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AIMS: Fasting periods represent a significant challenge for glycaemic control in hospitalised patients. The study aims to evaluate the efficacy and safety of basal-bolus insulin therapy in managing glycaemia during fasting periods in hospitalised patients with type 2 diabetes. MATERIALS AND METHODS: We performed a post-hoc analysis of two prospective, uncontrolled interventional trials that applied electronic decision support system guided basal-bolus (meal-related and correction) insulin therapy. We searched for fasting periods (invasive or diagnostic procedures, medical condition) during inpatient stays. In a mixed-model analysis, patients' glucose levels and insulin doses on days with regular food intake were compared to days with fasting periods. RESULTS: Out of 249 patients, 115 patients (33.9% female, age 68.3±10.3 years, diabetes duration 15.1±10.9 years, BMI 30.1±5.4 kg/m2 , HbA1c 69±20 mmol/mol) had 194 days with fasting periods. Mean daily blood glucose was lower (Modelled difference (ModDiff): -0.5±0.2 mmol/L, p=0.006), and the proportion of glucose values within the target range (3.9-10.0 mmol/L) increased on days with fasting periods compared to days with regular food intake (ModDiff: +0.06±0.02, p=0.005). Glycaemic control on fasting days was driven by a reduction in daily bolus insulin doses (ModDiff: -11.0±0.9 IU, p

Deep learning currently struggles with tabular data, but it can benefit from multimodal learning. SAINT is a deep learning model for tabular data on which we base our presented developments.In this study, we introduce SAINTENS as a new deep learning method, specifically for the in healthcare predominant tabular real world data.For this purpose, we compare SAINTENS with SAINT and the State of the Art Machine Learning methods for tabular data. We use tabular data from geriatrics to predict four different targets (dysphagia, pressure ulcers, decompensated heart failure and delirium). We determine the relevant feature sets and train the models on these sets.Both SAINTENS and SAINT models are at least on the same performance level as the current State of the Art (Gradient Boosting Decision Trees).In combination with multimodal learning SAINTENS and SAINT may be used on real world data comprising tabular, text and image data, for discovery and development of new digital biomarkers.

Background: GlucoTab, an electronic diabetes management system (eDMS), supports healthcare professionals (HCPs) in inpatient blood glucose (BG) management at point-of-care and was implemented for the first time under routine conditions in a regional hospital to replace the paper insulin chart. Method: To investigate quality of the eDMS for inpatients with type 2 diabetes mellitus a monocentric retrospective before-after evaluation was conducted. We compared documentation possibilities by assessing a blank paper chart vs the eDMS user interface. Further quality aspects were compared by assessing filled-in paper charts ( n = 106) vs filled-in eDMS documentation ( n = 241). HCPs ( n = 59) were interviewed regarding eDMS satisfaction. Results: The eDMS represented an improvement of documentation possibilities by offering a more structured and comprehensive user interface compared to the blank paper chart. The number of good diabetes days averaged to a median value of four days in both groups (paper chart: 4.38 [0-7] vs eDMS: 4.38 [0-7] days). Median daily BG was 170 (117-297) mg/dL vs 168 (86-286) mg/dL and median fasting BG was 152 (95-285) mg/dL vs 145 (69-333) mg/dL, and 0.1% vs 0.4% BG values Conclusions: The eDMS completely replaced the paper chart, showed comparable glycemic control, was positively accepted by HCPs, and is suitable for inpatient diabetes management.

