INFO 6061 FC EHR Functionality Audit Checklist

INFO-6061 – Health Systems Environments IIIndividual Assignment – Unit 1
Total Marks – 36
Weight – 10%
Due Date: Week 3 @ Weekly Course Schedule Date and Time
General Instructions:
This is an INDIVIDUAL assignment.
Use correct APA citation formatting and referencing. Use Microsoft Office application with default
margin, line spacing, font color and size. Remember to include a cover page and a reference page.
Late submissions will receive a 20% per day penalty for lateness beginning at the time the drop-box due
date and time ends.
Please review the marking rubric to ensure that you have completed the assignment accordingly. You
should be submitting two documents to the drop-box.
Assignment Instructions:
Fanshawe College’s Health Systems Management post-graduate program aims to allow its students
to understand the need, requirements, and application of electronic health records. This aim began
in Level 1 and will be continued in Level 2.
For this INDIVIDUAL assignment, you are going to read contents of a provided resource and develop
an audit checklist based on the resource.
Read the provided resource ‘Key Capabilities of E.H.R. (IA) as a PDF within this folder.
Next, develop an audit checklist of ONE-PAGE that captures the key content of the resource.
Be advised that a typical checklist will have criteria or parameter, compliant or non-compliant
(yes/no or met/unmet option), date, responsible party, and corrective action.
In addition, provide a one-paragraph explanation why you develop your audit checklist with the
parameters you select.
Ensure your final submission is free of spelling and grammar errors. Ensure you apply the appropriate
APA citing/referencing. Ensure you are researching each item above without just listing it – keep in
mind an ‘evidence-based’ process.
RUBRIC
Criteria
Unacceptable (1)
Does not provide
appropriate
content needed
Marginal (2) Partially Proficient (3) Meets
covers the content
the required content
needed
needed
Exemplary (4)
Exceeds the
required content
needed with critical
thinking and
analysis
Development of an
audit checklist (format)
Parameters
identified
Parameters of
compliance identified
Administrative
requirements shown
(name, date)
Correction action
mechanism shown
Paragraph of
explanation
Grammar & Spelling
APA referencing/citing
Professionally Finished
Submission
TOTAL OUT OF 36 MARKS
ADDENDUM TO A136 ACADEMIC INTEGRITY POLICY – Pursuant to Fanshawe College’s A136 Academic
Integrity policy, the Health Systems Management program does not permit the use of any unauthorized
technology tools. Technology tools include, but are not limited to, calculators, textbooks, translation tools,
course notes and resources, search engines (e.g. Google), and artificial intelligence applications (e.g. ChatGPT
or any other similar/equivalent platform). The unauthorized use of these technology tools in any academic
deliverable will result in the applicable penalties as per A136 Academic Integrity policy. This can be applied
individually or group capacity, dependent on the offence identified and resulting investigation and
verification.
Key Capabilities of an Electronic Health Record
System: Letter Report
Committee on Data Standards for Patient Safety
ISBN: 0-309-55877-8, 35 pages, 8 1/2 x 11, (2003)
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Key Capabilities of an Electronic Health Record System: Letter Report
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Key Capabilities of an
Electronic Health Record System
Letter Report
Committee on Data Standards for Patient Safety
Board on Health Care Services
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presented in this report are those of the Institute of Medicine Committee on Data Standards for Patient
Safety and are not necessarily those of the funding agencies.
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Copyright © National Academy of Sciences. All rights reserved.
Key Capabilities of an Electronic Health Record System: Letter Report
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Key Capabilities of an Electronic Health Record System: Letter Report
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KEY CAPABILITIES OF AN ELECTRONIC HEALTH RECORD SYSTEM
Letter Report
July 31, 2003
Dr. Carolyn Clancy
Director, Agency for Healthcare Research and Quality
John M. Eisenberg Building
540 Gaither Road
Rockville MD, 20850
Dear Dr. Clancy:
In May 2003, the Department of Health and Human Services (DHHS) asked the Institute of
Medicine (IOM) to provide guidance on the key care delivery-related capabilities of an
electronic health record (EHR) system. An EHR system includes (1) longitudinal collection of
electronic health information for and about persons, where health information is defined as
information pertaining to the health of an individual or health care provided to an individual; (2)
immediate electronic access to person- and population-level information by authorized, and only
authorized, users; (3) provision of knowledge and decision-support that enhance the quality,
safety, and efficiency of patient care; and (4) support of efficient processes for health care
delivery. Critical building blocks of an EHR system are the electronic health records (EHR)
maintained by providers (e.g., hospitals, nursing homes, ambulatory settings) and by individuals
(also called personal health records).
There is a great deal of interest within both the public and private sectors in encouraging all
health care providers to migrate from paper-based health records to a system that stores health
information electronically and employs computer-aided decision support systems. In part, this
interest is due to a growing recognition that a stronger information technology (IT) infrastructure
is integral to addressing such national concerns as the need to improve the safety and quality of
health care, rising health care costs, and matters of homeland security related to the health sector.
The efforts of all parties—purchasers, regulators, providers, and vendors—to advance the
deployment of EHR systems, would benefit from a common set of expectations about EHR
capabilities.
The IOM was asked to respond very rapidly to this request from DHHS. Fortunately, a
sizable project focused on patient safety data standards was already under way at the IOM, and
this new task proved to be an appropriate expansion of that ongoing work. Thus the charge to
the IOM Committee on Data Standards for Patient Safety (the IOM Committee) was expanded to
address this additional task, and the committee devoted a portion of its previously scheduled
1
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Electronic Health Record Functional Model: Letter Report
meeting of June 9–10, 2003, to the development of this letter report. The IOM Committee’s full
report on data standards will be issued in fall 2003.
BACKGROUND
The development of an IT infrastructure has enormous potential to improve the safety,
quality, and efficiency of health care in the United States (Institute of Medicine, 2001).
