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Despite a consensus that the use of health information technology should lead to more efficient, safer, and higher-quality care, there are no reliable estimates of the prevalence of adoption of electronic health records in U.S. hospitals. We surveyed all acute care hospitals that are members of the American Hospital Association for the presence of specific electronic-record functionalities. Using a definition of electronic health records based on expert consensus, we determined the proportion of hospitals that had such systems in their clinical areas. We also examined the relationship of adoption of electronic health records to specific hospital characteristics and factors that were reported to be barriers to or facilitators of adoption. On the basis of responses from 63.1% of hospitals surveyed, only 1.5% of U.S. hospitals have a comprehensive electronic-records system (i.e., present in all clinical units), and an additional 7.6% have a basic system (i.e., present in at least one clinical unit). Computerized provider-order entry for medications has been implemented in only 17% of hospitals. Larger hospitals, those located in urban areas, and teaching hospitals were more likely to have electronic-records systems. Respondents cited capital requirements and high maintenance costs as the primary barriers to implementation, although hospitals with electronic-records systems were less likely to cite these barriers than hospitals without such systems. The very low levels of adoption of electronic health records in U.S. hospitals suggest that policymakers face substantial obstacles to the achievement of health care performance goals that depend on health information technology. A policy strategy focused on financial support, interoperability, and training of technical support staff may be necessary to spur adoption of electronic-records systems in U.S. hospitals.
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The
new england journal
of
me dicine
n engl j med 10.1056/nejmsa0900592
1
special article
Use of Electronic Health Records
in U.S. Hospitals
Ashish K. Jha, M.D., M.P.H., Catherine M. DesRoches, Dr.Ph.,
Eric G. Campbell, Ph.D., Karen Donelan, Sc.D., Sowmya R. Rao, Ph.D.,
Timothy G. Ferris, M.D., M.P.H., Alexandra Shields, Ph.D., Sara Rosenbaum, J.D.,
and David Blumenthal, M.D., M.P.P.
From the Department of Health Policy
and Management, Harvard School of Pub -
lic Health (A.K.J.); the Division of General
Medicine, Brigham and Women’s Hospi-
tal (A.K.J.); the Veterans Affairs Boston
Healthcare System (A.K.J.); and the Inst i-
tute for Health Policy (C.M.D., E.G.C.,
K.D., S.R.R., T.G.F., A.S., D.B.) and the
Biostatistics Center (S.R.R.), Massachu-
setts General Hospital — all in Boston;
and the Department of Health Polic y,
George Washington University, Washing-
ton, DC (S.R.). Address reprint requests
to Dr. Jha at the Harvard School of Public
Health, 677 Huntington Ave., Boston, MA
02115, or at ajha@hsph.harvard.edu.
This article (10.1056/NEJMsa0900592) was
published at NEJM.org on March 25, 2009.
N Engl J Med 2009;360.
Copyright © 2009 Massachusetts Medical Society.
Abs tr act
Bac kgro und
Despite a consensus that the use of health information technology should lead to
more eff icient, safer, and higher-qualit y care, t here are no reliable estimates of the
prevalence of adoption of electronic health records in U.S. hospitals.
Methods
We surveyed all acute care hospitals that are members of the American Hospital
Association for the presence of specif ic electronic-record functionalities. Using a
definition of electronic health records based on expert consensus, we determined
the proportion of hospitals that had such systems in their clinical areas. We also
examined the relat ionship of adopt ion of electronic health records to specif ic hos-
pital characteristics and factors that were reported to be barriers to or facilitators
of adoption.
Re sult s
On t he basis of responses from 63.1% of hospitals su r ve yed, only 1.5% of U.S. hos-
pitals have a comprehensive electronic-records system (i.e., present in all clinical
un it s), and a n add ition al 7.6% have a basic system (i.e., present i n at least one cl inic a l
unit). Computerized provider-order entry for med ications h as been implemented i n
only 17% of hospitals. Larger hospitals, those loc ated in urban areas, and teaching
hospit a ls were more like ly t o ha ve ele ctronic-records systems. Resp ondents cited cap -
it al req u ireme nts and high m a i ntenanc e cost s as the p r i ma r y barrier s t o imp leme n-
tation, although hospitals with electronic-records systems were less likely to cite
these barriers t han hospitals without such systems.
