• Trust in physicians

    We found that, as has been previously reported, public trust in the leaders of the U.S. medical profession has declined sharply over the past half century. In 1966, nearly three fourths (73%) of Americans said they had great confidence in the leaders of the medical profession. In 2012, only 34% expressed this view. […]

    Indeed, the level of public trust in physicians as a group in the United States ranks near the bottom of trust levels in the 29 industrialized countries surveyed by the ISSP [International Social Survey Programme]. […]

    If the medical profession and its leaders cannot raise the level of public trust, they’re likely to find that many policy decisions affecting patient care will be made by others, without consideration of their perspective.

    And yet, satisfaction in receipt of care remains high. More by Robert Blendon, John Benson, and Joachim Hero.


    Comments closed
  • How often should I get dental x-rays?

    My dentist offered me bitewing x-rays yesterday. From the chair, I asked Twitter whether I needed them.

    I received two, informative replies. First, oral surgeon Sarah Davies, DDS, MD suggested annual bitewings and “maybe” panoramic x-rays every five years. Second, NYU Associate Professor Heather Gold pointed me to these ADA/FDA recommendations. If I’m reading them correctly (and this is based on just a quick look), I believe they recommend posterior bitewing x-rays on 1.5-3 year intervals.

    Anyone with knowledge or references have anything else to say on this matter? Comments open for one week from date of posting.

    P.S./pro tip: Future Austin or you can pull this post up for reference the next time the dentist offers x-rays. That’s why I’m posting this.


    Comments closed
  • Social impact bonds

    [A] growing body of literature [] calls for a renewed focus on [] nonmedical determinants of health. Unfortunately, interventions that address them— home visits, education, service-enriched housing, workforce training, healthy eating, exercise, and other nonmedical activities—often exist outside the scope of the traditional health care payment system.

    This is largely the result of two related challenges: The existing payment system is designed to pay for treatment after illness has occurred, and the funding necessary to pay for proven nonmedical interventions is tied up remediating the illnesses that the interventions could have prevented. […] Fortunately, there is a new tool—pay-for-success—that could be used to increase spending on the nonmedical determinants of health while maintaining society’s commitment to treating people who are already sick.

    All pay-for-success projects, which are also known as social impact bonds, or SIBs, begin with a performance-based contract between a service provider, usually a nonprofit organization, and a payer, usually a government agency. The service provider agrees to administer a program designed to produce a future outcome that is valuable to the payer—which, in turn, commits to pay the service provider when that outcome is delivered.

    Once the performance-based contract is in place, the service provider raises money from foundations, banks, and other investors that agree to supply the provider with up-front program funding. In exchange, the investor or investors receive “success payments” in the future based on projected cost savings (usually by a government agency), should the agreed-upon outcome be produced on schedule. […]

    Once the outcome has been verified, the payer’s success payment is [] passed along to the investor or investors. Depending on the contract, the service provider may also receive a bonus for executing the program successfully. However, if the outcomes are not present, no success payment is disbursed, the investors lose their investment, and the service provider loses credibility as an evidence-based organization.

    There have been four such pay-for-success projects. Others are in the works and many more are imaginable. Ian Galloway, quoted above, has more in the latest Health Affairs.

    (Bill mentioned social impact bonds in a prior post about financing hepatitis C treatment, pointing to this Harvard Magazine piece.)


    Comments closed
  • *Relieving Pain in America*

    Below are my notes from skimming Relieving Pain in America, by the IOM (2011). There’s also a four-page summary here. The report covers the following topics, among others:

    • What pain is, who gets it, its impact on health
    • The prevalence and cost of pain and its treatment
    • Types of pain care
    • Opioid abuse
    • Pain care under different types of insurance and systems, including the Department of Veterans Affairs
    • Education and research challenges
    • Problems with RCTs in studying pain and what’s been done about them

    All of the following are quotes. I’ve left out links to references since you can freely download the report and find them yourself.*

