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Weight entry errors can cause significant patient harm in pediatrics due to pervasive weight-based dosing practices. While computerized algorithms can assist in error detection, they have not achieved high sensitivity and specificity to be further developed as a clinical decision support tool. To train an advanced algorithm, expert-annotated weight errors are essential but difficult to collect. In this study, we developed a visual annotation tool to gather large amounts of expertly annotated pediatric weight charts and conducted a formal user-centered evaluation. Key features of the tool included configurable grid sizes and annotation styles. The user feedback was collected through a structured survey and user clicks on the interface. The results show that the visual annotation tool has high usability (average SUS=86.4). Different combinations of the key features, however, did not significantly improve the annotation efficiency and duration. We have used this tool to collect expert annotations for algorithm development and benchmarking.

Weight entry errors can cause significant patient harm in pediatrics due to pervasive weight-based dosing practices. While computerized algorithms can assist in error detection, they have not achieved high sensitivity and specificity to be further developed as a clinical decision support tool. To train an advanced algorithm, expert-annotated weight errors are essential but difficult to collect. In this study, we developed a visual annotation tool to gather large amounts of expertly annotated pediatric weight charts and conducted a formal user-centered evaluation. Key features of the tool included configurable grid sizes and annotation styles. The user feedback was collected through a structured survey and user clicks on the interface. The results show that the visual annotation tool has high usability (average SUS=86.4). Different combinations of the key features, however, did not significantly improve the annotation efficiency and duration. We have used this tool to collect expert annotations for algorithm development and benchmarking.

Inaccurate body weight measures can cause critical safety events in clinical settings as well as hindering utilization of clinical data for retrospective research. This study focused on developing a machine learning-based automated weight abnormality detector (AWAD) to analyze growth dynamics in pediatric weight charts and detect abnormal weight values. In two reference-standard based evaluation of real-world clinical data, the machine learning models showed good capacity for detecting weight abnormalities and they significantly outperformed the methods proposed in literature (p-value

Frequently overridden alerts in the electronic health record can highlight alerts that may need revision. This method is a way of fine-tuning clinical decision support. We evaluated the feasibility of a complementary, yet different method that directly involved pediatric emergency department (PED) providers in identifying additional medication alerts that were potentially incorrect or intrusive. We then evaluated the effect subsequent resulting modifications had on alert salience. Methods We performed a prospective, interventional study over 34 months (March 6, 2014, to December 31, 2016) in the PED. We implemented a passive alert feedback mechanism by enhancing the native electronic health record functionality on alert reviews. End-users flagged potentially incorrect/bothersome alerts for review by the study's team. The alerts were updated when clinically appropriate and trends of the impact were evaluated. Results More than 200 alerts were reported from both inside and outside the PED, suggesting an intuitive approach. On average, we processed 4 reviews per week from the PED, with attending physicians as major contributors. The general trend of the impact of these changes seems favorable. Discussion The implementation of the review mechanism for user-selected alerts was intuitive and sustainable and seems to be able to detect alerts that are bothersome to the end-users. The method should be run in parallel with the traditional data-driven approach to support capturing of inaccurate alerts. Conclusions User-centered, context-specific alert feedback can be used for selecting suboptimal, interruptive medication alerts.

Patient weights can be entered incorrectly into electronic health record (EHR) systems. These weight errors can cause significant patient harm especially in pediatrics where weight-based dosing is pervasively used. Determining weight errors through manual chart reviews is impractical in busy clinics, and current EHR alerts are rudimentary. To address these issues, we seek to develop an advanced algorithm to detect weight errors using supervised machine learning techniques. The critical first step is to collect labelled weight errors for algorithm training. In this paper, we designed and preliminarily evaluated a visual annotation tool using Agile software development to achieve the goal of supporting the rapid collection of expert-annotated weight errors. The design was based on the fact that weight errors are infrequent and medical experts can easily spot potential errors. The results show positive user feedback and prepared us for the formal user-centered evaluation as the next step.