Conclusions
Th e ver y low l e vels o f adopt i on of elect ronic hea lt h recor ds in U.S. hospit a ls s uggest
that policymakers face substantial obstacles to the achievement of healt h care per-
formance goals that depend on health information technology. A policy strategy fo-
cused on f inancia l support, interoperabi lit y, and t rain ing of t echnical support staf f
may be necessary to spur adoption of electronic-records systems in U.S. hospitals.
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The
new england journal
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n engl j med 10.1056/nejmsa0900592
2
T
he U. S. h eal th c are sys tem faces cha l-
lenges on multiple fronts, including rising
costs and inconsistent qualit y.
1-3
Health in-
formation technology, especia lly electronic healt h
records, has the potential to improve the efficienc y
and ef fe ct iveness of hea lt h c ar e p ro vi ders.
4,5
Met h-
ods to speed the adoption of health information
tech nolo gy have r eceive d bi part isan support among
U.S. policy makers, and t he Amer ic an Recover y a nd
Reinvestment Act of 2009 has made t he promotion
of a national, int eroperable hea lt h inform at ion s ys-
tem a priority. Despite broad consensus on the po-
tential benefits of electronic health records and
other forms of health information technolog y, U. S.
health care providers have been slow to adopt
them.
6,7
Usi ng a well-spe cif ied def init ion of elec-
tronic health records in a recent study, we found
that only 17% of U.S. physicians use either a min-
imally functional or a comprehensive electronic-
records system.
8
Prior data on hospitals’ adoption of electronic
health records or key functions of electronic rec-
ords (e.g., computerized provider-order entry for
medicat ions) suggest level s of adoption that range
between 5%
9
and 59%.
10
This broad range ref lects
different definitions of what constitutes an elec-
tronic health record,
10,11
use of convenience sam-
ples,
12
and low survey response rates.
13
To provide
more precise estimates of adoption of electronic
health records among U.S. hospitals, the Off ice
of the National Coordinator for Health Informa-
tion Technology of the Department of Health and
Human Ser vices commissioned a study to measure
current levels of adopt ion to facilit ate tracki ng of
these levels over time.
As in our previous study,
8
we identif ied key
clinical functions to def ine the minimum func-
tionalities necessary to call a system an electronic-
records system in the hospital setting. We also
defined an advanced conf iguration of functional-
ities that might be termed a comprehensive elec-
tronic-records system. Our survey then determined
the proportion of U.S. hospitals reporting the use
of electronic health records for either of these set s
of functionalities. We hypothesized that large hos-
pitals would have a higher prevalence of adoption
of electronic health records than smaller hospit a ls.
Similarly, we hypothesized that major teaching
hospitals would have a higher prevalence of adop-
tion than nonteaching hospitals and private hos-
pitals a higher prevalence than public hospitals.
Finally, to guide pol icymakers, we sought to iden-
tify frequently reported barriers to adoption and
potential mechanisms for facilitating it.
Methods
Survey Development
We developed our survey by examining and syn-
thes i zing prior hospit al-b ase d su rveys of elec tronic-
records systems or related functionalities (e.g.,
comp uter ized pro v ider- order entry) that have bee n
administered in the past 5 years.
9,13,14
Working
with e xperts who had led hospit a l-based su r vey s,
we developed an initial draft of the instrument.
To get feedback, we shared the survey with chief
information officers, other hospital leaders, and
survey experts. We then obtained input from a
cons ensus panel of ex per t s in t he f ields of he a lth
information technology, health services research,
survey research, and health policy. Further survey
modif ications were appr oved by our expert pan-
el. The final survey instrument was approved for
use by the institutional review board of Partners
HealthCare.
Survey Sample and Administration
We collaborated with the American Hospital As-
sociation (AHA) to survey all acute care general
medical and su rgical member hospitals. The sur-
vey was presented as an information technology
supplement to the association’s annual survey of
membe rs, and l i ke t he ove rall AHA que stion n a i re,
was sent to the hospital’s chief executive officer.
Hospital chief executive officers generally assigned
the m o st know ledge able person i n t he inst it ut ion
(in this case, typically the chief information of-
ficer or equivalent) to complete the survey. Non-
responding hospitals re ceive d mult iple telephone
calls and reminder letters asking them to com-
plete the survey. The survey was initially mailed
in March 2008, and our in-field period ended in
September 2008.
