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H E A L T H T R A C K I N G
M A R K E T W A T C H
19 January 2005
The Value Of Health Care Information
Exchange And Interoperability
There is a business case
to be made for spending money
on a fully standardized nationwide system.
By Jan
Walker, Eric Pan, Douglas Johnston, Julia Adler-Milstein,
David W. Bates, and
Blackford Middleton
ABSTRACT:
In this paper we assess the value of electronic health
care information exchange and interoperability (HIEI) between providers (hospitals
and medical group practices) and independent laboratories, radiology centers,
pharmacies, payers, public health departments, and other providers. We have
created an HIEI taxonomy and combined published evidence with expert opinion
in a cost-benefit model. Fully standardized HIEI could yield a net value of
$77.8 billion per year once fully implemented. Nonstandardized HIEI offers
smaller positive financial returns. The clinical impact of HIEI for which quantitative
estimates cannot yet be made would likely add further value. A compelling business
case exists for national implementation of fully standardized HIEI.
Attention to the use of information
technology (IT) in health care is intensifying rapidly, with President George
W. Bush calling for widespread adoption of electronic medical records (EMRs)
within the next ten years.1 In addition
to digitizing the information that providers use to care for their patients
within organizations, clinicians, patients, and policymakers are looking ahead
to sharing appropriate information electronically among organizations. David
Brailer, newly appointed national health information technology coordinator,
recently called for expansion of such interoperability to the flow
of clinical and other administrative data, citing its importance for encouraging
health care IT investment and facilitating health care reform.2
To explore the qualitative and economic implications of
health care information exchange and interoperability (HIEI), we studied the
value of electronic data flow between providers (hospitals and medical group
practices) and other providers, and between providers and five stakeholders
with which they exchange information most commonly: independent laboratories,
radiology centers, pharmacies, payers, and public health departments. We hypothesized
that the clinical benefits of electronic data exchange would be substantial
and that financial benefits would outweigh costs. In this paper we report on
the results of our analysis.
Study Data And Methods
We used a range of methods to gather evidence, including
literature reviews, expert interviews, and estimates by an expert panel. We
focused our efforts on analyzing published sources for data but, where these
were lacking, turned to experts to fill critical gaps. We then created a cost-benefit
model to project value to organizations and to the country. A full project
report that contains a detailed description of the methods we employed is forthcoming.3
Literature review. We
worked with a medical librarian to complete a systematic review of the U.S.
literature addressing the clinical, financial, and organizational value of
HIEI in these interorganizational relationships, and we also searched trade
press, general press, and online sources. Not surprisingly, given the lack
of real-world implementation of interoperable systems in health
care, we found few sources targeting HIEI value specifically.
Experts.
We convened a panel of nationally known experts to advise us throughout this
project. They brought expertise in regional data-sharing initiatives, economics,
public health, payment systems, informatics, and public policy. With relatively
little research and literature on the value of HIEI, the panelists played an
important role, participating in structured telephone interviews, a one-day
meeting, e-mail polling, and discussions. We also interviewed more than twenty
other experts, including provider information systems executives working with
various facets of interoperability and directors of regional data-sharing
initiatives. The panelists and other experts helped identify data sources and
estimated key data points that were not available in published sources.
Analytic framework. We
devised a conceptual framework describing how health care entities share information
and created a functional taxonomy reflecting the amount of human involvement
required, the sophistication of IT, and the level of standardization. The taxonomy
has four levels. Level 1: Nonelectronic data—no use of IT to share information
(examples: mail, telephone). Level 2: Machine-transportable data—transmission
of nonstandardized information via basic IT; information within
the document cannot be electronically manipulated (examples: fax or personal
computer [PC]–based exchange of scanned documents, pictures, or portable
document format [PDF] files). Level 3: Machine-organizable data—transmission
of structured messages containing nonstandardized data; requires interfaces
that can translate incoming data from the sending organization’s vocabulary
to the receiving organization’s vocabulary; usually results in imperfect
translations because of vocabularies’ incompatible levels of detail (examples:
e-mail of free text, or PC-based exchange of files in incompatible/proprietary
file formats, HL-7 messages). Level 4: Machine-interpretable data—transmission
of structured messages containing standardized and coded data; idealized state
in which all systems exchange information using the same formats and vocabularies
(examples: automated exchange of coded results from an external lab into a
provider’s EMR, automated exchange of a patient’s “problem
list”).
