Health Information Technology and the Patient Centered Medical Home: Seven Additional Caveats

The Disease Management Care Blog is scheduled to participate in a November 12 PCPCC webinar on the timely topic of population health management (PHM).  We'll be focusing on the October 2015 PCPCC report "Managing Populations, Maximizing Technology." Readers can download it here and refer to page 2 where the DMCB, among other luminaries, is acknowledged for its thoughtful review.

The PCPCC report effectively reminds health system architects and policymakers that the electronic health record (EHR) is necessary - but nowhere near sufficient - for a high performing patient-centered medical neighborhood.  Other information technology (IT) components include intelligent shared decision making, registries, health information exchanges, analytics, referral tracking, telemonitoring, automated outreach, patient communications, mobile apps, decision support and risk stratification.

And that's just for starters. 

The good news here is that while Washington DC's EHR weenies remain focused on the dreary stages "meaningful use," innovative health systems with medical homes and neighborhoods are really using IT to make a thousand PHM flowers bloom.   

Naturally, during the PCPCC webinar, the DMCB isn't going to stop there. If given a chance, it will also point out:

1. Build vs. buy: While health systems generally believe that PHM - with or without its IT  trappings - can be built using local resources, a better answer may be to buy it from a vendor.  Why own it when you can rent it?

2. And speaking of outsourcing: While its physician-colleagues prize the stature that comes from "quarterbacking" a medical home team, what is less appreciated is the distinct possibility that a quarterback is often not the most important position.  Get out of the way and let the IT-empowered and enabled non-physicians do their thing.

3. The EHR gone wrong: "Portals" are preferred by EHR vendors because they push patients toward their products, often run by lawyers who fear HIPAA and typically programmed by IT geeks who only think about code. It's time to put patients first.

4. "This is not my patient!": While predictive modeling generates lists of patients that annoy physicians with multiple inaccuracies, the science is getting better.  That being said, many other tests like EKGs and chest x-rays are notorious for false negative and false positive results.  It's all part of being a doctor.

5. Apply a filter.... please!: The biggest threat from health IT is a data glut of numbers, labs, tests, surveys, messages, alerts, prompts, readings, alarms, vitals and figures that overwhelm medical home team members. That's going to involve setting thresholds and priorities.

6. It's not about the revenue: Forget about using health IT to justify additional payment. In a health system without anymore money, the purpose of health IT is to generate savings.  That means it has to pay for itself.

7. Watch out! The under-appreciated health IT event that is going to change the relationship between insurers and providers: the move from using paid insurance claims to submitted EHR claims to assess population outcomes.

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The High Price of High Deductible Plans and the Potential Role of Population Health Management

Your bronze plan ticket to health care?

Should patients be forced to reach a spending threshold before their insurance kicks in? At first glance, it makes sense, because health consumers' "skin in the game" forces them to think twice before going to the emergency room for a sore throat, or an orthopedic surgeon for simple back pain.

Wharam and colleagues examine the science behind high deductible insurance in this just-written article in the New England Journal.

And the science says there is a lot we do not know.

Once insurance risk is monetized into premiums, policymakers as well as insurers are operating in the dark about calculating the right deduction for a given income level. One example is Cover Oregon's $5000 deductible for persons who are at 200% to 400% of the federal poverty level. That means a family with a yearly income as low as $47,000 would have to spend more than 10% of their income on health care before seeing a dime of insurance coverage.

"Egads," says the DMCB.

Given that stark reality, the challenge is to figure out how an up-front deductible influences "buying behavior" once persons get sick. Unfortunately, most of the research out there is on the impact of relatively "small" amounts of out-of-pocket expenses on health care utilization, especially in low-income populations. The bad news is that lay-persons - who are unable to discern the difference between a simple headache vs. a brain tumor - tend to "indiscriminately" lower all utilization as their cost sharing goes up.

There has also been no research on the impact of high deductible plans on mortality or chronic condition control.

Concluding that the U.S. is "poorly prepared" for what will happen under Obamacare's bronze high deductible plans, Wharam et al recommend there be more research on the topic.  Pending that, they suggest consumers be educated about their insurance purchases and be encouraged to chose low-deductible plans. They note that the star-crossed insurance exchanges (once they're fixed) can be configured to help do that. When there is employer-based insurance, employers could be encouraged to make the deductibles more proportional to income. In addition, health savings accounts could also help.