Survey Content
We asked respondents to report on the presence
or absence of 32 cl i nic a l functionalitie s of an elec -
tronic-records system and on whether their hos-
pital had fully implemented these functionalities
in a ll m ajor cl in ic a l u ni t s, h a d i mplemented t hem
in one or more (but not all) major clinical units,
or had not yet fully implemented them in any unit
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Use of Electronic Health Records in U.S. Hospitals
n engl j med 10.1056/nejmsa0900592
3
in the hospita l. We asked respondents to identify
whet her cer t a in f act ors we re major or m inor b ar-
riers or were not barriers to the adoption of an
elec t ron ic-r ecords sy ste m and whet her specif ic p ol-
icy changes would have a positive or negative ef-
fe ct on thei r decision t o ad opt such a s yst e m. The
quest ion s and resp onse categ orie s used are l ist ed
in t he S upplem entar y App endix, a vai labl e with the
full text of this article at NEJM.org.
Measures of Electronic-Records Use
The Institute of Medicine has developed a com-
prehensive list of the potential functionalities of
an inpatient electronic health record,
15
but there
is no consensus on what functionalit ies const itut e
the essent ial elem ent s ne cessar y to def ine a n ele c-
tronic hea lth rec ord in the ho spital s etting. T here -
fore, we used the expert panel described earlier
to help def ine the functionalities that constitute
comprehensive and basic electronic-records sys-
tem s in t he hospit al se t t i ng. The panel was a ske d
to id entif y whe ther i ndividu al f unction al ities w ou ld
be necessary to classify a hospital as having a
comprehensive or basic electronic health record.
With the use of a modified Delphi process, the
panel reached a consensus on the 24 functions t hat
should be present in all major clinical units of a
hospital to conclude that it had a comprehensive
electronic-records system.
16
Similarly, the panel
reached a consensus on eight f unctiona l it ies that
should be present in at least one major clinical
unit (e.g., t he intensive care unit) in order for the
ho spit a l to b e cl assif ied a s hav ing a ba sic ele ctron i c-
records system. Because the panel disagreed on the
need for two additional functionalities (physicians’
note s and nursing assessment s) to classi f y a hos-
pital as having a basic system, we developed two
de finit ion s of a b asic ele ct ronic-records system, one
that included functionalities for nursing assess-
me nts a nd physici a ns’ no t es and a not her t h at did
not. We present the results with the use of both
definitions.
Statistical Analysis
We compared the char acterist ics of respondent a nd
nonrespondent hospitals and found modest but
signif icant differences. We estimated the propen-
sity to respond to the survey with the use of a lo-
gistic-regression model that included all these
characteristics and used the inverse of this pro-
pensity value as a weight in all analyses.
We examined the proportion of hospitals that
had each of the individual functionalities and sub-
sequently calculated t he prevalence of adoption of
an electronic-records system, using three defini-
tions of such a system: comprehensive, basic with
physicians’ and nurses’ notes, and basic without
physician and nursing notes. For all subsequent
analyses, we used t he definition of basic electronic
health records that included clinicians’ notes.
We explored bivariate relationships between key
hospital characteristics (size, U.S. Census region,
ownership, teaching status, urban vs. rural loca-
tion, and presence or absence of markers of a high-
technology institution) and adoption of a basic or
comprehensive electronic-records system. We con-
sidered the use of various potential markers of a
high-technology institution, including the pres-
ence of a dedicated coronary care unit, a burn unit,
or a positron-emission tomographic scanner. Be-
cause the results were similar for each of these
markers, we present data based on the presence
or absence of only one — a dedicated coronary
ca re unit. We subsequent ly built a multivar iable
mode l to calc ulat e le vels of adoption of elec t ron ic-
records systems, adjusted according to these hos-
pital characteristics. We present the unadjusted
results below and those from the multivariate mod-
els in the Supplementary Appendix.
Finally, we built logistic-regression models (ad-
justing for the hospital characteristics mentioned
above) to assess whether t he presence or absence
of electronic health records was associated with
respondents’ reports of the existence of specific
barriers and facilitators of adoption. Since the
number of hospit als with comprehensive elec-
tronic-records systems was small, we combined
hospitals with comprehensive systems and those
with basic electronic-records systems and com-
pared their responses with those from institutions
without electronic health records. In all analyses,
two-sided P values of less than 0.05 were consid-
ered to indicate statistical significance.
