Thomas R. Hellmich

PhotoExam: adoption of an iOS-based clinical image capture application at Mayo Clinic

Mayo Clinic developed an internal iOS‐based, point‐of‐care clinical image capture application for clinicians. We aimed to assess the adoption and utilization of the application at Mayo Clinic.

Optimization of Multidisciplinary Staffing Improves Patient Experiences at the Mayo Clinic

Common approaches to emergency department (ED) staffing are to optimize shifts based on historical patient volume or arrival patterns. The former is problematic because historical patient volumes are based on the volumes during existing shifts. Therefore, optimizing shifts based on these volumes can replicate the inefficiencies in these shifts. The latter approach ignores queueing effects. To address the shortcomings of these commonly used approaches, we use classification and regression trees to identify thresholds for patient-to-staff ratios, which split the patient subpopulations into two groups that have different empirical cumulative distribution functions (ecdfs) for patients’ lengths of stay in the ED; one has an extended length and the other has a shorter length. We apply these thresholds and ecdfs to historical patient volumes to calculate an ideal patient volume. After accounting for arrival patterns of ED patients, ideal patient volumes represent the load on the entire ED if patient-to-staff ra...

Application of sociometer badges in simulated health environments: Can wearable devices quantify individual's workload?

Workload experienced by health care workers continues to be a challenge to define and quantify. The purpose of the current study is to present descriptive data on the application of sociometers in an emergency care environment during simulated hand-off scenarios and discuss their potential and limitations for quantifying individual’s workload in health care settings. Sociometer devices, worn by nine actors, collected body movement, interactions, and speech data during four simulated hand-off scenarios in the emergency department wards. Results found that sociometers distinguished body movement differences between sitting, standing, lying, and walking individuals. Interactions quantified by the devices were limited by obstructions, distance, and angles between the sociometer devices. The data collected by these devices show promise in providing human factors researchers a tool for quantifying the dynamic exposures experienced by health care workers over time.

Identifying factors influencing patient alone time at the emergency department using RFID data: What is next?

Emergency Department (ED) represents a highly chaotic environment with a big responsibility to provide critical care to patients on a rapid fashion for life saving service. The operating capacity of ED is often challenged with overcrowding especially with patients at non-emergency situation using it as point of access for primary care. Application of Radio Frequency Identification Device (RFID) technology in ED is gaining significant attention with the potential to improve ED care service and subsequently reduce cost. Our previous pilot study demonstrated the feasibility of quantifying patient alone time and provider time in establishing relationship to the ED length of stay (LOS). In this work, RFID data within the ED on a larger patient group was used to quantify and understand the various factors influencing ‘patient alone’ time in ED. Results indicate that many factors such as patients per physician or nurse and order volumes are controllable to improve the operating efficiency. These findings motivates further investigation to explore the relationship of patient alone time to overall hospital LOS, readmission, patient leaving without being seen, mortality, patient satisfactions and other complications for a particular cohort of disease group to improve quality of care at ED.

Contact tracing with a real-time location system: A case study of increasing relative effectiveness in an emergency department.

HighlightsContact tracing is an essential step in infectious disease control and prevention.Using Electronic medical record (EMR) is challenging and misses a number of potential exposures.Real time location system (RTLS) doubled the potential exposures list for pertussis disease beyond the conventional method of EMR‐based contact identificationRTLS is more efficient and timely in the process of contact tracing.Further studies with larger sample size are needed to confirm the findings. Background: Contact tracing is the systematic method of identifying individuals potentially exposed to infectious diseases. Electronic medical record (EMR) use for contact tracing is time‐consuming and may miss exposed individuals. Real‐time location systems (RTLSs) may improve contact identification. Therefore, the relative effectiveness of these 2 contact tracing methodologies were evaluated. Methods: During a pertussis outbreak in the United States, a retrospective case study was conducted between June 14 and August 31, 2016, to identify the contacts of confirmed pertussis cases, using EMR and RTLS data in the emergency department of a tertiary care medical center. Descriptive statistics and a paired t test (&agr; = 0.05) were performed to compare contacts identified by EMR versus RTLS, as was correlation between pertussis patient length of stay and the number of potential contacts. Results: Nine cases of pertussis presented to the emergency department during the identified time period. RTLS doubled the potential exposure list (P < .01). Length of stay had significant positive correlation with contacts identified by RTLS (&rgr; = 0.79; P = .01) but not with EMR (&rgr; = 0.43; P = .25). Conclusions: RTLS doubled the potential pertussis exposures beyond EMR‐based contact identification. Thus, RTLS may be a valuable addition to the practice of contact tracing and infectious disease monitoring.