Improving non-invasive hemoglobin measurement accuracy using nonparametric models.

Uncontrolled hemorrhage is a leading cause of preventable death among patients with trauma. Early recognition of hemorrhage can aid in the decision to administer blood transfusion and improve patient outcomes. To provide real-time measurement and continuous monitoring of hemoglobin concentration, the non-invasive and continuous hemoglobin (SpHb) measurement device has drawn extensive attention in clinical practice. However, the accuracy of such a device varies in different scenarios, so the use is not yet widely accepted. This article focuses on using statistical nonparametric models to improve the accuracy of SpHb measurement device by considering measurement bias among instantaneous measurements and individual evolution trends. In the proposed method, the robust locally estimated scatterplot smoothing (LOESS) method and the Kernel regression model are considered to address those issues. Overall performance of the proposed method was evaluated by cross-validation, which showed a substantial improvement in accuracy with an 11.3% reduction of standard deviation, 23.7% reduction of mean absolute error, and 28% reduction of mean absolute percentage error compared to the original measurements. The effects of patient demographics and initial medical condition were analyzed and deemed to not have a significant effect on accuracy. Because of its high accuracy, the proposed method is highly promising to be considered to support transfusion decision-making and continuous monitoring of hemoglobin concentration. The method also has promise for similar advancement of other diagnostic devices in healthcare.

Non-invasive Hemoglobin Measurement Predictive Analytics with Missing Data and Accuracy Improvement Using Gaussian Process and Functional Regression Model.

Recent use of noninvasive and continuous hemoglobin (SpHb) concentration monitor has emerged as an alternative to invasive laboratory-based hematological analysis. Unlike delayed laboratory based measures of hemoglobin (HgB), SpHb monitors can provide real-time information about the HgB levels. Real-time SpHb measurements will offer healthcare providers with warnings and early detections of abnormal health status, e.g., hemorrhagic shock, anemia, and thus support therapeutic decision-making, as well as help save lives. However, the finger-worn CO-Oximeter sensors used in SpHb monitors often get detached or have to be removed, which causes missing data in the continuous SpHb measurements. Missing data among SpHb measurements reduce the trust in the accuracy of the device, influence the effectiveness of hemorrhage interventions and future HgB predictions. A model with imputation and prediction method is investigated to deal with missing values and improve prediction accuracy. The Gaussian process and functional regression methods are proposed to impute missing SpHb data and make predictions on laboratory-based HgB measurements. Within the proposed method, multiple choices of sub-models are considered. The proposed method shows a significant improvement in accuracy based on a real-data study. Proposed method shows superior performance with the real data, within the proposed framework, different choices of sub-models are discussed and the usage recommendation is provided accordingly. The modeling framework can be extended to other application scenarios with missing values.

Unnecessary wait hours: a novel ED system-level metric.

BACKGROUND: Emergency department (ED) operations leaders are under increasing pressure to make care delivery more efficient. Publicly reported ED efficiency metrics are traditionally patient centred and do not show situational or facility-based improvement opportunities. We propose the consideration of a novel metric, the 'Number of Unnecessary Waits (NUW)' and the corresponding 'Unnecessary Wait Hours (UWH)', to measure space efficiency, and we describe how we used NUW to evaluate operational changes in our ED. METHODS: UWH summarises the relationship between the number of available rooms and the number of patients waiting by returning a value equal to the number of unnecessary patient waits. We used this metric to evaluate reassigning a clinical technician assistant (CTA) to the new role of flow CTA. RESULTS: We retrospectively analysed 3.5 months of data from before and after creation of the flow CTA. NUW metric analysis suggested that the flow CTA decreased the amount of unnecessary wait hours, while higher patient volumes had the opposite effect. CONCLUSIONS: Situational system-level metrics may provide a new dimension to evaluating ED operational efficiencies. Studies focussed on system-level metrics to evaluate an ED practice are needed to understand the role these metrics play in evaluation of a department's operations.

Maximizing Patient Coverage Through Optimal Allocation of Residents and Scribes to Shifts in an Emergency Department.

