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Background Recruiting healthcare providers as research subjects often rely on in-person recruitment strategies. Little is known about recruiting provider participants via electronic recruitment methods. In this study, conducted during the COVID-19 pandemic, we describe and evaluate a primarily electronic approach to recruiting primary care providers (PCPs) as subjects in a pragmatic randomized controlled trial (RCT) of a decision support intervention. Methods We adapted an existing framework for healthcare provider research recruitment, employing an electronic consent form and a mix of brief synchronous video presentations, email, and phone calls to recruit PCPs into the RCT. To evaluate the success of each electronic strategy, we estimated the number of consented PCPs associated with each strategy, the number of days to recruit each PCP and recruitment costs. Results We recruited 45 of 63 eligible PCPs practicing at ten primary care clinic locations over 55 days. On average, it took 17 business days to recruit a PCP (range 0–48) and required three attempts (range 1–7). Email communication from the clinic leaders led to the most successful recruitments, followed by brief synchronous video presentations at regularly scheduled clinic meetings. We spent approximately $89 per recruited PCP. We faced challenges of low email responsiveness and limited opportunities to forge relationships. Conclusion PCPs can be efficiently recruited at low costs as research subjects using primarily electronic communications, even during a time of high workload and stress. Electronic peer leader outreach and synchronous video presentations may be particularly useful recruitment strategies. Trial registration ClinicalTrials.gov, NCT04295135. Registered 04 March 2020.

Objective Given time constraints, poorly organized information, and complex patients, primary care providers (PCPs) can benefit from clinical decision support (CDS) tools that aggregate and synthesize problem-specific patient information. First, this article describes the design and functionality of a CDS tool for chronic noncancer pain in primary care. Second, we report on the retrospective analysis of real-world usage of the tool in the context of a pragmatic trial. Materials and methods The tool known as OneSheet was developed using user-centered principles and built in the Epic electronic health record (EHR) of 2 health systems. For each relevant patient, OneSheet presents pertinent information in a single EHR view to assist PCPs in completing guideline-recommended opioid risk mitigation tasks, review previous and current patient treatments, view patient-reported pain, physical function, and pain-related goals. Results Overall, 69 PCPs accessed OneSheet 2411 times (since November 2020). PCP use of OneSheet varied significantly by provider and was highly skewed (site 1: median accesses per provider: 17 [interquartile range (IQR) 9–32]; site 2: median: 8 [IQR 5–16]). Seven “power users” accounted for 70% of the overall access instances across both sites. OneSheet has been accessed an average of 20 times weekly between the 2 sites. Discussion Modest OneSheet use was observed relative to the number of eligible patients seen with chronic pain. Conclusions Organizations implementing CDS tools are likely to see considerable provider-level variation in usage, suggesting that CDS tools may vary in their utility across PCPs, even for the same condition, because of differences in provider and care team workflows.

BACKGROUND: The objective of this qualitative study is to better understand primary care clinician decision making for managing chronic pain. Specifically, we focus on the factors that influence changes to existing chronic pain management plans. Limitations in guidelines and training leave clinicians to use their own judgement and experience in managing the complexities associated with treating patients with chronic pain. This study provides insight into those judgments based on clinicians’ first-person accounts. Insights gleaned from this study could inspire innovations aimed at supporting primary care clinicians (PCCs) in managing chronic pain. METHODS: We conducted 89 interviews with PCCs to obtain their first-person perspective of the factors that influenced changes in treatment plans for their patients. Interview transcripts were analyzed thematically by a multidisciplinary team of clinicians, cognitive scientists, and public health researchers. RESULTS: Seven themes emerged through our analysis of factors that influenced a change in chronic pain management: (1) change in patient condition, (2) outcomes related to treatment, (3) non-adherent patient behavior, (4) insurance constraints, (5) change in guidelines, laws, or policies, (6) approaches to new patients, and (7) specialist recommendations. CONCLUSIONS: Our analysis sheds light on the factors that lead PCCs to change treatment plans for patients with chronic pain. An understanding of these factors can inform the types of innovations needed to support PCCs in providing chronic pain care. We highlight key insights from our analysis and offer ideas for potential practice innovations.

