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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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new england journal
me dicine
n engl j med 10.1056/nejmsa0900592
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
This article (10.1056/NEJMsa0900592) was
published at 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.
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.
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.
Copyright © 2009 Massachusetts Medical Society. All rights reserved.
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new england journal
me dicine
n engl j med 10.1056/nejmsa0900592
he U. S. h eal th c are sys tem faces cha l-
lenges on multiple fronts, including rising
costs and inconsistent qualit y.
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.
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
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.
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%
and 59%.
This broad range ref lects
different definitions of what constitutes an elec-
tronic health record,
use of convenience sam-
and low survey response rates.
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,
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.
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.
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
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
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
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,
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.
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
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.
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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new england journal
me dicine
n engl j med 10.1056/nejmsa0900592
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.*
(N = 2952)
(N = 1862)
Small (6–99 beds) 48 50
Medium (100–399 beds) 43 43
Large (≥400 beds) 10 7
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
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
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
in All Units
in at Least
One Unit
Begun or
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-
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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new england journal
me dicine
n engl j med 10.1056/nejmsa0900592
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.*
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
√ √
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
n engl j med 10.1056/nejmsa0900592
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.*
EHR System
Basic EHR
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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n engl j med 10.1056/nejmsa0900592
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,
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.
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.
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.
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.
Hospitals with EHR Hospitals without EHR
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Inadequate capital
for purchase Unclear ROI Maintenance
cost Physicians’
resistance Inadequate
IT staff
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
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,
can be detrimental to adop-
tion efforts.
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.
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
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.
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-
Proportion of Hospitals (%)
Hospitals with EHR Hospitals without EHR
for HIT use
incentives for
support for
Objective EHR
evaluation List of certified
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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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new england journal
me dicine
n engl j med 10.1056/nejmsa0900592
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.
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
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. ...
... When PLMs process natural language texts, the input is first segmented and tokenized, then the tokens are converted into embeddings according to the pre-trained vocabulary, and finally the network calculates the embeddings into probabilities [19][20][21][22][23][24]. However, since structured EHRs are heavily used in disease prediction tasks (such as the three datasets used in this work) [25,26], the tokenizer of PLMs cannot handle it well. As shown in Fig. 2, input from the Alzheimer's diagnosis task is segmented by the PLMs' tokenizer, the decimal is split into at least three parts, and the long continuous number is split into multiple segments (e.g., "54.5455" is split into "54", decimal point, "54" and "## 55"). ...
Full-text available
Machine learning (ML) has been extensively involved in assistant disease diagnosis and prediction systems to emancipate the serious dependence on medical resources and improve healthcare quality. Moreover, with the booming of pre-training language models (PLMs), the application prospect and promotion potential of machine learning methods in the relevant field have been further inspired. PLMs have recently achieved tremendous success in diverse text processing tasks, whereas limited by the significant semantic gap between the pre-training corpus and the structured electronic health records (EHRs), PLMs cannot converge to anticipated disease diagnosis and prediction results. Unfortunately, establishing connections between PLMs and EHRs typically requires the extraction of curated predictor variables from structured EHR resources, which is tedious and labor-intensive, and even discards vast implicit information. In this work, we propose an Input Prompting and Discriminative language model with the Mixture-of-experts framework (IPDM) by promoting the model's capabilities to learn knowledge from heterogeneous information and facilitating the feature-aware ability of the model. Furthermore, leveraging the prompt-tuning mechanism, IPDM can inherit the impacts of the pre-training in downstream tasks exclusively through minor modifications. IPDM remarkably outperforms existing models, proved by experiments on one disease diagnosis task and two disease prediction tasks. Finally, experiments with few-feature and few-sample demonstrate that IPDM achieves significant stability and impressive performance in predicting chronic diseases with unclear early-onset characteristics or sudden diseases with insufficient data, which verifies the superiority of IPDM over existing mainstream methods, and reveals the IPDM can powerfully address the aforementioned challenges via establishing a stable and low-resource medical diagnostic system for various clinical scenarios.
