How SAS can improve the IoT experience

The Internet of Things, event stream processing and wearable devices such as Apple Watch and FitBit, just to name a few, all have massive potential to meaningfully contribute to the broader health care world. They accomplish this by transmitting personal health data in near-real time in support of potential diagnosis and treatments scenarios aligned with what we understand as eHealth, but there are many challenges.

Here are three of many examples that help you understand the practical implications of this trend. In the first, we are introduced to Apple HealthKit:

"With HealthKit, developers can make their apps even more useful by allowing them to access your health data, too. And you choose what you want shared. For example, you can allow the data from your blood pressure app to be automatically shared with your doctor. Or allow your nutrition app to tell your fitness apps how many calories you consume each day. When your health and fitness apps work together, they become more powerful. And you might, too."


Under Armour takes a slightly different perspective, they are embedding their clothing and fitness gear with sensors to create “the ultimate performance and health app.”

And, finally, a real-world example of the potential of all of this technology to save lives:

“Paul Houle, a 17-year old who plays nose tackle for the Tabor Academy football team, credits his brand new Apple Watch for saving his life. Houle had just wrapped up a practice session when he started feeling pain in his chest and back. Using his Apple Watch, he discovered that he had a heart rate of 145 for two hours after practice ended. After calling his trainer, Houle's heart rate was checked manually, resulting in an immediate visit to the hospital.

Houle was diagnosed with rhabdomyolysis, a condition that results when heavy exercise leads muscle cells to leak enzymes and proteins. Rhabdo can lead to kidney failure and death.”


We are experiencing explosive growth of individuals using commercially available personal health monitoring devices, which are able to exchange data with a variety of connected software and hardware platforms. As those data become available for analysis, as part of an ever changing and personal “digital health picture”, it is inevitable that information will make their way into more formal data streams that clinicians use to diagnose and treat ailments.

This situation presents a significant amount of risk to individuals and institutions that are interested in using these devices and data to make health care decisions at the personal and population levels. More importantly, the challenge of embedding into these devices a level of intelligence so that clinically risky or dangerous readings will appropriately alert the individual and/or the clinician, without that alert being ignored (alert fatigue), is a profound and untested challenge.

As the Internet of Things becomes omnipresent and the concept of eHealth an everyday reality, the problem of alert fatigue will grow geometrically and the distinction between data collected, transmitted and processed from clinically validated systems to personal, consumer devices will be difficult to distinguish.

Today, consumers and clinicians are at risk for not noticing or ignoring alarms and alerts generated by IoT connected devices that may lead to injury or death. For SAS, this represents an opportunity to develop embedded, in-line analytic processes that facilitate the use of streaming data quality functionality as well as advanced analytics to develop intelligent, proactive and learning alert systems. Paraphrasing a well-known marketing slogan, “SAS may not make IoT devices but, we make the data they generate better”. And SAS is beginning to do just that.

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Maximize global revenue from pharmaceutical product launches

The high cost of pricing myopia

When launching a pharmaceutical product, it’s not enough to negotiate the best reimbursable price for each local market. Overall financial performance will depend on successfully managing go-to-market strategies at the global level. That means determining the optimal price and launch sequence for all the countries in which you intend to commercialize a new product.

It’s a complex equation. External market forces, governmental pricing pressures, parallel trade and global reference price modifications have not only reduced launch revenue but caused in-market selling prices to erode by 3-6 percent a year. Even a slight price change in one country can mean a big hit to prices, revenues and margins around the globe.

A key influence in this trend is international reference pricing (IRP), also known as external reference pricing (ERP), a cost-control approach that is widespread. It’s the practice of regulators using the pricing of a medicinal product in one or more countries (a reference basket) to derive a benchmark for setting or negotiating the reimbursable price of the same (or similar) product in that country. For example, a commonly used rule stipulates that when a product is launched in a given country, its price must be no higher than the average price for that country’s reference basket.

Why it matters

Traditionally, products were often launched as soon as regulatory approval was received – or larger markets were launched first. Now that pricing across international borders is based on a complex web of interdependencies, it’s no longer enough to use broad-brush rules of thumb and a local focus. The challenge for pharmaceutical companies is to negotiate the best possible launch prices across all countries and carefully time the launch to minimize the influence of reference pricing as long as possible – all while balancing global revenue targets.


The benefits of optimizing the launch sequence strategy are self-evident, but there are challenges to the ideal.