Computer-assisted diagnosis and chronic care management programs can improve clinical
decision making and adherence to clinical guidelines, and can provide focus on patients with
those diseases (Durieux et al., 2000; Evans et al., 1998). Computer-based reminder systems for
patients and clinicians can improve compliance with preventive service protocols (Balas et al.,
2000). More immediate access to computer-based clinical information, such as laboratory and
radiology results, can reduce redundancy and improve quality. Likewise, the availability of
complete patient health information at the point of care delivery, together with clinical decision
support systems such as those for medication order entry, can prevent many errors and adverse
events (injuries caused by medical management rather than by the underlying disease or
condition of the patient) from occurring (Bates et al., 1998, 1999; Evans et al., 1998). Via a
secure IT infrastructure, patient health information can be shared amongst all authorized
participants in the health care community (National Research Council, 2000).
An IT infrastructure also has great potential to contribute to achieving other important
national objectives, such as enhanced homeland security and improved and informed public
health services (Institute of Medicine, 2002b; National Committee on Vital and Health Statistics,
2001; Wagner et al., 2001). EHRs, combined with Internet-based communication, may enable
early detection of and rapid response to bioterrorism attacks, including the organization and
execution of large-scale inoculation campaigns and ongoing monitoring, detection, and treatment
of complications arising from exposure to biochemical agents or immunizations (Tang, 2002;
Teich et al., 2002). A more advanced health information infrastructure is also crucial for various
forms of biomedical and health systems research, as well as educating patients, informal
caregivers, and citizens about health (Detmer, 2003; National Committee on Vital and Health
Statistics, 2001).
EHR system implementation and its continuing development is a critical element of the
establishment of an IT infrastructure for health care. In 1991, the IOM issued a report calling for
the elimination of paper-based patient records within 10 years, but progress has been slow, and
this goal has not yet been met (Institute of Medicine, 1991; Overhage et al., 2002). It should be
noted that the motivation is not to have a paperless record per se, but to make important patient
information and data readily available and useable. In addition, computerizing patient data
enables the use of various computer-aided decision supports.
There are some noteworthy examples of health care settings in both the private and public
sectors in which EHRs have been deployed. A handful of communities and systems have
established secure platforms for the exchange of data among providers; suppliers; patients; and
other authorized users, such as the Veterans Health Administration, the New England Healthcare
Electronic Data Interchange Network, the Indiana Network for Patient Care, the Santa Barbara
County Care Data Exchange, the Patient Safety Institute’s National Benefit Trust Network, and
the Markle Foundation’s Healthcare Collaborative Network (CareScience, 2003; Kolodner and
Douglas, 1997; Markle Foundation, 2003b; New England Healthcare EDI Network, 2002;
Overhage, 2003; Patient Safety Institute, 2002). But these examples are the exception, not the
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Key Capabilities of an Electronic Health Record System: Letter Report
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Electronic Health Record Functional Model: Letter Report
3
rule. In most of the nation’s hospitals, orders for medications, laboratory tests, and other services
are still written on paper, and many hospitals lack even the capability to deliver laboratory and
other results in an automated fashion. The situation is no different in most small practice
settings, where there has been little if any migration to electronic records.
In addition to the technical challenges, there are sizable policy, organizational, financial, and
technological challenges that must be addressed to facilitate the adoption of EHR systems
(Overhage et al., 2002). Some attempts to introduce order entry systems and other components of
an EHR system have been unsuccessful (Auber and Hamel, 2001; Ornstein, 2003). Also,
currently available personal health records, which allow patients to enter their own information,
have demonstrated limited functionality to date (Kim and Johnson, 2002).
Government health care programs, along with various private-sector stakeholders, are
considering options for encouraging the implementation of EHR systems by providers. To
achieve widespread implementation, some external funding or incentive programs will be
necessary (Institute of Medicine, 2001, 2002a). For example, the Centers for Medicare and
Medicaid Services might provide some form of financial reward to providers participating in the
Medicare program that have deployed EHR systems. On the private-sector side, various
insurers, purchasers, and employer groups are instituting quality incentive programs for specific
EHR system functionalities, such as computerized provider order entry for prescription drugs
and electronic reporting of performance measures (National Health Care Purchasing Institute,
2003). In addition, a number of employers, health plans, and physicians have recently formed a
coalition called Bridges to Excellence, which will provide financial bonuses to providers to
encourage improved patient care management systems, including EHR systems (Bridges to
Excellence, 2003). Another option is to provide grant funding or access to “low-cost” capital to
enable providers, especially those with a safety net role, to invest in acquiring EHR systems
(Health Technology Center and Manatt, Phelps and Phillips, LLP, 2003). Certain regulatory
strategies might also be pursued, such as requiring providers to have an EHR system as a
condition of participation in Medicare (Department of Health and Human Services, 2003).
To implement any of the above strategies, one must first clearly define a functional model of
key capabilities for an EHR system. There have been many different views of what constitutes
an EHR system. Some EHR systems include virtually all patient data, while others are limited to
certain types of data, such as medications and ancillary results. Some EHR systems provide
decision support (e.g., preventive service reminders, alerts concerning possible drug interactions,
clinical guideline-driven prompts), while others do not. Most current EHR systems are
enterprise-specific (e.g., operate within a specific health system or multi-hospital organization),
and only a few provide strong support for communication and interconnectivity across the
providers in a community. The functionality of EHR systems also varies across multiple
settings—from the perspective of both what is available from vendors and what has actually been
implemented. Some EHR systems have been developed locally and others by commercial
vendors. In summary, EHR systems are actively under development and will remain so for many
years.
A “functional model” of an EHR system will assist providers in acquiring and vendors in
developing software. For most providers, the migration to an electronic environment will take
place over a period of years. The development of a common set of requirements for the
functional capabilities of various EHR system software components would allow providers to
compare and contrast the systems that are available, and enable vendors to build systems more in
line with providers’ expectations. To be most useful, a functional model of an EHR system must
Copyright © National Academy of Sciences. All rights reserved.
