  PepperSlice is designed to deliver insights and Evidence Graphs by structuring analytics and evidence in a simple way.

Capturing insights with PepperSlice.

Let’s look at how we can visually present the concerns with ICER’s Value Assessment Framework using PepperSlice insights and data visualization.

The images below depict the issues around RWE and the ICER framework, in easy-to-consume graphics that are transparent, show the relationship between the data inputs, and the data source behind the images.

The first “board” (Image 1 ) explains the issue, graphically on this topic: “ICER’s value assessment framework is not ready for widespread adoption by stakeholders”.

Image 1.

Below each board are “slices” (Image 2), designed as cards, and each of these slices offers an insight. In the PepperSlice platform, these slices can be pinned to a single board or multiple boards, can be saved, and are searchable and reusable.

Image 2.

A database provides the underlying structure to the insight slices (Image 3 below), and provides the relationship (reduces, lacks, improves, etc.) between the two inputs, the result, the analyst name (i.e., the “slicer”), and the evidence collection method.

Image 3.

Through this method of PepperSlicing, one can present insights with a click of a button, build and manage the inventory of evidence-based insights, use them for one or multiple analyses, and see how an insight was derived (What are the supporting data? How was the evidence analyzed? Who did the work?).   In just a few images, one can see inputs, outputs, and relationships.

Summary

In this blog, we’ve given you an overview of the ICER Value Assessment Framework and the major concerns of the decision framework according to key stakeholders.  We’ve also showed you, by example, how insights can be captured about this issue – or any decision/choice – using a new methodology called PepperSlice.

What do you think are the major concerns with Value Assessment Frameworks in general and the ICER framework, in particular?  Let us know what you think about the two presentations of the data: long-form vs short-form visual presentation using PepperSlice?

We’d like to hear from you.  Just leave us a comment below, and sign up to be informed of all blog posts from tHEORetically Speaking.

If you’d like to be a guest blogger, give us a shout at by emailing patti@healtheconomics.com.

 

 

 

By

tracy Tracy Allison Altman, PhD – Founder of Ugly Research   

 

 pattiPatti Peeples, PhD – CEO of HealthEconomics.Com

, , , , , , , , ,

No Comments