Classification of interruptions in a hospital central pharmacy

Interruptions in the healthcare field are prevalent. They are constantly occurring in central pharmacies within hospitals, and are a major distraction to the pharmacists who are working on vital tasks to prepare and dispense medications. An interruption is considered to be anything that makes the pharmacist stop their work to focus on a different task. This observational research project recorded the interruptions in central pharmacy of a large hospital. Analyzing interruptions can lead to process improvement aims to address issues causing such interruptions. This poster was originally presented at the 2010 MU Spring Undergraduate Research and Creative Achievements Forum

Stressors in the pharmacy: An observational of interruptions in pharmacy

Errors in the healthcare field are a significant problem. Interruptions leading to distractions can cause errors as these interruptions can distract the pharmacy workers from their tasks. Hence it is important to study interruptions, their types, how they are caused, where they come from, when they occur, how long they last, and how pharmacists and technicians feel about them. The objectives of this observational study were to: 1) classify interruptions based on the type of interruption and cause, time, location, and duration, 2) identify differences in interruption types, duration and frequency across days of the week or time of day, and the analysis of these stressors can aid in improving the processes and increasing safety within the pharmacy. Poster originally presented at the MU Spring 2011 Undergraduate Research and Creative Achievements Forum

Decision support system in a patient-centered medical home

Lack of sufficient primary care to manage chronic diseases has been quoted as a major drawback of the healthcare system within the United States. Patient-Centered Medical Home is a care delivery model to transform how primary care is delivered. The information technology revolution has brought about several advancements and solutions for medicine and care delivery, and medical homes are no exception to this. However, it is only through a robust decision support system that these medical homes can in fact provide truly coordinated and patient-centered care. The paper describes preliminary work that has been completed at the University of Missouri Health System and next steps in achieving high quality care delivery through a decision support system implementation. Originally presented at the IEEE HealthCon Medial Home conference in June 2011

Accepted and presented at The Design of Medical Devices Conference (DMD2015)

Sociometers are wearable devices that record speech patterns, body movements, user proximities, and face-to-face interactions [1], see The potential of these devices has not been tested in unstructured and more complex environments. Research is needed to compare sociometers data against gold standards to understand their limitations and potential. The objective of this paper is to understand the limitations and potential of sociometer devices in a live in situ field disaster preparedness simulation (1) with field observation notes to see if sociometers can capture macrolevel interactions; and (2) to video recorded (ground truth) interactions to test the granularity and accuracy of sociometer data. These results may facilitate use of sociometers in similar chaotic environments with complexity and uncertainty such as the emergency department. Methods The study was conducted in a dynamic disaster preparedness simulation environment involving over 150 actors and community participants for a total of 3.4 hr Five participants from the aid station and two observers wore sociometers around the neck Each sociometer device contained a WT12 Bluetooth module Comparison of interaction data was conducted using two methods. First, observer-O1 recorded major activities real-time using an electronic tablet application. Second method compared sociometer data with a 15 min video recording of a debrief session where the group primarily remained stationary in a circle with occasionally movement observed for LD, MM, and O

Social Computing for Healthcare Organizations

Social computing has taken the world by storm in the past decade. Today, there are around 500 million users on Facebook alone. Previous research studied how and why individuals use Facebook in social life; yet relatively little research has been conducted on how organizations utilize Facebook to interact with an expansive population of social computing users. The healthcare sector has been investing a lot of time and money to improve patient-centeredness and patient involvement in the provision of care. This poster was also presented at the 2011 American Medical Informatics Association meeting

Profiling women's breast cancer screening practices using data mining

Breast cancer is a major chronic disease and early identification is necessary for treatment. Mammograms are clinical tests that increase the detection of breast cancer. Funded by the Centers for Disease Control and Prevention and the Missouri Department of Health and Senior Services, this preliminary work identifies profiles of women's breast cancer screening practices based on socio-economic factors using a data mining technique called CHAID. Major factors include age, cost of access, income and number of children in household. Originally presented at American Medical Informatics Association conference 2009.Funded by the Centers for Disease Control and Prevention (CDC) and the Missouri Department of Health and Senior Services (MoDHSS

A work systems analysis approach to understanding fatigue in hospital nurses.

