FT: Data Prescription for Better Healthcare

“It’s quantifying intuition,”

April Dembosky, The Financial Times, December 11, 2012  

Machines beep and drone throughout the neonatal intensive care unit at Toronto’s Hospital for Sick Children. Intimidated parents stand by as nurses scurry between the glass cases. 

Among their various checks, nurses chart a baby’s heart rate once an hour. This is standard practice at most hospitals, but Carolyn McGregor, a professor of health informatics at the University of Ontario, Institute of Technology, says it leaves a lot of critical data to waste.

While a baby’s heart beats around 120 times a minute, it is the pattern of those beats over time that that can give early warning if something is wrong.

With the help of Watson, IBM’s supercomputer that trounced the top Jeopardy! champions last year, Ms McGregor analysed live streams of every heartbeat in a project nicknamed “data baby”. She found patterns that revealed signs of infection 24 hours before the baby showed any visible symptoms. In premature babies, advancing treatment by even an hour can be life-saving.

“At the moment, all nurses have are some very poor alarms that go off all the time, so everyone ignores them,” Ms McGregor says. “We’re trying to watch more streams of data that will give more intelligent alarms.”

The healthcare industry is under extreme pressure from regulators and market forces to reduce costs and produce better outcomes. The US, in particular, spends 30 per cent more on healthcare per capita than other developed countries – about $750bn in total. But despite this higher level of spending, its life expectancy or infant mortality rates are not significantly better, according to a McKinsey report.

The US healthcare industry has therefore become the target of sweeping reforms aimed at reducing tests and procedures that bulk up the bottom line but show minimal to no impact on making patients better. Hospitals and insurance companies must demonstrate efficiency and the improved health of its customers to remain compliant with government policy changes and stay competitive in the free market.

Harnessing vast troves of data is increasingly seen as the solution. From medical devices and insurance claims, to scribbled doctors’ prescriptions and social media sites, the data in healthcare is massive and messy.

One of the promises of organising and analysing all those data is the ability to predict the future, with the goal of early intervention preventing heart attacks or hip fractures from happening in the first place.

“Historically, quality in medicine has been a retrospective affair,” says Stephan Fihn, a doctor and director of analytics and business intelligence at the US Veterans’ Health Administration. “The Holy Grail right now is what would be termed ‘predictive analytics’.”

One of the earliest health systems to begin keeping health records electronically, the VA now has data on 20m patients, including 2bn text notes, 16.2m X-rays, and 1.5bn drug prescriptions. From analysing that data, it can, for example, identify characteristics of patients who suffered renal failure after getting a certain drug, then use that to predict who else is likely to have the same reaction.

Such measures are now of supreme interest to private hospitals in the US. Under new health reform laws, hospitals now face financial penalties if certain patients are readmitted to the hospital within 30 days of being discharged. To maintain profit margins over the long term, they must invest now to develop new protocols that conserve resources and improve performance.

Several hospitals are running data analyses to determine which patients are at the highest risk of being readmitted, for example after a heart attack or pneumonia, then prescribing in-home monitoring devices and outreach programmes to keep them on track.

“We’re moving away from pay-per-pill and pay-per-procedure, and going into a business model that is pay-per-person,” says David Dimond, healthcare strategist at EMC.

Health insurance companies are just as invested in correcting the inefficiencies of the system. Premiums have risen in step with the overspending, and customers, whether employers that sponsor health plans for their employees, or individual consumers, are at a breaking point of what they are willing to pay. Under US reforms, insurers cannot deny coverage based on pre-existing conditions, so they must manage the risk instead of avoiding it.

WellPoint, a health insurer that covers more than 33m patients, has contracts with dozens of technology suppliers, including several to manage and analyse its data.

One of the main goals is to use computers to make the human decision-making process more efficient, says Elizabeth Bigham, vice-president of health IT strategy for WellPoint. For example, the current process for pre-authorising claims can take up to two weeks and involve input from several nurses and physicians. Once a computer is trained on how the company decides to pay or reject claims, most of these humans could be replaced and the patient could have an answer almost instantaneously.

It is even exploring what it can do with all the data generated by medical tracking devices, such as glucometers and heart rate monitors, and even health tracking devices such as the FitBit, a pedometer that tracks the wearer’s activity, calorie intake and sleep patterns. It uses those data to inform diet and lifestyle coaching programmes for patients with diabetes, obesity and hypertension with an eye on preventing a catastrophic – and expensive – heart attack or amputation.
       