Due to public health measures enacted in response to the Covid-19 pandemic, educators and students alike have been suddenly thrust into the realm of online learning. To better understand how active and collaborative learning methods can apply to students studying in isolation, we compared the effects of two teach-and-question assignments: one that utilizes the active learning method of reciprocal peer tutoring and a solo version that requires individual verbalized studying and elaborative interrogation. We used a quasi-experimental design, with student participants enrolled in an online introductory human anatomy course. The first treatment group completed regular teach-and-question study assignments virtually with a peer, and the second treatment group completed the same assignment independently. We found no differences in exam scores between treatments, even for students with high social anxiety; however, student attitudes about the social versus individual assignment did differ for specific types of students. Students who reported experiencing high social anxiety preferred completing the active learning exercise by themselves, and students with low scientific reasoning ability preferred the partnered assignment. This research has potential implications for online classrooms. For instance, our results indicate that students who study independently, or in isolation, may have learning outcomes similar to those of students who study with a peer as long as they study actively. Because we found no negative impact on examination results, it also could be that virtually partnered or independent teach-and-question assignments could be helpful for instructors teaching large online classes to ensure all students are getting individualized feedback and attention.

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Introduction OpenMRS is an open source medical record system that was first released in 2004. This research study analyzed OpenMRS implementations by conducting a survey of implementers and by reviewing publicly available data reported to the OpenMRS Community to learn about the utilization and impact of OpenMRS over the past 15 years. Methods Data about the use of OpenMRS were collected by conducting a survey of OpenMRS implementers that included both quantitative and qualitative questions. Data were also gathered from the OpenMRS community-hosted Atlas website and the OpenMRS Community Annual report to arrive at a comprehensive view of OpenMRS implementations. Results OpenMRS has been implemented in over 62 countries worldwide (Community Annual report). The survey was responded to by 16 organizations with projects spanning 16 countries, which were launched over 15 years (2004–2019). Fourteen of these sites reported a total of 1,436,357 patients; 4,248,248 visits; 18,028,204 encounters; 312,068,205 observations; and 5088 users, of which 3933 were health providers, recorded in the system database. Implementers reported a positive impact from implementing OpenMRS in streamlining operational processes for healthcare delivery; improved interoperability; improved reporting; improved availability and quality of data for decision making, advocacy, and research; and, improvement in the quality of healthcare delivery. Key challenges in implementing OpenMRS included finding skilled technical staff; acceptability of electronic health records by clinical staff; poor training provided to staff when transitioning from a paper-based to an electronic system; technical challenges, including infrastructure availability (computers, servers, equipment, connectivity, power); missing clinical/programmatic functionality in OpenMRS; poor documentation; and, difficulties faced when contributing code to the open source project. Conclusion OpenMRS has a broad reach globally in a variety of settings. Organizations have reported a positive impact on health care delivery after implementing OpenMRS. Several risks and challenges were identified by implementers that need to be addressed to deliver successful implementations. Continued investment in the development of OpenMRS is needed to sustain and scale its impact.

SummaryObjectives: Despite significant awareness on the value of leveraging patient relationships across the healthcare continuum, there is no research on the potential of using Electronic Health Record (EHR) systems to store structured patient relationship data, or its impact on enabling better health-care. We sought to identify which EHR systems supported effective patient relationship data collection, and for systems that do, what types of relationship data is collected, how this data is used, and the perceived value of doing so.Materials and methods: We performed a literature search to identify EHR systems that supported patient relationship data collection. Based on our results, we defined attributes of an effective patient relationship model. The Open Medical Record System (OpenMRS), an open source medical record platform for underserved settings met our eligibility criteria for effective patient relationship collection. We performed a survey to understand how the OpenMRS patient relationship model was used, and how it brought value to implementers.Results: The OpenMRS patient relationship model has won widespread adoption across many implementations and is perceived to be valuable in enabling better health care delivery. Patient relationship information is widely used for community health programs and enabling chronic care. Additionally, many OpenMRS implementers were using this feature to collect custom relationship types for implementation specific needs.Conclusions: We believe that flexible patient relationship data collection is critical for better healthcare, and can inform community care and chronic care initiatives across the world. Additionally, patient relationship data could also be leveraged for many other initiatives such as patient centric care and in the field of precision medicine.