Introduction: Basal insulin is the first choice for geriatric persons with type 2 diabetes (T2D) who require insulin therapy. A digital workflow and decision support system (GlucoTab@MobileCare) with an integrated basal or basal-plus insulin algorithm has demonstrated to be an effective and safe treatment in geriatric persons with T2D receiving home health care during a three months study period. The aim of this study was to investigate the long-term effect on diabetes management following treatment by GlucoTab@MobileCare. Methods: Diabetes related aspects (glycemic control, acute hospital admissions, general practitioner (GP) consultations under routine conditions) were assessed in nine patients (5 female, age 77±10 years, BMI 27.7±4.7 kg/m2, HbA1c 60±13 mmol/mol [start of intervention] vs. 57±12 mmol/mol [end of intervention]) during the three months study intervention, six months before (n=9) and six months after (n=8, due to the death of one patient) study intervention. Data from electronic health records and diabetes documentation were analyzed. Results: Mean fasting blood glucose (BG) was 212±72 mg/dL before, 156±33 mg/dL during and 162±42 mg/dL after intervention. During the study, no acute hospital admissions due to BG derailments were observed. Eight derailments before and four derailments after intervention occurred. Causes for diabetes-related emergency department (ED) admissions were hyperglycemia (n=10), hypoglycemia (n=1) and undefined (n=1). Nine of the twelve admissions resulted in hospitalizations. GPs were consulted for diabetes management 19-times before and 16-times after GlucoTab@MobileCare treatment. Reasons for consultations were routine check-up (n=12), therapy adjustment (n=22) and HbA1c measurement (n=1). Conclusion: A sustained effect on glycemic control with reduced requirements for hospital admissions was observed six months after GlucoTab@MobileCare treatment, whereas GP consultations were not affected. Disclosure J. Kopanz: None. T. Pieber: Advisory Panel; Self; ADOCIA, Arecor, AstraZeneca, Eli Lilly and Company, Novo Nordisk A/S, Sanofi, Research Support; Self; AstraZeneca, Novo Nordisk A/S. J. K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Roche Diabetes Care, Speaker’s Bureau; Self; Abbott Diabetes, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Stock/Shareholder; Self; decide Clinical Software GmbH. L. K. Scholle: None. K. M. Lichtenegger: None. G. Ambrosch: None. A. Libiseller: None. F. Aberer: None. M. Pandis: None. K. Donsa: Stock/Shareholder; Self; decide Clinical Software GmbH. T. Truskaller: None.

The aim was to investigate the applicability of a clinical decision support system in a real-world inpatient setting for patients with type 2 diabetes on general hospital wards. A total of 150 patients with type 2 diabetes requiring subcutaneous insulin therapy were treated with basal-bolus insulin therapy guided by a decision support system (GlucoTab) providing automated workflow tasks and suggestions for insulin dosing to health care professionals. By using the system, a mean daily blood glucose (BG) of 159 ± 32 mg/dL was achieved. 68.8% of measurements were in the target range (70 to 93% and 91%, respectively). The decision support system facilitates safe and efficacious inpatient diabetes care by standardizing treatment workflow and providing decision support for basal-bolus insulin dosing.

Background: About 25% of adults >70 years suffer from type 2 diabetes. Due to the heterogeneity of the geriatric population, guidelines emphasize the need to individualize glycemic goals and simplify treatment strategies with the main focus of avoiding hypoglycemia. The aim of this study was to assess glycemic control in patients with type 2 diabetes in geriatric care facilities based on their individual health status. Methods: 170 medical records of older adults with type 2 diabetes in geriatric care facilities were retrospectively assessed (64.7% female, age 80 ± 9 years; glycated hemoglobin 6.8% ± 3.6% [51 ± 16 mmol/mol]; body mass index 27.9 ± 5.8 kg/m2). Based on the individual health status, patients were allocated to three groups (healthy n = 27, complex n = 86, and poor n = 57). Results: The overall blood glucose (BG) value was highest in the poor health group with 188 ± 47 mg/dL (poor) vs 167 ± 42 mg/dL (complex) vs 150 ± 34 mg/dL (healthy). BG values of 1.6% (poor) vs 2.8% (complex) vs 1.5% (healthy) of patients were below 90 mg/dL. 36.8% (poor) vs 23.4% (complex) vs 18.5% (healthy) of patients received insulin as the main diabetes therapy, but of these only 14.3% (poor) vs 20% (complex) vs 40% (healthy) were treated with basal insulin. Conclusions: Overall, BG values were higher in the poor and complex health group. There were a few low BG values in all groups. Although recommended by international guidelines, basal insulin therapy with its low complexity and low hypoglycemic risk is still underused, especially in the poor health group. Therefore, simplification of diabetes therapy should be considered further.

