Category Archives: policy

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How to Make Health Care Accountable When We Don’t Know What Works

 

https://hbr.org/2014/11/how-to-make-health-care-accountable-when-we-dont-know-what-works

How to Make Health Care Accountable When We Don’t Know What Works

NOVEMBER 25, 2014
How to Make Health Care Accountable When We Don’t Know What Works
NOV14_25_83286050

Accountable care organizations (ACOs) are widely regarded as part of the solution to a fragmented health care system — one plagued by duplicative services, avoidable errors, and other impediments to efficiency and quality. But 20 years of reform efforts have led to a wave of provider consolidation that has made little headway in efficiently coordinating care. Providers continue to follow a strategy that has shown minimal evidence of success.

We should admit that we don’t know what works and, instead, test a variety of potential solutions that could address fragmentation. Before I explore the concrete steps we can take to encourage that kind of innovation, let me provide some important historical context.

Payment Reform’s First Life

Early efforts to promote coordinated care emphasized payment reform. Toward that end, managed-care and health maintenance organizations used payment schedules and gatekeeper physicians to create provider networks. In addition, the Clinton administration introduced proposals to implement “pay for performance” and dedicated quality-improvement initiatives, suggesting that financial pressures might force the coordination and rationalization of care. But Congress rejected payment-focused reform, and market preferences eliminated managed-care pressures.

Commentators then suggested that payment reform could happen only in conjunction with provider-based reforms, and the Institute of Medicine later issued a series of reports calling for pairing payment solutions with structural reform. Then, when the Affordable Care Act instituted Medicare’s Shared Savings Program in 2010, it invited providers to create ACOs and to accept changes in reimbursement that allowed them recoup part of any savings they generated. Eventually, however, prospective ACOs were given the option ofcontinuing under Medicare’s traditional fee-for-service payments. In other words, providers were encouraged to pursue structural reform while being permitted to avoid any constraint from payment reform.

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The Disappointments of Provider Reform

The continued failure of payment-driven reform has sadly given provider-based reform a blank check. The U.S. health sector has been in a merger-and-acquisition frenzy for nearly 20 years, and much of the integration has been justified as an effort to construct ACOs. Buzz phrases such as “clinical integration” and “eliminating fragmentation” are routinely paraded before regulators who scrutinize proposed mergers.

The problem, of course, is that after waves of acquisitions, most hospital markets are now highly concentrated and lack meaningful competition. And, consistent with basic economic theory, hospital systems that acquired dominant market shares dramatically increased prices for health care services. Perhaps even worse is that these large entities have shown little capacity for achieving the efficiencies they promised through coordinated care. Newly integrated delivery systems retain their inefficiencies and bring higher prices without any evident reduction in costs or errors.

We don’t know exactly why efforts at integration have not yielded efficiencies, and it seems we simply didn’t think very hard about it. The health reform debate focused primarily on a handful of success stories we all can repeat in our sleep: Kaiser, Geisinger, Intermountain. The plan was to have other hospital systems mimic them. That is like instructing all high-tech companies to mimic Apple, as if what makes Apple successful is an easy-to-follow cookbook for large-scale structural change.

It is a curiosity about the U.S. health system that producers with better outcomes and lower costs than their competitors cannot dominate the market. Kaiser, for example, has tried but failed to enter more local markets. But it is foolhardy to think that the systems that have not achieved Kaiser’s success can replicate it simply with the help of government regulators. This duplication strategy at best seems mindless, and at worst smacks of a Khrushchev-era economic policy.

The truth is, despite a glut of business press and how-to manuals, we still understand very little about why certain organizations succeed and others do not. With all the complexities of delivering medical care, we should expect even more variation among health care providers than among manufacturers. We likewise should be very hesitant to claim we understand what works and prescribe nationwide structural reforms.

Concrete Steps for the Future of ACOs

Precisely because we don’t know what works at this juncture, we cannot continue encouraging the formation of vast integrated systems that are difficult to disentangle. Until we have more evidence that integration yields efficiencies, regulators should continue to halt mergers that harm competition.

But scrutinizing mergers will only prevent further damage. We also must improve our delivery system, and we cannot give up on the ACO as a potential source of innovative configurations. Specifically, we should:

  1. Redefine and broaden our concept of an ACO. Too much ACO formation has emphasized linking hospitals with other providers. Instead of this top-down approach, we should work from the bottom up by linking providers withconsumers and payors, so that the focus is on serving patients’ needs and managing budgets.
  2. Encourage nontraditional parties — such as social workers, professionals who help people navigate the health care system (often called “navigators”), and IT companies — to lead efforts at ACO formation. These parties would be well equipped to construct networks that provide accountability, given their expertise in connecting consumers to complex organizations and advocating on behalf of those consumers.
  3. Use contract-based and virtual provider collaborations instead of relying on mergers. Joining providers under common ownership might not be necessary. Electronic health records (EHRs) and other information technologies have the potential to create platforms that enable coordination without incurring the high costs of integration. EHR tools can also allow patients to control their own information and tailor collaborations to individual patients’ needs.
  4. Entertain disruptively innovative reconstructions of the health care delivery system — ones that make use of mobile health, medical tourism, and informatics. Many technology companies that traditionally have not participated in the health sector are now offering improvements to our delivery system. Because business scholarship tells us that outsiders frequently introduce the most valuable innovations to a market, we should ensure that regulatory barriers do not preclude participation from unconventional participants.
  5. Perhaps most important, we cannot pursue structural reform withoutpayment reform. We will distinguish valuable provider reforms from ineffective ones only if sustained revenue pressures force ACOs to be truly “accountable” to consumer demands and other economic realities.

Not every solution we try will work, but we’re likely to have more success letting providers figure out what works than telling them how to do it.

Barak Richman is the Edgar P. and Elizabeth C. Bartlett Professor of Law
 and a professor of business administration at Duke University.

 

data saving lives, EF, healthcare, policy, quantified self, rapid learning health systems

McKinsey on Digital Health

Good observations on mega-trends in healthcare…

Patients arm themselves with information about product safety and efficacy gleaned from websites and online communities such as PatientsLikeMe, pore over cost and quality indicators from healthcare start-ups such as Castlight Health or HealthGrades, and comparison shop using information synthesized by their insurance providers.