    • Acute and chronic pain affects large numbers of Americans, with approximately 100 million U.S. adults burdened by chronic pain alone. [Page 1 and similar on many other pages]
    • Section 4305 of the 2010 Patient Protection and Affordable Care Act required the Secretary, Department of Health and Human Services (HHS), to enter into an agreement with the IOM for activities “to increase the recognition of pain as a significant public health problem in the United States.” [Page 2]
    • [S]ome types of chronic pain are diseases in their own right. […] Understanding chronic pain as a disease means that it requires direct treatment, rather than being sidelined while clinicians attempt to identify some underlying condition that may have caused it. [Page 4]
    • [T]he annual economic cost of chronic pain in the United States is at least $560-635 billion. This estimate combines the incremental cost of health care ($261-300 billion) and the cost of lost productivity ($297-336 billion) attributable to pain. The federal Medicare program bears fully one-fourth of U.S. medical expenditures for pain; in 2008, this amounted to at least $65.3 billion, or 14 percent of all Medicare costs. In total, federal and state programs—including Medicare, Medicaid, the Department of Veterans Affairs, TRICARE, workers’ compensation, and others—paid out $99 billion in 2008 in medical expenditures attributable to pain. Lost tax revenues due to productivity losses compound that expense. [Page 5]
    • National Health and Nutrition Examination Survey (NHANES) data show that during the 7-year period 1988- 1994, 3.2 percent of Americans reported using opioids for pain (2.8 percent of men and 3.6 percent of women). During the 4-year period 2005-2008, by contrast, 5.7 percent of the population was using these drugs (5.2 percent of men and 6.2 percent of women), including 7 percent of people 65 and older. [Page 130]
    • A meta-analysis found [rehabilitation/physical therapy] programs achieved significant reductions in both pain intensity and use of pain medications (Hoffman et al., 2007). […] Rehabilitative/physical therapy has increasingly been found to reduce pain even in end-of-life situations, such as advanced cancer (Chang et al., 2007). […] A systematic review of 18 randomized controlled trials showed that physical conditioning programs “seem to be effective in reducing the number of sick days for some workers with chronic back pain, when compared to usual care” (Schonstein et al., 2003, p. 1). […] A meta-analysis of 20 studies showed that exercise had a statistically significant effect in reducing disability for work over the long term but not over the short or intermediate term (Oesch et al., 2010). […] In a systematic review of 43 studies of exercise for chronic low back pain, the researchers concluded that only 6 showed statistically significant and clinically important results in improving functioning, and only 4 showed such results in reducing pain intensity (van Tulder et al., 2007). [Pages 133-134]
    • Health professionals’ general awareness of the importance of pain and recognition of the need to ask patients about it have been buttressed by efforts of the Joint Commission to establish and enforce pain management standards (Phillips, 2000). Beginning in 2001, following the lead of pain medicine professional associations and the Department of Veterans Affairs, the then Joint Commission on Accreditation of Healthcare Organizations introduced a new hospital accreditation standard that requires monitoring of patients’ pain level as a “fifth vital sign.” […] The Joint Commission’s effort quickly led to clinically appropriate increases in opioid use in postanesthesia care units (Frasco et al., 2005). It also led many health facilities to implement routine efforts to relieve patients of pain immediately, identify and address causes of pain, initiate treatments other than medication, and prevent postsurgical acute pain from developing into chronic pain. […] In a veterans’ outpatient clinic, monitoring pain as a fifth vital sign failed to improve pain management as the assessment was not followed up with recommended treatment, even for patients reporting substantial pain (Mularski et al., 2006). Similarly, in a study of eight veterans’ facilities in the Los Angeles area, documentation of pain—necessary for pain care planning—was frequently absent from the medical records of patients with moderate and severe pain (Zubkoff et al., 2010). Taken together, these studies suggest the need to exercise careful clinical judgment based on a comprehensive patient assessment instead of merely monitoring pain (meeting, in a sense, the letter of the law and not the spirit), using opioids to the exclusion of other treatment approaches, or routinely using these powerful medications when their use is not clinically indicated. [Pages 138-139]
    • Despite the many variables involved in people’s responses to pain, different measures of pain can yield consistent results. For low back pain, high degrees of correlation have been found among three different types of measures: a patient’s global assessment of response to therapy (often a score given by the patient from zero to 4), a well-validated questionnaire about the extent of pain-related disabilities, and use of a “visual analog” or graphic rather than a numeric scale to report pain levels (Sheldon et al., 2008). [Page 140]
    • The April 2011 White House comprehensive action plan on prescription drug abuse notes that “. . . any policy in this area must strike a balance between our desire to minimize abuse of prescription drugs and the need to ensure access for their legitimate use” (The White House, 2011, pp. 1-2). While most of the plan’s provisions relate to substance abuse, it does include some measures to assess the adequacy and effectiveness of pain treatment and to “facilitate appropriate prescribing, including development of Patient-Provider Agreements and guidelines” (The White House, 2011, p. 4). The same day the White House action plan was released, the Food and Drug Administration (FDA) announced that it will require an Opioids Risk Evaluation and Mitigation Strategy (REMS) (Okie, 2010; FDA, 2011) for all extended release and long-acting opioid medications. [Pages 142-143]
    • A reasonable degree of access to pain medication—such as the stepped approach of the World Health Organization’s Pain Relief Ladder for cancer—has been considered a human right under international law since the 1961 adoption of the U.N. Single Convention on Narcotic Drugs (Lohman et al., 2010; WHO, 2011). Similarly, countries are expected to provide appropriate access to pain management, including opioid medications, under the International Covenant on Economic, Social, and Cultural Rights, which guarantees “the highest attainable standard of physical and mental health” (Brennan et al., 2007, p. 213). [Page 143]
    • Certainly in recent years, opioid prescriptions for chronic noncancer pain have increased sharply (Dhalla et al., 2009; Chapman et al., 2010). According to the White House action plan, between 2000 and 2009, the number of opioid prescriptions dispensed by retail pharmacies grew by 48 percent—to 257 million (The White House, 2011). But are patients who really need opioids able to get them? Twenty-nine percent of primary care physicians and 16 percent of pain specialists report they prescribe opioids less often than they think appropriate because of concerns about regulatory repercussions (Breuer et al., 2010). […] The effectiveness of opioids as pain relievers, especially over the long term, is somewhat unclear. [A lit review follows, omitted here. Pages 143-144]