Introduction Diabetes management can be especially complex for older adults who receive health care at home. Thus, international guidelines recommend basal-insulin regimens due to simpler handling and low hypoglycaemia risk. A basal-insulin algorithm (including basal-plus) was developed to also include participant's health status and subsequently implemented into a tablet-based workflow and decision support system, GlucoTab@MobileCare. This study protocol describes a proof-of-concept study to investigate user acceptance, safety and efficacy of the GlucoTab@MobileCare system in participants receiving home health care. Methods The open-label, single-centre, uncontrolled study will recruit a maximum of ten participants with insulin treated type-2-diabetes (age ≥18 years) who receive home health care. During a three month study period participants will receive basal- or basal-plus-insulin therapy once daily as suggested by the GlucoTab@MobileCare system. Statistical analysis will be conducted on an intention-to-treat basis. The primary endpoint is the percentage of tasks (BG measurements, insulin dose calculations, insulin injections) that were performed according to GlucoTab@MobileCare suggestions relative to the total of suggested tasks. Secondary endpoints include user acceptance, safety and efficacy parameters. The study was approved by the ethics committee and regulatory authorities. Before obtaining written informed consent, all participants will receive oral and written information about all aspects of the study. Results will be published in a peer-reviewed journal and at diabetes and geriatric conferences. Discussion Potential implications may be improved quality and safety of basal-insulin therapy in older adults as well as support for health-care-providers in daily routine including evidence-based knowledge. Trial registration German Clinical Trials Register (DRKS00015059)

Seit 2008 nutzen Gesundheitsdienstleister in Osterreich und Deutschland das von JOANNEUM RESEARCH entwickelte System BARS (Benchmarking and Reporting Service) bei der Behandlung chronischer Erkrankungen durch strukturierte Erfassung von Primardaten. Die erfassten pseudonymisierten Real World Daten (RWD) werden von den Einrichtungen zur Qualitatssicherung und fur einrichtungsubergreifende Vergleiche der Therapiemasnahmen genutzt. Zusatzlich werden die seit uber 13 Jahren erhobenen Daten verwendet, um mit Methoden der kunstlichen Intelligenz (KI) die Fruherkennung von Risikopotenzialen sowie die (De-)Intensivierung von medikamentosen Therapien zu erforschen.

Glycemic goals of inpatients should consider the prevention of hypoglycemia and hyperglycemia. To achieve glycemic control in the hospital, guidelines recommend basal-bolus insulin therapy as the preferred method for treatment of persistent hyperglycemia and the use of clinical decision support systems. The aim of this study was to investigate the efficacy, safety and usability of the GlucoTab® decision support system in hospital routine care of non-critically ill patients with type 2 diabetes at 3 general wards of a university medical center. In total, 150 patients with type 2 diabetes requiring insulin therapy (56 female, age 68 ± 11 years, HbA1c 77 ± 24 mmol/mol, diabetes duration 15 ± 11 years, BMI 30 ± 6 kg/m2, length of hospital stay 9 ± 7 days) were treated with basal-bolus insulin therapy guided by GlucoTab®, a mobile decision support system providing automated workflow tasks and suggestions for insulin dosing to nurses and physicians. In total 4879 capillary blood glucose values (BG) were documented. By using the GlucoTab® system in routine care, a mean daily BG of 159 ± 32 mg/dl could be achieved. 68.8% of all BG measurements were in the acceptable range (70-93% and 91%, respectively). The study confirmed that the GlucoTab® system supports a safe, efficacious and user-friendly glycemic management of basal-bolus insulin therapy in hospital routine care. Further research will include decision support for alternative therapy types for patients with less stringent goals to prevent hypoglycemia in hospital routine care. Disclosure K.M. Lichtenegger: None. F. Aberer: None. A.C. Tuca: None. K. Donsa: Stock/Shareholder; Self; decide Clinical Software GmbH. B. Höll: Employee; Self; decide Clinical Software GmbH. L. Schaupp: None. J. Plank: None. P. Beck: Employee; Self; decide Clinical Software GmbH. Stock/Shareholder; Self; decide Clinical Software GmbH. F. Fruhwald: None. T.R. Pieber: Advisory Panel; Self; ADOCIA, Arecor Limited, AstraZeneca, Novo Nordisk A/S, Sanofi. Speaker's Bureau; Self; Novo Nordisk A/S. J.K. Mader: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Prediktor Medical, Roche Diabetes Care, Sanofi. Speaker's Bureau; Self; Abbott, AstraZeneca, Dexcom, Inc., Novo Nordisk Inc. Stock/Shareholder; Self; decide Clinical Software GmbH. Funding Research Studio Austria "GlucoTab“ (844737)