Software model.
Using Analytica software (version 3.0.1) from Lumina Decision Systems Inc.
(Los Gatos, California), we created the analytic model as an influence diagram.
This software allowed us to depict complex factor relationships graphically,
to consider many factors simultaneously, to incorporate probability distributions
to represent uncertainties, and to test the sensitivity of projections to variations
in key inputs. Although we cannot include full model specifications in this
brief paper, we reference important data sources in each topic area.
Projections of costs. We
projected costs for the interfaces required by each participating organization’s
computers for communicating with external computers and for internal HIEI-capable
systems for providers. To calculate national costs, we allocated relevant costs
to relevant organizations. The only exception to this approach was for provider-payer
costs, which were taken directly from the Health Insurance Portability and
Accountability Act’s (HIPAA’s) Final Impact Analysis.4
Interfaces are programs that enable different systems to
communicate with one another. We estimated Level 3 interface development costs
based on expert opinion, assigning $50,000 per interface for hospitals, labs,
radiology centers, pharmacies, and public health departments, and $20,000 per
interface in group- practice offices. Experts were divided on whether Level
3 or Level 4 interfaces would cost more; we assumed that they would cost the
same. Level 3 requires a unique interface to each external organization, and
we assumed from eight to twenty interfaces per provider, depending on the provider’s
size. Level 4 HIEI requires one interface to each type of external organization—for
example, one interface to all external laboratories, totaling five per provider.
For both Level 3 and Level 4, each external organization requires an interface
to providers, and we assumed one per laboratory, radiology center, and pharmacy
and two per local public health department—one to hospitals and one to
office practices.
Relatively few providers currently have broad and mature
clinical information systems.5 Thus,
we assumed that all U.S. providers would install new systems, using the Institute
of Medicine’s (IOM’s) definition of minimal functional specifications
for the electronic health records that would be required for HIEI Levels 3
and 4.6 To estimate the national
costs of these systems, we applied Christian Birkmeyer’s cost estimates
to hospital providers and our earlier estimates for advanced ambulatory systems
to outpatient providers.7 Acquisition
costs include initial licenses, hardware, implementation, and training. For
both interfaces and provider systems, we assigned annual maintenance costs
equal to 17.5 percent of the initial acquisition costs to cover ongoing license
fees, system upgrades, and hardware replacement costs.
Projections of benefits. We
searched for evidence about information flows between organizations and asked
the expert panel to estimate the impact of each level of HIEI on these flows.
The model calculated benefits to organizations and to the country as a whole
by combining published quantitative evidence, national provider statistics,
other data points, and expert-panel estimates of HIEI impact.
As an example, Exhibit
1 illustrates the projection of
benefits from Level 4 HIEI between outpatient providers and independent laboratories.
As in our other calculations, we asked expert panelists to estimate the impact
on participating organizations once they are connected at each HIEI level.
To simplify our analysis, we assumed that this was effective 100 percent of
the time.
The model first estimates baseline total lab test costs:
a combination of fees billed by laboratories and administrative costs incurred
by providers in handling the paper and phone calls associated with tests.
Then it estimates the proportion of tests (and costs) that are redundant
and avoidable with HIEI. For the remaining tests, it estimates the impact
of HIEI on the administrative portion of test costs. Finally, the model
sums these cost savings and applies them to recent population statistics
to calculate national benefit.
National rollout scenario. To
allocate benefits and costs over time, we developed a ten-year national implementation
scenario. We assumed that 20 percent of organizations would install systems
in each of the first five years, incurring all acquisition and start-up costs
in year 1, and maintenance costs in years 1 through 10. We postulated that
each organization would accrue 50 percent of potential benefits in year 1,
and that benefits would increase by 10 percent each year. We did not attempt
to account for inflation, discounting, or changes in utilization from changes
in the national population. Therefore, amounts are in 2003 dollars and reflect
current care patterns and population figures. Again, provider-payer costs are
an exception, as they were amortized over three years to be consistent with
HIPAA’s Final Impact Analysis.
Results
Costs of HIEI.
Level 2 HIEI is cost-free, as faxing is universally available. Level 3 and
Level 4 costs are presented in Exhibit
2.
Benefits of HIEI. Where
we could find sufficient evidence, we quantified the benefits of HIEI. In presenting
those results, we reference important data sources and describe additional
qualitative benefits of HIEI for which we could not develop quantitative estimates.
