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Motivated by the need for readily available data for testing an open-source health information exchange platform, we developed and evaluated two methods for generating synthetic messages. The methods used HL7 version 2 messages obtained from the Indiana Network for Patient Care. Data from both methods were analyzed to assess how effectively the output reflected original 'real-world' data. The Markov Chain method (MCM) used an algorithm based on transitional probability matrix while the Music Box model (MBM) randomly selected messages of particular trigger type from the original data to generate new messages. The MBM was faster, generated shorter messages and exhibited less variation in message length. The MCM required more computational power, generated longer messages with more message length variability. Both methods exhibited adequate coverage, producing a high proportion of messages consistent with original messages. Both methods yielded similar rates of valid messages.

Environmental tobacco smoke (ETS) exposure remains an important cause of morbidity and mortality in children. Pediatricians are well positioned to help smoking parents quit. Parents who smoke may be particularly responsive to advice to quit, repeated smoking cessation messages can be effective, and parents visit the pediatrician 8-10 times for well care in the first two years of their child's life. Yet most pediatricians do not provide smoking cessation advice. We developed a parental smoking cessation module for an established pediatric primary care decision support system (CDSS) that runs as a front-end to the Regenstrief Medical Record System. The system collects data directly from parents and guides the physician through smoking cessation counseling, using stages of change. We present the CDSS and the smoking module as well as descriptive data from our smoking cessation system. We also describe a randomized controlled trial of the system that is now underway.

The challenges of creating and maintaining concept dictionaries are compounded in resource-limited settings. Approaches to alleviate this burden need to be based on information derived in these settings. We created a concept dictionary and evaluated new concept proposals for an open source EMR in a resource-limited setting. Overall, 87% of the concepts in the initial dictionary were used. There were 5137 new concepts proposed, with 77% of these proposed only once. Further characterization of new concept proposals revealed that 41% were due to deficiency in the existing dictionary, and 19% were synonyms to existing concepts. 25% of the requests contained misspellings, 41% were complex terms, and 17% were ambiguous. Given the resource-intensive nature of dictionary creation and maintenance, there should be considerations for centralizing the concept dictionary service, using standards, prioritizing concept proposals, and redesigning the user-interface to reduce this burden in settings with limited resources.

Millions of people are continue to die each year from HIV/AIDS. The majority of infected persons (>95%) live in the developing world. A worthy response to this pandemic will require coordinated, scalable, and flexible information systems. We describe the OpenMRS system, an open source, collaborative effort that can serve as a foundation for EMR development in developing countries. We report our progress to date, lessons learned, and future directions.

Clinical guideline authors, health information technology (HIT) standards development organizations, and information system implementers all work to improve the processes of healthcare, but have long functioned independently towards realizing these goals. This has led to clinical standards of care that often poorly align with the functional and technical HIT standards developed to realize them. We describe the shortcomings and inefficiencies inherent in this current process and introduce two national initiatives that attempt to unite these communities. The mission of these two initiatives is to create examples of unambiguous, decidable, and executable clinical guidelines which both utilize and inform HIT terminology and logical expression standards. All of the products of this work aim to facilitate enterprise-wide guideline implementation and create a rising tide which lifts all ships.

OpenMRS is an open source infrastructure for the creation of medical record systems in developing countries. Produced and maintained collaboratively across multiple institutions, this framework consists of an open source data model, a set of core application functions, and a default implementation. The goal of this implementation is to provide the beginnings of an EMR that is suitable for all groups involved with healthcare in developing countries.

Clinical guidelines translate complex research findings and expert opinion into actionable recommendations. However, the effectiveness of even evidence-based guidelines is rarely tested as a whole in a real clinical environment. We have developed a decision support system for implementing clinical guidelines in a busy pediatric practice. We have added to this system the ability to randomize patients to receive care with or without system support of the guideline or guideline components. The randomization is part of the Arden Syntax that implements the system logic. The result is a relatively effortless process for testing guidelines, as they are implemented, to assure that they are effective. We describe the system and the process by which this guideline evaluation functionality was built in, using two guidelines (asthma management and maternal depression screening) both of which have been applied to thousands of patients to date.

