ß-(1→3)-D-glucan- and mannan-guided early termination of antifungal therapy in ICU patients: a randomized controlled study.

Candida spp. are frequently encountered in specimens from ICUs. However, most of these detections represent colonization. Nevertheless, clinical practice shows that a considerable proportion of these patients will receive antifungal therapy (AT). ß-(1→3)-D-glucan (BDG) and mannan are fungal biomarkers with high negative predictive values. We aimed to examine whether biomarker-guided discontinuation of AT can reduce the antifungal consumption. Therefore, we conducted a prospective, randomized intervention study between 1 April 2019 and 31 March 2020. All adult ICU patients with a newly started systemic AT but without fungal infection were eligible for inclusion. Enrolled patients were randomized into an intervention and a control group. In both groups, serum BDG and mannan were determined on days 1 and 2 of AT. If all measurements were negative, AT was discontinued in the intervention group. The primary endpoint was antifungal use. The study was terminated after 12 months. Until this time-point, 41 patients had been included. In the intervention group (n = 19), AT was stopped in only two patients because all others showed either positive BDG and/or mannan levels. One of these two patients developed candidemia and AT had to be restarted. There was no significant difference in the primary and secondary endpoints. In summary, the strategy of using two negative BDG and mannan levels to stop AT failed to reduce antifungal consumption in our cohort. Indeed, there will inevitably be patients with invasive candidiasis in whom necessary AT is discontinued. The optimal patient population, biomarker set, and termination criteria are critical to the success of biomarker-based termination strategies.

Ethical conflicts associated with COVID-19 pandemic, triage and frailty-unexpected positive disease progression in a 90-year-old patient: A case report.

During the COVID 19 pandemic, advanced age, scoring systems, and a shortage of ICU beds were used as cut-offs for ICU admission. This case report describes the epicrisis of an elderly patient who was almost mistakenly not treated in an ICU.

(1 → 3)-ß-D-Glucan-guided antifungal therapy in adults with sepsis: the CandiSep randomized clinical trial.

PURPOSE: To investigate whether (1 → 3)-ß-d-Glucan (BDG)-guidance shortens time to antifungal therapy and thereby reduces mortality of sepsis patients with high risk of invasive Candida infection (ICI). METHODS: Multicenter, randomized, controlled trial carried out between September 2016 and September 2019 in 18 intensive care units enrolling adult sepsis patients at high risk for ICI. Patients in the control group received targeted antifungal therapy driven by culture results. In addition to targeted therapy, patients in the BDG group received antifungals if at least one of two consecutive BDG samples taken during the first two study days was ≥ 80 pg/mL. Empirical antifungal therapy was discouraged in both groups. The primary endpoint was 28-day-mortality. RESULTS: 339 patients were enrolled. ICI was diagnosed in 48 patients (14.2%) within the first 96 h after enrollment. In the BDG-group, 48.8% (84/172) patients received antifungals during the first 96 h after enrollment and 6% (10/167) patients in the control group. Death until day 28 occurred in 58 of 172 patients (33.7%) in the BDG group and 51 of 167 patients (30.5%) in the control group (relative risk 1.10; 95% confidence interval, 0.80-1.51; p = 0.53). Median time to antifungal therapy was 1.1 [interquartile range (IQR) 1.0-2.2] days in the BDG group and 4.4 (IQR 2.0-9.1, p < 0.01) days in the control group. CONCLUSIONS: Serum BDG guided antifungal treatment did not improve 28-day mortality among sepsis patients with risk factors for but unexpected low rate of IC. This study cannot comment on the potential benefit of BDG-guidance in a more selected at-risk population.