    * Plus, this “research notebook” post is principally for a future me. Delighted if you also find it of value, but I’m clearly not trying to write a complete piece for a general audience here.


    Comments closed
  • Distributions

    Via Adrianna’s viral tweet:

    paranormal dist


    Comments closed
  • On Google’s new Inbox

    Google’s new alternative to Gmail is here, and it’s called Inbox, available by invitation only at the moment. You can see Google’s pitch here and read how-to use guides here and here. My tweets on Inbox are here. Joshua Gans has written a review. Mine is below, informed by three days of Inbox use.

    What I Like

    For me, Inbox’s killer app is the ability to schedule emails and reminders to return to one’s inbox at a time and/or place of one’s choosing. This is called snoozing. (To date I have only used the time-based reminders, not place-based.) Some smarty-pants at Google has recognized that I, and no doubt gazillions of others, use their inbox like a task list anyway, leaving emails hanging around to remind us to do something, emailing ourselves to remind us to do something, starring things as reminders to do something, and so forth.

    Rather than forcing users to kludge all this onto an email program, Inbox has integrated it more intelligently. This makes sense, and it works. It’s for this feature alone that I have switched to using Inbox for a guestimated 95% of my email use. (More on the other 5% below.)

    I also like that the Done list (the analog to Gmail’s All Mail list) is presented in the order in which the user marked items as done. The most recent done stuff is at the top. In Gmail, the All Mail list is in order of when the last email was received. This is less useful to me, as I’m frequently scrolling my archive for that item I know I put away recently despite having received it weeks ago. The receipt date is less salient and relevant than the archived (“done’d”) date.

    What Google highly promotes is bundling, which is the combining of similar email conversations into groups. This is a bit like Gmail’s tabs, only bundles aren’t stored and presented as tabs, but as blocks of related email. And it’s a bit like tags only with tags, email conversations are only presented as grouped within their tag, not wherever they appear. I never liked Gmail’s tabs (disabled them on day 1) and I’m not a big tagger either. So, though potentially useful in rare instances, I won’t be bundling much. Google hypes bundling, but it’s not where the action is (for me). Still, no harm. I give bundling a mild “like.”

    Google could have done all the above right in Gmail and I’d have been happy.

    What is Not Ready or Not Useful

    Google claims that Inbox isn’t done, and it’s clear a lot of needed features are missing (one example: you can’t edit and manage signatures, which don’t appear on any emails, to my annoyance*). Indeed, how-to guides admit you’ll need to go back to Gmail to do some things like selecting multiple emails for bulk deletes and moves, doing anything in settings, emptying Trash, and on and on). Still, over three days, I’m not going back to Gmail much (5% of the time, maybe), yet I cannot completely abandon it. In time, I hope I can.

    Inbox allows you to “pin” stuff to your inbox, which keeps it there. I don’t get why this is useful. I can already keep stuff in my inbox by not removing it from my inbox. And, the whole point of Inbox, as far as I can see, is to help keep your inbox from being cluttered. This is why scheduling things to pop up later — and disappear in the meantime — is useful. Why pin when you can snooze and schedule?

    Google thinks pinning is key because it includes one-click pin buttons on emails and a switch at the top of one’s Inbox to toggle to a pined-only view. I just don’t get why Google thinks pinning is so important. There are almost no other buttons, an attempt at a very clean presentation.