... To adjust for non-response, we constructed a logistic regression model in which returning the survey was the primary outcome and hospital characteristics, including size, teaching status, ownership, urban location, and region were predictors, as has been done previously. 18,19 Each hospital received a likelihood of response based on this model; responses were then weighted with the inverse of this likelihood. These weights make the sample more reflective of all surveyed hospitals, regardless of the distribution of hospitals that actually responded. ...
Background The COVID-19 pandemic caused massive disruption in usual care delivery patterns in hospitals across the USA, and highlighted long-standing inequities in health care delivery and outcomes. Its effect on hospital operations, and whether the magnitude of the effect differed for hospitals serving historically marginalized populations, is unknown.Objective To investigate the perspectives of hospital leaders on the effects of COVID-19 on their facilities’ operations and patient outcomes.MethodsA survey was administered via print and electronic means to hospital leaders at 588 randomly sampled acute-care hospitals participating in Medicare’s Inpatient Prospective Payment System, fielded from November 2020 to June 2021. Summary statistics were tabulated, and responses were adjusted for sampling strategy and non-response.ResultsThere were 203 responses to the survey (41.6%), with 20.7% of respondents representing safety-net hospitals and 19.7% representing high-minority hospitals. Over three-quarters of hospitals reported COVID testing shortages, about two-thirds reported staffing shortages, and 78.8% repurposed hospital spaces to intensive care units, with a slightly higher proportion of high-minority hospitals reporting these effects. About half of respondents felt that non-COVID inpatients received worsened quality or outcomes during peak COVID surges, and almost two-thirds reported worsened quality or outcomes for outpatient non-COVID patients as well, with few differences by hospital safety-net or minority status. Over 80% of hospitals participated in alternative payment models prior to COVID, and a third of these reported decreasing these efforts due to the pandemic, with no differences between safety-net and high-minority hospitals.ConclusionsCOVID-19 significantly disrupted the operations of hospitals across the USA, with hospitals serving patients in poverty and racial and ethnic minorities reporting relatively similar care disruption as non-safety-net and lower-minority hospitals.
... Then, they were pulverized in a cereal grinder for 5 min and sieved, using a 100 mesh sieve, to obtain a fine and homogeneous powder that was stored in hermetic sealed plastic bags and stored at -20 °C until for further chemical analysis. All seaweeds were identified taxonomically following the methods of [7][8][9][10]. The names of the species were used according to Guiry, [11] and were confirmed using algae base website. ...
... Our findings are also consistent with those of Gagnon, Nsangou [37], Kruse, Kothman [72], Jha, DesRoches [94], and Gagnon, Desmartis [95], who found that cost reduction constitutes a major facilitator of EHRS implementation. In addition, the findings illustrated that higher perceived usefulness of an EHRS increases the end user's willingness to use the system, which has been recorded as another facilitator of EHRS implementation. ...
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Introduction The introduction of information technology was one of the key priorities for policy-makers in health care organisations over the last two decades due to the potential benefits of this technology to improve health care services and quality. However, approximately 50% of those projects failed to achieve their intended aims. This was a result of several factors, including the cost of these projects. The Saudi Ministry of Health (MoH) planned to implement an electronic health record system (EHRS) in approximately 2100 primary health care centres nationwide. It was acknowledged that this project may face hurdles, which might result in the failure of the project if implementation facilitators were not first determined. According to the Saudi MoH, previous electronic health record system implementation in primary health care centres failed as a consequence of several barriers, such as poor infrastructure, lack of connectivity and lack of interoperability. However, the facilitators of successful electronic health record system implementation in Saudi primary health care centres are not understood. Aim To determine the facilitators that enhance the success of the implementation of an EHRS in public primary health care centres in SA. Method A mixed methods approach was used with both qualitative and quantitative methods (qualitative using semistructured interviews and quantitative with a closed survey). The purpose of the utilisation of exploratory mixed methods was to identify a wide range of facilitators that may influence EHRS implementation. The data were obtained from two different perspectives, primary health care centre practitioners and project team members. A total of 351 practitioners from 21 primary health care centres participated in the online survey, and 14 key informants at the Saudi Ministry of Health who were directly involved in the electronic health record system implementation in the primary health care centres agreed to be interviewed face to face. Results The findings from both studies revealed several facilitators. Among these facilitators, financial resources were found to be the most influential factor that assisted in overcoming some barriers, such as software selection. The size of the primary health care centres was the second facilitator of successful implementation, despite the scale of the project. Perceived usefulness was another facilitator identified in both the interviews and the survey. More than 90% of the participants thought that the electronic health record system was useful and could contribute to improving the quality of health care services. While a high level of satisfaction was expressed towards the electronic health record system’s usability and efficiency, low levels of satisfaction were recorded for organisational factors such as user involvement, training and support. Hence, system usability and efficiency were documented to be other facilitators of successful electronic health record system implementation in Saudi primary health care centres. Conclusion The findings of the present study suggest that sufficient financial support is essential to enhance the success of electronic health record system implementation despite the scale of the project. Additionally, effective leadership and project management are core factors to overcome many obstacles and ensure the success of large-scale projects.
