Evolution in Health Care System- EMR 2slides

 

Demonstrate in a Power point an in depth understanding of the impact of health care systems on an organizations capacity to deliver quality care. Using EMR as a significant trend and how this will help improve health care. 2 slides based on the outline provided to show the description of how technology has affected or could affect delivery of health care and the quality associated to this. See attached OutlineUse one of the sources referenced along with one other. Must be 250 words per Slide in documented notes and slide outlines included. APA formatted and citations noted in documented slide notes.

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30 April 2013 ProQuest

Table of contents

1. Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings, And
Costs……………………………………………………………………………………………………………………………………………. 1

Bibliography

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Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings,
And Costs
Author: Hillestad, Richard; Bigelow, James; Bower, Anthony; Girosi, Federico; et al
Publication info: Health Affairs 24. 5 (Sep/Oct 2005): 1103-17.
ProQuest document link
Abstract: To broadly examine the potential health and financial benefits of health information technology (HIT),
this paper compares health care with the use of IT in other industries. It estimates potential savings and costs of
widespread adoption of electronic medical record (EMR) systems, models important health and safety benefits,
and concludes that effective EMR implementation and networking could eventually save more than $81 billion
annually – by improving health care efficiency and safety – and that HIT-enabled prevention and management of
chronic disease could eventually double those savings while increasing health and other social benefits.
However, this is unlikely to be realized without related changes to the health care system. [PUBLICATION
ABSTRACT]
Links: Linking Service
Full text: Headnote The adoption of interoperable EMR systems could produce efficiency and safety savings of
$142-$371 billion. Headnote ABSTRACT: To broadly examine the potential health and financial benefits of
health information technology (HIT), this paper compares health care with the use of IT in other industries. It
estimates potential savings and costs of widespread adoption of electronic medical record (EMR) systems,
models important health and safety benefits, and concludes that effective EMR implementation and networking
could eventually save more than $81 billion annually-by improving health care efficiency and safety-and that
HIT-enabled prevention and management of chronic disease could eventually double those savings while
increasing health and other social benefits. However, this is unlikely to be realized without related changes to
the health care system. THE U.S. HEALTH CARE INDUSTRY is arguably the world’s largest, most inefficient
information enterprise. However, although health absorbs more than $1.7 trillion per year-twice the Organization
for Economic Cooperation and Development (OECD) average-premature mortality in the United States is much
higher than OECD averages.1 Most medical records are still stored on paper, which means that they cannot be
used to coordinate care, routinely measure quality, or reduce medical errors. Also, consumers generally lack the
information they need about costs or quality to make informed decisions about their care. It is widely believed
that broad adoption of electronic medical record (EMR) systems will lead to major health care savings, reduce
medical errors, and improve health.2 But there has been little progress toward attaining these benefits. The
United States trails a number of other countries in the use of EMR systems.3 Only 15-20 percent of U.S.
physicians’ offices and 20-25 percent of hospitals have adopted such systems.4 Barriers to adoption include
high costs, lack of certification and standardization, concerns about privacy, and a disconnect between who
pays for EMR systems and who profits from them. In 2003 the RAND Health Information Technology (HIT)
Project team began a study to (1) better understand the role and importance of EMRs in improving health care
and (2) inform government actions that could maximize the benefits of EMRs and increase their use. This paper
summarizes that study’s results about benefits and costs. A companion paper by Roger Taylor and colleagues
in this volume describes the policy implications of our findings.5 Study Data And Methods Here we summarize
the methodologies we used to estimate the current adoption of EMR systems and the potential savings, costs,
and health and safety benefits. We use the word potential to mean “assuming that interconnected and
interoperable EMR systems are adopted widely and used effectively.” Thus, our estimates of potential savings
are not predictions of what will happen but of what could happen with HIT and appropriate changes in health

30 April 2013 Page 1 of 11 ProQuest

http://search.proquest.com/docview/204645298?accountid=35812

http://AV4KC7FG4G.search.serialssolutions.com/?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&rfr_id=info:sid/ProQ:abiglobal&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.jtitle=Health%20Affairs&rft.atitle=Can%20Electronic%20Medical%20Record%20Systems%20Transform%20Health%20Care?%20Potential%20Health%20Benefits,%20Savings,%20And%20Costs&rft.au=Hillestad,%20Richard;Bigelow,%20James;Bower,%20Anthony;Girosi,%20Federico;et%20al&rft.aulast=Hillestad&rft.aufirst=Richard&rft.date=2005-09-01&rft.volume=24&rft.issue=5&rft.spage=1103&rft.isbn=&rft.btitle=&rft.title=Health%20Affairs&rft.issn=02782715