Objective This study attempts to characterize the inpatient communication network within a quaternary pediatric academic medical center by applying network analysis methods to secure text-messaging data. Methods We used network graphing and statistical software to create network models of an inpatient communication system with secure text-messaging data from physicians, nurses, and other ancillary staff in an academic medical center. Descriptive statistics about the network, users within the network, and visualizations informed the team's understanding of the network and its components. Results Analysis of messages exchanged over approximately 23 days revealed a large, scale-free network with 4,442 nodes and 59,913 edges. Quantitative description of user behavior (messages sent and received) and network metrics (i.e., importance of nodes within a network) revealed several operational and clinical roles both sending and receiving > 1,000 messages over this time period. While some of these nodes represented expected “dispatcher” roles in our inpatient system, others occupied important frontline clinical roles responsible for bedside clinical care. Conclusion Quantitative and network analysis of secure text-messaging logs revealed several key operational and clinical roles at risk for alert fatigue and information overload. This analysis also revealed a communication network highly reliant on these key roles, meaning disruption to these individuals or their workflows could lead to dysfunction of the communication network. While secure text-messaging applications play increasingly important roles in facilitating inpatient communication, little is understood about the impact these systems have on health care providers. Developing methods to understand and optimize communication between inpatient providers might help operational and clinical leaders to proactively prevent poorly understood pitfalls associated with these systems and build resilient and effective communication structures.

Dosing errors due to erroneous body weight entry can be mitigated through algorithms designed to detect anomalies in weight patterns. To prepare for the development of a new algorithm for weight-entry error detection, we compared methods for detecting weight anomalies to human annotation, including a regression-based method employed in a real-time web service. Using a random sample of 4,000 growth charts, annotators identified clinically important anomalies with good inter-rater reliability. Performance of the three detection algorithms was variable, with the best performance from the algorithm that takes into account weights collected after the anomaly was recorded. All methods were highly specific, but positive predictive value ranged from < 5% to over 82%. There were 203 records of missed errors, but all of these were either due to no prior data points or errors too small to be clinically significant. This analysis illustrates the need for better weight-entry error detection algorithms.

Objectives: Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications.Materials and Methods: We created an order and alert analysis system that identified antibiotic medication orders and evaluated user response to dosing alerts. Orders associated with overridden alerts were examined for evidence of administration and the delivered dose was compared to pharmacy-derived dosing rules to confirm true overdoses. True overdose cases were reviewed for association with known ADEs.Results: Of 55 546 orders reviewed, 539 were true overdose orders, which lead to 1965 known overdose administrations. Documentation of loose stools and diarrhea was significantly increased following drug administration in the overdose group. Dosing rule thresholds were altered to reflect clinically accurate dosing. These rule changes decreased overall alert burden and improved the salience of alerts.Discussion: Electronic algorithm-based detection systems can identify antibiotic overdoses that are clinically relevant and are associated with known ADEs. The system also serves as a platform for evaluating the effects of modifying electronic dosing rules. These modifications lead to decreased alert burden and improvements in response to decision support alerts.Conclusion: The success of this test case suggests that gains are possible in reducing medication errors and improving patient safety with automated algorithm-based detection systems. Follow-up studies will determine if the positive effects of the system persist and if these changes lead to improved safety outcomes.

Objectives To examine healthcare worker's perceptions, expectations, and experiences regarding how work processes, patient-related safety, and care were affected when a quaternary care center transitioned from one computerized provider order entry (CPOE) system to a full electronic health record (EHR). Methods The I-SEE survey was administered prior to and 1-year after transition in systems. The construct validity and reliability of the survey was assessed within the current population and also compared to previously published results. Pre- and 1-year post-implementation scale means were compared within and across time periods. Results The majority of respondents were nurses and personnel working in the acute care setting. Because a confirmatory factor analysis indicated a lack of fit of our data to the I-SEE survey's 5-factor structure, we conducted an exploratory factor analysis that resulted in a 7-factor structure which showed better reliability and validity. Mean scores for each factor indicated that attitudes and expectations were mostly positive and score trends over time were positive or neutral. Nurses generally had less positive attitudes about the transition than non-nursing respondents, although the difference diminished after implementation. Conclusions Findings demonstrate that the majority of responding staff were generally positive about transitioning from CPOE system to a full electronic health record (EHR) and understood the goals of doing so, with overall improved ratings over time. In addition, the I-SEE survey, when modified based on our population, was useful for assessing patient care and safety related expectations and experiences during the transition from one CPOE system to an EHR.

The widespread adoption of electronic health records presents a number of benefits to the field of clinical genomics. They include the ability to return results to the practitioner, to use genetic findings in clinical decision support, and to have data collected in the electronic health record that serve as a source of phenotypic information for analysis purposes. Not all electronic health records are created equal, however. They differ in their features, capabilities, and ease of use. Therefore, to understand the potential of the electronic health record, it is first necessary to understand its capabilities and the impact that implementation strategy has on usability. Specifically, we focus on the following areas: (i) how the electronic health record is used to capture data in clinical practice settings; (ii) how the implementation and configuration of the electronic health record affect the quality and availability of data; (iii) the management of clinical genetic test results and the feasibility of electronic health record integration; and (iv) the challenges of implementing an electronic health record in a research-intensive environment. This is followed by a discussion of the minimum functional requirements that an electronic health record must meet to enable the satisfactory integration of genomic results as well as the open issues that remain. Genet Med 15 10, 786–791.