PDF: A digital prescription for pharma companies McKinsey

http://www.mckinsey.com/Insights/Health_systems_and_services/A_digital_prescription_for_pharma_companies

A digital prescription for pharma companies

Pharmaceutical and medical-device companies have been slow to adopt digitization. Here are five reasons they should get moving.

November 2014 | bySastry Chilukuri, Rena Rosenberg, and Steve Van Kuiken

The US healthcare industry is undergoing a major transformation as healthcare reform encourages consumers to play a far more active decision-making role. Yet despite this traditionally business-to-business industry moving quickly to a business-to-consumer model, companies have been slow to join the digital movement. Unlike successful B2C companies in other industries—which offer mobile solutions, provide personalized product recommendations, and empower customer-service agents with a 360-degree view of the customer—most healthcare providers and payors are lagging, as are pharmaceutical companies and medical-device manufacturers. That’s problematic when customers are increasingly expecting a better, more personalized experience from companies taking advantage of the host of digital tools and analytics at their disposal.

Healthcare is not immune to this reality. The sudden increase in the individual market1through the creation of exchanges and growth in Medicare Advantage2 has forced US payors to adopt some of these digital tools, while the growing cost burden for healthcare absorbed by consumers inspires many would-be patients to jump on the web or social networks to conduct research. So why, with a few exceptions, are pharmaceutical and device companies taking a “wait and watch” approach? Government agencies, payors, disease advocates, and disrupters are launching digital solutions that threaten product sales and take advantage of the opportunity to respond to patient needs. This role should be a natural extension for pharmaceutical and medical-device companies, and we have identified five compelling reasons they must get moving before it is too late.

1. Patient behavior is changing

As with many other industries, consumers in the healthcare sector are becoming more informed, empowered, and demanding. The vast majority of connected patients are using an array of digital tools to take control of their health and the healthcare services they access and buy: more than 70 percent of patients who are online in the United States use the Internet to find healthcare information, and more than 40 percent of people who diagnosed their condition through online research had it confirmed by a physician.3Patients arm themselves with information about product safety and efficacy gleaned from websites and online communities such as PatientsLikeMe, pore over cost and quality indicators from healthcare start-ups such as Castlight Health or HealthGrades, and comparison shop using information synthesized by their insurance providers.

The more that healthcare data becomes digitally accessible, the more patients will use it to weigh—and potentially reject—expensive healthcare treatments. This is particularly true in the United States, where patients pay a greater percentage of the cost of their drug therapies (25 percent is not unusual) than they do for other healthcare expenses such as inpatient services. Not surprisingly, these consumers are demanding more information so they can apply the same cost-benefit analysis and research techniques they use to purchase cars or phones when they purchase healthcare; they are also making more informed, rational choices about where they put their money. Data and information about insurance plans, pharmaceutical products, and manufacturers are discussed in a variety of virtual forums. If companies do not join the digital dialogue and influence the conversation, they will lose an opportunity to shape it, and they may be put on the defensive trying to refute the statements made by those that do take part.

2. Government agencies are moving surprisingly quickly

As patient and consumer demand for information grows, the government is beginning to supply healthcare data either directly, through the release of information, or indirectly, by providing incentives for collection and aggregation of relevant clinical data. A recent McKinsey Global Institute report4 found that healthcare is one of seven sectors that could generate billions of dollars of value per year as companies use open data—machine-readable information made available to others, often free of charge—to develop new products and improve the efficiency and effectiveness of operations.

Government health agencies, from national health services in Asia and Europe to government organizations in the United States, are already harnessing the power of big data to figure out what’s working and what isn’t and encouraging others to do the same. The Health Data Initiative launched in 2010 by the US Department of Health & Human Services (HHS) was one of the first and is still among the most prominent examples. In June 2011, former HHS chief technology officer Todd Park described an ambition to make HHS the “NOAA of health data.”5 It appears that his vision is becoming reality, as HHS reported that more than 1,000 data sets were available on healthdata.gov at the end of 2013,6 and the agency’s catalog continues to expand.

The hope is that greater “data liquidity” will both enable more collaborative research among academics and inspire healthcare innovation. Greater access to data is already driving changes in care protocols, allowing the benchmarking of physicians, aiding the identification of clinical best practices, informing the adjustment of benefits and reimbursement structures, and resulting in actual behavioral change. At the federal level in the United States, for example, the recent release by the Centers for Medicare & Medicaid Services of Medicare reimbursements to providers put some physicians on the defensive to explain billing perceived as excessive, and the organization also proposed rescinding the prohibition against releasing prescriber, pharmacy, and plan identifiers related to Medicare Part D payments.

In another example, the new openFDA application-programming-interface initiative for drug-adverse events allows researchers to synthesize, interrogate, and generate insights from a decade (2004–13) of adverse-event reports—an effort that is almost certain to stir conversation. And at the US state level, Arkansas and Tennessee are examining treatment protocols and zeroing in on the relatively small number of care episodes that comprise the majority of medical costs. The states’ shared goal is cutting waste and revising reimbursement policies to encourage high-quality and efficient care.

These efforts mean that providers and manufacturers of drugs and devices only control a small fraction of the data relevant to their work or products. If healthcare follows the path of other consumer-oriented sectors that compete on data analytics, such as high tech and retailing, winners and losers will be determined in part by who makes the best use of the data available and the strongest case for change. Government agencies across the globe are leading the way, and entrepreneurs are taking advantage of government’s interest in facilitating data exchange. However, pharmaceutical and medical-device companies are on the sidelines, leaving others to dictate how information related to their products is used.

3. Trial data is necessary but no longer sufficient

Pharmaceutical companies have used data generated from long-running randomized controlled trials as the gold standard to demonstrate the efficacy and safety of products and gain regulatory approval or formulary listings. Yet many of their customers—payors, increasingly providers, and even patients—are looking for real-world evidence. Both access to and quality of real-world data are increasing exponentially, spanning everything from patient electronic health records to social platforms, healthcare claims, demographic trends, and genomic insights.