care. We also provide a more thorough explanation of our data and methods in an online supplement.6 *
Estimation of current HIT adoption and related factors. Our primary data source was the Healthcare Information
and Management Systems Society (HIMSS)-Dorenfest survey, which represents a broad canvassing of acute
care hospitals, chronic care faculties, and ambulatory practices on their adoption and plans to adopt various HIT
components.7 We included in the adoption category the provider organizations that had contracted for but not
yet installed an EMR system. To examine the factors related to differences in adoption, we merged additional
data about the providers and then performed probit regression analysis. Our lower-bound estimate of HIT
adoption assumed an integrated system that had an EMR, clinical decision support, and a central data
repository-from the same vendor to ensure interoperability. We adjusted the estimates according to the known
underrepresentation of smaller providers in this survey. * Estimation of potential HIT efficiency savings. We
conducted a broad literature survey to capture evidence of HIT effects. The survey was primarily from peer-
reviewed literature, but it included some information from non-peer-reviewed literature. Expert opinion was used
to validate some of this evidence. In some cases, such as savings from transcription, reported results covered a
broad range, and we used these ranges to estimate a possible distribution of savings. For effects supported by
only a few useful articles, we superimposed the same degree of dispersion.8 In general, the currently useful
evidence is not robust enough to make strong predictions, and we describe our results only as “potential.”
However, we do not believe that they represent the “best-case scenario,” for three reasons: (1) We have not
included many other effects (such as transaction savings, reductions in malpractice costs, and research and
public health savings), and there may be more sizable savings from HIT-motivated health care changes that we
are not able to predict: Modern EMR systems may be more effective than the legacy systems reporting
evidence; (2) we have not included certain domains such as long-term care; and (3) we do not report possible
values above the mean. The results are not worst-case, either. We chose to interpret reported evidence of
negative or no effect of HIT as likely being attributable to ineffective or not-yet-effective implementation.
Characteristics of the provider organizations that reported the savings were used to scale the results for cases
of broader EMR adoption. Assuming ten- and fifteen-year HIT adoption periods, we used Monte Carlo
simulation to generate the range of savings that might be achieved at different points in the future, assuming
that at least part of the reported benefit could be achieved by each newly adopting provider organization. We
generally report the mean value of the potential savings. * Estimating the costs of adoption. For hospital
adoption, we built a model of EMR system costs based on the literature and on information supplied directly to
us from hospitals. We included one-time implementation costs, such as provider downtime and hardware costs,
and ongoing maintenance costs. Our data allowed us to relate hospital adoption costs to size and operating
expenses of hospitals and generally represented the adoption of newer, more complete EMR systems, including
clinical decision support and computerized physician order entry (CPOE). For the acquisition and setup costs of
ambulatory systems, we used a publicly available database of commercial systems and excluded products that
did not have most of the desirable features of an ambulatory EMR system.9 To these costs, we added a
productivity loss of 15 percent for three months, $3,000 per physician for additional hardware costs, and yearly
maintenance costs equal to 20 percent of the one-time cost. Starting with current adoption rates of EMR
systems, we simulated ten- and fifteen-year adoption periods, in which physicians’ choices were approximated
by random selections from the ambulatory EMR list, and hospitals adopted systems and paid costs consistent
with our data related to size and operating expenses. From these simulations, we report the mean and show
sensitivity to assumptions about the initial adoption rate and assumed adoption period. * Estimating potential
safety benefits. Using medication error and adverse drug event rates from the literature, as well as limited
evidence of CPOE’s reduction of medication error rates, we extrapolated these potential safety benefits to a
future with broad national adoption of CPOE.10 Several databases-the Medical Expenditure Panel Survey
(MEPS) 1999 Inpatient File (which tracks a large number of patients and their interaction with health care), the
American Hospital Association (AHA) 2000 Hospital Survey, and the Healthcare Cost and Utilization Project