Background For complex patients with chronic conditions, electronic health records (EHRs) contain large amounts of relevant historical patient data. To use this information effectively, clinicians may benefit from visual information displays that organize and help them make sense of information on past and current treatments, outcomes, and new treatment options. Unfortunately, few clinical decision support tools are designed to support clinical sensemaking. Objective The objective of this study was to describe a decision-centered design process, and resultant interactive patient information displays, to support key clinical decision requirements in chronic noncancer pain care. Methods To identify key clinical decision requirements, we conducted critical decision method interviews with 10 adult primary care clinicians. Next, to identify key information needs and decision support design seeds, we conducted a half-day multidisciplinary design workshop. Finally, we designed an interactive prototype to support the key clinical decision requirements and information needs uncovered during the previous research activities. Results The resulting Chronic Pain Treatment Tracker prototype summarizes the current treatment plan, past treatment history, potential future treatments, and treatment options to be cautious about. Clinicians can access additional details about each treatment, current or past, through modal views. Additional decision support for potential future treatments and treatments to be cautious about is also provided through modal views. Conclusion This study designed the Chronic Pain Treatment Tracker, a novel approach to decision support that presents clinicians with the information they need in a structure that promotes quick uptake, understanding, and action.

Decision support system designs often do not align with the information environments in which clinicians work. These work environments may increase Clinicians’ cognitive workload and harm their decision making. The objective of this study was to identify information needs and decision support requirements for assessing, diagnosing, and treating chronic noncancer pain in primary care. We conducted a qualitative study involving 30 interviews with 10 primary care clinicians and a subsequent multidisciplinary systems design workshop. Our analysis identified four key decision requirements, eight clinical information needs, and four decision support design seeds. Our findings indicate that clinicians caring for chronic pain need decision support that aggregates many disparate information elements and helps them navigate and contextualize that information. By attending to the needs identified in this study, decision support designers may improve Clinicians’ efficiency, reduce mental workload, and positively affect patient care quality and outcomes.

Healthcare is an information business with expanding use of information and communication technologies (ICTs). Current ICT tools are immature, but a brighter future looms. We examine 7 areas of ICT in healthcare: electronic health records (EHRs), health information exchange (HIE), patient portals, telemedicine, social media, mobile devices and wearable sensors and monitors, and privacy and security. In each of these areas, we examine the current status and future promise, highlighting how each might reach its promise. Steps to better EHRs include a universal programming interface, universal patient identifiers, improved documentation and improved data analysis. HIEs require federal subsidies for sustainability and support from EHR vendors, targeting seamless sharing of EHR data. Patient portals must bring patients into the EHR with better design and training, greater provider engagement and leveraging HIEs. Telemedicine needs sustainable payment models, clear rules of engagement, quality measures and monitoring. Social media needs consensus on rules of engagement for providers, better data mining tools and approaches to counter disinformation. Mobile and wearable devices benefit from a universal programming interface, improved infrastructure, more rigorous research and integration with EHRs and HIEs. Laws for privacy and security need updating to match current technologies, and data stewards should share information on breaches and standardize best practices. ICT tools are evolving quickly in healthcare and require a rational and well-funded national agenda for development, use and assessment.

Recent focus on Precision medicine (PM) has led to a flurry of research activities across the developed world. But how can understaffed and underfunded health care systems in the US and elsewhere evolve to adapt PM to address pressing healthcare needs? We offer guidance on a wide range of sources of healthcare data / knowledge as well as other infrastructure / tools that could inform PM initiatives, and may serve as low hanging fruit easily adapted on the incremental pathway towards a PM based healthcare system. Using these resources and tools, we propose an incremental adoption pathway to inform implementers working in underserved communities around the world on how they should position themselves to gradually embrace the concepts of PM with minimal interruption to existing care delivery.

