Anderson, Philip Dean

Forberg, Jakob Lundager

Isenberg Ravn, Lisbet

Jacobsen, Peter

Sørup, Christian Michel

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 20, 2017An integrated performance measurement model for emergency departmentassessmentSørup, Christian Michel; Jacobsen, Peter; Anderson, Philip Dean ; Isenberg Ravn, Lisbet; Forberg,Jakob LundagerPublication date:2014Link back to DTU OrbitCitation (APA):Sørup, C. M., Jacobsen, P., Anderson, P. D., Isenberg Ravn, L., & Forberg, J. L. (2014). An integratedperformance measurement model for emergency department assessment. Poster session presented at 6thDanish Emergency Medicine Conference, Odense, Denmark.INTRODUCTION  New ways of organizing acute care delivery need to be developed and assessed  Numerous larger changes have affected the ED in recent years and will continue to do so  Impact on the ED as a system not clear  To develop a performance measurement (PM) tool that enables ED decision makers to 1. monitor the effect of new performance enhancing initiatives 2. show how internal processes are connected METHODS  Multiple case study involving three EDs  Builds on a prior literature study on recommended performance indicators for ED assessment  42 semi-structured interviews were conducted with key staff representatives, evenly distributed among the cases, to identify interconnections between performance indicators  Performance data was retrieved from all included cases  Process control charts were utilized to assess variation in each performance indicator  RESULTS RESULTS DISCUSSION  A first step towards data-driven management of acute care delivery  Draws on systems thinking and statistics  Novel causal tracing of how performance enhancing initiatives impact the system  The PM tool should be applied together with other available information sources    Potential to include a cost module and indicators for patient specific conditions (e.g. pneumonia and sepsis)       AN INTEGRATED PERFORMANCE MEASUREMENT MODEL FOR EMERGENCY DEPARTMENT ASSESSMENT Christian Michel Sørup*1,3,4, Peter Jacobsen1, Philip Dean Anderson2, Lisbet Isenberg Ravn3  & Jakob Lundager Forberg4 for the 6th Danish Emergency Medicine Conference   1Department of Management Engineering, Technical University of Denmark, Kongens Lyngby, Denmark; 2Department of Emergency Medicine,  Beth Israel Deaconess Medical Center, Boston, MA; 3Department of Emergency Medicine, Herlev Hospital, Herlev, Denmark; 4Department of Emergency Medicine, Nordsjællands Hospital, Hillerød, Denmark OBJECTIVE LIMITATIONS  Manual data entry and analysis (PM tool is currently a prototype)  Data validity is questionable  No way of determining time delays from an intervention is launched to effect kicks in  Turn attention from suggesting connections to apply data in a system dynamics simulation model, enhancing understanding of the ED as a complex system CONCLUSION  A PM tool was developed that can track all-round ED performance on selected indicators  Connects indicators based on causal causes and effects  Can potentially be implemented in existing data repositories for automation purposes  Potential extensions include 1) turn PM tool into a system dynamics model and 2) develop an add-on that considers financial aspects  User: DEFAULTTime window: 01-10-13 to 31-10-13SafetyPatientsSatisfactionTotal ED performance EmployeesPlanningOperations UtilizationFront end processesTime intervals Throughput processes Back end processesAdverse eventsMissed diagnosesMortalityED returns (< 72 hours)LWBSLAMALBTCSatisfaction, waitSatisfaction, treatmentSatisfaction, personnelSatisfaction, info deliverySatisfaction, physical settingsVacant positionsNurse practitionersMDs in trainee positionsOvertime hours per employeeSatisfaction, physical work. environmentSatisfaction, phycological Employee absencePatient loadPatient case mixStock (medication, medical equipment, supplies etc.)ECG's takenX-rays, CTs, MRIs, ultrasonic studies conductedBlood samples drawnAmbulance off-loading timeGreeter timeTriage timeTreatment timeLaboratory turnaroundImaging turnaroundBoarding timeLEFT WITHOUT BEING SEEN (LWBS)NARRATIVELWBS rates achieved a highscore on October 22 for several reasons. The number of patients in the waiting room reached 27 patients on October 19 which was the highest during the month. As a result, average waiting time in the waiting room exceeded 300 minutes. A high proportion of ESI level 1 patients (October 19 = 11 patients and October 20 = 10 patients) leading to delays and increased waiting times for patients requring less immediate attention. Qualitative measurements through patient satisfaction surveys serve as an indicator for waiting time contentment. A flatscreen depicting up-to-date waiting times could be installed in the waiting room with benefit for the patients.   01234567PatientsLWBS countCLUCLLCLBack to overviewCauses (explanatory)Adverse eventsReturn visits (<72 hours)Vacant positionsEmployee absenceLaboratory turnaroundImaging turnaround--------------------------------Effects (outcomes)Satisfaction, waitSatisfaction, treatmentSatisfaction, info deliverySatisfaction, physical settingsEmpl. satisfaction, physicalEmpl. satisfaction, psychologicalPatient loadGreeter timeTreatment time

An integrated performance measurement model for emergency department assessment

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An integrated performance measurement model for emergency department assessment

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