Background: Diabetes management requires complex and interdisciplinary cooperation of health care professionals (HCPs). To support this complex process, IT-support is recommended by clinical guidelines. The aim of this article is to report on results from a clinical feasibility study testing the prototype of a mobile, tablet-based client-server system for computerized decision and workflow support (GlucoTab®) and to discuss its impact on hypoglycemia prevention. Methods: The system was tested in a monocentric, open, noncontrolled intervention study in 30 patients with type 2 diabetes mellitus (T2DM). The system supports HCPs in performing a basal-bolus insulin therapy. Diabetes therapy, adverse events, software errors and user feedback were documented. Safety, efficacy and user acceptance of the system were investigated. Results: Only 1.3% of blood glucose (BG) measurements were 300 mg/dl. The availability of the system (97.3%) and the rate of treatment activities documented with the system (>93.5%) were high. Only few suggestions from the system were overruled by the users (>95.7% adherence). Evaluation of the 3 anonymous questionnaires showed that confidence in the system increased over time. The majority of users believed that treatment errors could be prevented by using this system. Conclusions: Data from our feasibility study show a significant reduction of hypoglycemia by implementing a computerized system for workflow and decision support for diabetes management, compared to a paper-based process. The system was well accepted by HCPs, which is shown in the user acceptance analysis and that users adhered to the insulin dose suggestions made by the system.

AIMS To investigate efficacy, safety and usability of the GlucoTab system for glycaemic management using insulin glargine U300 in non-critically ill hospitalized patients with type 2 diabetes (T2D). MATERIALS AND METHODS In this open, non-controlled single-arm pilot study, glycaemic control at the general ward of a tertiary care hospital was guided by a mobile decision support system (GlucoTab) for basal-bolus insulin dosing using the novel basal insulin analogue insulin glargine U300 for the first time. Glycaemic control was surveilled with capillary glucose measurements and continuous glucose monitoring (CGM). The primary endpoint was efficacy of glycaemic management, defined as the percentage of blood glucose measurements within the target range of 3.9 to 7.8 mmol/L. RESULTS A total of 30 patients with T2D (12 female; age, 67 ± 11 years; HbA1c, 70 ± 26 mmol/mol; BMI, 31.8 ± 5.6 kg/m2 ; length of study, 8.5 ± 4.5 days) were included. In total, 894 capillary glucose values and 49 846 data points of CGM were available, of which 56.1% of all measured capillary glucose values and 54.3% of CGM values were within the target area (3.9-7.8 mmol/L). Overall capillary mean glucose was 8.5 ± 1.2 and 8.4 ± 1.2 mmol/L assessed by CGM. Time within glucose target improved continuously during the course of treatment, while time within hypoglycaemia (

