医疗领域的黑客攻击：有意义的使用与超越

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"Hacking Healthcare: Meaningful Use and Beyond" 本书《Hacking Healthcare》由Fred Trotter和David Uhlman合著，深入探讨了医疗保健领域的信息化及其潜在影响。"Meaningful Use"是美国政府推出的一项政策，旨在通过鼓励医疗机构采用和有效利用电子健康记录（EHRs）来提升医疗服务的质量、安全性和效率。该政策是美国医疗改革的关键组成部分，旨在通过技术手段推动医疗数据的标准化和互操作性。 "Meaningful Use"的实施包括多个阶段，要求医疗机构在特定时间内完成一系列目标，如改善患者参与度、提高临床决策支持以及确保数据的安全共享。这一政策不仅影响了医疗保健机构的技术选择，还对医疗信息系统的开发、集成和升级产生了深远影响。书中可能涵盖了如何符合Meaningful Use标准的具体策略和技术解决方案，以及这些变化对医疗行业参与者，包括医生、护士、信息技术专业人员以及病人的影响。作者Fred Trotter和David Uhlman在医疗信息领域具有深厚的专业背景，他们可能通过实际案例和深入分析，揭示了Meaningful Use政策实施中的挑战和机遇。书中可能讨论了数据安全和隐私保护的问题，因为随着医疗数据的数字化，保护患者信息安全成为了重大课题。此外，还可能探讨了医疗保健行业的未来趋势，如人工智能、大数据分析和远程医疗在提升医疗保健水平方面的作用。编辑Andy Oram的参与确保了内容的专业性和深度，而生产编辑Jasmine Perez、复制编辑Teresa Horton和校对者O'Reilly Production Services等团队成员的工作则保证了书籍的高质量呈现。封面设计和内部布局的精心打造，使得这本书在视觉上也引人入胜。总体来说，《Hacking Healthcare》对于理解医疗保健行业的数字化转型、Meaningful Use政策的实施以及它如何塑造未来的医疗服务体系，提供了宝贵的洞见。这本书不仅是医疗IT专业人士的重要参考，也是政策制定者、医疗管理者以及关心医疗领域发展的人士的理想读物。

The Veterans Affairs (VA) hospitals in the United States offer the most substantial

example of systemic improvements in quality using health IT. Since the 1970s, the VA

has gone from a system with a reputation as a low-quality provider to a system widely

regarded as the safest and most effective healthcare delivery system in the world. VA

hospitals almost obsessively measure the quality of the healthcare they deliver, and they

have the numbers to back up the assertion that they are tops. The quality of the VA

system, and its focus on health IT to deliver quality, is documented in the book The

Best Care Anywhere by Phillip Longman. Rather than quote all of the quality statistics

in that and many other books, we will relate two simple cases that show the power of

leveraged health IT systems at the VA.

In 2004, the drug company Merck voluntarily withdrew Vioxx from the market. Vioxx

had been used to treat chronic pain, but it had become clear, over time, that Vioxx had

a dangerous side effect: fatal heart attacks. Evidence also emerged that by 2000, Merck

had evidence that Vioxx was dangerous. The fact that Vioxx was approved by the Food

and Drug Administration (FDA), and that it was used so long after it was known to be

dangerous, has been the subject of intense scrutiny.

But years before the healthcare profession as a whole was aware of the dangers of Vioxx,

the VA discovered on its own that it was a dangerous medication. Data from the VA’s

electronic healthcare record, VistA, had alerted the VA that something was amiss with

Vioxx. The VA took steps to ensure that Vioxx was prescribed only with careful mon-

itoring and only in special circumstances, a drug of last resort. By doing so, the VA

saved thousands of lives.

The second case is the level of integration experienced by VA hospitals. If a veteran

receives treatment in one VA hospital for a decade and then moves to another hospital,

even another state, he can expect a decade’s worth of VA records to be available at the

new hospital on his first visit. The VA has achieved near-complete health data liquidity

for its covered veterans. In comparison, most other healthcare systems typically still

use fax machines to exchange health information.

This is the stage that has been set for health IT. Medical errors are too common, costs

are out of control, and effective deployment of computerized records and workflow

can dramatically reduce these errors and lower costs. This book will discuss preventable

medical errors in detail, and show how many different health IT functions, from health

data exchange to different types of reporting, can help to address healthcare quality

and reduce medical errors.

