REQUEST FOR INFORMATION (RFI)
ISSUED BY THE U.S. ARMY MEDICAL RESEARCH ACQUISITION ACTIVITY,
ON BEHALF OF THE
JOINT TECHNOLOGY COORDINATING GROUP ONE (JTCG1)
ADVANCED HEALTH INFORMATION TECHNOLOGIES
W81XWH-Advanced Health Information Technologies
The Defense Medical Research and Development Program (DMRDP) is being initiated in Fiscal Year 2010
(FY10) to augment the related medical research and development programs of the Army, Navy, Air Force, and
Defense Advanced Research Projects Agency. The program is intended to discover and explore innovative
approaches to protect, support, and advance the health and welfare of military personnel; to accelerate the
transition of medical technologies into deployed products; and to accelerate the translation of advances in
knowledge into new standards of care for injury prevention and treatment of casualties that can be applied in the
field and clinic. It includes advanced medical health information technologies and medical simulation and
training technologies and systems.
The Joint Technical Coordinating Area has been established to address DoD-identified needs in medical
information and training technologies. These information technologies focus on the prototyping of emerging
and enabling healthcare information technologies which have the potential to improve healthcare access,
availability, accessibility, continuity, cost-effectiveness, and quality.
Emerging healthcare information technologies arise out of basic computer science and information technologies
development and research. They have the potential for transforming healthcare, but are not yet commercialized.
Enabling healthcare information technologies are those emerging technologies which have been
commercialized, and which have started to be used in the marketplace; but have not yet reached their full
potential for transforming healthcare delivery. Both emerging and enabling healthcare information technologies
may be disruptive to healthcare delivery and may result in a need for clinical and business process
reengineering. Some of these technologies will attain user acceptance and achieve success in improving
healthcare; others may disappear and be replaced by yet other emerging and enabling technologies. Some of
these technologies may reach their maximum productivity within a few years; others may take ten years or more
to mature. The cycles between the emergence and disappearance of technologies within the same class can be
The conversion of data into wisdom is often described as following an evolutionary path that transforms data
into information, information into knowledge, knowledge into understanding, and understanding into wisdom.
The final step is to transfer the wisdom into action that will make a positive difference in patient outcomes.
Technologies are proliferating that can attain this movement from data to action; however these technologies are
largely unproven in healthcare. The government anticipates that application of emerging and enabling
technologies will help facilitate the transformation of data to wisdom to action in the healthcare space.
Ultimately, this will benefit our healthcare providers and the patient population they serve by delivering
evidence-based medicine across all spectrums of care and by promoting improved public health.
In order to assist the government in identifying needs, the government is conducting market research on
emerging and enabling healthcare information technologies in seven (7) critical areas:
1. Acquisition-Access Refers to projects that can either assist with combining large disparate data to
allow access to actionable information, or projects that capture computable data
in new ways. Examples include new human-computer interfaces that can
facilitate novel data entry, such as speech recognition, natural language
processing, and electronic paper and digital pens.
2. Analysis-Execution Refers to projects that analyze or use novel tools to allow analysis of large
amount of information to find patterns or relationships among data sets. These
technologies may also predict certain events or conditions or analyze data to
optimize scheduling or resource allocation. It also refers to technologies that
allow creation, storage, management, and execution of clinical guidelines and
rules to assist decision making (e.g. clinical decision support)
3. Interoperability-Standards Refers to projects that help promote interoperability between systems
(particularly semantic interoperability and computability) or help define,
harmonize, and implement recognized health information technology standards.
4. Distribution-Portability Refers to projects that distribute information or make information portable (both
software and hardware) across all spectrums of care, and across the life of an
5. Representation-Visualization Refers to projects that develop novel ways to represent or visualize data or
processes. Examples include novel graphical user interfaces, new 3D graphical
technologies, and virtual world technologies. Examples include a technology
that may improve how providers can abstract increasing amounts of data,
display data based on a problem orientation or any novel methods that will
increase decision making or improve cognitive performance to allow a more
holistic and systematic management of patient. These technologies can also
provide a way to integrate and provide instant access to a comprehensive view
of each patient that include images, scanned documents, proteomic/ genomic,
and other clinical observational data.
6. Archive-Retrieval Refers to projects that demonstrate methods of archiving data and/or retrieving
data in novel more efficient ways. It should handle large sets of data distributed
over large distances. (May include data warehousing, data modeling,
extraction, transformation and load [ETL] functions, etc.).
7. Systems Engineering/Program Refers to systems analysis, engineering and program management support
Support necessary to facilitate the prototype projects in (1) to (6) above, and to also
facilitate technology transfer to the Military Health System IM/IT Program
Executive Officers (who design, develop, test, and deploy systems of record),
and to support formal processes of Defense Acquisition Programs for
This Request for Information (RFI) seeks information about emerging and enabling healthcare information
technologies that can be applied in production or licensed in existing Department of Defense (DOD) programs
of record or commercialized for use to improve healthcare delivery in the private sector. The technologies
should address the need to improve both the clinical and business delivery of healthcare in the military or
address the transfer of information between the military, other government, and other civilian healthcare
institutions for the public good. This RFI also seeks applied research studies that can measure the effectiveness
of these applied technologies towards healthcare improvement, or otherwise measure the maturity of these
technologies and readiness for deployment.
This RFI also seeks information from commercial vendors, not-for-profit and for-profit healthcare delivery
organizations, universities, consulting firms, and healthcare trade associations or information societies, that have
capability and experience in assessing and applying emerging and enabling healthcare information technologies.
