The business case for integrating claims and clinical data

Curated on Posted on by Stefaan Verhulst

Claudia Williams at MedCityNews: “The path to value-based care is arduous. For health plans, their ability to manage care, assess quality, lower costs, and streamline reporting is directly impacted by access to clinical data. For providers, the same can be said due to their lack of access to claims data. 

Providers and health plans are increasingly demanding integrated claims and clinical data to drive and support value-based care programs. These organizations know that clinical and claims information from more than a single organization is the only way to get a true picture of patient care. From avoiding medication errors to enabling an evidence-based approach to treatment or identifying at-risk patients, the value of integrated claims and clinical data is immense — and will have far-reaching influence on both health outcomes and costs of care over time.

On July 30, Medicare announced the Data at the Point of Care pilot to share valuable claims data with Medicare providers in order to “fill in information gaps for clinicians, giving them a more structured and complete patient history with information like previous diagnoses, past procedures, and medication lists.” But that’s not the only example. To transition from fee-for-service to value-based care, providers and health plans have begun to partner with health data networks to access integrated clinical and claims data: 

Health plan adoption of integrated data strategy

A California health plan is partnering with one of the largest nonprofit health data networks in California, to better integrate clinical and claims data. …

Providers leveraging claims data to understand patient medication patterns 

Doctors using advanced health data networks typically see a full list of patients’ medications, derived from claims, when they treat them. With this information available, doctors can avoid dangerous drug to-drug interactions when they prescribe new medications. After a visit, they can also follow up and see if a patient actually filled a prescription and is still taking it….(More)”.

Complex Systems Change Starts with Those Who Use the Systems

Curated on Posted on by Stefaan Verhulst

Madeleine Clarke & John Healy at Stanford Social Innovation Review: “Philanthropy, especially in the United States and Europe, is increasingly espousing the idea that transformative shifts in social care, education, and health systems are needed. Yet successful examples of systems-level reform are rare. Concepts such as collective impact (funder-driven, cross-sector collaboration), implementation science (methods to promote the systematic uptake of research findings), and catalytic philanthropy (funders playing a powerful role in mobilizing fundamental reforms) have gained prominence as pathways to this kind of change. These approaches tend to characterize philanthropy—usually foundations—as the central, heroic actor. Meanwhile, research on change within social and health services continues to indicate that deeply ingrained beliefs and practices, such as overly medicalized models of care for people with intellectual disabilities, and existing resource distribution, which often maintains the pay and conditions of professional groups, inhibits the introduction of reform into complex systems. A recent report by RAND, for example, showed that a $1 billion, seven-year initiative to improve teacher performance failed, and cited the complexity of the system and practitioners’ resistance to change as possible explanations. 

We believe the most effective way to promote systems-level social change is to place the voices of people who use social services—the people for whom change matters most—at the center of change processes. But while many philanthropic organizations tout the importance of listening to the “end beneficiaries” or “service users,” the practice nevertheless remains an underutilized methodology for countering systemic obstacles to change and, ultimately, reforming complex systems….(More)”.

The Why of the World

Curated on Posted on by Stefaan Verhulst

Book review by Tim Maudlin of The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie: “Correlation is not causation.” Though true and important, the warning has hardened into the familiarity of a cliché. Stock examples of so-called spurious correlations are now a dime a dozen. As one example goes, a Pacific island tribe believed flea infestations to be good for one’s health because they observed that healthy people had fleas while sick people did not. The correlation is real and robust, but fleas do not cause health, of course: they merely indicate it. Fleas on a fevered body abandon ship and seek a healthier host. One should not seek out and encourage fleas in the quest to ward off sickness.

The rub lies in another observation: that the evidence for causation seems to lie entirely in correlations. But for seeing correlations, we would have no clue about causation. The only reason we discovered that smoking causes lung cancer, for example, is that we observed correlations in that particular circumstance. And thus a puzzle arises: if causation cannot be reduced to correlation, how can correlation serve as evidence of causation?

The Book of Why, co-authored by the computer scientist Judea Pearl and the science writer Dana Mackenzie, sets out to give a new answer to this old question, which has been around—in some form or another, posed by scientists and philosophers alike—at least since the Enlightenment. In 2011 Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning,” and this book sets out to explain what all that means for a general audience, updating his more technical book on the same subject, Causality, published nearly two decades ago. Written in the first person, the new volume mixes theory, history, and memoir, detailing both the technical tools of causal reasoning Pearl has developed as well as the tortuous path by which he arrived at them—all along bucking a scientific establishment that, in his telling, had long ago contented itself with data-crunching analysis of correlations at the expense of investigation of causes. There are nuggets of wisdom and cautionary tales in both these aspects of the book, the scientific as well as the sociological…(More)”.