Results
We received responses from 3049 hospitals, or
63.1% of all acute care genera l hospitals t hat were
surveyed. After excluding federal hospitals and
those located outside the 50 states and the Dis-
trict of Columbia, we were lef t with 2952 institu-
tions. There were modest differences between re-
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4
spondents and nonrespondents (
Table 1
), and a ll
re sult s repo r t ed be low h ave b een adju sted for p o-
tential nonresponse bias.
Adoption of Clinical Functionalities
in Electronic Format
We found large variations in t he implement at ion
of key clinical functionalities across U.S. hospi-
ta ls. Only 12% of hospitals had instituted electron-
ic physicians’ notes across all clinical units, and
computerized provider-order entry for medications
was r eporte d as hav ing be en implement ed across
al l clinica l un its in 17% of hospitals (
Table 2
). In
contrast, more than 75% of hospitals reported
adoption of electronic laboratory and radiologic
report i ng systems. A sizable number of hospit a ls
reported having implemented several key func-
tionalities in one or more (but not all) units,
having begun such implementation, or having
identified resources for the purpose of such im-
plementation. These functionalities included phy-
sicians’ notes (among 44% of the hospitals) and
computerized provider-order entry (38%).
Adoption of Electronic Records
The pr esence of cer ta in ind i v idu a l f u nctio n a l ities
was considere d nece ss ar y f or an ele ct ro ni c-reco rd s
system to be def ined as comprehensive or basic
by our exper t pane l (
Table 3
). On t he ba sis of t hese
def initions, we fou nd t hat 1.5% (95% confidence
interval [CI], 1.1 to 2.0) of U.S. hospitals had a
comprehensive electronic-records system imple-
mented across all major clinical units and an ad-
ditional 7.6% (95% CI, 6.8 to 8.1) had a basic sys-
tem that included functionalities for physicians’
notes and nursing assessments in at least one
clinical unit. When defined without the require-
ment for cl i nica l notes, a b asic elec t ron ic-re cords
system was found in 10.9% of hospita ls (95% CI,
9.7 to 12.0). I f we i nclude f e dera l ho spi t a l s run by
the Veterans Health Administration (VHA), the
proportion of hospitals with comprehensive elec-
tronic-records systems increases to 2.9% (95% CI,
2.3 to 3.5), the proportion with basic systems that
include clinicians’ notes increases to 7.9% (95% CI,
6.9 to 8.8), a nd the pr oport ion w ith basic s y s t e ms
that do not include clinicians’ notes increases to
11.3% (95% CI, 10.2 to 12.5).
Hospitals were more likely to report having an
electronic-records system if they were la rger insti-
tutions, major teaching hospitals, part of a larger
hospital system, or located in urban areas and if
they had dedic at ed corona r y c are un it s (
Table 4
);
these differences were small. We found no rela-
tionship bet ween ownership status and level of
adoption of electronic health records: the preva-
lence of electronic-records systems in public hos-
pitals was similar to that in private institutions.
Even when we compared for-profit with nonprofit
(public and private) institutions, there were no
significant differences in adoption. In multivari-
able analyses, each of these differences diminishe d
Table 1. Characteristics of Responding and Nonresponding U.S. Acute Care
Hospitals, Excluding Federal Hospitals.*
Characteristic
Respondents
(N = 2952)
Nonrespondents
(N = 1862)
percent
Size
Small (6–99 beds) 48 50
Medium (100–399 beds) 43 43
Large (≥400 beds) 10 7
Region
Northeast 14 12
Midwest 33 24
South 37 41
West 17 22
Ownership status
For-profit hospital 14 22
Private nonprofit hospital 62 55
Public hospital 24 23
Teaching status
Major teaching hospital 7 4
Minor teaching hospital 16 16
Nonteaching hospital 77 80
Member of hospital system
Yes 43 47
No 57 53
Location
Urban 62 60
Nonurban 38 40
Dedicated coronary care unit†
Yes 35 25
No 65 75
* P<0.05 for all comparisons. Numbers may not add to 100 because of rounding.
† The presence of a coronary care unit is a marker of technological capability.