Information gained from these organizations may assist the government to develop and refine its requirements
and needs. The government will review this information pursuant to developing potential future Requests for
C. INFORMATION REQUESTED
In summary, this RFI is intended to help the government gather information to advance the maturity and/or
promote the development and understanding of emerging and enabling healthcare information technology
components. The intent is to use this information to determine the extent to which these technologies can be
integrated into existing government programs of record or commercialized for the public good. Products and
outcomes to be described should relate to healthcare information technology systems or related support systems.
Studies and/or methods to assess healthcare information technology training effectiveness and transfer of
healthcare information technology skills are also of interest.
The RFI requests information on product technologies or proposed studies that are consistent with the objectives
of this RFI and clearly address topics in one of the aforementioned seven (7) areas. A SUMMARY of these
seven (7) areas follows, to include representative functionalities, concepts of operation, use cases, and examples
of emerging and enabling technologies that the government is seeking. The government is amenable to
receiving information on other emerging and enabling technologies as long as a rationale case can be made with
respect to applying these technologies towards the improvement of healthcare delivery in the military,
government, or civilian health sectors. Improving healthcare delivery requires clinical, administrative, material
management (pharmacy, supplies, and equipment) information management capabilities.
1. EMERGING AND ENABLING HEALTHCARE INFORMATION TECHNOLOGIES WHICH MAY
IMPROVE ACQUISITION OR ACCESS OF DATA
Prototype technologies and conduct technical risk reduction activities associated with the following functional
areas of interest. Where applicable, demonstrate how these prototype technologies can be integrated into
government systems of record, or new systems already under development:
A. Prototypes that facilitate both structured and unstructured data collection and entry at the point of
care (be it in theater or garrison) for inclusion in the electronic health record and other healthcare
clinical and business systems. The government seeks methods by which data can be entered in a
manner acceptable to clinicians, nurses, ancillary personnel, and logisticians. The goal is to provide
data and information that has value for decision-making at the point of care, and also is fully-
computable for use retrospectively in analysis to potentially improve population healthcare,
pharmacovigilance, biosurveillance, and medical asset availability and planning. The government is
particularly interested in collecting demographic, clinical, and logistical data along the lifespan of an
individual in order to better promote and guarantee quality and continuity of care for its beneficiaries.
This continuum of care includes data collected on active duty personnel from their time of birth, through
accession and active duty military service, through transitioning into care received as a veteran or
military retiree, in VA, DOD, or other government and civilian healthcare facilities. Prototypes should
also address data collection for eligible active duty dependents and retired dependents and other eligible
B. Prototypes that can improve the collection of pre-hospital care data in theaters of operation are of
particular interest to the government (e.g. capturing optimal data during mass casualty scenario). This
data must be collected in a way that leverages or improves use of the Tactical Combat Casualty Card
(TC3) in the field and can subsequently be transferred in an intra-operable manner to Theater Data
Medical Stores (TMDS), DOD’s Electronic Health Record Clinical Data Repository (CDR), as well as
used in Service Trauma Registries, to include the Army’s Joint Theater Trauma Registry (under the
auspices of the Institute of Surgical Research), and the Navy Marine Corps Combat Trauma Registry
(under the auspices of the Naval Health Research Center).
C. Prototypes that can ease data entry in combat theater and garrison use. These technologies include,
but are not limited to, hands-free technologies such as speech recognition tools, which transform voice
to text, and natural language processing tools, which transform text to codes. These example
technologies are felt to be necessary to achieve acceptable computability to foster implementation of
clinical decision support and logistics engines. The government is particularly interested in how to
apply these technologies in austere surroundings to include noisy, multi-voice, and foreign language
environments. Furthermore, the government is interested in the use of radio frequency identification for
hands free management of medical material, equipment, and drugs.
D. Prototypes that can collect and store data in the austere theater environment, which may have little
or no Internet connectivity. Such technologies might include USB or other based electronic information
cards with high storage capacities, and ultra-wide or other broadband communications. These devices
would utilize available electronic communication channels to facilitate the transfer of information
throughout the echelons of care, but also be able to work in a disconnected environment through
retrospective, manual synchronization techniques, or have the ability to come back on-line when field
communications were restored.
E. Prototype virtualization technologies to support the use of net-centric software applications in
limited/no communications environments to establish a common strategy to deliver a local runtime
capability that provides an internet application experience to the desktop where communications are
constrained or intermittent. This technology should allow legacy web enabled technologies such as
Traumatic Brain Injury/Behavioral Health (TBI/BH), Neurocognitive Assessment Tool (NCAT), and
Clinical Case Management (CCM) to use the same code to operate in disconnected operations for the
F. Prototypes involving the use of electronic paper and digital pen technologies for data capture in
theater and garrison.
G. Prototypes that demonstrate the value of touch screen technologies at the bedside.
H. Prototypes that involve the use of small mobile devices including Net-Books, Ultra-Mobile
computers and pen tablets, Personal Digital Assistants (PDAs), and mobile SmartPhones in healthcare.
As one example, these devices should be secure and safe for use in harsh environment conditions (heat,
cold, wet, sand, blood, etc.) and that can use: voice commands, enable secure voice communication, use
device independent specific use medical applications (iPhone/Droid like apps) that may answer multiple
mission critical medical functional applications (i.e., trauma report documentation as part of the initial
encounter, blood availability and location information).
I. Prototypes that use various Alternative Input Methods for computer data, including re-designed
keyboards, and control of computer screen data using human eye movement.
