Big Data Helps Kaiser Close Healthcare Gaps

 Analytics from massive clinical data repository are central to closing gaps in care, HIMSS attendees told.

Neil Versel,  Information Week, March 07, 2013

One benefit of Kaiser Permanente spending an estimated $6 billion for an integrated electronic health records (EHR) system to serve 9 million people across eight regions from coast to coast is it that has amassed a vast repository of clinical data. That storehouse also contains information from a patient portal, ancillary systems, smart medical devices and even home-based patient monitoring systems.

All those terabytes of electronic data now are helping to fuel a massive analytics operation, part of an overall organizational goal of improving care and reining in costs. "It's all about the data and information, not the electronic health record," Carol Cain, senior director of clinical information services for the Kaiser Permanente Care Management Institute, said this week at the Healthcare Information and Management Systems Society (HIMSS) annual conference in New Orleans.

Kaiser has embraced a concept of "complete care," which one Southern California Permanente Medical Group described as "giving my patients everything they need, whether they know it or not," according to Cain's presentation.

"We need to incorporate so much more data that is available," Cain said. Data needs to be "synthesized in a meaningful way" and delivered to primary care physicians at the point of care to help suggest appropriate interventions.

Cain said Kaiser views big data as being characterized by "volume, variety and velocity." The term "refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze," she said.

"Our ability to monitor our members' health is greater than our members' ability to know what needs to be monitored," Cain explained.

[ Are your patients taking leadership for their own health? See 7 Portals Powering Patient Engagement. ]

Kaiser Permanente has developed several modules of population management, all designed to identify and close gaps in care. If a patient shows up with knee pain, for example, management tools suggest doctors ask about a cancer screening, in an effort to make office visits "proactive" and organize care around the concept of the patient-centered medical home, Cain said.

The analytics also has to be done in a way that won't make patients feel like Big Brother is watching over them, Cain said. Instead, Kaiser wants people to think that the integrated delivery system is helping to prevent illness and find health problems early. If patients allow Kaiser to access information linked to their supermarket loyalty cards, the organization will not send warnings every time they purchase a candy bar, Cain said.

What Kaiser can do is rely on its platform to combine patient-specific knowledge, such as whether an individual has filled a prescription. This can help with medication adherence, according to Cain. Analytics are helpful for developing care plans before patients are discharged from hospitals, too.

Kaiser also can advise patients to telephone or schedule e-visits if a primary care physician determines a problem is not worth an in-person appointment. "That is something that is often appreciated by our members," Cain noted.

Cain said that patient needs are not always clinical, either. During a 12-hour hackathon in the analytics department, Kaiser IT professionals were able to correlate access to parks with rates of obesity in Oakland, Calif. "In some of our communities, we are investing in building parks," Cain said. Kaiser also has partnerships with YMCA and schools in some areas to address lifestyle issues that can affect health.

A Cure for Cancer? This ‘Big Data’ Startup Says It Can Deliver

Christina Farr, The Washington Post, January 17

‘Big data’ is one of the most over-used buzzwords in the startup vernacular, and founders rarely have the goods to back it up. So you’ll understand that I was intrigued — but highly skeptical — when an email with the subject line “using data to cure cancer” popped into my inbox.

But Ayasdi, a startup that closed $10 million in venture funding Wednesday, doesn’t just talk the talk. Stanford researchers have been baking the complex algorithms behind Ayasdi (its quirky name means “to seek,” in Cherokee) for over a decade, with the goal of unlocking the hidden value in human genetic data.
In 2008, the founders, Gurjeet Singh, Dr. Gunnar Carlsson, and Harlan Sexton, decided to commercialize the technology.

With the government stepping up its funding for science, they were able to pull in $3.5 million in grants from DARPA, the department of defense agency responsible for building new technology for the military, and the National Science Foundation. The result? A synthesis of machine learning technology and topological data analysis (TDA) that has impressed a score of Silicon Valley investors.

Rather than typing in search-style queries, the tools allow for automated discovery of information. As Dr. Carlsson explained in an interview, “The idea is to answer questions that you didn’t know to ask.”

