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Background Capturing accurate clinical data in real time is a challenge during public health emergencies. The United States Critical Illness and Injury Trials Group-Program for Emergency Preparedness is committed to improving these preparedness efforts. Objectives We aimed to create an electronic Acute Physiology and Chronic Health Evaluation (APACHE) II data collection instrument that (1) leverages Research Electronic Data Capture (REDCap) automated calculations and logic, (2) may be shared across sites, (3) overcomes limitations in existing APACHE II instruments in the REDCap library, and (4) suggests changes to be made to data collection instruments during emergencies. Methods The APACHE II instrument was implemented using REDCap. Data fields were divided into four sections: age, Acute Physiology, Glasgow Coma Scale, and chronic health status. Usability testing was followed by two preliminary evaluations: a comparison to existing APACHE II instruments and a simulated emergency exercise. Results The final instrument consisted of 34 data fields. It produced an accurate APACHE II score and was faster to complete than two previous implementations (average of 97.5 seconds vs. 323.5 and 183.5 seconds). During the simulated emergency exercise, the instrument was used at 10 sites to create 34 patient records; median time to complete the instrument was 150.5 seconds. Conclusion This project demonstrated feasibility of improving the accuracy and efficiency of a data collection instrument. Future efforts should focus on expanding these methods to develop other scoring tools for use during emergencies and additional testing to ensure it is ready for use during a real emergency.

Summary Background: The Centers for Medicare & Medicaid Services’ Stage 2 final rule requires that eligible hospitals provide a visit summary electronically at transitions of care in order to qualify for “meaningful use” incentive payments. However, Massachusetts state law and Federal law prohibit the transmission of documents containing “sensitive” data unless there is a new patient consent for each transmission. Objectives: To describe the implementation and evaluation of a rule-based decision support system used to screen transition of care documents for sensitive data. Methods: We implemented a rule-based document screening system to identify transition of care documents that might contain sensitive data. The transmission of detected documents is withheld until a new patient consent is obtained. The documents that were flagged as containing sensitive data were reviewed in two different time periods to verify that the decision support system was not missing documents or withholding more documents than necessary. Results: The rule-based screening system has been in regular production use for the past 18 months. During the first evaluation period, 3% of 5,841 documents were identified as containing sensitive data (true-positive rate of 44%). After additional enhancements to the rules, the system was evaluated a second time and 4.5% of 6,935 documents were identified as containing sensitive data (true-positive rate of 98.4%). Conclusion: The analysis of the system demonstrates that production rules can be used to automatically screen the content of transition of care documents for sensitive data. The utilization of the rule-based decision support system enabled our hospitals to achieve meaningful use and, at the same time, remain compliant with state and federal laws.

SummaryFor children who present to emergency departments (EDs) due to blunt head trauma, ED clinicians must decide who requires computed tomography (CT) scanning to evaluate for traumatic brain injury (TBI). The Pediatric Emergency Care Applied Research Network (PECARN) derived and validated two age-based prediction rules to identify children at very low risk of clinically-important traumatic brain injuries (ciTBIs) who do not typically require CT scans. In this case report, we describe the strategy used to implement the PECARN TBI prediction rules via electronic health record (EHR) clinical decision support (CDS) as the intervention in a multicenter clinical trial.Thirteen EDs participated in this trial. The 10 sites receiving the CDS intervention used the Epic® EHR. All sites implementing EHR-based CDS built the rules by using the vendor’s CDS engine. Based on a sociotechnical analysis, we designed the CDS so that recommendations could be displayed immediately after any provider entered prediction rule data. One central site developed and tested the intervention package to be exported to other sites. The intervention package included a clinical trial alert, an electronic data collection form, the CDS rules and the format for recommendations.The original PECARN head trauma prediction rules were derived from physician documentation while this pragmatic trial led each site to customize their workflows and allow multiple different providers to complete the head trauma assessments. These differences in workflows led to varying completion rates across sites as well as differences in the types of providers completing the electronic data form. Site variation in internal change management processes made it challenging to maintain the same rigor across all sites. This led to downstream effects when data reports were developed.The process of a centralized build and export of a CDS system in one commercial EHR system successfully supported a multicenter clinical trial.