J. Prototypes of information technologies that can help the government comply with Section 508 (29
U.S.C. 794d), which states that government agencies must give disabled employees and members of the
public access to information that is comparable to the access available to others.
K. Prototypes of new emerging technologies to convert from paper to digital records, including
automated indexing of scanned documents, and storage as HITSP compliant C. 32 documents.
Prototypes would be able to demonstrate extraction and conversion of information content from
different scanned files into computable data, using text mining, conceptualization, or natural language
processing technologies. The solution should be able to extract and store the metadata for an artifact or
image. It should also convert the contents of structured documents or forms (e.g. blood test results) to
L. Prototype innovative patient portal leveraging the Tricare OnLine infrastructure using the clinical
data repository of AHLTA (the DOD's Electronic Health Record) as the source to populate and manage
selected data elements, utilizing the infrastructure of the Nationwide Health Information Network
(NHIN), to promote consumer empowerment. Such work might leverage previous work on the MiCare
project at Madigan Army Medical Center, which provides military beneficiaries a choice of Google
Health or Microsoft HealthVault as a health information exchange platform and personal health record
which is tethered to AHLTA, or McKesson RelayHealth, which in use in the Medical Home Concept at
NNMC Bethesda, and integrating these commercial services into the NHIN.
M. Prototypes involving the use, advantages, and risks of using open source technologies in healthcare.
Create prototype integrations between various open source healthcare technologies and standards, as
part of an overall strategy to mitigate systems development risk.
N. Prototypes that demonstrate the use of emerging medical logistics technologies, including new
supply chain techniques, RFID, bar-codes, and other technologies, and how they might be integrated
into The Theater Enterprise-Wide Logistics System, TEWLS 2.0, DMLSS, JMEWS, JPTA, and other
logistics systems of record. The government is particularly interested in prototypes that shift the medical
materiel supply chain towards a clinical data driven, evidence based, proactive function, utilizing the
diagnosis data that medical facilities capture in an electronic health record. Include driving
requirements for all medical materiel (pharmaceuticals, medical/surgical supplies, and equipment).
The collection of meaningful electronic data at the point of care is the key to all other capabilities which are
subsequently described in this RFI. If sufficient or meaningful data is not collected, or data is collected in
incompatible formats, the rest of the capabilities in this RFI will be difficult to execute. Thus, the government
seeks information on how the human-computer interface can be improved to collect intra-operable data
throughout the life of the individual, and across multiple spectrums of care. To date, many military,
government, and civilian healthcare IT systems have been developed without full consideration of the impact to
the user, resulting in lower than expected adoption rates by clinicians and business users.
The government is particularly interested in applying the academic domains of cognitive psychology and
human-factors to improve computer data entry methods. Relevant example work may be found in the
publications of Dr. Jakob Nielsen, the Norman-Nielsen Group, Human Factors, International, Inc.; Dr. Vilma
Patel, Dr. Jiaje Zhang, Dr. Linji Chen, Mr. Jim Ong at Stottler Henkle, Inc., the Parsons Institute for Information
Mapping, KLAS Research, GartnerResearch, the Healthcare Information Management Systems Society
(HIMSS), the American College of Healthcare Executives (ACHE), Mayo Clinic, Partners Healthcare,
Intermountain Healthcare, Kaiser Permanente, and others.
To the extent that new technologies can facilitate clinical and logistics data collection, this data will then be
available for execution and analysis. The electronic health record will then move from simply a collector of
information, to an assistant, colleague, and mentor for the clinician as purported by GartnerResearch.
2. EMERGING AND ENABLING HEALTHCARE INFORMATION TECHNOLOGIES WHICH MAY
IMPROVE EXECUTION OR ANALYSIS OF DATA
A. Prototype revolutionary clinical intelligence tools such as Clinical Looking Glass, created at Montifiore
Medical Center, under a Congressional Special Interest project, which democratize the availability of
information, and allow a clinician to easily build study and control cohorts at the point of care, using rich
clinical encounter data from the transactional electronic health record, for the purposes of improving clinical
outcomes, quality assurance, and clinical research. Such tools should allow immediate comparison of statistical
differences in health outcomes between study cohorts (e.g. morbidity, mortality, and readmission rates), so as to
improve quality assurance, allow for translational medicine, and provide for immediate patient remediation in
the healthcare delivery setting. The government is particularly interested in a clinical analysis tool that can
compare multiple parameters across many time periods (lab, pharmacy, radiology, demographics, clinical
encounter data, admissions, claims, and medical material used during treatment) in a time event canvas. The
tools should also support advanced epidemiological metrics, risk-windows and black-out periods. The tools
should be able to support SQL, OLAP, and statistical comparisons in one integrated package, for use by
clinicians in improving quality assurance, without the need for a qualified statistician, or data priest or priestess.
Lastly, the tools should provide for storage and reuse of all queries as objects, and provide for self-
documentation of parameters, and easily shared reports for use in quality assurance and research publications.
Note that this capability refers to SQL and OLAP generated queries, and not to true data mining (discovering
previously undiscovered relationships through pattern recognition), which is defined subsequently.
B. Closely related to the capabilities in (A) above are prototypes that facilitate the collection and analysis of
medical quality data in a manner that is minimally intrusive to the user. Initial medical quality standards would
focus on data collection and analysis for Health Plan Employer Data and Information Set (HEDIS), the Joint
Commission on the Accreditation of Healthcare Organizations’ ORYX required performance measurement
system, and specific JCAHO standards such as advance directives, medication reconciliation. The research
should identify optimal ways to insert data collection and reminders to each member of the healthcare team to
optimize quality while minimizing healthcare documentation time.