30 April 2013 Page 2 of 11 ProQuest

(HCUP) 2000 National Inpatient Sample-were used to distribute the errors across hospitals and patients.11 A
spreadsheet model was then used to calculate the potential adverse drug events and costs avoided as a
function of hospital size and patient age. For ambulatory care, our model used error and adverse drug event
reductions reported in the literature for ambulatory CPOE. Using the 2000 National Ambulatory Medical Care
Survey (NAMCS) database on office visits, we extrapolated the effects to full national adoption and show the
likely distribution of possible savings and adverse drug events avoided as a function of practice characteristics
and size.12 * Estimating other potential health benefits. We considered two kinds of interventions-disease
prevention and chronic disease management-that would exploit key features of HIT. To estimate the potential
effects enabled by EMR systems, we used several years of the MEPS data to develop a representative national
patient sample, with its associated information on health care use, diagnosis, and self-reported health status.
We applied recommended disease management and prevention interventions to appropriate members of that
population. Then, given the literature and clinicians’ opinions regarding the effect of the interventions, we
calculated the differences in cost, use, health status, and other outcomes measured in MEPS, such as sick
days in bed and workdays lost. We evaluated a representative sample of near-term (some effects within one or
two years of intervention) prevention, near-term disease management, and long-term (most effects five or more
years into the future) chronic disease management and prevention interventions. We report the health benefits
and savings associated with various degrees of patient participation in these programs, as might be obtained
with HIT support. What Can We Learn From Other Industries? We examined a range of industries to
understand IT’s effects on productivity and related enabling factors. During the 1990s, many industries-most
notably, telecommunications, securities trading, and retail and general merchandising-invested heavily in IT.13
Consumers saw the fruits of this investment in bar-coded retail checkouts, automated teller machines,
consumer reservation systems, and online shopping and brokerages. During the late 1990s and continuing into
this century, these industries recorded 6-8 percent annual productivity growth, of which at least one-third to one-
fourth annually can be attributed to IT. But dramatic productivity improvements did not follow automatically from
IT investments. For example, the hotel industry, which underused its IT investment in the late 1990s, did not see
sizable productivity increases. What if health care could produce productivity gains similar to those in
telecommunications, retail, or wholesale? Exhibit 1 superimposes a range of productivity improvements on a
plot of estimated growth in national health care spending from 2002 to 2016. The smaller improvement (1.5
percent per year) is similar to the productivity gains in retail/wholesale attributed to IT; the upper end (4 percent
per year) is half the IT-enabled gains in telecommunications. Either level of productivity improvement could
greatly reduce national health care spending. The lower improvement implies an average annual spending
decrease of $346 billion, and the upper end, $813 billion. However, we believe that when thought leaders
discuss transforming health care with HIT, they are talking about the kinds of benefits seen in the telecom and
securities industries: gains of 8 percent or more per year, year after year. These sectors illustrate that it can be
done. But our analysis found that the ingredients needed to achieve this growth (strong competition on quality
and cost, substantial investments in EMR systems, an enhanced infrastructure that can accommodate
increased future demand or reduce costs without increasing labor, a strong champion firm or institution that
drives change, and integrated systems) are mostly absent in today’s health care industry. Achieving savings at
the upper end of the range will be limited by the degree of transformation that accompanies HIT. What Are The
Potential Efficiency Savings From HIT? There are few comprehensive estimates of savings from HIT at the
national level.14 Using a simulation model of HIT adoption and scaling literature-based HIT effects, we built a
national estimate.15 At 90 percent adoption, we estimate that the potential HIT-enabled efficiency savings for
both inpatient and outpatient care could average more than $77 billion per year (an average annual savings of
$42 billion during the adoption period). Exhibit 2 shows the most important sources of the savings we estimated:
The largest come from reducing hospital lengths-of-stay, nurses’ administrative time, drug usage in hospitals,
and drug and radiology usage in the outpatient setting.16 These potential savings, while quite large, are