    Meh. Obsessive cleanliness is an overrated design principle. Sure, one doesn’t want too much clutter. But a handful of buttons at the top, for archiving, marking as spam, moving to trash, etc. is a reasonable balance, as achieved in Gmail. In a nearly button-free Inbox, one has to click twice to do some of those things, or use keyboard shortcuts. (There are gestures for the touch-screen implementations on Android and iOS, which are kind of nice.) I also noticed that I have to now click twice to download an attachment. I don’t like this. One click should do it, per Gmail.

    Emails are presented in Inbox with some of their internal contents more visible without opening them, like dates of events, contact info, and thumbnails of attachments. I do not like this because it makes the email look big, take up too much real-estate on my page. Opening the email is not hard. I would toggle this off if I could. Or, I encourage Google to find a way to make it more space-efficient.

    Stars are gone. I don’t mind too much. It took me a bit of time to realize that “snooze until someday (unspecified)” is the equivalent to my use of Gmail stars. Inbox offers a ready way to see all one’s snoozed items (whether scheduled for a specific time or not). And that’s how I used Gmail’s Starred list. Still, the advantage of stars is that, with the couple of dozen or so that Gmail offers, users can flexibly mark emails as they see fit. I don’t see why stars need to go away, except to satisfy some need for cleanliness or to force people into snoozing things. Maybe not everyone wants to snooze!

    To Sum Up

    I like Inbox. I am using it almost exclusively. Scheduling/snoozing is the killer app. It helps me manage my life and my inbox. This is good.

    However, Google needs to bring in or bring back more of Gmails functionality to make Inbox a full-service email app. I believe this is their intention, but I don’t fully trust them. (Sorry, they lost my trust long ago.) Let’s wait and see.

    If you get an Inbox invitation, take it! I wish I could give you one, but Google has not granted me any (yet?). Perhaps my invites have been snoozed.

    * Really, this is very, very bad and should be added immediately.


    Comments closed
  • The latest research on ACOs

    Today in NEJM you’ll find two studies and an editorial pertaining to ACO performance. Below is a brief summary and commentary.

    In Changes in Health Care Spending and Quality 4 Years into Global Payment, Zirui Song et al. examined cost and quality of care for patients served by providers participating in Blue Cross Blue Shield of Massachusetts’ Alternative Quality Contract (AQC). They compared them to the experience of comparable patients enrolled in certain employer-sponsored plans in other Northeastern states over 2009-2012. (If you’re not familiar with what the AQC is and does, read this.)

    In the 2009 AQC cohort, medical spending on claims grew an average of $62.21 per enrollee per quarter less than it did in the control cohort over the 4-year period (P<0.001). This amount is equivalent to a 6.8% savings when calculated as a proportion of the average post-AQC spending level in the 2009 AQC cohort. Analogously, the 2010, 2011, and 2012 cohorts had average savings of 8.8% (P<0.001), 9.1% (P<0.001), and 5.8% (P = 0.04), respectively, by the end of 2012. Claims savings were concentrated in the outpatient-facility setting and in procedures, imaging, and tests, explained by both reduced prices and reduced utilization. Claims savings were exceeded by incentive payments to providers during the period from 2009 through 2011 but exceeded incentive payments in 2012, generating net savings. Improvements in quality among AQC cohorts generally exceeded those seen elsewhere in New England and nationally.

    Here are two charts that illustrate some of the findings:

    AQC cost

    AQC qual

    In Changes in Patients’ Experiences in Medicare Accountable Care Organizations, J. Michael McWilliams et al. considered patients’ experiences with Medicare ACO contracts after one year, relative to before ACOs formed, comparing the change to that of matched Medicare patients not served by ACOs.

    Overall ratings of care and physicians and ratings of interactions with primary physicians did not change differentially in the ACO group, as compared with the control group, from the preintervention period to the postintervention period. In contrast, reports of timely access to care differentially improved in the ACO group. […]

    Overall ratings of care reported by patients in the ACO group with seven or more [chronic] conditions and HCC scores of 1.10 or higher improved significantly as compared with similarly complex patients in the control group (differential change, 0.11; 95% confidence interval [CI], 0.02 to 0.21; P = 0.02; differential change with adjustment for preceding trends, 0.20; 95% CI, 0.06 to 0.35; P = 0.005).