Both freestanding and hospital-based outpatient clinicians
use external laboratories. Interoperability between these organizations would
enable computer-assisted reduction of redundant tests, and it would reduce
delays and costs associated with paper-based ordering and reporting of results.8 These
savings would produce an annual national benefit of $8.09 billion at Level
2, $18.8 billion at Level 3, and $31.8 billion at Level 4. In addition, provider-laboratory
connectivity would give clinicians better access to patients’ longitudinal
test results, eliminate errors associated with reporting results orally, optimize
ordering patterns by making information on test costs readily available to
clinicians, and make testing more convenient for patients.
Most imaging procedures ordered by office-based clinicians,
and some ordered by those in hospital-based ambulatory practices, are performed
in external radiology centers. Connectivity between these organizations would
reduce redundant tests and would save time and costs associated with paper-
and film-based processes.9 Our model
projects annual national savings from avoided tests and improved efficiencies
of $8.34 billion at Level 2, $14.4 billion at Level 3, and $26.2 billion
at Level 4. Although we did not model additional potential impacts, interoperability
here could also improve ordering by giving radiologists access to relevant
clinical information, thereby enabling them to recommend optimal testing;
improve patient safety by alerting both the provider and the radiologist to
test contraindications; facilitate coordination of care and help prevent errors
of omission by enabling automated reminders when follow-up studies are indicated;
and lessen adverse environmental impacts by reducing the use of chemicals and
paper in film processing.
Interoperability between outpatient providers and pharmacies
would reduce the number of medication-related phone calls for both clinicians
and pharmacists, saving $2.19 billion at Level 2, $2.66 billion at Level 3,
and $2.71 billion at Level 4 each year.10 It
would also improve clinical care by facilitating the formation of complete
medication lists, thereby reducing duplicate therapy, drug interactions and
other adverse drug events, and medication abuse. It could also enable automated
refill alerts, offer clinicians easy access to information about whether patients
fill prescriptions, and complete insurance forms required for some medications.
In addition, it could help identify affected patients in the event of drug
recalls, uncover new side effects, and improve formulary management.
Provider-provider connectivity would save time associated
with handling chart requests and referrals.11 The
model assumes that all needed charts are requested and projects annual national
benefits from these time savings of $2.92 billion at Level 2, $8.11 billion
at Level 3, and $13.2 billion at Level 4. Moreover, connectivity would reduce
fragmentation of care from scattered records and improve referral processes.
Provider connectivity to the U.S. public health system
would make reporting of vital statistics and cases of certain diseases more
efficient and complete, potentially saving the nation $63.2 million at Level
2, $107 million at Level 3, and $195 million at Level 4 each year.12 However,
the most important impact of public health interoperability would almost certainly
derive from earlier recognition of emerging disease outbreaks and biosurveillance,
as it becomes easier to identify warning signs and trends by aggregating data
from many sources. Since robust quantitative evidence about the value of HIEI
in earlier recognition of disease and biosurveillance does not yet exist, we
did not project value from these sources.
Provider-payer transactions enjoy a relatively high degree
of standardization, largely because of HIPAA. HIPAA does not allow Level 2
and Level 3 connectivity. Some transactions are highly automated, but others
are not, particularly in smaller organizations. We combined recent statistics
about electronic transaction rates with estimates of HIEI impact on the nonelectronic
transactions. Our model estimates that moving to Level 4 interoperability for
all provider-payer transactions would save the nation $20.1 billion each year.13
An example at the organization level helps bring perspective
to these numbers. A medium-size hospital, defined in our analysis as one with
50–199 beds, would invest $2.7 million in clinical systems and interfaces
to achieve Level 4 interoperability. Beginning in year 2, it would spend $250,000
per year to maintain those systems. Benefits would increase over time as the
hospital moved up its “learning curve” and as more of its care
partners were connected. Once it reached its steady state, it would accrue
benefits of $1.3 million annually, from its transactions with other providers
($570,000), laboratories ($200,000), radiology centers ($170,000), payers ($250,000),
and pharmacies ($70,000). Since hospitals provide in-house services for most
laboratory and radiology tests, their greatest need for—and benefit from—external
information exchange is with other providers.
Net value of HIEI.
Combining the benefits and costs quantified above, we present the net value
of three different levels of HIEI in Exhibit
3.
