Some functions of an electronic health record system are much more important in providing pediatric care than in adult care. Pediatricians commonly complain about the absence of these "pediatric functions" when they are not available in electronic health record systems. To stimulate electronic health record system vendors to recognize and incorporate pediatric functionality into pediatric electronic health record systems, this clinical report reviews the major functions of importance to child health care providers. Also reviewed are important but less critical functions, any of which might be of major importance in a particular clinical context. The major areas described here are immunization management, growth tracking, medication dosing, data norms, and privacy in special pediatric populations. The American Academy of Pediatrics believes that if the functions described in this document are supported in all electronic health record systems, these systems will be more useful for patients of all ages.

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BackgroundWith the aim of improving health care processes through health information technology (HIT), the US government has promulgated requirements for “meaningful use” (MU) of electronic health records (EHRs) as a condition for providers receiving financial incentives for the adoption and use of these systems. Considerable uncertainty remains about the impact of these requirements on the effective application of EHR systems.ObjectiveThe Agency for Healthcare Research and Quality (AHRQ)-sponsored Centers for Education and Research in Therapeutics (CERTs) critically examined the impact of the MU policy relating to the use of medications and jointly developed recommendations to help inform future HIT policy.MethodsWe gathered perspectives from a wide range of stakeholders (N=35) who had experience with MU requirements, including academicians, practitioners, and policy makers from different health care organizations including and beyond the CERTs. Specific issues and recommendations were discussed and agreed on as a group.ResultsStakeholders’ knowledge and experiences from implementing MU requirements fell into 6 domains: (1) accuracy of medication lists and medication reconciliation, (2) problem list accuracy and the shift in HIT priorities, (3) accuracy of allergy lists and allergy-related standards development, (4) support of safer and effective prescribing for children, (5) considerations for rural communities, and (6) general issues with achieving MU. Standards are needed to better facilitate the exchange of data elements between health care settings. Several organizations felt that their preoccupation with fulfilling MU requirements stifled innovation. Greater emphasis should be placed on local HIT configurations that better address population health care needs.ConclusionsAlthough MU has stimulated adoption of EHRs, its effects on quality and safety remain uncertain. Stakeholders felt that MU requirements should be more flexible and recognize that integrated models may achieve information-sharing goals in alternate ways. Future certification rules and requirements should enhance EHR functionalities critical for safer prescribing of medications in children.

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.

The widespread of adoption of EHRs presents a number of benefits to the field of clinical genomics. They include the ability to return results to the practitioner, the ability to use genetic findings in clinical decision support, and to have data collected in the EHR serve as a source of phenotypic information for analysis purposes. Not all EHRs are created equal, however. They differ in their features, capabilities and ease-of-use. Therefore, in order to understand the potential of the EHR, it is first necessary to understand its capabilities and the impact that implementation strategy has on usability. Specifically, we focus on the following areas: 1) how the EHR is used to capture data in clinical practice settings; 2) how the implementation and configuration of the EHR affects the quality and availability of data; 3) the management of clinical genetic test results and the feasibility of EHR integration; and 4) the challenges of implementing an EHR in a research-intensive environment. This is followed by a discussion of the minimum functional requirements that an EHR must meet to enable the satisfactory integration of genomic results as well as the open issues that remain.

BackgroundIn this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and built a computerized algorithm for medication reconciliation. Our specific aims are: (1) to develop a computerized algorithm for medication discrepancy detection between patients’ discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm on real-world medication reconciliation data.MethodsWe collected clinical notes and discharge prescription lists for all 271 patients enrolled in the Complex Care Medical Home Program at Cincinnati Children’s Hospital Medical Center between 1/1/2010 and 12/31/2013. A double-annotated, gold-standard set of medication reconciliation data was created for this collection. We then developed a hybrid algorithm consisting of three processes: (1) a ML algorithm to identify medication entities from clinical notes, (2) a rule-based method to link medication names with their attributes, and (3) a NLP-based, hybrid approach to match medications with structured prescriptions in order to detect medication discrepancies. The performance was validated on the gold-standard medication reconciliation data, where precision (P), recall (R), F-value (F) and workload were assessed.ResultsThe hybrid algorithm achieved 95.0%/91.6%/93.3% of P/R/F on medication entity detection and 98.7%/99.4%/99.1% of P/R/F on attribute linkage. The medication matching achieved 92.4%/90.7%/91.5% (P/R/F) on identifying matched medications in the gold-standard and 88.6%/82.5%/85.5% (P/R/F) on discrepant medications. By combining all processes, the algorithm achieved 92.4%/90.7%/91.5% (P/R/F) and 71.5%/65.2%/68.2% (P/R/F) on identifying the matched and the discrepant medications, respectively. The error analysis on algorithm outputs identified challenges to be addressed in order to improve medication discrepancy detection.ConclusionBy leveraging ML and NLP technologies, an end-to-end, computerized algorithm achieves promising outcome in reconciling medications between clinical notes and discharge prescriptions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0160-8) contains supplementary material, which is available to authorized users.

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.

Alert burden plays a role in influencing provider response to medication alerts. An increased number of alerts a provider saw during a one-day period did not directly lead to decreased response to alerts. Given the multiple factors influencing the response to alerts, efforts focused solely on burden are not likely to be effective.