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Project HIE STANDARD
Strategic Health IT Advanced Research Project (SHARP) Program
health research project
Strategic Health IT Advanced Research Project (SHARP) Program
The SHARP program is a research program that has been awarded $60 million, funded by The Office of the National Coordinator for Health Information Technology (ONC). This program provides for new competitively-awarded agreements to establish Strategic Health IT Advanced Research Projects (SHARP). Awardees will put into action a collaborative, inter-disciplinary research program that addresses one of four research focus areas. They will develop as well as implement a cooperative program among researchers, industry, health care providers, and other health IT sector stakeholders in order to transition the results of research into practice.
Seeking to support dramatic improvements in the quality, safety, and efficiency of healthcare using advanced information technology, the program was created to achieve breakthrough advances in order to deal with obstacles that have long hindered the essential adoption of health IT as well as the acceleration of progress towards achieving a nationwide meaningful use of health IT. Current SHARP priorities include research focused on achieving breakthrough advances to address well-documented problems that have impeded adoption.
Research projects supported by the SHARP program will focus on solving current and expected future challenges that represent obstacles to adoption and meaningful use of health IT, through the proliferation of new methods and advanced technologies. These projects will focus on areas where “breakthrough” advances are needed to realize the full potential of health IT.
Awardees in the SHARP Program will implement a focused research project in one of the following four areas where breakthrough advances are needed to address barriers to the adoption of health IT to meet the goal of making electronic health records (EHRs) available for all Americans by 2014.
1. Security of Health Information Technology
2. Patient-Centered Cognitive Support
3. Healthcare Application and Network Platform Architectures
4. Secondary Use of EHR Data.
The goals for each SHARP project are to develop technology solutions in the selected area of research, make the technology solutions available to the health information technology market, and make research results available through publication.
Each project’s research focus and outcomes will include both near-term results and longer term results. The near-term results (those that can be addressed in two years) will be developed to meet the specific needs of the healthcare community to achieve meaningful use. The longer-term results (those that require longer than two years) will be contributions to methods and fundamental knowledge that can be leveraged over time for continuous improvement in health care.
The SHARP program will develop numerous applications, all of which spring from four critical areas of health IT that present barriers which hinder the adoption of health IT, as well as the acceleration of progress towards achieving a nationwide meaningful use of health IT.
-- Applications Include --
Electronic Health Records (EHRs)
Health Information Exchanges (HIEs)
Telemedicine (TEL), with Personal Health Records (PHRs)
Critical Areas of Focus Impeding Adoption of Health IT:
Strategic Healthcare IT Advanced Research Projects on Security (SHARPS)
The University of Illinois at Urbana-Champaign
: Deals with Security of Health IT
Develop technologies and policy recommendations that reduce privacy and security risks and increase public trust
Advance the requirements, foundations, design, development, and deployment of security and privacy tools and methods.
Organized around three major healthcare environments:
Electronic Health Records (EHRs)
Health Information Exchange (HIE)
- Program Goals -
The maturity of security and privacy technologies and policies through the removal of key barriers that prevent the use of valuable health information.
The creation of an integrated security and privacy research community for HIT that will exist following the culmination of the SHARPS program.
-- Projects --
- Electronic Health Records (EHR) -
Will focus on issues related to the security and privacy of health records within a single care delivery organization (CDO), such as a hospital or doctor’s office.
Addresses defense-in-depth protection of records within an enterprise or in outsourcing by using attribute-based encryption to enforce SHARPS-developed protection requirements
Policy Terrain and Implications of HIT
Addresses the inadequacy of existing frameworks for formulating and understanding privacy policies by developing contextual integrity underpinnings for application-enabling privacy practices.
Privacy-Aware Health Information Systems
Meets needs for highly assured conformance to privacy policies by developing new strategies for building such systems based on trust management systems.
- Health Information Exchange (HIE)
- Is concerned with the security and privacy of health records as they are exchanged between CDOs and/or individuals.
project has three components:
Responsive, Secure Health Information Exchange
Addresses the inadequacy of current service models for exchanges by demonstrating how model-based design can be applied to HIT
Experience-Based Access Management
Addresses the need for an engineering model for the evolution of access controls limiting insider threats with a lifecycle model based on strategies from attribute-based rule sets and machine learning
Personal Health Records
Addresses the inadequacy of privacy standards for third-party PHRs through policy exploration with PHR stakeholders, leading to development and transition of supporting technology.