Objective Regenstrief Institute developed one of the seminal computerized order entry systems, the Medical Gopher, for implementation at Wishard Hospital nearly three decades ago. Wishard Hospital and Regenstrief remain committed to homegrown software development, and over the past 4 years we have fully rebuilt Gopher with an emphasis on usability, safety, leveraging open source technologies, and the advancement of biomedical informatics research. Our objective in this paper is to summarize the functionality of this new system and highlight its novel features. Materials and methods Applying a user-centered design process, the new Gopher was built upon a rich-internet application framework using an agile development process. The system incorporates order entry, clinical documentation, result viewing, decision support, and clinical workflow. We have customized its use for the outpatient, inpatient, and emergency department settings. Results The new Gopher is now in use by over 1100 users a day, including an average of 433 physicians caring for over 3600 patients daily. The system includes a wizard-like clinical workflow, dynamic multimedia alerts, and a familiar ‘e-commerce’-based interface for order entry. Clinical documentation is enhanced by real-time natural language processing and data review is supported by a rapid chart search feature. Discussion As one of the few remaining academically developed order entry systems, the Gopher has been designed both to improve patient care and to support next-generation informatics research. It has achieved rapid adoption within our health system and suggests continued viability for homegrown systems in settings of close collaboration between developers and providers.

Objectives Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and “off the shelf” tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. Materials and methods We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Results Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Conclusion Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing “off the shelf” approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches.

Objective Little evidence exists on effective interventions to integrate HIV-care guidelines into practices within developing countries. This study tested the hypothesis that clinical summaries with computer-generated reminders could improve clinicians' compliance with CD4 testing guidelines in the resource-limited setting of sub-Saharan Africa. Design A prospective comparative study of two randomly selected outpatient adult HIV clinics in western Kenya. Printed summaries with reminders for overdue CD4 tests were made available to clinicians in the intervention clinic but not in the control clinic. Measurements Changes in order rates for overdue CD4 tests were compared between and within the two clinics. Results The computerized reminder system identified 717 encounters (21%) with overdue CD4 tests. Analysis by study assignment (regardless of summaries being printed or not) revealed that with computer-generated reminders, CD4 order rates were significantly higher in the intervention clinic compared to the control clinic (53% vs 38%, OR=1.80, CI 1.34 to 2.42, p

Forage kochia [Kochia prostrata (L.) Schrad.] has been shown to be nutritious fall and winter forage on western U.S. rangelands; however, its utilization by livestock is not well understood. This study compared relative cattle preferences among forage kochia accessions, alfalfa (Medicago sativa L.), and winterfat (Krascheninnikovia spp.), and how they related to nutritional and morphological characteristics. Relative preference was measured as biomass consumed on experimental plots near Promontory, UT, during September of 2002 and 2003. Preference differences (P < 0.01) were found among forage kochia accessions; however, all were consumed to some level, ranging from 71 to 35% of biomass consumed. Traits highly associated with biomass consumed included pregrazing biomass (r = 0.93), phenological maturity (r = −0.85), branch density (r = 0.80), and leafiness (r = 0.70) suggesting that productive, leafy, less mature plants are preferred by cattle. Crude protein, fiber, and digestibility were moderately associated with cattle preference but were not consistent predictors. Overall, we conclude that forage kochia is a palatable shrub for grazing during the fall, with preference and utilization of some entries being comparable to alfalfa and greater than winterfat. Grazing behavior also suggested that forage kochia growing in a mixture with adapted grasses would enhance livestock utilization.

We sought to enable better interoperability and easy adoption of healthcare applications by developing a standardized domain independent Application Programming Interface (API) for an Electronic Medical Record (EMR) system. We leveraged the modular architecture of the Open Medical Record System (OpenMRS) to build a Fast Healthcare Interoperability Resources (FHIR) based add-on module that could consume FHIR resources and requests made on OpenMRS. The OpenMRS FHIR module supports a subset of FHIR resources that could be used to interact with clinical data persisted in OpenMRS. We demonstrate the ease of connecting healthcare applications using the FHIR API by integrating a third party Substitutable Medical Apps & Reusable Technology (SMART) application with OpenMRS via FHIR. The OpenMRS FHIR module is an optional component of the OpenMRS platform. The FHIR API significantly reduces the effort required to implement OpenMRS by preventing developers from having to learn or work with a domain specific OpenMRS API. We propose an integration pathway where the domain specific legacy OpenMRS API is gradually retired in favor of the new FHIR API, which would be integrated into the core OpenMRS platform. Our efforts indicate that a domain independent API is a reality for any EMR system. These efforts demonstrate the adoption of an emerging FHIR standard that is seen as a replacement for both Health Level 7 (HL7) Version 2 and Version 3. We propose a gradual integration approach where our FHIR API becomes the preferred method for communicating with the OpenMRS platform.