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Use of Electronic Health Records in U.S. Hospitals
n engl j med 10.1056/nejmsa0900592
5
further and was less consistently significant (see
the Supplementary Appendix).
Barriers to and Facilitators of Electronic-
Records Adoption
Among hospitals without electronic-records sys-
tems, the most commonly cited barriers were in-
ade qu at e c apit al f or pu rc ha se ( 74%) , c on ce rn s a bou t
maintenance costs (44%), resistance on the part
of physicians (36%), unclear return on investment
(32%), and lack of availability of staff with ade-
quate expertise in informat ion technology (30%)
(Fig. 1). Hospitals that had adopted electronic-
records systems were less likely to cit e four of t hese
five concerns (all except physicians’ resistance) as
major bar riers to adoption tha n were hospit a ls t h at
had not adopted such systems (Fig. 1).
Most hospitals that had adopted electronic-
records systems identified financial factors as hav-
ing a major positive effect on the likelihood of
adopt ion: add it ion al r eimbu rsement for electronic
health record use (82%) and financial incentives
Table 2. Selected Electronic Functionalities and Their Level of Implementation in U.S. Hospitals.
Electronic Functionality
Fully
Implemented
in All Units
Fully
Implemented
in at Least
One Unit
Implementation
Begun or
Resources
Identified*
No
Implementation,
with No
Specific Plans
percent of hospitals
Clinical documentation
Medication lists 45 17 18 20
Nursing assessments 36 21 18 24
Physicians’ notes 12 15 29 44
Problem lists 27 17 23 34
Test and imaging results
Diagnostic-test images (e.g., electrocar-
diographic tracing) 37 11 19 32
Diagnostic-test results (e.g., echocardio-
graphic report) 52 10 15 23
Laboratory reports 77 7 7 9
Radiologic images 69 10 10 10
Radiologic reports 78 7 7 8
Computerized provider-order entry
Laboratory tests 20 12 25 42
Medications 17 11 27 45
Decision support
Clinical guidelines (e.g., beta-blockers af-
ter myocardial infarction) 17 10 25 47
Clinical reminders (e.g., pneumococcal
vaccine) 23 11 24 42
Drug-allergy alerts 46 15 16 22
Drug–drug interaction alerts 45 16 17 22
Drug–laboratory interaction alerts (e.g.,
digoxin and low level of serum potas-
sium)
34 14 21 31
Drug-dose support (e.g., renal dose gui-
dance) 31 15 21 33
* These hospitals reported that they were either beginning to implement the specified functionality in at least one unit
or had identified the resources required for implementation in the next year.
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6
for adoption (75%). Other facilitators of adoption
included the availability of technical support for
the implementation of information technology
(47%) and objective third-party evaluations of elec-
tronic health record products (35%). Hospitals
with and those without electronic-records systems
were equally likely to cite these factors (P>0.10
for each comparison) (Fig. 2).
Table 3. Electronic Requirements for Classification of Hospitals as Having a Comprehensive or Basic Electronic-
Records System.*
Requirement
Comprehensive
EHR System
Basic EHR
System with
Clinician Notes
Basic EHR
System without
Clinician Notes
Clinical documentation
Demographic characteristics of patients
√ √
Physicians’ notes
√ √
Nursing assessments
√ √
Problem lists
√ √
Medication lists
√ √
Discharge summaries
√ √
Advanced directives
Test and imaging results
Laboratory reports
√ √
Radiologic reports
√ √
Radiologic images
Diagnostic-test results
√ √
Diagnostic-test images
Consultant reports
Computerized provider-order entry
Laboratory tests
Radiologic tests
Medications
√ √
Consultation requests
Nursing orders
Decision support
Clinical guidelines
Clinical reminders
Drug-allergy alerts
Drug–drug interaction alerts
Drug–laboratory interaction alerts (e.g., digoxin
and low level of serum potassium)
Drug-dose support (e.g., renal dose guidance)
Adoption level — % of hospitals (95% CI) 1.5 (1.1–2.0) 7.6 (6.8–8.1) 10.9 (9.7–12.0)
* A comprehensive electronic-health-records (EHR) system was defined as a system with electronic functionalities in all
clinical units. A basic electronic-records system was defined as a system with electronic functionalities in at least one
clinical unit.