John Edwards, director of the health advisory at PwC, expects health insurers to offer lower premiums in the next one to two years to customers willing to wear a mobile health tracking device, similar to car insurance companies that give discounts to drivers willing to install a GPS device on their cars that tracks speed.
“It’s not far-fetched at all that tracking exercise could become part of an insurance design that says ‘you’re doing the right thing’ in order to control healthcare costs,” he says.

Wireless sensors are also seen as a way to control costs associated with the ageing population. Motion sensors in beds, walls and floors of elderly people’s homes can track patterns in activity and movement to predict devastating falls. Researchers at the Sinclair School of Nursing at the University of Missouri are identifying patterns of restlessness during sleep and changes in physical activity that precede a devastating fall or visit to the emergency room.

Bonnie Wakefield, a research professor at the University of Missouri School of Nursing, says such data patterns and predictive algorithms could soon aid nurses making critical decisions at all levels of healthcare.

“It’s quantifying intuition,” she says.

Big Data Won't Save Pharma, But Smart Data Might

Data analytics has the potential to do much more if applied across the pharmaceutical enterprise.

Guy Cavet, Genetic Engineering and Biotechnology News, November 2012

Intelligent use of large-scale data has become fundamental to other industries: finance, insurance—even sports. But despite its importance in areas of research, data analytics has the potential to do much more if applied across the pharmaceutical enterprise.

In the past 15 years, biology has been transformed by the availability of large-scale genetic and genomic data. The first ten years of work on the human genome yielded one draft genome. The last ten years have yielded over ten thousand. Advances in technology have enabled high-throughput gene expression profiling, cancer genome analysis, and other disciplines to change the way biology is studied. Cheminformatics allows companies like Numerate to screen millions of compounds for activity by purely computational prediction. However, there are much greater opportunities for data-driven transformation across the broader pharmaceutical enterprise.

These opportunities arise, in part, because of the broad trend toward data being tracked and recorded in new and far-reaching ways. Importantly, many of these are outside the pharma industry. Medical records are collected electronically on an unprecedented scale, driven in part by federal “meaningful use” programs. These records reveal how diseases manifest and how treatments are used in the real world. Social media also contains vast amounts of information on real patient experiences with both diseases and treatments. And in the sales and marketing of drugs, data on program effectiveness is collected in real-time by reps, and companies like Aktana are interpreting it to understand where physicians perceive value.

Getting data is only the first step. The true value arises from analytics that generate actionable insights. In many cases, this means predictive modeling: developing algorithms that reveal what drives an outcome of interest (such as response to therapy or drug choice) and allowing that outcome to be predicted in the future. The data scientists that can carry out this type of analysis are multidisciplinary experts with skills from statistics, computer science, biology, chemistry and other fields, and they are highly sought after.

The conventional ways to engage data analysts involve building internal teams of scientists or buying time from consultants. However, data analytics is also particularly well-suited to crowdsourcing, which opens up a problem for many people to address. It’s inevitable that most of the world’s experts in any domain are outside any single pharma company. Even with strong internal teams, as Bill Joy of Sun Microsystems insightfully noted, “Most of the smartest people work for someone else.” Crowdsourcing allows those people to be tapped in a highly flexible and cost-effective manner. A team of experts can coalesce around a problem, working on it only as long as necessary, and then move on.

In 2006, Netflix used crowdsourcing to improve their ability to suggest movies to their customers. Rather than just inviting people to work in isolation, they set up an online competition in which people submitted entries in real-time and vied to come up with the best solution. This is a particularly effective approach to predictive modeling analytics. Seeing their rivals above them on a leaderboard drives people to continuously generate better results. In the Netflix competition, the company’s internal method was surpassed within six days, and the eventual winner was more than 10% better.

The competition approach is equally applicable to pharmaceutical industry problems. For example, Boehringer Ingelheim sponsored a contest to develop methods to predict small molecule safety that resulted in a 25% improvement over an industry standard approach. In the Heritage Health Prize competition, methods are being developed to predict which patients will require hospitalization, and for how long, over the next twelve months. Other competitions have been used to predict patient outcomes, sales patterns, and clinical outcomes. In each case, the results were better than any methods that had previously existed.