The difference in emphasis by certain stakeholders creates pressure on pharmaceutical companies to respond. As data integration and analyses take precedence over data ownership or sponsorship, competitive advantage will rest with those organizations that innovatively use multiple data sources to uncover true insights. Meeting long-standing requirements regarding clinical-trial data continues to be necessary for approval, but it is no longer enough for other stakeholders when more and more targeted and timely data are available. Consider this: Thomson Reuters found that the number of observational research studies tripled from roughly 80,000 between 1990 to 2000 to more than 263,000 in the following decade from 2001 through 2011.7

There is a concerted effort to facilitate collaboration by making more real-world data available at a fairly low cost. Initiatives such as PCORnet, a distributed research network, were launched to advance researchers’ ability to conduct comparative-effectiveness and clinical-outcomes research more efficiently. Aggregating data across “networks of networks” dramatically reduces the cost of observational studies and more quickly generates insights about patient care. Innovative methods enable randomization using real-world data to improve the quality of findings.

Pharmaceutical companies can’t discount observational data because such data already affect product pricing and reimbursement levels. European markets are using real-world evidence to limit reimbursements on new drugs to the competitor’s level until real-world evidence is provided to demonstrate that the new therapy is better. The International Society for Pharmacoeconomics and Outcomes Research reported in 2007 that countries were using reference pricing for new treatments assessed to add little incremental medical value, and real-world data was part of that effectiveness assessment.8 In short, pharmaceutical companies need a data strategy that reflects the shift in how data are shared and analyzed, as well as a plan to manage all types of data that affect product sales, pricing, and reimbursement.

4. Care is evolving

Healthcare is moving from a focus on addressing point-in-time issues toward coordinated, continuous health management. The need to provide ongoing management of chronic diseases and to predict and prevent severe episodes and events offers new opportunities and places new communication demands on every member of the healthcare team, including pharmaceutical companies. Sensor technology, such as that produced by Proteus Digital Health, allows continuous collection of physiological data (for example, electroencephalograph, electrocardiogram, movement, heart rate, and glucose levels), which could vastly improve disease management by providing real-time status reports that can alert providers to impending patient problems. When scaled broadly, these innovations also may reduce the need for many courses of treatment. Pharmaceutical companies need to be at the forefront of developing “beyond the pill” services that deliver value to patients and evolve from a mind-set that measures success based largely on the number of prescriptions written.

Some innovators already are combining technology-enabled monitoring and insight to deliver new solutions to patients. Propeller Health inserted GPS technology in inhalers to identify environmental triggers that caused asthma sufferers to use their device, thus allowing consumers to head off severe attacks. Similarly, a pharmaceutical company that made a pain medication equipped patients with Jawbone devices to continuously capture patient mobility. This showed that patients experienced greater relief that allowed them to increase their movement, even if they did not report lower pain scores. The evidence was used to convince payors to relist the pain medication on formularies.

Not all wraparound services rely on new technology. Telemedicine outreach and coaching efforts by nurses at one of the largest government hospital systems in the United States dramatically reduced the risk of complications from conditions such as diabetes.

Whether low or high tech, patient services aimed at preventing acute episodes or supporting compliance deliver significant benefits to patients. Pharmaceutical companies that remain fixated solely on prescription volume, rather than on sustaining relationships between a brand and patients, risk ceding the role of trusted provider to others. For industry participants to thrive in the digital era, they must build a broader menu of service offerings instead of merely using technology solutions to increase prescriptions.

5. Competition is faster and fiercer

Technology cycles are getting shorter and the cost of experimentation cheaper. The run-up to the passage of the Health Information Technology for Economic and Clinical Health Act in 2009 and Affordable Care Act in 2010 saw significant investment in companies developing systems, solutions, or applications to support electronic health records. From 2010 to the end of 2013, seed and Series A–stage healthcare investments continued to grow, multiplying fivefold in the United States in that time. In the first half of 2014, investors spent $2.3 billion, with more than 140 digital companies each raising more than $2 million,9 as the investment focus shifted from providers of electronic-health-records solutions to developers of consumer-oriented applications, makers of wearable health technology, and health data and analytics. There are thousands of healthcare-related apps available from the US Apple App Store, but only a fraction are patient facing with genuine health content, according to a new study from the IMS Institute for Healthcare Informatics. The recent announcement of the Apple Watch and the company’s release of its HealthKit developer tool are likely to increase the variety of functions and number of health-related apps that are available.

Google Glass is the most high-profile wearable being tested for numerous healthcare applications—for example, surgeons are using it to facilitate and record operations, office physicians are reducing interruptions in patient engagement by retrieving and sending information to electronic medical records through the device, and emergency-medicine physicians are getting specialist consults by transmitting video or images taken by Glass.10 Beyond Google, Intel acquired BASIS Science, MC10 raised a $41.9 million investment, and Proteus raised $183.4 million to develop its line of sensor-based products. Services or applications that facilitate consumer communication with doctors such as Doctor on Demand and HealthTap+ also secured financing.

These new entrants to the healthcare sector have different ways of thinking about solving healthcare problems and using proven agile iterative techniques to bring products to market rapidly and in iterations as improvements are made. Pharmaceutical companies need to recognize the value and impact of these disrupters and learn from them.

Digitally enabled healthcare is here, and most pharmaceutical companies aren’t ready. Despite access to unprecedented data and technologies that can be used to drive better health outcomes by influencing customer behavior, few are truly exploring digital-engagement models. The opportunity to learn more about consumers and develop better, more targeted products and services far outweighs the threat digitization presents companies—for now. Unless incumbent pharmaceutical companies move quickly, innovative competitors may grab a greater share of benefits and stronger customer loyalty.

About the authors

Sastry Chilukuri and Rena Rosenberg are principals in McKinsey’s New Jersey office, where Steve Van Kuiken is a director.

The authors wish to thank Elizabeth Doshi for her contribution to this article.

complex adaptive systems, data saving lives, EF, healthy habits, meaning, nutrition, policy, politics, rapid learning health systems

McKinsey’s Plan to fight obesity…

http://www.mckinsey.com/Insights/Economic_Studies/How_the_world_could_better_fight_obesity

Executive Summary: Innovation vs Obesity_McKinsey

MGI Obesity_Full report_November 2014

Sensible stuff. Possibly the most sensible stuff I’ve seen on this. Good for them…

How the world could better fight obesity

November 2014 | byRichard Dobbs, Corinne Sawers, Fraser Thompson, James Manyika, Jonathan Woetzel, Peter Child, Sorcha McKenna, and Angela Spatharou

Obesity is a critical global issue that requires a comprehensive, international intervention strategy. More than 2.1 billion people—nearly 30 percent of the global population—are overweight or obese.1 That’s almost two and a half times the number of adults and children who are undernourished. Obesity is responsible for about 5 percent of all deaths a year worldwide, and its global economic impact amounts to roughly $2 trillion annually, or 2.8 percent of global GDP—nearly equivalent to the global impact of smoking or of armed violence, war, and terrorism.