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Use of Electronic Health Records in U.S. Hospitals
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Discussion
We found that less than 2% of acute care hospi-
tals have a c omprehensive electron ic-records sys-
tem, and that, depending on the defin it ion used,
bet ween 8 and 12% of hospitals have a basic elec-
tronic-records system. With the use of the def ini-
tion that requires the presence of functionalities
for phy sician s’ notes and nu rsi ng assessments, in -
for mat ion systems i n more th a n 90% of U.S. h os-
pit als do not e ven meet t he req u i remen t for a basic
electronic-records system.
Alt hough le vels of ad option of elect ronic hea lt h
records were low, many functionalities that un-
derlie electronic-records systems have been widely
implemented. A sizable proportion of hospitals
reported that laboratory and radiologic reports,
radiologic images, medication lists, and some de-
cision-support functions are available in electronic
format. Others reported that they planned to up-
grade their information systems to an electronic-
records system by adding functionalities, such as
computerized provider-order entry, physicians’
notes, and nursing assessments. However, these
Table 4. Adoption of Comprehensive and Basic Electronic-Records Systems According to Hospital Characteristics.*
Characteristic
Comprehensive
EHR System
Basic EHR
System†
No EHR
System
Overall
P Value
percent of hospitals
Size <0.001
Small (6–99 beds) 1.2±0.3 4.9±0.6 93.9±0.6
Medium (100–399 beds) 1.7±0.4 8.1±0.8 90.2±0.8
Large (≥400 beds) 2.6±0.9 15.9±2.2 81.5±2.3
Region 0.77
Northeast 1.1±0.5 8.9±1.4 90.1±1.5
Midwest 1.7±0.4 6.6±0.8 91.7±0.9
South 1.4±0.4 7.3±0.8 91.3±0.8
West 1.9±0.6 7.0±1.2 91.1±1.3
Profitability status 0.08
For-profit hospital 1.3±0.5 5.2±1.1 93.5±1.2
Private nonprofit hospital 1.5±0.3 8.4±0.6 90.1±0.7
Public hospital 1.7±0.5 5.8±0.9 92.4±1.0
Teaching status <0.001
Major teaching hospital 2.6±1.1 18.5±2.6 78.9±2.7
Minor teaching hospital 2.4±0.7 10.6±1.4 87.0±1.6
Nonteaching hospital 1.3±0.2 5.6±0.5 93.1±0.5
Member of hospital system 0.006
Yes 2.1±0.4 8.4±0.9 89.5±0.9
No 1.1±0.2 6.3±0.6 92.6±0.6
Location <0.001
Urban 1.9±0.3 8.4±0.6 89.7±0.6
Nonurban 0.6±0.3 4.0±0.7 95.3±0.8
Dedicated coronary care unit‡ 0.002
Yes 1.9±0.4 9.7±0.9 88.4±1.0
No 1.3±0.3 6.3±0.6 92.4±0.6
* Plus–minus values are means ±SE. EHR denotes electronic health record.
† The definition of a basic system that included functionalities for physicians’ notes and nursing assessments was used
for this analysis.
‡ The presence of a coronary care unit is a marker of technological capability.
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8
functionalities are typically more difficult to im-
plement than the others that we examined, and
it remains unclear whether hospitals will be able
to do so successfully.
We found high levels of decision support in
the absence of a comparable prevalence of com-
puterized provider-order entry. It is possible that
respondents reporting that their hospitals have
implemented electronic decision support were i n-
cluding in that category decision-support capabili-
ties that are available only for electronic pharmacy
systems, t hereby overstating the preparedness of
hospitals to provide physicians with electronic de-
cision support for patient care.
We found somewhat higher levels of adopt ion
among larger, urban, teaching hospitals, proba-
bly ref lecting greater availability of the f inancial
re sou rces nec ess ar y to a cqu ire a n ele ctron ic-rec or ds
system. We expected to find lower levels of adop-
tion among public hospitals, which might be fi-
nancially stressed and therefore less able to pur-
chase these systems. Although our results do not
support this hypothesis, we did not directly ex-
amine detailed indicators of the f inancial health
of the hospitals, such as their operating margins.
In 2006, we performed a comprehensive review
of the literature on hospital adoption of electronic-
records systems in the United States and found
that the most rigorous assessment made was for
computerized provider-order entry and that its
prevalence was between 5 and 10%.