This year, the 30-person team of engineers will expand its marketing and sales efforts with funding from Khosla Ventures, Floodgate, Data Collective’s Matt Ocko, serial entrepreneur, Steve Blank, and more.

Storied investor Vinod Khosla, who rocked the medical world with the statement that 80 percent of doctors would be replaced by machines, said Ayasdi’s “machine powered intelligence” has the potential to unearth “previously unattainable insights that will help solve some of our most pressing global, social, and economic issues.”

Eric Schadt, Director of the Institute for Genomics and Multiscale Biology, has a team of researchers using the technology to identify the genetic predispositions of many diseases, including cancer, which they hope will help them “glean new insights that will lead to breakthrough drug therapies.”

Related: In the burgeoning field of genomics, entrepreneurs aim to deliver more personalized treatments for life-threatening diseases.

Ayasdi is working with the nation’s top hospitals and medical researchers to uncover more targeted treatments for disease. Singh, the company’s CEO, told me that hospitals and big pharmas are routinely pulling data from public sources — medical researchers are required to publish their data  – and combine it with private data to yield new insights.

The data isn’t anything new — it’s the technology that has evolved. “We have automated the discovery of knowledge from data,” said Singh in a phone interview. “We were able to discover a new type of breast cancer without asking questions.”

Singh was referring to a recent breakthrough where Ayasdi mapped 14 variants of breast cancer. Using data collected during a 15 year period, and studied by thousands of scientists, the algorithms discovered a sub-group of patients that have a higher chance of survival based on their genetic profile.

If a patient falls into this sub-group, it is unlikely that they will require chemotherapy.

In another recent partnership, with Mount Sinai Medical Center, Ayasdi was used to point to targeted treatment options for E. Coli sufferers. E. Coli affects more than 265,000 people in the U.S. every year, and millions around the world. It is known in the medical community for developing resistance to many drugs, and doctors are never 100 percent sure if a treatment will work or not.

Mount Sinai is using Ayasdi to analyze the entire E. Coli genome sequence, which includes more than 1 million DNA variants. This will further our understanding of why some types of E. Coli develop resistance to antibiotics and how we can combat the spread of the bacteria.

Singh, a former researcher at Stanford, told me that the company has secured 20 customers in the oil and gas, government, pharmaceutical, and healthcare sectors. Big name customers include Merck, the Food and Drug Administration, and the U.S. Department of Agriculture.

Copyright 2013, VentureBeat

Approaching Illness as a Team

The New York Times, December 25, 2012

The Cleveland Clinic, long considered a premier medical system, is gaining new renown for innovation in improving the quality of care while holding down costs.

In its most fundamental reform, the clinic in the past five years has created 18 “institutes” that use multidisciplinary teams to treat diseases or problems involving a particular organ system, say the heart or the brain, instead of having patients bounce from one specialist to another on their own.

The Neurological Institute, for example, provides both inpatient or outpatient care for those 
with strokes and brain tumors, as well as those with epilepsy, multiple sclerosis, depression and sleep disorders, among other conditions.

On a recent visit, we observed one such team, consisting of a neurosurgeon, a neurologist, a neuroradiologist, a neurologist with advanced training in intensive care, a physical and rehabilitation doctor, a medical resident, a physical therapist and a nurse. As they made rounds from patient to patient, they had a portable computer that displayed electronic medical records so that the whole team could see how the patient was doing and plan the course of care for the day.

This team approach can improve the quality of care because all the experts are involved in deciding the best treatment option, which can save time and money. The neurological team, by consensus, has been better able to determine which acute stroke patientsneed a risky and expensive treatment that involves threading a catheter through an artery in the leg up into the brain to destroy a clot. It cut the use of that treatment in half, reducing costs and deaths and improving outcomes.

The Cleveland Clinic has strong leverage to drive such reforms because its staff physicians are salaried and are granted only one-year contracts and subjected to annual performance reviews. Those reviews apply measures of quality, like patient improvement, patient satisfaction and cost reductions. It raises the pay of those who get high marks, reduces the pay of poor performers and even terminates some doctors who fall short. This approach could become more widespread as more hospitals and doctors move toward the salary-based model.