The full potential of data analytics requires accessing and using data in creative ways. For example, after a drug launches, information about the drug is rapidly generated in the outside world through patient and physician experiences. This information is currently largely untapped. It is entered into electronic medical records, tweeted, posted on Facebook, and entered into community sites such as Patients Like Me. This data is often unstructured and very noisy, but companies such as Israeli startup Treato are beginning to systematically organize it. Despite the complexities of working with data like this, skilled data scientists can extract meaningful patterns about drug-drug interactions, what drives patients to start and stop medications, or which patients will not adhere to their prescriptions, to name a few.

Predictive models even have the potential to tackle some of the most critical decisions in drug development, such as whether a clinical trial will be successful or whether a licensing deal will eventually lead to a drug. Billions of dollars rest on these decisions, but it is rare that all available relevant data is systematically employed to predict the probability of success. Of course, no algorithm can make such predictions with perfect accuracy, and no computation can replace a clinical trial. However, for an organization deciding between multiple costly development programs, having any improvement in ability to predict results is immensely valuable.
Putting data beyond the company firewall for outside experts to use may not be a natural step for organizations that are accustomed to carefully protecting their sensitive information. However, with the appropriate steps, the confidentiality and privacy of pharmaceutical and medical data can be carefully preserved. For example, when Boehringer Ingelheim sponsored a competition to predict small molecule activity, neither the structures of the molecules nor the specifics of the activity were revealed. In a competition to identify patients with type 2 diabetes using electronic medical records, the data was carefully de-identified to meet HIPAA standards. Privacy and confidentiality concerns can also be addressed by restricting access to trained and trusted individuals.

With drug development costs rising and approvals declining, new approaches are sorely needed. It’s too simplistic to see “big data” as a knight in shining armor, but the intelligent use of rich data, regardless of size, has the potential to help dramatically with problems from basic research to commercial operations.

Tim O'Reilly: Open Health Data in Practice: Increase Your Access to Lab Results Voice Your Support for a Proposed Federal Rule that Expands Patients' Access to Test Results

Tim O'Reilly, O'Reilly Radar, October 16, 2012

I’m convinced that there’s a wave of innovation coming in healthcare, driven by new kinds of data, new ways of extracting meaning from that data, and new business models that data can enable.  That’s one of the reasons why we launched our StrataRx Conference, which focuses on the importance of data science to the future of health care.

Unfortunately, much of the data that will enable an entrepreneurial explosion is still locked up — in paper records, in proprietary data formats, and by well-intentioned but conflicting privacy regulations.

We’re making progress towards open data in healthcare, but there are still so many obstacles!  Ann Waldo recently introduced me to one of these.

A 2009 law modernized patient access rights by allowing individuals to get copies of their medical records in electronic format. Unfortunately, however, these patients’ access rights surprisingly do not include lab test results – one of the types of medical records that people are most likely to find urgent and useful. Due to the interaction of HIPAA (the Federal medical privacy law), CLIA (a Federal laboratory regulatory law), and state laws, patients can only get direct access to their their test results from labs in a handful of states.
A recent New York Times story highlighted just how much pain and suffering can be caused by this inability to get access to your own lab results.

In 2011, the Department of Health and Human Services put forward a proposed Rule that would give patients the right to get their test results directly from laboratories. This Rule is still waiting to be finalized. In hopes of breaking the logjam, O’Reilly Media and a variety of other players have written a consensus letter that voices our whole-hearted support for that proposed Rule and encourages the Federal government to finalize it promptly.
We’d love to invite you to join us in signing this letter.

Patients’ rights should include direct access to their lab results, just like all their other medical records!

Big Data, Bigger Outcomes

Healthcare is embracing the big data movement, hoping to revolutionize HIM by distilling vast collections of data for specific analysis

Lorraine Fernandes, Michele O'Conner and Victoria Weaver, Journal of the American Health Information Management Association, October 2012

One only needs to open a recent conference brochure, read an electronic newsletter, or preview marketing materials to appreciate that “Big Data” is getting a lot of buzz in healthcare—as well as many other sectors of the global economy. Big Data tries to make sense out of information overload, and provides new insights from the growing volumes and sources of data with the goal of answering business, operational, and clinical questions in near-real time. As technology grows, the various types of data available for research grow with it. Big Data solutions aim to harness large and complex collections of digital data and extract focused knowledge and insights from it. In healthcare, experts say Big Data empowers caregivers, scientists, and management to make better decisions that have the potential to save lives, improve efficiencies, and decrease costs. Big Data also has the potential to revolutionize the way health information management (HIM) professionals collect, store, and transmit data.