Key Capabilities of an Electronic Health Record System: Letter Report
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Electronic Health Record Functional Model: Letter Report
also reflect a balance between what is desirable and what can feasibly be implemented
immediately or within a short time frame. It will be important to update the functional model
from time-to-time to reflect advancements in health care technology and care delivery.
PROJECT OVERVIEW
In response to the request from DHHS in May 2003, the charge to the IOM Committee on
Data Standards for Patient Safety was expanded as follows:
Provide guidance to DHHS on a set of “basic functionalities” that an electronic
health record system should possess to promote patient safety. The IOM
committee will consider functions, such as the types of data that should be
available to providers when making clinical decisions (e.g., diagnoses, allergies,
laboratory results); and the types of decision-support capabilities that should be
present (e.g., the capability to alert providers to potential drug-drug interactions).
The IOM Committee was asked to focus on care delivery functions, and did not address
infrastructure functions, such as database management and the use of health care data standards
(e.g., terminology, messaging standards, network protocols). Although not within the scope of
this project, the IOM Committee would like to emphasize the importance of two infrastructure
functions—privacy and security (e.g., access control, encryption). It is absolutely critical that an
EHR system be capable of safeguarding privacy and security.
DHHS requested a rapid response because of its desire to implement various programs in
2004 that would benefit from the availability of a functional model for an EHR system.
Specifically, the Center for Medicare and Medicaid Services (CMS) is considering offering
financial and other incentives to providers to encourage the deployment of EHR systems. The
Agency for Healthcare Research and Quality is implementing an applied research program that
will provide funding for the implementation and evaluation of innovative IT-related programs.
The federal government is also working collaboratively with private sector stakeholders to
facilitate the development of a national health information infrastructure (Department of Health
and Human Services, 2003).
In addition, the IOM work is the first step of a two-step process. IOM is being asked to
identify core care delivery–related functionalities of an EHR system. Health Level Seven (HL7),
a leading standards-setting organization working on the development of an EHR functional
model, will incorporate these core functionalities into the model, and further specify each
functionality along three dimensions: (1) develop a functional statement or definition (what),
(2) establish a rationale for the functionality (why included), and (3) establish a compliance
metric or test (Dickinson et al., 2003).
Because of the quick turnaround required, the IOM Committee convened a small working
group that met at the National Academies’ Jonsson Conference Center in Woods Hole,
Massachusetts, on June 7–8, 2003. The work of this group served as a starting point for
discussions of the full IOM Committee at its June 9–10, 2003, meeting.
FRAMEWORK FOR IDENTIFYING CORE EHR FUNCTIONALITIES
In recent years, several IOM reports have recommended that the U.S. health care system
make a commitment to the development of a health information infrastructure by the year 2010
Copyright © National Academy of Sciences. All rights reserved.
Key Capabilities of an Electronic Health Record System: Letter Report
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Electronic Health Record Functional Model: Letter Report
5
(Institute of Medicine, 2001, 2002a, 2002c). This IOM Committee concurs with those
recommendations.
It is recognized that the EHR system will be built incrementally utilizing clinical information
systems and decision support tools as building blocks of the EHR, and the IOM Committee has
strived to identify reasonable steps that can be taken by health care providers over the next 7
years to advance the accomplishment of this overall goal. It will be important for the Agency for
Healthcare Research and Quality and others to pursue a robust research agenda if the EHR
system is to reach full maturity in the years ahead.
Key EHR functionalities have been identified for four settings—hospital, ambulatory care,
nursing home, and care in the community (i.e., the personal health record). Additional settings
will need to be addressed in the future, such as home health agencies, pharmacies, and dental
care.
In considering the core functionalities of EHR systems, it is important to recognize their
many potential uses (see Box 1). EHR systems must support the delivery of personal health care
services, including care delivery (e.g., care processes), care management, care support processes,
and administrative processes (e.g., billing and reimbursement). As individuals engage more
actively in management of their own health, they too become important users of electronic health
information. There are also important secondary uses, including education, regulation (e.g.,
credentialing), clinical and health services research, public health and homeland security, and
policy support. There are both individual users (e.g., patients, clinicians, managers) and
institutional users (e.g., hospitals, public health departments, accreditation organizations,
educators, and research entities).
Box 1. Primary and Secondary Uses of an Electronic Health Record System
Primary Uses
• Patient Care Delivery
• Patient Care Management
• Patient Care Support Processes
• Financial and Other Administrative Processes
• Patient Self-Management
Secondary Uses
• Education
• Regulation
• Research
• Public Health and Homeland Security
• Policy Support
SOURCE: Adapted from Institute of Medicine (1997).
To guide the process of identifying core EHR system functionalities, the IOM Committee
formulated five criteria, which are listed below. Although each functionality independently may
not fulfill all five criteria, when taken together as part of an EHR system, the core functionalities
should address all criteria.
•
•
Improve patient safety. Safety is the prevention of harm to patients. Each year in the
United States, tens of thousands of people die as a result of preventable adverse events
due to health care (Institute of Medicine, 2000).
Support the delivery of effective patient care. Effectiveness is providing services based
on scientific knowledge to those who could benefit and at the same time refraining from
providing services to those not likely to benefit (Institute of Medicine, 2001). Only about
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Electronic Health Record Functional Model: Letter Report
•
•
•
one-half (55 percent) of Americans receive recommended medical care that is consistent
with evidence-based practice guidelines (McGlynn et al., 2003).
Facilitate management of chronic conditions. Chronic conditions are now the leading
cause of illness, disability, and death in the United States (Hoffman et al., 1996). Persons
with chronic conditions account for over 75 percent of all health care spending, and more
than half of that spending is on behalf of people with multiple such conditions
(Partnership for Solutions, 2002; U.S. Department of Health and Human Services, 2002).
More than half of those with chronic conditions have three or more different providers
and report that they often receive conflicting information from those providers; moreover,
many undergo duplicate tests and procedures, but still do not receive recommended care
(Leatherman and McCarthy, 2002; Partnership for Solutions, 2002). Physicians also
report difficulty in coordinating care for their patients with chronic conditions, and
believe that this lack of coordination produces poor outcomes (Partnership for Solutions,
2002).