Data analysis to evaluate how well treatments work is also a big part of the medical practice. For instance, the clinic analyzed outcomes for heart surgery patients and found that those who had received blood transfusions during surgery had higher complication rates afterward and a lower long-term survival rate. As a result, it has adopted strict guidelines that limit the use of transfusions.

Such judgments about a treatment’s effectiveness are made by doctors, not by financial administrators, so they tend to be accepted. One analysis found that suturing could be done as well with a $5 silk stitch as with a $400 staple, leading to a big drop in the use of the staples. At the same time, the clinic has also carried out simpler reforms, like improving sterile conditions, which has reduced catheter-related bloodstream infections by more than 40 percent and urinary tract infections by 50 percent. All this has happened in a remarkably short time. Patients seem to like the treatment they get. A federal government survey of patient opinion last fall found that 80 percent of the patients gave the Cleveland Clinic a high rating over all and 84 percent would recommend it to others, well above the state and national averages in the 69 percent to 71 percent range.

Still, many patients are clearly unhappy. A series this year about confusing medical bills and unexpectedly high charges by The Plain Dealer of Cleveland elicited hundreds of patients’ complaints mostly directed against the clinic, because it had reclassified off-campus physician practices and health centers as hospital outpatient facilities and tacked on a “facility fee” for services previously billed at lower doctor’s office rates. The clinic says the added fees are justified because it provides better quality controls and health information technologies in its outpatient units than that available in a typical doctor’s office.

Medicare’s spending per patient at the clinic for an episode of illness that requires hospitalization is below the national median, suggesting that the clinic’s cost-cutting efforts are working. The University HealthSystem Consortium, an alliance of the nation’s leading nonprofit academic medical centers and teaching hospitals, gave the clinic one of its “rising star” awards in September for significant improvements over the previous year in quality, patient safety and clinical effectiveness, an indication that its quality efforts are taking hold.
The Cleveland Clinic’s progress in restructuring itself, said Michael Porter, a Harvard professor who analyzes health care delivery and organizational change, is “light speed” compared with other institutions. The clinic is “a model of where we need to go,” he said, “Not perfect, not done, but far along.”

Social Media a Healthcare Data Gold Mine

April Dembosky, The Financial Times, December 11, 2012

Bill Schmarzo envisions a future where holiday photos posted on Facebook become a gauge of a person’s weight loss or gain over time.

The chief technology officer for EMC’s consultant services acknowledges that privacy advocates are unlikely to allow his fantasy to become reality, but the technology that can measure minute body changes in photographs and feed it into someone’s electronic health record already exists.

 “The scary thing is if that data could be used to deny care and insurability,” Mr Schmarzo says.
Healthcare companies are loathe to tread into such sensitive territory, but they are keenly aware of the gold mine of health data stored in people’s social media accounts.

“Studies have shown that people are more willing to share more private medical information in social media than they’re willing to share with their medical providers,” says Martin Kohn, chief medical scientist at IBM.

Pharmaceutical companies already analyse social media sites to track reports of side-effects of their drugs. That data can help correct formulas more quickly than waiting for the results of years-long clinical trials; it can also be used to set prices and test marketing slogans.

Public health officials are also interested in social media data as a source of information on disease outbreaks. Several start-ups are working on algorithms that study Facebook, Twitter, and blog posts to track early signs of infectious disease outbreaks, as people generally complain to their friends long before public health agencies can collect doctors’ reports and issue official warnings.

One outbreak of the Norovirus stomach bug at a student journalism conference in Canada was live-tweeted earlier this year, with posts such as “Motion that nobody else on this bus puke” and “36 hours without leaving my hotel room” signalling its coming and going before any traditional surveillance system had noted it.

The trouble with social media data are that they are fragmented and incomplete, warns James Kaufman, manager of public health research at IBM’s Silicon Valley lab, and there are limits to the types of insights that can be drawn from such patient-reported data.

“Colds, flus, sure people report that,” he says, “but no one’s going to report Aids or haemorrhoids.”

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.

Who Are the Doctors Most Trusted by Doctors? Big Data Can Tell You.