30 April 2013 Page 3 of 11 ProQuest

considerably lower than the annual IT-enabled productivity gains just described in other industries. Although
achieving these more limited savings would not require radical changes in the health care delivery system, it
would require process changes and, in some cases, resource reduction. Also, the potential savings would not
be realized immediately. They would require widespread adoption of HIT by providers, and most of the savings
would start only after a successful implementation period and associated process changes or resource
reductions had taken place. Also, the efficiencies could be used to improve health care quality rather than to
reduce costs. Although the savings would accrue to different stakeholders, in the long run they should accrue to
payers. If we allocate the savings using the current level of spending from the National Health Accounts (kept by
the Centers for Medicare and Medicaid Services), Medicare would receive about $23 billion of the potential
savings per year, and private payers would receive $31 billion per year. Thus, both have a strong incentive to
encourage the adoption of EMR systems. Providers face limited incentives to purchase EMRs because their
investment typically translates into revenue losses for them and health care spending savings for payers. What
Are The Potential Safety Benefits Of EMR Systems? Studies showing improved patient safety from EMR use in
hospital and ambulatory care largely focus on alerts, reminders, and other components of CPOE.17 CPOE
makes information available to physicians at the time they enter an order-for example, warning about potential
interactions with a patient’s other drugs. Once the order has been entered, the system can track the steps
involved in executing the order, providing an additional mechanism for identifying and eliminating errors. In the
longer term, CPOE provides the information needed to redesign the order-execution process so that errors
become even harder to make. To provide these benefits, CPOE must be an integrated component of a more
comprehensive health care information system that is designed and used well.18 We addressed the safety
benefits of CPOE by using models to extrapolate existing evidence to the national level and estimated
separately the potential to reduce adverse drug events in inpatient and outpatient settings. * Reducing adverse
drug events in the inpatient setting. The measures-adverse drug events avoided, and bed days and dollars
saved-all follow the same pattern, which suggests that CPOE could eliminate 200,000 adverse drug events and
save about $1 billion per year if installed in all hospitals. But the bulk of the savings could be realized by
installation in hospitals with more than 100 beds. About two-thirds of the CPOE benefits are attributable to
adverse drug events avoided for patients age sixty-five and older. Although this group comprises only 13
percent of the population, it accounts for a much larger fraction of hospital bed days, and its members are more
susceptible than others to adverse drug events. * Reducing adverse drug events in the ambulatory setting.
Medication errors and adverse drug events in ambulatory settings have been studied much less than in
hospitals. The available data suggest that roughly eight million outpatient events occur each year, of which one-
third to one-half are preventable. About two-thirds of preventable adverse drug events might be avoided through
widespread use of ambulatory CPOE. Each avoided event saves $1,000-$2,000 because of avoided office
visits, hospitalizations, and other care.19 Scaling these numbers to the national level, we estimate that two
million such events could be avoided, generating annual savings of $3.5 billion.20 Avoided adverse drug events
in patients age sixty-five and older account for 40 percent of the savings. Our models also show that to obtain
the benefits of ambulatory CPOE, one cannot ignore small providers. About 37 percent of the potential savings
and error avoidance would come from solo practitioners. Recent estimates suggest that CPOE systems can be
cost-effective even for small offices.21 What Are The Potential Health Benefits Of EMR Systems? Beyond
safety, the literature provides little evidence about EMR systems’ effects on health. We must, therefore,
hypothesize about both mechanisms and magnitudes of effects. We considered two kinds of interventions
intended to keep people healthy (or healthier): disease prevention measures and chronic disease management.
These interventions are key to understanding HIT’s potential. First, they would exploit important features and
capabilities of EMR systems: communication, coordination, measurement, and decision support. Second, they
are potentially high-leverage areas for improving health care. Physicians deliver recommended care only about
half of the time, and care for patients with chronic illnesses absorbs more than 75 percent of the nation’s health

30 April 2013 Page 4 of 11 ProQuest

care dollars.22 Third, evidence from regional health information network (RHIN) demonstrations suggests that
these are key applications of HIT.23 * Using HIT for short-term preventive care. EMR systems can integrate
evidence-based recommendations for preventive services (such as screening exams) with patient data (such as
age, sex, and family history) to identify patients needing specific services. The system can remind providers to
offer the service during routine visits and remind patients to schedule care. Reminders to patients generated by
EMR systems have been shown to increase patients’ compliance with preventive care recommendations when
the reminders are merely interjected into traditional outpatient workflows.24 More systemic adaptation-for
example, by Kaiser Permanente and Group Health Cooperative-appears to achieve greater compliance.25 We
estimated the effects of influenza and pneumococcal vaccination and screening for breast cancer, cervical
cancer, and colorectal cancer, using data about the current compliance rate, the recommended population, and
the costs.26 We assumed that the services are rendered to 100 percent of people not currently complying with
the U.S. Preventive Services Task Force recommendation.27 We also applied the health benefit estimates from
the literature to this population (Exhibit 3). We conclude that all of these measures, except for pneumococcal
vaccination, will increase health care use and spending modestly. But the costs are not large, and the health
benefits are significant: for example, 13,000 life-years gained from cervical cancer screening at a cost of $0.1-
$0.4 billion. * Using HIT for near-term chronic disease management. The U.S. burden of chronic disease is
extremely high and growing. In one study, fifteen chronic conditions accounted for more than half of the growth
in health care spending between 1987 and 2000, and just five diseases accounted for 31 percent of the
increase.28 Disease management programs identify people with a potential or active chronic disease; target
services to them based on their level of risk (sicker patients need more-tailored, more-intensive interventions,
including case management); monitor their condition; attempt to modify their behavior; and adjust their therapy
to prolong life, minimize complications, and reduce the need for costly acute care interventions. EMR systems
can be instrumental throughout the disease management process. Predictive-modeling algorithms can identify
patients in need of services. EMR systems can track the frequency of preventive services and remind
physicians to offer needed tests during patients’ visits. Condition-specific encounter templates implemented in
an EMR system can ensure consistent recording of disease-specific clinical results, leading to better clinical
decisions and outcomes. Connection to national disease registries allows practices to compare their
performance with that of others. Electronic messaging offers a low-cost, efficient means of distributing
reminders to patients and responding to patients’ inquiries. Web-based patient education can increase the
patient’s knowledge of a disease and compliance with protocols. For higher-risk patients, case management
systems help coordinate workflows, including communication between multiple specialists and patients. In what
may prove to be a transformative innovation, remote monitoring systems can transmit patients’ vital signs and
other biodata directly from their homes to their providers, allowing nurse case managers to respond quickly to
incipient problems. Health information exchange via RHINs or personal health records promises great benefits
for patients with multiple chronic illnesses, who receive care from multiple providers in many settings. We
examined disease management programs for four conditions: asthma, congestive heart failure (CHF), chronic
obstructive pulmonary disease (COPD), and diabetes (Exhibit 4) and estimated the effects of 100 percent
participation of people eligible for each program.29 By controlling acute care episodes, these programs greatly
reduce hospital use at the cost of increased physician office visits and use of prescription drugs. As shown, the
programs could generate potential annual savings of tens of billions of dollars. Keeping people out of the
hospital is, of course, a health benefit, but we can also expect important outcomes such as reductions in days
lost from school and work and in days spent sick in bed. Exhibit 4 also highlights an important disincentive for
health care providers to offer these kinds of services or to invest in HIT to effectively perform them: The savings
come out of provider receipts, as patients spend less time in acute care. This key misalignment of incentives is
an important barrier to EMR adoption and, more generally, to health care transformation. * Using HIT for long-
term chronic disease prevention and management. A program of EMR-enhanced prevention and disease