BACKGROUND: Although pediatric electronic prescribing systems are increasingly being used in pediatric care, many of these systems lack the clinical decision-support infrastructure needed to calculate a safe and effective rounded medication dose. This infrastructure is required to facilitate tailoring of established dosing guidance while maintaining the medication's therapeutic intent. OBJECTIVE: The goal of this project was to establish best practices for generating an appropriate medication dose and to create an interoperable rounding knowledge base combining best practices and dose-rounding information. METHODS: We interviewed 19 pediatric health care and pediatric pharmacy experts and conducted a literature review. After using these data to construct initial rounding tolerances, we used a Delphi process to achieve consensus about the rounding tolerance for each commonly prescribed medication. RESULTS: Three categories for medication-rounding philosophy emerged from our literature review: (1) medications for which rounding is used judiciously to retain the intended effect; (2) medications that are rounded with attention to potential unintended effects; and (3) medications that are rarely rounded because of the potential for toxicity. We assigned a small subset of medications to a fourth category—inadequate data—for which there was insufficient information to provide rounding recommendations. For all 102 medications, we were able to arrive at a consensus recommendation for rounding a given calculated dose. CONCLUSIONS: Results of this study provide the pediatric information technology community with a primary set of recommended rounding tolerances for commonly prescribed drugs. The interoperable knowledge base developed here can be integrated with existing and developing electronic prescribing systems, potentially improving prescribing safety and reducing cognitive workload.

A personal health record (PHR) is a repository of information from multiple contributors (eg, patient, family, guardians, physicians, and other health care professionals) regarding the health of an individual. The development of electronic PHRs presents new opportunities and challenges to the practice of pediatrics. This policy statement provides recommendations for actions that pediatricians can take to support the development and use of PHRs for children. Pediatric health care professionals must become actively involved in developing and adopting PHRs and PHR systems. The American Academy of Pediatrics supports development of: educational programs for families and clinicians on effective and efficient use of PHRs;incentives to facilitate PHR use and maintenance; andchild- and adolescent-friendly standards for PHR content, portability, security, and privacy. Properly designed PHR systems for pediatric care can empower patients. PHRs can improve access to health information, improve coordination of preventive health and health maintenance activities, and support emergency and disaster management activities. PHRs provide support for the medical home for all children, including those with special health care needs and those in foster care. PHRs can also provide information to serve as the basis for pediatric quality improvement efforts. For PHRs to be adopted sufficiently to realize these benefits, we must determine how best to support their development and adoption. Privacy and security issues, especially with regard to children and adolescents, must be addressed.

Progress in using information technology to achieve the goal of high-quality health care is hindered by the lack of coordinated data standards. To accelerate quality improvement in pediatrics, child health providers must participate actively in the creation of health care data standards. To do so will require far greater understanding on the part of pediatricians and other pediatric providers regarding the scope and role of data standards in advancing health information systems for children, as well as how data standards could improve quality in child health, what kinds of data standards hold the most promise for quality improvement in child health, and how child health professionals can engage in the work of creating data standards. Child health professionals in organized and academic medicine should participate in standards development organizations, to present the pediatric point of view as data standards emerge. They also should support efforts to certify electronic health record systems that include pediatric functionality. A major challenge to academic pediatrics is to prove that data standards can lead to improved health outcomes for children; this is only a compelling conjecture as of this writing.

US adoption of health information technology as a path to improved quality of patient care (effectiveness, safety, timeliness, patient-centeredness, efficiency, and equity) has been promoted by the medical community. Children and infants (especially those with special health care needs) are at higher risk than are adults for medical errors and their consequences (particularly in environments in which children are not the primary patient population). However, development and adoption of health information technology tools and practices that promote pediatric quality and patient safety are lagging. Two inpatient clinical processes—medication delivery and patient care transitions—are discussed in terms of health information technology applications that support them and functions that are important to pediatric quality and safety. Pediatricians and their partners (pediatric nurses, pharmacists, etc) must develop awareness of technical and adaptive issues in adopting these tools and collaborate with organizational leaders and developers as advocates for the best interests and safety of pediatric patients. Pediatric health information technology adoption cannot be considered in terms of applications (such as electronic health records or computerized physician order entry) alone but must be considered globally in terms of technical (health information technology applications), organizational (structures and workflows of care), and cultural (stakeholders) aspects of what is best.