Podcast

Implementing an Obesity Abatement Program

MGI’s Richard Dobbs and Corinne Sawers discuss how a holistic strategy, using a number of interventions, could reverse rising rates of obesity around the world.

And the problem—which is preventable—is rapidly getting worse. If the prevalence of obesity continues on its current trajectory, almost half of the world’s adult population will be overweight or obese by 2030.

Much of the global debate on this issue has become polarized and sometimes deeply antagonistic. Obesity is a complex, systemic issue with no single or simple solution. The global discord surrounding how to move forward underscores the need for integrated assessments of potential solutions. Lack of progress on these fronts is obstructing efforts to address rising rates of obesity.

A new McKinsey Global Institute (MGI) discussion paper,Overcoming obesity: An initial economic analysis, seeks to overcome these hurdles by offering an independent view on the components of a potential strategy. MGI has studied 74 interventions (in 18 areas) that are being discussed or piloted somewhere around the world to address obesity, including subsidized school meals for all, calorie and nutrition labeling, restrictions on advertising high-calorie food and drinks, and public-health campaigns. We found sufficient data on 44 of these interventions, in 16 areas.

Although the research offers an initial economic analysis of obesity, our analysis is by no means complete. Rather, we see our work on a potential program to address obesity as the equivalent of the maps used by 16th-century navigators. Some islands were missing and some continents misshapen in these maps, but they were still helpful to the sailors of that era. We are sure that we have missed some interventions and over- or underestimated the impact of others. But we hope that our work will be a useful guide and a starting point for efforts in the years to come, as we and others develop this analysis and gradually compile a more comprehensive evidence base on this topic.

We have focused on understanding what it takes to address obesity by changing the energy balance of individuals through adjustments in eating habits or physical activity. However, some important questions we have not yet addressed require considerable further research. These questions include the role of different nutrients in affecting satiety hormones and metabolism, as well as the relationship between the gut microbiome and obesity. As more clarity develops in these research areas, we look forward to the emergence of important insights about which interventions are likely to work and how to integrate them into an antiobesity drive.

The main findings of this discussion paper include:

  • Existing evidence indicates that no single intervention is likely to have a significant overall impact. A systemic, sustained portfolio of initiatives, delivered at scale, is needed to reverse the health burden. Almost all the identified interventions (exhibit) are cost effective for society—savings on healthcare costs and higher productivity could outweigh the direct investment required by the intervention when assessed over the full lifetime of the target population. In the United Kingdom, for instance, such a program could reverse rising obesity, saving the National Health Service about $1.2 billion a year.
  • Education and personal responsibility are critical elements of any program aiming to reduce obesity, but they are not sufficient on their own. Other required interventions rely less on conscious choices by individuals and more on changes to the environment and societal norms. They include reducing default portion sizes, changing marketing practices, and restructuring urban and education environments to facilitate physical activities.
  • No individual sector in society can address obesity on its own—not governments, retailers, consumer-goods companies, restaurants, employers, media organizations, educators, healthcare providers, or individuals. Capturing the full potential impact requires engagement from as many sectors as possible. Successful precedents suggest that a combination of top-down corporate and government interventions, together with bottom-up community-led ones, will be required to change public-health outcomes. Moreover, some kind of coordination will probably be required to capture potentially high-impact industry interventions, since any first mover faces market-share risks.
  • Implementing an obesity-abatement program on the required scale will not be easy. We see four imperatives: (1) as many interventions as possible should be deployed at scale and delivered effectively by the full range of sectors in society; (2) understanding how to align incentives and build cooperation will be critical to success; (3) there should not be an undue focus on prioritizing interventions, as this can hamper constructive action; and (4) while investment in research should continue, society should also engage in trial and error, particularly where risks are low.

Exhibit

Cost-effective interventions to reduce obesity in the United Kingdom include controlling portion sizes and reducing the availability of high-calorie foods.

The evidence base on the clinical and behavioral interventions to reduce obesity is far from complete, and ongoing investment in research is an imperative. However, in many cases this requirement is proving a barrier to action. It need not be so. Rather than wait for perfect proof of what works, we should experiment with solutions, especially in the many areas where interventions are low risk. We have enough knowledge to do more.

About the authors

Richard Dobbs, James Manyika, and Jonathan Woetzel are directors of the McKinsey Global Institute, where Corinne Sawers is a fellow and Fraser Thompson is a senior fellow; Peter Child is a director in McKinsey’s London office; Sorcha McKenna is a principal in the Dublin office; and Angela Spatharou is a principal in the Mexico City office.

 

MGI_Implementing_an_Obesity_Abatement_Program_Exibit18 MGI_Implementing_an_Obesity_Abatement_Program_Exibit3 MGI_Implementing_an_Obesity_Abatement_Program_ExibitE3 MGI_Implementing_an_Obesity_Abatement_Program_Exibit1

data saving lives, healthcare, policy, rapid learning health systems

Drs don’t care for interoperability

 

http://www.politico.com/story/2014/10/health-care-data-records-112039.html?hp=l13

Few motives to fix busted health data

 

 

A doctor is shown. | Getty

  • The records are difficult to use, reduce interaction with patients and cost a fortune. | Getty

  • By ARTHUR ALLEN | 10/20/14 1:32 PM EDT Updated: 10/21/14 3:00 PM EDT

  • Someday, doctors will have our data at their fingertips and will use it to prevent drug reactions, nip diabetes and cancers in the bud and lengthen our lives while preventing unpleasant and costly hospital stays.

    But for most doctors, that free-flowing information highway is a beautiful dream that doesn’t pay the bills.

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    Many hospitals don’t have any incentive to improve the clunky $30 billion federal electronic health records program: They still make most of their money by filling beds. Most doctors still get paid through procedures and visits.

    (Also on POLITICO: Weiner: Done with politics, not life)

    So a new 10-year plan for fixing the system from the Office of the National Coordinator for Health IT may have a hard time getting off the ground.