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management should change the incidence of chronic conditions and their complications. We considered
cardiovascular diseases (hypertension, hyperlipidemia, coronary artery disease/acute myocardial infarction,
CHF, cerebrovascular disease/stroke, and other heart diseases), diabetes and its complications (retinopathy,
neuropathy, lower extremity/foot ulcers and amputations, kidney diseases, and heart diseases), COPD
(emphysema and chronic bronchitis), and the cancers most strongly associated with smoking (cancers of the
bronchus and lung, head and neck, and esophagus, and other respiratory and intrathoracic cancers). Using our
MEPS-based model, we estimated how combinations of lifestyle changes and medications that reduced the
incidence of these conditions would affect health care use, spending, and outcomes (Exhibit 5). Savings are
evenly divided between the populations under age sixty-five and those age sixty-five and older, despite the fact
that the older population constitutes only 13 percent of the total. Since chronic diseases are, by and large,
diseases of the elderly, a large fraction of the long-term savings attributable to prevention and disease
management would accrue to Medicare. Yet, to realize these benefits, people would have to begin participating
in these programs as relatively young adults. We combined the effects of the reduced incidence attributable to
long-term prevention and management and reduced acute care due to disease management. We estimated the
potential combined savings, again assuming 100 percent participation, to be $147 billion per year.30 * Realizing
the potential of these interventions. Realizing the benefits of prevention and disease management requires that
a substantial portion of providers and consumers participate. Since, on average, patients comply with
medication regimens about half the time, it is plausible to assume that about half of the chronically ill would
participate in disease management programs and, therefore, the health care system would reap about half of
the estimated short-term benefits, assuming that EMR systems and community-based connectivity were
operational.31 Patients comply with their physician’s lifestyle recommendations only about 10 percent of the
time.32 We assumed that in a future with EMR-based reminders and decision support and patient-physician
messaging, we could realize at least 20 percent of the long-term benefits shown in Exhibit 5. Under these
assumptions, the net savings would be on the order of $40 billion per year. We varied the participation in
disease management and prevention activities parametrically to show the potential beyond these estimates.33
What Will It Cost To Implement EMR Systems? There are a few published estimates of the costs of widespread
implementation of EMR systems in the United States. Samuel Wang and colleagues have provided a model for
estimating the cost and return on investment for a physician office practice.34 Jan Walker and colleagues have
estimated the costs ($28 billion per year during a ten-year deployment, $16 billion per year thereafter) and net
savings ($21.6-$77.8 billion per year, depending on the level of standardization) of a broadly adopted,
interoperable EMR system.35 The Patient Safety Institute estimated the initial cost of widespread connectivity of
EMR systems (not of the EMR system itself) to be $2.5 billion.36 * Adoption costs for hospitals. From cost data
obtained from the literature, as well as from direct discussions with providers, we used simulation to estimate
that the cumulative cost for 90 percent of hospitals to adopt an EMR system is $98 billion if 20 percent of
hospitals now have such a system. Average yearly costs for the fifteen-year adoption period are $6.5 billion-
about one-fifth of our earlier-described estimate of potential efficiency savings in hospitals.37 * Adoption costs
for physicians. Our models for adoption by physicians show that the cumulative costs to reach 90 percent
adoption are $17.2 billion, almost equally split between one-time costs and maintenance costs. The average
yearly cost during the adoption period is about $1.1 billion. In comparison, we estimated the potential annual
average efficiency and safety benefits from ambulatory EMR systems during the same period to be $11 billion.
What Are The Potential Net Savings From EMR Systems? Exhibit 6 plots the net cumulative and yearly
potential savings (benefits over costs) from EMR systems in hospital and outpatient settings over time. Because
we do not take credit for savings from providers already in the adoption process and because process changes
and related benefits take time to develop, net savings are initially low and then rise steeply. Over fifteen years,
the cumulative potential net efficiency and safety savings from hospital systems could be nearly $371 billion;
potential cumulative savings from physician practice EMR systems could be $142 billion. This potential net