Each year in the Level 4 steady state, providers benefit
from connectivity with other providers ($12.2 billion), radiology centers
($8.82 billion), payers ($10.3 billion), and laboratories ($13.9 billion).
Providers lose money from connectivity to pharmacies (–$0.037 billion) and public
health departments (–$0.98 billion), effectively subsidizing those connections,
and they incur annual costs of $10.5 billion to run the systems required, leaving
them with an annual net value of $33.7 billion. Payers realize net value from
improved efficiency of provider transactions ($9.84 billion), and from avoided
lab ($3.76 billion) and radiology ($8.04 billion) tests. Other organizations
realize net value from improved efficiency of provider transactions (laboratories,
$13.1 billion; radiology centers, $8.17 billion; pharmacies, $1.29 billion;
and public health departments, $0.094 billion). The total annual net value
to these stakeholders is $77.8 billion (rounded).
We measured the sensitivity of net value to variations
of 50 percent in model inputs. Results are most sensitive to the average costs
of laboratory tests and radiology procedures, with 50 percent decreases in
those averages reducing annual Level 4 net value to $68.7 billion and $72.2
billion, respectively. Inflating the average cost by 50 percent raises annual
net value to $105 billion for more expensive laboratory tests, and to $94.6
billion with more expensive radiology procedures. Given the unexpected pitfalls
that accompany large systems installations, we also tested a less favorable
cost scenario. Doubling the systems and interface costs reduces annual net
value to $61.3 billion. To test the potential impact of reduced technology
costs, we halved the systems and interface costs and calculated an annual
projected net value of $86.1 billion.
Discussion
Based on our analysis of those elements of interoperability
for which we can assign dollar values, net savings from national implementation
of fully standardized interoperability between providers and five other types
of organizations could yield $77.8 billion annually, or approximately 5 percent
of the projected $1.661 trillion spent on U.S. health care in 2003.14 In
addition, the model did not quantify many potentially important costs
and benefits. On balance, we believe that their net value is largely positive;
the value of fully standardized interoperability is likely to be higher than
our quantified results suggest. Overall, we believe that a compelling business
case exists for national implementation of fully standardized HIEI.
We suspect that the clinical payoff in improved patient
safety and quality of care could dwarf the financial benefits projected from
our model, which are derived from redundancies that are avoided and administrative
time saved. Giving clinicians access to data about their patients’ care
from providers outside their organizations would likely result in fewer medical
errors and better continuity of care. But electronic exchange of clinical data
between organizations is nascent, and few data exist about the clinical impact
it would bring. It will be important for future inquiries to explore such impact
in depth.
Both Level 2 and Level 3 nonstandardized electronic communication
offer positive financial returns, although they pale in comparison with the
value of fully standardized interoperability. The most basic form
of electronic interoperability—Level 2 faxing—is already universally
available (and therefore nearly cost free) and could offer immediate reductions
in the time spent on many transactions if it were more widely used.
Level 3 interoperability requires a hefty investment in
interfaces to translate heterogeneous electronic vocabularies, although it
eventually accumulates benefits that outweigh those costs. However, it is
not realistic for the nation as a whole to plan to “step up” over time,
hoping for an orderly progression from nonstandardized Level 3 to standardized
Level 4 interoperability, as national standards are gradually adopted. Level
3 HIEI requires that organizations develop interfaces to others’ coding
schemes, an investment that locks in local solutions, diverts resources from
developing more-universal approaches, delays conversion to national standards,
and guarantees additional costs down the road to convert to national standards
once they exist.
With national standards today neither completely
defined nor adopted, it is tempting to develop a nonstandardized Level 3
system. Level 3 systems can indeed aggregate information from remote
sources. However, they must reconcile diverse codes, data structures,
and terminologies. Through such inevitably imprecise processes, Level
3 systems may generate errors and redundant information, limit the efficacy
of clinical decision support, and create information and cognitive overload
for clinicians. A Level 4 system, with on-demand, seamless integration
of local and remote records, is far more likely to offer clinicians the
integrated information they need for providing optimal care.
If one considers the difference between Levels 3 and 4
to be the value of national standards, they could be worth billions of dollars.
We did not estimate the cost of developing such standards, but it seems reasonable
to assume that it would be in the millions, rather than billions, making development
of coherent, universal standards a sound investment.