“Today’s episode-oriented discrete data does not allow us to be as prescriptive as we need to be in delivering better healthcare and empowering consumers,” says Lisa Khorey, vice president of enterprise systems and data management, information technology at the University of Pittsburgh Medical Center. “Medicine can get closer to the action when it is prescriptive, predictive, and precise. Big Data allows organizations to focus on wellness and standardize care processes.”

Big Data Basics
Big Data can be defined by reviewing its basic characteristics, sometimes referred to as the 3 Vs: volume, velocity, and variety.

·       Volume refers to the rapid rate at which data is growing. In 2020 it is estimated there will be 44 times more data than in 2009—35 zettabytes compared to 800,000 petabytes. Big Data techniques and software work to manage large data blocks and make sense of the information.

·       Velocity represents the increasing frequency with which data is delivered. Data such as social media, monitoring and sensing devices, and embedded chips— now in every imaginable device from refrigerators and airplanes to bodily implants—all add to the growing mounds of available data.

·       Variety signifies the many forms in which data exists. In healthcare this includes unstructured data in text format, scanned documents, streams of data from monitoring devices, email or text messages, and audio and video from images and procedures that add to the wide variety of existing structured healthcare data.

The intrigue of Big Data technologies in many industries, including healthcare, is its promise to transform how an industry operates. Scott Schumacher, PhD, IBM chief scientist and distinguished engineer, says these technologies can allow physicians to have predictive analytics that can lead to both long-term and immediate care decisions.

“Technologies aimed at the first V, volume, support the analysis of the large quantity of data required for meaningful statistics and finer grained personalization,” Schumacher says. “The second V, velocity, delivers the transformational promise of Big Data through predictive analytics tied to real-time measurements.
“The third V, variety, leverages natural language processing, semantic normalization using standard ontologies, and image and video extraction to bring more and varied evidence into analytic systems.”

How Big Data Helps Healthcare
Big Data has tremendous potential to add value in all healthcare settings. Big Data solutions can help organizations personalize care, engage patients, reduce variability and costs, and improve quality. Once Big Data is managed and integrated, organizations can apply analytics to better understand the clinical and operational states of their business based on historical and current trends, and predict what might occur in the future with a trusted level of reliability.

Personalization, whether based on genomic data, standard test data, or a combination of the two, requires the integration and analysis of much larger volumes of data than is used today, Khorey says.

“Big Data provides a rich context to shape many areas of healthcare, especially genomics where massive amounts of data are required and costs are rapidly decreasing,” she says.

While these technologies center on vast collections of data, they can also be used for select and specific analysis. For example, Big Data can be used to define patient populations at a level of granularity previously unobtainable, according to Dr. Richard Tayrien, DO, FACOL, chief health information officer for the Hospital Corporation of America. By referencing a patient to a cohort of several million similar patients, aligned by hundreds of clinical features and modeled through numerous therapeutic pathways, Big Data tools can be used to find outcomes that are predicted with a high degree of sensitivity and specificity, Tayrien says.
“Big Data solutions can result in personalized medicine that makes a dramatic difference by redirecting the care of a patient toward the most favorable outcome before predictably sustaining an adverse clinical event,” he says.

Big Data solutions will benefit healthcare providers, payers, research, and government organizations. The following is an overview of what Big Data delivers for each of these sections of the healthcare industry.

Providers Get Patient-Specific Best Practices
Healthcare providers have massive amounts of unstructured data in the form of images, scanned documents, and encounter or progress notes. Big Data solutions enable providers to analyze unstructured data in its native state, integrate it with structured data, and address priorities based on their findings. Priorities may include care pattern identification that aids in process modifications; predictive identification of risk factors to avoid never or sentinel events and untoward outcomes; and comparisons of images, procedures, and surgeries to improve education, research, and care.

Kristen Wilson-Jones, vice president of data and online services for Sutter Health, describes Big Data as a means for provider organizations to apply “mass personalization” principles to healthcare in ways similar to those used in consumer product design and manufacturing.

“Big Data will allow traditional claims and procedure data to be integrated with data created outside of healthcare to break down artificial barriers between healthcare settings,” Wilson-Jones says. “For example, data from grocery store purchases, social media, and personal preferences can be integrated to better understand what impacts individual and population health.”