Display Omitted Cancer cases can be identified in unstructured clinical data to support public health reporting.Such cancer detection methods do not require complex external ontologies or human intervention.Such approaches can identify cases with sensitivity, specificity, PPV, accuracy, and AUC exceeding 80-90%.Automated cancer detection methods perform as well as approaches that require costly clinician input.These approaches may be generalized for other health analytics applications and healthcare domains. ObjectivesIncreased adoption of electronic health records has resulted in increased availability of free text clinical data for secondary use. A variety of approaches to obtain actionable information from unstructured free text data exist. These approaches are resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches. We sought to evaluate the potential to obtain actionable information from free text pathology reports using routinely available tools and approaches that do not depend on dictionary-based approaches. Materials and methodsWe obtained pathology reports from a large health information exchange and evaluated the capacity to detect cancer cases from these reports using 3 non-dictionary feature selection approaches, 4 feature subset sizes, and 5 clinical decision models: simple logistic regression, naive bayes, k-nearest neighbor, random forest, and J48 decision tree. The performance of each decision model was evaluated using sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. ResultsDecision models parameterized using automated, informed, and manual feature selection approaches yielded similar results. Furthermore, non-dictionary classification approaches identified cancer cases present in free text reports with evaluation measures approaching and exceeding 80-90% for most metrics. ConclusionOur methods are feasible and practical approaches for extracting substantial information value from free text medical data, and the results suggest that these methods can perform on par, if not better, than existing dictionary-based approaches. Given that public health agencies are often under-resourced and lack the technical capacity for more complex methodologies, these results represent potentially significant value to the public health field.

The Indiana Network for Patient Care (INPC) is a local health information infrastructure (LHII) that includes information from the five major hospital systems (fifteen separate hospitals), the county and state public health departments, and Indiana Medicaid and RxHub and that carries 660 million separate results. It provides cross-institutional access to physicians in emergency rooms and hospitals based on patient-physician proximity or on hospital credentialing. The network includes and delivers laboratory, radiology, dictation, and other documents to a majority of Indianapolis office practices. The INPC began operation seven years ago and is one of the first and best examples of an LHII.

Two papers in this issue of JAMIA discuss computerized physician order entry (CPOE) and a third one discusses patient clinical information systems (PCISs), which often include CPOE. The first paper, by Ash and colleagues, simply reports the rate at which U.S. hospitals and their care providers are adopting physician order entry systems.1 The other two challenge the current push toward rapid adoption of CPOE and PCIS in the health care industry—Berger and Kichak,2 by challenging the evidence base for the push, and Ash et al.,3 by calling attention to many failure points that occur when rigid computer system designs meet the reality of really complex clinical systems. Before commenting on these papers, we should confess our long-term infatuation with computers and a 30-year conviction that computers could be the “chicken soup” for many illnesses of the health care system. We proved that computer reminders systems are chicken soup for preventive care in a series of studies starting in 1976.4–7 Then, during the early 1980s, we spent our nights and weekends and as much time as we could scrape from our workday writing, testing, implementing, and studying software for what would become the Medical Gopher ,8 the first PC-based order entry system used, and studied, in outpatient care.9–11 More years were required to tune and adapt this system, born in an outpatient setting, for an inpatient service. Then we performed the first, and what may be the only, randomized trial of CPOE in the hospital and proved that our Medical Gopher order entry system is chicken soup to hospital inefficiency. It reduced the cost of care and improved the workflow among CPOE users by 13% compared with the control group who used the traditional paper orders. An example of its benefit …

