Tech

Tech

A Tale of Two IT Procurements

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Recently, the President of the United States, the most powerful person on earth, the man whose finger rests on the nuclear button, struck a bold blow for . . . procurement reform?

“There are a whole range of things that we’re going to need to do once we get [the Affordable Care Act (ACA) rollout] fixed—to talk about federal procurement when it comes to IT and how that’s organized,” the president said on November 4, speaking to a group of donors and supporters.

People are clamoring for heads to roll, and the president is talking about what just could be the geekiest, most obscure topic ever to clog a federal bureaucrat’s inbox. Procurement reform? Has he gone off the deep end?

Well, not really. Among the causes of healthcare.gov’s difficulties, the federal process for purchasing goods and services could rank right up there with toxic politics, lack of funding for ACA implementation, and management goofs. Let me explain why, from personal experience.

From 2009 to 2011, I served as National Coordinator for Health Information Technology. My job was to implement the HITECH ACT, which aims to create a nationwide, interoperable, private, and secure electronic health information system. As national coordinator I had to lead a lot of federal contracts.

This is how that went.

FHIR Use Cases: Population Health and Value-Based Care

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Screen Shot 2016-07-06 at 3.22.36 PMAs a practicing internist, I have followed the gamut of the sturm and drang surrounding interoperability, and have experienced its pros and cons first hand.

What’s important now is that interoperability must evolve into population health management and value-based care use cases to match where healthcare delivery and payment is quickly going. Along with the approaching permanence of alternative payment models, population-based payments, either condition-specific or comprehensive, are on the ONC/CMS roadmap.

The FHIR API can can advance how the healthcare industry exchanges data, and not just for EMRs. All healthcare information technology products—from lab systems to HIEs, and even population health management tools—will have the opportunity to leverage the new framework.

An Open Note to Open Notes Objectors

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Screen Shot 2014-07-20 at 5.59.22 PMThere is a growing group of articulate and engaged patients committed to getting access to all their medical information in order to be better positioned to work collaboratively with their clinical teams. Published studies like the OpenNotes project have consistently shown significant benefits and a lack of serious problems. Health care systems are slow to change and just beginning to understand both the need and value to this more transparent and collaborative approach.

My institution, for example, is not ready (or even interested) in anything approaching opening chart notes to patients. In fact, although our secure portal will be launched in the near future, there was some resistance to making even problem lists, medication lists, lab and x-rays available through the portal.

That need not prevent individuals from contributing to change. A few years ago I began providing every patient with a copy of their office visit note as they left the office after their visit. The intent was for us to do the assessment and plan collaboratively and make sure they have a copy of our (collaborative) plan.  Patients have been very appreciative, and use it to share the assessment and plan with family and consultants, and as a reference. A few bring it back at the next visit with notes on it about what they did and what happened.

To the objectors who say that one cannot be honest in a note if the patient is going to see it, I say: balderdash. (Actually, what I say is much stronger…)  For one thing (the smaller point) the patient is already allowed to see it if they but ask.  More importantly, this argument depends entirely on the principle that the clinician knows best and needs to keep secrets in the interest of the patient. What I have experienced is a variation on the advice I got many years ago regarding relationships: if it’s important, then it’s important enough to be open about and deal with. If you aren’t willing to deal with it openly, you are not allowed to save it up and spring it on your partner (patient) later.

Could Digital Rights Management Solve Healthcare’s Data Crisis?

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Today, academic medicine and health policy research resemble the automobile industry of the early 20th century — a large number of small shops developing unique products at high cost with no one achieving significant economies of scale or scope.

Academics, medical centers, and innovators often work independently or in small groups, with unconnected health datasets that provide incomplete pictures of the health statuses and health care practices of Americans.

Health care data needs a “Henry Ford” moment to move from a realm of unconnected and unwieldy data to a world of connected and matched data with a common support for licensing, legal, and computing infrastructure. Physicians, researchers, and policymakers should be able to access linked databases of medical records, claims, vital statistics, surveys, and other demographic data.

To do this, the health care community must bring disparate health data together, maintaining the highest standards of security to protect confidential and sensitive data, and deal with the myriad legal issues associated with data acquisition, licensing, record matching, and the Health Insurance Portability and Accountability Act of 1996 (HIPAA).

Just as the Model-T revolutionized car production and, by extension, transit, the creation of smart health data enclaves will revolutionize care delivery, health policy, and health care research. We propose to facilitate these enclaves through a governance structure know as a digital rights manager (DRM).