C. Prototype clinical decision support algorithms. Such algorithms can help the clinician to evaluate, diagnose,
and evaluate patients at the point of care. The government is particularly interested in piloting existing clinical
decision support rules engines such as Proteus, EON, GLIF, SAGE, ATHENA, ProForma, Asbru7, and other
new engines that may exist. The government will give preference to the demonstration of open source
technologies such as GELLO and JBoss Drools.
D. Prototype highly scalable and high performing databases that support relational, object-oriented, and
massive parallel processing to support complex, analysis of healthcare of 9.5 million military medical
beneficiaries in the Military Health System (MHS). Such analysis could involve a need for high computing
power to analyze combinations of proteomic, genomic, and clinical observational data, and to conduct clinical
intelligence predicting health outcomes, that involves multiple epidemiological parameters, risk windows, and
E. Prototype new, improved healthcare logical data models, and physical databases in warehouses and data
marts, based on traditional entity-relationship diagramming and newer object-oriented techniques, as well as
Kimball-based fact and dimension modeling.
F. Prototype new health data models that can accommodate combined, semantically-interoperable clinical
observational, proteomic, genomic, and non-healthcare data such as body armor, explosive devices, and armored
equipment, as well as adverse events.
G. Pilot Simply Query Language (SQL), On-Line Analytical Processing (OLAP), Relational On-Line
Analytical Processing (ROLAP), Multi-Dimensional On-Line Analytical Processing (MOLAP), and other
emerging database query technologies. These query techniques would be evaluated with respect to their ability
to support relational, object-oriented, and hierarchical databases, integrated into a physician or patient portal.
H. Prototype true data mining algorithms that can discover previously undiscovered relationships in healthcare
clinical observational, proteomic, and genomic data, and help predict outcomes. Note that data mining
capabilities are different than SQL, OLAP, ROLAP, or MOLAP capabilities. If one has a question already in
mind, one should employ SQL, OLAP, ROLAP, MOLAP. If one does not know the question, or is seeking to
discover previously undiscovered relations or patterns in data, data mining tools apply. Initial emphasis should
be given on providing clinicians with SQL, OLAP, ROLAP, and MOLAP tools that can support quality
assurance, translational medicine, and patient remediation at the point of care, vice data mining tools that are
just seeking interesting patterns in data. Such data mining tools might expand upon, and improve, previous data
mining work in from a U.S. Army Telemedicine and Advanced Technology Research Center (TATRC) –
managed Small Business Innovative Research (SBIR) project with KBSI, Inc., which produced a Health Data
Mining Algorithms Library, to the extent permitted by intellectual property laws.
I. Pilot grid computing technologies for image sharing and integration into the electronic health record.
J. Provide support for implementing the MHS Clinical Knowledge Roadmap.
K. Prototype the NIH i2b2 data warehouse for military medicine.
L. Prototype new claims fraud algorithms.
M. Prototype enterprise-wide workload forecasting and scheduling systems, and nursing staffing solutions
based on patient acuity.
N. Pilot solutions for secure messaging solutions that integrate personal health records, NHIN
infrastructure/capablity, and links to the patient medical and dental record across services as well as service
components (Active Duty, National Guard, and Reserve).
-Determine how to integrate key functionality contained in the MHS MiCare solution (Google Health
and Microsoft HealthVault) and NNMC Medical Home Concept McKesson RelayHealth solution with systems
of record, such as the Tricare On Line Patient Portal, and AHLTA, the DOD Electronic Health Record.
-Prototype integrations of other secure messaging/personal health records solutions, such as mCare,
VitalChart Dental PHR for Reserves, and the Text for Baby Secure Messaging prototype, for integration with
the Tricare OnLine Patient Portal.
Prototypes would include applications that implement military beneficiaries’ preferred way for medical
information notification to include test results, follow-up, etc. Solutions can include texting to specific cell
phones, internet secure messaging, social networking, mail, etc. Research should also include patient preferred
method of reporting self-care (including home-based monitoring) and of providing follow-up or continuity of
care to the beneficiaries. Patient sensitive information and access to such must maintain privacy and restrict
access according to federal law. This should include ways to address access of complex patient family groups
and the right to privacy according to the federal and state laws in place (i.e., parental access to dependent
children’s information particularly those of a sensitive nature (pregnancy or behavioral health issues).
O. Prototype delivery of patient educational content for use at the point of care, using HL-7 InfoButton Services
being developed for the NHIN.
P. Prototype closed loop medication administration systems and systems to improve pharmacovigilance and
improved pharmacy decision support applications for point of care use. Current pharmacy decision support is a
cross reference of prescribed medications and allergies. It does not take into account patient diagnosis or test
results. Improved systems should take into account specific patient labs such as Indium Phosphide (INR) when
on Coumadin, or Creatinine and when medications are renally excreted. Other evidence based interactions
would be included and provide point of care actionable decision support.
Q. Prototype text mining, text analytics, and concept search for document classification, and in use in clinical
research and portfolio management. This would include prototypes of the application of natural language
processes (NLP) to historic EHR documents that include free text. Research would include determining what
information is most used by providers in medical decision making or necessary to facilitate clinical-business
needs. Research focus included accuracy of data identification and best options for clinical validation of data
identified by NLP processing. There is a need to explore the feasibility and limitations of creating such a
software service, especially in the context of different applications (such as Vista, CHCS, AHLTA and Third
Party Outpatient Collection System [TPOCS]) employing this service while also maintaining data integrity and
R. Prototype new intelligent patient identity management matching algorithms (fuzzy logic, artificial
intelligence), for use in health information exchanges.
