Next steps toward clinical trial data transparency

Industry leaders shift the conversation from “why” to “how”

Imagine that you and a committee of your competitors have been tasked to collaboratively design and build an automobile. You don’t know how it will be used, how many passengers it must transport, or what kind of cargo it will carry. You don’t know what EPA and DOT regulations might exist when your creation rolls off the production line. Got it? Now hit the drawing board and come up with a design that works, wins the public’s trust and forestalls unwanted government involvement.

That’s essentially the assignment set before the life sciences industry in the quest for clinical trial data transparency.

From many of the conversations we’re having, we’re seeing some uncertainty as to what the industry needs to be doing. You need to make clinical trial data available in an easy and inclusive way, but there are many questions still open for debate and consensus.

Finding the questions to ask

What information should be shared, with whom and for what purposes? How much demand is anticipated? How should data access and use be managed? How do you ensure patient privacy without hindering the research value of the data? What should the information delivery and analytics platform look like?

The answers are not challenging from a technical perspective, but for clinical data transparency to succeed, everybody needs to agree on (or concede to) consistent policies and processes – and that’s a work in progress.

“It’s amazing that you have a group of people who all want to do the same thing, but you can’t get them to agree on how to do it,” said a participant at the Clinical Trial Data Transparency Forum we hosted at SAS headquarters in October 2013. Said another: “We go around and around on the same topics and end up with the same unease. Is it just a matter of moving forward with our own individual strategies – or moving forward with a group strategy?”

It’s important for the industry to work together. Sharing and collaboration are critical. That’s why SAS is hosting this multi-part forum. The second installment on February 11 brought together 70 leaders from 28 companies – plus more via webcast.

It’s encouraging to note that conversations are becoming more positive. Conference participants are not talking about whether we should do this; they’re talking about how we can get it right. The recent event focused on the latest news from regulatory fronts, considerations of policy and platform to hammer out, and lessons learned from four pioneers in a joint data-sharing initiative – GlaxoSmithKline, Roche, Sanofi and Boehringer Ingelheim.

Agreed-upon fundamental requirements

From the presentations, Q&A and breakout sessions of the day, we heard agreement on the fundamental requirements of a successful clinical trial data transparency initiative:

  • Public access to a library of available studies, coupled with a consistent, user-friendly and auditable online data request process.
  • An independent review panel process to review research proposals and grant or deny data access based on agreed-upon criteria that may be specific to each sponsor and study type.
  • A scalable, secure computing environment with advanced analytics built in, where researchers can create, run and save their own analyses using multiple tools.
  • Access to patient-level data, de-identified as appropriate for the research at-hand and protected from possible re-identification.
  • Protection of data from uncontrolled distribution or misuse – restricting data sharing across research projects or exports of raw data from the shared computing environment.
  • The ability for multiple researchers to securely collaborate on a project, and for a project to analyze data from multiple sponsors in the same environment.

Inaction is not an option, unless the industry will be content with a framework imposed by others. The better option is to press forward even if the path is unclear.

“Data sharing in a multi-sponsor environment is new, so the more we do it, the more we’ll learn and ease concerns,” said a conference participant. “We need to continue to sell “the why” as we figure out “the how.” Remember, Martin Luther King didn’t have a plan, he had a dream.”

Forum attendees represented: AbbVie, Amgen, Astellas, AstraZeneca, Bayer Pharma AG, Boehringer Ingelheim, Celgene, EIi Lilly & Company, F. Hoffmann-La Roche AG, Forest Laboratories, Gilead Sciences, GlaxoSmithKline, ideaPoint Inc., Janssen R&D, Johnson & Johnson, MedImmune, Merck & Company, Novartis, Novo Nordisk, Otsuka Pharmaceutical, Paarlberg & Associates, Pfizer, Roche, Sanofi, Shire Inc., SAS, Takeda Development Center Americas, UCB Biosciences and ViiV Healthcare. Comments in this article represent a compendium of general discussion at the forum and not the opinion of any particular organization.

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Building an analytics capability for engaging consumers - or members, patients or citizens

2014 . . . the year that millions upon millions of previously uninsured individuals flood the US market and create unprecedented demand for health insurance and health care services – right? Well, not exactly how things have played out so far.  But regardless of how the initial phase of ACA implementation has unfolded, it’s impossible to deny that our industry is beginning a transformation in its approach to understanding and serving individual consumers.