6,9 ,14
An ear-
lier AHA survey showed a higher prevalence of
computerized provider-order entry,
13
but the re-
sponse rate was only 19%. A Mathematica survey
showed that 21% of U.S. hospitals had comput-
erized provider-order entry and 59% had elec-
tronic clinical documentation.
10
However, this
survey’s definition of clinical documentation al-
lowed for the inclusion of syst ems t hat were only
capable of recording demographic characteristics
of patients, a definition that is likely to have in-
f lated adopt ion levels, given t hat Medicare require s
electronic reporting of demographic data. A re-
cent analysis, based on a propriet ary database wit h
an unclear sampling frame and an unknown re-
sponse rate, showed that 13% of the hospitals had
implemented computerized provider-order entry,
a prevalence similar to that in our study.
11
Most reports of a benef icial effect of electronic-
records systems involved systems capable of com-
puterized provider-order entry with clinical-deci-
sion support.
4
Our experts took a lenient approach
by not requi ring the presence of clin ic al-decision
support as part of a basic electronic-records sys-
tem and by requiring adoption of computerized
provider-order entry in only one clinical unit.
33p9
80
70
60
40
30
10
50
20
0
Hospitals with EHR Hospitals without EHR
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Inadequate capital
for purchase Unclear ROI Maintenance
cost Physicians’
resistance Inadequate
IT staff
Barriers
Proportion of Hospitals (%)
Figure 1. Major Perceived Barriers to Adoption of Electronic Health Records (EHRs) among Hospitals with Electronic-
Records Systems as Compared with Hospitals without Systems.
Hospitals with electronic-records systems include hospitals with a comprehensive electronic-records system and
those with a basic electronic-records system that includes functionalities for physicians’ notes and nursing assess-
ments. P<0.01 for all comparisons except physicians’ resistance (P = 0.20). IT denotes information technology, and
ROI return on investment.
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Use of Electronic Health Records in U.S. Hospitals
n engl j med 10.1056/nejmsa0900592
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Whether a hospital that has successfully imple-
mented computerized provider-order entry in one
unit can easily implement in other units and add
clinical-decision support is unclear. Furthermore,
a nonuniform information system within the hos-
pital (paper-based in some units and electronic in
others) may increase clinical hazards as patients
move from one unit to another. Whether the ben-
efits of adoption of an electronic-records system
in some clinical units outweigh the theoretical
hazards posed by uneven adoption within the hos-
pital requires examination.
Respondents identif ied f inancial issues as t he
predominant barriers to adoption, dwarf ing is-
sues such as resist a nce on t he par t of physicians.
Other studies have shown that physicians’ resis-
tance, partly driven by concerns about negative
effects of the use of electronic health records on
clinical productivity,
17
can be detrimental to adop-
tion efforts.
18
Whether our respondents, most of
whom have not adopted elect ronic hea lt h rec ords,
underestimated the challenges of overcoming this
barrier or whether physicians are becoming more
receptive to adoption is unclear. Either way, ob-
taining the support of physicians — often by get-
ting the backing of clinical leaders — can be help-
ful in ensuring successful adoption.
19
Another potential barrier to adoption is con-
cern about interoperabilit y: few electronic-records
systems allow for easy exchange of clinical data
between hospitals or from hospitals to physicians’
off ices. Low levels of health information exchange
in the marketplac e
20,21
reduce the potentia l value
of these systems and may have a dampening ef-
fect on adoption.
From a policy perspective, our data suggest that
rewarding hospitals — especially financially vul-
nerable ones — for using health information tech-
nolog y may play a central role in a comprehensive
approach to stimulating the spread of hospital
electronic-records systems. Creating incentives for
increasing information-technology staff and har-
monizing information-technology standards and
creating disincentives for not using such technol-
ogy may also be helpful approaches.
Some providers, such as the VHA, have success-
ful ly i mplemented elect ron ic-records sy stems. VHA
hospitals have used electronic health records for
more than a decade with dramatic associated im-
provements in clinical quality.