Improve efficiency. Efficiency is the avoidance of waste, in particular, waste of
equipment, supplies, ideas, and energy (Institute of Medicine, 2001). Methods must be
found to enhance the efficiency of health care professionals and reduce the administrative
and labor costs associated with health care delivery and financing. Staffing shortages
have developed in multiple health care professions, placing added pressure on providers
to continually improve care processes with current staffing levels (AHA Commission on
Workforce for Hospitals and Health Systems, 2002). The cost of private health insurance
is increasing at an annual rate of greater than 12 percent, while individuals are paying
more out of pocket and receiving fewer benefits (Edwards et al., 2002; Kaiser Family
Foundation and Health Research and Educational Trust, 2002). And rising health care
costs will likely contribute to growing numbers of uninsured, who currently total over 41
million, or 1 in 7 Americans (U.S. Census Bureau, 2002). Addressing these issues
represents a major challenge.
Feasibility of implementation. The IOM Committee considered this criterion in
determining the time frames within which it is reasonable to expect providers’ EHR
systems will be capable of demonstrating the key functionalities. The timing of this
study did not allow for a thorough evaluation of feasibility, so the IOM Committee had to
rely on its collective knowledge of the field. In assessing feasibility, the IOM Committee
considered whether software is currently available or under development; the time period
necessary for vendors to develop, produce, and market new software to achieve certain
functionalities; and the willingness of users to purchase and implement such systems. It
would be advisable to reassess periodically the feasibility of implementing certain EHR
functionalities and modify expectations regarding timing, as appropriate.
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Electronic Health Record Functional Model: Letter Report
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CORE EHR FUNCTIONALITIES
The IOM Committee identified core functionalities falling into eight categories (see Box 2).
Box 2. Core Functionalities for an Electronic Health Record System
•
Health information and data
•
Patient support
•
Results management
•
Administrative processes
•
Order entry/management
•
Reporting & population health
•
Decision support
•
Electronic communication and
management
connectivity
Health Information and Data
Although not truly a functionality attribute per se, in order to achieve the objectives set forth
for an EHR system, it must contain certain data about patients. Physicians and other care
providers require certain information to make sound clinical decisions; however, their
information needs are often not met (Bates et al., 2003; Covell et al., 1985; McKnight et al.,
2001; Tang et al., 1994). This lack of information can lead to lesser-quality and inefficient care.
As noted, for example, the capability to display previous laboratory test results can
significantly reduce the number of redundant tests ordered, not only saving money, but also
preventing the patient from undergoing unnecessary tests (Bates et al., 1999; Stair, 1998; Tierney
et al., 1987). Also as noted earlier, information on patient allergies and other medications, in
combination with alerts and reminders, can decrease the number of medication-related adverse
events and improve the prescribing practices of physicians and nurse practitioners (Bates et al.,
1999; Kuperman et al., 2001; McDonald, 1976; Teich et al., 2000). In addition, urgent matters,
such as abnormal test results, can be addressed on a more timely basis if the physician has the
information at the point of care (Bates et al., 2003). EHR systems with a defined dataset that
includes such items as, medical and nursing diagnoses, a medication list, allergies,
demographics, clinical narratives, and laboratory test results, can therefore ensure improved
access to at least some types of information needed by care providers when they need it.
It is also important to note that too much information and data may overwhelm or distract the
end user, so EHR systems must have well designed interfaces. The health information and data
captured by an EHR system must also evolve over time, as new knowledge becomes available,
both clinical knowledge and knowledge regarding the information needs of different users.
Results Management
Managing results of all types (e.g., laboratory test results, radiology procedure results
reports) electronically has several distinct advantages over paper-based reporting in terms of
improved quality of care. Computerized results can be accessed more easily by the provider at
the time and place they are needed; the reduced lag time increases both efficiency and patient
Copyright © National Academy of Sciences. All rights reserved.
Key Capabilities of an Electronic Health Record System: Letter Report
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Electronic Health Record Functional Model: Letter Report
safety by allowing for quicker recognition and treatment of medical problems (Bates et al.,
2003). Additionally, the automated display of previous test results makes it possible to reduce
redundant and additional testing, thus not only improving efficiency of treatment, but also
decreasing costs (Bates et al., 2003; Shea et al., 2002; Tierney et al., 1987). Having electronic
results can allow for better interpretation and for easier detection of abnormalities, thereby
ensuring appropriate follow-up (Bates et al., 2003; Overhage et al., 2001; Schiff et al., 2003).
Finally, access to electronic consults and patient consents can establish critical linkages and
improve care coordination among multiple providers, as well as between provider and patient
(Bates et al., 2003).
Order Entry/Order Management
The benefits of computerized provider order entry (CPOE) have been well documented
(Bates and Gawande, 2003; Bates et al., 1998, 1999; Butler and Bender, 1999; Kuperman and
Gibson, 2003; Kuperman et al., 2001; Mekhjian et al., 2002; Schiff and Rucker, 1998; Sittig and
Stead, 1994; Teich et al., 2000; Tierney et al., 1993). Even with little or no decision support
capabilities, such systems can improve workflow processes by eliminating lost orders and
ambiguities caused by illegible handwriting, generating related orders automatically, monitoring
for duplicate orders, and reducing the time to fill orders (Lepage et al., 1992; Mekhjian et al.,
2002; Sittig and Stead, 1994). The use of computerized order entry, in conjunction with an
electronic health record, is also beginning to demonstrate a positive effect on clinician
productivity (Overhage et al., In press).