Ki Mae Heussner, GigaOm, November 16, 2012

ZocDoc, Healthgrades, Vitals, Yelp and other sites can tell you what patients think of their doctors. But finding out in any aggregate way what doctors think of their peers has been much harder, if not near impossible, for patients — up until now.

By accessing information in government databases through FOIA (Freedom of Information Act) requests, healthcare innovators are now able to share connections between doctors that are based on millions of physician referrals — a valuable indicator of who doctors hold in esteem.

Last month, Fred Trotter, a self-identified “hacktivist,” revealed that he had obtained a dataset of Medicare physician referrals through a FOIA request and was making the initial data available to those who supported a Medstartr crowdfunding campaign meant to build out his “DocGraph” and make it freely available. This week, he announced that he not only blew past his $15,000 funding goal, but was launching a second campaign to integrate his current data with an additional dataset.

HealthTap, a Palo Alto-based startup that connects patients with an online network of 17,000 doctors, also this week launched a new feature based partly on Trotter’s data. Called “DOConnect,” it combines Trotter’s Medicare data with physician data from its own site and other sources to give patients a new window into their doctors’ networks.

“This isn’t just friendships and business connections. This is who doctors trust,” said HealthTap co-founder and CEO Ron Gutman. “If you could know who your doctor’s doctor is, if you knew who they would choose, this lets you see that for the first time.”

The new tool, which reflects 25 million doctor referral connections, enables patients to see how many doctors are linked to a particular doctor, as well as their locations. As patients search for new physicians and specialists, being able to see who their current doctors are linked with could help them decide who to visit.  
It also gives doctors an opportunity to build online networks that reflect their offline networks, Gutman said. In a post about his “DocGraph” project, Trotter said that his data wasn’t strictly a “referral” data set because, in some cases, doctors might be linked through a patient they both happened to see at the same time, not through an active referral. But Gutman emphasized that HealthTap’s DOConnect considered more than Medicare referrals in mapping connections between doctors.

In releasing the dataset, Trotter said his main goal was to create doctor-rating algorithms that “patients find useful and doctors find fair.” But he also hoped that academics, health policy wonks, entrepreneurs and others would use it to bring more transparency to health care overall.

Todd Park, the U.S. Chief Technology Officer, has frequently talked up the value of “setting data free” and has backed hackathons, “datapaloozas” and other open data initiatives to highlight the need for innovators to use government data for the public good — this is a great example of that vision and, hopefully, points to more similar projects in the future.

“Our goal is to empower the patient, make the system transparent and accountable, and release this d

Crowdsourcing Medical Treatments

Samahope Crowdsources Simple, Life-Saving Surgeries For The Poor

Ellen McGirt, Fast Company, November 2, 2012

Veteran social entrepreneur Leila Janah of Samasource recently co-launched a new project to crowdfund medical treatment for the very poor. Think of it as Kiva for surgery.


“I just started bawling,” Leila Janah is telling me about a trip she took to Sierra Leone earlier this year. "I’m usually pretty steely as a matter of course. But I’ve been crying a lot more lately.”

Janah, the founder of Samasource, a nonprofit organization that brings paid digital work to very poor women and youth, is no stranger to harsh realities. She studies them for a living.

But a sweet teenaged girl named Tiangay Kaiwo moved Janah to tears. Kaiwo was waiting for surgery at the government hospital in Bo to repair the extensive damage to her body after her teacher brutally raped her. Traditional tribal remedies involving herbs and a bath in boiling water exacerbated her condition. Kaiwo had been living with a painful rectovaginal fistula for over a year, but best as Janah could tell, the rapes began when she was 12 years old. Janah met dozens of girls and women with similar stories, all needing life-changing surgeries that nobody could afford. “Nobody even to hold their hands, to tell them that this wasn’t their fault,” Janah said.

When the slice of the market you are trying to corner is as troubled as the one that Janah is, then tears are clearly a rational response. But after tears, at least if you’re built like Janah, comes action.