In sum, the government seeks emerging and enabling technologies that can provide easy-to use methods to store
transactional data in standards based healthcare information models, aggregate it, and query it to answer pre-
defined questions, or more open ended questions regarding patterns in data. These technologies are intended to
make use of collected data to improve decision-making at the point of care, support quality assurance, patient
safety, quality outcomes, and patient remediation. They can also be used in retrospective analysis to improve
population health, public health, biosurveillance, and pharmacovigilance. The key to generate easy-to-
understand ad-hoc clinical and business reports is crucial to improving healthcare delivery and managed care for
the Military Health System population. Our EHR contains a wealth of data on our patient population. However,
the systems that our providers use today do not fully utilize that data to provide robust "Clinical Decision
Support" (CDS) or clinical business intelligence tools for our providers. Nor do the tools provide for an effective
capability to use that discrete data in a business intelligence (BI) fashion.
*Both SQL, OLAP, MOLAP, ROLAP, and true data mining tools are applicable to this goal. Note that
data mining capabilities are different than SQL, OLAP, ROLAP, or MOLAP capabilities. If one has a question
already in mind, one should employ SQL, OLAP, ROLAP, MOLAP.
*If one does not know the question, or is seeking to discover previously undiscovered relations or
patterns in data, data mining tools apply.
Initial emphasis should be given on providing clinicians with SQL, OLAP, ROLAP, and MOLAP tools that can
support quality assurance, translational medicine, and patient remediation at the point of care, vice data mining
tools that are just seeking interesting patterns in data. Once sufficient capability is implemented to support
quality assurance and patient safety at the point of care, more sophisticated data mining can then be
implemented to discover previously undiscovered relationships in data about a patient or group of patients.
Research is needed to understand how our data can be fully leveraged to provide our physicians the robust CDS/
BI tools that can be integrated into their existing workflows at the point of care.
3. EMERGING AND ENABLING HEALTHCARE INFORMATION TECHNOLOGIES WHICH MAY
IMPROVE INTEROPERABILITY BETWEEN SYSTEMS, OR PROMOTE NATIONAL AND
INTERNATIONAL HEALTHCARE STANDARDS
A. Prototype new open source technologies for use in subscribing to, and consuming, health information
available from Health Information Network (NHIN), NHIN CONNECT, and Virtual Lifetime Electronic Record
(VLER) initiatives. Prototype new technologies to provide for future functionality in production pilots in the
HHS, DOD, and VHA Beacon Communities, per http://www.hhs.gov/news/press/2009pres/12/20091202a.html.
Of particular interest are those projects which seek to promote and contribute to national efforts in open source
health information technology. Such effort would typically be focused on advanced concept development for
Phase 2 and beyond.
B. Prototype technologies to implement HHS Office of the National Coordinator for Healthcare IT (ONC) and
the Healthcare Information Technology Standards Panel (HITSP) standards and specifications. Standards of
near-term interest include C32, C84, TP-20, IS-12, IS-03, and many others listed at http://www.hitsp.org/. Of
particular interest are those projects which seek to promote and contribute to national efforts in open source
health information technology.
C. Prototype methods to fully implement HL-7 3.0 Reference Information Model (RIM) in healthcare systems.
D. Prototype alternative technologies to migrate systems to ICD-10, SNOMED-CT, and other terminology
standards. Example focus area is determining strategies to mitigate risks that existing terminologies introduce
when one-to-many translations to newer terminology sets are possible (e.g. ICD-9 to SNOMED-CT).
E. Provide a terminology service that can be used and maintained as open source software as part of the
National Health Information Network (NHIN). The terminology translations should initially occur that would
enable medical practice situations with electronic medical records (EMRs) or other technologies, that do not
store data in the NHIN defined sharing format, to access diagnosis, allergies, and medications, and procedures
shared across the NHIN in a computable format. The service should be extensible to allow transition to ICD10
and the gambit of other major health information standard languages. Pilot terminology mediation and semantic
web-related services necessary for healthcare inter-operability. Leverage emerging Web 3.0 standards where
appropriate. Such work would require analysis of existing health data dictionaries such as the 3M HDD, and
medical code sets, terminologies, and ontologies, such as ICD-9 and 10, CPT-4, Logical Observation Identifiers
Names and Codes (LOINC), Systematized Nomenclature of Medicine (SNOMED), Unified Medical Language
System (UMLS), MedRa, NDC Codes, and Language and Computing’s LinkBase, as well as Mayo Clinic’s
LexGrid, Oracle Healthcare Transaction Base, and terminology services from Apelon, Health Language,
Intelligent Medical Objects, and similar companies.
F. Prototype medical device interoperability using HL-7 3.0, DICOM, and MD PnP (e.g. ICE) standards, and
integration with MHS systems of record, such as AHLTA, CHCS, Clinicomp Essentris, and others which may
be developed or licensed.
G. Prototype new code to an emerging U.S. Army TATRC Common Development Environment and Patient
Ancillary Web Services which can provide a fully replicated DOD electronic health record and NHIN
development and test environment, using virtualization, to support A to F above.
In sum, DOD is playing a critical role in developing software components and other technologies to support
healthcare interoperability and the semantic exchange of data between disparate healthcare systems and medical
devices, but seek new information from industry on available technologies. Achieving healthcare
interoperability is a Presidential Executive Order and national priority by 2014. Many of the projects will
involve health data exchanges across local, regional, state, and national lines, and involve multiple federal
agencies. The government welcomes additional thoughts from industry regarding how to best achieve success
for this laudable goal, and what open source and other technologies may be useful in this regard.