How to Build Artificial Intelligence We Can Trust

Curated on Posted on by Stefaan Verhulst

Gary Marcus and Ernest Davis at the New York Times: “Artificial intelligence has a trust problem. We are relying on A.I. more and more, but it hasn’t yet earned our confidence.

Tesla cars driving in Autopilot mode, for example, have a troubling history of crashing into stopped vehicles. Amazon’s facial recognition system works great much of the time, but when asked to compare the faces of all 535 members of Congress with 25,000 public arrest photos, it found 28 matches, when in reality there were none. A computer program designed to vet job applicants for Amazon was discovered to systematically discriminate against women. Every month new weaknesses in A.I. are uncovered.

The problem is not that today’s A.I. needs to get better at what it does. The problem is that today’s A.I. needs to try to do something completely different.

In particular, we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets — often using an approach known as deep learning — and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality….

We face a choice. We can stick with today’s approach to A.I. and greatly restrict what the machines are allowed to do (lest we end up with autonomous-vehicle crashes and machines that perpetuate bias rather than reduce it). Or we can shift our approach to A.I. in the hope of developing machines that have a rich enough conceptual understanding of the world that we need not fear their operation. Anything else would be too risky….(More)”.

What statistics can and can’t tell us about ourselves

Curated on Posted on by Stefaan Verhulst

Hannah Fry at The New Yorker: “Harold Eddleston, a seventy-seven-year-old from Greater Manchester, was still reeling from a cancer diagnosis he had been given that week when, on a Saturday morning in February, 1998, he received the worst possible news. He would have to face the future alone: his beloved wife had died unexpectedly, from a heart attack.

Eddleston’s daughter, concerned for his health, called their family doctor, a well-respected local man named Harold Shipman. He came to the house, sat with her father, held his hand, and spoke to him tenderly. Pushed for a prognosis as he left, Shipman replied portentously, “I wouldn’t buy him any Easter eggs.” By Wednesday, Eddleston was dead; Dr. Shipman had murdered him.

Harold Shipman was one of the most prolific serial killers in history. In a twenty-three-year career as a mild-mannered and well-liked family doctor, he injected at least two hundred and fifteen of his patients with lethal doses of opiates. He was finally arrested in September, 1998, six months after Eddleston’s death.

David Spiegelhalter, the author of an important and comprehensive new book, “The Art of Statistics” (Basic), was one of the statisticians tasked by the ensuing public inquiry to establish whether the mortality rate of Shipman’s patients should have aroused suspicion earlier. Then a biostatistician at Cambridge, Spiegelhalter found that Shipman’s excess mortality—the number of his older patients who had died in the course of his career over the number that would be expected of an average doctor’s—was a hundred and seventy-four women and forty-nine men at the time of his arrest. The total closely matched the number of victims confirmed by the inquiry….

In 1825, the French Ministry of Justice ordered the creation of a national collection of crime records. It seems to have been the first of its kind anywhere in the world—the statistics of every arrest and conviction in the country, broken down by region, assembled and ready for analysis. It’s the kind of data set we take for granted now, but at the time it was extraordinarily novel. This was an early instance of Big Data—the first time that mathematical analysis had been applied in earnest to the messy and unpredictable realm of human behavior.

Or maybe not so unpredictable. In the early eighteen-thirties, a Belgian astronomer and mathematician named Adolphe Quetelet analyzed the numbers and discovered a remarkable pattern. The crime records were startlingly consistent. Year after year, irrespective of the actions of courts and prisons, the number of murders, rapes, and robberies reached almost exactly the same total. There is a “terrifying exactitude with which crimes reproduce themselves,” Quetelet said. “We know in advance how many individuals will dirty their hands with the blood of others. How many will be forgers, how many poisoners.”

To Quetelet, the evidence suggested that there was something deeper to discover. He developed the idea of a “Social Physics,” and began to explore the possibility that human lives, like planets, had an underlying mechanistic trajectory. There’s something unsettling in the idea that, amid the vagaries of choice, chance, and circumstance, mathematics can tell us something about what it is to be human. Yet Quetelet’s overarching findings still stand: at some level, human life can be quantified and predicted. We can now forecast, with remarkable accuracy, the number of women in Germany who will choose to have a baby each year, the number of car accidents in Canada, the number of plane crashes across the Southern Hemisphere, even the number of people who will visit a New York City emergency room on a Friday evening….(More)”

Misinformation Has Created a New World Disorder

Curated on Posted on by Stefaan Verhulst

Claire Wardle at Scientific American: “…Online misinformation has been around since the mid-1990s. But in 2016 several events made it broadly clear that darker forces had emerged: automation, microtargeting and coordination were fueling information campaigns designed to manipulate public opinion at scale. Journalists in the Philippines started raising flags as Rodrigo Duterte rose to power, buoyed by intensive Facebook activity. This was followed by unexpected results in the Brexit referendum in June and then the U.S. presidential election in November—all of which sparked researchers to systematically investigate the ways in which information was being used as a weapon.