To date little data exists on diabetes management during fasting periods on the general ward. The aim of the analysis was to assess standardized diabetes management using GlucoTab® - a digital algorithm driven basal-bolus insulin therapy - in hospitalized patients with T2D in the fasting period. Data from two clinical trials were pooled. Patients were included in the analysis if they had at least one fasting period during hospital stay at the general ward. Periods of fasting were compared with periods of regular food intake. Out of 191 T2D patients 91 (48%, 5 on surgical and 86 patients on internal medicine wards, 32 female, age 68 ± 12 years, HbA1c 67 ± 19 mmol/mol, BMI 30 ± 15 kg/m2, diabetes duration 15 ± 11 years, creatinine 1.8 ± 1.4 mg/dl, length of hospital stay 10 ± 6 days) were included comprising overall 131 fasting periods. Mean blood glucose was lower during fasting periods as compared to periods with regular food intake (153 ± 38 vs. 158 ± 24 mg/dl, p = 0.033). Hypoglycemia ( Disclosure R. Rainer: None. H. Elsayed: None. F. Aberer: None. P. Beck: Employee; Self; decide Clinical Software GmbH. T.R. Pieber: Consultant; Self; Arecor, AstraZeneca, Eli Lilly and Company, Novo Nordisk A/S, Sanofi. Employee; Self; CBmed. Research Support; Self; Novo Nordisk A/S, AstraZeneca. K. Donsa: Stock/Shareholder; Self; decide Clinical Software GmbH. J. Plank: None. J.K. Mader: Speaker's Bureau; Self; Roche Diabetes Care Health and Digital Solutions, Novo Nordisk A/S. Advisory Panel; Self; Eli Lilly and Company. Consultant; Self; Becton, Dickinson and Company. Speaker's Bureau; Self; Sanofi. Research Support; Self; ConvaTec Inc., Menarini Group. Speaker's Bureau; Self; Medtronic. Research Support; Self; Novo Nordisk A/S, Sanofi.

A fast and accurate data transmission from glucose meter to clinical decision support systems (CDSSs) is crucial for the management of type 2 diabetes mellitus since almost all therapeutic interventions are derived from glucose measurements.Aim was to develop a prototype of an automated glucose measurement transmission protocol based on the Continua Design Guidelines and to embed the protocol into a CDSS used by healthcare professionals.A literature and market research was performed to analyze the state-of-the-art and thereupon develop, integrate and validate an automated glucose measurement transmission protocol in an iterative process.Findings from literature and market research guided towards the development of a standardized glucose measurement transmission protocol using a middleware. The interface description to communicate with the glucose meter was illustrated and embedded into a CDSS.A prototype of an interoperable transmission of glucose measurements was developed and implemented in a CDSS presenting a promising way to reduce medication errors and improve user satisfaction.

The Surgical Safety Checklist (SSC) is routinely used in operating rooms (OR) but its acceptance is low. One promising way to improve acceptance of the SSC and thus quality of patient care is digitalization.To investigate how a digitalization of the SSC could be implemented in a teaching hospital. Based on the identified user requirements we designed a first user interface (UI).We performed a literature review, identified user perceptions and requirements during 12 interviews including a standardized questionnaire in surgical departments at the University Hospital Graz (Austria). Subsequently a first prototype of a UI was designed.Seven different approaches for digital SSC were identified in literature. Our interviews showed that 90% of the participants had a positive attitude towards a digitalization of SSC. The most favoured version of a digitalized SSC was a tablet-based client-server system with integration in the EHR and projection on an OR monitor.Digitalization of the SSC is requested by medical and nursing personnel. Based on the identified user requirements we designed a process oriented UI of a digital SSC.