    In the editorial Accountable Care Organizations — The Risk of Failure and the Risks of Success, Lawrence P. Casalino wrote that

    ACOs represent the best attempt to date to move away from business as usual and toward health care that will improve patients’ health and will not bankrupt the country. If ACOs fail, it may be a long time before a similarly bold concept emerges. […]

    [Yet, t]he performance of ACOs to date has been promising but not overwhelming. Although some ACOs have gained a substantial return on their investment in improving the health of their patients, many have not. […]

    The ACO movement is unlikely to succeed unless health insurance plans dramatically increase their number of ACO contracts and unless CMS modifies specifications for its ACO programs — a course that the agency is considering.

    I think Casalino strikes the right tone. There are some encouraging findings about ACOs in the literature, both in the new work by McWilliams, Song, and colleagues, and in prior work. But it’s both early yet and unclear whether the most promising findings from the AQC can be generalized.

    Across public and private ACOs, 18 million Americans receive care from one. Massachusetts is particularly dense in ACOs, as Song et al. write: 85% of physicians in the state have entered the AQC, 72% of Tufts Health Plan commercial managed care enrollees are under global budgets, and five organizations have joined the Medicare Shared Savings Program. This makes Massachusetts a convenient laboratory for ACO-like models, but it also makes Massachusetts unusual and threatens generality of findings from the state. Perhaps other features of Massachusetts are responsible for a tendency for ACO participation and their outcomes.

    I would give ACOs another five or so years before drawing any strong conclusions about what they can do. Even a few years of generally positive results is insufficient to declare victory. It’s reasonable to be optimistic, but cautiously so. A lot could still go wrong.


    Comments closed
  • Choosing a health plan is hard, even for a health economist (me)

    The following originally appeared on The Upshot (copyright 2014, The New York Times Company).

    A confession: I am a health economist, and I cannot rationally select a health plan.

    I buy health insurance through the Federal Employees Health Benefits Program, or F.E.H.B.P., which is very similar to the Affordable Care Act’s exchanges. Like the exchanges, the federal employee program runs an online marketplace with a choice of plans, which vary by region.

    Most workers don’t have a lot of choice among plans offered by their employer. But the federal employee program offers me about 20 plans to choose from, and a similar number to almost all other federal employees. This puts me in a position akin to a consumer selecting among many plansin an Affordable Care Act exchange or a Medicare beneficiary selecting among many Medicare Advantage plans.

    I have a lot of sympathy for consumers in these markets. Comparing health plans is hard, even for a health economist like me. (And it’s arguably harder on the Affordable Care Act exchanges, where consumers may also need to report income and apply for subsidies. Federal employees just need to choose a plan.) Each year when I shop for coverage through my employer, I feel like I’m buying myself at least as much grief as I am insurance.

    In one sense, buying health insurance is not different from buying any other product, like a laptop computer or a refrigerator. There are two things to consider: how much you pay (the price) and what you get (the quality). Quality can mean a lot of things for a health plan, and your criteria may differ from mine. For me, the most important aspect is which doctors and hospitals are in its network and, hence, most generously covered. (Some plans cover out-of-network providers less generously; some not at all.)

    A health plan’s price is more amenable to quantitative analysis, but still hard to assess.

    Each laptop has a sticker price, as does each refrigerator. Health insurance has not one but many price-like characteristics. The premium is the most salient price, perhaps. But there are lots of others like co-payments (fixed dollar amounts you pay each time you visit a doctor, get a lab test or pick up a prescription), co-insurance (a percentage of the cost you pay for each visit, test or prescription), and deductibles (how much you pay before your plan pays a single dollar). Complicating matters, deductibles do not apply to every service, and co-payments and co-insurance can vary by service — a different amount for a hospital stay vs. a primary care visit vs. a visit to a specialist, for a brand-name drug vs. a generic, and so forth.

    Given all this, computing something like a sticker price for a plan is daunting. The actual amount an insurance plan will cost me next year is its premium plus a complex interaction of its various other prices with the specific types of health care services my family will use. Fortunately, for federal employee plans, there is an online resource that helps simplify this calculation. Using The Guide to Health Plans for Federal Employees & Annuitants, federal employees can compare the total cost of premiums and cost sharing of plans for low, average and high levels of health care spending. (Low is about $3,000 in annual health care spending, and high is about $30,000.) This guide, which I have used, also includes plan quality ratings. A similar guide exists for the Illinois Affordable Care Act exchange, but for no others.

    One problem is that I don’t know how much or what kind of health care services my family will use next year. But based on past experience, I can make a reasonable guess as to whether it will be low, average or high. Seeing how my total cost for each plan varies across that range helps me understand the consequences of increases or decreases in use.

