Evaluation of a command-line parser-based order entry pathway for the Department of Veterans Affairs electronic patient record

Effect of Rapid Response Order Panels on Resident and Nurse Satisfaction

Background

Hospitals are required to have rapid response (RR) systems in place to respond to acute changes in a patient's condition. In high-stress situations like RR, medical residents face decision-making challenges due to time constraints and perceived pressure. Instituting order panels (OPs) can facilitate clinical decision making and improve residents' and nurses' satisfaction and patient safety.

Objective

This quality improvement (QI) project aimed to create and institute standardized OPs for common RR clinical scenarios to improve satisfaction of internal medicine residents and nurses with the RR process.

Methods

This was a single tertiary care center QI project that developed OPs for 10 common RR scenarios. Resident and nursing satisfaction with RR was assessed before and after OP implementation via survey and qualitative data collection.

Results

Residents and nurses expressed high levels of satisfaction across various aspects of the RR process before and after OP implementation in both quantitative and qualitative analysis. Increased satisfaction was observed among residents regarding time spent placing orders (94%; P = 0.02) and time spent correcting wrong orders (87%; P = 0.03) after OP implementation. The nurses' survey revealed no statistically significant differences in satisfaction before and after the implementation of OPs regarding communication, collaboration, efficiency, and organization of the team.

Conclusion

The introduction of standardized OPs for RRs resulted in increased satisfaction among internal medicine residents in terms of order placement and correcting wrong orders. Nurse satisfaction based on survey responses remained neutral. Qualitative data from both groups demonstrated a positive impact on communication, efficiency, and teamwork.

Distribution of Problems, Medications and Lab Results in Electronic Health Records: The Pareto Principle at Work

BACKGROUND: Many natural phenomena demonstrate power-law distributions, where very common items predominate. Problems, medications and lab results represent some of the most important data elements in medicine, but their overall distribution has not been reported. OBJECTIVE: Our objective is to determine whether problems, medications and lab results demonstrate a power law distribution. METHODS: Retrospective review of electronic medical record data for 100,000 randomly selected patients seen at least twice in 2006 and 2007 at the Brigham and Women's Hospital in Boston and its affiliated medical practices. RESULTS: All three data types exhibited a power law distribution. The 12.5% most frequently used problems account for 80% of all patient problems, the top 11.8% of medications account for 80% of all medication orders and the top 4.5% of lab result types account for all lab results. CONCLUSION: These three data elements exhibited power law distributions with a small number of common items representing a substantial proportion of all orders and observations, which has implications for electronic health record design.

Use of electronic health records in disaster response: the experience of Department of Veterans Affairs after Hurricane Katrina

OBJECTIVES

We describe electronic health data use by the Department of Veterans Affairs (VA) in the month after Katrina, including supporting technologies, the extent and nature of information accessed, and lessons learned.

METHODS

We conducted a retrospective study using cross-sectional panels of data collected sequentially over time.

RESULTS

By September 30, 2005, clinical data were accessed electronically for at least 38% (14941 of 39910) of patients cared for prior to Hurricane Katrina by New Orleans-area VA medical facilities. Approximately 1000 patients per day had data accessed during the month following Hurricane Katrina, a rate approximately two thirds of pre-Katrina values. Health care data were transmitted to more than 200 sites in 48 states and to at least 2300 users.

CONCLUSIONS

The VA electronic health records supported continuity of care for evacuated veterans after Katrina. Our findings suggest that pharmacy and laboratory computerization alone will not be sufficient for future disaster support systems.

eQuality: electronic quality assessment from narrative clinical reports

OBJECTIVE

To evaluate an electronic quality (eQuality) assessment tool for dictated disability examination records.

METHODS

We applied automated concept-based indexing techniques to automated quality screening of Department of Veterans Affairs spine disability examinations that had previously undergone gold standard quality review by human experts using established quality indicators. We developed automated quality screening rules and refined them iteratively on a training set of disability examination reports. We applied the resulting rules to a novel test set of spine disability examination reports. The initial data set was composed of all electronically available examination reports (N=125,576) finalized by the Veterans Health Administration between July and September 2001.

RESULTS

Sensitivity was 91% for the training set and 87% for the test set (P-.02). Specificity was 74% for the training set and 71% for the test set (P=.44). Human performance ranged from 4% to 6% higher (P<.001) than the eQuality tool in sensitivity and 13% to 16% higher in specificity (P<.001). In addition, the eQuality tool was equivalent or higher in sensitivity for 5 of 9 individual quality indicators.

CONCLUSION

The results demonstrate that a properly authored computer-based expert systems approach can perform quality measurement as well as human reviewers for many quality indicators. Although automation will likely always rely on expert guidance to be accurate and meaningful, eQuality is an important new method to assist clinicians in their efforts to practice safe and effective medicine.

Structuring order sets for interoperable distribution

A major portion of patient care planning occurs during the process of writing orders. Computerized order entry can present collections of predefined orders to the user during the ordering process. These order sets are useful for promoting standards of care, and provide one element of structured clinical knowledge to be used by Computerized Provider Order Entry (CPOE) systems at the point of care. Since the creation, confirmation and maintenance of order sets is resource intensive, sharing order sets is a useful goal. We describe a standard representation of order sets that supports maintenance, sharing and interoperation of pre-defined order sets. A dialogue within the HL7 community seeks to harmonize this proposal with the Clinical Document Architecture and the HL7 Reference Information Model.