Background: This study investigated the efficacy, safety, and usability of standardized glycemic management by a computerized decision support system for non-critically ill hospitalized patients with type 2 diabetes on four different wards. Materials and Methods: In this open, noncontrolled intervention study, glycemic management of 99 patients with type 2 diabetes (62% acute admissions; 41 females; age, 67±11 years; hemoglobin A1c, 65±21 mmol/mol; body mass index, 30.4±6.5 kg/m2) on clinical wards (Cardiology, Endocrinology, Nephrology, Plastic Surgery) of a tertiary-care hospital was guided by GlucoTab® (Joanneum Research GmbH [Graz, Austria] and Medical University of Graz [Graz, Austria]), a mobile decision support system providing automated workflow support and suggestions for insulin dosing to nurses and physicians. Results: Adherence to insulin dosing suggestions was high (96.5% bolus, 96.7% basal). The primary outcome measure, percentage of blood glucose (BG) measurements in the range of 70–140 mg/dL, occurred in 50.2±22.2% of all measurements. The overall mean BG level was 154±35 mg/dL. BG measurements in the ranges of 60–70 mg/dL, 40–60 mg/dL, and

Inpatient glucose management is based on four daily capillary blood glucose (BG) measurements. The aim was to test the capability of continuous glucose monitoring (CGM) for assessing the clinical impact and safety of basal-bolus insulin therapy in non-critically ill hospitalized patients with type 2 diabetes mellitus (T2DM).Eighty-four patients with T2DM (age, 68±10 years; glycosylated hemoglobin, 72±28 mmol/mol; body mass index, 31±7 kg/m(2)) were treated with basal-bolus insulin. CGM was performed with the iPro(®)2 system (Medtronic MiniMed, Northridge, CA) and calibrated retrospectively.A remarkable consistency between CGM and BG measurements and therapy improvement was shown over the study period of 501 patient-days. The number of CGM and BG measurements (CGM/BG) in the range from 3.9-10 mmol/L increased from 67.7%/67.2% (on Day 1) to 77.5%/78.6% (on the last day) (P0.04). The number of low glycemic episodes (3.3 to3.9 mmol/L) during nighttime detected by CGM was 15-fold higher, and the number of episodes13.9 mmol/L detected by CGM during night was 12.5-fold higher than the values from the BG measurements. Ninety-nine percent of data points were in the clinically accurate or acceptable Clarke Error Grid Zones A+B, and the relative numbers of correctly identified episodes of3.9 and13.9 mmol/L detected by CGM (sensitivity) were 47.3% and 81.5%, respectively.Our data exhibit a good agreement between overall CGM and BG measurements, but there were a high number of missed hypo- and hyperglycemic episodes with BG measurements, particularly during nighttime. Overall assessment of glycemic control using CGM is feasible, whereas the use of CGM for individualized therapy decisions needs further improvement.

Summary Background: Standardized insulin order sets for subcutaneous basal-bolus insulin therapy are recommended by clinical guidelines for the inpatient management of diabetes. The algorithm based GlucoTab system electronically assists health care personnel by supporting clinical workflow and providing insulin-dose suggestions. Objective: To develop a toolbox for improving clinical decision-support algorithms. Methods: The toolbox has three main components. 1) Data preparation: Data from several heterogeneous sources is extracted, cleaned and stored in a uniform data format. 2) Simulation: The effects of algorithm modifications are estimated by simulating treatment workflows based on real data from clinical trials. 3) Analysis: Algorithm performance is measured, analyzed and simulated by using data from three clinical trials with a total of 166 patients. Results: Use of the toolbox led to algorithm improvements as well as the detection of potential individualized subgroup-specific algorithms. Conclusion: These results are a first step towards individualized algorithm modifications for specific patient subgroups. Citation: Donsa K, Beck P, Plank J, Schaupp L, Mader JK, Truskaller T, Tschapeller B, Höll B, Spat S, Pieber TR. A toolbox to improve algorithms for insulin-dosing decision support. Appl Clin Inf 2014; 5: 548–556 http://dx.doi.org/10.4338/ACI-04-RA-0033