* Vioxx, the implosion of Merck, and aftershocks at the FDA

2 | Chapter 1: Introduction

Health IT and Medical Science

Most of those who are deeply involved in healthcare IT have chosen this field as a

mission or vocation, rather than merely a career. Many health IT professionals have

historically taken substantial pay cuts compared with IT professionals in other areas

(although this is improving now). Many of us work in this industry because we lost a

loved one to a simple medical error, or some other failure of the healthcare delivery

system. For many of us, this is our life’s passion. To us, “reducing the costs and im-

proving the quality of healthcare” is a dry and frail description of our ambition for

health IT. To paraphrase Steve Jobs, we want to make a dent in the human condition.

Before we can talk about what that next stage will be like, we should acknowledge that

it will not be anything like past medical advances. Pasteur’s microorganism model of

disease, Darwin’s theory of evolution, Florence Nightingale’s redefinition of nursing,

Roentgen’s X-ray, or perhaps even the discovery of DNA by Watson and Crick are

examples of game-changing insights. These are classic examples of massive improve-

ments in healthcare delivery that come from a new fundamental insight. The improve-

ments to healthcare that happen because of computerization will not be a revolution,

but an evolution.

Fundamental to the ambitions in the health IT community is a humble acknowledg-

ment that these huge game-changing insights are rare. We can expect fewer and fewer

of them as the science of medicine progresses. Instead, medicine must now begin the

difficult work of chronicling the immense complexity of a single cell’s DNA, proteins

and other structures, and how that cell cooperates with other cells in the human or-

ganism. We can no longer expect that individual insights will leap medical science

forward, but instead the medical community will make hundreds of thousands of small

incremental advances on tens of thousands of diseases.

If we hope to continue the rate of improvement in healthcare we must find a way to

coordinate the contributions of countless clinicians, researchers, and patients. To make

any sense out of the genotype, we must have a understanding of phenotype —the

manifest characteristics of individuals, such as their age, weight, medical symptoms,

mental status, and many other measurable traits —than is several orders of magnitude

deeper than it is today. We must be able to gather and parse a hundred times more data

about each patient than we do today, and we must be able to compare that rich data

among millions of patients. Today, the sciences and the software that support clinical

trials, genomics, and standard clinical operations are separate and distinct, with infre-

quent overlap. Tomorrow, these disciplines will merge into a single enormous effort to

improve healthcare. Science on this scale is impossible without mass high-quality com-

puterization. There is no reason why all of this cannot be accomplished while respecting

patient privacy and other basic notions of human dignity.

We hope to use technology to improve every aspect of healthcare. We hope to create

information systems that help to turn medicine into a higher art and a higher science.

Health IT and Medical Science | 3

As you can imagine, with such ambitions, the health IT community frequently has

delusions of grandeur. But we also suffer from frequent and stifling disillusionment.

Although most of us agree that health IT has tremendous potential, progress in the field

has been far too slow. We have a few good examples, like the VA with VistA, demon-

strating that massive improvements to healthcare delivery are possible by leveraging

technology. But we must admit that although we have a few good examples, we have

countless examples of failure.

The authors of this book believe both that health IT has tremendous potential and that

health IT is surprisingly difficult. As we discuss its difficulties, and the methods that

have been used to successfully overcome them, we hope to avoid the pessimism that is

all too common in health IT. Having said that, when pessimism and discouraging voices

abound, it is often for good reason. There are real pitfalls in health IT, and this book

should show you how to avoid many of them.

Meaningful Use and What It Means to Be an EHR

Health IT has changed tremendously over the last few years. The biggest change in the

United States has come from the simple phrase “meaningful use.” The term is now

solidly entrenched as the catchphrase for health IT in the United States. Most impor-

tant, meaningful use represents reasonable first steps toward the long-term potential

for health IT. For better or worse, the dreams and ambitions of the healthcare infor-

matics industry are tied to the concept.

The phrase first appeared in the Health Information Technology for Economic and

Clinical Health (HITECH) portion of the American Recovery and Reinvestment Act of

2009 (ARRA). ARRA defined that a substantial portion ($20 billion) of the money set

aside by Congress to stimulate the United States economy after the financial and fore-

closure crisis would go to doctors and hospitals who “meaningfully use” clinical soft-

ware. The HITECH act was the first step in President Barack Obama’s comprehensive

plan for healthcare reform. Clinicians would receive the stimulus money to pay for

software to improve the delivery of patient care.

The bill referred to that software by the currently popular term electronic health re-

cord (EHR) software. But software designed to improve the delivery of clinical care has

been around for decades, under different names. Such software has been called com-

puterized patient records (CPR), electronic medical records (EMR), electronic health

records (EHR), and countless other similar names with corresponding abbreviations.

Even more confusing, there was no set definition of what this class of software was

supposed to achieve. Unlike software products such as word processors, spreadsheets,

or database storage engines, which all have well-understood definitions, the software

category of EHR has meant very different things to different people. Passionate users

would assume that EHR software meant the features that they wanted. Developers

assumed that EHR meant the set of features in the software that they had developed.

