Cynthia S. Gadd

The financial impact of health information exchange on emergency department care

Objective To examine the financial impact health information exchange (HIE) in emergency departments (EDs). Materials and Methods We studied all ED encounters over a 13-month period in which HIE data were accessed in all major emergency departments Memphis, Tennessee. HIE access encounter records were matched with similar encounter records without HIE access. Outcomes studied were ED-originated hospital admissions, admissions for observation, laboratory testing, head CT, body CT, ankle radiographs, chest radiographs, and echocardiograms. Our estimates employed generalized estimating equations for logistic regression models adjusted for admission type, length of stay, and Charlson co-morbidity index. Marginal probabilities were used to calculate changes in outcome variables and their financial consequences. Results HIE data were accessed in approximately 6.8% of ED visits across 12 EDs studied. In 11 EDs directly accessing HIE data only through a secure Web browser, access was associated with a decrease in hospital admissions (adjusted odds ratio (OR)=0.27; p<0001). In a 12th ED relying more on print summaries, HIE access was associated with a decrease in hospital admissions (OR=0.48; p<0001) and statistically significant decreases in head CT use, body CT use, and laboratory test ordering. Discussion Applied only to the study population, HIE access was associated with an annual cost savings of

Forecasting Emergency Department Crowding: A Discrete Event Simulation

1.9 million. Net of annual operating costs, HIE access reduced overall costs by

A Computerized Provider Order Entry Intervention for Medication Safety During Acute Kidney Injury: A Quality Improvement Report

1.07 million. Hospital admission reductions accounted for 97.6% of total cost reductions. Conclusion Access to additional clinical data through HIE in emergency department settings is associated with net societal saving.

A quantitative analysis of qualitative studies in clinical journals for the 2000 publishing year

STUDY OBJECTIVE To develop a discrete event simulation of emergency department (ED) patient flow for the purpose of forecasting near-future operating conditions and to validate the forecasts with several measures of ED crowding. METHODS We developed a discrete event simulation of patient flow with evidence from the literature. Development was purely theoretical, whereas validation involved patient data from an academic ED. The model inputs and outputs, respectively, are 6-variable descriptions of every present and future patient in the ED. We validated the model by using a sliding-window design, ensuring separation of fitting and validation data in time series. We sampled consecutive 10-minute observations during 2006 (n=52,560). The outcome measures--all forecast 2, 4, 6, and 8 hours into the future from each observation--were the waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion. Forecasting performance was assessed with Pearsons correlation, residual summary statistics, and area under the receiver operating characteristic curve. RESULTS The correlations between crowding forecasts and actual outcomes started high and decreased gradually up to 8 hours into the future (lowest Pearsons r for waiting count=0.56; waiting time=0.49; occupancy level=0.78; length of stay=0.86; boarding count=0.79; boarding time=0.80). The residual means were unbiased for all outcomes except the boarding time. The discriminatory power for ambulance diversion remained consistently high up to 8 hours into the future (lowest area under the receiver operating characteristic curve=0.86). CONCLUSION By modeling patient flow, rather than operational summary variables, our simulation forecasts several measures of near-future ED crowding, with various degrees of good performance.

Mediating the intersections of organizational routines during the introduction of a health IT system.

BACKGROUND Frequently, prescribers fail to account for changing kidney function when prescribing medications. We evaluated the use of a computerized provider order entry intervention to improve medication management during acute kidney injury. STUDY DESIGN Quality improvement report with time series analyses. SETTING & PARTICIPANTS 1,598 adult inpatients with a minimum 0.5-mg/dL increase in serum creatinine level over 48 hours after an order for at least one of 122 nephrotoxic or renally cleared medications. QUALITY IMPROVEMENT PLAN Passive noninteractive warnings about increasing serum creatinine level appeared within the computerized provider order entry interface and on printed rounding reports. For contraindicated or high-toxicity medications that should be avoided or adjusted, an interruptive alert within the system asked providers to modify or discontinue the targeted orders, mark the current dosing as correct and to remain unchanged, or defer the alert to reappear in the next session. OUTCOMES & MEASUREMENTS Intervention effect on drug modification or discontinuation, time to modification or discontinuation, and provider interactions with alerts. RESULTS The modification or discontinuation rate per 100 events for medications included in the interruptive alert within 24 hours of increasing creatinine level improved from 35.2 preintervention to 52.6 postintervention (P < 0.001); orders were modified or discontinued more quickly (P < 0.001). During the postintervention period, providers initially deferred 78.1% of interruptive alerts, although 54% of these eventually were modified or discontinued before patient death, discharge, or transfer. The response to passive alerts about medications requiring review did not significantly change compared with baseline. LIMITATIONS Single tertiary-care academic medical center; provider actions were not independently adjudicated for appropriateness. CONCLUSIONS A computerized provider order entry-based alerting system to support medication management after acute kidney injury significantly increased the rate and timeliness of modification or discontinuation of targeted medications.

The Online Bioinformatics Resources Collection at the University of Pittsburgh Health Sciences Library System—a one-stop gateway to online bioinformatics databases and software tools

BackgroundQuantitative studies are becoming more recognized as important to understanding health care with all of its richness and complexities. The purpose of this descriptive survey was to provide a quantitative evaluation of the qualitative studies published in 170 core clinical journals for 2000.MethodsAll identified studies that used qualitative methods were reviewed to ascertain which clinical journals publish qualitative studies and to extract research methods, content (persons and health care issues studied), and whether mixed methods (quantitative and qualitative methods) were used.Results60 330 articles were reviewed. 355 reports of original qualitative studies and 12 systematic review articles were identified in 48 journals. Most of the journals were in the discipline of nursing. Only 4 of the most highly cited health care journals, based on ISI Science Citation Index (SCI) Impact Factors, published qualitative studies. 37 of the 355 original reports used both qualitative and quantitative (mixed) methods. Patients and non-health care settings were the most common groups of people studied. Diseases and conditions were cancer, mental health, pregnancy and childbirth, and cerebrovascular disease with many other diseases and conditions represented. Phenomenology and grounded theory were commonly used; substantial ethnography was also present. No substantial differences were noted for content or methods when articles published in all disciplines were compared with articles published in nursing titles or when studies with mixed methods were compared with studies that included only qualitative methods.ConclusionsThe clinical literature includes many qualitative studies although they are often published in nursing journals or journals with low SCI Impact Factor journals. Many qualitative studies incorporate both qualitative and quantitative methods.