The strongest evidence of the clinical effectiveness of CPOE is seen in medication order
entry. Relatively simple systems have been shown to reduce the number of non-intercepted
medication errors by up to 83 percent by using “forcing functions” for medication dose and
frequency (Bates and Gawande, 2003), displaying relevant laboratories, and checking for drug–
allergy and drug–drug interactions. CPOE is expected to offer similar benefits for laboratory,
microbiology, pathology, radiology, nursing, and supply orders, as well as for ancillary services
and consults (Butler and Bender, 1999; Sanders and Miller, 2001; Schiff et al., 2003; Schuster et
al., 2003; Teich et al., 1992; Wang et al., 2002). Financial benefits—such as reducing the
amount of money spent on preprinted forms, assuring that prescribing practices are consistent
with a facility’s established formulary, and informing physicians and other providers about costsaving options and duplicate test orders—have also been demonstrated (Butler and Bender,
1999; Mekhjian et al., 2002; Sittig and Stead, 1994).
Decision Support
Computerized decision support systems have demonstrated their effectiveness in enhancing
clinical performance for many aspects of health care, including prevention, prescribing of drugs,
diagnosis and management, and detection of adverse events and disease outbreaks (Bates and
Gawande, 2003; Hunt et al., 1998; Johnston et al., 1994; Tang et al., 1999b). In two metaanalyses, computer reminders and prompts were shown to significantly improve preventive
practices in such areas as vaccinations, breast cancer screening, colorectal screening, and
cardiovascular risk reduction (Balas et al., 2000; Shea et al., 1996). Several studies have also
been conducted on the use of computerized decision support to improve drug dosing, drug
selection, and screening for drug interactions; these studies have shown overall positive effects
Copyright © National Academy of Sciences. All rights reserved.
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on the quality of patient care (Abookire et al., 2000; Evans et al., 1998; Hunt et al., 1998; Schiff
and Rucker, 1998). A study comparing clinical decisions made by physicians in the same
practice using an EHR system and traditional paper records found that the former group made
more appropriate clinical decisions as a result of all the tools available in an EHR system,
including decision support (Tang et al., 1999a).
There is also a small but growing evidence base for the effectiveness of such systems in the
area of computer-assisted diagnosis and disease treatment and management. In 1992, an expert
diagnostic system demonstrated the ability to detect more serious quality problems arising from
diagnostic errors than those detected by a state-based peer review organization, suggesting that
computerized tools may help prevent such diagnostic misadventures (Lee and Warner, 1992). A
1999 study comparing the performance of clinicians with and without the aid of a diagnostic
computerized decision support system found a significant improvement in the generation of
correct diagnoses when the system was used (Friedman et al., 1999). Two additional recent
studies have revealed that decision support tools could improve clinician compliance with
established evidence-based guidelines and protocols (Morris, 2003; Starmer et al., 2000). Other
studies on the use of decision support tools have not found improvements, however (Eccles et al.,
2002; Rollman et al., 2002).
More sophisticated tools, such as artificial neural networks, have also demonstrated their
effectiveness in detecting acute myocardial infarction, breast cancer, and cervical cancer (Bates
and Gawande, 2003; Heden et al., 1997; Kok and Boon, 1996; Petrick et al., 2002). In addition,
computerized tools can be used to identify and track the frequency of adverse events (Bates et
al., 2001; Classen et al., 1991; Honigman et al., 2001) and hospital-acquired infections (Evans et
al., 1986), as well as disease outbreaks and bioterrorism events (Pavlin, 2003; Tsui et al., 2003).
Electronic Communication and Connectivity
Effective communication—among health care team members and other care partners (e.g.,
laboratory, radiology, pharmacy) and with patients—is critical to the provision of quality health
care. Its lack can contribute to the occurrence of adverse events (Bates and Gawande, 2003;
Petersen et al., 1994; Schmidt and Svarstad, 2002; Wanlass et al., 1992). Improved
communication among care partners, such as laboratory, pharmacy, and radiology, can enhance
patient safety and quality of care (Schiff et al., 2003), and improve public health surveillance
(Schiff and Rucker, 1998; Wagner et al., 2001). Electronic connectivity is essential in creating
and populating EHR systems, especially for those patients with chronic conditions, who
characteristically have multiple providers in multiple settings that must coordinate care plans
(Wagner, 2000; Wagner et al., 1996). While communication interfaces are becoming well
established for administrative data exchange, there are very few such interfaces for the exchange
of clinical data.
Electronic communication tools, such as e-mail and web messaging, have been shown to be
effective in facilitating communication both among providers and with patients, thus allowing
for greater continuity of care (Balas et al., 1997; Liederman and Morefield, 2003; Worth and
Patrick, 1997) and more timely interventions (Kuebler and Bruera, 2000). One recent study
found that automatic alerts to providers regarding abnormal laboratory results reduced the time
until an appropriate treatment was ordered (Kuperman et al., 1999). Another important
communication tool is an integrated health record, both within a setting and across settings and
institutions. Such a record allows for improved access to patient data at the point where clinical
Copyright © National Academy of Sciences. All rights reserved.
Key Capabilities of an Electronic Health Record System: Letter Report
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Electronic Health Record Functional Model: Letter Report
decisions are made (Institute of Medicine, 1997). In addition, telemedicine has demonstrated
effectiveness in certain settings, including pulmonary clinics and intensive care units (Pacht et
al., 1998; Rosenfeld et al., 2000; Shafazand et al., 2000); home telemonitoring has been shown to
be successful as well (Finkelstein et al., 2000; Johnston et al., 2000; Rogers et al., 2001; Shea et
al., 2002; Whitlock et al., 2000).
Patient Support
Patient education has demonstrated significant effectiveness in improving control of chronic
illnesses (Weingarten et al., 2002). Computer-based patient education in particular has been
found to be successful in primary care (Balas et al., 1996). In a 1997 study of 22 clinical trials,
interactive educational interventions showed positive results for several major clinical
applications, the most frequently targeted of these being diabetes (Krishna et al., 1997).
Additionally, as noted earlier, several studies have demonstrated the feasibility of home
monitoring by patients (Finkelstein et al., 2000; Johnston et al., 2000; Rogers et al., 2001;
Whitlock et al., 2000). In a recent study, for instance, spirometry self-testing by asthma patients
during home telemonitoring was found to provide valid results comparable to those of tests
collected under the supervision of a clinician (Finkelstein et al., 2000). A multidimensional
telehealth system has also demonstrated the ability to decrease stress for some caregivers of
patients with Alzheimer’s disease (Bass et al., 1998).