Enter her latest project, Samahope, an experiment in crowdfunding medical treatment--like burn care--for the very poor. Think of it as Kiva for surgery. “There are millions of people who need corrective surgeries that we take for granted in the West,” she says. But the very poor often need types of care, like fistula surgery, that are now wholly unfamiliar and largely unnecessary in the developed world. (You can contribute to the development of the site via their Indiegogo campaign here.)

The site launched last month and has already funded a handful of surgeries; there are over 70 profiles on the site. If you believe in the premise of the League of Extraordinary Women, then the business case is clear: If you get one girl back on her feet, she can go to school. If she goes to school, she can get a job. Enough girls join the workforce and a country gets uplifted. But Janah sees another benefit. “They want to say what happened to them, to tell their own stories,” she says.

She has collected so many of these stories--of the poor and the embattled and their search for basic human rights through employment--that she’s writing a book. “I just interviewed a security guard at a hotel in Freetown," Janah said. "He grew up as a rebel and child soldier in the conflict--think about that for a minute--then forced into the diamond mines. His life was so full of conflict, a constant struggle to access basic human resources, that it’s impossible to wrap my mind around.” Recalling young Kaiwo, “For someone like her, being able to tell her story and help other girls not become a victim is a very powerful thing.”

Samasource, which last month closed a $7.5 million round of philanthropic funding led by The MasterCard Foundation, has become the darling of the tech crowd for its deft use of the Internet to match an excess capacity of potential workers with the jobs they need to live in dignity. “But the scale of the problems can seem so great compared to the resources you have to address them,” says Janah--thus, the crowdsourcing project. Unlike her peers in the for-proft tech world, she is not going to be able to turn to her staffers with breathless reports of sky-high valuations, rounds of venture funding, or promises of equity upside.
And yet, Janah is convinced that dignified work can resurrect even the most damaged lives, and that her own business case is sound. “We’ve gotten the microwork model on the agenda of a lot of foundations and government entities. We just have to prove it can scale.”

Janah recalls with fondness the “aha” moment when she knew that Samasource could actually be a business. But the slog of ramping up to achieve a massive goal is largely free of lightbulb moments. “Not a day goes by when I don’t doubt myself or question something,” she says. So instead, she takes the power of microwork and puts it to work for herself and her team. “I had to manage my own psychology around this. So, I’ve trained myself to pause and celebrate each step.”

She rattles off a list of things that sound more startup than do-good: Realign your expectations, hit your goals, stay close to the customer, stay connected to your mission. She ends up sounding more like a Zen master than elevator pitcher. “It’s about looking down and doing what’s in front of you. Truly savor it. Then do the next thing. My job is to make sure we’re all going in the right direction, and at the end of the year, the sum of those steps adds up to something really great.”

Data from Health Care Reviews Could Power "Yelp for Health Care" Startups

Data-driven decision engines will need patient experience to complete the feedback loop.

Alex Howard, O'Reilly Radar, October 17, 2012

Given where my work and health has taken me this year, I’ve been thinking much more about the relationship of the Internet and health data to accountability and patient-driven health care.

When I was looking for a place in Maine to go for care this summer, I went online to look at my options. I consulted hospital data from the government at HospitalCompare.HHS.gov and patient feedback data on Yelp, and then made a decision based upon proximity and those ratings. If I had been closer to where I live in Washington D.C., I would also have consulted friends, peers or neighbors for their recommendations of local medical establishments.

My brush with needing to find health care when I was far from home reminded me of the prism that collective intelligence can now provide for the treatment choices we make, if we have access to the Internet.

Patients today are sharing more of their health data and experiences online voluntarily, which in turn means that the Internet is shaping health care. There’s a growing phenomenon of “e-patients” and caregivers going online to find communities and information about illness and disability.

Aided by search engines and social media, newly empowered patients are discussing health conditions with others suffering from disease and sickness — and they’re taking that peer-to-peer health care knowledge into their doctors’ offices with them, frequently on mobile devices. E-patients are sharing their health data of their own volition because they have a serious health condition, want to get healthy, and are willing.

From the perspective of practicing physicians and hospitals, the trend of patients contributing to and consulting on online forums adds the potential for errors, fraud, or misunderstanding. And yet, I don’t think there’s any going back from a networked future of peer-to-peer health care, anymore than we can turn back the dial on networked politics or disaster response.