A1 Meet the needs: Important questions for ICU relatives Magdalena M. Hoffmann1,2, Anna K. Holl3, Harald Burgsteiner4, Thomas Pieber1,5, Philipp Eller6, Karin Amrein1 A2 Daily use of the Surgical Safety Checklist: results of a real-time audit Gerald Sendlhofer1,2,3, Karina Leitgeb1, Veronika Gombotz1, Peter Tiefenbacher1, Lars-Peter Kamolz2,3 A3 Best-practice projects concerning patient safety in Austria Gerald Sendlhofer1,2,3,4, , Karina Leitgeb1,4, Magdalena Hoffmann1,4, Sabine Papst1,4, Susanne Gasteiner1,4, Lars Peter Kamolz2,3,4 A4 Comprehensive Cancer Center Graz: evaluation of availability of mandatory healthcare professionals in tumorboards Peter Tiefenbacher1,2, Yvonne Muller1,2, Gerald Sendlhofer1,2,3,4 A5 Comprehensive Cancer Center Graz: protocol to evaluate communication and recommendation finding in tumorboards Marlies Hart1, Peter Tiefenbacher1,2, Yvonne Muller1,2, Gerald Sendlhofer1,2,3,4 A6 Generating a new insulin prescription chart at a university hospital Julia Kopanz1, Katharina M. Neubauer1, Gerald Sendlhofer2,3,4, Barbara Semlitsch1, Andres Pak5, Gerald Cuder1, Thomas R. Pieber1, Johannes Plank6 A7 The Speech and Language Therapy teaching practice at the Institute for Speech and Language Therapy at FH JOANNEUM in Graz Annemarie Schinko1, Angelika Rother1 A8 Development and implementation of a progress test in undergraduate dental education: a prospective Austrian pilot project Barbara Kirnbauer1, Petra Rugani1, Norbert Jakse1, Johannes Bernhardt-Melischnig2, Rudolf Egger3 A9 What they need and what they get: protocol to identify the needs of information of patients and what they receive so far Magdalena M. Hoffmann1,2, Karina Leitgeb1,3, Esther Trampusch4,5, Angelika Hofer4, Regina Riedl6, Karin Amrein2, Christa Tax7, Gernot Brunner7, Gerald Sendlhofer,1,3,8 A 10 Medication-related problems resolved and money saved: results of a clinical pharmacy service evaluation in the surgical setting Emilie Tudela-Lopez1, Petra Polzleitner1, Klara Jadrna1, Christina Labut1, Maria Kundracikova1, Helga Fend1, Gunar Stemer1 A 11 Intercultural communication management in radiology Jutta Maria Lorenz1 A 12 Spotlight on data quality: comparison of data input by physicians vs. non-physicians in the German Resuscitation Registry Gerhard Prause1, Paul Zajic1, Philipp Zoidl1, Geza Gemes1 A 13 MRSA prevalence and eradication with octenidine Gerald Pichler1, Christian Pux1, Rita Babeluk2, Brigitte Hermann3, Eric Stoiser3, Antonella De Campo4, Andrea Grisold5, Ines Zollner-Schwetz6, Robert Krause6, Walter Schippinger4 A 14 QUIPS: a pain registry with impact in science and daily routine work Alexander Avian1,2, Brigitte Messerer3, Claudia Weinmann2, Winfried Meisner2 A 15 Patient empowerment for the youngest Karina Leitgeb1, Magdalena Hoffmann1, Gerald Sendlhofer1,2,3 A 16 Development of a pocket guide for parenteral nutrition in hospitalized adults Anna Maria Eisenberger1, Siegrid Fuchs2 A 17 Safety and efficacy of a clinical decision support system for blood glucose management in patients with diabetes mellitus type 2 at a plastic-surgical ward Alexandru-C. Tuca1, Katharina M. Neubauer2, Julia K. Mader2, Felix Aberer2 , David B. Lumenta1, Birgit Bauer1 ,Klaus Donsa3 , Thomas Augustin3 , Bernhard Holl3 , Lukas Schaupp2, Peter Beck4, Johannes Plank2 , Thomas R. Pieber2,3, Lars-P. Kamolz1,5 A 18 Urban dwellers show higher readiness in participating actively in the shaping of the healthcare system in Austria Christian Smolle1, Frederike Reischies2, Gudrun Pregartner3, Daryousch Parvizi2, Gerald Sendlhofer1,4, Lars-Peter Kamolz1,4