The concept of a DRM is common in the entertainment (The American Society of Composers, Authors and Publishers or ASCAP would be an example) and legal industries.  If successful, DRMs would be a vital component of a data-enhanced health care industry.

Giving birth to change. The data enhanced health care industry is coming, but it needs a midwife.There has been explosive growth in the use of electronic medical records, electronic prescribing, and digital imaging by health care providers. Outside the physician’s office, disease registries, medical associations, insurers, government agencies, and laboratories have also been gathering digital pieces of information on the health status, care regimes, and health care costs of Americans.

However, little to none of these data have been integrated, and most remain siloed within provider groups, health plans, or government offices.

Apple and the 3 Kinds of Privacy Policies

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Screen Shot 2016-02-21 at 8.01.34 PM

Why Are Apple’s Competitors Staying Silent On the iPhone Unlocking Fight? is the question of the day on tech blogs. The answer is hardly technical and may not be legal, it’s all about privacy policies and business strategy and it is very evident in healthcare.

There are three classes of privacy policy in healthcare and everywhere else:

Class 1 – “Apple will not see your data.” This is Apple’s privacy policy for ResearchKit and HealthKit and apparently for whatever data the FBI is hoping to read from the terrorist’s phone. Obviously, in this case the person is in complete control of the data and it can be shared only with third-parties that the person authorizes.

Class 2 – “We will see and potentially use your data but you will have first-class access to your data”. This is the kind of privacy policy we see with Apple’s calendar and many Google services. The personal data is accessible to the service provider but it is also completely accessible via an interface or API. In healthcare, the equivalent would be having the FHIR API equally and completely accessible to patients and to _any_ third-parties authorized by the patient. This is Patient Privacy Rights’ recommendation as presented to the API Task Force.

Class 3 – “We will use your data according to xyz policy and if you don’t like it, take your illness elsewhere.” This is pretty much how healthcare and much of the Web world runs today. We have limited rights to our own data. On the other hand, the services that have our data can sell it and profit in dozens of ways. This includes selling de-identified data. In Class 3, you, the subject of the data are a third-class citizen, at best. In many cases, the subject doesn’t even know that the data exists. See, for example, The Data Map.

We are so completely engulfed by Class 3 privacy policies that we have lost perspective on what could or should be. A Class 1 policy like Apple’s is widely seen as un-American. A Class 2 policy like PPR’s is indirectly attacked as “insurmountable”.

Is the Direct Primary Care Model Dead?

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A recent Medical Economics article asked “Is the DPC model at risk of failing?”

The piece focuses on two large DPC-like organizations, Qliance Medical Management of Seattle, Washington and Turntable Health of Las Vegas, NV, working in partnership with Iora Health, which recently closed their doors. Qliance and Turntable were not actually DPC practices by strict definition; they were innovative large business operations providing healthcare services to patients and excluding third party payers. Their idea was commendable, but their closure indicates little cause for concern in regard to the growing Direct Primary Care movement.

Robert Berenson, MD, who admits to not being a fan of the DPC model, said “Qliance has been the poster child for DPC… If that one can’t make it… it suggests the business model (of DPC) is flawed.”  He is correct about one thing; the “business” model of medicine is certainly flawed.

What Dr Berenson fails to realize is that DPC is not a “business” model; it is a “care” model. Whether accepting insurance or DPC in structure, we already know solo and two-physician practices deliver the best care and have been doing so for the past 100 years. These intimate clinics know their customers better than anyone else in the industry, and can devote the time necessary to their clientele; these micro-practices should be known as the small giants of healthcare.

Internet Self-Diagnosis: Mapping the Information Seeking Process

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We’ve all been there. It’s early morning, and you wake up feeling groggier than usual, sensing the onset of a sore throat and a runny nose. Before crawling out of bed, you grab your smart phone and, naturally, Google “groggy sore throat runny nose symptoms.” Hundreds of results pop up, suggesting various illnesses and links to seemingly promising remedies. How could anyone filter through page after page of links, ranging from everyday allergies to deadly diseases?

Many of our health choices are made outside the doctor’s office. The simple decision of whether symptoms are severe enough to warrant visiting a healthcare provider is one of them. For some patients, that decision is easy, because regardless of the severity of symptoms, from a simple cough to leg pain, getting in to see a healthcare provider is easy. Unfortunately, many people still struggle to find a healthcare provider, get an appointment, and/or obtain transportation. These individuals are left to turn to other health information resources, such as the Internet, to determine whether their symptoms are severe enough to navigate these barriers.