I see it every day when I work with health plan executives who say that “consumerism” or “consumer engagement” is at the very top of their enterprise priority list. I see it when I speak with leaders of analytics divisions and chief financial officers – executives who previously would not have given a thought to the “individual” or the “consumer,” but now have key divisional performance metrics linked to successful management of this constituency. And the same thoughts are coming from health care delivery organizations that want to create a better experience for their patients.

Knowing and engaging your customer

Whether this transformation is being driven by legislation, a changing employer-sponsored insurance environment, or simple consumer demand – we are in the midst of a tremendous shift in how we do business. In this new world, everything we do will somehow be linked to successfully engaging the individual consumer. From care management plans and risk mitigation strategies to expansion plans and government reimbursement, success of virtually every initiative will hinge on how well we know our customers. As the provider world changes, and new models such as ACOs take hold, this will hold true for provider organizations as well.

Consumer analytics makes it possible

Industry leaders have long understood that predictive analytics is the key to successfully engaging individuals – and that the same analytical approach can be leveraged across all touch-points with an individual: from marketing to care delivery to customer service. In 2013 we saw a dramatic refocusing of energy around advancing analytical competencies and using them to engage and manage individual consumers. We expect 2014 to usher in breakthroughs that mirror those we see in more advanced consumer-focused industries – like telecom and financial services.

Join me at HIMSS 2014 to learn how health care organizations are transforming themselves to be insight-driven. Florida Blue, the leading health insurer in the state of Florida, is building competitive advantage and providing extraordinary value to their customers by understanding and engaging them in new and meaningful ways. Our presentation on Building an Analytics Capability for Consumer Engagement will take place from 10-11 a.m. on Wednesday, February 26th in Room 330D of the Orange County Convention Center. If you’re not able to make our presentation, please see us in SAS Booth #935, and schedule a demo session to learn more about how you can use analytics to engage consumers in their own health care.

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Data meets design: How visual analytics is transforming health care - Part 2

In my last post, I introduced visual analytics for health care, and I shared how it can be a game-changer for ACOs. While there are many more use cases for visual analysis in health care, public health and personal health programs are two areas that are particularly fascinating.

Public health
Since the explosion of health care price transparency dialogs last year, huge volumes of data are being collected and made available by the US federal and state governments. For example,,, and state APCDs are all sources of big data the public sector is generating that can help guide policymaking. Moreover, these data will grow expeditiously as more states establish APCDs, and the federal and state-run insurance marketplaces generate new data on individuals.

However, the use of these datasets remains challenging, and in general, the databases are unexploited. Fortunately – with visual analytics – the possibilities to support public health and policy making with all this data are incredible. Being able to visualize how disease is spreading across communities, predict outbreaks, evaluate public health strategies and in general provide policymakers with timely, easy-to-view information is invaluable.

Check out this report I created using SAS Visual Analytics with the Medicare claims public use files. Also be sure to check out the interactive demo of APCD visualizations.


Report using SAS Visual Analytics with the Medicare claims public use files

Personal health
Mobile health has grown exponentially over the last several years and is expected to reach $20.7 billion by 2018, with nearly 96 million users. Thousands of applications exist, and many more are being developed, to collect personal health and lifestyle data. Through smartphones, wearables and computers, people are measuring and logging everything from calories burned to medications and supplements taken. As these initiatives advance, visual analytics is inevitable if the data collected is to provide any value to the patients. And it goes beyond personal; primary care physicians can use visual analytics to better assist with individual lifestyle change, and researchers can use it to explore cohorts of individuals. For example - being able to visualize patients who've had certain sequences of events, such as a stroke, a common blood pressure medication, and are of similar age and weight. Furthermore, with the inclusion of social media data, we’ll be able to analyze and visualize how medical trends are emerging and spreading. The possibilities are just starting to emerge.

Using visual analytics for service delivery, public health and personal health programs merely scratches the surface of what data visualization can offer in health care. As we continue to plunge into big data and transparency initiatives, it’s critical that we find ways to synthesize big data for more human-centered insights; that’s what will make big data a big success!

Schedule a demo session at SAS booth #935 at HIMSS in Orlando, or just stop by to learn more about visual analytics for health care.

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Data meets design: How visual analytics is transforming health care

In the midst of the big data, open data and transparency revolutions, ideas around data consumption and usability are those that should be (but often are not) discussed as part of these modernizations. In health care, information usability is perhaps most critical; the fact that we’re making decisions regarding human life is in itself a reason to better use our data. Not to mention the exorbitant health care spending in the US. However, usability of data has traditionally been less advanced in health care compared to other fields. The good news? Visual analytics is changing that.