Complexity of the question

The reference pricing matrix around the globe is daunting and ever changing. Most countries use their own formulas, and the rules that govern IRP are complex. Several of the largest, most influential and most referenced countries are evolving their reimbursement and referencing rules. Even a slight price change in a referenced country can have a significant impact on prices, revenues and margins around the globe. Navigating this matrix to limit price erosion becomes a serious optimization exercise.

For example, a product launch across 75 countries in 60 months represents trillions x trillions x trillions of possible price/launch date sequence combinations. Not all of those combinations will be viable, but you still get the idea. Finding the optimal combination is an enormous needle-in-the-haystack calculation.

Lack of global vision and governance

Organizational capabilities for price optimization are often cobbled together and do not adequately support today’s business requirements. Few pricing teams have a corporate mandate to manage launch strategies across geographies. As a result, pricing and launch sequencing decisions are often made at the country level without awareness of the big-picture ramifications. Companies that don’t have a centralized framework for managing launch strategies tend to make reactive pricing decisions and forego tens to hundreds of millions in revenue.

Absence of advanced analytics

According to an Accenture study on the pharmaceutical industry, 80 percent of executives surveyed said that pricing optimization is one of the top three strategic priorities for their companies, two-thirds of pharmaceutical companies do not have sophisticated pricing capabilities. In fact, about 70 percent of the industry is still using spreadsheets to figure out this problem. A few are using basic analytics in homegrown systems.

Slow time to results

Global pricing teams are tasked to run many analyses to simulate different market pricing situations. One client reported that each scenario required 12 hours of computer time to run, only to restart the next day with new assumptions. This iterative approach slows the pace of decision making, limits the number of scenarios that can be considered, consumes valuable resources and leads to organizational bottlenecks.

What is needed

Some life sciences companies have taken steps to formalize the launch process, but most still need to gain the abilities to:

  • Quickly simulate new product launches to see the effect of different pricing and sequence strategies.
  • Optimize the launch price and country launch sequence, including launch date as a variable.
  • Monitor in-market prices while considering the impact of mandated price changes and market events.
  • Centralize business structure and processes around global pricing

Launch revenue optimization is one of the most complex areas of analytics to solve, but the returns for the business are significant. Join our experts Patrick Homer and Ivan Oliveira at Analytics 2015 to discover more.

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The top 10 use cases for analytics in high-growth health technologies

Healthcare IT News recently published an article on 18 health technologies poised for big growth, a list culled from a HIMSS database. The database is used to track an extensive list of technology products that have seen growth of 4-10 percent since 2010, but have not yet reached a 70 percent penetration rate.

It strikes me that advanced analytics could have a significant impact on the greater adoption of these technologies and to further their capabilities and deliver increased value.

Because some of health technologies are already based on analytics – forecasting or optimization (e.g. cost/utilization analytics) – I didn’t include them here. This post instead takes a look at 10 use cases that can benefit from the greater use of analytics:

  1. Bed management – Remaining first-time buyers: 49.7 percent

Bed management technologies provide visibility into how beds are used and when they’re in use. What if you applied forecasting to this data so that you could see peaks and troughs of use across the hospital and network and the forecasted demand for specific skill sets to staff those beds? This information could improve planning for capital purchases of new beds, allow for preventive maintenance scheduling on beds and improve staffing across hospital and network operations.

  1. Business intelligence – Remaining first-time buyers: 40.3 percent

Traditional business intelligence is a historical assessment to determine what happened and why it happened. Forecasting analytics and scenario analysis can provide a forward-looking view to what is likely to happen based on various courses of action. Optimization analytics provides the insight to deliver the ideal outcome based upon a specific course of action. If you’re not using forecasting, scenario analysis and optimization, you are missing a significant opportunity to improve operations and clinical care. Forecasting enables you to understand what is most likely to occur and allows you to assess various scenarios to choose the optimal course of action.

  1. Data warehouse – Remaining first-time buyers: 39.7 percent

Modern data warehousing can be distilled to one word – Hadoop. And this is not a what-if option (as with the other technologies). This is a you-must-embrace-it-now solution. It’s a data store environment that is low-cost, flexible, scalable, fault tolerant and can process analytic models using huge volumes of data within the data store.

  1. Dictation with speech recognition – Remaining first-time buyers: 44.4 percent

You can now use text analytics to move natural language processing closer to unstructured text and also use it to update patient records. Neuro-linguistics and medical ontology can be used look for inaccuracies or contra-indicated treatment activity so that they could be identified sooner rather than later. Ultimately, this could mean identifying the appropriate patient record to automatically append the new dictation or halt an inappropriate procedure or treatment.