    “From the business perspective, there’s no financial benefit for the majority of hospitals and physicians to be interoperable,” says Steve Waldren, director of the Alliance for eHealth Innovation at the American Academy of Family Physicians. “If we don’t change the business end of it … it’s just checking a box.”

    Lobbying efforts have yielded a bill that would slow down federal requirements — and patient-centered care in the process. It could be taken up in the lame-duck session or added to another bill that directly addresses the interoperability issue next year, Hill staff say.

    (Also on POLITICO: Super PAC fundraising soars for conservatives)

    “It may already be too late,” says Thomson Kuhn, a researcher with the American College of Physicians. “Short of some crazy scheme to make a change at the end of the year I just don’t see how we’re going to get what we need. And at that point they’ve killed the program.”

    To address that issue, the Office of the National Coordinator’s plan is likely to require EHR vendors to include software interfaces that will make it easier for smooth communication across health care systems. But it may not work unless the economics of medicine shift in a way that forces doctors to require shared information to function.

    The HITECH Act of 2009 was designed to let health care professionals use information to improve care and reduce spending and shifting their economic motivation to keeping people healthy, rather than charging for individual treatments.

    (Also on POLITICO: Battle begins for NRSC chair)

    Five years later, most doctors have electronic health records (EHRs) — that’s where most of the money was spent — but doctors and nurses are unhappy with the time-consuming clumsiness of the software, much of which wasn’t ready for the medical profession.

    The records are difficult to use, reduce interaction with patients and cost a fortune. And for the most part, they haven’t made information sharing easier.

    There are some places — a growing number, in fact — where interoperability makes business sense. In Massachusetts, for example, many health systems such as Partners HealthCare have more than a third of their patients in value-based care systems built on the state’s network of health information.

    (Also on POLITICO: Tillis claims ‘momentum’ in N.C.)

    Some patients in these systems already are benefiting from “wrap-around” care that relies on good data, shared among medical professionals.

    Such success stories have popped up around the country in tandem with growing consolidation of health networks and the Affordable Care Act-fostered creation of accountable care organizations — in which doctors are nudged toward focusing on patients, rather than procedures.

    In such organizations, information sharing among doctors and hospitals is vital. Shifts in the Medicare payment schemes for doctors next year could also force the medical profession into more reliance on sharing.

    “Value-based care may not succeed with good information, but it can’t succeed without it,” says Josh Seidman, a former HHS health IT official who is now a vice president at Avalere Health.

    To date, though, most hospitals and provider networks aren’t financially motivated to freely share their patients and data with others. It will cost money to get their computer to share data. Providers, having already spent plenty, aren’t enthusiastic about spending more to meet the demands of the federal incentive program.

  • For some, accepting a penalty will beat buying new software, which they will need to upgrade again for the next stage of the program in a few years, says Rob Tennant of the Medical Group Management Association.
  • A retreat from the beautiful dream: It’s enough to make some health visionaries sick. One of them, a former official who was involved in creating the incentive program, sniffs that providers have themselves to blame.

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  • “A lot of people made bad choices,” he said, speaking on condition of anonymity. “They didn’t do half as much research before buying their EHRs as they would getting a new car, and they’re paying for it.
  • “The people complaining now are the ones at the trough — the vendors and doctors and hospitals. They gladly took the money, but when it comes time to step up or take the penalty, they say, ‘Let’s drop out of the program.’ It’s always, ‘Slow it down, dumb it down, give me money, don’t take anything away.’”
  • The biggest provider complaint about the current stage of the incentive program is its requirements that doctors share summary-of-care documents and get a percentage of their patients to receive or send health data through secure portals.
  • They often must shell out tens of thousands of dollars to software vendors to set up portals to transmit data across health care networks or even outside their offices. Their patients aren’t interested in accessing their records, they add — how can the government penalize them for what their patients won’t do?
  • Republican members of Congress are skeptical of the federal program’s top-down requirements, and equally incensed by its slow advance.
  • Rep. Phil Gingrey (R-Ga.) epitomizes this contradiction. This summer he threatened to investigate Epic, the leading EHR vendor, because he said it had taken billions in federal money to create information silos. A month later, he supported a bill put forward by Renee Ellmers (R-N.C.) that would slow down the meaningful use program, giving providers — and vendors — more time to learn how to share.
  • Some Hill staff who track the issue think the answer is to relax federal standards while focusing narrowly on freeing the information — a position that federal health IT advisory committees share.
  • “It’s hard to argue with physicians who say their systems aren’t ready, so they shouldn’t be penalized,” said a Hill staffer. “But the administration has to make this happen. … Every year we don’t have interoperability, public health issues go unaddressed and that’s unfortunate.”
  • A parade of witnesses appearing before the House Energy and Commerce Committee this year as part of its 21st Century Cures Initiative have stressed the importance of free-flowing health data — not only for patient care but also for research leading to cures.
  • “If we had a truly networked health care system, it would allow us to gather data on patients treated with drugs on and off label to see what kinds of adverse events are occurring,” said William Hanlon of drug testing company Covance, in testimony last month. “It could have huge benefits for clinical trials.”
  • Energy and Commerce staff say that information sharing will be part of legislation they expect to put out early next year.
  • The Health and Human Services Department is feeling pressure from Congress — in multiple directions. CMS has delayed the incentive program twice already in response to provider unhappiness. At the same time, ONC has taken a harder line on enforcing interoperability.
  • Earlier this month, HHS official Kelly Cronin noted that some EHR vendors were charging up to $20,000 to create computer interfaces for users to access out-of-network laboratories. She suggested her office might pass along such information to the Federal Trade Commission, to investigate as obstructions to free trade.
  • HHS’s health IT coordinator, Karen DeSalvo, has indicated that her agency may toughen its stance.
  • “[The Office of the National Coordinator] believes the market has not solved this problem on behalf of the American people,” she said in an interview. “Patient data is a public good so there has to be a public involvement in making sure everyone … has the data available in an appropriate way.”
  • The version of this story has corrected the spelling of Thomson Kuhn’s name.
  • Read more: http://www.politico.com/story/2014/10/health-care-data-records-112039.html#ixzz3HN1pB9eO
data saving lives, health market quality, healthcare, policy, rapid learning health systems

Why Big Health Insurance is pouring money into startups

 

http://fortune.com/2014/09/24/health-insurance-invest-startups/

TECH HEALTHCARE

Why Big Health Insurance is pouring money into startups

A part of the Affordable Care Act forces insurers to redeploy capital rather than distribute it to shareholders. And that’s where things get interesting.