Interleukin-3 is a predictive marker for severity and outcome during SARS-CoV-2 infections.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a worldwide health threat. In a prospective multicentric study, we identify IL-3 as an independent prognostic marker for the outcome during SARS-CoV-2 infections. Specifically, low plasma IL-3 levels is associated with increased severity, viral load, and mortality during SARS-CoV-2 infections. Patients with severe COVID-19 exhibit also reduced circulating plasmacytoid dendritic cells (pDCs) and low plasma IFNα and IFNλ levels when compared to non-severe COVID-19 patients. In a mouse model of pulmonary HSV-1 infection, treatment with recombinant IL-3 reduces viral load and mortality. Mechanistically, IL-3 increases innate antiviral immunity by promoting the recruitment of circulating pDCs into the airways by stimulating CXCL12 secretion from pulmonary CD123+ epithelial cells, both, in mice and in COVID-19 negative patients exhibiting pulmonary diseases. This study identifies IL-3 as a predictive disease marker for SARS-CoV-2 infections and as a potential therapeutic target for pulmunory viral infections.

Disseminated Multifocal Intracerebral Bleeding Events in Three Coronavirus Disease 2019 Patients on Extracorporeal Membrane Oxygenation As Rescue Therapy.

OBJECTIVES: To describe three coronavirus disease 2019 patients suffering from acute respiratory distress syndrome under venovenous extracorporeal membrane oxygenation therapy and tight anticoagulation monitoring presenting a novel pattern of multifocal brain hemorrhage in various degrees in all cerebral and cerebellar lobes. DESIGN: Clinical observation of three patients. Post mortem examinations. SETTING: Two ICUs at the University Hospital Erlangen. PATIENTS: Three patients (medium age 56.6 yr, two male with hypertension and diabetes, one female with no medical history) developed severe acute respiratory distress syndrome on the basis of a severe acute respiratory syndrome coronavirus 2 infection. All required mechanical ventilation and venovenous extracorporeal membrane oxygenation support. INTERVENTIONS: Clinical observation, CT, data extraction from electronic medical records, and post mortem examinations. MAIN RESULTS: We report on an unusual multifocal bleeding pattern in the white matter in three cases with severe acute respiratory distress syndrome due to coronavirus disease 2019 undergoing venovenous extracorporeal membrane oxygenation therapy. Bleeding pattern with consecutive herniation was found in CT scans as well as in neuropathologic post mortem examinations. Frequency for this unusual brain hemorrhage in coronavirus disease 2019 patients with extracorporeal membrane oxygenation therapy at our hospital is currently 50%, whereas bleeding events in extracorporeal membrane oxygenation patients generally occur at 10-15%. CONCLUSIONS: Multifocality and high frequency of the unusual white matter hemorrhage pattern suggest a coherence to coronavirus disease 2019. Neuropathological analyses showed circumscribed thrombotic cerebrovascular occlusions, which eventually led to microvascular and later on macrovascular disseminated bleeding events. However, signs of cerebrovascular inflammation could not be detected. Polymerase chain reaction analyses of brain tissue or cerebrospinal fluid remained negative. Increased susceptibility for fatal bleeding events should be taken into consideration in terms of systemic anticoagulation strategies in coronavirus disease 2019.

A method for the graphical modeling of relative temporal constraints.

Searching for patient cohorts in electronic patient data often requires the definition of temporal constraints between the selection criteria. However, beyond a certain degree of temporal complexity, the non-graphical, form-based approaches implemented in current translational research platforms may be limited when modeling such constraints. In our opinion, there is a need for an easily accessible and implementable, fully graphical method for creating temporal queries. We aim to respond to this challenge with a new graphical notation. Based on Allen's time interval algebra, it allows for modeling temporal queries by arranging simple horizontal bars depicting symbolic time intervals. To make our approach applicable to complex temporal patterns, we apply two extensions: with duration intervals, we enable the inference about relative temporal distances between patient events, and with time interval modifiers, we support counting and excluding patient events, as well as constraining numeric values. We describe how to generate database queries from this notation. We provide a prototypical implementation, consisting of a temporal query modeling frontend and an experimental backend that connects to an i2b2 system. We evaluate our modeling approach on the MIMIC-III database to demonstrate that it can be used for modeling typical temporal phenotyping queries.

Effects of staff training and electronic event monitoring on long-term adherence to lung-protective ventilation recommendations.