4. EMERGING AND ENABLING HEALTHCARE INFORMATION TECHNOLOGIES WHICH
MAY IMPROVE THE DISTRIBUTION OR PORTABILITY OF DATA
A. Prototype solutions for regional storage and distribution of high volumes of clinical encounter data,
admissions, pharmacy, lab, radiology images, medical equipment repair records, and facility computer aided
drawings, for use in a highly reliable, high performing, and secure electronic health record and associated data
warehouses, serving 9.5 million military beneficiaries. Such technical guidance could include complete or
partial technical solutions including client, mid-tier, regionally-distributed back-end, or other architectures. The
government is particularly interested in service-oriented architectures that can provide for loose-coupling of
components, distributed architectures, cloud computing, and grid computing, that not only help distribute data
and make it more portable, but also provide for highly reliable, available, and serviceable platforms.
B. Develop strategies and prototype alternatives for data replication across five data centers, Unified Structure
Regional Distribution (USRD). The current centralized data repository will be replaced with five geographically
distributed data centers. The strategy needs to enable real-time or near-real-time (current applications have batch
updates but near real-time has been defined as a goal of not less than every 15 minutes) data replication. The
strategy must accommodate the capability to record individual transactions while the data is being replicated
across the regional data centers (local store and forward – supports low communication/no communication
operations in remote operational conditions when no connection to the network is possible).
C. Prototype emerging and enabling data replication strategies and extract, transform, and load processes to
support nightly transfer of high volumes of data between transactional and data warehouse systems.
D. Prototype integration of mobile platforms (netbooks, PDAs, and SmartPhones) to access military health data,
and provide for physician alerts on critical issues, improved clinical handoff, secure messaging between
physicians and patients, patient appointment scheduling, patient drug refills, and access to patient education
content from anyplace, anytime. A similar requirement exists for rapid situational awareness of availability and
location of medical material to promote rapid response to medical emergencies world-wide. The government is
particularly interested in developing applications for Smart Phones such as the Blackberry, Apple iPhone and
Google Android platforms.
E. Prototype new secure messaging and next-generation PHR functionality that provide for integration with
personal health records using the Nationwide Health Information Network (NHIN) to promote distribution and
portability of data. The goal should be the creation of secure messaging between patient and providers as well
as provider to provider across practice groups. The solutions may use Web 2.0 and other native social
networking capabilities, as well as Web 3.0 capabilities.
F. Prototype new mobile technologies for use by clinicians in healthcare.
G. Prototype existing and new online social networking utilities such as Twitter and FaceBook in healthcare
and in military medicine. Prototype related applications of value to military medicine which are officially
sanctioned and HIPAA compliant if required by law. The government is particularly interested in use of these
social networking applications for suicide prevention.
H. Create a prototype LinkedIn or related professional social network application, and evaluate its effectiveness
on recruiting and retaining highly-qualified personnel with healthcare IT skills.
I. Pilot improved single sign-on solutions, roaming profiles, and biometric identity solutions to support the
mobile clinician who works in multiple locations in a healthcare setting.
In sum, the Military Health System seeks information on how to improve data access services for its mobile
clinical staff and patients. The goal of the Military Health System is to provide patients and its staff with
anytime, anywhere access to critical data systems, including both transactional and analytical systems. Design
of such systems for mobile platforms is different than for desktop systems. There has been little research or
prototypes to date which have been tested in military medicine.
5. EMERGING AND ENABLING HEALTHCARE INFORMATION TECHNOLOGIES TO
IMPROVE REPRESENTATION OR VISUALIZATION OF DATA
A. Prototype improved ways to visualize data in transactional electronic health records or in data marts or data
B. Prototype means of displaying graphical data, including complex, longitudinal data over time, such as blood
pressure, blood sugars, weight, and other parameters important for chronic disease management.
C. Prototype ways of simplifying the display of data on an electronic health record by only calling back and
portraying information important to the evaluation and management of the patient at hand (i.e. context-sensitive
data). This is important in that the amount of information grows significantly over the life of the individual, and
it is important for a system to be able to display only the most critical information about a patient at the time of
care. Some military health records can contain 60 or 70 years of data. This prototype would also execute user
interface enhancements to improve the clinician's workflow and alignment with relevant clinical practice
guidelines used in daily practice. This should address the density of information in a single display versus
structured paths to more detailed data. Enhancements should also accommodate a strategy for accommodating
individual user preferences and content.
D. Prototype new ways of integrating and displaying near real-time data imported from multiple external and
disparate systems into a meaningful unified view for clinicians. Consideration must be given to including data
from medical materiel and logistics systems to provide clinicians with the supplies, equipment, and facilities at
their disposal to ensure patient safety, clinician efficiency, and quality of care.
E. Prototype methods of displaying complex patterns in data, particularly relationships between clinical
observational data, and proteomic and genomic data.
F. Prototype the use of virtual worlds to evaluate new data displays, or new architectural designs, or new
processes in healthcare.
G. Prototype new visualization technologies that build upon, and improve technologies that have been
previously funded by Small Business Innovative Research Program (SBIR) funding, to the extent permitted by
patent, trademark, open source, government use rights, and other intellectual property laws:
• Data Montage technology, depicted at http://www.stottlerhenke.com/datamontage/ and
• AHLTA Point Display, developed by http://www.ssci.com/
H. Prototype and apply new visualization technologies that build upon, and improve state-of-the-art
visualization technologies frequently cited in the literature:
Dr. Edward Tufte’s work at http://www.edwardtufte.com/tufte/
Dr. Jakob Nielsen’s work at http://www.useit.com/
The government is particular interested in the pilot of open source, non-proprietary technologies.
