Eric V. Jackson

Improving Infusion Pump Safety Through Usability Testing.

With the recognition that the introduction of new technology causes changes in workflow and may introduce new errors to the system, usability testing was performed to provide data on nursing practice and interaction with infusion pump technology. Usability testing provides the opportunity to detect and analyze potentially dangerous problems with the design of infusion pumps that could cause or allow avoidable errors. This work will reduce preventable harm through the optimization of health care delivery.

Using electronic health records and nursing assessment to redesign clinical early recognition systems

As health-care organizations transition from paper to electronic documentation systems, capturing the nursing assessment electronically can play a fundamental role in transforming health-care delivery. Especially in preventive health, electronic capture of nursing assessment, combined with vital sign-based monitoring, can support early detection of physiological deterioration of patients. While vital sign-based Early Warning Systems have the potential to detect signals of physiological deterioration, their clinical interpretation and integration into the workflow in hospital-based care setting remain a challenge. This study presents a clinical early recognition algorithm using electronic health records (EHRs) coupled with an electronic Nurse Screening Assessment (NSA) that targets various health assessment categories and its integration into the nursing workflow. Data was collected retrospectively from a single institution (N=2,405 visits). χ2 tests showed significant differences between algorithms with and without NSA (P<0.01). This study provides a practical framework for facilitating the meaningful use of EHRs in hospitals.

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit

BACKGROUND Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. OBJECTIVE Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. METHODS We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. RESULTS The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. CONCLUSIONS Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

Sepsis alert presentation: Developing a framework to optimize alert design for clinical decision support

The Value Institute at Christiana Care Health System is developing and evaluating a framework to optimize alert design for clinical decision support (CDS) to target sepsis, the most impactful disease process to our system. Our multifaceted approach takes into account technological, organizational, and provider factors that influence how providers interact with alerts throughout the hospitalization process when appropriate clinical care can change the trajectory of a patient with a systemic infection. Effective presentation of the alert, including how and what is displayed, may help providers extract information quickly, offering better cognitive support during busy patient encounters. Our simulated usability evaluation, using real clinical scenarios, investigates how clinicians detect sepsis and respond to CDS based on the way the information is presented visually. Usability testing is designed to better understand the decision-making process analyzing varied designs that utilize various levels of visual presentations to promote situational awareness and measure diagnosis- and treatment- related variables.

Data-driven approach to Early Warning Score-based alert management

Background Increasing adoption of electronic health records (EHRs) with integrated alerting systems is a key initiative for improving patient safety. Considering the variety of dynamically changing clinical information, it remains a challenge to design EHR-driven alerting systems that notify the right providers for the right patient at the right time while managing alert burden. The objective of this study is to proactively develop and evaluate a systematic alert-generating approach as part of the implementation of an Early Warning Score (EWS) at the study hospitals. Methods We quantified the impact of an EWS-based clinical alert system on quantity and frequency of alerts using three different alert algorithms consisting of a set of criteria for triggering and muting alerts when certain criteria are satisfied. We used retrospectively collected EHRs data from December 2015 to July 2016 in three units at the study hospitals including general medical, acute care for the elderly and patients with heart failure. Results We compared the alert-generating algorithms by opportunity of early recognition of clinical deterioration while proactively estimating alert burden at a unit and patient level. Results highlighted the dependency of the number and frequency of alerts generated on the care location severity and patient characteristics. Conclusion EWS-based alert algorithms have the potential to facilitate appropriate alert management prior to integration into clinical practice. By comparing different algorithms with regard to the alert frequency and potential early detection of physiological deterioration as key patient safety opportunities, findings from this study highlight the need for alert systems tailored to patient and care location needs, and inform alternative EWS-based alert deployment strategies to enhance patient safety.