PURPOSE: To investigate long-term effects of staff training and electronic clinical decision support (CDS) on adherence to lung-protective ventilation recommendations. MATERIALS AND METHODS: In 2012, group instructions and workshops at two surgical intensive care units (ICUs) started, focusing on standardized protocols for mechanical ventilation and volutrauma prevention. Subsequently implemented CDS functions continuously monitor ventilation parameters, and from 2015 triggered graphical notifications when tidal volume (VT) violated individual thresholds. To estimate the effects of these educational and technical interventions, we retrospectively analyzed nine years of VT records from routine care. As outcome measures, we calculated relative frequencies of settings that conform to recommendations, case-specific mean excess VT, and total ICU survival. RESULTS: Assessing 571,478 VT records from 10,241 ICU cases indicated that adherence during pressure-controlled ventilation improved significantly after both interventions; the share of conforming VT records increased from 61.6% to 83.0% and then 86.0%. Despite increasing case severity, ICU survival remained nearly constant over time. CONCLUSIONS: Staff training effectively improves adherence to lung-protective ventilation strategies. The observed CDS effect seemed less pronounced, although it can easily be adapted to new recommendations. Both interventions, which futures studies could deploy in combination, promise to improve the precision of mechanical ventilation.

Using Arden Syntax for the creation of a multi-patient surveillance dashboard.

OBJECTIVE: Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not natively support patient overviews from customizable CDS routines, but local physicians indicated a demand for multi-patient tabular overviews of important clinical parameters such as key laboratory measurements. As our PDMS installation provides Arden Syntax support, we set out to explore the capability of Arden Syntax for multi-patient CDS by implementing a prototypical dashboard for visualizing laboratory findings from patient sets. METHODS AND MATERIAL: Our implementation leveraged the object data type, supported by later versions of Arden, which turned out to be serviceable for representing complex input data from several patients. For our prototype, we designed a modularized architecture that separates the definition of technical operations, in particular the control of the patient context, from the actual clinical knowledge. Individual Medical Logic Modules (MLMs) for processing single patient attributes could then be developed according to well-tried Arden Syntax conventions. RESULTS: We successfully implemented a working dashboard prototype entirely in Arden Syntax. The architecture consists of a controller MLM to handle the patient context, a presenter MLM to generate a dashboard view, and a set of traditional MLMs containing the clinical decision logic. Our prototype could be integrated into the graphical user interface of the local PDMS. We observed that with realistic input data the average execution time of about 200ms for generating dashboard views attained applicable performance. CONCLUSION: Our study demonstrated the general feasibility of creating multi-patient CDS routines in Arden Syntax. We believe that our prototypical dashboard also suggests that such implementations can be relatively easy, and may simultaneously hold promise for sharing dashboards between institutions and reusing elementary components for additional dashboards.

Accessing complex patient data from Arden Syntax Medical Logic Modules.