Limitations. Our
analysis is limited in several important ways. Beyond transactions with payers,
health care organizations have little real-world experience with electronic
information exchange and almost no experience with transactions that bear on
clinical matters. Our analysis incorporates the best quantitative evidence
available from a small number of studies, but we had to rely on expert estimates
more often than would be optimal. We were also not able to impute values for
clinical or organizational
effects of HIEI or for probable societal impacts, such as faster detection
of disease outbreaks, other improvements in biosurveillance, and broad
impacts on workflow based on electronic rather than paper information. HIEI
could affect these health care processes in powerful ways, well beyond the
individual patient-clinician encounter.
In addition, the model did not account for lost revenues
from avoided tests and other changes in utilization, or for the cost of major
workflow disruptions during systems implementation. If employees are redeployed,
the financial benefits projected from time savings may be realized as improved
productivity or service quality, rather than pocketed dollar savings, and the
model did not distinguish between these endpoints.
Finally, although we included costs for providers’ and
payers’ HIEI-capable systems, we did not account for corresponding costs
to laboratories, radiology centers, pharmacies, and public health departments.
Such an undertaking would include collecting sensitive—and sometimes
proprietary—data about very complex systems from heterogeneous organizations,
an effort beyond the scope of this project. For these entities, we assumed
that these costs are subsumed in the costs of doing business. In spite of these
limitations, our confidence in our model is bolstered by the results of multiple
sensitivity analyses that found that the findings are not materially affected
when we vary important inputs.
Policy considerations. Although
it is beyond the scope of this paper to speculate in depth about who would
benefit from HIEI, patients and providers most likely have the most to gain.
Organizations such as regulatory agencies, research institutions, and others
not considered here could benefit from aggregate information about care. However,
those who depend in subtle ways on redundancy and excess could find such change
costly.
National HIEI may well grow from regional data-sharing
initiatives. If incentives can be established to encourage these local efforts,
and national standards can be established for them to adopt from the start,
these networks may one day be knit together into a seamless, national Level
4 health care information system, although this will not occur without some
federal leadership. Achieving Level 3 and Level 4 interoperability will require
sizable investment in HIEI systems by providers and stakeholders. Participants
realize different levels of return on HIEI investments, and the conflicting
financial incentives of the health care system raise complex policy questions
about who should pay for development and implementation.15
Thus, achieving Level 4 interoperability
will require strong policy incentives, federal leadership, and possibly state
and federal legislative mandates.16 At
a time of national tumult over quality, safety, and cost, achieving seamless
interoperability among vital sectors of the delivery system must proceed in
parallel with the move from paper to EMRs. Both will be enormously valuable,
and they will be synergistic. Creating an environment that encourages this
transformation represents an opportunity that must be seized.
This project was supported by a grant from the Foundation for the eHealth
Initiative. Expert panelists included David Brailer, William Braithwaite, Paul
Carpenter, Daniel Friedman, Robert Miller, Arnold Milstein, Marc Overhage,
Scott Young, and Kepa Zubeldia. The authors are indebted to Ellen Rosenblatt
and Jen Hartling for invaluable assistance in preparing this manuscript.
NOTES
1. M. Allen, “Bush Touts Plan for Electronic Medicine,” Washington
Post, 28 May 2004.
2. J. Dodge, “New Health-IT Czar’s First Press
Conference Hints at Plans,” Health IT World, 20
May 2004, www.health-itworld.com/enews/05-20-2004_171.html (6
January 2005).
3. E. Pan et al., The Value of Healthcare Information
Exchange and Interoperability (Chicago: Health Information
Management and Systems Society, forthcoming).
4. U.S. Department of Health and Human Services, “Standards
for Electronic Transactions and Code Sets: Final Impact Analysis,” Federal
Register 65, no. 160 (2000): 50311–50372.
5. S. Barlow, J. Johnson, and J. Steck, “The Economic
Effect of Implementing an EMR in an Outpatient Clinical Setting,” Journal
of Healthcare Information Management 18, no. 1 (2004): 46–51;
and D.J. Brailer and E.L. Terasawa, Use and Adoption of
Computer-based Patient Records in the United States, October 2003, www.chcf.org/documents/ihealth/UseAdoptionComputerizedPatientRecords.pdf (13
January 2005).
6. Committee on Data Standards for Patient Safety, Key
Capabilities of an Electronic Health Record System, Letter
Report, October 2003,
www.nap.edu/books/NI000427.pdf (18
November 2004).