Residents and scribes in an Emergency Department (ED) work closely with an attending physician. Residents care for patients under the supervision of the attending physician, whereas scribes assist physicians with documentation contemporaneously with the patient encounter. Optimal allocation of these roles to shifts is crucial to improve patient care, physician productivity, and to increase learning opportunities for residents. Since resident and scribe availability varies on a monthly basis, the allocation of these roles into different shifts within a pre-designed ED physician shift template must be dynamically adjusted. Using historical patient flow timestamp data as well as information about the patient-coverage capacity of an ED care team, a data-driven model was developed for optimally determining which shifts must be staffed by residents and scribes to maximize patient coverage and to calculate the relative importance of a shift. This relative importance metric aids decision-making in adjusting the allocation of residents and scribes to various shifts as their availability fluctuates. Since the model uses historical timestamp data, which all EDs are mandated to collect, the approach is generalizable to all EDs.

Determining steady-state trough range in vancomycin drug dosing using machine learning.

BACKGROUND: Vancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate dosing for each ICU patient. METHODS: Observed vancomycin trough levels were categorized into sub-therapeutic, therapeutic, and supra-therapeutic levels to train and compare different classification models. We included adult ICU patients (≥ 18 years) with at least one vancomycin concentration measurement during hospitalization at Mayo Clinic, Rochester, MN, from January 2007 to December 2017. RESULT: The final cohort consisted of 5337 vancomycin courses. The XGBoost models outperformed other machine learning models with the AUC-ROC of 0.85 and 0.83, specificity of 53% and 47%, and sensitivity of 94% and 94% for sub- and supra-therapeutic categories, respectively. Kinetic estimated glomerular filtration rate and other creatinine-based measurements, vancomycin regimen (dose and interval), comorbidities, body mass index, age, sex, and blood pressure were among the most important variables in the models. CONCLUSION: We developed models to assess the risk of sub- and supra-therapeutic vancomycin trough levels to improve the accuracy of drug dosing in critically ill patients.

Patient-Care Team Contact Patterns Impact Treatment Length of Stay in the Emergency Department.

Real-time location systems (RTLS) has found extensive application in the healthcare setting, that is shown to improve safety, save cost, and increase patient satisfaction. More specifically, some studies have shown the efficacy of RTLS leading to an improved workflow in the emergency department. However, due to substantial implementation costs of such technologies, hospital administrators show reluctance in RTLS adoption. Our previous preliminary studies with RFID data in the emergency department (ED) demonstrated for the first time the quantification of `patient alone time' and its relationship to outcomes such as 30-day hospitalization. In this study, we use ED RTLS data to analyze patient-care team contact time (PCTCT) and its relationship to the total treatment length of stay (LOS) in ED. An observational cohort study was performed in the ED using RTLS data from Jan 17 - Sep 17, 2017, which included a total of 51,697 patients. PCTCT within the first hour of a patient's placement in a treatment bed was calculated and its relationship to treatment LOS was analyzed while controlling for confounding factors affecting treatment LOS. Results show that treatment LOS is highly correlated with the ED crowding captured by the patient-perprovider ratio, negatively correlated to the physician and resident visit frequency, and positively correlated to nurse visit frequency. The results can inform designing new guidelines for ideal patient-care team interactions and be used to determine optimal ED staffing levels and care team composition for effective care delivery.

Development of data integration and visualization tools for the Department of Radiology to display operational and strategic metrics.

Visualizing process metrics can help identify targets for improvement initiatives. Dashboards and scorecards are tools to visualize important metrics in an easily interpretable manner. We describe the development of two visualization systems: a dashboard to provide real-time situational awareness to frontline coordinators, and a scorecard to display aggregate monthly performance metrics for strategic process improvement efforts. Both systems were designed by a multidisciplinary team of physicians, allied health staff, engineers and information technology specialists. We describe the process of defining important metrics, gathering and cleaning data, and designing the visualization interfaces. We also describe some improvement initiatives that stemmed. These systems were implemented in our hospital and improved the availability of data to our staff and leadership, making performance gaps visible and generating new targets for quality improvement projects.