Medicine is an increasing complex domain, with new sources of data being identified frequently as important
for decision support, and necessary for improved health outcomes. Yet most healthcare delivery systems are
awash in data, but lacking in the ability to transform that data to information, understanding, knowledge, and/or
wisdom. On the other hand, there are new thoughts and technologies emerging as to how to best display
information for improved decision-making.
6. EMERGING AND ENABLING HEALTHCARE INFORMATION TECHNOLOGIES TO
IMPROVE ARCHIVING AND RETRIEVAL OF DATA
A. Prototype emerging and enabling technologies which can automatically archive growing volumes of
military health and logistics data, including images, but still allow for quick retrieval when required. Such work
would involve coordination with TATRC, the MHS Tri-Service Infrastructure Management Program Office
(TIMPO), and the Defense Information Systems Agency (DISA). Representative technologies may involve
Storage Area Networks (SANs), Fibre Channels, and other backup and recovery solutions from major IT
B. Prototype data integration and visualization technologies, which provide a way to quickly integrate disparate
data sources into a unified view, such as Microsoft Amalga, which is being considered for use in the Joint
Federal Facility at N. Chicago, IL., and other electronic healthcare development tools that promote
implementation of the HL-7 3.0 Reference Information Model, such as Oracle Healthcare Transaction Base.
C. Prototype the Globus Medicus image archiving and retrieval system in a new prototype that leverages where
appropriate Internet 2 and/or the National LambdaRail ultra high-performance networks. See
http://dev.globus.org/wiki/Incubator/MEDICUS and http://www.nlr.net/
Demonstrate alignment and integration into ongoing archiving efforts such as the DOD Healthcare Artifact and
Image Management Solution (HAIMS), and integration with existing healthcare networks such as the
Nationwide Health Information Network (NHIN). Further demonstrate integration with projects similar to the
American College of Radiology (ACR) Triad tool at http://www.acrin.org/ or open source OsiriX DICOM
D. Prototype technical alternatives for a robust, continuously improving, data archiving, back-up and recovery,
and retrieval plan for the Military Health System.
The Military Health System (MHS) serves 9.5 million beneficiaries, and collects tens of thousands of electronic
clinical encounter notes, admissions, and pharmacy/lab orders each day. Currently, the DOD AHLTA
Electronic Health Record (EHR) Repository is approximately 40 terabytes and is growing exponentially. In
addition, the MHS is implementing a system to bring image and document access into the EHR. The MHS
seeks information on the best technologies for archive and retrieval for this massive amount of information. The
MHS could benefit from a robust, continuously improving, data archiving, back-up and recovery, and retrieval
plan. This plan would also provide for increasing requirements to integrate civilian and VHA encounter notes
and claims into the AHLTA Clinical Data Repository, Clinical Data Mart, MDR, M2 or related Service
Systems. In addition, the Military Health Service Data Repository (MDR) warehouse, associated M2, and
Clinical Data Marts could also benefit from a clear archive and retrieval strategy. The government seeks ideas
for appropriate archival and retrieval technologies to prototype. The goal is to provide for a highly reliable,
available, and serviceable platform in a cost-effective manner, with access to data as required to support high
quality, available, accessible, acceptable, and continuous care.
7. SYSTEMS ANALYSIS, ENGINEERING, AND PROGRAM MANAGEMENT SUPPORT SERVICES
Refers to systems analysis, engineering and program management support necessary to facilitate the prototype
projects in (1) to (6) above, and to also facilitate technology transfer to the Military Health System IM/IT
Program Executive Officers (who design, develop, test, and deploy systems of record), and to support formal
processes of Defense Acquisition Programs for Information Management/Technology:
A. Research and market surveys concerning federal and civilian healthcare information technology initiatives,
which could then be considered in development of the MHS Strategic Plan for Information Management and
B. Analysis of technical alternatives support.
C. Development and evaluation of alternative strategic, operational, and technical architectures, and data
integration strategies. As one example, determine the best data integration approach for easy vendor software
(COTS/GOTS or Government Developed) replacement. Evaluate alternatives and strategies of converting the
existing legacy clinical or logistics data to a standard format and then store it so that it can be consumed by a
new application with minimum impact on the overall system vs. migrating the data at the time the vendor
replacement. The strategy must accommodate an ability to upgrade to new version of a product with minimal to
no impact on the legacy data.
D. Requirements management, use case, and CONOPS program support
E. Prototype design support
F. Data integration and engineering alternatives support
G. Testing and evaluation support. This would include support to develop alternative testing and evaluation
environments, including the TATRC Common Development Environment, and the Joint Integration Testing
H. Training and deployment support
The functions in (A) to (H) provide research, analysis of alternatives, systems engineering and program
management support to carry out the prototype mentioned in (1) to (6). Such support is necessary by both
MRMC TATRC and the MHS IM/IT Program Executive Office.
Responses to this RFI should be e-mailed as either a PDF or MS Word attachment. Emails exceeding 3MB in
size must be sent via US Mail on both paper and CD. Your packet must include the following components:
1. A cover letter on institution letterhead including the institution name, point of contact, address,
phone number, e-mail, web page (optional), fax number, DUNS number, and CAGE code.