Forecasting Emergency Department Crowding: A Prospective, Real-time Evaluation

Public interest in the quality and safety of health care has spurred examination of specific organizational routines believed to yield risk in health care work. Medication administration routines, in particular, have been the subject of numerous improvement projects involving information technology development, and other forms of research and regulation. This study draws from ethnographic observation to examine how the common routine of medication administration intersects with other organizational routines, and why understanding such intersections is important. We present three cases describing intersections between medication administration and other routines, including a pharmacy routine, medication administration on the next shift and management reporting. We found that each intersection had ostensive and performative dimensions; and furthermore, that IT-enabled changes to one routine led to unintended consequences in its intersection with others, resulting in misalignment of ostensive and performative aspects of the intersection. Our analysis focused on the activities of a group of nurses who provide technology use mediation (TUM) before and after the rollout of a new health IT system. This research offers new insights on the intersection of organizational routines, demonstrates the value of analyzing TUM activities to better understand the relationship between IT introduction and changes in routines, and has practical implications for the implementation of technology in complex practice settings.

Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: A literature review and case study

To bridge the gap between the rising information needs of biological and medical researchers and the rapidly growing number of online bioinformatics resources, we have created the Online Bioinformatics Resources Collection (OBRC) at the Health Sciences Library System (HSLS) at the University of Pittsburgh. The OBRC, containing 1542 major online bioinformatics databases and software tools, was constructed using the HSLS content management system built on the Zope® Web application server. To enhance the output of search results, we further implemented the Vivísimo Clustering Engine®, which automatically organizes the search results into categories created dynamically based on the textual information of the retrieved records. As the largest online collection of its kind and the only one with advanced search results clustering, OBRC is aimed at becoming a one-stop guided information gateway to the major bioinformatics databases and software tools on the Web. OBRC is available at the University of Pittsburghs HSLS Web site ().

Forecasting Emergency Department Crowding: An External, Multicenter Evaluation

OBJECTIVE Emergency department crowding threatens quality and access to health care, and a method of accurately forecasting near-future crowding should enable novel ways to alleviate the problem. The authors sought to implement and validate the previously developed ForecastED discrete event simulation for real-time forecasting of emergency department crowding. DESIGN AND MEASUREMENTS The authors conducted a prospective observational study during a three-month period (5/1/07-8/1/07) in the adult emergency department of a tertiary care medical center. The authors connected the forecasting tool to existing information systems to obtain real-time forecasts of operational data, updated every 10 minutes. The outcome measures included the emergency department waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion; each forecast 2, 4, 6, and 8 hours into the future. RESULTS The authors obtained crowding forecasts at 13,239 10-minute intervals, out of 13,248 possible (99.9%). The R(2) values for predicting operational data 8 hours into the future, with 95% confidence intervals, were 0.27 (0.26, 0.29) for waiting count, 0.11 (0.10, 0.12) for waiting time, 0.57 (0.55, 0.58) for occupancy level, 0.69 (0.68, 0.70) for length of stay, 0.61 (0.59, 0.62) for boarding count, and 0.53 (0.51, 0.54) for boarding time. The area under the receiver operating characteristic curve for predicting ambulance diversion 8 hours into the future, with 95% confidence intervals, was 0.85 (0.84, 0.86). CONCLUSIONS The ForecastED tool provides accurate forecasts of several input, throughput, and output measures of crowding up to 8 hours into the future. The real-time deployment of the system should be feasible at other emergency departments that have six patient-level variables available through information systems.

Barriers and facilitators to the use of computer-based intensive insulin therapy

INTRODUCTION Evaluations of computerized clinical decision support systems (CDSS) typically focus on clinical performance changes and do not include social, organizational, and contextual characteristics explaining use and effectiveness. Studies of CDSS for intensive insulin therapy (IIT) are no exception, and the literature lacks an understanding of effective computer-based IIT implementation and operation. RESULTS This paper presents (1) a literature review of computer-based IIT evaluations through the lens of institutional theory, a discipline from sociology and organization studies, to demonstrate the inconsistent reporting of workflow and care process execution and (2) a single-site case study to illustrate how computer-based IIT requires substantial organizational change and creates additional complexity with unintended consequences including error. DISCUSSION Computer-based IIT requires organizational commitment and attention to site-specific technology, workflow, and care processes to achieve intensive insulin therapy goals. The complex interaction between clinicians, blood glucose testing devices, and CDSS may contribute to workflow inefficiency and error. Evaluations rarely focus on the perspective of nurses, the primary users of computer-based IIT whose knowledge can potentially lead to process and care improvements. CONCLUSION This paper addresses a gap in the literature concerning the social, organizational, and contextual characteristics of CDSS in general and for intensive insulin therapy specifically. Additionally, this paper identifies areas for future research to define optimal computer-based IIT process execution: the frequency and effect of manual data entry error of blood glucose values, the frequency and effect of nurse overrides of CDSS insulin dosing recommendations, and comprehensive ethnographic study of CDSS for IIT.