OBJECTIVE: To evaluate the architecture, integration requirements, and execution characteristics of a remote clinical decision support (CDS) service used in a multicenter clinical trial. The trial tested the efficacy of implementing brain injury prediction rules for children with minor blunt head trauma. MATERIALS AND METHODS: We integrated the Epic(®) electronic health record (EHR) with the Enterprise Clinical Rules Service (ECRS), a web-based CDS service, at two emergency departments. Patterns of CDS review included either a delayed, near-real-time review, where the physician viewed CDS recommendations generated by the nursing assessment, or a real-time review, where the physician viewed recommendations generated by their own documentation. A backstopping, vendor-based CDS triggered with zero delay when no recommendation was available in the EHR from the web-service. We assessed the execution characteristics of the integrated system and the source of the generated recommendations viewed by physicians. RESULTS: The ECRS mean execution time was 0.74 ±0.72s. Overall execution time was substantially different at the two sites, with mean total transaction times of 19.67 and 3.99s. Of 1930 analyzed transactions from the two sites, 60% (310/521) of all physician documentation-initiated recommendations and 99% (1390/1409) of all nurse documentation-initiated recommendations originated from the remote web service. DISCUSSION: The remote CDS system was the source of recommendations in more than half of the real-time cases and virtually all the near-real-time cases. Comparisons are limited by allowable variation in user workflow and resolution of the EHR clock. CONCLUSION: With maturation and adoption of standards for CDS services, remote CDS shows promise to decrease time-to-trial for multicenter evaluations of candidate decision support interventions. Language: en

BACKGROUND There is growing interest in developing systems to overcome barriers for acquiring and interpreting family health histories in primary care.

Background There are several challenges in encoding guideline knowledge in a form that is portable to different clinical sites, including the heterogeneity of clinical decision support (CDS) tools, of patient data representations, and of workflows. Methods We have developed a multi-layered knowledge representation framework for structuring guideline recommendations for implementation in a variety of CDS contexts. In this framework, guideline recommendations are increasingly structured through four layers, successively transforming a narrative text recommendation into input for a CDS system. We have used this framework to implement rules for a CDS service based on three guidelines. We also conducted a preliminary evaluation, where we asked CDS experts at four institutions to rate the implementability of six recommendations from the three guidelines. Conclusion The experience in using the framework and the preliminary evaluation indicate that this approach has promise in creating structured knowledge, to implement in CDS systems, that is usable across organizations.

Authors evaluated the impact of computerized alerts on the quality of outpatient laboratory monitoring for transplant patients. For 356 outpatient liver transplant patients managed at LDS Hospital, Salt Lake City, this observational study compared traditional laboratory result reporting, using faxes and printouts, to computerized alerts implemented in 2004. Study alerts within the electronic health record notified clinicians of new results and overdue new orders for creatinine tests and immunosuppression drug levels. After implementing alerts, completeness of reporting increased from 66 to >99 %, as did positive predictive value that a report included new information (from 46 to >99 %). Timeliness of reporting and clinicians' responses improved after implementing alerts (p

Summary Objectives: To discuss the advantages and disadvantages of an interfaced approach to clinical information systems architecture. Methods: After many years of internally building almost all components of a hospital clinical information system (HELP) at Intermountain Health Care, we changed our architectural approach as we chose to encompass ambulatory as well as acute care. We now seek to interface applications from a variety of sources (including some that we build ourselves) to a clinical data repository that contains a longitudinal electronic patient record. Results: We have a total of 820 instances of interfaces to 51 different applications. We process nearly 2 million transactions per day via our interface engine and feel that the reliability of the approach is acceptable. Interface costs constitute about four percent of our total information systems budget. The clinical database currently contains records for 1.45 m patients and the response time for a query is 0.19sec. Discussion: Based upon our experience with both integrated (monolithic) and interfaced approaches, we conclude that for those with the expertise and resources to do so, the interfaced approach offers an attractive alternative to systems provided by a single vendor. We expect the advantages of this approach to increase as the costs of interfaces are reduced in the future as standards for vocabulary and messaging become increasingly mature and functional.

Objective Clinical data warehouses have accelerated clinical research, but even with available open source tools, there is a high barrier to entry due to the complexity of normalizing and importing data. The Office of the National Coordinator for Health Information Technology's Meaningful Use Incentive Program now requires that electronic health record systems produce standardized consolidated clinical document architecture (C-CDA) documents. Here, we leverage this data source to create a low volume standards based import pipeline for the Informatics for Integrating Biology and the Bedside (i2b2) clinical research platform. We validate this approach by creating a small repository at Partners Healthcare automatically from C-CDA documents. Materials and methods We designed an i2b2 extension to import C-CDAs into i2b2. It is extensible to other sites with variances in C-CDA format without requiring custom code. We also designed new ontology structures for querying the imported data. Results We implemented our methodology at Partners Healthcare, where we developed an adapter to retrieve C-CDAs from Enterprise Services. Our current implementation supports demographics, encounters, problems, and medications. We imported approximately 17 000 clinical observations on 145 patients into i2b2 in about 24 min. We were able to perform i2b2 cohort finding queries and view patient information through SMART apps on the imported data. Discussion This low volume import approach can serve small practices with local access to C-CDAs and will allow patient registries to import patient supplied C-CDAs. These components will soon be available open source on the i2b2 wiki. Conclusions Our approach will lower barriers to entry in implementing i2b2 where informatics expertise or data access are limited.