While the authors don't use the words "population health management," they tap this discipline as one solution to this Obamacare problem. They point out that predictive modeling/risk stratification can be used to create "personalized" insurance designs that optimize high-risk patients' access to care. Patients in these plans could have access to decision-aids and coaching that help them figure out when it's a simple headache and then they should seek medical care.

Version 1 Care Management to Prevent Hospital Readmission Fails (Unsurprisingly)

Does this high profile randomized study "prove" that telephone follow-up of recently discharged inpatients fails to prevent readmissions? 

Should hospital leaders reconsider care management programs aimed at reducing readmissions?

Hardly, says the Population Health Blog.

Here's how the study was designed:

To be eligible, patients had to be aged 55 or older and without mental illness or serious cancer. They also had to be able to use a telephone. If the patient and their doctor agreed, patients were then randomly assigned to either:

1) "usual" care that consisted of a pre-discharge review of medications, follow up and other instructions plus a 10 day medication supply, or

2) "intervention" care that was comprised of pre-discharge disease-specific education using motivational interviewing, personalized notification of the primary care physician for follow-up, a medication schedule, an in-person follow-up by a registered nurse within 24 hours and follow-up telephone calls on days 1-3 and 6-10 after discharge.

Over 6300 patients were reviewed, 1781 patients were considered and 700 were enrolled in the study. 679 patients completed 30 days, 581 patients completed 90 days and 561 patients completed 180 days of follow-up.  The mean age of the study population was 66 years, 56% had mild cognitive impairment, 33% had visited an emergency room in the prior six months and 62% used English as their primary language. 

In the intervention group, nurses managed to complete their two phone calls 83% of the time.

Results?

"There were no statistically significant differences in the number of ED visits or readmissions between the intervention and usual care groups at 30 days (0.33 vs. 0.26 per person-month; 112 vs. 89 events), 90 days (0.23 vs. 0.20 per person-month; 238 vs. 203 events), or 180 days (0.20 vs. 0.18 per person-month; 392 vs. 370 events)." 

There was also no different in the number of primary care visits between the two groups.

Ouch.

The authors speculate that this patient population already had a high level of support from primary care providers and good access to medications.  In addition, a high prevalence of cognitive impairment may have blunted the nurse interventions.  Last but not least, the authors state that further reductions in ED visits or readmissions may require more in-person home visits in lieu of just telephone calls.

The Population Health Blog offers another thought:  the study was doomed from the start.

Years ago, the Ver. 1.0 "disease management" vendors learned the hard way that aggressively "calling" every patient with did not reduce complications, costs or health care utilization. 

The study described above was a reprise of that long discredited approach. Calling every person being discharged from a hospital may help some patients, but not all. 

Since that time, "population health" vendors have discovered risk stratification. By restricting their in-person and telephonic follow-up to patients discovered to be at greatest risk by advances in"big data" analytics, resources can be better focused on the patients who are most likely to benefit and the likelihood of a return on investment is accordingly increased.

And it's not like this is rocket science.  Surveys and clinical algorithms like this and this respectively can help identify recently discharged patients at high risk of readmission. 

If the study above had incorporated this approach and only enrolled the high risk patients, they might have had a positive study. 

That's the real lesson for hospital leaders and their care management programs.

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Doubling Down on Accountable Care Organizations and Health Information Networks

Want to achieve effective health care, reduced costs, increased quality, population health, widespread prevention and seamless health information access? 

It's easy, says  this article in Population Health Management: mix one part PHO with one part HRB to create a HAPPI.

The Population Health Blog was confused too, but that's what's proposed by three smart academics from Johns Hopkins, Arizona State University and UC Berkeley.

As the PHB understands it, Population Health Organizations (PHOs) would be responsible for all medical, public health, community and social services in a defined geographic area and coordinate them with local education, housing and labor. Much of it would be paid for by a pooled risk-adjusted global or capitated payment (budget) from all insurers.

Each organization would be paired with a Health Record Bank (HRB), which would act as a huge data warehouse that not only stores all medical information, but any other publically available information on every individual enrolled in the PHO. The HRBs would be owned and operated by "trusted custodial organizations." Data access would be ultimately controlled by each patient.

The authors believe that patient payments would be a source of additional revenue for their PHOs. Examples include buying "apps" that are tailored to their individual health needs, or selling their personal health information, especially if it means helping physicians buy an electronic health record or access cutting edge research.