7. C.M. Birkmeyer et al., “Will Electronic Order Entry
Reduce Health Care Costs?” Effective Clinical Practice 5,
no. 2 (2002): 67–74; and D. Johnston et al., The
Value of Computerized Provider Order Entry in Ambulatory Settings (Chicago:
HIMSS, 2003), 59–66.
8. D.W. Bates et al., “What Proportion of Common Diagnostic
Tests Appear Redundant?” American Journal of Medicine 104,
no. 4 (1998): 361–368; D. Brailer et al., Moving
toward Electronic Health Information Exchange: Interim Report on the Santa
Barbara County Data Exchange, July 2003,
www.chcf.org/documents/ihealth/SBCCDEInterimReport.pdf (18
November 2004); and S.J. Wang et al., “A Cost-Benefit Analysis of
Electronic Medical Records in Primary Care,” American
Journal of Medicine 114, no. 5 (2003): 397–403.
9. Bates et al., “What Proportion of Common Diagnostic Tests?”;
Brailer et al., Moving toward Electronic Health Information
Exchange; and Johnston et al., The Value of
Computerized Provider Order Entry.
10. Workgroup for Electronic Data Interchange, “Appendix 9: Financial
Implications,” Technical Advisory Group White Paper (Reston, Va.: WEDI,
October 1993); H. Ogura et al., “Online Support Functions of Prescription
Order System and Prescription Audit in an Integrated Hospital Information System,” Medical
Informatics (London) 13, no. 3 (1988): 161–169; and National
Institute for Health Care Management Research and Educational Foundation, Prescription
Drug Expenditures in 2001: Another Year of Escalating Costs (Washington:
NIHCM, May 2002).
11. C.B. Forrest, “Primary Care Gatekeeping and Referrals:
Effective Filter or Failed Experiment?” British Medical Journal 326,
no. 7391 (2003): 692– 695; and J.D. Wassenaar and S.L. Thran, eds., Physician
Socioeconomic Statistics 2000–2002 Edition: Profiles for Detailed Specialties,
Selected States, and Practice Arrangements (Chicago: AMA, 2003).
12. T.J. Doyle, M.K. Glynn, and S.L. Groseclose, “Completeness
of Notifiable Infectious Disease Reporting in the United States: An Analytical
Literature Review,” American Journal of Epidemiology 155,
no. 9 (2002): 866–874; and Texas Department of Health, “Communicable
Disease Reporting,” Disease Prevention News 55,
no. 17 (1995): 1–8.
13. Utah Health Information Network, “HIPAA Cost Tool” (Murray,
Utah: UHIN, December 2001); and Computer Sciences Corporation, “HIPAA
Provider ROI Model” (El Segundo, Calif.: CSC, 25 September 2000).
14. CMS, “Table 3: National Health Expenditures Aggregate and
per Capita Amounts, Percent Distribution and Average Annual Percent Change
by Source of Funds: Selected Calendar Years 1980– 2012,” 17 September
2004, www.cms.hhs.gov/statistics/nhe/projections-2002/t3.asp (18
November 2004).
15. B. Middleton et al., “Accelerating U.S. HER Adoption:
How to Get There from Here, Recommendations Based on the 2004 ACMI Retreat,” Journal
of the American Medical Informatics Association 12, no. 1 (2005):
13–19.
16. M. Overhage et al., “Does National Regulatory Mandate
of Provider Order Entry Portend Greater Benefit than Risk for Health Care Delivery?
The 2001 ACMI Debate,” Journal of the American Medical
Informatics Association 9, no. 3 (2002): 199–208.
The authors are with
the Center for Information Technology Leadership, Partners HealthCare System,
in Boston, Massachusetts. Jan Walker (Jwalker{at}citl.org) is its executive director;
Eric Pan, associate fellowship director and a senior analyst; Douglas Johnston,
a senior analyst; Julia Adler-Milstein, a research analyst; David Bates, a member
of the executive committee; and Blackford Middleton, chairman. Bates is also
chief of the Department of General Internal Medicine at Brigham and Women's Hospital
(Boston) and director, clinical and quality analysis, Partners HealthCare System.
Middleton is also corporate director, clinical informatics research and development,
at Partners.
DOI:
10.1377/hlthaff.w5.10
©2005 Project HOPE–The People-to-People Health
Foundation, Inc.
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