30 April 2013 Page 6 of 11 ProQuest

financial benefit could double if the health savings produced by chronic disease prevention and management
were included. Barriers To Realizing The Health Benefits And Savings Our analysis shows that moving the U.S.
health care system quickly to broad adoption of standards-based EMR systems could dramatically reduce
national health care spending at a cost far below the savings. Further, these potential savings would outweigh
the costs relatively quickly during the adoption cycle. But key barriers in the HIT market directly impede
adoption and effective application of EMR systems; these include acquisition and implementation costs, slow
and uncertain financial payoffs, and disruptive effects on practices.38 In addition, providers must absorb the
costs of EMR systems, but consumers and payers are the most likely to reap the savings. Also, even if EMR
systems were widely adopted, the market might fail to develop interoperability and robust information exchange
networks. Given our analysis, we believe that there is substantial rationale for government policy to facilitate
widespread diffusion of interoperable HIT. Actions now, in the early stages of adoption, would provide the most
leverage. Taylor and colleagues discuss several alternatives for government action to remove barriers, correct
market failures, and speed the realization of EMR system benefits.39 We have shown some of the potential
benefits of HIT in the current health care system. However, broad adoption of EMR systems and connectivity
are necessary but not sufficient steps toward real health care transformation. For example, adoption of EMR
systems and valid comparative performance reporting would enable the development of value-based
competition and quality improvement to drive transformation. HIT also should facilitate system integration for
broader optimization, and comparative benchmarking should encourage development of market-leading
examples of ways to better organize, pay for, and deliver care. It is not known what changes should or will take
place after widespread EMR system adoption-for example, increased consumer-directed care, new methods of
organizing care delivery, and new approaches to financing. But it is increasingly clear that a lengthy, uneven
adoption of nonstandardized, noninteroperable EMR systems will only delay the chance to move closer to a
transformed health care system. The government and other payers have an important stake in not letting this
happen. The time to act is now. This report is a product of the RAND HIT Project. It benefited from the guidance
of an independent Steering Committee, chaired by David Lawrence, and was sponsored by Cerner, General
Electric, Hewlett-Packard, Johnson and Johnson, and Xerox. Footnote NOTES 1. Organization for Economic
Cooperation and Development, “Health at a Glance-OECD Indicators 2003,” 17 September 2003,
www.oecd.org/document/11/0,2340,en_2649_201185_16502667_1_1_1_1,00.html (20 July 2005). 2. The term
EMR systems as used here includes the electronic medical record (EMR), containing current and historical
patient information; clinical decision support (CDS), which provides reminders and best-practice guidance for
treatment; and a central data repository (CDR), for the information. It also includes IT-enabled functions, such
as computerized physician order entry (CPOE). We use the terms health information technology (HIT) and EMR
systems interchangeably. 3. H. Taylor and R. Leitman, “European Physicians, Especially in Sweden,
Netherlands, and Denmark, Lead U.S. in Use of Electronic Medical Records,” Harris Interactive 2, no. 16 (8
August 2002), www.harris interactive.com/news/newsletters_healthcare.asp (20 July 2005). 4. K. Fonkych and
R. Taylor, The State and Pattern of Health Information Technology Adoption (Santa Monica, Calif.: RAND,
2005). 5. R. Taylor et al., “Promoting Health Information Technology: Is There a Case for More-Aggressive
Government Action?” Health Affairs 24, no. 5 (2005): 1234-1245. 6. The online supplement is available at
content.healthaffairs.org/cgi/content/full/24/5/1103/DC1. The RAND Web site provides a comprehensive
description of our methods, data, and models. See www.rand.org/ publications/MG/MG408; MG409; and
MG410. 7. HIMSS AnalyticsSM Database (formerly the Dorenfest IHDS+TM Database), second release, 2004.
8. See J.H. Bigelow, K. Fonkych, and F. Girosi, Technical Executive Summary in Support of “Can Electronic
Medical Record Systems Transform Healthcare?” This online summary of our methods includes a table listing
the most important literature findings and some measures of their quality; see Note 6. 9. See K.C. Voelker,
“Electronic Medical Record (EMR) Comparisons by Physicians for Physicians,” www .elmr-electronic-medical-
records-emr.com (26 May 2005). 10. In R. Koppel et al., “Role of Computerized Physician Order Entry Systems