Capturing coded clinical data for clinical decision support can improve care, but cost and disruption of clinic workflow present barriers to implementation. Previous work has shown that tailored, scannable paper forms (adaptive turnaround documents, ATDs) can achieve the benefits of computer-based clinical decision support at low cost and minimal disruption of workflow. ATDs are highly accurate under controlled circumstances, but accuracy in the setting of busy clinics with untrained physician users is untested. We recently developed and implemented such a system and studied rates of errors attributable to physician users and errors in the system. Prompts were used in 63% of encounters. Errors resulting from incorrectly marking forms occurred in 1.8% of prompts. System errors occurred in 7.2% of prompts. Most system errors were failures to capture data and may represent human errors in the scanning process. ATDs are an effective way to collect coded data from physicians. Further automation of the scanning process may reduce system errors.

Millions of people are either living with or dying from HIV/AIDS; most of this living and dying is taking place in developing countries. There is an immediate need for electronic medical record systems to help scale up HIV/AIDS prevention and treatment programs, reduce critical human errors, and support the research necessary to guide future efforts. Several groups are working on this problem, but most of this work is occurring within silos. To be more effective, we must find ways to collaborate.

Millions of people are either living with or dying from HIV/AIDS; most of this living and dying is taking place in developing countries. There is an immediate need for electronic medical record systems to help scale up HIV/AIDS prevention and treatment programs, reduce critical human errors, and support the research necessary to guide future efforts. Several groups are working on this problem, but most of this work is occurring within silos. To be more effective, we must find ways to collaborate. We describe a system built on the 30+ years of experience at Regenstrief Institute to serve as the seed for building toward a common infrastructure. We discuss the design goals, data model, and implementation of a data entry component. Further details are available online at amrs.iukenya.org.

An ever-growing plethora of preventive services guidelines threatens to overwhelm primary care providers who are expected to recognize and prioritize these needed services for each patient. The Child Health Improvement through Computer Automation (CHICA) system was designed to facilitate this process through a workflow-sensitive interface that gathers and distills the most relevant patient data within pediatric settings. We evaluated family responses to 21 CHICA questions that assess risk factors and health behaviors over a three month period. 3005 patients provided 15,434 responses to these questions, and 1756 or 11.3% of these suggest risk factors which merit attention. This preliminary analysis suggests that, using CHICA, families identify significant risk factors that our clinicians acknowledge are often overlooked given the realities of practicing within this setting.

Although many organizations are beginning to develop strategies to implement and study regional and national health information exchanges, there are few operational examples to date. The Indiana Network for Patient Care (INPC) is an example of a currently operational Regional Health Information Organization (RHIO) built upon a foundation of open, robust healthcare information standards. Having demonstrated the scalability of this design, the Indiana State Department of Health (ISDH) contracted with the Regenstrief Institute to implement a statewide disease surveillance system incorporating encounter data from all 114 Indiana hospitals with emergency departments. We describe the 4-year implementation plan, including our design rationale and how we plan to address the specific implementation challenges of data collection, connectivity in diverse environments and current hospital buy-in. To date, 36 hospitals are in various stages of engagement, with 19 hospitals actively providing real-time surveillance data. We will discuss how this project creates the foundation for a potential statewide health information exchange.

We developed adaptive turnaround documents (ATDs) to address longstanding challenges inherent in acquiring structured data at the point of care. These computer-generated paper forms both request and receive patient tailored information specifically for electronic storage. In our pilot, we evaluated the usability, accuracy, and user acceptance of an ATD designed to enrich a pediatric preventative care decision support system. The system had an overall digit recognition rate of 98.6% (95% CI: 98.3 to 98.9) and a marksense accuracy of 99.2% (95% CI: 99.1 to 99.3). More importantly, the system reliably extracted all data from 56.6% (95% CI: 53.3 to 59.9) of our pilot forms without the need for a verification step. These results translate to a minimal workflow burden to end users. This suggests that ATDs can serve as an inexpensive, workflow-sensitive means of structured data acquisition in the clinical setting.