During the past three years the discussion around the causes of our polluted information ecosystem has focused almost entirely on actions taken (or not taken) by the technology companies. But this fixation is too simplistic. A complex web of societal shifts is making people more susceptible to misinformation and conspiracy. Trust in institutions is falling because of political and economic upheaval, most notably through ever widening income inequality. The effects of climate change are becoming more pronounced. Global migration trends spark concern that communities will change irrevocably. The rise of automation makes people fear for their jobs and their privacy.

Bad actors who want to deepen existing tensions understand these societal trends, designing content that they hope will so anger or excite targeted users that the audience will become the messenger. The goal is that users will use their own social capital to reinforce and give credibility to that original message.

Most of this content is designed not to persuade people in any particular direction but to cause confusion, to overwhelm and to undermine trust in democratic institutions from the electoral system to journalism. And although much is being made about preparing the U.S. electorate for the 2020 election, misleading and conspiratorial content did not begin with the 2016 presidential race, and it will not end after this one. As tools designed to manipulate and amplify content become cheaper and more accessible, it will be even easier to weaponize users as unwitting agents of disinformation….(More)”.

Credit: Jen Christiansen; Source: Information Disorder: Toward an Interdisciplinary Framework for Research and Policymaking, by Claire Wardle and Hossein Derakhshan. Council of Europe, October 2017

Investigators Use New Strategy to Combat Opioid Crisis: Data Analytics

Curated on Posted on by Stefaan Verhulst

Byron Tau and Aruna Viswanatha in the Wall Street Journal: “When federal investigators got a tip in 2015 that a health center in Houston was distributing millions of doses of opioid painkillers, they tried a new approach: look at the numbers.

State and federal prescription and medical billing data showed a pattern of overprescription, giving authorities enough ammunition to send an undercover Drug Enforcement Administration agent. She found a crowded waiting room and armed security guards. After a 91-second appointment with the sole doctor, the agent paid $270 at the cash-only clinic and walked out with 100 10mg pills of the powerful opioid hydrocodone.

The subsequent prosecution of the doctor and the clinic owner, who were sentenced last year to 35 years in prison, laid the groundwork for a new data-driven Justice Department strategy to help target one of the worst public-health crises in the country. Prosecutors expanded the pilot program from Houston to the hard-hit Appalachian region in early 2019. Within months, the effort resulted in the indictments of dozens of doctors, nurses, pharmacists and others. Two-thirds of them had been identified through analyzing the data, a Justice Department official said. A quarter of defendants were expected to plead guilty, according to the Justice Department, and additional indictments through the program are expected in the coming weeks.

“These are doctors behaving like drug dealers,” said Brian Benczkowski, head of the Justice Department’s criminal division who oversaw the expansion.

“They’ve been operating as though nobody could see them for a long period of time. Now we have the data,” Mr. Benczkowski said.

The Justice Department’s fraud section has been using data analytics in health-care prosecutions for several years—combing through Medicare and Medicaid billing data for evidence of fraud, and deploying the strategy in cities around the country that saw outlier billings. In 2018, the health-care fraud unit charged more than 300 people with fraud totaling more than $2 billion, according to the Justice Department.

But using the data to combat the opioid crisis, which is ravaging communities across the country, is a new development for the department, which has made tackling the epidemic a key priority in the Trump administration….(More)”.

The Internet Freedom League: How to Push Back Against the Authoritarian Assault on the Web

Curated on Posted on by Stefaan Verhulst

Essay by Richard A. Clarke And Rob Knake in Foreign Affairs: “The early days of the Internet inspired a lofty dream: authoritarian states, faced with the prospect of either connecting to a new system of global communication or being left out of it, would choose to connect. According to this line of utopian thinking, once those countries connected, the flow of new information and ideas from the outside world would inexorably pull them toward economic openness and political liberalization. In reality, something quite different has happened. Instead of spreading democratic values and liberal ideals, the Internet has become the backbone of authoritarian surveillance states all over the world. Regimes in China, Russia, and elsewhere have used the Internet’s infrastructure to build their own national networks. At the same time, they have installed technical and legal barriers to prevent their citizens from reaching the wider Internet and to limit Western companies from entering their digital markets. 