OBJECTIVE: Arden Syntax is a standard for representing and sharing medical knowledge in form of independent modules and looks back on a history of 25 years. Its traditional field of application is the monitoring of clinical events such as generating an alert in case of occurrence of a critical laboratory result. Arden Syntax Medical Logic Modules must be able to retrieve patient data from the electronic medical record in order to enable automated decision making. For patient data with a simple structure, for instance a list of laboratory results, or, in a broader view, any patient data with a list or table structure, this mapping process is straightforward. Nevertheless, if patient data are of a complex nested structure the mapping process may become tedious. Two clinical requirements - to process complex microbiology data and to decrease the time between a critical laboratory event and its alerting by monitoring Health Level 7 (HL7) communication - have triggered the investigation of approaches for providing complex patient data from electronic medical records inside Arden Syntax Medical Logic Modules. METHODS AND MATERIALS: The data mapping capabilities of current versions of the Arden Syntax standard as well as interfaces and data mapping capabilities of three different Arden Syntax environments have been analyzed. We found and implemented three different approaches to map a test sample of complex microbiology data for 22 patients and measured their execution times and memory usage. Based on one of these approaches, we mapped entire HL7 messages onto congruent Arden Syntax objects. RESULTS: While current versions of Arden Syntax support the mapping of list and table structures, complex data structures are so far unsupported. We identified three different approaches to map complex data from electronic patient records onto Arden Syntax variables; each of these approaches successfully mapped a test sample of complex microbiology data. The first approach was implemented in Arden Syntax itself, the second one inside the interface component of one of the investigated Arden Syntax environments. The third one was based on deserialization of Extended Markup Language (XML) data. Mean execution times of the approaches to map the test sample were 497ms, 382ms, and 84ms. Peak memory usage amounted to 3MB, 3MB, and 6MB. CONCLUSION: The most promising approach by far was to map arbitrary XML structures onto congruent complex data types of Arden Syntax through deserialization. This approach is generic insofar as a data mapper based on this approach can transform any patient data provided in appropriate XML format. Therefore it could help overcome a major obstacle for integrating clinical decision support functions into clinical information systems. Theoretically, the deserialization approach would even allow mapping entire patient records onto Arden Syntax objects in one single step. We recommend extending the Arden Syntax specification with an appropriate XML data format.

Using Arden Syntax Medical Logic Modules to reduce overutilization of laboratory tests for detection of bacterial infections-Success or failure?

OBJECTIVE: Bacterial infections frequently cause prolonged intensive care unit (ICU) stays. Repeated measurements of the procalcitonin (PCT) biomarker are typically used for early detection and follow up of bacterial infections and sepsis, but those PCT measurements are costly. To avoid overutilization, we developed and evaluated a clinical decision support system (CDSS) in Arden Syntax which computes necessary and preventable PCT orders. METHODS: The CDSS implements a rule set based on the latest PCT value, the time period since this measurement, and the PCT trend scenario. We assessed the CDSS effects on the daily rate of ordered PCT tests within a prospective study having two ON and two OFF phases in a surgical ICU. In addition, we performed interviews with the participating physicians to investigate their experience with the CDSS advice. RESULTS: Prior to the deployment of the CDSS, 22% of the performed PCT tests were potentially preventable according to the rule set. During the first ON phase the daily rate of ordered PCT tests per patient decreased significantly from 0.807 to 0.662. In subsequent OFF, ON and OFF phases, however, PCT utilization reached again daily rates of 0.733, 0.803, and 0.792, respectively. The interviews demonstrated that the physicians were aware of the problem of PCT overutilization, which they primarily attributed to acute time constraints. The responders assumed that the majority of preventable measurements are indiscriminately ordered for patients during longer ICU stays. CONCLUSION: We observed an 18% reduction of PCT tests within the first four weeks of CDSS support in the investigated ICU. This reduction may have been influenced by raised awareness of the overutilization problem; the extent of this influence cannot be determined in our study design. No reduction of PCT tests could be observed during the second ON phase. The physician interviews indicated that time critical ICU situations can prevent extensive reflection about the necessity of individual tests. In order to achieve an enduring effect on PCT utilization, we will have to proceed to electronic order entry.

Standards-Based Procedural Phenotyping: The Arden Syntax on i2b2.

Phenotyping, or the identification of patient cohorts, is a recurring challenge in medical informatics. While there are open source tools such as i2b2 that address this problem by providing user-friendly querying interfaces, these platforms lack semantic expressiveness to model complex phenotyping algorithms. The Arden Syntax provides procedural programming language construct, designed specifically for medical decision support and knowledge transfer. In this work, we investigate how language constructs of the Arden Syntax can be used for generic phenotyping. We implemented a prototypical tool to integrate i2b2 with an open source Arden execution environment. To demonstrate the applicability of our approach, we used the tool together with an Arden-based phenotyping algorithm to derive statistics about ICU-acquired hypernatremia. Finally, we discuss how the combination of i2b2's user-friendly cohort pre-selection and Arden's procedural expressiveness could benefit phenotyping.