Visual analytics is an integrated approach that combines visualization, human factors and data analysis (see Visual analytics: Scope and challenges). The goal is to allow users to draw conclusions from data by representing information through human-centered, intuitive visualizations. It’s much more than what meets the eye, though. Behind the scenes, it’s the work of advanced analytics that prepare and organize massive amounts of data so that users can make sense of hundreds of thousands of variables. It’s what makes visual interaction with big data possible so that users can pose known questions to the data, and also explore the data for the unknown.

So what does that mean for health care? It’s quite transformational and can reinvent the way stakeholders of the health care system, including consumers, payers, providers, researchers, and employers, make health care decisions.  More specifically, it can transform the way service delivery, public health and personal health is approached. In this first of two parts, I'll talk about service delivery...

Service delivery

New delivery models introduced by the US ACA, such as accountable care and patient-centered medical homes, rely heavily on data and analytics to fulfill the goal of improved, coordinated care. As a result, new users of data such as primary care physicians, specialists, hospital administrators and care managers, need to access and utilize large databases of claims, EHRs and more. Unfortunately, for the first crop of Medicare ACOs, a recent survey by NAACOS found that learning to access and process data has been a significant challenge. More specifically, ACOs have been challenged with finding suitable software, building new skill sets to analyze data, and translating data into useful information for care managers and providers.

Visual analytics addresses each of these challenges and completely changes the way ACOs can approach data. It can give these users the unique population health insights they need from data – such as performance measures, trends, costs and outcomes– across multiple sites of care. Moreover, it provides these insights in a consumable format that can improve decision-making at the patient-level. This is not only a huge advancement for provider efficiency, but a game changer in making the ACO model successful. For example, the state of New Hampshire uses SAS® Visual Analytics in conjunction with the state’s all-payer claims database (APCD) to allow ACOs to dynamically view data and critical measures on their populations’ health. Through a Web-based portal, ACOs in NH will soon be able to use visualization and interactive reports to better coordinate care, replacing monthly documents of over 800-pages! 

By making visual analytics an essential component of their IT infrastructure, ACOs will gain measurable success even faster. In fact, visual analytics spans well beyond ACOs, and provides benefits across the entire health care ecosystem. Next week I'll share how visual analytics can improve public health and personal health programs in Part 2 of this series.

Schedule a demo session at SAS booth #935 at HIMSS in Orlando, or just stop by to learn more about visual analytics for health care.


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Building trust for patients with a clinical trial data transparency system

On October 16th 2013, I participated in the European Forum for Good Clinical Practice’s (EFGCP) roundtable discussion about clinical trial data in the interests of patients and research in Brussels, Belgium.

Clinical trial data transparency was arguably the most publicized pharmaceutical industry topic in 2013, at least in Europe (see 2013 in reflection). The arguments to the debate are well known by now: data transparency should lead to more external scrutiny of trial results, including comparison between different competitor products. However, it could also jeopardize intellectual property of inventors, and patient privacy and interests, if done too fast or without proper controls.

What’s the chronology?

Since the European Medicines Agency (EMA) announced their commitment to complete transparency regarding patient-level clinical data and analysis results in late 2012, a constant stream of announcements and debates have erupted.  After conducting stakeholder workshops, EMA published a draft guidance in 2013 for which it received an unprecedented number of comments. In December 2013, EMA announced plans to publish a detailed implementation plan that would lead to transparency before a management board meeting in March of 2014. Key guiding principles in that plan are to be publication of clinical study reports, development of methods for de-identification of patient information and the development of a standard data format for submission.

During the roundtable discussion at the EFGCP, a key message was that “no one needs access to all the data.” One major concern of most participants was how to make the raw data available while also respecting their ethical and legal obligations to patients that participated in trials. Another major concern involved ensuring data access by responsible researchers that have knowledge of good analytical and statistical principles and the science behind the trial.

Best practices for successful data sharing

If data transparency is to move forward with the full support of the pharmaceutical industry, the EMA and the health community, it is of utmost importance that all voices are incorporated in the final transparency “solution.” Therefore the following key points result in a good data transparency process:

  • The need for a proposal evaluation committee that can grant or revoke access to researchers.
  • Simplicity of loading clinical data in a controlled environment that gives the researcher all degrees of freedom to execute statistical programs and explore data.
  • An advanced analytical environment, built into the transparency solution.
  • De-identification of patient data according to agreed specifications adapted for the trial at hand.
  • A technology environment that protects the interests of the trial data’s owner and avoids uncontrolled distribution of patient data.
  • The ability for a researcher to analyze data of multiple sponsors in the same environment.