... utility than in other non-health sectors. 11,12 It is therefore unsurprising that the community care sector has lagged behind in this respect. The reasons for this are well documented but typically include the lack of investment in the social care sector, the fractured nature of the provider supply network and a lack of standardization in community digital health records and digital software. ...
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Cera, a homecare provider, uses digital care plans (DCP), to streamline the provision of home care. DCP rollout is part of a larger digitization initiative, including carer visit reports collected through a mobile app and branch actions recorded in a web application supported by a secure central database. This retrospective cohort study aimed to assess the association of a DCP rollout with service user hospitalization rates. his study utilized retrospective data from 2 groups of service users, those for whom their first 30 days of Cera membership occurred prior to DCP rollout (pre-DCP group) versus those whose first 30 days of Cera membership occurred after DCP rollout (post-DCP group). The 30-day hospitalization rate was the primary outcome measure and was determined through a combination of carer reports, reporting from service users or their families, and branch staff follow-up. There were 55 hospitalizations among 392 users in the pre-DCP group in the 30 days after joining Cera (14.0% hospitalization rate), compared to 23 hospitalizations among 297 users in the post-DCP group (7.7% hospitalization rate). This represented a significant reduction in hospitalizations in the post-DCP group (6.3% absolute difference in hospitalization rate; 45% relative reduction; P < .001). This result was robust to multiple sensitivity analyses. The implementation of a DCP was associated with a 45% relative reduction in the 30-day hospitalization rate for new service users when compared to pre-DCP enrollment. These benefits could be further amplified by combining the DCP with additional initiatives aimed at the prediction and prevention of avoidable hospitalizations.
This paper explores factors in the implementation of a management information system in Zambia called SmartCare. Since 2017, the SmartCare electronic health record management system (EHR) has been adopted as the national EHR by the Ministry of Health (MoH) Zambia in with funding from the Centres for Disease Control and Prevention (CDC) and many other implementing partners. This study was a systematic review of literature to ascertain other findings and the writer, who adds the implementer's experiences. SmartCare has been implemented by the ministry of health in Zambia with support from various partners. This paper presents a systematic literature review in addition to the idealist ontology and epistemology. Some experiences from other countries have also been included. Organizations and funders looking to develop the healthcare electronic records management in Zambia and other resource limited countries alike can use this document to learn some lessons which can aid implementational success. This paper details the factors associated with SmartCare EHR's implementation in Zambia. The factors include general successes and challenges involved in the implementation. Thereby providing lessons to other EHR implementers in similar resource settings.
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Background Health data is one of the most valuable assets in health service delivery yet one of the most underutilized in especially low-income countries. Health data is postulated to improve health service delivery through availing avenues for optimal patient management, facility management, and public health surveillance and management. Advancements in information technology (IT) will further increase the value of data, but will also call for capacity readiness especially in rural health facilities. We aimed to understand the current knowledge, attitudes and practices of health workers towards health data management and utilization. Methods We conducted key informant interviews (KII) for health workers and data staff, and focus group discussions (FGD) for the village health teams (VHTs). We used both purposive and convenience sampling to recruit key informants, and convenience sampling to recruit village health teams. Interviews and discussions were audiotaped and transcribed verbatim. We manually generated the codes and we used thematic analysis to identify the themes. We also developed a reflexivity journal. Results We conducted a total of 6 key informant interviews and 3 focus group discussions of 29 participants. Our analysis identified 7 themes: One theme underscored the health workers’ enthusiasm towards an optimal health data management setting. The rest of the six themes resonated around working remedies to the systemic challenges that grapple health data management and utilization at facilities in rural areas. These include: Building human resource capacity; Equipping the facilities; Improved coordination with partners; Improved data quality assurance; Promotion of a pull supply system and Reducing information relay time. Conclusion Our findings reveal a plethora of systematic challenges that have persistently undercut optimal routine health data management and utilization in rural areas and suggest possible working remedies. Health care workers express enthusiasm towards an optimal health management system but this isn’t matched by their technical capacity, facility readiness, systems and policy willingness. There is an urgent need to build rural lower facilities’ capacity in health data management and utilization which will also lay a foundation for exploitation of information technology in health.