Combine a PHO and HRB and you have a Health and Prevention Promotion Initiative (HAPPI). Its size and scale would warrant contributions from community and provider organizations "without the need for additional reimbursement or outside funding." It would efficiently "align incentives" for insurers, hospitals and ACOs - with money left over for prevention, care coordination, decision support and a learning health system.

Breathtaking, isn't it?  If any PHB readers thought accountable care organizations (ACOs) and health information networks (HINs) weren't big enough, along comes Tyrannosaurus rex-sized PHOs, HRBs and HAPPIs. 

The PHB worries that while we'd want to see how pint-sized ACOs (not a slam dunk) and HINs (likewise not a slam dunk) perform before we apply the massive steroid doses, the opposite could happen: their messy failure could be just the justification for doubling down and going even bigger. 

As pointed out in a recent Wall Street Journal Notable and Quotable:

Economist Michael Munger writing in the Freeman, Aug. 11:

When I am discussing the state with my [academic] colleagues, it's not long before I realize that, for them, almost without exception, the State is a unicorn. I come from the Public Choice tradition, which tends to emphasize consequentialist arguments more than natural rights, and so the distinction is particularly important for me. My friends generally dislike politicians, find democracy messy and distasteful, and object to the brutality and coercive excesses of foreign wars, the war on drugs, and the spying of the NSA.

 

But their solution is, without exception, to expand the power of "the State." That seems literally insane to me—a non sequitur of such monstrous proportions that I had trouble taking it seriously.

 

Then I realized that they want a kind of unicorn, a State that has the properties, motivations, knowledge, and abilities that they can imagine for it. When I finally realized that we were talking past each other, I felt kind of dumb. Because essentially this very realization—that people who favor expansion of government imagine a State different from the one possible in the physical world—has been a core part of the argument made by classical liberals for at least three hundred years.

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Commitment Devices, Behavior Change and Population Health

A new addition to the
behavior change tool box

Whenever the Population Health Blog encountered a tobacco user in its clinic, it would gauge the patient's readiness to quit. For those patients who were ready, it then established a future "quit date" (to facilitate planning), a "contract" (a jointly signed prescription for display on the home fridge) and advice to use any money savings (tobacco is expensive) for a nice reward once seven days of success (for example, a restaurant dinner) was achieved.

The PHB didn't know it at the time, but that seven-day reward was a variation of a "commitment device."  That's what it learned after reading this just-published JAMA manuscript by Todd Rogers and colleagues.

Commitment devices are a way that "present" persons can commit their "future selves" to a sufficient level of needed behavior change.  The threat of a penalty, such as the loss of a night out on the town, imposes a limit on future choices and makes success more likely. 

Other examples of commitment devices described by the authors include applying cash to a success contract (for example, employers could link a bonus to participation in a exercise program that would otherwise be lost), "temptation" bundling that limits access to a gratifying experience in exchange for "consistent" behaviors (used with repeated success by the crafty PHB spouse), limiting bad choices to small packages (smaller portion sizes) and partnering (to avoid disappointing a buddy who shares the commitment).

In retrospect, "commitment devices" have been used in population health for decades.  As Rogers et al point out, however, despite some good research on how effective this approach is, they're generally underused by providers and patients.  One potential way to overcome that is to offer them routinely on an "opt-out" basis, 401k savings-plan style.  The authors also point out that a series of commitment devices on a longitudinal basis could be used to blunt drop outs and maintain long-term behavior change. Last but not least, leveraging social networks with or without handheld "apps" remains an area ripe for future research.

As medical homes spread and shared-risk payment reforms gain traction, the art and science of commitment devices will likely grow. Not only is it a cool piece of insider jargon ("Hey, Mary, I like this care management proposal, but have you any plans to develop commitment devices?"), but any addition to the behavior-change tool box can only help.

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I'm From CMS and I'm Here to Help

Writing in JAMA "online first," CMS Administrator Tavenner and colleagues offer a payment reform "framework" that includes "multipayer collaboration."  The article is wonky, so the Population Health Blog dons its universal adminispeak translator so us normal humans can better understand what CMS is up to.

According to the writers, CMS has a history of innovatively implementing reforms that were later adapted by other insurers. The most famous example is the hospital "DRG" system that, starting in 1983, paid for a diagnosis in lieu of a daily room rate.  Suddenly, hospitals had an incentive to shorten hospital stays, which is precisely what happened in the years that followed.