    But I would find it helpful to supplement that approach with a more precise calculation based on the level of health care my family tends to use. For instance, how much would each plan have cost me last year? The answer is a much closer analog to the type of sticker price one sees for refrigerators and laptops. I’m aware of no online insurance market that provides this type of information.

    Already, the lack of price transparency is enough to make a health economist despair. But it gets worse.

    Some aspects of plan quality are available to most consumers, like consumers’ ratings of customer service and how well doctors in each plan communicate with their patients. But a crucial feature of health plans is not as easily or widely accessible: the extent to which each covers services provided by one’s favorite doctors and hospitals. Except on a handful of Affordable Care Act exchanges and for federal-employee participants in the Washington area, such network information is typically available only on plans’ websites, making gathering and comparing plan networks prohibitively difficult. Moreover, which doctors are in a plan’s network can change over time.

    If I could not precisely price a laptop or assess the size of a refrigerator (or if that size could change after I bought it), I’d have a great deal of difficulty selecting the right one for me and my family. So when I shop for a health insurance plan each year, I have very little confidence that the one I select is the one I would choose were I to have more information available to me.

    And, as a health economist, I have very little confidence that a market with this degree of opacity of prices and quality can serve consumers well. Indeed, research has shown that Medicare beneficiaries have great difficultyin selecting the lowest-cost prescription drug plan. (A Medicare drug-plan pricing and coverage tool is available on the Medicare website, but it’s a fair bet that most beneficiaries do not use it.)

    Insurance markets do not need to be this opaque. We have the technology to track health care use electronically and to create online tools that could tell every health insurance consumer exactly how much each plan would have cost them based on prior-year utilization (as well as for a range of other utilization levels) and to what extent services provided by the doctors they saw and hospitals they visited would be covered next year. This is an effort that some policy experts have called for.

    Though the Affordable Care Act’s exchanges are new, some markets in which consumers can select among many plans are not. The F.E.H.B.P. has existed with plans in competition since 1960. The competitive Medicare Advantage market grew out of predecessor programs that stretch back nearly 30 years.

    Despite this long history, we do not yet offer consumers the tools they would need to become anything like rational market participants. This could change. Companies such as Picwell and Consumers’ Checkbook are working on developing and expanding consumers’ access to such tools. This is welcome news, and it’s about time.

    If even a health economist will confess to needing better access to price and quality information when choosing a health plan, it’s a sure bet that many other Americans need it as well.


    Comments closed
  • Five more big data quotes: The ambitions and challenges

    For this roundup of quotes, I received input from Darius Tahir and David Shaywitz. Prior TIE posts on big data are here. As always, the quotes below do not reflect the views of the authors, but those of the people or community they’re covering. Click through for details.

    1. David Shaywitz in “Why Causation Is (Often) Not Causation – The Retro Humility Of Empiricism,” articulates the “strong version” of the “big data thesis”:

    A strong version of the canonical big data thesis is that when you have enough information, you can make unbiased predictions that don’t require an underlying understanding of the process or context – the data are sufficient to speak for themselves. This is the so-called “end of theory.”

    2. Darius Tahir reports on the content of a Rock Health slide deck:

    Healthcare accelerator Rock Health is predicting big advances for startups and healthcare providers using personalized, predictive analytic tools. The firm has observed $1.9 billion in venture dollars pouring into the subsector since 2011, with major venture capital firms keeping active.

    The use of predictive analytics, essentially looking at historic data to predict future developments to directly intervene in patient care, will only increase as data multiplies, the report argues.

    In 2012, the healthcare system had stored roughly 500 petabytes of patient data, the equivalent of 10 billion four-drawer file cabinets full of information.

    By 2020, the healthcare system is projected to store 50 times as much information, 25,000 petabytes, meaning machine intelligence will be essential to complement human intelligence to make sense of it all.

    See, in particular, pages 18, 19 of the slide deck from Rock Health. I found it interesting that there’s lots of use of “prediction” and “predictive” throughout the deck and no direct language of causality. This is appropriate. I also think that organizations that didn’t understand just this limitation would slip into causal language now and then. In other words, Rock Health, and likely others, know exactly what they’re selling. (I am not disparaging prediction here. It is useful. I am merely distinguishing it from causal inference.)