Requirements Analysis for a Clinical Decision Support System Aiming at Improving the Artificial Nutrition of Critically Ill Patients.

BACKGROUND: Nutrition support is an important aspect regarding the care of critically ill patients. Malnutrition affects the recovery process negatively. However, the impact on the clinical outcome is often underestimated in complex clinical settings due to several factors hindering optimization of nutrition. OBJECTIVE: To identify the requirements for a clinical decision support system that enables the medical staff to improve its patients' nutritional status. METHODS: A literature review and interviews with two senior physicians were conducted to refine the requirements for the support system as well as to determine the inclusion criteria for a subsequent intervention study. RESULTS: The analysis resulted in: (i) the identification of 4 measurement parameters for the assessment of the nutrition status; (ii) the graphical layout in adherence to the standards-based implementation approach for the creation of multi-patient dashboards; (iii) the definition of the study group. The nutrition dashboard will be implemented and integrated based on the set requirements, followed by an intervention study evaluating the dashboard's efficacy.

Using Arden Syntax for the Generation of Intelligent Intensive Care Discharge Letters.

Discharge letters are an important means of communication between physicians and nurses from intensive care units and their colleagues from normal wards. The patient data management system (PDMS) used at our local intensive care units provides an export tool to create discharge letters by inserting data items from electronic medical records into predefined templates. Local intensivists criticized the limitations of this tool regarding the identification and the further processing of clinically relevant data items for a flexible creation of discharge letters. As our PDMS supports Arden Syntax, and the demanded functionalities are well within the scope of this standard, we set out to investigate the suitability of Arden Syntax for the generation of discharge letters. To provide an easy-to-understand facility for integrating data items into document templates, we created an Arden Syntax interface function which replaces the names of previously defined variables with their content in a way that permits arbitrary custom formatting by clinical users. Our approach facilitates the creation of flexible text sections by conditional statements, as well as the integration of arbitrary HTML code and dynamically generated graphs. The resulting prototype enables clinical users to apply the full set of Arden Syntax language constructs to identify and process relevant data items in a way that far exceeds the capabilities of the PDMS export tool. The generation of discharge letters is an uncommon area of application for Arden Syntax, considerably differing from its original purpose. However, we found our prototype well suited for this task and plan to evaluate it in clinical production after the next major release change of our PDMS.

Increased Rate of Poor Laryngoscopic Views in Patients Scheduled for Cardiac Surgery Versus Patients Scheduled for General Surgery: A Propensity Score-Based Analysis of 21,561 Cases.

OBJECTIVES: Former analyses reported an increased rate of poor direct laryngoscopy view in cardiac surgery patients; however, these findings frequently could be attributed to confounding patient characteristics. In most of the reported cardiac surgery cohorts, the rate of well-known risk factors for poor direct laryngoscopy view such as male sex, obesity, or older age, were increased compared with the control groups. Especially in the ongoing debate on anesthesia staff qualification for cardiac interventions outside the operating room a detailed and stratified risk analysis seems necessary. DESIGN: Retrospective, anonymous, propensity score-based, matched-pair analysis. SETTING: Single-center study in a university hospital. PARTICIPANTS: No active participants. Retrospective, anonymous chart analysis. INTERVENTIONS: The anesthesia records of patients undergoing cardiac surgery in a period of 6 consecutive years were analyzed retrospectively. The results were compared with those of a control group of patients who underwent general surgery. Poor laryngoscopic view was defined as Cormack and Lehane classification grade 3 or 4. MEASUREMENTS AND MAIN RESULTS: The records of 21,561 general anesthesia procedures were reviewed for the study. The incidence of poor direct laryngoscopic views in patients scheduled for cardiac surgery was significantly increased compared with those of the general surgery cohort (7% v 4.2%). Using propensity score-based matched-pair analysis, equal subgroups were generated of each surgical department, with 2,946 patients showing identical demographic characteristics. After stratifying for demographic characteristics, the rate of poor direct laryngoscopy view remained statistically significantly higher in the cardiac surgery group (7.5% v 5.7%). CONCLUSIONS: Even with stratification for demographic risk factors, cardiac surgery patients showed a significantly higher rate of poor direct laryngoscopic view compared with general surgery patients. These results should be taken into account for human resource management and distribution of difficult airway equipment, especially when cardiac interventional programs are implemented in remote hospital locations.