Buoyed by this success, the authors describe the merits of championing Medicare's transition from "category 1" fee-for-service without any link to quality to "category 4" population-based payments that are linked to quality. And, as CMS embarks on this excellent payment journey toward accountable care, they'll get other commercial insurers to mirror their efforts by:

"Being conveners" as in "working with" other insurers in a region or a state to implement large payment reforms.  Working with may include grants;

"Incentivizing," as in requiring the participation of other payers prior to funding any large pilot programs.

"Working with states" to implement additional reforms, when the state has sufficient influence over the health insurance or delivery system.

The Population Health Blog's take:

"Category 4 population-based payments" are a form of capitation that are ultimately designed to transfer insurance risk from CMS to providers. The PHB hopes the bureaucrats at CMS are aware of the risk re-introducing some 1990s-style managed care abuses. 
 
What also goes unmentioned by the JAMA article are examples of CMS payment reform unintentionally gone awry, including RVUs, regional payment variation and the SGR with lingering fraud. While CMS has had its successes, it's also had more than its share of problems.  Time will tell which track record will apply to population-based payments.

Convening was an art developed by Medicaid programs.

Ms. Tavenner implies that population-based payments (a form of capitation) are intrinsically linked to quality.  Nothing could be further from the truth, since it's possible to reward quality while also relying on a FFS methodology. 

Accountable population-based care remains a large experiment.  Ms. Tavenner implies that there is an aura of inevitability.  The PHB learned long ago that the sign of a good plan is an exit strategy in case things go south.  The PHB didn't read that here.

Ten Things to Know About the mHealth App Ecosystem.

A mHealth app walled garden:
enter at your own risk?

If, like the Population Health Blog, you're interested in the hand-held mHealth app ecosystem, you may want to check out this just published JAMA review article "In Search of a Few Good Apps." 

Naturally, for time-pressed readers who'd rather not read it all, your PHB is happy to provide this ten point summary.

1) There are more than 40,000 of mHealth apps and the industry is still in its infancy.

2) Despite their faddish sexiness, there is very little hard evidence that many of the commercially available apps to lead to measurable improvements in clinical or economic outcomes. However, some of the underlying technology (such as pedometers) does provide a benefit.

3) The Food and Drug Administration (FDA) will assert its regulatory authority if the app "acts" like a "medical device" or as an accessory to a "medical device." Logging data, retrieving content or communicating won't be regulated, but medication dosing guides or the provision of diagnostic information will be.

4) 3) Little is known about the physician prescribing patterns for apps.  We also haven't figured out if or how a patient's access to an app should depend on a licensed professional's approval/prescription.

5) There is a possibility that many currently available apps are putting users' privacy at risk.

6) Little is known about apps' compatibility with electronic health records (EHRs).  This may be less of an "ecosystem" and more a bunch of isolated "walled gardens."

7) One vulnerability to any app's usefulness is data overload. Hundreds of food entries, for example, may do little to increase user insight about his or her diet.

8) Other than the FDA and its fussing over apps' medical "deviceness", there is no agency or entity that provides certification for apps. Consumers are on their own, based largely on on-line reviews and word of mouth.  One organization tried to do it and conspicuously failed.

9) The time is right to create "guidelines" for app developers, such as how to provide useful data summaries as well as visual displays, maximize patient safety, ensure information accuracy and protect consumer privacy.

10) The time is also right for funding agencies to support research on apps, especially for persons with chronic illness.

Naturally, the PHB offers commentary:

It remains to be seen if the FDA can keep up, especially with apps that are in the "grey zone" between offering advice/possibilities vs. diagnosis/treatment. That shortcoming is vulnerable to overlawyering and regulatory overreach. That means prolonged time to market, increased uncertainty, hampered innovation and the threat of retroactive and potentially capricious reviews.

As you are reading this, many apps are undoubtedly being developed by the population health service providers.  It may be time for entities like the Population Health Alliance or stakeholder organized medicine organizations to take the lead in establishing app benchmarks, best practices and guidelines.  If they don't lead on this, someone will do it to them. 

While vendors that offer apps along with their coaching may be inclined to regard them as proprietary and shield them from the scrutiny of peer review research, apps that are proven to improve outcomes will ultimately rise to the top.  It's not just the funding agencies but the companies that offer these apps that have a stake in "proving it," while also advancing medical knowledge for the betterment of all of us.

Finally, wouldn't it be neat if there was a generic mHealth app that could be used by medical homes to facilitate nurse-patient coaching, link the patient to the EHR and enhance communication with providers?  If there is one that the PHB isn't aware of, it wants to know about it.

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