But despite handwringing in Washington and Brussels about authoritarian schemes to split the Internet, the last thing Beijing and Moscow want is to find themselves relegated to their own networks and cut off from the global Internet. After all, they need access to the Internet to steal intellectual property, spread propaganda, interfere with elections in other countries, and threaten critical infrastructure in rival countries. China and Russia would ideally like to re-create the Internet in their own images and force the world to play by their repressive rules. But they haven’t been able to do that—so instead they have ramped up their efforts to tightly control outside access to their markets, limit their citizens’ ability to reach the wider Internet, and exploit the vulnerability that comes with the digital freedom and openness enjoyed in the West.

The United States and its allies and partners should stop worrying about the risk of authoritarians splitting the Internet. Instead, they should split it themselves, by creating a digital bloc within which data, services, and products can flow freely…(More)”.

Sharing Private Data for Public Good

Curated on Posted on by Stefaan Verhulst

Stefaan G. Verhulst at Project Syndicate: “After Hurricane Katrina struck New Orleans in 2005, the direct-mail marketing company Valassis shared its database with emergency agencies and volunteers to help improve aid delivery. In Santiago, Chile, analysts from Universidad del Desarrollo, ISI Foundation, UNICEF, and the GovLab collaborated with Telefónica, the city’s largest mobile operator, to study gender-based mobility patterns in order to design a more equitable transportation policy. And as part of the Yale University Open Data Access project, health-care companies Johnson & Johnson, Medtronic, and SI-BONE give researchers access to previously walled-off data from 333 clinical trials, opening the door to possible new innovations in medicine.

These are just three examples of “data collaboratives,” an emerging form of partnership in which participants exchange data for the public good. Such tie-ups typically involve public bodies using data from corporations and other private-sector entities to benefit society. But data collaboratives can help companies, too – pharmaceutical firms share data on biomarkers to accelerate their own drug-research efforts, for example. Data-sharing initiatives also have huge potential to improve artificial intelligence (AI). But they must be designed responsibly and take data-privacy concerns into account.

Understanding the societal and business case for data collaboratives, as well as the forms they can take, is critical to gaining a deeper appreciation the potential and limitations of such ventures. The GovLab has identified over 150 data collaboratives spanning continents and sectors; they include companies such as Air France, Zillow, and Facebook. Our research suggests that such partnerships can create value in three main ways….(More)”.

The Ethics of Hiding Your Data From the Machines

Curated on Posted on by Stefaan Verhulst

Molly Wood at Wired: “…But now that data is being used to train artificial intelligence, and the insights those future algorithms create could quite literally save lives.

So while targeted advertising is an easy villain, data-hogging artificial intelligence is a dangerously nuanced and highly sympathetic bad guy, like Erik Killmonger in Black Panther. And it won’t be easy to hate.

I recently met with a company that wants to do a sincerely good thing. They’ve created a sensor that pregnant women can wear, and it measures their contractions. It can reliably predict when women are going into labor, which can help reduce preterm births and C-sections. It can get women into care sooner, which can reduce both maternal and infant mortality.

All of this is an unquestionable good.

And this little device is also collecting a treasure trove of information about pregnancy and labor that is feeding into clinical research that could upend maternal care as we know it. Did you know that the way most obstetricians learn to track a woman’s progress through labor is based on a single study from the 1950s, involving 500 women, all of whom were white?…

To save the lives of pregnant women and their babies, researchers and doctors, and yes, startup CEOs and even artificial intelligence algorithms need data. To cure cancer, or at least offer personalized treatments that have a much higher possibility of saving lives, those same entities will need data….

And for we consumers, well, a blanket refusal to offer up our data to the AI gods isn’t necessarily the good choice either. I don’t want to be the person who refuses to contribute my genetic data via 23andMe to a massive research study that could, and I actually believe this is possible, lead to cures and treatments for diseases like Parkinson’s and Alzheimer’s and who knows what else.

I also think I deserve a realistic assessment of the potential for harm to find its way back to me, because I didn’t think through or wasn’t told all the potential implications of that choice—like how, let’s be honest, we all felt a little stung when we realized the 23andMe research would be through a partnership with drugmaker (and reliable drug price-hiker) GlaxoSmithKline. Drug companies, like targeted ads, are easy villains—even though this partnership actually couldproduce a Parkinson’s drug. But do we know what GSK’s privacy policy looks like? That deal was a level of sharing we didn’t necessarily expect….(More)”.