The contours of a successful clinical trial data transparency initiative are becoming visible – step by step. The stepwise approach seems to be the only way to create a robust transparency environment that benefits all stakeholders.  As mentioned during the Brussels discussion – the key word is “trust.” Transparency without trust doesn’t lead very far. Some companies like GlaxoSmithKline and Sanofi have started sharing patient-level data.  SAS is excited to facilitate the discussion within the industry and provide a solution to realize the full potential of trial data transparency, in the interest of the patients.

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Big data gets personal on individual health – Part II

If you’ll remember, the first words of Part 1 of my blog about genetic testing were: “The buzz is starting to brew for 23andMe….”

Little did I know however, the controversy that was about to erupt between 23andMe and the U.S. Food and Drug Administration (FDA).

An FDA Warning

On November 22, 2013, the FDA issued a warning letter to Ann Wojcicki, CEO of 23andMe, Inc., stating that FDA regulators believed that the Personal Genome Service (PGS) kits issued by the company to its customers are medical devices and should be regulated as such.

The FDA states in the letter:  “This product is a device within the meaning of section 201(h) of the FD&C Act, 21 U.S.C. 321(h), because it is intended for use in the diagnosis of disease or other conditions or in the cure, mitigation, treatment, or prevention of disease, or is intended to affect the structure or function of the body.”

Further, the FDA demanded that 23andMe stop marketing the kits as personal genomic units until the company receives clearance from the FDA.

While Wojcicki fought the FDA initially, she relented in early December, and in a letter to me and her other customers, she said:  “We remain firmly committed to fulfilling our long-term mission to help people everywhere have access to their own genetic data and have the ability to use that information to improve their lives. Our goal is to work cooperatively with the FDA to provide that opportunity in a way that clearly demonstrates the benefit to people and the validity of the science that underlies the test.”

My results came in under the wire

I was perhaps one of the “lucky” ones whose saliva was being processed prior to the FDA mandate, and as a result, my results arrived near the end of 2013.

A few items of interest from the report:

  • I am 98.9 percent European (which is very interesting to my family, as we’ve always been told that we have significant American Indian heritage);
  • I am 2.5 percent Neanderthal;
  • My famous relatives include Marie Antionette, Napoelon, Prince Philip, and Susan Sarandon; and
  • I do not have the genetic markers for the cancers for which they test.

You may remember that I ended Part 1 of my blog post with information about a program that one of my primary care doctors suggested that I participate in to assess my risk for hereditary cancer. The laboratory assessment checked for genetic mutations known to cause the following cancers:  female and male breast, colorectal, endometrial, gastric, ovarian, pancreatic, prostate and melanoma.

Well, despite a pretty rich family history of cancer, I learned that of the 27 genes tested, my results were “NEGATIVE:  NO CLINICALLY ACTIONABLE MUTATION IDENTIFIED.”

I will never forget the smile on my doctor’s face as she entered the consult room that morning. “Good news” were some of the sweetest words I’ve ever heard.

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Can pulling a pharmaceutical drug from a European market help you achieve optimal revenue? - Part 2

In Part 1 of my blog, I listed nine questions your global pricing team should ask to determine if they’re ready to support C-suite discussions on reference pricing and revenue optimization strategy.  Given recent events, these discussions may include the consideration of pulling a product from the market. Part 2 describes the capabilities they need to be ready.

Pricing is a key strategic imperative, yet underserved

Pricing and market access are top strategic priorities for life science companies. And most organizations I spoke with this year admit their organizations are not efficient nor transparent in addressing current global pricing practices. Organizational capabilities are often cobbled together and do not adequately support today’s business requirements. This can result in lost revenue and lower productivity.

Pricing teams are frequently asked to run analyses to simulate numerous market scenarios. This can be a drain on productivity and slow the pace of decision-making. One executive explained  each pricing scenario requires extensive computer processing, literally on an overnight basis, which then must be restarted with new assumptions, or to reflect changes across markets. Such slow processing limits the number of effective scenarios considered, and leads to organizational bottlenecks.  On the other extreme, some organizations are running scenarios  very quickly, yet they admit they have no way of knowing if they are choosing the optimal scenario, just offering several options for consideration.  The team of the future requires more sophisticated  analytical support  enabling them to optimize the market as each market situation evolves.