The discipline of clinical informatics endeavors to improve the process and outcomes of health care by enabling efficient access to information. Care providers can then use this information, both in the form of medical knowledge and in the form of patient data collected during clinical practice, to make decisions and comply with appropriate standards of care. The Regenstrief Institute began work on clinical information systems in 1972, when Dr. Clement McDonald and colleagues conceptualized and began construction of a computerized patient management system for outpatient diabetes care, developed to meet three primary goals: first, it was built to eliminate the problems inherent in paper records by making clinical data available to authorized users “just-in-time” as medical decisions are made; second, it was designed to aid in the recognition of diagnoses and adoption of pertinent care practices by assisting clinicians during their record-keeping activities; third, the system was designed to aggregate and analyze clinical information to be used in health care support systems, such as those for public health, health services research, and quality improvement. The first installation of the Regenstrief Medical Record System (RMRS) at Wishard Memorial Hospital occurred in 1974 and, over the next few years, the use of this system expanded outside of the diabetic clinic into a few of the hospital’s many general medicine clinics. From early in its history, the Regenstrief system has included mechanisms for tailoring rules based on the data, to generate reminders and alerts to care providers. This chapter provides a history of the development and growth of the RMRS into a region-wide source of clinical data, the Indiana Network for Patient Care (INPC), and a summary of the research on the decision support interventions themselves, made possible by this infrastructure. Additionally, lessons learned throughout the more than 30 years of experience in both building and maintaining this system are detailed, alongside some reflections that may be useful for future system builders.

Few studies have focused on how youth develop agency to organize and participate in online unstructured creative collaborations. This paper describes and analyzes how youth programmers organized collaborative groups in response to a programming “Collab Challenge” in the Scratch Online Community and in an accompanying workshop with high school students. The analyses focused on modalities of online collaborations, determined the breadth of online participation, and examined local teens’ awareness of the online community. The discussion addresses youth’s collaborative agency in these new networked contexts, studied the role that online social awareness plays in completing tasks and makes recommendations for the support of online programming communities.

Purpose: We implemented computer-based reminders for CD4 count tests at an HIV clinic in Western Kenya though an open-source Electronic Medical Record System. Within a month, providers had stopped complying with the reminders. Methods: We used a multi-method qualitative approach to determine reasons for failure to adhere to the reminders, and took multiple corrective actions to remedy the situation. Results: Major reasons for failure of the reminder system included: not considering delayed data entry and pending test results; relying on wrong data inadvertently entered into the system; inadequate training of providers who would sometimes disagree with the reminder suggestions; and resource issues making generation of reminders unreliable. With appropriate corrective actions, the reminder system has now been functional for over eight months. Conclusion: Implementing clinical decision support in resource-limited settings is challenging. Understanding and correcting root causes of problems related to reminders will facilitate successful implementation of the decision support systems in these settings.

Objective OpenMRS (www.openmrs.org) is a configurable open source electronic medical record application developed and maintained by a large network of open source developers coordinated by the Regenstrief Institute and Partners in Health and mainly used for HIV patient and treatment information management in Africa. Our objective is to develop an open Implementers Network for OpenMRS to provide regional support for the growing number of OpenMRS implementations in Africa and to include African developers and implementers in the future growth of OpenMRS. Methods We have developed the OpenMRS Implementers Network using a dedicated Wiki site and e-mail server. We have also organized annual meetings in South Africa and regional training courses at African locations where OpenMRS is being implemented. An OpenMRS Internship program has been initiated and we have started collaborating with similar networks and projects working in Africa. To evaluate its potential, OpenMRS was implemented initially at one site in South Africa by a single implementer using a downloadable OpenMRS application and only the OpenMRS Implementers Network for support. Results The OpenMRS Implementers Network Wiki and list server have grown into effective means of providing implementation support and forums for exchange of implementation experiences. The annual OpenMRS Implementers meeting has been held in South Africa for the past three years and is attracting successively larger numbers of participants with almost 200 implementers and developers attending the 2008 meeting in Durban, South Africa. Six African developers are presently registered on the first intake of the OpenMRS Internship program. Successful collaborations have been started with several African developer groups and projects initiated to develop interoperability between OpenMRS and various applications. The South African OpenMRS Implementer group successfully configured, installed and maintained an integrated HIV/TB OpenMRS application without significant programming support. Since then, this model has been replicated in several other African sites. Conclusions The OpenMRS Implementers Network has contributed substantially to the growth and sustainability of OpenMRS in Africa and has become a useful way of including Africans in the development and implementation of OpenMRS in developing countries. The Network provides valuable support and enables a basic OpenMRS application to be implemented in the absence of onsite programmers.