These new insights can improve health at many levels, Wilson-Jones feels. With Big Data, best practices are more readily identified, variability decreases, and costs and quality are enhanced by providers, delivering a truly personalized patient experience.

Payers Leverage Data Pool
Payers have massive amounts of claims data they would like to harness to provide insights that improve wellness, patient compliance, fraud detection, and enable early warning to negative patient trends. Whether they are private payers or the government, payers increasingly use incentive programs to reward better outcomes while controlling costs. Many also want to utilize social media as a wellness and patient intervention tool that drives lifestyle changes, improves care, and reduces costs. Big Data solutions enable payers to integrate high volumes of different varieties and sources of data to enable these diverse initiatives.

Research Enabled with Unprecedented Reach
Research that requires the integration of large amounts of data has historically been underserved due to computational limitations. With Big Data solutions, researchers can contextually integrate and correlate large amounts of information automatically to gain faster insights.

For example, the State University of New York (SUNY) at Buffalo has deployed a Big Data solution to better understand the complex causes of multiple sclerosis. The system combines and analyzes variables such as diet, exercise, living, and working conditions, as well as clinical and genetic data. This approach used to take days of computing time, but now takes minutes due to the advanced computing power of today’s systems.

“Big Data allows us to take our research to a new level,” says Dr. Murali Ramanathan, PhD, lead researcher at SUNY Buffalo. “We can now rapidly analyze larger data sets including thousands of genetic variations, many environmental factors, and the interaction between them to gain valuable new insights that weren’t possible before.”

Benefit of Using Vast Government Data Stores
Government organizations may be the biggest beneficiary of Big Data solutions. Organizations already have vast stores of data sitting in data warehouse silos. With Big Data solutions these data silos can be quickly integrated to provide valuable insights such as detection of fraud and abuse patterns, identification of best practices for safer and more efficient care delivery, and better epidemiology surveillance.

“Proceeding with the implementation of Big Data healthcare solutions requires organizations to make a cultural commitment to use data to improve quality and reduce waste,” Wilson-Jones comments. “Information must be recognized as the strategic enterprise asset it is, and must be mastered and governed to break down the large number of silos and barriers in today’s healthcare systems.”

Ensuring Success for Big Data Solutions
Big Data solutions can provide significant benefits, but to ensure their successful implementation healthcare organizations need to take the following four steps:

1. Establish data governance, define data objectives
Before organizations implement Big Data solutions, stakeholders should convene an executive council made up of senior leadership to develop an information governance model that clearly defines Big Data objectives and expected outcomes, as well as drives Big Data initiatives.

“Data must be managed and treated as a strategic enterprise asset, and data governance or active management of the data should be vital, especially in light of Big Data,” Wilson-Jones says.

An effective Big Data governance program should include the basic tenets of people, process, and policies. Specific people that should be included are data stewards, who can assist with the interpretation and use of data, and a data governance council that provides representation for key stakeholders across the organization. Special consideration must be given to the new automated processes, inferences, metrics, and monitoring tools provided by Big Data solutions. Policies and procedures will also be required that govern the use of data, define the required actions and quality control processes, and optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions.

2. Identify data and information requirements
Once an organization has established an information governance model, its next step is to identify where all of the required data resides, what information should be gleaned from data, and how data will be leveraged to help prevent adverse situations, improve care, and keep patients healthy. Most structured healthcare data, estimated to make up 20 percent of all data in a healthcare facility, resides in automated systems such as the hospital information system, the radiology information system, laboratory systems, etc. The remaining 80 percent of healthcare data consists of rich unstructured data that historically has only been leveraged using labor-intensive processes or, more commonly, has not been leveraged at all.

Big Data solutions provide healthcare organizations with the ability to access and analyze unstructured data to assist them in making more informed decisions and reducing errors and missed opportunities. However, unstructured data introduces new challenges for data stewards, specifically verifying that new information is extracted correctly (i.e., proper handling of negations such as “… tests indicate lack of evidence of …”) and that individual patient records are accurate. To properly identify and remediate errors, organizations will need to develop and deploy new data mining tools.

Organizations need to understand what data they will use today, and any potential data that they may want to access in the future. This can include data from mobile or remote devices, implanted devices, text messages and e-mails between patients and providers, and data from third parties or health information exchanges. Organizations will also need to establish a data acquisition roadmap based on business and analysis priorities.