Administrative Processes
Electronic scheduling systems for hospital admissions, inpatient and outpatient procedures,
and visits not only increase the efficiency of heath care organizations, but also provide better,
more timely service to patients (Everett, 2002; Hancock and Walter, 1986; Woods, 2001). Use
of communication and content standards is equally important in the billing and claims
management area—close coupling of authorization and prior approvals can, in some cases,
eliminate delays and confusion. Additionally, immediate validation of insurance eligibility
should add value for both providers and patients through improved access to services, more
timely payments and less paperwork.
Moreover, computerized decision support tools are being used in a variety of settings to
identify eligible or potentially eligible patients for clinical trials (Breitfeld et al., 1999; Carlson et
al., 1995; Ohno-Machado et al., 1999; Papaconstantinou et al., 1998). Other effective electronic
administrative tools include reporting tools that support drug recalls (Schiff and Rucker, 1998)
and artificial neural networks that can assist in identifying candidates for chronic disease
management programs (Heden et al., 1997; Kok and Boon, 1996; Petrick et al., 2002).
Reporting and Population Health Management
Institutions currently have multiple public and private sector reporting requirements at the
federal, state, and local levels for patient safety and quality, as well as for public health. In
addition, the internal quality improvement efforts of many health care organizations include
routine reporting of key quality indicators (sometimes referred to as clinical dashboards) to
clinicians. Most of the data for these reports must be abstracted from claims data, paper records,
and surveys, a process that is labor-intensive and time-consuming, and usually occurs
Copyright © National Academy of Sciences. All rights reserved.
Key Capabilities of an Electronic Health Record System: Letter Report
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11
retrospectively. Thus such reporting is often limited to entities that have sufficient
administrative infrastructure to develop the necessary data (Institute of Medicine, 2002c).
Additionally, chart abstraction has been shown to involve a number of significant errors (Green
and Wintfeld, 1993). Having clinical data represented with a standardized terminology and in a
machine-readable format would reduce the significant data collection burden at the provider
level, as well as the associated costs, and would likely increase the accuracy of the data reported.
CORE FUNCTIONAL REQUIREMENTS
When identifying the core functional requirements for an EHR system, the IOM Committee
was asked to consider both the care setting of each function and the time frame for its
introduction. Table 1 at the end of this report lists the eight key EHR system capabilities
described above, broken down at a more detailed level, according to these two dimensions. The
committee was asked to provide guidance pertaining to four care settings: (1) hospitals; (2)
ambulatory care settings, including small practice settings, community health centers, and group
practices; (3) nursing homes; and (4) care in the community.
In addressing the fourth setting, care in the community, the IOM Committee focused on
functional requirements for the personal health record (PHR), defined to include (1) a subset of
data from the individual’s EHR, and (2) information recorded by the individual, including health
maintenance and monitoring data. A PHR may be used in a number of ways by the patient to
support their care, disease management, and clinical communication. (Markle Foundation,
2003a). As computer-based PHRs become part of the EHR system, being able to access patients’
own narratives of their illnesses will become a valuable source of information for improving care
through comparisons with the clinicians’ records.
Assuming that the migration from paper records to a comprehensive EHR system will take 7
or more years for most providers, the IOM Committee strived to identify functional requirements
for three time periods:
•
•
•
In the immediate future (2004–2005), it is assumed that providers (i.e., ambulatory care
settings, hospitals, and nursing homes) will focus on (1) the capture of essential patient
data already found frequently in electronic form, such as laboratory and radiology results;
(2) the acquisition of limited decision support capabilities for which software is readily
available in the marketplace (e.g., order entry, electronic prescribing); and (3) the
generation of reports required by external organizations for quality and safety oversight
and public health reporting.
In the near term (2006–2007), providers’ EHR systems should (1) allow for the capture
of defined sets of health information, (2) incorporate a core set of decision support
functions (e.g., clinical guideline support, care plan implementation), and (3) support the
exchange of basic patient care data and communication (e.g., laboratory results,
medication data, discharge summaries) among the care settings (e.g. pharmacies,
hospitals, nursing homes, home health agencies, etc.) within a community.
In the longer term (2008–2010), the committee believes that fully functional,
comprehensive EHR systems will be available and implemented by some health systems
and regions. It may take considerably longer, however, for all providers to be using a
comprehensive EHR system that provides for the longitudinal collection of complete
Copyright © National Academy of Sciences. All rights reserved.
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Electronic Health Record Functional Model: Letter Report
health information for an individual; immediate access to patient information by all
authorized users within a secure environment; extensive use of knowledge support and
decision support systems; and extensive support for applications that fall outside
immediate patient care (e.g., homeland security, public health, clinical research).
In identifying core functionalities for specific provider settings, the IOM Committee also
considered the current level of information technology capabilities within a sector. Specifically,
the IOM Committee assumed that the migration pathway for hospitals would be more rapid than
that for nursing homes, recognizing that many hospitals have some EHR system capabilities
already in place while most nursing homes do not, and that hospitals generally have greater
access to technical expertise. The migration can also be expected to take longer for physicians’
offices than for hospitals, given the differences between the two in financial resources available
for IT investments. The IOM Committee set these targets within the context of the current
momentum it is observing in the public and private sectors. A loss of momentum would
adversely affect these estimates. It is recognized that not every provider will meet the functional
requirements by the times indicated. The functional requirements are intended to be challenging
but achievable for a sizable proportion of the health care sector.
CONCLUSION
The IOM Committee is pleased to have had the opportunity to provide guidance on this
important issue. The committee hopes its work will be useful to HL7 in its efforts to develop
functional statements for an EHR system; to government programs and private purchasers in
their efforts to encourage and assist health care providers in deploying EHR systems; to
providers and vendors as they strive to acquire and build software products that form part of the
foundation for a comprehensive health information infrastructure; and to patients as they seek to
participate more fully in decisions regarding their own care.