One effect of the Affordable Care Act, a.k.a. Obamacare? A spike in venture interest in health care startups. Digital health care companies raised $2.3 billion in the first half of 2014, which surpasses the total raised in all of 2013, according Rock Health, a health care-focused seed fund. That figure is impressive given that 2013 was already a record year at $1.9 billion raised. (That’s a 39% increase year over year, even as biotech investment grew just 8% and medical devices investment fell by 17%.)

Funding rounds that engaged traditional venture capital firms grabbed most of the headlines: Flatiron Health raised $130 million in May, Doxmity raised $54 million in April, Zenefits raised $66 million in June, Teledoc raised $50 million this week. Look past the big VC firms, though, and you may notice that non-traditional investors—in particular, large health insurance companies—are increasingly participating and investing in the startup ecosystem.

In August, Blue Cross and Blue Shield of Florida, a Jacksonville-based insurer better known as Florida Blue, established an accelerator for health care startups based in Jacksonville. The program is run by Healthbox, an accelerator company that has invested in at least 47 health care startups—and, on its own behalf, raised seed funding from Blue Cross Blue Shield Venture Partners. BCBSVP has also funded its own incubator, called Sandbox Industries, that invests in startups. Lemhi Ventures, which invests in health care services companies, is said to be backed by UnitedHealth Group UNH -2.49% , though the firm declined to comment on its limited partners. The firms have been around for some time—Sandbox was founded in 2003, Lemhi was founded in 2006—but ramped up deal activity in the last year.

On Tuesday, Horizon Healthcare Services, a New Jersey insurance provider with 3.7 million members, announced it invested $3.7 million into Cota, a big data company focused on oncology. Glenn Pomerantz, Horizon’s chief medical officer, said the company has been investing in startups for the past three years. (He refused to disclose how many startups the company has backed.) The investments are part of the health insurance industry’s transition to a more “value-based” system, he says. “This is a dawn of a new day for us. You have the state’s largest insurance company taking an equity interest in the supply side of technology . . . That is a real shift.”

Insurers are interested in startup investments for the same reason venture capitalists are: because the Affordable Care Act is shaking things up.

For insurance companies, an esoteric part of the Affordable Care Act mandates that insurers spend somewhere between 80% and 85% of the premiums they collect from patients on quality and efficiency measures. Called the medical loss ratio rule, it forces insurers to redeploy capital rather then distribute it to shareholders. Josh Kaye, a partner at the multinational law firm DLA Piper, believes the rule has been a big driver of venture deals for insurance companies. “They’re mandated by law to use these funds in a variety of different ways,” he says. “Why not use it in a way that could result in additional returns while at the same time meeting the requirements?”

The ACA’s emphasis on efficiency measures is intended to lower the cost of health care by removing incentives to conduct unnecessary tests and procedures. In turn, health care providers are looking to big data and data analytics technologies to create best practices, make better decisions, and get better outcomes.

In this way, the Affordable Care Act blurs the categorical line between insurance company and health care provider by making insurers more responsible for measuring the quality and efficiency of providers. Meanwhile, providers take on more risk by conducting fewer tests and procedures, even if they’re deemed unnecessary. “Payers and providers who learn to work as if they are owned by a single entity are going to drive the marketplace,” Pomerantz says.

Corporate venture investors tend to have a bad reputation in the startup and venture capital world. At best, they are a potential acquirer of the startups they invest in. At worst, they cull ideas and strategies from the startups in which they invest, only to compete with them down the road. (In the words of prominent venture investor Fred Wilson, “They suck” because they may not be interested in the success of the company or of the entrepreneur. He later clarified his position by stating that some corporate VC firms are better than others.) With health insurance companies serving as investors, they have the chance to help ambitious but possibly naïve startups figure out the country’s complicated healthcare system.

data saving lives, policy, rapid learning health systems

data mining health records

“The goal in health care is not to protect privacy, the goal is to save lives. We need to have that as starting point,” said David Castro, director of the Center for Data Innovation. “Is the doctor treating me based on the last couple patients he saw, or is he treating me based on the rigorous analysis of millions of patents and finding the 5,000 that are actually just like me, and treating me in a much more accurate way?”

For data mining to succeed would also require recruiting top data scientists to health care, which isn’t easy given the demand in the hot field.

http://www.washingtonpost.com/blogs/innovations/wp/2014/10/01/the-incredible-potential-and-dangers-of-data-mining-health-records/

The incredible potential and dangers of data mining health records

By Matt McFarland October 1
Using the world’s best computers to analyze our health data could revolutionize how we live. (Connie Zhou/Google)

Imagine if your doctor could compare your physical health, diet and lifestyle to a thousand Americans with similar characteristics, and realize that you need treatment to prevent heart failure next month.

What if an analysis of your genome could help a physician give you a customized cancer treatment that saves your life?

Unleashing the modern power of computers, data crunching and artificial intelligence could revolutionize health care, improving and extending lives.

It’s the kind of potential Google chief executive Larry Page hinted at when he told the New York Times earlier this year that “we’d probably save 100,000 lives next year,” if we data mined health care data.

“Imagine you had the ability to search people’s medical records in the U.S.,” Page said in another interview this summer. “I imagine that would save 10,000 lives in the first year.”

Page’s numbers sound impressive, but are speculative and unfounded, according to many in the medical industry.

Interviews with more than a dozen health care professionals and data scientists found no evidence backing Page’s specific claims. While they universally agree that data mining — the examination and analysis of huge batches of information – could invigorate health care, they caution that any sort of accurate estimate would be impossible.

“Usually when I see someone put a number on it and throw around saving lives it usually means one, they aren’t usually a clinician or someone who provides care, or No. 2 it’s someone who really knows better, but is trying to grab a headline,” said Nicholas Marko, the department head of data science at the Geisinger Medical Center.

A Google spokeswoman declined to offer an explanation of Page’s numbers, or make him available for comment.

In one other instance where Page has used an unsubstantiated health care statistic, he told Time Magazine  last year that solving cancer would only “add about three years to people’s average life expectancy.” That’s a figure the American Cancer Society and National Cancer Institute had never heard of before. A Google spokeswoman didn’t have an answer when asked for an explanation.