3. Normalize, integrate, and organize Big Data solutions
After all data sources have been identified, a plan needs to be developed for how data will be normalized, integrated with, and organized into the Big Data solution. The plan should address technology requirements as well as business objectives, and must ensure that data are accurate and complete. Big Data solutions present even greater challenges than traditional data and analytic solutions as the volume of data is multiplied many times. The quality of many data sources accessed may have never been evaluated before.

For individually-focused analytics, most Big Data solutions require a complete view of patient and provider data. The ability to recognize relationships between patients and providers, households, payers, and organizations may also be helpful but difficult to achieve given the number of data sources. Any Big Data solution should support the systems, data, and information needs that organizations have today, but also must be configurable and flexible enough to adapt and meet future requirements.

4. Protect security and privacy of Big Data
Data privacy and security must also be a key component of any Big Data solution. All systems, data flows, and information lifecycles must be accounted for and the privacy of personally identifiable information protected. Organizations need to consider what types of information they expect to generate, and whether it will be individually identified or population-based. Data that are used for population-based clinical research to detect diseases or disease patterns usually masks or removes the identities of individuals before the database is populated with the clinical information. But due diligence should be taken to check if the information is de-identified before using the data.

Since healthcare Big Data solutions may use data from many different sources and be predictive and inferential in nature, there may be uncertainty within an organization about how to apply privacy and security mandates like the HIPAA requirements, the Fair Credit Reporting Act, and the Federal Trade Commission’s Fair Information Practice Principles (FIPPs). The best way to address privacy concerns or requirements is for Big Data solutions to support FIPPs. FIPPs are industry-agnostic, basic information privacy principles that can guide the thorny discussions that may be required when analytic projects cross industries, data sources, and data types.

“FIPPs are a roadmap for good data stewardship and the foundation for regulations or policies understood and practiced around the globe,” says Deven McGraw, JD, director of the Health Privacy Project, Center for Democracy and Technology, and member of the Office of the National Coordinator for Health IT’s Health IT Policy Committee. “Since many organizations will deploy healthcare Big Data solutions that use data from outside their walls, they must be able to assure consumers that they have put the appropriate privacy practices in place and that only authorized personnel can access data.”

Big Data’s HIM Opportunities
The move to Big Data solutions provides HIM professionals with significant opportunities for advancement. Those professionals who have an understanding of Big Data and know how to apply HIM principles and data management skills to Big Data implementations will have the most growth opportunities.

Big Data offers HIM professionals the chance to play a strategic role in crafting the next level of healthcare information management, and act as key stakeholders in advancing the strategic use of Big Data across the healthcare ecosystem.

As the industry transforms, it becomes essential for HIM professionals to move beyond the principles of record maintenance and documentation and develop an understanding for data transport, mapping processes, and other Big Data characteristics. Continuing education can help to expand individual knowledge and expertise in health informatics, data management, clinical vocabularies, and data standards—all important aspects of Big Data solution planning. For example, being well-versed in key concepts such as the Systematized Nomenclature of Medicine (SNOMED) classification system and the Logical Observation Identifiers Names and Codes (LOINC) can empower an HIM professional to champion the use of data across systems and facilitate interoperability.

From a broad perspective, HIM professionals should ensure that industry leadership understands the value that HIM brings to Big Data. Not only is it important for HIM professionals to get involved in Big Data planning, but they must come prepared to work with the organizational team and address data and information on a whole new level.

References
Office of Science and Technology Policy, Executive Office of the President of the United States of America. “Obama administration unveils ‘Big Data’ initiative: announces $200 million in new R&D investments.” March 29, 2012. http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_press_release_final_2.pdf.
US Department of Health and Human Services National Institutes of Health. “1000 Genomes Project data available on Amazon Cloud.” March 29, 2012. http://www.nih.gov/news/health/mar2012/nhgri-29.htm.
Federal Trade Commission. “Fair Information Practice Principles.” http://www.ftc.gov/reports/privacy3/fairinfo.shtm.
Lorraine Fernandes (lfernand@us.ibm.com) is global healthcare industry ambassador and Michele O’Connor (moconno@us.ibm.com) is global MDM sales at IBM. Victoria Weaver (victoria.weaver@hcahealthcare.com) is assistant vice president, clinical data management at HCA.

Article citation:
Fernandes, Lorraine; O’Connor, Michele; Weaver, Victoria. "Big Data, Bigger Outcomes." Journal of AHIMA 83, no.10 (October 2012): 38-43.