  1. Enterprise master patient index (EMPI) – Remaining first-time buyers: 39.6 percent

EMPI can use existing data management tools (instead of a purpose-built application with limited utility) to create a master patient record. The benefit of this approach is that the data management software has more utility across the enterprise. Additionally, robust data management tools allows you to make better use unstructured data to enhance match rates and verify (or improve) data accuracy for match decisioning. This results in easier record review and reconciliation.

  1. Enterprise resource planning (ERP) – Remaining first-time buyers: 65 percent

ERP enables inventory optimization. By optimizing your inventory, you can keep just enough supplies to help ensure there are no stock outs that could affect the quality of care or operations. It also means you minimize carrying costs, required shelf space and real estate and can help you avoid holding items beyond their shelf life.

  1. Infection surveillance system – Remaining first-time buyers: 49.3 percent

Infection surveillance systems use analyses to identify areas of operational improvement for preventing infection, monitoring infection control initiatives and assessing the impact of infection control programs. These systems can be further enhanced by using a Hadoop data store to access a significantly larger data set for correlation analyses, forecasting and prediction analysis. Imagine what it would mean for your organization if data such as patient longitudinal views, staffing levels, patient volumes, procedures, complications, event locations, time of day (and more) could be used for uncovering insights.

  1. Laboratory (outreach services) – Remaining first-time buyers: 41.8 percent

Imagine if you could use analytics to stratify patients based on their likelihood to adhere to treatment and then have laboratory outreach services focus their attention on those patients likely to benefit the most (e.g., those most likely to be non-adherent). Outreach services could orchestrate patient compliance programs and use resource allocation models to determine support resources needed at the individual level.

  1. Medical necessity checking – Remaining first-time buyers: 32 percent

Medical necessity checking has been referred to as "next-level clinical decision support." This ability is largely rules-based. What if you could use advanced analytics (in real time) to assess risk and receive recommendations when additional tests are warranted; or, that a patient is at low risk and testing is not warranted? This concept also applies to prescription treatments and other procedures. It provides another source of information and context for clinical recommendations.

  1. Staff scheduling – Remaining first-time buyers: 42.2 percent

It is interesting to note that this technology has less than 60 percent penetration in the market. The reason could that many smaller hospitals do not see a need for it. Or, perhaps the benefits of collecting the data are unclear because of limits of their existing technology. But what if, in addition to the traditional forecasting staffing demands, they also used analytics to assess staffing levels based on patient outcomes? Often, staffing levels are based upon patient volume, not patient outcomes. But what if you had access to recommendations (and the associated cost forecasts) comparing the use of full-time vs. part-time employees to cover demand peaks, weekends and holidays? This enables you to build the case for different staffing models in the future so that there is time to act rather than react.

Analytics can further increase the value of each of the above health care technologies by using the underlying data to provide insights and forecasts about current operations and the advantages and disadvantages of alternate courses of action. This analytical insight helps enable an organization to be proactive and realize additional savings and better patient outcomes.

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Knowledge transfer: 3 real-world evidence strategy considerations

“The most successful life sciences companies will be the ones that can convince their customers – patients, health care professionals, government authorities and health plans – that new treatments are the most effective and provide true value compared with alternatives.”

Jamie Powers, DrPH, Principal Consultant and Practice Lead, SAS Health and Life Sciences

The value of real-world evidence to pharma and life sciences organizations is well understood but creating a real-world data solution is no small task. In a recent FiercePharma webinar, industry experts collectively discussed three important aspects in developing a RWE strategy.Real-world evidence

Collaboration involves knowing your stakeholders and data use cases. Dr. David Memel, VP of Health Economics and Outcomes Research at Boehringer-Ingelheim, outlined the importance of understanding the entire health care ecosystem to transform data into insight. He posed the following questions and encouraged iterative discussions among health care payers, providers and pharma throughout a product’s lifecycle.

  1. What data is needed and how is its value defined?
  2. How will the data be used? What are the objectives for the data?
  3. Will the data indicate unmet medical needs?
  4. Does the data help optimize the design of a randomized clinical trial?
  5. Does the data support market access or effective utilization of the product?

Broaden the horizon by evaluating the full enterprise. The pathway to address strategic data and research challenges is evolving, according to Louis Brooks, VP of Marketing Analytics at Optum. Creation of a RWE strategy never starts with a clean slate, so bridging gaps with organizational silos, addressing privacy concerns and enabling scalability is to be expected.