The revenue targets are more difficult to meet today due to the increasing complexity of reference pricing, often among dozens of countries, each with a commitment to reduce their  national drug spend.  And for a life science organization, achieving optimal pricing across geographies at every stage of the product life cycle is key to sustainable global revenue. So how does an organization launch a product and consider the sequence opportunity across 75 countries within a 90-month period?   They would actually be evaluating  8,960 to the 2,030th power of possible price/launch date sequence combinations.   Does your team have the ability to evaluate all these combinations? Clearly, there are limitations in considering all these iterations with basic tools. Recognizing not all combinations are viable in the market, industry experts could be utilizing market knowledge, advanced analytics and global goals to create the optimal market launch scenario as well as modifications as the market conditions change.

The global pricing team requires sophisticated analytics capabilities

Today’s pricing and market access teams deliver corporate pricing analysis to the organization both at the corporate and affiliate levels. These analyses and market insights are required to maximize the reimbursed prices to the greatest number of eligible patients. Fortunately these are seasoned teams that understand the market nuances and the products. And without these teams to monitor and manage global pricing, the organization is at risk for significant revenue leakage. Proper affiliate insight and forecasting enables global organizations to meet financial targets and allows them to be nimble in reacting to unscheduled market events.

So where is your team?

With so many senior executives questioning the strategies and the supporting analytics for these critical pricing decisions, it’s important to evaluate the current tools and methodologies, consider benchmarking studies and question standard hypotheses. Without doing so, there’s no way to answer that fundamental question “Are we moving forward with the optimal pricing strategy for today's market?  How about when the market dynamics shift?”  Can we inform our C-suite of the financial ramifications of pulling a product off the market in a country  vs. staying with the original launch plan?

I’m interested to know – are you concerned  more products will be pulled from the European market in the future?  What approaches do you think the market should adopt to  minimize revenue risk?

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Can pulling a pharmaceutical drug from a European market help you achieve optimal revenue?

How do you make the tough decision? Gut, analytics or a combination? Is your global pricing team ready to support reference pricing decisions with your market analysis capabilities?

Part 1

Today I read that a diabetes medicine is being pulled from the German market because the parties failed to agree on the product’s value. The market has seen several of these critical decisions made recently by life sciences. Why? Because governments are addressing their budget challenges by increasingly scrutinizing the clinical value a product brings to market, leading to pricing and volume pressures on new launches as well as in-market products. These decisions have serious consequences for patients and providers, as well as the obvious adverse impact on corporate revenue projections. As a result, pricing has been elevated to a life sciences senior executive level issue.

How are these difficult decisions made at the C-suite level? Is your pricing team equipped to deliver the market analysis to make these informed decisions? Nine questions every global pricing team should ask themselves to support these tough market decisions are:

  1. Are we using all available data, internal and external, to estimate accurate market sizing and market value?
  2. Are we prepared to run the critical analysis necessary to provide the C-suite with the information required to make pivotal market decisions?
  3. How confident are we in our analysis?
  4. Do we adjust our launch price and launch sequence recommendations when a referenced country launch is delayed or withdrawn?
  5. How quickly are we able to analyze and course correct when a market price event, both expected and unexpected, occurs?
  6. Can we estimate the financial impact to the global organization when a country launches out of sequence, or at a price below our established floor?
  7. Are we confident our price and country launch sequence strategy will deliver the optimal revenue stream?
  8. Are we best-in-class in determining our global pricing strategy?
  9. Are we too reliant on consultants, empirical knowledge, Excel models and/or gut feel to determine our launch price strategy and adjust our in-market life science pricing?

Depending on your answers, you may want to know more about the capabilities your team will need as country governing bodies continue to critically evaluate their health care spending – so stay tuned to Part 2 of this blog tomorrow.

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Big data equals big opportunity in pharma sales and marketing

While shopping online in the past few weeks, I personally experienced the business value of data and analytics when the Amazon product recommendations included two additional items that I ultimately decided to purchase. It’s pretty impressive when the recommendations are so accurate. This is a real-world example of the potential competitive advantage organizations can gain from applying data and analytics to increase sales revenue.

Big data can equal big opportunity for pharmaceutical sales and marketing as well. There is a lot a data available for analysis in pharma sales and marketing today, and important new data sources are growing at a rapid rate.

Data rich, insight poor?