- Telemedicine (TEL) -
Will address the security and privacy of implantable medical devices, remote monitoring, tele-immersion, and safety.
project has four components:
Implantable Medical Devices
Addresses control operations on implanted medical devices without proper authorization by developing techniques for achieving measurable security for such devices relative to specified infrastructure
Remote Monitoring for Mobile and Assisted Living
Addresses usable security for remote monitoring and home healthcare with an mHealth security framework and service model
Addresses the need for efficient provisioning for security and privacy in tele-immersion by linking classification to encryption
Patient Safety Assessment
Addresses inadequate quantification of safety risks for medical devices in the face of security threats with a plan based on using Food and Drug Administration (FDA) adverse event reports to develop risk assessments.
National Center for Cognitive Informatics and Decision Making in Healthcare (NCCD)
University of Texas – Houston
: Deals with patient-centered cognitive support
Harness the power of health IT to integrate and support physician reasoning and decision-making as providers care for patients.
Nationwide collaboration established in response to the urgent and long-term cognitive challenges in Health Information Technology (HIT) adoption and meaningful use.
NCCD’s vision is to become a national resource which provides strategic leadership in patient-centered cognitive support research and applications in healthcare.
-- Program Goals --
NCCD has a three part mission:
Bring together an interdisciplinary team of researchers from biomedical and health informatics, cognitive science, computer science, clinical sciences, industrial and systems engineering, and health services who focus on patient-centered cognitive support.
Conduct short-term research that addresses the usability, workflow, and cognitive support issues of HIT. Conduct long-term research that can remove key cognitive barriers to HIT adoption and meaningful use.
Maximize HIT benefits for quality, efficiency, and safety by translating research findings to the real world through a cooperative program involving all stakeholders.
-- Projects --
- Work-Centered Design of Care Process Improvements in HIT -
Generate a set of EHR-specific metrics which foster usability, best practices, system comparisons, and guide certification.
This project aims to provide tools to increase HIT adoption and cost effectiveness by integrating functions and reducing risks associated with variegated user behavior.
- Cognitive Foundations for Decision Making: Implications for Decision Support -
Form a new approach to clinical decision support (CDS) based on the cognitive constructs of accurate decision making and develop the theoretical basis for clinical summarizations.
This project will develop and pilot a small EMR that evolves, adapts, and proactively reacts to patient and provider needs.
- Modeling of Setting-Specific Factors to Enhance Clinical Decision Support Adaptation -
Develop methodologies which improve the efficacy and applicability of CDS by integrating patient and environmental specific factors.
This project will focus on tailoring CDS to support chronic disease management by incorporating guidance and workflow optimization techniques into EHRs.
- Automated Model-Based Clinical Summarization of Key Patient Data -
Develop a stand-alone automated clinical summarization engine that yields condition specific, actionable, 1-2 page summaries which can be integrated into existing EHRs.
- Cognitive Information Design and Visualization: Enhancing Accessibility and Understanding of Patient Data -
Construct an interface which supports the integration of clinical understanding, decision making, and problem solving.
This project will also provide metrics to evaluate and compare the efficacy of this open-source interface as compared to commercial interfaces.
Substitutable Medical Apps, Reusable Technologies
Deals with Healthcare Application and Network Platform Architectures
Create new and improved system designs that facilitate information exchange while ensuring the accuracy, privacy, and security of electronic health information.
The HIT environment is largely populated by outdated one-size-fits-most systems; customization is difficult, expensive, and only a few established electronic health record (EHR) vendor developers can innovate.
To achieve the promise of HIT reform, the United States needs a platform by which the grassroots of the community – patients, physicians, and small agile software vendors – can continuously drive innovation.
To achieve this goal, medicine needs to learn from the successful implementation of information technology in other sectors.
Substitutability is the capability of a system to replace one application with another of similar functionality.
This requires that the purchaser of an application can replace one application with another without being technically expert, without requiring re-engineering of other applications they are using, and without having to seek assistance from any of the vendors of previously or currently installed applications.
This allows developers to rapidly create a large marketplace of apps for consumers to choose from.
A HIT environment characterized by substitutable apps constructed around shared core components would drive down healthcare technology costs, support standards evolution, accommodate difference in care workflow, foster competition in the market, and accelerate innovation.
Competition on quality, cost, and usability would become fierce.
With the cost of switching kept low, a physician using an electronic health record (EHR), a CIO running a hospital system, or a patient using a personally controlled health record (PHR) would all be empowered to readily discard an under-performing app and install a better one.
-- Program Goals --
The major deliverable of this project will be the Substitutable Medical Apps, reusable technologies (SMArt) platform architecture. Therefore SMArt will achieve two major goals:
Develop a user interface which allows “iPhone-like” substitutability for medical apps based upon shared basic components.
Create a set of services that enables efficient data capture, storage, retrieval and analytics, which are scalable to the national level and respectful of institutional autonomy and patient privacy.
