The SMART Platform: early experience enabling substitutable applications for electronic health records.

OBJECTIVE: The Substitutable Medical Applications, Reusable Technologies (SMART) Platforms project seeks to develop a health information technology platform with substitutable applications (apps) constructed around core services. The authors believe this is a promising approach to driving down healthcare costs, supporting standards evolution, accommodating differences in care workflow, fostering competition in the market, and accelerating innovation. MATERIALS AND METHODS: The Office of the National Coordinator for Health Information Technology, through the Strategic Health IT Advanced Research Projects (SHARP) Program, funds the project. The SMART team has focused on enabling the property of substitutability through an app programming interface leveraging web standards, presenting predictable data payloads, and abstracting away many details of enterprise health information technology systems. Containers--health information technology systems, such as electronic health records (EHR), personally controlled health records, and health information exchanges that use the SMART app programming interface or a portion of it--marshal data sources and present data simply, reliably, and consistently to apps. RESULTS: The SMART team has completed the first phase of the project (a) defining an app programming interface, (b) developing containers, and (c) producing a set of charter apps that showcase the system capabilities. A focal point of this phase was the SMART Apps Challenge, publicized by the White House, using http://www.challenge.gov website, and generating 15 app submissions with diverse functionality. CONCLUSION: Key strategic decisions must be made about the most effective market for further disseminating SMART: existing market-leading EHR vendors, new entrants into the EHR market, or other stakeholders such as health information exchanges.

Sharing data for public health research by members of an international online diabetes social network.

BACKGROUND: Surveillance and response to diabetes may be accelerated through engaging online diabetes social networks (SNs) in consented research. We tested the willingness of an online diabetes community to share data for public health research by providing members with a privacy-preserving social networking software application for rapid temporal-geographic surveillance of glycemic control. METHODS AND FINDINGS: SN-mediated collection of cross-sectional, member-reported data from an international online diabetes SN entered into a software application we made available in a "Facebook-like" environment to enable reporting, charting and optional sharing of recent hemoglobin A1c values through a geographic display. Self-enrollment by 17% (n = 1,136) of n = 6,500 active members representing 32 countries and 50 US states. Data were current with 83.1% of most recent A1c values reported obtained within the past 90 days. Sharing was high with 81.4% of users permitting data donation to the community display. 34.1% of users also displayed their A1cs on their SN profile page. Users selecting the most permissive sharing options had a lower average A1c (6.8%) than users not sharing with the community (7.1%, p = .038). 95% of users permitted re-contact. Unadjusted aggregate A1c reported by US users closely resembled aggregate 2007-2008 NHANES estimates (respectively, 6.9% and 6.9%, p = 0.85). CONCLUSIONS: Success within an early adopter community demonstrates that online SNs may comprise efficient platforms for bidirectional communication with and data acquisition from disease populations. Advancing this model for cohort and translational science and for use as a complementary surveillance approach will require understanding of inherent selection and publication (sharing) biases in the data and a technology model that supports autonomy, anonymity and privacy.

Indivo x: developing a fully substitutable personally controlled health record platform.

To support a rich ecosystem of third-party applications around a personally controlled health record (PCHR), we have redesigned Indivo, the original PCHR, as a web-based platform with feature-level substitutability. Core to this new release is the Indivo X Application Programming Interface (API), the contract between the PCHR platform and the end-user apps. Using rapid iterative development to build a minimal feature set from real-world requirements, the resulting Indivo X API, now in public stable beta, is enabling developers, including third-party contributors, to quickly create and integrate novel features into patients' online records, ultimately building a fully customizable experience for diverse patient needs.

What are the benefits and risks of fitting patients with radiofrequency identification devices.

BACKGROUND TO THE DEBATE: In 2004, the United States Food and Drug Administration approved a radiofrequency identification (RFID) device that is implanted under the skin of the upper arm of patients and that stores the patient's medical identifier. When a scanner is passed over the device, the identifier is displayed on the screen of an RFID reader. An authorized health professional can then use the identifier to access the patient's clinical information, which is stored in a separate, secure database. Such RFID devices may have many medical benefits--such as expediting identification of patients and retrieval of their medical records. But critics of the technology have raised several concerns, including the risk of the patient's identifying information being used for nonmedical purposes.

GenePING: secure, scalable management of personal genomic data.

BACKGROUND: Patient genomic data are rapidly becoming part of clinical decision making. Within a few years, full genome expression profiling and genotyping will be affordable enough to perform on every individual. The management of such sizeable, yet fine-grained, data in compliance with privacy laws and best practices presents significant security and scalability challenges. RESULTS: We present the design and implementation of GenePING, an extension to the PING personal health record system that supports secure storage of large, genome-sized datasets, as well as efficient sharing and retrieval of individual datapoints (e.g. SNPs, rare mutations, gene expression levels). Even with full access to the raw GenePING storage, an attacker cannot discover any stored genomic datapoint on any single patient. Given a large-enough number of patient records, an attacker cannot discover which data corresponds to which patient, or even the size of a given patient's record. The computational overhead of GenePING's security features is a small constant, making the system usable, even in emergency care, on today's hardware. CONCLUSION: GenePING is the first personal health record management system to support the efficient and secure storage and sharing of large genomic datasets. GenePING is available online at http://ping.chip.org/genepinghtml, licensed under the LGPL.