    3. Tim Hartford has written one of the best pieces on the limitations of big data I’ve read to date. Big data is often also “found data,” hence typically suffers from selection bias. It also invites a multiplicity of hypothesis testing; query the data enough and something (meaningless) will appear statistically significant, eventually. (More by David Shaywitz on this point here.) I recommend reading his piece in full; it includes many examples from Google, Twitter, Target, the city of Boston, and the history of polling. Here’s an excerpt, cobbled from snippets throughout:

    Cheerleaders for big data have made four exciting claims, each one reflected in the success of Google Flu Trends [which Hartford summarizes, as well as its later comeuppance]: that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”. [I quoted from and linked to that Wired article here.]

    Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge university, they can be “complete bollocks. Absolute nonsense.” […]

    A recent report from the McKinsey Global Institute reckoned that the US healthcare system could save $300bn a year – $1,000 per American – through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes. […]

    “There are a lot of small data problems that occur in big data,” says Spiegelhalter. “They don’t disappear because you’ve got lots of the stuff. They get worse.” […]

    The Literary Digest, in its quest for a bigger data set, fumbled the question of a biased sample. It mailed out forms to people on a list it had compiled from automobile registrations and telephone directories – a sample that, at least in 1936, was disproportionately prosperous. To compound the problem, Landon supporters turned out to be more likely to mail back their answers. The combination of those two biases was enough to doom The Literary Digest’s poll. For each person George Gallup’s pollsters interviewed, The Literary Digest received 800 responses. All that gave them for their pains was a very precise estimate of the wrong answer.

    The big data craze threatens to be The Literary Digest all over again. Because found data sets are so messy, it can be hard to figure out what biases lurk inside them – and because they are so large, some analysts seem to have decided the sampling problem isn’t worth worrying about. It is. […]

    [B]ig data do not solve the problem that has obsessed statisticians and scientists for centuries: the problem of insight, of inferring what is going on, and figuring out how we might intervene to change a system for the better. […]

    Statisticians are scrambling to develop new methods to seize the opportunity of big data. Such new methods are essential but they will work by building on the old statistical lessons, not by ignoring them.

    4. David Shaywitz in “Turning Information Into Impact: Digital Health’s Long Road Ahead”:

    A leading scientist once claimed that, with the relevant data and a large enough computer, he could “compute the organism” – meaning completely describe its anatomy, physiology, and behavior. Another legendary researcher asserted that, following capture of the relevant data, “we will know what it is to be human.” The breathless excitement of Sydney Brenner and Walter Gilbert —voiced more than a decade ago and captured by the skeptical Harvard geneticist Richard Lewontin – was sparked by the sequencing of the human genome. Its echoes can be heard in the bold promises made for digital health today. […]

    [T]echnologists, investors, providers, and policy makers all exalt the potential of digital health. Like genomics, the big idea – or leap of faith — is that through the more complete collection and analysis of data, we’ll be able to essentially “compute” healthcare – to the point, some envision, where computers will become the care providers, and doctors will at best be customer service personnel, like the attendants at PepBoys, interfacing with libraries of software driven algorithms.

    5. David Shaywitz in “A Database of All Medical Knowledge: Why Not?” writes about the challenges of finding and assembling big data. Here’s the set-up:

    For scientists and engineers today, perhaps the greatest challenge is the structure and assembly of a unified health database, a “big data” project that would collect in one searchable repository all of the parameters that measure or could conceivably reflect human well-being. This database would be “coherent,” meaning that the association between individuals and their data is preserved and maintained. A recent Institute of Medicine (IOM) report described the goal as a “Knowledge Network of Disease,” a “unifying framework within which basic biology, clinical research, and patient care could co-evolve.”

    The information contained in this database — expected to get denser and richer over time — would encompass every conceivable domain, covering patients (DNA, microbiome, demographics, clinical history, treatments including therapies prescribed and estimated adherence, lab tests including molecular pathology and biomarkers, info from mobile devices, even app use), providers (prescribing patterns, treatment recommendations, referral patterns, influence maps, resource utilization), medical product companies (clinical trial data), payors (claims data), diagnostics companies, electronic medical record companies, academic researchers, citizen scientists, quantified selfers, patient communities – and this just starts to scratch the surface.


    Comments closed
  • Management

    In his recent book David Cutler wrote about the importance of good management in the efficiency and productivity of health care organizations, among others. Here’s my summary:

    An instrument developed by McKinsey asks organizations about performance monitoring, target setting, and incentives/people management. In high performing organizations, information relevant to performance is fed back to workers who are empowered to stop processes to fix problems; goals are established to focus attention on areas for improvement; people are hired and promoted on the basis of performance.

    Bloom et al. used the McKinsey survey instrument to examine over 10,000 organizations internationally, mostly manufacturing firms, but also several hundred hospitals and some schools, including those in the U.S., U.K., Japan, Germany, and some developing countries.