With all that pharmaceutical sales and marketing data available for analysis, one major challenge is bringing together the many available data sources, and then preparing that data for analysis. This need to integrate data sources is causing many pharmaceutical companies to be data rich, but insight poor.

And data volumes are increasing. Many companies are using data sources such as prescription data, CRM data, longitudinal patient data and managed care data. Some additional data sources that can provide key insights in pharmaceutical sales and marketing include:

  • Social media.
  • Digital marketing.
  • Claims databases.
  • Co-pay card consumer fill data.

The volume and complexity of all this available data is starting to overwhelm traditional approaches. Especially when business units request important, yet difficult, analyses that require the integration of marketing tactics with sales efforts.

Making the organizational commitment to harness available data sources for analysis can certainly give sales and marketing new insights. But sometimes, it may not be obvious what insights are possible. For example, health outcomes research and social media analytics are potentially rich sources of insights that perhaps were not previously investigated.

Health outcomes research

One big opportunity is to better quantify health outcomes in order to prove the value of medicines to health care professionals and payers. Today’s health care market demands evidence from health economics and outcomes research (HEOR) studies to inform payer and health care decision making. The ability to quickly explore and visualize real-world data sources is critical to decisions about treatment regimens, pricing, reimbursement and formulary access. Outcomes research can also identify any patient populations where your treatment can show a competitive advantage.

Separating truth from fiction in social media

A second big opportunity is to score internet sources and social media content to separate truth from fiction. The amount of information electronically available through social media channels about health care, pharmaceuticals and medical devices is staggering. But how much of this information is valid or truthful? How much is inaccurate or biased? What if you could use analytics to find the truth in social media channels?

Analytical methods can apply a “veracity score” to data that’s been collected and prepared for analysis from a variety of internet sources, including social media. The “veracity score” is in context of the source data (i.e. Twitter, Facebook, blog/comment, product reviews, etc.), and reflects a measure of confidence associated with the specific data collected.

The big opportunity

These are just two examples of different types of insights available to your organization by taking advantage of all your available data. Of course, you probably can find more. What Is Big Data? can get you started on finding your big opportunities from big data.

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SAS® Claims Analytics: Bringing the next generation of APCDs to life

Recently, SAS announced a new product offering - SAS® Claims Analytics - designed to transform the way health care data is collected, managed and analyzed. More specifically, SAS Claims Analytics will change the way state governments approach their all-payer claims databases, delivering on the vision of high-performance analytics, custom data visualizations, integration of various types of structured and unstructured data, and wide access to the database through secure, role-based views.

Click on image to view.

As you may have learned through my previous posts, a lot has changed in the world of APCDs since they were first established by the pioneer states in the early-2000s. With the increasing demand for health care price transparency, the critical need for cost reduction, and the sheer demand from stakeholders across the board to better understand where our health care costs lie, a new landscape for APCDs is emerging. In fact, this year we saw this landscape change rapidly as more states adopted a claims database and more legislators and stakeholders began advocating for one. And the conversations have gone well beyond the collection of data to the consumption of this data by various stakeholders. States have become more visionary than ever in the ways they will utilize this rich source of data.  Furthermore, with the provisions of health reform, APCDs are becoming a necessary platform for successful reform initiatives like ACOs and value-based payment.

A big data analytics solution

We’ve heard it time and time again that APCDs have “something for everyone;” there are a plethora of use cases for researchers, policymakers, payers, providers, employers and others. What we’ve seen over the years though is a long data collection process, little or no dissemination of the data, and the general delay in APCDs delivering value through analytics. We haven’t seen the technology to support the big data analytics that we can achieve with APCDs, nor have we seen the infrastructure necessary to create role-based access to the database and merge other datasets successfully. Overcoming these challenges will surely usher in APCD's full potential for impact.

I’m excited to say that SAS Claims Analytics will support and expand this vision and establish the new generation of APCDs. Moreover, I’m excited about the unique opportunity APCDs have to be much more than a collection of claims; they can and will evolve into a repository of all types of health data. As transparency demands increase and APCDs grow and mature, they will transform into health information hubs for the states, serving as platforms for informed decision-making across the entire health care ecosystem.

To learn more about SAS Claims Analytics for APCD, see and be sure to check out the interactive demo.

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  • About this blog

    Welcome to the SAS Health and Life Sciences blog. We explore how the health care ecosystem – providers, payers, pharmaceutical firms, regulators and consumers – can collaboratively use information and analytics to transform health quality, cost and outcomes.
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