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Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this 'data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.
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Electronic health records (EHRs) are promising tools to improve quality and efficiency in health care, but data on their adoption rate are limited. We identified surveys on EHR adoption and assessed their quality. Although surveys returned widely different estimates of EHR use, when available information is limited to studies of high or medium quality, national estimates are possible: Through 2005, approximately 23.9 percent of physicians used EHRs in the ambulatory setting, while 5 percent of hospitals used computerized physician order entry. Large gaps in knowledge, including information about EHR use among safety-net providers, pose critical challenges for the development of policies aimed at speeding adoption.
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In April 2005, the Centers for Medicare & Medicaid Services and the Health Quality Alliance launched Hospital Compare, a web-based tool that helps acute care and critical access hospitals publish quality data for 17 clinical measures on heart attack, heart failure, and pneumonia. Approximately 4,200 hospitals across the country currently use Hospital Compare to disclose their scores for some or all of the 17 measures. On the basis of a nationally representative survey of senior hospital executives (typically the vice president of medical affairs or the chief medical officer) and directors of quality improvement departments, this report assesses how public reporting and Hospital Compare have affected hospitals. The assessment addresses experiences with public reporting; reactions of the media, payers, and purchasers to the data; changes in quality improvement efforts as a result of public reporting; and hospital leadership views on the federal role in future quality improvement programs, including pay for performance.
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We surveyed regional health information organizations (RHIOs) to assess the state of electronic health information exchange in the United States. We found fifty-five operational RHIOs, and most were focused on exchanging test results. Forty-one percent of operational RHIOs reported receiving sufficient revenue from participating entities to cover operating costs. Of the remainder, only 28 percent expected to ever do so. RHIOs in the planning stage were far more optimistic. Operational RHIOs from our 2007 survey had made little progress in expanding the breadth of their activities. Although the number of operational RHIOs is growing, their scope remains limited and their viability uncertain.
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In the mid-1990s, the Department of Veterans Affairs (VA) health care system initiated a systemwide reengineering to, among other things, improve its quality of care. We sought to determine the subsequent change in the quality of health care and to compare the quality with that of the Medicare fee-for-service program. Using data from an ongoing performance-evaluation program in the VA, we evaluated the quality of preventive, acute, and chronic care. We assessed the change in quality-of-care indicators from 1994 (before reengineering) through 2000 and compared the quality of care with that afforded by the Medicare fee-for-service system, using the same indicators of quality. In fiscal year 2000, throughout the VA system, the percentage of patients receiving appropriate care was 90 percent or greater for 9 of 17 quality-of-care indicators and exceeded 70 percent for 13 of 17 indicators. There were statistically significant improvements in quality from 1994-1995 through 2000 for all nine indicators that were collected in all years. As compared with the Medicare fee-for-service program, the VA performed significantly better on all 11 similar quality indicators for the period from 1997 through 1999. In 2000, the VA outperformed Medicare on 12 of 13 indicators. The quality of care in the VA health care system substantially improved after the implementation of a systemwide reengineering and, during the period from 1997 through 2000, was significantly better than that in the Medicare fee-for-service program. These data suggest that the quality-improvement initiatives adopted by the VA in the mid-1990s were effective.