Successful strategies embrace innovation and long-term approaches. That way you can harness and analyze data to accomplish goals for the entire organization up front rather than for specific aspects of a product’s lifecycle. The outcome is visibility into the complete patient journey.

Data science education drives success in research projects. From the perspective of Aman Bhandari, PhD, Executive Director of Data Science and Insights at Merck, value is realized when computer science meets scientific research. Emphasizing strong collaboration within the health care market, he described the ideal partnership as one that appreciates the scientific, informatics and data expertise of both parties. Likewise, infrastructure to support big data and IT leadership talent within regulatory bodies, government entities and health care foster proactive strategic development.

In short, health care collaboration, enterprise innovation and big data education create opportunities to reach the ultimate objective: producing quality drugs and devices that enable the highest quality of patient care.

Interested in some next steps? Watch the full on-demand webinar, complete with use cases. Then check out the Making Real-World Evidence Real white paper.

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Getting to “Wow” with analytics

Recently, I experienced a minor accident that damaged my car's front fender. Based on prior experience, I completely dreaded the process of getting the car repaired.  To my surprise, the claims process and repair service provided by my automobile insurance company and the local repair shop were absolutely flawless and the car was quickly restored to its original condition.   Their impeccable business processes resulted in one satisfied customer and this experience made me think “Wow – what an excellent process.”

My amazement at the efficiency of these business processes and the positive customer experience they produced caused me think about a few ways life sciences companies could improve business processes in clinical trials or drug safety in order to elicit a "Wow” response.

Getting patient enrollment right

Clinical trials are a significant expense in the total cost to bring a new drug to market - and patient enrollment is a major challenge in many clinical trials. One global contract research organization (CRO) is increasing efficiency and customer satisfaction by using analytics to forecast patient enrollment in clinical trials.

In order to maintain customer satisfaction with the sponsors of clinical research, CROs need to deliver increased visibility into trial timelines in order to estimate the duration of a clinical trial or detect and notify sponsors concerning potential schedule changes. Using analytics to gain better insight into clinical trial patient enrollments is key to improving business processes in clinical research so that the sponsor’s experience matches the “Wow” of my automotive claim and repair experience.

In addition, the application of analytics to gain better insight into trial schedules helps you model when patient data will be available – allowing cost savings through better use of the CRO’s human resources.

And from a sponsor perspective, once a product is approved analytics can help to increase the efficiency of post-marketing surveillance and reporting.

Adverse events and product complaints – no problem

Each year, drug and device manufacturers receive large volumes of adverse event and product complaint reports – and the quantity continues to increase significantly. The challenge of processing and analyzing adverse event and product complaint reports on a global basis within regulatory reporting timelines can be daunting for many of these organizations. The noncompliance fines could cost millions for failing to report certain events within regulatory guidelines.

The ability to apply automation to this process through the application of analytics would elicit a WOW response in many companies. Using analytics, you can increase speed and reduce the expense of post-market surveillance processes through automatic coding and prioritization. Automation can reduce the manual burden of identifying and prioritizing significant adverse events for further investigation and possible mandatory reporting as required by regulatory authorities. This is accomplished by using advanced linguistic technologies to automatically apply your company’s business rules to extract insights from text data in real time on a high volume of customer-reported issues. Wow!

More "Wow" opportunities are available

There are many additional opportunities to use analytics and visualization to increase efficiency and improve processes in life sciences companies.   Whether it’s clinical development, drug safety and quality or commercialization activities such as product launch, imagine the ways your organization can use analytic insights to enjoy your own “Wow” experience.

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A case for real-world evidence

For health and life sciences organizations, discussions about big data include gaining value from that data in the form of real-world evidence.

Consider for a moment the amount of healthcare data that exists today thanks to the adoption of electronic health records. Then think about the future with data from wearables and health monitoring tools from your smartphone, as highlighted by Dr. Eric Topol at the SAS Health Analytics Forum.

Patient records, insurance claims, clinical trial research all add up to real-world data. So how is this data valuable?

For starters, more data means more information for biopharmaceutical researchers. From seeking cures for life-impacting diseases, to preventive measures for mundane health conditions, insights from this data offer confidence in the research. It offers better population targeting for clinical trials. Plus greater understanding of efficacy across demographics is derived.

