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 TECHNOLOGIESW81XWH-Advanced Health Information Technologies

A. BACKGROUND

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.

B. OBJECTIVES

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 rapid.

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.

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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 individual.

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 Support

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

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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 Proposals.

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

OBJECTIVE:

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

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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 beneficiaries.

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 Theater.

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).

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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 computable data.

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).

SUMMARY:

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

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(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

OBJECTIVES:

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 black-out periods.

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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

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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 validity.

R. Prototype new intelligent patient identity management matching algorithms (fuzzy logic, artificial intelligence), for use in health information exchanges.

SUMMARY:

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.

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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

OBJECTIVES:

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.

SUMMARY:9

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

OBJECTIVES:

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

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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.

SUMMARY:

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

OBJECTIVES:

A. Prototype improved ways to visualize data in transactional electronic health records or in data marts or data warehouses.

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:

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Data Montage technology, depicted at http://www.stottlerhenke.com/datamontage/ and http://www.openclinical.org/dm_dataMontage.html

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.

SUMMARY:

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

OBJECTIVES:

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 vendors.

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 viewer. http://www.osirix-viewer.com/

D. Prototype technical alternatives for a robust, continuously improving, data archiving, back-up and recovery, and retrieval plan for the Military Health System.

SUMMARY:

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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 Technology

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 Center (JITC).

H. Training and deployment support

SUMMARY:

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.

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D. RESPONSES

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.

E. AMENDMENTS

This RFI may be amended or modified at any time at the discretion of the Government.

F. DISCLAIMER

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.

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JTCG1 – ADVANCED HEALTHCARE INFORMATION TECHNOLOGIESAttachment 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.)

Acquisition-AccessRefers 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.

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)

Interoperability-StandardsRefers to projects that help promote interoperability between systems (particularly semantic interoperability and computability) or help define, harmonize, and implement recognized health information technology standards.

Distribution-PortabilityRefers to projects that distribute information or make information portable (both software and hardware) across all spectrums of care, and across the life of an individual.

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 includes images, scanned documents, proteomic/ genomic and other clinical observational data.

Archive-RetrievalRefers 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.).

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Systems Engineering or Program Management Support

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.

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 larger consortium?

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 list below]

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 below]

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.

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