Of course, different users and different developers rarely agreed on which features were

4 | Chapter 1: Introduction

the most important. Dr. Ignacio Valdes (a medical doctor possessing a master’s degree

in computer science with a stellar reputation in the health IT community) has frequently

said, “For decades, doctors had no idea what they wanted, and software developers

have given it to them.”

For clinicians, these terms served as a source of confusion and frustration. It was totally

unclear what different names implied about the functionality of the software. Even a

few years ago, when a doctor would say “I want an EHR!”, the right response from a

health IT software vendor would have been “Fine, exactly what do you mean by EHR?”

The meaningful use EHR certification requirements have finally dictated exactly what

EHR software needs to do for the doctors, hospitals, and other eligible providers who

purchase, use, and deploy the software to receive payments from the ARRA-HITECH

stimulus plan. In fact this has made the meaningful use requirements even more im-

portant than the term EHR. What is an EHR? That which can be used by a clinician to

achieve meaningful use.

Why So Late?

IT experts, as well as the general public, often fail to ask one simple question that will

help focus any discussion of healthcare informatics: Why did the United States health-

care industry need to be paid to computerize? Every other industry computerized when,

and to the degree that, computerization held intrinsic competitive advantages for

members of that industry. Market forces compelled computerization, and companies

that refused or resisted the move to computerization were squeezed out by competitors

who were leaner and faster as the result of automated processes.

This has not happened in healthcare. Why not? It seems like such a simple and obvious

step! Almost all hospitals and clinics already have some computers. They use them to

type letters and send emails, to research on the web and coordinate schedules. They

also automate some clinical tasks, most notably medical imaging, which is almost en-

tirely computer-based. But, with few and notable exceptions, clinicians have not com-

puterized the most central information resource they possess, the patient chart. The

patient chart remains a paper record, usually a set of papers wrapped in a simple manila

envelope.

For most information professionals (or clinical professionals with good information

instincts), the use of computers to achieve standardization in data and work processes

is a mantra. It is almost beyond question that computerized automation of processes

and record-keeping would dramatically improve the performance of any industry. Still,

healthcare has resisted computerization for decades.

In the answer to the question “Why hasn’t this happened on its own?” we will find the

heart of meaningful use. The reasons that healthcare has not computerized can be

summarized as screwy incentives and a difficult domain. Specifically:

Why So Late? | 5

• Healthcare is orders of magnitude more complex than most other industries. This

complexity has generated extensive clinical specialization. In some cases this spe-

cialization calls for extensive changes to the “normal” health IT workflow. It also

means that each medical specialization has its own diagnostic categories, termi-

nology, and procedures.

• Healthcare is constantly changing. Treatments and best practices that are even two

or three years old are frequently so out of date as to be nearly unethical. (Remember

Vioxx.)

• Attempts to computerize healthcare facilities have typically failed badly. Many ex-

planations have been offered for this problem, and will be discussed throughout

this book.

• Even successful efforts are typically only partial automations, resulting in half-

paper and half-computerized workflows that have the benefits of neither system

and the drawbacks of both.

• Healthcare providers in the United States, for the most part, are paid for their time.

EHR typically slow doctors down, ensuring that they are paid less for the same

work.

• Computerization has typically been a very expensive proposition. Healthcare pro-

viders have been saddled with this cost, despite the fact that most of the benefits

of computerization accrue to either patients or insurers. Essentially, until now,

EHR systems have been disincentivized.

These factors, and others like them, have resulted in an abysmal value equation for

EHR systems. For the average doctor or hospital, before ARRA funding, buying an EHR

system was risky and expensive, with little benefit. To say that adoption of EHR soft-

ware has been sluggish would be generous. It was widely known that fewer than 20%

of small practices in the United States had purchased an EHR before the HITECH act.

For a time, conventional wisdom held that although small practices had not purchased

EHR systems, they were common (at least over 50%) in hospitals. But a study published

in the New England Journal of Medicine in 2009 largely destroyed that illusion.

†

The

study compiled a list of the 30 or so tasks that an EHR should do that actually improve

the quality of healthcare, and found that less than 10% of all hospitals have installed

software that accomplished even a few of these important tasks. The health IT industry

was treating EHR deployment as a yes/no question, but if you asked “Is this software

actually helping doctors deliver improved care in the hospital?” rather than just “Is

something electronic in the hospital?” it became clear that hospital EHR software was

typically underperforming.

One of the authors of that study was David Blumenthal. It was not an accident that he

was chosen as the National Coordinator for health IT during a critical phase, when

† The Use of Electronic Health Records in U.S. Hospitals

6 | Chapter 1: Introduction

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