In short, life sciences companies can make smarter investment decisions. Drug development costs are reduced, as are poor clinical trial outcomes. Healthcare payers benefit from less expensive prescription drugs and providers have greater assurance in care provided to their patients.

But there are significant business and infrastructure challenges. Disparate data sets, huge organizational silos, lack of analytical resources and the necessary time to manage all of these factors exist. This is typically where the conversation around real-world data falters.

However, it doesn’t have to stop there. The right combination of data and analytics tools means benefits can be realized from clinical and claims data.

If you would like to dive a bit deeper, take a look at the FiercePharma on-demand webinar, Maximizing the Value of Real World Evidence and join us in continuing the conversation!

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What's new with clinical trial data transparency?

After being involved with data transparency for a few years, I continue to be pleasantly surprised by the continuous progress with respect to data transparency for clinical trials.  At the Fifth Installment of the SAS Clinical Trial Data Transparency Forum, I was amazed by the amount of new information that speakers added to my existing knowledge of data transparency for clinical trials data.

Interesting new information included the human resources challenge, the growth of disease-specific repositories and the availability of an assessment on the research potential for increased access to the clinical trial data.

Resources, resources, resources

Speakers from sponsor companies clearly outlined the human resource challenge inherent in the gathering and de-identification of the clinical trial data.   Some companies are changing processes for new trials to ease the future data transparency support challenge.

Allocating resources to support data transparency efforts was highlighted as a major issue in smaller firms. Possible approaches to solve the data transparency data preparation human resources challenge are:

  • Creating dedicated transparency data preparation teams.
  • Outsourcing the activity to a vendor.
  • Using the existing clinical study teams.

Disease-specific data repositories

Prior to the conference, I was aware of the success of Project Data Sphere – an initiative of the CEO Roundtable on Cancer’s Life Sciences Consortium. Project Data Sphere aims to accelerate cancer research by allowing access to the comparator arms of historical cancer trial data sets.

During the conference, I learned of the availability of schizophrenia clinical trials data through the Open Translational Science in Schizophrenia (OPTICS). OPTICS fosters translational science in schizophrenia research by making clinical trial data plus observational studies and trials available through the NIH (via dbGaP) accessible to qualified investigators.

This is welcome news that new disease-specific repositories are being developed. The potential for disease-specific data repositories to advance science and impact human health in the future is immense.

Assessing the research potential - Wellcome Trust report

And speaking of the potential impact of increased data transparency for clinical trials data, a thought-provoking presentation from a representative of the Wellcome Trust  discussed a report commissioned by their organization titled Assessing the research potential of access to clinical trial data.

The study is comprehensive and an excellent source of information for anyone interested to learn more about clinical trial data transparency and the potential benefits to medical research.

Want to learn more?

Increased access to clinical trial data for authorized researchers can be a valuable resource for a generation of new medical knowledge. The new insights will have the potential to improve public health over for decades to come.

No matter how closely you have followed data transparency issues, I suspect you will learn a great deal from the video recording of the Fifth Installment of the SAS Clinical Trial Data Transparency ForumAnd you can learn more about the SAS data transparency solution.

Clinical trial data transparency is a rapidly evolving topic where sources of new information are a valuable resource. The Fifth Installment of the SAS Clinical Trial Data Transparency Forum did an excellent job of providing me with new knowledge.

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Visualizing the customer journey through analytics

42-23789610 (1)SAS customers across all industries are solving amazingly complex business problems – in record time – with advanced analytics from SAS. And health care is no exception. In fact, the health care industry may soon be leading the way in rapid analytics innovation.

Read what Ravi Shanbhag from UnitedHealthCare has to say about using social media data and SAS® analytics to build lightning-fast insights around competitor behavior and customer risk, including this tidbit about the customer journey:

To find out which customers might be likely to default on their premiums, they decided to take a birds-eye view of all systems.  The team used grid computing to look at structured data, then loaded a broad set of possible attributes to SAS® Visual Analytics for a decision tree analysis. And that’s what helped them pinpoint where the problems might be. Armed with that knowledge, United can now help people into more affordable products or even change their payment plans.

Want to hear more stories like this? Don’t miss the SAS Health Analytics Virtual Forum (free registration) streaming live from SAS Headquarters on May 20, 2015.

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The NHS: Time to care about big data

Considered to be one of the UK’s most precious institutions, it's no surprise that the National Health Service (NHS) has been ranked as one of the most important issues in this year’s general election. Phrases like ‘building an NHS with the time to care,’ will no doubt be ringing in the ears of the electorate and public alike over the next month.

