Archive for category #RWE

Closing Gaps in Real-World Evidence through Data Linkage

Interview with Dr. Kevin Haynes, Principal Scientist, HealthCore-NERI.

Dr. Patti Peeples, CEO of HealthEconomics.Com, sat down with Dr. Kevin Haynes, Principal Scientist at HealthCore-NERI, to discuss closing gaps in real-world evidence through data linkage. The mission of HealthCore-NERI is to provide clarity that empowers decision makers to act with precision to improve quality, safety, and affordability in health care. HealthCore-NERI works with life science companies, payers and providers, and government and academic organizations to provide real-world evidence in support of a wide variety of health care decisions.

Dr. Peeples: Why is fragmentation in health care so important to health care researchers, and specifically real-world evidence?

Dr. Haynes: The fact that patients can seek care across health care systems, may move across geographies over time, and may change health plans fragments the patient journey within the health system. This fragmentation within the US health care system creates fragmentation of health care data and this data fragmentation inhibits our ability to generate high-quality evidence. In my opinion, the biggest gap to close is a gap in data. When we close the data gap, then we can begin to close the gaps in evidence. The same gaps in data that prevent us from generating high-quality evidence also create gaps in care. These gaps are closing as Health Information Exchanges, all-payer claims databases, and data integration with prescription dispensing records feed back into electronic medical record systems. As these data gaps close, our ability to generate high-quality evidence will improve. One area of data fragmentation is in the patient journey across health care systems creating a long-term fragmentation. As people traverse a lifetime of follow up periods, we traverse periods of our lives moving from childhood to college and early careers. This creates a fluid space of data moving across health plans as jobs, spouse, and life circumstances change our insurance coverage and access to health care systems. Today we need to be developing the infrastructure to be able to address these research issues moving forward. This is a long-term game as fragmentation of health care delivery currently has an impact on our ability to conduct observational comparative effectiveness research over a lifetime. Another area of fragmentation is cross-sectional data fragmentation which occurs as we seek care across health systems. For example in cancer we invariably fragment care as we instruct patients to seek a second opinion. As such tests and work up may be contained within one health system’s electronic records and treatment and longitudinal follow-up may be contained in a second health system’s electronic record.

Dr. Peeples: What are some of the biggest issues among stakeholders (providers, payers, patients, researchers, policy makers)?

Dr. Haynes: The biggest issue for stakeholders is data privacy and the governance required to manage the use of data for various purposes. There is clear governance with regards to HIPPA and other privacy considerations that govern the research aspects of data. Often the use of observational data at the institutional level notes loss of confidentiality as the only risk to the patient, and ensures that all data will be stored on secure servers with limited access. However, we are in an era now where we need to link to other data sources to close gaps in data fragmentation. This creates a need to utilize protected health information or personal identifiable information which may increase the risk to study participants in observational research. Therefore, we need to implement the technology to improve data privacy.

Dr. Peeples: With the fragmentation of health care across health systems, what do you see as the opportunities to overcome these data fragmentation challenges to enhance real-world evidence?

Dr. Haynes: There is tremendous opportunity as there is a lot going on in the data linkage space as people develop relationships with patients. For example, researchers involved in PCORnet’s Patient or People Powered Research Networks (PPRNs), the NIH’s All of Us, and other commercial ventures – are developing relationships with patients. They are able to get and seek, not only the consent, but also the authorization to link data across resources to develop the evidence that is needed. When researchers have access to patients, it is important to obtain sufficient patient authorization to conduct these linkage activities. Other opportunities exist in the space of protecting patient privacy in observational research, such as creating privacy protection record linkage.

Dr. Peeples: How do we do the data linkage? Are there specific use cases that have been successful?   

Dr. Haynes: One must either obtain patient consent through a relationship with the patient or utilize privacy-preserving record linkage strategies. For example, patients participating in PCORnet’s ADAPTABLE study, which seeks to identify the most appropriate dose of aspirin for secondary prevention of cardiovascular morbidity – have consented to participate. The study is therefore able to outreach and obtain authorization from participants to allow their health plan to share limited longitudinal information to help address one of the data gaps with regards to this study. Among PCORnet’s demonstration studies is a large obesity observational study looking at the effect of pediatric antibiotic exposure on the microbiome and the effect of weight gain at 5 and 10 years. This study is involving hundreds of thousands of patients. Considering the impossibility of obtaining the consent of or relationship with all patients, researchers are employing privacy-preserving record linkage to facilitate linkage of health plan pharmacy claims with clinical data.

Dr. Peeples: Patient privacy is of vital importance in the conduct of data linkage. How do you conduct the research and ensure patients their privacy is not being breached? What changes have occurred over time? What do you do that is a higher bar than is necessary, if anything?

Dr. Haynes: We as researchers need to develop the trust with our patients, especially patients who are recruited and enrolled in clinical trials where we have a relationship with the patient, to seek the necessary authorizations to do these linkages. We must also ensure that these linkages are used only for that intended purpose. As such, there is a need for governance around data use. When we have a relationship with the patient, we have an obligation to educate and inform patients in things like the ADAPTABLE study, the All of Us study, potential patient registries, and others, to inform patients of the importance of the linkage and that the linkage activities will be governed in such a way as to protect patient privacy. We also have a societal obligation to ensure that any linkage activities utilizing privacy-preserving record linkage modalities protect patients and their privacy.

Dr. Peeples: Expanding on the concept of data linkage, are there disease or therapeutic areas that are particularly challenging? Or areas where this data linkage has shown success?  

Dr. Haynes: There are tremendous opportunities and areas that are particularly challenging in this space of linkage. These challenges focus on both linking longitudinally and on linking over defined time periods to get deep clinical data. One example is in the opioids space where many states have prescription drug monitoring programs (PDMP). These programs are designed to capture all of the opioid prescriptions such that providers can access this resource and ensure that they have a complete picture of exposure to opioids across health systems. Pharmacists have an opportunity to assess this system to evaluate opioid utilizations beyond their pharmacy. These systems are designed to close a gap in care. However, one challenge is that these systems are not able to be utilized by health plans to potentially close gaps in evidence. The high-quality exposure information from state PDMP and the high quality outcome data from health plans would provide an opportunity to address evidence in the opioid epidemic.

Dr. Peeples: What are the ultimate rewards of linked data resources for RWE?  

Dr. Haynes: The most important thing from an epidemiologic standpoint to linked data is to reduce what we call information bias. There are several forms of information bias, including misclassification. Therefore, capturing outcomes or exposures of interest and knowing that you have complete capture is vital to the conduct of real-world data analysis.


Dr. Kevin Haynes, PharmD, MSCE, is a Principal Scientist at HealthCore-NERI. He is the Principal Investigator on two Patient Centered Outcomes Research Institute (PCORI) awards and the site Principal Investigator for HealthCore within the FDA Sentinel Initiative as well as a Data Core Co-Lead on Sentinel. At HealthCore-NERI, Dr. Haynes is currently responsible for developing responses to proposals and providing clinical pharmacoepidemiology expertise to various projects. Dr. Haynes has more than 14 years of experience in clinical pharmacy, clinical research, epidemiology, pharmacoepidemiology, surveillance, medical informatics, and project management. In addition, he has extensive experience collaborating with the Food and Drug Administration as well as multiple investigators on pharmacoepidemiology projects.

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

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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

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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. 

OUTCOMES-AS-KEY

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BIG-DATA

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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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