This chapter presents a complete program for clinical knowledge management from an institutional perspective, including motivation, requirements, and implementation strategies. The knowledge engineering process and the knowledge asset lifecycle are also discussed, taking into account extensible modeling approaches that ensure consistent knowledge representation and effective collaboration. Special emphasis is given to requirements for a comprehensive software infrastructure to support the knowledge management program and enable effective integration with clinical information systems. New opportunities and challenges related to personalized clinical decision support interventions and advanced curation tools are also outlined, taking into account inter-institutional collaborations leading to a sustainable exchange of knowledge assets. The chapter concludes with a brief overview of the ongoing program implementation efforts at Partners HealthCare.

Objective: To analyze whether computer-generated reminders about infections could influence clinicians' practice patterns and consequently improve the detection and manage- ment of nosocomial infections. Design: The conclusions produced by an expert system developed to detect and manage infections were presented to the attending clinicians in a pediatric hospital to determine whether this infor- mation could improve detection and management. Clinician interventions were compared before and after the implementation of the system. Measurements: The responses of the clinicians (staff physicians, physician assistants, and nurse practitioners) to the reminders were determined by review of paper medical charts. Main outcome measures were the number of suggestions to treat and manage infections that were followed before and after the implementation of COMPISS (Computerized Pediatric Infection Surveillance System). The clinicians' opinions about the system were assessed by means of a paper questionnaire distrib- uted following the experiment. Results: The results failed to show a statistical difference between the clinicians' treatment strategies before and after implementation of the system (P > 0.33 for clinicians working in the emergency room and P > 0.45 for clinicians working in the pediatric intensive care unit). The questionnaire results showed that the respondents appreciated the information presented by the system. Conclusion: The computer-generated reminders about infections were unable to influence the practice patterns of clinicians. The methodologic problems that may have contributed to this negative result are discussed. � J Am Med Inform Assoc. 2001;8:117-125.

Traditionally, rule interactions are handled at implementation time through rule task properties that control the order in which rules are executed. By doing so, knowledge about the behavior and interactions of decision rules is not captured at modeling time. We argue that this is important knowledge that should be integrated in the modeling phase. In this project, we build upon current work on a conceptual schema to represent clinical knowledge for decision support in the form of ifformula/formulathen rules. This schema currently captures provenance of the clinical content, context where such content is actionable (i.e. constraints) and the logic of the rule itself. For this project, we borrowed concepts from both the Semantic Web (i.e., Ontologies) and Complex Adaptive Systems (CAS), to explore a conceptual approach for modeling rule interactions in an enterprise-wide clinical setting. We expect that a more comprehensive modeling will facilitate knowledge authoring, editing and update; foster consistency in rules implementation and maintenance; and develop authoritative knowledge repositories to promote quality, safety and efficacy of healthcare.

Objective To create a clinical decision support (CDS) system that is shareable across healthcare delivery systems and settings over large geographic regions. Materials and methods The enterprise clinical rules service (ECRS) realizes nine design principles through a series of enterprise java beans and leverages off-the-shelf rules management systems in order to provide consistent, maintainable, and scalable decision support in a variety of settings. Results The ECRS is deployed at Partners HealthCare System (PHS) and is in use for a series of trials by members of the CDS consortium, including internally developed systems at PHS, the Regenstrief Institute, and vendor-based systems deployed at locations in Oregon and New Jersey. Performance measures indicate that the ECRS provides sub-second response time when measured apart from services required to retrieve data and assemble the continuity of care document used as input. Discussion We consider related work, design decisions, comparisons with emerging national standards, and discuss uses and limitations of the ECRS. Conclusions ECRS design, implementation, and use in CDS consortium trials indicate that it provides the flexibility and modularity needed for broad use and performs adequately. Future work will investigate additional CDS patterns, alternative methods of data passing, and further optimizations in ECRS performance.