More than 55 % of US hospitals have electronic health records (EHRs); frequently these contain computerized decision support (CDS) in the form of alerts. Alerts are a common form of CDS often implemented for medication ordering and decision support to improve patient care. EHRs implement rules supplied by third-party vendors to help guide the dosing process include weight-based dosing. Since many of these rules are conservative, they result in noisy alerting and are therefore overridden by users. Alert fatigue is commonly studied and reported by providers. EHR implementers customize these rules to reduce noise. Adverse drug events are a common occurrence, prevalent in both adult and pediatric populations. However, there are few automated ways to identify adverse drug events. Weight-based dosing guidance for medication orders has limited functionality if the patient’s body weight is entered incorrectly. Despite safeguards intended to prevent weight data-entry errors, erroneous weights exist in patients’ charts. These pose a safety threat to patients, especially inpatients, whose medication doses may be calculated from the last recorded weight. In this chapter we will give an overview of pediatric clinical decision support in EHRs, the different modes and forms of CDS, as well as diving into several related and specific areas of CDS – medication dosing alerts and the detection of weight data entry errors. We will review these common sources of error and frustration in the EHR, how errors can be identified, and changes implemented to mitigate the errors.

Most pediatric healthcare providers use an electronic health record (EHR) system in both office-based and hospital-based practice in the United States. While some pediatric-specific EHR systems exist for the office-based market, the majority of EHR systems used in the care of children are designed for general use across all specialties. Pediatric providers have succeeded in influencing the development of these systems to serve the special needs of child health (e.g., immunization management, dosing by body weight, growth monitoring, developmental assessment), but the pediatric community continues to press for further refinement of these systems to meet the advanced needs of pediatric specialties. These clinical systems are typically integrated with administrative (scheduling, billing, registration, etc.) systems, and the output of both types of systems are often used in research. A large portion of the data from the clinical side remains in free-text form, which raises challenges to the use of these data in research. In this chapter, we discuss workflows with data implications of special importance in pediatrics. We will also summarize efforts to create standard quality measures and the rise in EHR-based registry systems.

Information technology used in the child health setting must take into account wide variations in body size across the age range and the changes in those dimensions over time. These changes are particularly important in medication dosing, which depends directly on body weight, especially for smaller infants. Care of older children and adolescents raises the issue of privacy, where the interests of the young person may be at odds with that of the parent, in particular when reproductive health issues are involved.

Semantic interoperability between clinical information systems is a major goal of current pediatric IT implementations and ongoing research. For both clinical care and research collaboration, successful exchange of meaningful clinical data depends on flexible, standard formats for the construction of messages, and widely accepted terminologies to capture the clinical concepts. In 2016, most messages take the form of delimited character strings that adhere to the Health Level 7 (HL7) version 2.X standard. We review the state of the art of version 2 HL7 messaging types and describe a well known example of this kind of messaging in the CDC Implementation Guide for Immunization Messaging. Version 3 of HL7, built on XML, has the potential for richer semantics but is not yet widely used. We use the Continuity of Care Document as a pediatric example of the use of this standard in real systems. Terminology systems, both open and proprietary, are used to encode clinical and administrative concepts in pediatrics. We will review terminology systems in current use and their pediatric-specific limitations, and mention some current efforts to create a platform for applications that interact with EHRs 18 (SMART) with the messaging standard that supports it (FHIR).

In the United States and other industrialized countries, laws demand that all individually identifiable health information be secured from unintended disclosure and handled as private, sensitive information. While this protection extends equally to all information in a health record, information that pertains to mental health, reproductive health, physical abuse, and certain other areas with social impact is usually considered even more sensitive than other types of health information. The latter types of information may have special laws or professional standards that apply to how it is handled. All of these privacy and security issues become more complex in situations where minors are involved, because of real or perceived conflicts between the interests of the child and the interests of parents or guardians. In the care of adolescents, these issues become particularly difficult, and may affect how data are recorded or displayed in the EHR system, and the extent to which data may be available for research. Additional areas that present difficult challenges to privacy include fetal care, foster care, and situations where genetic information must be stored and interpreted. Security policies for access to systems intended to be used by patients (personal health records and patient portals) are complex. They can become even more challenging when the child has participated in clinical research and unexpected clinically relevant results are obtained. In this chapter we will discuss the prevailing regulations in the United States and the European Union that apply to privacy and security, and highlight pediatric aspects of these rules that apply to data.