To assess the state of health information technology (HIT) adoption and use in seven industrialized nations. We used a combination of literature review, as well as interviews with experts in individual nations, to determine use of key information technologies. We examined rates of electronic health record (EHR) use in ambulatory care and hospital settings, along with current activities in health information exchange (HIE) in seven countries: the United States (U.S.), Canada, United Kingdom (UK), Germany, Netherlands, Australia, and New Zealand (NZ). Four nations (the UK, Netherlands, Australia, and NZ) had nearly universal use of EHRs among general practitioners (each >90%) and Germany was far along (40-80%). The U.S. and Canada had a minority of ambulatory care physicians who used EHRs consistently (10-30%). While there are no high quality data for the hospital setting from any of the nations we examined, evidence suggests that only a small fraction of hospitals (<10%) in any single country had the key components of an EHR. HIE efforts were a high priority in all seven nations but the early efforts have had varying degrees of active clinical data exchange. We examined HIT adoption in seven industrialized nations and found that many have achieved high levels of ambulatory EHR adoption but lagged with respect to inpatient EHR and HIE. These data suggest that increased efforts will be needed if interoperable EHRs are soon to become ubiquitous in these seven nations.
Ten years ago, it would have been hard to imagine the publication of an issue of a scholarly journal dedicated to applying lessons from the transformation of the United States Department of Veterans Affairs Health System to the renewal of other countries national health systems. Yet, with the recent publication of a dedicated edition of the Canadian journal Healthcare Papers (2005), this actually happened. Veterans Affairs health care also has been similarly lauded this past year in the lay press, being described as the best care anywhere in the Washington Monthly, and described as top-notch healthcare in US News and World Report s annual health care issue enumerating the Top 100 Hospitals in the United States (Longman, 2005; Gearon, 2005).
We have little systematic information about the extent to which standard processes involved in health care--a key element of quality--are delivered in the United States. We telephoned a random sample of adults living in 12 metropolitan areas in the United States and asked them about selected health care experiences. We also received written consent to copy their medical records for the most recent two-year period and used this information to evaluate performance on 439 indicators of quality of care for 30 acute and chronic conditions as well as preventive care. We then constructed aggregate scores. Participants received 54.9 percent (95 percent confidence interval, 54.3 to 55.5) of recommended care. We found little difference among the proportion of recommended preventive care provided (54.9 percent), the proportion of recommended acute care provided (53.5 percent), and the proportion of recommended care provided for chronic conditions (56.1 percent). Among different medical functions, adherence to the processes involved in care ranged from 52.2 percent for screening to 58.5 percent for follow-up care. Quality varied substantially according to the particular medical condition, ranging from 78.7 percent of recommended care (95 percent confidence interval, 73.3 to 84.2) for senile cataract to 10.5 percent of recommended care (95 percent confidence interval, 6.8 to 14.6) for alcohol dependence. The deficits we have identified in adherence to recommended processes for basic care pose serious threats to the health of the American public. Strategies to reduce these deficits in care are warranted.
To determine the availability of inpatient computerized physician order entry in U.S. hospitals and the degree to which physicians are using it. Combined mail and telephone survey of 964 randomly selected hospitals, contrasting 2002 data and results of a survey conducted in 1997. Availability: computerized order entry has been installed and is available for use by physicians; inducement: the degree to which use of computers to enter orders is required of physicians; participation: the proportion of physicians at an institution who enter orders by computer; and saturation: the proportion of total orders at an institution entered by a physician using a computer. The response rate was 65%. Computerized order entry was not available to physicians at 524 (83.7%) of 626 hospitals responding, whereas 60 (9.6%) reported complete availability and 41 (6.5%) reported partial availability. Of 91 hospitals providing data about inducement/requirement to use the system, it was optional at 31 (34.1%), encouraged at 18 (19.8%), and required at 42 (46.2%). At 36 hospitals (45.6%), more than 90% of physicians on staff use the system, whereas six (7.6%) reported 51-90% participation and 37 (46.8%) reported participation by fewer than half of physicians. Saturation was bimodal, with 25 (35%) hospitals reporting that more than 90% of all orders are entered by physicians using a computer and 20 (28.2%) reporting that less than 10% of all orders are entered this way. Despite increasing consensus about the desirability of computerized physician order entry (CPOE) use, these data indicate that only 9.6% of U.S. hospitals presently have CPOE completely available. In those hospitals that have CPOE, its use is frequently required. In approximately half of those hospitals, more than 90% of physicians use CPOE; in one-third of them, more than 90% of orders are entered via CPOE.