Paul C. Tang, Chair
Committee on Data Standards for Patient Safety
Cc: Ann Marie Lynch, Acting Assistant Secretary for Planning and Evaluation (ASPE),
Department of Health and Human Services
Thomas A. Scully, Administrator, Centers for Medicare and Medicaid Services, Department
of Health and Human Services
Gary Christopherson, Senior Advisor for the Undersecretary for Health, Department of
Veterans Affairs
Copyright © National Academy of Sciences. All rights reserved.
Copyright © National Academy of Sciences. All rights reserved.
NA
NA
X
X
X
X
X
X
X
X
X
X
X
2004–5
Note: NA=not applicable
Minimum dataset (MDS) for
nursing homes
– Defined MDS for nursing
homes
– Expanded/refined MDS
1. Health Information and Data
Key data (using standardized
code sets where available)
– Problem list
– Procedures
– Diagnoses
– Medication list
– Allergies
– Demographics
– Diagnostic test results
– Radiology results
– Health maintenance
– Advance directives
– Disposition
– Level of service
Core Functionality
X
Hospitals
2006–7 2008–10
NA
NA
X
X
X
X
X
X
X
X
X
X
X
X
Ambulatory Care
2004–5 2006–7 2008–10
X
X
See Minimum Dataset Below
Nursing Homes
2004–5 2006–7 2008–10
Table 1 EHR System Capabilities by Time Frame and Site of Care
NA
NA
X
X
X
X
X
X
X
X
X
X
X
X
Care in the Community
(Personal Health Record)
2004–5 2006–7 2008–10
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13
X
X
X
X
Capture of identifiers
– People and roles
– Products/devices
– Places (including
directions)
X
X
X
X
NA
X
X
X
X
X
X
X
NA
NA
X
X
NA
X
X
X
X
NA
NA
X
X
X
X
X
X
X
X
X
Care in the Community
(Personal Health Record)
2004–5 2006–7 2008–10
X
X
Nursing Homes
2004–5 2006–7 2008–10
X
X
X
X
Patient acuity/severity of
illness/risk adjustment
– Nursing workload
– Severity adjustment
X
X
X
X
X
Ambulatory Care
2004–5 2006–7 2008–10
Hospitals
2004–5 2006–7 2008–10
1. Health Information and Data continued
Narrative (clinical and patient
narrative)
X
– Free text
X
– Template-based
– Deriving structure from
unstructured text
– Natural Language
X
Processing
– Structured and coded
X
– Signs and Symptoms
X
– Diagnoses
X
– Procedures
X
– Level of service
– Treatment plan
X
– Single discipline
X
– Interdisciplinary
Functionality
Table 1 continued
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Electronic Health Record Functional Model: Letter Report
Copyright © National Academy of Sciences. All rights reserved.
X
Multiple views of data /
Presentation
3. Order Entry/Management
Computerized provider order
entry
– Electronic prescribing
– Laboratory
– Microbiology
– Pathology
– XR
– Ancillary
– Nursing
– Supplies
– Consults
X
X
X
X
X
X
X
X
X
X
Results Notification
Multimedia support
– Images
– Waveforms
– Scanned documents
– Patient consents
– Pictures
– Sounds
X
X
X
X
2. Results Management
Results Reporting
– Laboratory
– Microbiology
– Pathology
– Radiology Reports
– Consults
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Copyright © National Academy of Sciences. All rights reserved.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
NA
NA
NA
NA
NA
NA
NA
NA
NA
X
X
X
X
X
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X
X
X
NA
Reminders
– Preventive services
Clinical guidelines and
pathways
– Passive
– Context-sensitive passive
– Integrated
Chronic disease management
X
X
X
X
X
Copyright © National Academy of Sciences. All rights reserved.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Nursing Homes
2004–5 2006–7 2008–10
X
X
X
X
Ambulatory Care
2004–5 2006–7 2008–10
X
Other rule-based alerts (e.g.,
significant lab trends, lab test
because of drug)
X
X
X
X
X
X
X
2004–5
Hospitals
2006–7 2008–10
Drug alerts
– Drug dose defaults
– Drug dose checking
– Allergy checking
– Drug interaction
checking
– Drug–lab checking
– Drug–condition checking
– Drug–diet checking
4. Decision Support
Access to knowledge sources
– Domain knowledge
– Patient education
Core Functionality
Table 1 continued
NA
X
X
NA
NA
NA
NA
NA
NA
NA
NA
X
X
X
X
Care in the Community
(Personal Health Record)
2004–5 2006–7 2008–10
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Electronic Health Record Functional Model: Letter Report
X
X
X
Copyright © National Academy of Sciences. All rights reserved.
Trading partners (external)
– Outside pharmacy
– Insurer
– Laboratory
– Radiology
Medical devices
Patient–provider
– E-mail
– Secure web messaging
Team coordination
X
X
X
X
X
X
X
X
5. Electronic Communication & Connectivity
Provider–provider
X
Automated real-time
surveillance
– Detect adverse events and
near misses
– Detect disease outbreaks
– Detect bioterrorism
X
Use of epidemiologic data
X
X
X
Diagnostic decision support
Incorporation of patient and/or
family preferences
Clinician work list
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
NA
NA
X
NA
X
X
X
X
X
X
X
X
X
X
X
X
X
X
NA
NA
X
X
NA
NA
NA
NA
NA
NA
NA
NA
X
X
Key Capabilities of an Electronic Health Record System: Letter Report
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17
Copyright © National Academy of Sciences. All rights reserved.
X
X
X
X
X
X
X
X
X
NA
NA
NA
X
X
X
X
X
X
X
X
X
X
Nursing Homes
2004–5 2006–7 2008–10
NA
X
NA
X
X
NA
X
X
X
X
X
X
X
Care in the Community
(Personal Health Record)
2004–5 2006–7 2008–10
Defined as the extent to which a single record integrates data from different settings, providers, and organizations (e.g., Primary Care Physician,
specialist, hospital).