To a cynic, Page is a shrewd businessman twisting facts to shape the national dialogue so that he can profit from absorbing our health data into the Google cloud, where his world-class engineers will find ways to make money off all of that information.

An optimist might remember Page’s assertion that Google is a company devoted to solving “huge problems for hundreds of millions of people,” and offer him the benefit of the doubt.

“Health care has been pretty archaic. We’re pretty behind the curve on things,” said Lorren Pettit, a vice president for the Healthcare Information and Management Systems Society, which aims to improve health care through information technology.  “We need the innovation of people from outside health care to come in and take a look and challenge this industry, and yes with data mining there’s a great world of possibility.”

Shaking up industries is part of Google’s DNA. Its self-driving car project could in theory eliminate the 1.24 million fatalities a year on global roads. If Page can soften a country’s fears about sharing our health data — which ends up saving lives — does the end justifies his means of fuzzy math?

“There’s tremendous opportunity if we start taking individualized genomic data and health histories and assuming you can perfectly de-identify it, my gosh, if you can mine that and look for patterns between genomic sequences and types of illnesses and effects of treatment on those illnesses you could potentially do a tremendous amount for society and the health of our individuals,” said Christopher Jaeger, Sutter Health’s chief medical information officer.

The average person might spend a few hours a year with their physician, during which data about their health (blood pressure, alcohol consumption, weight, etc.) is written down. If a patient’s health data was tracked 24-7 — as devices such as smartwatches are making realistic — there would be an exponential leap in the amount of data about someone’s health. More information — and the comparison of that information to other patients — should lead to better treatments.

“It would be great if when the patient walked in our Bluetooth sensors picked up their phone and it pushed in all their exercise and diet history, and then there were analytics that were performed in real time,” said Thomas Graf, chief medical officer at Geisinger Health System. “When the doc walked in the room they can say ‘Oh, looks like you’re exercising at 80 percent of what we were talking about.’ ”

But fear of litigation, privacy concerns, regulations and the challenge of collecting and standardizing data all stand in the way of realizing this health care utopia. Still, there are some early examples that hint at what could be done.

Researchers at the University of Bern in Switzerland have built a computer program to better measure the size of brain tumors.

Traditionally radiologists look at MRI scans and measure in two dimensions the size of a tumor. The computer program — called BraTumIA — is capable of a 3D analysis of the tumor’s volume, which better measures whether it’s shrinking or growing. Getting measurements right is crucial as physicians determine the best treatment plan for a patient.

“If I ask two radiologists to do the same job, you will see differences,” said researcher Mauricio Reyes. “The computer has the ability to be more consistent and more objective over time. Even if you have an error in the computer this error is consistent over time. What really matters is the trend.”

Here’s how the program works. A set of annotated brain scans — in which different parts of a tumor are labeled — are preloaded into the program. The program uses those as a guide to teach itself to identify different parts of future brain scans as a tumor or not.


A screenshot of BraTumIA in action. (Mauricio Reyes)

The end result is being able to run a scan for five minutes on a laptop and having a better understanding of a tumor. If more medical images made their way into databases such as BraTumIA, those services would get even better.

But what if health data we think is anonymous gets identified or hacked? The threat of being sued deters health organizations from sharing data and embracing the full potential of data mining.

For example, MRI exams and CT scans of a patient’s head could be used to reconstruct a person’s face. A hacker with access to such a database could use face-detection software to crosscheck the scans with a Web site where users post photos of themselves.

“If the same person has a Facebook account there’s a good chance that you could identify this person. If I had access to such a database I could give you a list of people in Facebook with names of who has a brain tumor,” cautioned Bjoern Menze, a computer science professor at TU Munchen who researches medical imaging.

“There will be criminals. There will be people who are bad actors. At some point something is going to get out,” Graf said. “It’s not an irrational fear. At the same time, people die driving every year and we still choose to drive cars, or most of us do. It’s a risk every person has to decide where they fall on the line.”

Many of those I interviewed anticipated a situation where patients could decide whether to opt into data mining of their health records. A tax benefit might even be given to encourage involvement.

If health records are ever going to be data mined, it’ll happen when consumers are convinced the perks outweigh the costs. The world has already seen dramatic changes to privacy norms as services such as Facebook grow in popularity. It’s incredibly popular Newsfeed — which funnels the latest information about friends into a feed — was initially met with uproar by users concerned about their privacy.

But as users saw the utility of the feed, the tradeoff in privacy became acceptable.

“The goal in health care is not to protect privacy, the goal is to save lives. We need to have that as starting point,” said David Castro, director of the Center for Data Innovation. “Is the doctor treating me based on the last couple patients he saw, or is he treating me based on the rigorous analysis of millions of patents and finding the 5,000 that are actually just like me, and treating me in a much more accurate way?”

For data mining to succeed would also require recruiting top data scientists to health care, which isn’t easy given the demand in the hot field.

“It’s hard,” said John Weinstein, chair of bioinformatics and computational biology at MD Anderson Cancer Center. “You really have to battle with Silicon Valley and the Boston academic scene.”

“Why would someone who is really really good at analyzing data come to work for a health care organization and make X dollars when they could go to Google and make 10X dollars?” Marko added.

Matt McFarland is the editor of Innovations. He’s always looking for the next big thing. You can find him on Twitter and Facebook.
cool, healthy habits, nutrition, policy, rapid learning health systems

Advertising tells you how affluent your suburb is…

 

http://www.news.com.au/finance/work/how-suburban-commuters-are-coaxed-into-unhealthy-eating-habits/story-fnkgbb6w-1227089160388

How suburban commuters are coaxed into unhealthy eating habits

  • 3 DAYS AGO OCTOBER 13, 2014 5:33PM
If you’re surrounded by ice coffee ads, you’re probably in a poorer suburb. Real coffee o

If you’re surrounded by ice coffee ads, you’re probably in a poorer suburb. Real coffee on the other hand … well, you could be well off. Source: News Corp Australia

EVER wondered whether your suburb is well-off or disadvantaged? There’s a simple test you can use to find the answer as you head home from work this evening.

Just check out the food advertisements around your train station or bus stop.