Occupational fatigue is an important challenge in improving health and safety in health care systems. A secondary analysis of cross-sectional data from a survey sample comprised 340 hospital nurses was conducted to explore the relationships between components of the nursing work system (person, tasks, tools and technology, environment, organisation) and nurse fatigue and recovery levels. All components of the work system were significantly associated with changes in fatigue and recovery. Results of a tree-based classification method indicated significant interactions between multiple work system components and fatigue and recovery. For example, the relationship between a task variable of \u27excessive work\u27 and acute fatigue varied based on an organisation variable related to \u27time to communicate with managers/supervisors\u27. A work systems analysis contributes to increased understanding of fatigue, allowing for a more accurate representation of the complexity in health care systems to guide future research and practice to achieve increased nurse health and safety. Practitioner Summary: This paper explored the relationships between nursing work system components and nurse fatigue. Findings revealed significant interactions between work system components and nurses\u27 fatigue and recovery. A systems approach allows for a more accurate representation of complexity in work systems and can guide interventions to improve nurse health and safety

Prediction and detection models for acute kidney injury in hospitalized older adults.

BACKGROUND: Acute Kidney Injury (AKI) occurs in at least 5 % of hospitalized patients and can result in 40-70 % morbidity and mortality. Even following recovery, many subjects may experience progressive deterioration of renal function. The heterogeneous etiology and pathophysiology of AKI complicates its diagnosis and medical management and can add to poor patient outcomes and incur substantial hospital costs. AKI is predictable and may be avoidable if early risk factors are identified and utilized in the clinical setting. Timely detection of undiagnosed AKI in hospitalized patients can also lead to better disease management. METHODS: Data from 25,521 hospital stays in one calendar year of patients 60 years and older was collected from a large health care system. Four machine learning models (logistic regression, support vector machines, decision trees and naïve Bayes) along with their ensemble were tested for AKI prediction and detection tasks. Patient demographics, laboratory tests, medications and comorbid conditions were used as the predictor variables. The models were compared using the area under ROC curve (AUC) evaluation metric. RESULTS: Logistic regression performed the best for AKI detection (AUC 0.743) and was a close second to the ensemble for AKI prediction (AUC ensemble: 0.664, AUC logistic regression: 0.660). History of prior AKI, use of combination drugs such as ACE inhibitors, NSAIDS and diuretics, and presence of comorbid conditions such as respiratory failure were found significant for both AKI detection and risk prediction. CONCLUSIONS: The machine learning models performed fairly well on both predicting AKI and detecting undiagnosed AKI. To the best of our knowledge, this is the first study examining the difference between prediction and detection of AKI. The distinction has clinical relevance, and can help providers either identify at risk subjects and implement preventative strategies or manage their treatment depending on whether AKI is predicted or detected

Optimising the booking horizon in healthcare clinics considering no-shows and cancellations

Patient no-shows and cancellations are a significant problem to healthcare clinics, as they compromise a clinic's efficiency. Therefore, it is important to account for both no-shows and cancellations into the design of appointment systems. To provide additional empirical evidence on no-show and cancellation behaviour, we assess outpatient clinic data from two healthcare providers in the USA and EU: no-show and cancellation rates increase with the scheduling interval, which is the number of days from the appointment creation to the date the appointment is scheduled for. We show the temporal cancellation behaviour for multiple scheduling intervals is bimodally distributed. To improve the efficiency of clinics at a tactical level of control, we determine the optimal booking horizon such that the impact of no-shows and cancellations through high scheduling intervals is minimised, against a cost of rejecting patients. Where the majority of the literature only includes a fixed no-show rate, we include both a cancellation rate and a time-dependent no-show rate. We propose an analytical queuing model with balking and reneging, to determine the optimal booking horizon. Simulation experiments show that the assumptions of this model are viable. Computational results demonstrate general applicability of our model by case studies of two hospitals