Comparison of clinical outcome variables in patients with and without etomidate-facilitated anesthesia induction ahead of major cardiac surgery: a retrospective analysis.

INTRODUCTION: It is well known that etomidate may cause adrenal insufficiency. However, the clinical relevance of adrenal suppression after a single dose of etomidate remains vague. The aim of this study was to investigate the association between the administration of a single dose of etomidate or an alternative induction regime ahead of major cardiac surgery and clinical outcome parameters associated with adrenal suppression and onset of sepsis. METHODS: The anesthesia and intensive care unit (ICU) records from patients undergoing cardiac surgery over five consecutive years (2008 to 2012) were retrospectively analyzed. The focus of the analysis was on clinical parameters like mortality, ventilation hours, renal failure, and sepsis-linked serum parameters. Multivariate analysis and Cox regression were applied to derive the results. RESULTS: In total, 3,054 patient records were analyzed. A group of 1,775 (58%) patients received a single dose of etomidate; 1,279 (42%) patients did not receive etomidate at any time. There was no difference in distribution of age, American Society of Anesthesiologists physical score, duration of surgery, and Acute Physiology and Chronic Health Evaluation II score. Postoperative data showed no significant differences between the two groups in regard to mortality (6.8% versus 6.4%), mean of mechanical ventilation hours (21.2 versus 19.7), days in the ICU (2.6 versus 2.5), hospital days (18.7 versus 17.4), sepsis-associated parameters, Sequential Organ Failure Assessment score, and incidence of renal failure. Administration of etomidate showed no significant influence (P = 0.6) on hospital mortality in the multivariate Cox analysis. CONCLUSIONS: This study found no evidence for differences in key clinical outcome parameters based on anesthesia induction with or without administration of a single dose of etomidate. In consequence, etomidate might remain an acceptable option for single-dose anesthesia induction.

Employing heat maps to mine associations in structured routine care data.

OBJECTIVE: Mining the electronic medical record (EMR) has the potential to deliver new medical knowledge about causal effects, which are hidden in statistical associations between different patient attributes. It is our goal to detect such causal mechanisms within current research projects which include e.g. the detection of determinants of imminent ICU readmission. An iterative statistical approach to examine each set of considered attribute pairs delivers potential answers but is difficult to interpret. Therefore, we aimed to improve the interpretation of the resulting matrices by the use of heat maps. We propose strategies to adapt heat maps for the search for associations and causal effects within routine EMR data. METHODS: Heat maps visualize tabulated metric datasets as grid-like choropleth maps, and thus present measures of association between numerous attribute pairs clearly arranged. Basic assumptions about plausible exposures and outcomes are used to allocate distinct attribute sets to both matrix dimensions. The image then avoids certain redundant graphical elements and provides a clearer picture of the supposed associations. Specific color schemes have been chosen to incorporate preexisting information about similarities between attributes. The use of measures of association as a clustering input has been taken as a trigger to apply transformations which ensure that distance metrics always assume finite values and treat positive and negative associations in the same way. To evaluate the general capability of the approach, we conducted analyses of simulated datasets and assessed diagnostic and procedural codes in a large routine care dataset. RESULTS: Simulation results demonstrate that the proposed clustering procedure rearranges attributes similar to simulated statistical associations. Thus, heat maps are an excellent tool to indicate whether associations concern the same attributes or different ones, and whether affected attribute sets conform to any preexisting relationship between attributes. The dendrograms help in deciding if contiguous sequences of attributes effectively correspond to homogeneous attribute associations. The exemplary analysis of a routine care dataset revealed patterns of associations that follow plausible medical constellations for several diseases and the associated medical procedures and activities. Cases with breast cancer (ICD C50), for example, appeared to be associated with radiation therapy (8-52). In cross check, approximately 60 percent of the attribute pairs in this dataset showed a strong negative association, which can be explained by diseases treated in a medical specialty which routinely does not perform the respective procedures in these cases. The corresponding diagram clearly reflects these relationships in the shape of coherent subareas. CONCLUSION: We could demonstrate that heat maps of measures of association are effective for the visualization of patterns in routine care EMRs. The adjustable method for the assignment of attributes to image dimensions permits a balance between the display of ample information and a favorable level of graphical complexity. The scope of the search can be adapted by the use of pre-existing assumptions about plausible effects to select exposure and outcome attributes. Thus, the proposed method promises to simplify the detection of undiscovered causal effects within routine EMR data.