To the Editor .— Han et al1 describe a retrospective study in which the mortality rate for interfacility transfers into an ICU was compared before and after implementation of a computerized provider order entry (CPOE) system. The authors found that the mortality rate increased from 2.8% (30 deaths of 1394 patient transfers during 13 months) before CPOE implementation to 6.6% (36 deaths of 548 transfers during 5 months) after. The authors conclude that the increased mortality was associated directly with modifications in standard clinical processes, including the following changes: (1) not allowing order communication until the patient was physically present and registered in the admitting system; (2) relocating medication dispensing to a central (rather than a satellite) pharmacy; (3) increasing the physical separation of nursing and physician staff during the time that orders were generated; (4) implementing computerized order entry; and (5) system-wide provider role changes to support the CPOE system. Perhaps the most important lesson from this study is that there exists an intimate association between care-delivery processes and health information technology. Any shift in the methods used to manage patient care (such as implementing and using a CPOE system) is associated with significant changes in clinical workflows, communication among providers, and distribution of responsibilities.2–4 Decades of research in medical informatics have underscored the importance of this observation, a message that was not lost on the authors. In this study, they note that the increased unadjusted mortality may reflect problems with the process of change, including the extremely rapid implementation plan adopted by their organization. The authors describe other major changes in workflow and patient care processes that occurred coincident with the CPOE system implementation. For example, their …

1. S. Andrew Spooner, MD* 2. Kent R. Anderson† 1. 2. *Assistant Professor of Pediatrics, University of Alabama at Birmingham, Children’s Hospital, Birmingham, AL. 3. 4. †Director, Division of Medical Journals & Professional Periodicals, American Academy of Pediatrics, Elk Grove Village, IL. Type the word “pediatrics” into one of the more robust search engines on the World-Wide Web, and you will retrieve more than 90,000 different sites that refer to the specialty in some manner. With more than 5 million different destinations on the Web (and 80,000 being added every week), the growth of this information source and communication tool is remarkable. However, the quality and reliability of information can be uneven, despite vast improvements in the offerings over the past 1 to 2 years, and sites still fall into disrepair when their champions lose interest or funding. To help readers sort through this daunting information repository, the editors of Pediatrics in Review invited us to highlight some of the primary touchstones on the Web for pediatricians and to point to other sites that we believe offer excellent content, useful features, timely information, and interesting perspectives. The following list is by no means exhaustive, and it is not intended to represent a “best of” list. It is simply a list of stable, useful, and intriguing sites that can serve as a fine collection of online resources and a springboard for further investigations for the pediatric health care specialist. We organized most of this list according to clinical categories used by the American Board of Pediatrics and by the AAP’s PREP program. We then focused on sites that have one or more of the following priorities apparent in their design

Objectives The objective of this study was to determine whether Medical Informatics education among house staff is included in the current residency curriculum, and to present ways in which this type of education may be improved. Design During autumn 1996 a mailed survey was distributed to chief residents of American paediatric residency programmes. Questions included the resident’s use of computers, the influence of Informatics- trained faculty members on programme education and plans for curriculum expansion. Setting Division of General Paediatrics, University of Alabama, United States. Subjects Paediatric chief residents. Results Eighty programmes (63%) returned a survey. An average of 97% of residents use the computer for looking up patient laboratory results, while 70% use it for Medline searches. Less than half of all residents use the Internet or computer-based learning programmes. Thirty-seven per cent of programmes provided formal lectures on computer topics, and 22% of programmes provided hands-on Internet training. There was no association between the presence if Informatics faculty and current programmes; however, programmes with Informatics faculty were more likely to have future curriculum plans (P=0·04). Some of the programmes’ future plans are described. Conclusions We felt that an overall description of what is currently being done with computers would be helpful as a baseline for other programmes considering new curricula changes. Residents use the computer often to check patient laboratory results but not as much for self-education. Many programmes are expanding their Informatics curriculum. Faculty without formal Informatics training can take part in basic computer education for residents. A self-study approach by the residents has been received positively.