30 April 2013 Page 7 of 11 ProQuest

in Facilitating Medication Errors,” Journal of the American Medical Association 293, no. 10 (2005): 1197-1203, it
was reported that the medication error rate actually increased because of computer- and interface-induced
errors. We have assumed that this is not the case for a carefully redesigned medication process supported by
modern CPOE. 11. See Agency for Healthcare Research and Quality (AHRQ), Medical Expenditure Panel
Survey (MEPS) (multiple years of data and documentation), at www.meps.ahrq.gov (24 February 2005);
American Hospital Association, AHA Annual Survey Database (a survey conducted since 1946; data must be
purchased); and AHRQ, Nationwide Inpatient Sample (NIS), part of the Healthcare Cost and Utilization Project
(HCUP), at www.hcup-us.ahrq.gov/nisoverview.jsp (24 February 2005). 12. National Center for Health
Statistics, National Ambulatory Medical Care Survey (NAMCS)-multiple years of data and documentation
available at www.cdc.gov/nchs/about/major/ahcd/ahcd1.htm (24 February 2005). 13. A. Bower, The Diffusion
and Value of Healthcare Information Technology, Pub. no. MG-272-HLTH (Santa Monica, Calif.: RAND, 2005).
14. Recently, Jan Walker and colleagues quantified the value of full adoption of interoperable EMR systems. J.
Walker et al., “The Value of Health Care Information Exchange and Interoperability,” Health Affairs, 19 January
2005, content.healthaffairs.org/cgi/content/abstract/hlthaff.w5.10 (2 May 2005). Laurence Baker argued that
these savings were overestimated. L.C. Baker, “Benefits of Interoperability: A Closer Look at the Estimates,”
Health Affairs, 19 January 2005, content.healthaffairs.org.cgi/content/abstract/hlthaff.w5.22 (26 May 2005). 15.
F. Girosi et al., Extrapolating Evidence of Health Information Technology Savings and Costs, Pub. no. MG-410
(Santa Monica, Calif.: RAND, 2005). 16. Other factors and savings, mentioned in the Study Data and Methods
section, could increase this total potential. 17. D.W. Bates et al., “Effect of Computerized Physician Order Entry
and a Team Intervention on Prevention of Serious Medication Errors,” Journal of the American Medical
Association 280, no. 15 (1999): 1311-1316. 18. Koppel et al., “Role of Computerized Physician Order Entry
Systems.” 19. D. Johnston et al., Patient Safety in the Physician’s Office: Assessing the Value of Ambulatory
CPOE, April 2004, www.chcf.org/topics/view.cfm?itemID-101965 (26 May 2005). 20. J.H. Bigelow et al.,
Analysis of Healthcare Interventions That Change Patient Trajectories (Santa Monica, Calif.: RAND, 2005). 21.
S.J. Wang et al., “A Cost-Benefit Analysis of Electronic Medical Records in Primary Care,” American Journal of
Medicine 114, no. 5 (2003): 397-403. 22. E.A. McGlynn et al., “The Quality of Health Care Delivered to Adults in
the United States,” New England Journal of Medicine 348, no. 26 (2003): 2635-2645; and Centers for Disease
Control and Prevention, “Chronic Disease Overview,” 15 October 2004, www.cdc.gov/nccdphp/overview.htm
(26 May 2005). 23. See, for example, Institute for Healthcare Improvement, “My Shared Care Plan,”
www.ihi.org/IHI/ topics/chronicconditions/diabetes/tools/my+shared+care+plan.htm (26 May 2005). 24. R.C.
Burack and P.A. Gimotty, “Promoting Screening Mammography in Inner-City Settings: The Sustained
Effectiveness of Computerized Reminders in a Randomized Controlled Trial,” Medical Care 35, no. 9 (1997):
921-931. 25. B. Kaplan, “Evaluating Informatics Applications-Clinic Decision Support Systems Literature
Review,” International Journal of Medical Informatics 64, no. 1 (2001): 15-37. 26. Bigelow et al., Analysis of
Healthcare Interventions. 27. We make the 100 percent assumption to provide an upper bound on the net costs
and the health effects of their service. We do not suggest that 100 percent participation can be realized in
practice. 28. K.E. Thorpe, C.S. Florence, and P. Joski, “Which Medical Conditions Account for the Rise in
Health Care Spending?” Health Affairs, 25 August 2004,
content.healthaffairs.org/cgi/content/abstract/hlthaff.w4.437 (26 May 2005). 29. Bigelow et al., Analysis of
Healthcare Interventions. 30. This is less than the direct sum, because reduced incidence implies a lesser
requirement for disease management. 31. R.B. Haynes, H.P. McDonald, and A.X. Garg, “Helping Patients
Follow Prescribed Treatment: Clinical Applications,” Journal of the American Medical Association 288, no. 22
(2002): 2880-2883. 32. D.L. Roter et al., “Effectiveness of Interventions to Improve Patient Compliance: A Meta-
Analysis,” Medical Care 36, no. 8 (1998): 1138-1161. 33. Bigelow et al., Analysis of Healthcare Interventions.
34. Wang et al., “A Cost-Benefit Analysis.” 35. Walker et al., “The Value of Health Care Information Exchange.”
36. Because there is not much experience with regional connectivity, cost estimates fall within a wide range.