What’s needed in all three of these areas is better data that informs better data-driven decisions. Some of that data will come from industry, some from government, and some from citizens.

This fall, the Obama administration proposed a system for patients to report medical mistakes. The system would create a new “consumer reporting system for patient safety” that would enable patients to tell the federal government about unsafe practices or errors. This kind of review data, if validated by government, could be baked into the next generation of consumer “choice engines,” adding another layer for people, like me, searching for care online.

There are precedents for the collection and publishing of consumer data, including the Consumer Product Safety Commission’s public complaint database at SaferProducts.gov and the Consumer Financial Protection Bureau’s complaint database. Each met with initial resistance by industry but have successfully gone online without massive abuse or misuse, at least to date.

It will be interesting to see how medical associations, hospitals and doctors react. Given that such data could amount to government collecting data relevant to thousands of “Yelps for health care,” there’s both potential and reason for caution. Health care is a bit different than product safety or consumer finance, particularly with respect to how a patient experiences or understands his or her treatment or outcomes for a given injury or illness. For those that support or oppose this approach, there is an opportunity for public comment on proposed data collection at the Federal Register.

The power of performance data
Combining patients review data with government-collected performance data could be quite powerful in helping to drive better decisions and adding more transparency to health care.
In the United Kingdom, officials are keen to find the right balance between open data, transparency and prosperity.

“David Cameron, the Prime Minister, has made open data a top priority because of the evidence that this public asset can transform outcomes and effectiveness, as well as accountability,” said Tim Kelsey, in an interview this year. He used to head up the United Kingdom’s transparency and open data efforts and now works at its National Health Service.

“There is a good evidence base to support this,” said Kelsey. “Probably the most famous example is how, in cardiac surgery, surgeons on both sides of the Atlantic have reduced the number of patient deaths through comparative analysis of their outcomes.”

More data collected by patients, advocates, governments and industry could help to shed light on the performance of more physicians and clinics engaged in other expensive and lifesaving surgeries and associated outcomes.

Should that be extrapolated across the medical industry, it’s a safe bet that some medical practices or physicians will use whatever tools or legislative influence they have to fight or discredit websites, services or data that puts them in a poor light. This might parallel the reception that BrightScope’s profiles of financial advisors have received in industry.

When I talked recently with Dr. Atul Gawande about health data and care givers, he said more transparency in these areas is crucial:

“As long as we are not willing to open up data to let people see what the results are, we will never actually learn. The experience of what happens in fields where the data is open is that it’s the practitioners themselves that use it.”

In that context, health data will be the backbone of the disruption in health care ahead. Part of that change will necessarily have to come from health care entrepreneurs and watchdogs connecting code to research. In the future, a move to open science and perhaps establish a health data commons could accelerate that change.

The ability of caregivers and patients alike to make better data-driven decisions is limited by access to data. To make a difference, that data will also need to be meaningful to both the patient and the clinician, said Dr. Gawande. He continued:

“[Health data] needs to be able to connect the abstract world of data to the physical world of what really happens, which means it has to be timely data. A six-month turnaround on data is not great. Part of what has made Wal-Mart powerful, for example, is they took retail operations from checking their inventory once a month to checking it once a week and then once a day and then in real-time, knowing exactly what’s on the shelves and what’s not. That equivalent is what we’ll have to arrive at if we’re to make our systems work. Timeliness, I think, is one of the under-recognized but fundamentally powerful aspects because we sometimes over prioritize the comprehensiveness of data and then it’s a year old, which doesn’t make it all that useful. Having data that tells you something that happened this week, that’s transformative.”

Health data, in other words, will need to be open, interoperable, timely, higher quality, baked into the services that people use, and put at the fingertips of caregivers, as US CTO Todd Park explains in the video below:

There is more that needs to be done than simply putting “how to live better” information online or into an app. To borrow a phrase from Robert Kirkpatrick, for data to change health care, we’ll need to apply the wisdom of the crowds, the power of algorithms and the intuition of experts to find meaning in health data and help patients and caregivers alike make better decisions.