Objective To determine the accuracy of vendor-supplied dosing eRules for pediatric medication orders. Inaccurate or absent dosing rules can lead to high numbers of false alerts or undetected prescribing errors and may potentially compromise safety in this already vulnerable population. Materials and methods 7 months of medication orders and alerts from a large pediatric hospital were analyzed. 30 medications were selected for study across 5 age ranges and 5 dosing parameters. The resulting 750 dosing rules from a commercial system formed the study corpus and were examined for accuracy against a gold standard created from traditional clinical resources. Results Overall accuracy of the rules in the study corpus was 55.1% when the rules were transformed to fit a priori age ranges. Over a pediatric lifetime, the dosing rules were accurate an average of 57.6% of the days. Dosing rules pertaining to the newborn age range were as accurate as other age ranges on average, but exhibited more variability. Daily frequency dosing parameters showed more accuracy than total daily dose, single dose minimum, or single dose maximum. Discussion The accuracy of a vendor-supplied set of dosing eRules is suboptimal when compared with traditional dosing sources, exposing a gap between dosing rules in commercial products and actual prescribing practices by pediatric care providers. More research on vendor-supplied eRules is warranted in order to understand the effects of these products on safe prescribing in children.

* Abbreviations: AAP — : American Academy of Pediatrics EHR — : electronic health record MU — : Meaningful Use In this month’s issue of Pediatrics , we read a report on the state of clinical information technology adoption by pediatricians, based on an American Academy of Pediatrics (AAP) survey from 2009.1 The lessons from this report are that pediatricians were not adopting technology as fast as their nonpediatrician counterparts and, just as concerning, they were using technology that is not supportive of basic child health functions. Much has changed since 2009. The irresistible force in the electronic health record (EHR) market is the federal Meaningful Use (MU) program,2 a system of financial incentives funded through the 2009 American Recovery and Reinvestment Act intended to accelerate the adoption of EHRs. Those who work in EHR implementation know that the primary focus of EHR vendors and their customers during the past 2 years has been to modify their systems to meet the MU requirements. The work is not insignificant. A workgroup convened by the National Association of Children’s Hospitals estimated that it took hospitals 500 to 1000 person-hours each to create the functionality and reporting for … Address correspondence to S. Andrew Spooner, MD, MS, FAAP, Cincinnati Children’s Hospital Medical Center and Department of Pediatrics, University of Cincinnati College of Medicine, 3333 Burnet Ave, MLC-9009, Cincinnati, OH 45229. E-mail: andrew.spooner{at}cchmc.org

Graphical abstractDisplay Omitted We describe a novel algorithm for converting weight-based dosing guidelines into administerable doses for children.This algorithm features combines medication knowledge, dosing knowledge, and dose administering knowledge.We report the performance of this approach, and discuss the limitations encountered thus far. ObjectivePediatric dose rounding is a unique and complex process whose complexity is rarely supported by e-prescribing systems, though amenable to automation and deployment from a central service provider. The goal of this project was to validate an automated dose-rounding algorithm for pediatric dose rounding. MethodsWe developed a dose-rounding algorithm, STEPSTools, based on expert consensus about the rounding process and knowledge about the therapeutic/toxic window for each medication. We then used a 60% subsample of electronically-generated prescriptions from one academic medical center to further refine the web services. Once all issues were resolved, we used the remaining 40% of the prescriptions as a test sample and assessed the degree of concordance between automatically calculated optimal doses and the doses in the test sample. Cases with discrepant doses were compiled in a survey and assessed by pediatricians from two academic centers. The response rate for the survey was 25%. ResultsSeventy-nine test cases were tested for concordance. For 20 cases, STEPSTools was unable to provide a recommended dose. The dose recommendation provided by STEPSTools was identical to that of the test prescription for 31 cases. For 14 out of the 24 discrepant cases included in the survey, respondents significantly preferred STEPSTools recommendations (p

Electronic health records in pediatric care must connect to other systems to integrate information and to communicate to providers in other systems. In order to perform this data transfer, there must be formats for the construction of messages, and standard terminologies that communicate the clinical concepts. Messaging standards are usually in the form of delimited character strings that follow the HL7 version 2.X standard. We will review the state of the art of HL7 version 2 messaging types and describe the best known example of this kind of messaging in the CDC Implementation Guide for Immunization Messaging. HL7 version 3, built on XML, has the potential for richer semantics, but is not yet widely used. We will use the Continuity of Care Document as a pediatric example of the use of this standard in real systems. Terminology systems, both open and proprietary, are used to encode clinical and administrative concepts in pediatrics. We will review terminology systems in current use and their pediatric-specific limitations.