    In manufacturing, firms that score better are also more profitable and successful. Higher scoring schools do better on standardized tests. Higher scoring hospitals also have better survival rates for heart attacks. Across all three domains, U.S. hospitals have lower management scores than manufacturing firms, but higher than hospitals in other countries.

    I’ve now read the Bloom study (ungated here and published by the Academy of Management here). Below is the ten-point summary by the authors. For the health care-specific point I’ve included the chart. For the rest, you can go to the source at one of the prior links.

    Before I get to the authors’ summary, though, a word about my interest in this area: Many of my favorite health policy experts have emphasized in conversation the importance of management. To large degree, it seems, what separates a broadly high-quality and efficient health care organization from one that is neither is management and organizational culture, which are intertwined.

    This shouldn’t be surprising. If you think about organizations you interact with in other sectors (restaurants, retail stores, and the like) the ones that (a) stick around and prosper and (b) that you enjoy patronizing are, disproportionately and in general, the well-managed and efficient ones. The others, well, suck, and tend to get out-competed. That is, you, in particular, and competition, in general, select(s) for good management.

    Relative to some other sectors, we don’t have as much competition in health care for a variety of reasons, some inherent (like a high degree of product differentiation and information asymmetry) and some amenable to policy (a high degree of third-party payment, constraints on market entry). With no strong mechanism to promote the well managed and to weed out the badly manged organizations, what type of organization settles in a given area might be somewhat random. In some places, we find high quality and greater efficiency (e.g., Kaiser, Intermountain, Geisinger, etc.). In others, we don’t.

    This raises a set of crucial questions, among them: (1) Assuming weaker market forces in health care than in other sectors, how do we promote good management? (2) To what extent can we promote stronger market forces in health care without sacrificing the important reasons they are weak in the first place? Notice I’ve phrased these to be relevant no matter your policy preferences. The first asks you to assume weaker market forces, without claiming that assumption cannot be loosened. The second lifts that assumption but asks you to recognize that there may be some “important reasons” market forces are weak (you decide which, and note there is heterogeneity among people about what they are) that we may wish to retain.

    When I look at what’s happening in health care today, I see an attempt to establish or laud conditions under which better managed organizations will thrive, with all their attendant benefits for patients, efficiency, and so forth. That’s the point of price transparency, pay-for-performance, bundled payments, ACOs, etc. That’s the hope for retail clinics, greater patient cost-sharing, narrow networks, reference pricing, and so on. But, notice, none of them directly promote or measure good management. In well-functioning markets ,we need not worry about that. In health care, we might, particularly if we want to understand more precisely to what extent the myriad approaches to greater efficiency and quality noted above work, if any. Do we know how to measure good management or promote it? I am not qualified to say, but I suspect the answer is somewhere between “no” and “not very well.” Am I right?

    Bloom et al. offer a means of measuring good management, though it may be incomplete and have important limitations. Here’s what they found:

    1. US manufacturing firms score higher [in management performance] than any other country. Companies based in Canada, Germany, Japan and Sweden are also well managed. Firms in developing countries, such as Brazil, China and India are typically less well managed (Figure 1). [Click through for figures not included below.]

    2. In manufacturing, there is a wide spread of management practices within every country. This spread is particularly notable in developing countries, such as Brazil and India, which have a large tail of very badly managed firms (Figure 2).

    3. Looking at other sectors, US firms in retail and hospitals also appear to be the best managed internationally, but US schools score poorly (Figure 3).


    4. There is also a wide spread of management practices in non-manufacturing sectors (Figure 4).

    5. Publicly (Government) owned organizations have worse management practices across all sectors we studied. They are particularly weak at incentives: promotion is more likely to be based on tenure (rather than performance), and persistent low-performers are much less likely to be retrained or moved (Figures 5 and 6).

    6. Amongst private sector firms, those owned and run by their founder or their family descendants, especially firstborn sons, tend to be badly managed. Firms with professional (external, non-family) CEOs tend to be well managed (Figure 7).

    7. Multinationals appear able to adopt good management practices in almost every country in which they operate (Figure 8).

    8. There is strong evidence that tough product market competition is associated with better management practices, within both the private and public sectors (Figure 9).

    9. Light labor market regulation is correlated with the systematic use of monetary and non monetary incentives (related to hiring, firing, pay and promotions), but not monitoring or targets management (Figure 10).

    10. The level of education of both managers and non-managers is strongly linked to better management practices (Figure 11).


    Comments closed