22,23
Their medical
records are nearly wholly electronic, and includ-
ing them in our ana lyses led to a doubl ing of our
count of U.S. hospitals with a comprehensive sys-
tem. Some developed countries, such as the Un ite d
Kingdom and t he Netherlands, have also success-
fully spurred adoption of health information tech-
33p9
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Proportion of Hospitals (%)
80
90
70
60
40
30
10
50
20
0
Hospitals with EHR Hospitals without EHR
Additional
reimbursement
for HIT use
Financial
incentives for
implementation
Technical
support for
implementation
Facilitators
Objective EHR
evaluation List of certified
EHRs
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Figure 2. Perceived Facilitators of Adoption of Electronic-Records Systems among Hospitals with Systems
as Compared with Hospitals without Systems.
Hospitals with electronic-records systems include hospitals with a comprehensive system and those with a basic
system that includes functionalities for physicians’ notes and nursing assessments. P>0.10 for all comparisons. EHR
denotes electronic health record, and HIT health information technology.
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The
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of
me dicine
n engl j med 10.1056/nejmsa0900592
10
nology, although most of their progress has been
in a mbulatory care. Few countries have yet to ma ke
substantial progress in the inpatient setting.
24
There are limitations to our study. First, al-
though we achieved a 63% response rate, the hos-
pitals that did not respond to our survey were
somewhat di f ferent from t hose that did respond.
We attempted to compensate for t hese di f ferenc es
by adjusting for potential nonresponse bias, but
such adjustments are imperfect. Given that non-
responding hospitals were more likely to have
characteristics associated with lower levels of
adoption of electronic health records, residual bias
may have led us to overestimate adoption levels.
Second, we focused on adoption and could not ac-
curately gauge the actual use or effectiveness of
electronic-records systems. Third, we did not as-
certain whet her the systems that were adopted had
been independently certified (by parties such as
the Certification Commission for Health Informa-
tion Technology). Fourth, given low adoption lev-
els, we had li mited power to ident ify predictors of
the adoption of comprehensive electronic-records
syst ems as compared w it h ba sic system s. Finally,
we did not ascertain whether users of electronic
health records were satisfied with them.
In summary, we examined levels of electronic
health record adoption in U.S. hospitals and fou nd
that very few have a comprehensive electronic sys-
tem for recording clinical information and that
only a small minority have even a basic system.
However, many institutions have parts of an elec-
tronic-records system in place, suggesting that
policy interventions could increase the prevalence
of electronic health records in U.S. hospitals faster
than our low adoption levels might suggest. Criti-
ca l strategies for policymakers hoping to promote
the adoption of electronic health records by U.S.
hospitals should focus on financial support, in-
teroperability, and training of information tech-
nology support staff.
Supported by grants from the Off ice of t he National Coordi-
nator for Healt h Informat ion Technolog y in the Depart ment of
Health and Human Services and the Robert Wood Johnson
Foundation.
Dr. Jha reports recei ving consulting fees from UpToDate; Drs.
Donelan and R ao, receiving grant suppor t f rom GE Corporate
Healthcare; and Dr. Blumenthal, receiving gra nt support from
GE Corporate Healt hcare, the Macy Foundation, and the Office
of the Nat ional Coordi nat or for He alt h Informa tion Tech nology
in t he Depar tment of Health a nd Human Services and speaking
fees from the FOJ P Service Corporat ion and serving as an ad-
viser to the presidential campa ign of Barack Obama. No other
pot ent ia l conf li ct of int ere st re levant to t hi s ar ticle was r eported.
We thank our expert consensus panel for their assistance in
conducting this research and Paola Miralles of the Institute for
Healt h Polic y for assistance in the preparation of an earlier ver-
sion of the m anu script.
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... Pretrained language models (PLMs), which are currently the most popular transfer learning models, divide the training into two phases: pre-training and fine-tuning, pre-training on a large-scale opendomain corpus and fine-tuning on downstream tasks [14][15][16][17][18]. PLMs compensate for the negative effects of insufficient training data by transferring pre-training results to downstream tasks and have achieved impressive success in natural language processing (NLP) tasks [19][20][21][22][23][24]. However, PLMs are usually pre-trained on natural language corpus, which has a natural gap with the most commonly used structured electronic health records (EHRs) in disease diagnosis and prediction tasks [25,26]. Although there have been works like Med-BERT [27] to rearrange the pre-training task for structured EHRs, the large-scale data and the expensive training cost required for pre-training make it suffer from various deficiencies. ...
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