Board 355 - Research Abstract Training Non-Physician Anesthetists Using Medical Simulation In Freetown, Sierra Leone (Submission #1140)

Introduction/Background Medical simulation is a proven, powerful tool that is increasingly being used for training healthcare workers worldwide. Unfortunately, high-fidelity simulation has predominately remained an instrument found in high resource countries and experience with simulation for medical training in austere environments remains primarily anecdotal,1 and has not been extensively documented in the scientific literature.2,3 Anesthesia delivery in Sierra Leone, as with many other countries in Sub-Saharan Africa and worldwide, is performed primarily by non-physicians with two years of anesthesia training in a traditional apprenticeship model consisting of primarily didactic sessions, observation and direct patient care. We present our experience with introducing high fidelity, mobile simulation as a method for teaching anesthesia delivery using a new anesthesia machine at two tertiary-care hospitals in Freetown, Sierra Leone. Methods After obtaining IRB approval from the Johns Hopkins School of Medicine and the Ministry of Health and Sanitation, we conducted 100 high-fidelity medical simulations with non-physician anesthetists from seven different hospitals in and near Freetown, Sierra Leone. Educational goals of the training sessions were established based upon a targeted needs assessment from direct observation of over 700 anesthetics performed between April 2012 to January 2013 and upon informal discussions with nurse anesthetists at Connaught Hospital and Princess Christian Maternity Hospital (PCMH). A formal online presentation was developed and utilized for training. After participants viewed the online educational modules, using a mobile simulation system consisting of the IngMar Medical QuickLung® in combination with a RespiTrainer® and the Universal Anaesthesia Machine (UAM), we tested the ability of each participant to perform twelve cognitive/ psychomotor skills (Appendix A) with four scenarios: 1) Light anesthesia/ bronchospasm, 2) Management of anesthesia during a power outage with the UAM, 3) Routine airway management during a failed spinal and 4) A pre-use anesthesia machine check. The scenarios were conducted within the operating theaters at PCMH and Connaught Hospitals. We rated the participant’s ability to perform the twelve tasks on an ordinal scale from 1–5, as well as rating their need for more development/proficiency on a 1–5 scale. Immediate verbal feedback was given on their performance and an opportunity to train to greater proficiency was provided using the simulation platform. The perceived effect of medical simulation in this environment was surveyed before and after each session. Results We observed a total of 25 participants recruited from seven hospitals in and around Freetown. All were non-physician anesthetists, with years of experience performing anesthesia ranging from 0.25 to 6 years (2.5 +/− 1.68) and number of hours per week performing anesthesia ranging from 4 to 64 hours (44.8 +/− 17.2). We observed that more training was needed in almost all of the areas tested, but particularly with preparing the anesthesia machine and identifying hypoxia and bronchospasm (Table 1). Participants surveyed also sensed other areas were needed improvement, namely managing difficult airways and assessing hypoventilation. Conclusion Mobile medical simulation offers a versatile and effective tool for medical training but remains largely underutilized in the developing world. By tailoring our training to situations commonly found in these low resource hospitals (power outages, failed spinal, bronchospasm, etc.), our simulations carried a greater significance and as a result, participant feedback was strongly positive. Average level of comfort for each participant increased significantly with managing an airway, as did the average level of comfort during crisis situations such as bronchospasm and power failure. A common difficulty encountered during our mobile training simulations was power failure during the sessions due to an unreliable power grid to the hospital. We used the QuickLung® ventilator because simulated spontaneous ventilation can be hand-powered and thus used without electricity, allowing for simulation training to continue unimpeded by power outages. References 1. Bibliography: Unknown. 2013. The Inaugural SAFE Obstetrical Anesthesia Course in Rwanda: We Did It!!!. SAFE in Rwanda, [blog] January 19th, Available at: http://pattyalexrwanda.blogspot.com/2013/01/the-inaugural-safe-obstetrical.html [Accessed: July 30th, 2013]. 2. A low cost simulator for learning to manage postpartum hemorrhage in rural Africa. Perosky, J., Richter, R., Rybak, O., Gans-Larty, F., Mensah, M. A., Danquah, A., & Andreatta, P. (2011).A low-cost simulator for learning to manage postpartum hemorrhage in rural Africa. Simulation in Healthcare, 6(1), 42-47. 3. Addressing gaps in surgical skills training by means of low-cost simulation at Muhimbili University in Tanzania S. Taché, N. Mbembati, N. Marshall, F. Tendick, C. Mkony and P. O’Sullivan, Human Resources for Health 2009, 7:64. Disclosures Gradian Health Systems Gradian Health Systems LLC Gradian Health Systems, LLC Gradian Health Systems.