The challenge of scaling up HIV treatment in Africa has led to a new emphasis on improving health systems in impoverished areas. One aspect of this is the development and deployment of electronic medical record systems to support HIV and TB treatment. In this paper we describe the design and implementation of a new medical record architecture to support an HIV treatment program in rural Rwanda. The architecture is called OpenMRS and it has been developed to address the problem of configuring EMR systems to suit new sites, languages and diseases. OpenMRS uses a data dictionary called the concept dictionary to represent all the possible data items that can be collected. This allows new items to be added to the system by non-programmers. In addition, there are form creation tools that use drag and drop web technologies to simplify form construction. The OpenMRS system was first implemented in Kenya in February 2006 and then in Rwanda in August 2006. The system is now functioning well and we are developing extensions to improve the support for the clinic. These include improved, easy to use reporting tools, support for additional clinical problems including nutrition and child health, better database synchronization tools, and modules to collect laboratory data and support the pharmacy. The system is also in use in South Africa and Lesotho and is being deployed in Tanzania and Uganda.

Providing high-quality HIV/AIDS care requires high-quality, accessible data on individual patients and visits. These data can also drive strategic decision-making by health systems, national programs, and funding agencies. One major obstacle to HIV/AIDS care in developing countries is lack of electronic medical record systems (EMRs) to collect, manage, and report clinical data. In 2001, we implemented a simple primary care EMR at a rural health centre in western Kenya. This EMR evolved into a comprehensive, scalable system serving 19 urban and rural health centres. To date, the AMPATH Medical Record System contains 10 million observations from 400,000 visit records on 45,000 patients. Critical components include paper encounter forms for adults and children, technicians entering/managing data, and modules for patient registration, scheduling, encounters, clinical observations, setting user privileges, and a concept dictionary. Key outputs include patient summaries, care reminders, and reports for program management, operating ancillary services (e.g., tracing patients who fail to return for appointments), strategic planning (e.g., hiring health care providers and staff), reports to national AIDS programs and funding agencies, and research.

In this chapter, we review a timeline of Regenstrief Medical Record System (RMRS) studies related to decision support, describe the system’s architecture, and discuss the lessons we have learned from over a quarter century of experience with clinical decision support systems (CDSS). From the beginning, the RMRS has included mechanisms for writing rules for generating reminders to physicians, based on clinical data, including laboratory results, visit diagnosis, coded medications prescribed in the clinic, and vital signs collected on encounter forms.

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.

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.

Publisher Summary Regenstrief scientists have created a 40-year legacy of experience with decision support systems. Among these experiences are many lessons that are helpful to the larger informatics community. The Institute has demonstrated the value of a concurrent focus on standardized, structured data acquisition alongside the development of decision support systems. Having access to these data allowed researchers to perform dozens of reminder studies, many of which serve as foundations for other work in the medical informatics community. The research team has demonstrated that computerized reminders can change clinical behaviors, reduce errors, and improve adherence to practice guidelines. These changes have a strong and persistent effect on patient care. They also promote preventive medicine in both the outpatient and inpatient settings, and have a greater effect than delayed feedback for enhancing preventive care. A series of lessons are culled from the experience inherent in not only developing these systems from scratch, but additionally in serving as both the implementation and direct support team.

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.

Several experimental lines of forage kochia [Kochia prostrata (L. Shrad.)] have potential to provide taller forage on fall and winter rangelands than the only released variety of forage kochia, Immigrant. This study was conducted to determine differences in cattle utilization among experimental lines of forage kochia and relate them to forage quality and morphological attributes. The utilization of forage kochia was also compared to 'Ladak ' alfalfa [Medicago saliva (L.)] and two entries of winterfat [Krashnennikovia species (Guldenstaedt)]. Cattle utilization was determined by calculating a biomass consumed value and a percent biomass consumed value. Dry weights for each plant were taken after grazing and subtracted from a pre-grazing dry weight (predicted using a quadratic regression equation) that produced the biomass consumed value. The percent consumed value was calculated by dividing biomass consumed by pre-grazing dry weight. Ocularly estimated utilization scores (OU) were also used to measure utilization. Forage quality traits were analyzed on each entry using near infrared reflectance spectroscopy. Morphologic and phenological characteristics potentially related to utilization were made by physical measurements and visual evaluations. There were significant differences among forage kochia accessions for all traits evaluated. Four entries, including Immigrant and Ladak alfalfa, were significantly higher in percent herbage consumed than the other entries . These same entries were consistently high in OU ratings , forage quality analyses , and most morphological characteristics. Pre-grazing dry weight (r = -0.96 , P < 0.0001) , stem length (r = 0.67, P < 0.0001), and branch density (r = 0.63, P < 0.0001) were all highly correlated with utilization. Crude protein (partial R2 = 0.253 , P ��� 0.0001) was the only forage quality trait associated with utilization using a stepwise regression to predict utilization. From this study we conclude that some forage kochia accessions are very palatable. They are comparable to Ladak alfalfa, and utilized more than Krashnikova spp., a desirable forage on western rangelands.