1
X
NA
NA
X
Data entered by patient,
family, and/or informal
caregiver
– Home monitoring
– Questionnaires
X
X
X
Ambulatory Care
2004–5 2006–7 2008–10
X
X
X
Family and informal caregiver
education
7. Administrative Processes
Scheduling management
– Appointments
– Admissions
– Surgery/procedure
schedule
X
X
X
6. Patient Support
Patient education
– Access to patient
education materials
– Custom patient education
– Tracking
Hospitals
2004–5 2006–7 2008–10
5. Electronic Communication & Connectivity continued
Integrated medical record 1
X
– Within setting
– Cross-setting
X
– Inpatient–outpatient
X
– Other cross-setting
X
– Cross-organizational
Core Functionality
Table 1 continued
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Electronic Health Record Functional Model: Letter Report
Disease registries
Deidentifying data
X
X
X
X
X
X
X
X
NA
NA
NA
X
X
Public health reporting
– Reportable diseases
– Immunization
X
X
NA
NA
X
X
X
X
NA
NA
X
NA
X
X
X
X
X
NA
NA
X
X
X
X
X
X
X
8. Reporting and Population Health Management
Patient safety and quality
reporting
X
– Clinical dashboards
– External accountability
X
reporting
X
– Ad hoc reporting
Eligibility determination
– Insurance eligibility
– Clinical trial recruitment
– Drug recall
– Chronic disease
management
X
X
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Copyright © National Academy of Sciences. All rights reserved.
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Appendix A
Committee and Staff
COMMITTEE ON DATA STANDARDS FOR PATIENT SAFETY
PAUL C. TANG (Chair), Chief Medical Information Officer, Palo Alto Medical Foundation
MOLLY JOEL COYE (Vice Chair), Chief Executive Officer, Health Technology Center
SUZANNE BAKKEN, Alumni Professor of Nursing and Professor of Biomedical
Informatics, Columbia University
E. ANDREW BALAS, Dean, School of Public Health, Saint Louis University
DAVID W. BATES, Chief, Division of General Medicine, Brigham and Women’s Hospital
JOHN R. CLARKE, Professor of Surgery, Drexel University
DAVID C. CLASSEN, Associate Professor of Medicine, Vice President, University of Utah,
First Consulting Group
SIMON P. COHN, National Director of Health Information Policy, Kaiser Permanente
CAROL CRONIN, Consultant
JONATHAN S. EINBINDER, Assistant Professor, Harvard Medical School and Corporate
Manager, Partners Health Care Information Systems
LARRY D. GRANDIA, Chief Technology Officer, Executive Vice President, Premier, Inc.
W. ED HAMMOND, Professor, Division of Medical Informatics, Duke University
BRENT C. JAMES, Executive Director, Intermountain Health Care Institute for Health
Care Delivery Research, and Vice President for Medical Research, Intermountain Health Care
KEVIN JOHNSON, Associate Professor and Vice Chair, Department of Biomedical
Informatics and Associate Professor, Department of Pediatrics, Vanderbilt University
JILL ROSENTHAL, Program Manager, National Academy for State Health Policy
TJERK W. van der SCHAAF, Associate Professor of Human Factors in Risk Control,
Eindhoven University of Technology, Eindhoven Safety Management Group, Department of
Technology Management
Special Consultant
J. MARC OVERHAGE, Associate Professor of Medicine and Investigator, Regenstrief
Institute for Health Care, Indiana University School of Medicine
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Electronic Health Record Functional Model: Letter Report
Study Staff
JANET M. CORRIGAN, Director, Board on Health Care Services
PHILIP ASPDEN, Study Director
JULIE WOLCOTT, Program Officer
SHARI ERICKSON, Research Associate
REBECCA LOEFFLER, Senior Project Assistant
ANTHONY BURTON, Administrative Assistant
The committee wishes to thank the co-chairs of the Health Level Seven (HL7) Special
Interest Group (SIG), Linda Fischetti (U.S. Department of Veterans Affairs), Gary L.
Dickinson (Misys Healthcare), and Sam Herd (Ocean Informatics, Australia), for the briefing
and background materials they provided to the committee at its June 2003 meeting. The
committee would also like to thank Gary Christopherson of the U.S. Department of Veterans
Affairs, William C. Rollow of the Centers for Medicare and Medicaid Services, and Scott
Young of the Agency for Healthcare Research and Quality for their helpful contributions to the
report.
Copyright © National Academy of Sciences. All rights reserved.
Key Capabilities of an Electronic Health Record System: Letter Report
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Reviewers
31
Appendix B
Reviewers
This report has been reviewed in draft form by individuals chosen for their diverse
perspectives and technical expertise, in accordance with procedures approved by the NRC’s
Report Review Committee. The purpose of this independent review is to provide candid and
critical comments that will assist the institution in making its published report as sound as
possible and to ensure that the report meets institutional standards for objectivity, evidence, and
responsiveness to the study charge. The review comments and draft manuscript remain
confidential to protect the integrity of the deliberative process. We wish to thank the following
individuals for their review of this report:
REED M. GARDNER, Professor, Medical Informatics, University of Utah
BLACKFORD MIDDLETON, Director of Clinical Informatics Research and
Development, Partners Healthcare System, Inc., Brigham and Women’s Hospital
DAVID N. MOHR, Professor of Medicine, Area Medicine, Mayo Clinic
JUDITH J. WARREN, Associate Professor, School of Nursing, University of Kansas
Although the reviewers listed above have provided many constructive comments and
suggestions, they did not see the final draft of the report before its release. The review of this
report was overseen by Don E. Detmer, Dennis Gillings Professor of Health Management, The
Judge Institute of Management Studies, University of Cambridge, and Professor Emeritus,
Professor of Medical Education, University of Virginia, appointed by the National Research
Council and Institute of Medicine, he was responsible for making certain that an independent
examination of this report was carried out in accordance with institutional procedures and that all
review comments were carefully considered. Responsibility for the final content of this report
rests entirely with the authoring committee and the institution.
Copyright © National Academy of Sciences. All rights reserved.

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