If the ads encourage you to drink diet soft drink, tea or coffee, you reside in an area considered pretty plush.

But if a lot of ads push fast food restaurants, flavoured milk and fruit juice, there is a fair chance you can mark your suburb as “disadvantaged”.

These are the findings from research by Philippa J. Settle, Adrian J. Cameron and Lukar E. Thornton of Deakin University.

Their investigation of ads aimed at commuters in 20 Melbourne suburbs is published in the October issue of the Australian and New Zealand Journal of Public Health.

“This exploration of outdoor food advertising at Melbourne transit stops found 30 per cent displayed food advertisements, with those in more disadvantaged suburbs more frequently promoting chain-brand fast food and less frequently promoting diet varieties of soft drinks,” concluded the researchers.

“These findings may help raise awareness of unhealthy environmental exposures.”

The study reinforces the proposition there is a distinct difference in food eaten in various social-economic communities. And the lower the income, the higher the likelihood that unhealthy fast food will be promoted.

Kooyong station volunteer gardeners John Dale and Charlie Baxter were disappointed when n

Kooyong station volunteer gardeners John Dale and Charlie Baxter were disappointed when new billboards were installed at Kooyong Station in Melbourne. Source: News Limited

The researchers contend advertising influences the type of food we eat and that overseas studies have found that unhealthy foods are most likely to appear in these advertisements.

“This being the case, advertising is likely to have played a role in the current obesity epidemic,” write the researchers in their paper.

“Furthermore, targeted advertising of unhealthy foods may entrench and even increase existing socio-economic inequalities in the prevalence of obesity.”

So some advertising doesn’t just make you fat, it can keep you overweight.

Previous studies found ads at Sydney rail stations commonly advertised unhealthy snacks — although water was the most common beverage — while a Perth study found 23 per cent of commuter stops audited had ads for alcohol.

The Melbourne study is the first to cover all types of commuter public transport and to make socio-economic conclusions.

A total of 233 food advertisements were identified at the 558 public transit stops audited across the 20 sampled suburbs, the study reports.

If you’re seeing ads such as this at your local bus stop, you probably live in an affluen

If you’re seeing ads such as this at your local bus stop, you probably live in an affluent area. Picture: AP/PepsiCoSource: AP

Least-disadvantaged suburbs had a higher mean number of advertisements per suburb compared to the most-disadvantaged suburbs, although this difference was not statistically significant.

And it’s not just a matter of where you live which decides the exposure to food ads. It also depends on how you commute.

“… however, differences were observed by the type of stop. A higher proportion of train stations in the least-disadvantaged suburbs had at least one advertisement present (86 per cent v 42 per cent). Conversely, fewer tram shelters in the least-disadvantaged areas featured food (32 per cent v 50 per cent),” says the research.

“The proportion of bus stop shelters with food advertisements was similar in the least- and most-disadvantaged suburbs (22 per cent and 25 per cent).”

data saving lives, health market quality, healthcare, policy, politics

Health Data “Interoperability”: A $30 Billion Unicorn Hunt

too funny

http://www.forbes.com/sites/theapothecary/2014/09/03/health-data-interoperability-a-30-billion-unicorn-hunt/

Health Data “Interoperability”: A $30 Billion Unicorn Hunt

John R. GrahamJohn R. Graham , Contributor
Comment Now

Having cheered as $26 billion of taxpayers’ money has been spent since 2009 inducing hospitals and physicians to install electronic health records (EHRs), many champions of the effort are dismayed that the EHRs are not interoperable. That is, they cannot talk to each other – which was the whole point of subsidizing the exercise.

All this money has achieved a process goal: There has been a significant uptake of EHRs. According to a recent review, the proportion of physicians who have at least a basic EHR has increased from under 22 percent to 48 percent. Doctors were motivated by the bounty offered, plus the threat of having reimbursements being clawed back in 2015 if they have not adopted EHRs. The proportion of hospitals has similarly increased from 12 percent to 44 percent.

But these EHRs do not  talk to each other. According to the same review, “only 10 percent of ambulatory practices and 30 percent of hospitals were found to be participating in operational health information exchange efforts.”

All those billions of taxpayers’ dollars were paid out to providers who attest to “meaningful use” of EHRs. However, there are three stages of meaningful use.  Stage 1 was relatively simple. Stage 2 was originally supposed to be achieved by 2013, but that has been pushed back until 2016. The hang up is that stage 2 has a high hurdle for interoperability.

According to the final rule published in September 2012, requirements include “the expectation that providers will electronically transmit patient care summaries with each other and with the patient to support transitions in care. Increasingly robust expectations for health information exchange in Stage 2 and Stage 3 would support the goal that information follows the patient.”

Despite the delay, providers are still complaining that the requirements are too demanding. According to Russell Branzell, president and CEO of the College of Healthcare Information Management Executives: “Now the very future of Meaningful Use is in question.”

So it should be: Evidence from Congressional investigations suggests that meaningful-use bounties have encouraged the adoption of EHRs that are deliberately closed to exchange with other parties. The problem is that exchanging data with competitors is fundamentally against the self-interest of the party which created the data. Nobody would expect The U.S. Department of Transportation to set up a fund to incentivize car-markers to exchange data with each other, or the U.S. Department of Agriculture to set up a fund to incentivize grocery stores to exchange data with each other.

670px-obama_signing_health_care-201003231

That is not to say that there would be no value to such data exchange. IfSafeway were out of my favorite brand of breakfast cereal, I’d love for the clerk to tell me that Giant had plenty in stock just down the road, instead of selling me something similar. However, the amount of government funding required to overwhelm competitors’ resistance to doing this would surely not be worth it.

I’m sure readers can come up with many examples that would demonstrate the public benefit of competing hospital systems sharing data seamlessly. An epidemic or terrorist attack are easy ones. However, advocates of health information exchange emphasize how it would reduce friction in the day to day operations of our health system.

But at what cost? $26 billion has not done the trick. It is unlikely that the remaining $4 billion in the pot will get the job done. The Office of the National Coordinator of Health IT has been promoting a ten-year plan for more funding – even a trust fund like the Federal Highway Trust Fund!

When the Office of the National Coordinator of Health IT was established during the Bush Administration, its purpose was to “coordinate,” not underwrite nor regulate. Congress should be wary of appropriating yet more funding to hunt the unicorn of health data interoperability.