With the increasing availability of electronic health records, translational researchers frequently wish to extract information from or add information to these records to support various research projects. It is important that investigators be familiar with laws and regulations that protect the privacy of patients and restrict who can view or extract information from their EHRs. Laws and professional standards demand that all individually identifiable health information be secured at a high level and handled as private, sensitive information. Some information (mental health, reproductive health, abuse) is generally regarded as more sensitive than others in the health care of a patient at any age. In a situation where minors are involved, these security and privacy policies become more complicated because of possible conflicts between the interests of the child and the interests of parents or guardians. Situations specific to pediatric clinical practice and research increase the difficulty of implementing these policies: adolescent care, adoption, fetal care, foster care, and genetic disease. Security policies for access to systems intended to be used by patients (personal health records and patient portals) are complex. They can become even more challenging when the child has participated in clinical research and unexpected clinically relevant results are obtained.

Pediatric providers use electronic health record systems to review patient information, to document care, to order clinical interventions, and to perform related administrative tasks. All of these activities create data that might be useful in research, although research is seldom the objective of EHR-related data entry. Providers may use other information systems (e.g., specialized systems for analyzing electrocardiograms), but the EHR is the central application for clinical and administrative clinical activities. While there are a few EHR systems designed specifically for care of pediatric patients, most pediatric providers adopt general-purpose EHRs that must be customized for specialized pediatric environments. In this chapter we outline the special functional requirements of EHRs (e.g., growth monitoring, medication dosing, and immunization management), the relative difficulty of meeting these requirements with EHRs that are currently available in the marketplace, and current adoption trends. We discuss workflows that present special challenges to EHR implementation. We discuss the typical workflow phenomena that affect the use of data for research and other secondary uses. We also discuss special aspects of terminology systems employed by EHRs that have implications for pediatric usability. Lastly, we address special issues in the use of EHR data for the extraction of care quality measures.

Implementing electronic health records (EHR) in healthcare settings incurs challenges, none more important than maintaining efficiency and safety during rollout. This report quantifies the impact of offloading low-acuity visits to an alternative care site from the emergency department (ED) during EHR implementation. In addition, the report evaluated the effect of EHR implementation on overall patient length of stay (LOS), time to medical provider, and provider productivity during implementation of the EHR. Overall LOS and time to doctor increased during EHR implementation. On average, admitted patients' LOS was 6-20% longer. For discharged patients, LOS was 12-22% longer. Attempts to reduce patient volumes by diverting patients to another clinic were not effective in minimizing delays in care during this EHR implementation. Delays in ED throughput during EHR implementation are real and significant despite additional providers in the ED, and in this setting resolved by 3 months post-implementation.

Although electronic medical records (EMRs) are widely regarded as valuable tools in patient care, physicians in outpatient practices have been slow to adopt them. We sought to determine the current use of EMRs in area practices and identify physician attitudes related to their adoption.Fax and mail survey of randomly selected physician representatives of all outpatient practices of Internal Medicine (n=51) and Pediatrics (n=24) in Shelby County, Tenn. Scores on eight physician attitudes regarding barriers to EMR adoption were obtained using a Likert scale.Survey response rate was 55%, with 18% reporting current EMR use. This corresponds to an EMR penetration of 20% for Shelby County. Current users were significantly less likely (P=0.005) than non-users to feel that an EMR interferes with doctor-patient interaction and less likely (P=0.019) to have EMR privacy concerns. While differences noted in other attitudes did not reach statistical significance, a trend was seen toward EMR users being less concerned (P=.0502) about reliability of an EMR. Large practices were no more likely than smaller ones to be using an EMR. Internal Medicine and Pediatric participants responded similarly to all items. The number of years in practice had no demonstrable impact on physician responses to these survey items.In this West Tennessee physician population, EMR user and non-user attitudes markedly differed about impact on doctor-patient interaction and patient privacy. If such concerns could be addressed to the satisfaction of physicians considering EMRs in their practice, adoption rates might be increased.

The newly developing field of telemedicine has the potential to benefit pediatric care by in creasing access to pediatric specialists and services. This report explores the current uses and limitations of telemedicine in pediatrics.

Many computer applications may be considered to be clinical decision support systems. Programs that perform Medline searches or check drug interactions do support decisions, but they are not “clinical decision support systems” in the usual sense. What we usually mean by a clinical decision support system (CDSS) is a program that supports a reasoning task carried out behind the scenes and based on clinical data. For example, a program that accepts thyroid panel results and generates a list of possible diagnoses is what we usually recognize as a diagnostic decision support system, a particular type of CDSS. General purpose programs that accept clinical findings and generate diagnoses are typical diagnostic decision support systems. These programs employ numerical and logical techniques to convert clinical input into the kind of information that a physician might use in performing a diagnostic reasoning task. How these numerical techniques work is the subject of this chapter.