The “digital divide” has become a catchphrase for how differences in educational, social, and economic backgrounds can affect access to web-based tools and services, as well as the general ability to use the Internet.

That divide has serious healthcare consequences: Though the web is not intended to replace traditional medical care, it may offer one of the few available sources of information for those with limited access to health services. While patients who regularly visit a provider are privy to the diagnostic processes of medical professionals, web-based tools may be critical in weighing the severity of symptoms for those with fewer resources and less access. 

A Challenge to Control Blood Pressure Using HIT

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Screen Shot 2014-07-30 at 2.13.16 PMHeart disease and stroke are two of the leading causes of death in the United States. To combat these threats, the Department of Health and Human Services (co-led by Centers for Disease Control and Prevention [CDC] and the Centers for Medicare & Medicaid Services [CMS]) has joined with private and non-profit organizations such as the American Heart Association, American Pharmacists Association and the YMCA, to launch Million Hearts®, a national initiative to prevent one million heart attacks and strokes by 2017. The initiative is working to encourage clinicians nationwide to improve the quality of care through use of the ABCS strategies – Aspirin when appropriate,Blood pressure control, Cholesterol management and Smoking cessation.

On July 7th, as we marked the halfway point in this ambitious drive to improve America’s health, the Office of the National Coordinator for Health Information Technology (ONC), in collaboration with the CDC, launched the EHR Innovations for Improving Hypertension Challenge to accelerate improvement on the Million Hearts® “B” strategy – Blood Pressure Control. The goal is to show how professionals are using health IT to improve patients’ cardiovascular health. Evidence-based treatment protocols are an essential tool for providers to use in improving blood pressure control.

What makes this ONC challenge unique?  First, it taps the expertise of clinicians who care for patients with hypertension and are using health IT to improve their control. Second, the challenge is designed to promote the scalability of critical tools for maximum impact and reach.

Grading the Federal Health IT Strategic Plan

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Optimized-SalwitzIt is a heart pounding, head spinning, edge of your seat page-turner; the sort of rare saga that takes your breath away as it changes you, forever.  It hints at a radically different future, a completely new world a few years away, which will disrupt the lives of every man, woman and child.  Available now, from the National Coordinator for Health Information Technology (ONC), Office of the Secretary, United States Department of Health and Human Services, is finally, without further ado; the Federal Health IT Strategic Plan 2015 – 2020.

You think I am kidding.  A satirical dig at another monstrous, useless, governmental report?  Absolutely not.  The concepts outlined in this blueprint will transform healthcare.  It is a tight, clear, document, which at only 28 pages, delivers almost as much change per word as the Declaration of Independence.  This may be the most powerful application yet of computerized information technology.

If you want to know where healthcare and health IT are headed, The Plan is absolutely worth a read.

I have only one complaint; it is coated with too much sugar.  Restricted by policy structure and jargon, the report does not go far enough.

The Case For Traveling to the Center of Our Social Networks

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James FowlerMuch has been made of David Lazer’s finding that Google’s Flu Trends tracker seriously missed the mark in its measurement of flu activity for 2012-2013—and in previous years, too.

For those who don’t know, Flu Trends monitors Google search behaviors to identify regions where searches related to flu-like symptoms are spiking.In spite of Flu Trend’s notable misstep, Lazer still believes in the power of marrying health and social data.

In discussing the results of his study, he has maintained Google Flu is “a terrific” idea—one that just needs some refining. I agree.And, earlier this month, Nicholas Christakis, several other colleagues, and I—with funding from the Robert Wood Johnson Foundation—published a new method offering one such refinement.

Our paper shows that, in a given social network (in this study’s case, Twitter), a sample of its most connected, central individuals can hold significant predictive power.

We call this potentially powerful group of individuals a “sensor group.”

By finding and monitoring the tweets of a sensor group, we can catch—and sometimes even predict—the outbreak of contagious information early on. That detection edge could improve how we track the outbreak of disease epidemics, the rise of certain terms or phrases, or shifts in political sentiment.

Whereas Flu Trends relies on a relatively static, proprietary “dictionary” of flu-related search terms based on average Google search habits, the sensor method taps into what is really happening in social networks in real time.

By drawing on language being used by a sensor group—such as mentions of an emergent symptom or a popular newly coined name for a disease—Google could gain insight into what their dictionary might be missing.Sampling both the average Googler’s behavior and that of the exceptionally connected social network user can paint a much fuller picture of whatever landscape we are interested in tracking. We can more accurately see how it looks now—and how it could look in the near future.