That isn’t to say that health data, once published, can’t be removed or filtered. Witness the furor over the removal of a malpractice database from the Internet last year, along with its restoration.

But as more data about doctors, services, drugs, hospitals and insurance companies goes online, the ability of those institutions to control public perception of the institutions will shift, just as it has with government and media. Given flaws in devices or poor outcomes, patients deserve such access, accountability and insight.

Enabling better health-data-driven decisions to happen across the world will be far from easy. It is, however, a future worth building toward.

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.      

When data disrupts health care

The convergence of data, privacy and cost have created a unique opportunity to reshape health care.

Mac Slocum, O'Reilly Radar, September 18, 2012

Health care appears immune to disruption. It’s a space where the stakes are high, the incumbents are entrenched, and lessons from other industries don’t always apply.

Yet, in a recent conversation between Tim O’Reilly and Roger Magoulas it became evident that we’re approaching an unparalleled opportunity for health care change. O’Reilly and Magoulas explained how the convergence of data access, changing perspectives on privacy, and the enormous expense of care are pushing the health space toward disruption.

As always, the primary catalyst is money. The United States is facing what Magoulas called an “existential crisis in health care costs” [discussed at the 3:43 mark]. Everyone can see that the current model is unsustainable. It simply doesn’t scale. And that means we’ve arrived at a place where party lines are irrelevant and tough solutions are the only options.

“Who is it that said change happens when the pain of not changing is greater than the pain of changing?” O’Reilly asked. “We’re now reaching that point.” [3:55]

(Note: The source of that quote is hard to pin down, but the sentiment certainly applies.)

This willingness to change is shifting perspectives on health data. Some patients are making their personal data available so they and others can benefit. Magoulas noted that even health companies, which have long guarded their data, are warming to collaboration.

At the same time there’s a growing understanding that health data must be contextualized. Simply having genomic information and patient histories isn’t good enough. True insight — the kind that can improve quality of life — is only possible when datasets are combined.

“Genes aren’t destiny,” Magoulas said. “It’s how they interact with other things. I think people are starting to see that. It’s the same with the EHR [Electronic Health Record]. The EHR doesn’t solve anything. It’s part of a puzzle.” [4:13]

And here’s where the opportunity lies. Extracting meaning from datasets is a process data scientists and Silicon Valley entrepreneurs have already refined. That means the same skills that improve mindless ad-click rates can now be applied to something profound.

“There’s this huge opportunity for those people with those talents, with that experience, to come and start working on stuff that really matters,” O’Reilly said. “They can save lives and they can save money in one of the biggest and most critical industries of the future.” [5:20]

The language O’Reilly and Magoulas used throughout their conversation was telling. “Save lives,” “work on stuff that matters,” “huge opportunity” — these aren’t frivolous phrases. The health care disruption they discussed will touch everyone, which is why it’s imperative the best minds come together to shape these changes.

The full conversation between O’Reilly and Magoulas is available in the following video.

Here are key points with direct links to those segments:

· Internet companies used data to solve John Wanamaker’s advertising dilemma (“Half the money I spend on advertising is wasted; the trouble is I don’t know which half”). Similar methods can apply to health care. [17 seconds in]

· The “quasi-market system” of health care makes it harder to disrupt than other industries. [3:15]

· The U.S. is facing an existential crisis around health care costs. “This is bigger than one company.” [3:43]

· We can benefit from the multiple data types coming “on stream” at the same time. These include electronic medical records, inexpensive gene sequencing, and personal sensor data. [4:28]

· The availability of different datasets presents an opportunity for Silicon Valley because data scientists and technologists already have the skills to manage the data. Important results can be found when this data is correlated: “The great thing is we know it can work.” [5:20]

· Personal data donation is a trend to watch. [6:40]

· Disruption is often associated with trivial additions to the consumer Internet. With an undisrupted market like health care, technical skills can create real change. [7:04]

· “There’s no question this is going to be a huge field.” [8:15]

If the disruption of health care and associated opportunities interests you, O’Reilly has more to offer. Check out our interviews, ongoing coverage, our recent report, “Solving the Wanamaker problem for health care,” and the upcoming Strata Rx conference in San Francisco.


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