Aims: HIV-related renal diseases are increasingly prevalent and are associated with proteinuria and rapid progression to end-stage renal failure. Early treatment with highly active antiretroviral therapy (HAART) and ACE inhibition may prevent the development of chronic kidney disease (CKD), but studies evaluating the epidemiology of proteinuria and early CKD in HIV-infected patients are lacking. Methods: All consecutive patients at Wishard Memorial Hospital (Indiana University) whose initial HIV documentation occurred from 1990 to 1998, were retrospectively studied using a computerized medical record system. Clinical data were abstracted from time of first HIV documentation through 12/31/2000. The proportions of patients who developed CKD (doubling of serum creatinine from an initial level < 1.5 mg/dl) and who had proteinuria (≥ 1+ protein on the first urine dipstick after HIV documentation) were calculated. Case mix and laboratory variables at the time of HIV documentation were compared between those who did and did not develop CKD and between those who had and did not have initial proteinuria. Results: Of 487 subjects with initially normal renal function, 10 (2% (95% Cl, 1 - 4%)) developed CKD. In univariable analysis, black race, a diagnosis of diabetes or hypertension and proteinuria were all significantly associated with the development of CKD; 89 (29% (95% CI, 24 - 35%)) of 289 evaluable subjects had ≥ 1+ proteinuria on urine analysis. Multivariable regression revealed only older age (OR 1.08 per year increase; 95% CI, 1.03 - 1.14) to be associated with proteinuria. Conclusions: A small, but potentially clinically meaningful proportion of HIV-infected patients develop CKD, and there appears to be a high prevalence of proteinuria on the first urine analysis obtained after HIV documentation.

We describe the performance of a particular natural language processing system that uses knowledge vectors to extract findings from radiology reports. LifeCode® (A-Life Medical, Inc.) has been successfully coding reports for billing purposes for several years. In this study, we describe the use of LifeCode® to code all findings within a set of 500 cancer-related radiology reports against a test set in which all findings were manually tagged. The system was trained with 1400 reports prior to running the test set. Results: LifeCode® had a recall of 84.5% and precision of 95.7% in the coding of cancer-related radiology report findings. Conclusion: Despite the use of a modest sized training set and minimal training iterations, when applied to cancer-related reports the system achieved recall and precision measures comparable to other reputable natural language processors in this domain.

[1] The major magnetic storm of 4–5 June 1991 was well observed with the Combined Release and Radiation Experiment (CRRES) satellite in the duskside inner magnetosphere and with three Defense Meteorological Satellite Program (DMSP) spacecraft in the polar ionosphere. These observations are compared to results from the Rice Convection Model (RCM), which calculates the inner magnetospheric electric field and particle distribution self-consistently. This case study, which uses the most complete RCM runs to date, demonstrates two significant features of magnetospheric storms, the development of subauroral polarization streams (SAPS) and plasma-sheet particle injection deep into the inner magnetosphere. In particular, the RCM predicts the electric field peak near L = 4 that is observed by the CRRES satellite during the second injection. The RCM calculations and DMSP data both show SAPS events with similar general characteristics, though there is no detailed point-by-point agreement. In the simulation, SAPS are generated by the deep penetration of plasma sheet protons to L < 4 and Earthward of the plasma sheet electrons. Similarly, the vast majority of the ions that make up the storm-time ring current came from the plasma sheet; most of the particles that made up the prestorm quiet-time ring current escaped through the dayside magnetopause during ring current injection. The RCM demonstrates the capability of plasma sheet ions to reach all ring current orbits and predicts the location of the injected particles (both ions and electrons) reasonably well. However, it overpredicts the ion flux in the inner magnetosphere.