30 April 2013 Page 8 of 11 ProQuest

Our own scaling of data provided by the Santa Barbara Care Data Exchange indicates $2.4 billion for a non-
standards-based system. 37. Girosi et al., Extrapolating Evidence. 38. R. Miller and I. Sim, “Physicians’ Use of
Electronic Medical Records: Barriers and Solutions,” Health Affairs 23, no. 2 (2004): 116-126; and DW. Bates et
al., “A Proposal for Electronic Medical Records in U.S. Primary Care,” Journal of the American Medical
Informatics Association 10, no. 1 (2003): 1-10. 39. Taylor et al., “Promoting Health Information Technology.”
AuthorAffiliation Richard Hillestad (Richard_Hillestad@rand.org) and James Bigelow are senior management
scientists at RAND in Santa Monica, California. Anthony Bower is a senior economist there; Federico Girosi is a
policy researcher, and Robin Meili is a senior management systems analyst. Richard Scoville and Roger Taylor
are senior consultants at RAND Health-Scoville, in Chapel Hill, North Carolina, and Taylor, in Laguna Beach,
California.
Subject: Health care industry; Benefit cost analysis; Electronic health records; Safety management; Studies
MeSH: Aged, Delivery of Health Care — economics, Diffusion of Innovation, Efficiency, Organizational, Health
Expenditures — trends, Humans, Middle Aged, Quality of Health Care, United States, Cost; Control —
economics (major), Delivery of Health Care — organization & administration (major), Medical Records Systems,
Computerized (major)
Location: United States, US
Classification: 8320: Health care industry; 5260: Records management; 5340: Safety management; 9130:
Experimental/theoretical; 9190: United States
Publication title: Health Affairs
Volume: 24
Issue: 5
Pages: 1103-17
Number of pages: 15
Publication year: 2005
Publication date: Sep/Oct 2005
Year: 2005
Section: EMR SYSTEMS
Publisher: The People to People Health Foundation, Inc., Project HOPE
Place of publication: Chevy Chase
Country of publication: United States
Publication subject: Insurance, Public Health And Safety
ISSN: 02782715
Source type: Scholarly Journals
Language of publication: English
Document type: Journal Article
Document feature: graphs; tables; engravings
Accession number: 16162551

30 April 2013 Page 9 of 11 ProQuest

ProQuest document ID: 204645298
Document URL: http://search.proquest.com/docview/204645298?accountid=35812
Copyright: Copyright The People to People Health Foundation, Inc., Project HOPE Sep/Oct 2005
Last updated: 2013-02-06
Database: ProQuest Central,ProQuest Health Management,ProQuest Health & Medical Complete,ProQuest
Nursing & Allied Health Source,ABI/INFORM Complete,ProQuest Research Library

30 April 2013 Page 10 of 11 ProQuest

http://search.proquest.com/docview/204645298?accountid=35812

Bibliography
Citation style: APA 6th – American Psychological Association, 6th Edition

Hillestad, R., Bigelow, J., Bower, A., Girosi, F., & al, e. (2005). Can electronic medical record systems transform
health care? potential health benefits, savings, and costs. Health Affairs, 24(5), 1103-17. Retrieved from
http://search.proquest.com/docview/204645298?accountid=35812

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• Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings, And Costs

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1) Description of how technology has affected or could affect delivery, if applicable

a) Interoperability and widespread health information exchange;

i) Continuity of care

ii) Less medical error,

(1) Reduction in Malpractice claims and costs

b) Automated, real-time

i) Instant access to a medical record for billing patient and physician access

c) Quality and cost measurement;

i) Meaningful use

ii) Ability to report and measure outcomes, presentations and other quality information pertaining to the care of patients

(1) Physician performance and quality

d) smarter analytic capacities

i) The delivery of the actual costs of health care.

Hillestad, R., Bigelow, J., Bower, A., Girosi F., Meili R., Scoville R., and Taylor R. (2013) Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings, and costs.Health Aff September 2005 24:51103-1117; doi:10.1377/hlthaff.24.5.1103

Retrieved from http://content.healthaffairs.org/content/24/5/1103.full