Electoral pledges have been focused on investing more funding and human resources, joined up services from home to hospital, and guaranteeing appointments. Whilst investment is imperative, I’m keen to point out how the NHS can be more efficient and how big data could dramatically cut our country’s care bill.

It goes without saying that the NHS is a relentless producer of data – be it patient data, performance data, clinical data or textual unstructured data. However, despite the breadth of a nationalised health care system, this data is immensely siloed. Hidden within this data are valuable insights that can identify trends, potential outbreaks in diseases or peaks in medical conditions. This data, if used correctly, could be the pill the NHS has been looking for.


One example of data being put to good use is at Royal Brompton & Harefield NHS Foundation Trust, where they have in excess of 400 data systems and 20 clinical data sets. It uses data to make evidence-based decisions based on its research into heart and lung conditions. It has been able to bring numerous disparate data sources together into a single system. A single source of the truth makes it possible for a consultant to eventually have all the information about a patient immediately in front of them, giving a much clearer picture for earlier decision-making – e.g. whether to provide emergency treatment.

The Trust also uses advanced analytics to uncover unexpected or less obvious connections between data. This could be a previously unknown link between a medication and a certain condition, or links between lifestyle and recovery from operations. Crucially, the analytical capability means these insights can be rapidly identified. For example, in certain situations patients may no longer have to go into hospital and sit in a waiting room for hours. Through better understanding of the patient from their data, it may be possible to intervene early and deal with their situation in a different way. It should also lead to more rapid diagnosis and treatment. All these outcomes deliver obvious benefits in reduced costs and lower demands on services.

Using big data analytics techniques more widely could anticipate pandemics or peaks in demand on A&E (accident and emergency) services. This is where significant efficiency savings can be found by putting additional resources into A&E to manage expected peaks, and vice versa when demand subsides. The data can also be analysed to delve deeper into what causes peaks and troughs in demand, further improving predictions and resource allocation.

Our greatest medical discoveries are borne from repeated testing and repeated failure. Much effort is being spent across the health care industry to understand the correlating genome sequence for many patients covering different conditions and outcomes. This creates masses of data, but the good news is that the technology can now process all that data much quicker than before. Already medical researchers have begun tailoring treatments for different patients. For example, clinicians can prescribe drugs with a varying level of toxicity according to how susceptible the patient might be to treatment, which is based on the evidence from data.

Big data analytics provides a quicker route to efficiency savings and new breakthroughs, which can help revolutionise the NHS – unlike blindly throwing more money at it.

Find out how better use of data and analytics can prevent more NHS scandals.

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Research just got easier: Open clinical trial data holds tremendous promise for medical research

Our own Matt Becker and Christian Nimsch contributed to this post.

You might be surprised at the wealth of information you have access to!

The worldwide movement towards open data has swept through many industries in a highly publicized fashion. And for health care, there is a real promise of using data to achieve transformation.

The first step in using data is to foster an environment of data transparency that provides access to relevant data. Think of the Human Genome Project. One of the hallmarks of the project, and some would say the key to its success, is that sequence information was in the public domain and readily accessible to all.  Similarly, when we evaluate the efficacy, safety and effectiveness of pharmaceutical and biotech innovation, we often turn as a first resource to data generated via the clinical development process (clinical trials). Surprisingly to many, most major pharmaceutical companies have recently provided transparent access to their clinical trial data.

clinical trial data transparencyThe move towards open and transparent access to clinical trial data has been going on without much publicity. It’s now possible for researchers to access vast amounts of anonymized patient-level data from clinical trials at no cost. Rather than continue the practice of basing research on abstracted information and reports, researchers are free to use source data to enhance their projects and further validate their findings. In fact, many major organizations have created virtual workspaces for researchers to request, access and analyze data in a controlled environment from multiple sponsor companies within the same research project, all from the same place.

Almost all major pharmaceutical companies worldwide have a framework in place to make this patient-level clinical trial data available to researchers. Many biopharmaceutical sponsors have added information to their company websites regarding their data transparency initiatives and how to request access to clinical study data. Although there is currently no master list of organizations that participate in this movement, some industry groups are attempting to create such a record. is an excellent resource for medical researchers in that it posts transparency information for eleven sponsor organizations participating in clinical trial data transparency efforts. In addition, researchers can contact each pharmaceutical company directly and request access to the clinical trial data.

You’ll be surprised at the wealth of information you have access to!

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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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