-- Projects --
Led by Harvard Medical School in collaboration with Children’s Hospital Boston, Partners Healthcare, the Regenstrief Institute, the University of Texas, and the University of Wisconsin, four projects will be completed. The anticipated outcomes include foundational knowledge and useable, testable prototypes for a national-scale SMArt platform with a developing ecosystem, robust and scalable network data services, and advanced data analysis capabilities. Such a system should provide a mechanism to rapidly and flexibly serve the health of the nation.
Create a set of services for the SMArt platform that will enable efficient data capture, storage, retrieval, and analytics.
(A) Generate the basic shared components and healthcare core service building blocks (HCSBB) to run across multiple platforms.
(B) Generate the basic SMArt platform to deliver HCSBBs and run apps in practices and hospitals that currently have limited HIT.
(C) Lead to the creation of medication use apps that will be deposited into the App Exchange in collaboration with CVS/Caremark and SureScripts.
Enable existing HIT platforms to become SMArt-ready.
Generate the ecosystem for SMArt apps. This ecosystem will be characterized by inter-application integration, usability across multiple geographical sites, sustained responsiveness to HIT meaningful use criteria, and a one-stop shop for apps developers and consumers.
FOUR: Secondary Use of EHR Data
Mayo Clinic of Medicine
Deals with Secondary Use of EHR Data
Developing strategies to improve the overall quality of healthcare by leveraging existing EHR data to generate new, environmentally appropriate, best practice suggestions.
Mayo Clinic’s SHARP project will enhance patient safety and improve patient medical outcomes through the use of an electronic health record (EHR).
Medical information, such as medical history, exam data, hospital visits and physician notes, are inconsistently stored in multiple locations, both electronically and non-electronically.
The project aims to efficiently leverage EHR data to improve care, generate new knowledge, and address population needs.
-- Program Goals --
Mayo Clinic’s project will work towards creating a unified HER
Allowing the exchange of patient information among...
This will be done by creating
(All for large-scale health record data sharing)
Mayo Clinic’s project will ultimately advance the quality and efficiency of patient care through the use of an EHR.
-- Projects --
- Standardize health data elements and ensure data integrity
Patient information can be stored using several different abbreviations and representations for the same piece of data.
"diabetes mellitus” better known as “diabetes”
Referred to in a patient’s medical record alternately as:
The first phase of Mayo Clinic’s project:
Clinical Data Normalization
Will work towards transforming this non-standardized patient data into one unified set of terminology.
In this case, “diabetes mellitus,” “diabetic,” “249.00” and “DM” would all be re-named “diabetes.”
- Merge and standardize patient data from non-electronic forms with the EHR
Some important information is stored in “free text” form.
Mayo Clinic’s project will first work to merge the patient information in free texts with that in the EHR.
Natural Language Processing (NLP)
Will work towards classifying certain tags, such as “diabetic,” “DM” and “57 year old male” under specific categories.
Such as “disease” or “demographics.”
Will help improve patient care by reducing inconsistencies in patient data.
Providing physicians with more accurate and uniform information in a centralized location.
- Seek physically observable patient traits for further study -
Physically observable traits, or phenotypes, can include:
Growth and development
Absorption and processing of nutrients
The functioning of different tissues and organs.
These traits result from interactions between a patient’s genes and environmental conditions.
Mayo Clinic will use a process called “High-Throughput Phenotyping”
Uses clinical data normalization and NLP
To identify and group a particular phenotype
Such as Type 2 diabetes.
This process will enhance a physician’s ability to identify and study individual phenotypes or groups of phenotypes.
- Find processes to make clinical data normalization, NLP and high-throughput phenotyping more efficient using fewer resources
This part of the process will focus on building adequate computing resources and infrastructures to accomplish the previous steps.
Called “Performance Optimization”
This system will allow those seeking patient information:
To be received quickly
Increasing the efficiency of patient care
While using fewer resources.
- Detect and reconcile inconsistent data -
Mayo Clinic will utilize high-confidence services, or “data quality metrics,”
To identify and optionally correct inconsistent or conflicting data.
Evaluate the progress and efficiency of Mayo Clinic’s project
Mayo Clinic will use an “Evaluation Framework”
Using the Nationwide Health Information Network (NHIN)
An ONC program.
NHIN is a set of:
That enable secure health information exchange over the internet.
Senior Projects Officer Wil Yu
Focus Area One
Focus Area Two
Focus Area Three
Focus Area Four
Barriers Preventing Implementation
Health Information Exchange
Electronic Health Record
All information contained in this page comes from the following link.
HealthIT.hhs.gov SHARP Program
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