Integrating Arden-Syntax-based clinical decision support with extended presentation formats into a commercial patient data management system.

The purpose of this study was to introduce clinical decision support (CDS) that exceeds conventional alerting at tertiary care intensive care units. We investigated physicians' functional CDS requirements in periodic interviews, and analyzed technical interfaces of the existing commercial patient data management system (PDMS). Building on these assessments, we adapted a platform that processes Arden Syntax medical logic modules (MLMs). Clinicians demanded data-driven, user-driven and time-driven execution of MLMs, as well as multiple presentation formats such as tables and graphics. The used PDMS represented a black box insofar as it did not provide standardized interfaces for event notification and external access to patient data; enabling CDS thus required periodically exporting datasets for making them accessible to the invoked Arden engine. A client-server-architecture with a simple browser-based viewer allows users to activate MLM execution and to access CDS results, while an MLM library generates hypertext for diverse presentation targets. The workaround that involves a periodic data replication entails a trade-off between the necessary computational resources and a delay of generated alert messages. Web technologies proved serviceable for reconciling Arden-based CDS functions with alternative presentation formats, including tables, text formatting, graphical outputs, as well as list-based overviews of data from several patients that the native PDMS did not support.

Does introduction of a Patient Data Management System (PDMS) improve the financial situation of an intensive care unit?

BACKGROUND: Patient Data Management Systems (PDMS) support clinical documentation at the bedside and have demonstrated effects on completeness of patient charting and the time spent on documentation. These systems are costly and raise the question if such a major investment pays off. We tried to answer the following questions: How do costs and revenues of an intensive care unit develop before and after introduction of a PDMS? Can higher revenues be obtained with improved PDMS documentation? Can we present cost savings attributable to the PDMS? METHODS: Retrospective analysis of cost and reimbursement data of a 25 bed Intensive Care Unit at a German University Hospital, three years before (2004-2006) and three years after (2007-2009) PDMS implementation. RESULTS: Costs and revenues increased continuously over the years. The profit of the investigated ICU was fluctuating over the years and seemingly depending on other factors as well. We found a small increase in profit in the year after the introduction of the PDMS, but not in the following years. Profit per case peaked at 1039 € in 2007, but dropped subsequently to 639 € per case. We found no clear evidence for cost savings after the PDMS introduction. Our cautious calculation did not consider additional labour costs for IT staff needed for system maintenance. CONCLUSIONS: The introduction of a PDMS has probably minimal or no effect on reimbursement. In our case the observed increase in profit was too small to amortize the total investment for PDMS implementation.This may add some counterweight to the literature, where expectations for tools such as the PDMS can be quite unreasonable.

Implementation of a patient data management system - an evaluation study of workflow alterations.

This paper describes a combined evaluation approach for an information system on intensive care units. Staff self assessment of time needed for documentation activities during admission, daily treatment and discharge/transfer has been compared to a workflow analysis which demonstrated that the system eliminated process steps mainly in daily documentation activities. Interestingly, nursing staff reported major time savings rather during discharge/transfer than in daily documentation, whereas physicians noticed no time savings at all. We conclude that combining workflow analysis with either self assessment of time needed or alternatively appropriate time measurements or both increases insight into organizational changes and their implications after system implementation.