The Clinical Decision Support Consortium has completed two demonstration trials involving a web service for the execution of clinical decision support (CDS) rules in one or more electronic health record (EHR) systems. The initial trial ran in a local EHR at Partners HealthCare. A second EHR site, associated with Wishard Memorial Hospital, Indianapolis, IN, was added in the second trial. Data were gathered during each 6 month period and analyzed to assess performance, reliability, and response time in the form of means and standard deviations for all technical components of the service, including assembling and preparation of input data. The mean service call time for each period was just over 2 seconds. In this paper we report on the findings and analysis to date while describing the areas for further analysis and optimization as we continue to expand our use of a Services Oriented Architecture approach for CDS across multiple institutions.

Introduction: We evaluated the feasibility of using an electronic protocol developed for research use (Research-eProtocol-insulin) for blood glucose management in usual intensive care unit clinical practice. Methods: We implemented the rules of Research-eProtocol-insulin in the electronic medical record of the Intermountain Healthcare hospital system (Clinical-eProtocol-insulin) for use in usual clinical practice. We evaluated the performance of Clinical-eProtocol-insulin rules in the intensive care units of seven Intermountain Healthcare hospitals and compared this performance with the performance of Research-eProtocol-insulin at the LDS Hospital Shock/Trauma/Respiratory Intensive Care Unit. Results: Clinician (nurse or physician) compliance with computerized protocol recommendations was 95% (of 21,325 recommendations) with Research-eProtocol-insulin and 92% (of 109,458 recommendations) with Clinical-eProtocol-insulin. The blood glucose distribution in clinical practice (Clinical-eProtocol-insulin) was similar to the research use distribution (Research-eProtocol-insulin); however, the mean values (119 mg/dl vs 113 mg/dl) were statistically different ( P = 0.0001). Hypoglycemia rates in the research and practice settings did not differ: the percentage of measurements ≤40 mg/dl (0.11% vs 0.1%, P = 0.65) and the percentage of patients with at least one blood glucose ≤40 mg/dl (4.2% vs 3%, P = 0.23) were not statistically significantly different. Conclusion: Our electronic blood glucose protocol enabled translation of a research decision-support tool (Research-eProtocol-insulin) to usual clinical practice (Clinical-eProtocol-insulin).

Chronically anticoagulated patients taking the drug Warfarin require time intensive management and followup processes to avoid complications. The "Chronic Anticoagulation Clinic" (CAC) protocol is a set of production rules that help manage, treat, and follow-up such patients. The CAC protocol has been in regular use at Intermountain Healthcare (Salt Lake City, UT, USA) for over three years. The results demonstrate an improvement on the number of patients with anticoagulation levels within the desired target range. The protocol alerts have a high acceptance rate (83.4%) and were able to help patients remember to collect their next coagulation test. The CAC protocol results show that production rules can improve the management of chronically anticoagulated patients. Additional studies are required to verify if this experience can be transferred to other institutions.

Publisher Summary Intermountain's core clinical strategy is to provide high value care by effectively managing clinical conditions and processes, while improving medical outcomes and member satisfaction at the lowest necessary cost. This chapter discusses the Clinical Knowledge Management (CKM) software infrastructure that has been implemented at Intermountain, along with some utilization data demonstrating how extensively it is being used. Since 1995, Intermountain has been building a new clinical information systems infrastructure, known as HELP2. The clinical systems are being delivered to care providers through a web-based shell developed in-house. Currently, it offers functionality such as laboratory results, radiology images and reports, surgery reports, etc. The main components of the HELP2 core infrastructure are the Clinical Data Repository, the Healthcare Data Dictionary, and the Enterprise Master Patient Index. Another important component of the overall information systems infrastructure of Intermountain is the Enterprise Data Warehouse, which receives data feeds from almost all administrative and clinical databases used by Intermountain systems. The Clinical Knowledge Repository and Foresight are the primary components of Intermountain's CKM software infrastructure. The data presented reflects the knowledge content and software infrastructure that were in production use as of September 2005, unless stated otherwise.

Adverse drug events are known to be a major health problem worldwide. Decision support systems (DSSs) that assist drug ordering have demonstrated to be a powerful tool to prevent prescription errors and adverse drug events. On the other hand, some issues related to the development, implementation, configuration and evaluation of these DDSs still need further research. The objective of this project was the development of a DSS prototype that helps with the prevention of adverse drug events by detecting drug-drug interactions in drug orders. The structure of the system tries to solve some of the problems described by the literature, such as integration with hospital information systems, adaptability to local needs, and knowledge base maintenance. The proposed model has been shown to be an effective method for representing drug-drug interactions.