2. A white paper, not to exceed 5 pages in length, per objective, as is necessary to provide the
information requested for the technical areas of this RFI
3. A completed Questionnaire, Attachment 1
Response to this RFI is due to U.S. Army Medical Research Acquisition Activity (USAMRAA) by 4:00 PM,
Eastern Standard Time on 28 Feb 2010. Please submit e-mail responses to the following email address at Info-
JTCG1-HealthIT@tatrc.org No phone calls will be accepted. Responses must be identified with W81XWH-
ADVHEALTHIT. Electronic submissions are preferred but if submitted by mail, please provide one written
copy and two CD. Send to: US Army Medical Research Acquisition Activity, ATTN: MRMC-AAA-T, 820
Chandler Street, Fort Detrick, MD 21702-5014.
This RFI may be amended or modified at any time at the discretion of the Government.
In accordance with FAR 15.201(e), responses to this notice are not offers and cannot be accepted by the
Government to form a binding contract. This RFI is issued solely for information and planning purposes and
does not constitute a solicitation. Neither unsolicited proposals nor any other kind of offers will be considered
in response to this RFI. Responses to this notice are not offers and will not be accepted by the Government to
form a binding contract. Responders are solely responsible for all expenses associated with responding to this
RFI. All information received in response to this RFI that is marked Proprietary will be handled accordingly.
Responses to the RFI will not be returned. At this time, questions concerning the composition and requirements
for future RFPs will not be entertained.
JTCG1 – ADVANCED HEALTHCARE INFORMATION TECHNOLOGIES
Attachment 1: Advanced Development Evaluation Questionnaire
INSTRUCTIONS: Please answer all questions. In general, the questions below should be answered by inserting a simple “YES” or “NO” in the “Answer” column next to
each (or by selection of a single choice for multiple-choice questions). If you do not know the answer to a particular question, enter “UKNOWN”. If desired, you may also
insert a short (1-2 sentences) explanation of your answer in the “Comments” column. Lengthy discussions are strongly discouraged.
ORGANIZATION NAME: ___________________________________________________________________________________________
PRODUCT/TECHNOLOGY NAME (White Paper Title):__________________________________________________________________
WHICH OF THE SEVEN AREAS DOES THIS TECHNOLOGY OR SERVICE BEST FIT? PLEASE SELECT AT LEAST ONE.
(NOTE: May choose more than one priority area if represented in attached response. If selecting more than one priority area, please rank in ascending sequential order from most
appropriate fit to least appropriate fit.)
Refers to projects that can either assist with combining large disparate data to allow access to actionable information, or projects
Acquisition-Access that capture computable data in new ways. Examples include new human-computer interfaces that can facilitate novel data
entry, such as speech recognition, natural language processing, and electronic paper and digital pens.
Refers to projects that analyze or use novel tools to allow analysis of large amount of information to find patterns or
relationships among data sets. These technologies may also predict certain events or conditions or analyze data to optimize
Analysis-Execution scheduling or resource allocation. It also refers to technologies that allow creation, storage, management, and execution of
clinical guidelines and rules to assist decision making (e.g. clinical decision support)
Refers to projects that help promote interoperability between systems (particularly semantic interoperability and computability)
Interoperability-Standards or help define, harmonize, and implement recognized health information technology standards.
Refers to projects that distribute information or make information portable (both software and hardware) across all spectrums of
Distribution-Portability care, and across the life of an individual.
Refers to projects that develop novel ways to represent or visualize data or processes. Examples include novel graphical user
interfaces, new 3D graphical technologies, and virtual world technologies. Examples include a technology that may improve
Representation-Visualization how providers can abstract increasing amounts of data, display data based on a problem orientation or any novel methods that
will increase decision making or improve cognitive performance to allow a more holistic and systematic management of patient.
These technologies can also provide a way to integrate and provide instant access to a comprehensive view of each patient that
includes images, scanned documents, proteomic/ genomic and other clinical observational data.
Refers to projects that demonstrate methods of archiving data and/or retrieving data in novel more efficient ways. It should
Archive-Retrieval handle large sets of data distributed over large distances. (May include data warehousing, data modeling, extraction,
transformation and load [ETL] functions, etc.).
Refers to systems analysis, engineering and program management support necessary to facilitate the prototype projects in (1) to
Systems Engineering or (6) above, and to also facilitate technology transfer to the Military Health System IM/IT Program Executive Officers(who
Program Management design, develop, test, and deploy systems of record), and to support formal processes of Defense Acquisition Programs for
COMPANY REPRESENTATIVE, E-MAIL, AND PHONE NUMBER:
1. Technology Innovativeness / Maturity of proposed product ANSWER COMMENTS
a. Is this product or technology currently commercially available and fully developed
with all approvals?
b. If not currently commercially available, is the product or technology based on well
understood, mature technology, i.e., a modification of existing technology?
c. Is this product based on a novel technology with no prior commercialization?
2. Consortia and Collaboration ANSWER COMMENTS
a. What your experience and capability in regards to leading or working is as part of a
b. Is the product already validated by consortia? If so, by whom?
3. What is the estimated cost to complete development of this product? [Select one from ANSWER COMMENTS
a. Less than $1M
b. Greater than $1M to less than $10M
c. Greater than $10M to less than $20M
d. Greater than $20M
4. What is the potential for commercialization of this product? [Select one from list ANSWER COMMENTS
a. It has the potential to be widely used in the civilian commercial market.
b. It has some commercial value, but not wide universal use.
c. It has limited commercial value, perhaps as a humanitarian use in third world countries
but not within US.
d. It has no commercial use; only for the military.