The anatomy of decision support during inpatient care provider order entry (CPOE): empirical observations from a decade of CPOE experience at Vanderbilt

The authors describe a pragmatic approach to the introduction of clinical decision support at the point of care, based on a decade of experience in developing and evolving Vanderbilt's inpatient "WizOrder" care provider order entry (CPOE) system. The inpatient care setting provides a unique opportunity to interject CPOE-based decision support features that restructure clinical workflows, deliver focused relevant educational materials, and influence how care is delivered to patients. From their empirical observations, the authors have developed a generic model for decision support within inpatient CPOE systems. They believe that the model's utility extends beyond Vanderbilt, because it is based on characteristics of end-user workflows and on decision support considerations that are common to a variety of inpatient settings and CPOE systems. The specific approach to implementing a given clinical decision support feature within a CPOE system should involve evaluation along three axes: what type of intervention to create (for which the authors describe 4 general categories); when to introduce the intervention into the user's workflow (for which the authors present 7 categories), and how disruptive, during use of the system, the intervention might be to end-users' workflows (for which the authors describe 6 categories). Framing decision support in this manner may help both developers and clinical end-users plan future alterations to their systems when needs for new decision support features arise.

The use and interpretation of quasi-experimental studies in medical informatics

Quasi-experimental study designs, often described as nonrandomized, pre-post intervention studies, are common in the medical informatics literature. Yet little has been written about the benefits and limitations of the quasi-experimental approach as applied to informatics studies. This paper outlines a relative hierarchy and nomenclature of quasi-experimental study designs that is applicable to medical informatics intervention studies. In addition, the authors performed a systematic review of two medical informatics journals, the Journal of the American Medical Informatics Association (JAMIA) and the International Journal of Medical Informatics (IJMI), to determine the number of quasi-experimental studies published and how the studies are classified on the above-mentioned relative hierarchy. They hope that future medical informatics studies will implement higher level quasi-experimental study designs that yield more convincing evidence for causal links between medical informatics interventions and outcomes.

Information retrieval performance of probabilistically generated, problem-specific computerized provider order entry pick-lists: a pilot study

OBJECTIVE

The aim of this study was to preliminarily determine the feasibility of probabilistically generating problem-specific computerized provider order entry (CPOE) pick-lists from a database of explicitly linked orders and problems from actual clinical cases.

DESIGN

In a pilot retrospective validation, physicians reviewed internal medicine cases consisting of the admission history and physical examination and orders placed using CPOE during the first 24 hours after admission. They created coded problem lists and linked orders from individual cases to the problem for which they were most indicated. Problem-specific order pick-lists were generated by including a given order in a pick-list if the probability of linkage of order and problem (PLOP) equaled or exceeded a specified threshold. PLOP for a given linked order-problem pair was computed as its prevalence among the other cases in the experiment with the given problem. The orders that the reviewer linked to a given problem instance served as the reference standard to evaluate its system-generated pick-list.

MEASUREMENTS

Recall, precision, and length of the pick-lists.

RESULTS

Average recall reached a maximum of .67 with a precision of .17 and pick-list length of 31.22 at a PLOP threshold of 0. Average precision reached a maximum of .73 with a recall of .09 and pick-list length of .42 at a PLOP threshold of .9. Recall varied inversely with precision in classic information retrieval behavior.

CONCLUSION

We preliminarily conclude that it is feasible to generate problem-specific CPOE pick-lists probabilistically from a database of explicitly linked orders and problems. Further research is necessary to determine the usefulness of this approach in real-world settings.

Preparation and use of preconstructed orders, order sets, and order menus in a computerized provider order entry system

OBJECTIVE

To describe the configuration and use of the computerized provider order entry (CPOE) system used for inpatient and outpatient care at the authors' facility.

DESIGN

Description of order configuration entities, use patterns, and configuration changes in a production CPOE system.

MEASUREMENTS

The authors extracted and analyzed the content of order configuration entities (order dialogs, preconfigured [quick] orders, order sets, and order menus) and determined the number of orders entered in their production order entry system over the previous three years. The authors measured use of these order configuration entities over a six-month period. They repeated the extract two years later to measure changes in these entities.

RESULTS

CPOE system configuration, conducted before and after first production use, consisted of preparing 667 order dialogs, 5,982 preconfigured (quick) orders, and 513 order sets organized in 703 order menus for particular contexts, such as admission for a particular diagnosis. Fifty percent of the order dialogs, 57% of the quick orders, and 13% of the order sets were used within a six-month period. Over the subsequent two years, the volume of order configuration entities increased by 26%.

CONCLUSIONS

These order configuration steps were time-consuming, but the authors believe they were important to increase the ordering speed and acceptability of the order entry software. Lessons learned in the process of configuring the CPOE ordering system are given. Better understanding of ordering patterns may make order configuration more efficient because many of the order configuration entities that were created were not used by clinicians.

Two biomedical sublanguages: a description based on the theories of Zellig Harris

Natural language processing (NLP) systems have been developed to provide access to the tremendous body of data and knowledge that is available in the biomedical domain in the form of natural language text. These NLP systems are valuable because they can encode and amass the information in the text so that it can be used by other automated processes to improve patient care and our understanding of disease processes and treatments. Zellig Harris proposed a theory of sublanguage that laid the foundation for natural language processing in specialized domains. He hypothesized that the informational content and structure form a specialized language that can be delineated in the form of a sublanguage grammar. The grammar can then be used by a language processor to capture and encode the salient information and relations in text. In this paper, we briefly summarize his language and sublanguage theories. In addition, we summarize our prior research, which is associated with the sublanguage grammars we developed for two different biomedical domains. These grammars illustrate how Harris' theories provide a basis for the development of language processing systems in the biomedical domain. The two domains and their associated sublanguages discussed are: the clinical domain, where the text consists of patient reports, and the biomolecular domain, where the text consists of complete journal articles.