Objective: To illustrate the growth of pediatric-related material on the Internet as demonstrated by the growth and use of an index of pediatric Internet re Design: Descriptive analysis. Methods: The log files of the computer systems on which the PEDINFO index was implemented were examined. File size of the index was measured, and a record of Internet hosts that connected to the server each day was produced. A proportion of Internet sites (n=300) were examined for author reliability and practice enhancement value. Results: In a period of 14 months, the file size grew from 7 kilobytes (KB) to more than 80 KB. By November 1995, usage exceeded 250 individuals per day and has since then leveled off to about 180 users per day. Analysis of the domains of the users showed a shift from equal proportion of "com" (commercially obtained Internet addresses) and "edu" (educational institution) addresses to an increase in com addresses with a stable proportion of edu addresses. One hundred twenty-seven sites contained patient or parent information, and 62 sites contained reference material. There was a wide range of author reliability ratings. Conclusions: From the growth of PEDINFO, we conclude that an increase in pediatric-related information available on the Internet is steady, much of which is patient or parent educational material. We anticipate further growth and use of the Internet in the exchange of information and call for further education regarding its use so that pediatricians can more easily direct their patients to the most medically relevant sources. Arch Pediatr Adolesc Med. 1997;151:592-597

We used innovative state-of-the-art computer and collaboration technologies to teach first-year medical students an analytic methodology to solve difficult clinical cardiology problems to make informed medical decisions. Clinical examples included the decision to administer thrombolytic therapy considering the risk of hemorrhagic stroke, and activity recommendations for athletes at risk for sudden death. Students received instruction on the decision-analytic approach which integrates pathophysiology, treatment efficacy, diagnostic test interpretation, health outcomes, patient preferences, and cost-effectiveness into a decision-analytic model. The traditional environment of a small group and blackboard was significantly enhanced by using an electronic whiteboard, the Xerox LiveBoard™. The LiveBoard features an 80486-based personal computer, large (3’ × 4’) display, and wireless pens for input. It allowed the integration of decision-analytic software, statistical software, digital slides, and additional media. We developed TIDAL (Team Interactive Decision Analysis in the Large-screen environment), a software package to interactively construct decision trees, calculate expected utilities, and perform one- and two-way sensitivity analyses using pen and gesture inputs. The Live Board also allowed the novel incorporation of Gambler, a utility assessment program obtained from the New England Medical Center. Gambler was used to obtain utilities for outcomes such as non-disabling hemorrhagic stroke. The interactive nature of the LiveBoard allowed real-time decision model development by the class, followed by instantaneous calculation of expected utilities and sensitivity analyses. The multimedia aspect and interactivity were conducive to extensive class participation. Ten out of eleven students wanted decision-analytic software available for use during their clinical years and all students would recommend the course to next year's students. We plan to experiment with the electronic collaboration features of this technology and allow groups separated by time or space to collaborate on decisions and explore the models created.

We've all heard of the digital divide: for example, the rural poor are much less likely than others to have access to information technology. Despite this, the authors of “Early Childhood Computer Experience and Cognitive and Motor Development”1 found that over half of a group of rural, poor preschoolers had a computer at home. Another stereotype-smashing finding was that boys and girls in this sample used computers with equal frequency. Whether …

Objective: To determine the difference in reported and observed behavior regarding parents' and childrens' use of Child Restraint Devices(CRD's) and safety belts in a children's hospital outpatient population.

Objective: To evaluate the use before and after intervention of Child Restraint Devices (CRD's) and seat belts of children and drivers of families who had been seen in a children's hospital clinic setting. A pediatric resident sponsored Car Seat/Seat Belt Display booth in the outpatient clinic setting was the intervention.

We were very pleased to see the article "Medical Informatics and Pediatrics: Decision-Support System" in the December 1995 issue. 1 TheArchivesis taking the lead in providing a series of academic articles on informatics topics for the pediatrician . No one has done this before, and we want to comment on the value of such a series to encourage further publication of such articles. There are many reviews of informatics topics in the literature, but none of them is targeted at pediatricians. To successfully target an article to be of value to pediatricians, as Drs Johnson and Feldman 1 have done, one must add information not found in general articles. For example, in Table 3 of their article, the authors include information on whether each decision-support system includes the pediatric age range. In Table 5, 1 they describe guidelines for pediatric illnesses. In the text, they describe a decision-support system