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.

Estimation of Baseline Serum Creatinine with Machine Learning.

INTRODUCTION: Comparing current to baseline serum creatinine is important in detecting acute kidney injury. In this study, we report a regression-based machine learning model to predict baseline serum creatinine. METHODS: We developed and internally validated a gradient boosting model on patients admitted in Mayo Clinic intensive care units from 2005 to 2017 to predict baseline creatinine. The model was externally validated on the Medical Information Mart for Intensive Care III (MIMIC III) cohort in all ICU admissions from 2001 to 2012. The predicted baseline creatinine from the model was compared with measured serum creatinine levels. We compared the performance of our model with that of the backcalculated estimated serum creatinine from the Modification of Diet in Renal Disease (MDRD) equation. RESULTS: Following ascertainment of eligibility criteria, 44,370 patients from the Mayo Clinic and 6,112 individuals from the MIMIC III cohort were enrolled. Our model used 6 features from the Mayo Clinic and MIMIC III datasets, including the presence of chronic kidney disease, weight, height, and age. Our model had significantly lower error than the MDRD backcalculation (mean absolute error [MAE] of 0.248 vs. 0.374 in the Mayo Clinic test data; MAE of 0.387 vs. 0.465 in the MIMIC III cohort) and higher correlation (intraclass correlation coefficient [ICC] of 0.559 vs. 0.050 in the Mayo Clinic test data; ICC of 0.357 vs. 0.030 in the MIMIC III cohort). DISCUSSION/CONCLUSION: Using machine learning models, baseline serum creatinine could be estimated with higher accuracy than the backcalculated estimated serum creatinine level.

Mapping of critical events in disease progression through binary classification: Application to amyotrophic lateral sclerosis.

BACKGROUND: The progression of many degenerative diseases is tracked periodically using scales evaluating functionality in daily activities. Although estimating the timing of critical events (i.e., disease tollgates) during degenerative disease progression is desirable, the necessary data may not be readily available in scale records. Further, analysis of disease progression poses data challenges, such as censoring and misclassification errors, which need to be addressed to provide meaningful research findings and inform patients. METHODS: We developed a novel binary classification approach to map scale scores into disease tollgates to describe disease progression leveraging standard/modified Kaplan-Meier analyses. The approach is demonstrated by estimating progression pathways in amyotrophic lateral sclerosis (ALS). Tollgate-based ALS Staging System (TASS) specifies the critical events (i.e., tollgates) in ALS progression. We first developed a binary classification predicting whether each TASS tollgate was passed given the itemized ALSFRS-R scores using 514 ALS patients' data from Mayo Clinic-Rochester. Then, we utilized the binary classification to translate/map the ALSFRS-R data of 3,264 patients from the PRO-ACT database into TASS. We derived the time trajectories of ALS progression through tollgates from the augmented PRO-ACT data using Kaplan-Meier analyses. The effects of misclassification errors, condition-dependent dropouts, and censored data in trajectory estimations were evaluated with Interval Censored Kaplan Meier Analysis and Multistate Model for Panel Data. RESULTS: The approach using Mayo Clinic data accurately estimated tollgate-passed states of patients given their itemized ALSFRS-R scores (AUCs > 0.90). The tollgate time trajectories derived from the augmented PRO-ACT dataset provide valuable insights; we predicted that the majority of the ALS patients would have modified arm function (67%) and require assistive devices for walking (53%) by the second year after ALS onset. By the third year, most (74%) ALS patients would occasionally use a wheelchair, while 48% of the ALS patients would be wheelchair-dependent by the fourth year. Assistive speech devices and feeding tubes were needed in 49% and 30% of the patients by the third year after ALS onset, respectively. The onset body region alters some tollgate passage time estimations by 1-2 years. CONCLUSIONS: The estimated tollgate-based time trajectories inform patients and clinicians about prospective assistive device needs and life changes. More research is needed to personalize these estimations according to prognostic factors. Further, the approach can be leveraged in the progression of other diseases.

Measuring Sensitivity and Precision of Real-Time Location Systems (RTLS): Definition, Protocol and Demonstration for Clinical Relevance.

The ability of a Real Time Location System (RTLS) to provide correct information in a clinical environment is an important consideration in evaluating the effectiveness of the technology. While past efforts describe how well the technology performed in a lab environment, the performance of such technology has not been specifically defined or evaluated in a practice setting involving workflow and movement. Clinical environments pose complexity owing to various layouts and various movements. Further, RTL systems are not equipped to provide true negative information (where an entity is not located). Hence, this study defined sensitivity and precision in this context, and developed a simulation protocol to serve as a systematic testing framework using actors in a clinical environment. The protocol was used to measure the sensitivity and precision of an RTL system in the emergency department space of a quaternary care medical center. The overall sensitivity and precision were determined to be 84 and 93% respectively. These varied for patient rooms, staff area, hallway and other rooms.

Characterization of emergency department abandonment using a real-time location system.

BACKGROUND: Patients who present to emergency departments (EDs) for evaluation but are noted to have left without being seen (LWBS) are potentially at great risk. Governmental agencies, such as the Centers for Medicare and Medicaid, as well as hospitals and health organizations, are examining the factors which drive LWBS, including accurately quantifying patient tolerance to wait times and targeting interventions to improve patient tolerance to waiting. OBJECTIVE: Compare traditional methods of estimating time to LWBS with an objective method using a real-time location tracking system (RTLS); examine temporal factors associated with greater LWBS rates. METHODS: This is a retrospective cohort study of all ED visits to a large, suburban, quaternary care hospital in one calendar year. LWBS was calculated as patient registration to nurse recognition and documentation of patient abandonment (traditional method) vs registration to last onsite RTLS timestamp (study method). Descriptives of patterns of patient abandonment rates and patient demographic data were also included. RESULTS: Our study shows that traditional methods of measuring LWBS times significantly overestimate actual patient tolerance to waiting times (median 70, mean 92 min). Patients triaged to resource intensive categories (Emergency Severity Index (ESI) 2, 3) wait longer than patients triaged to less resource intensive categories (ESI 4, 5). CONCLUSION: Compared to traditional methods, RTLS is an efficient and accurate way to measure LWBS rates and helps set the stage for assessing the efficacy of interventions to reduce LWBS and reduce the gap between those seeking evaluation at emergency departments and those ultimately receiving it.

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.

Pattern-based strategic surgical capacity allocation.

Strategic allocation of limited operating room (OR) capacity to surgeons is crucial for the coordination of surgical work flow, including planning of consultation and surgery days, and staff assignment to perioperative teams. However, it is a challenging problem in practice, since the capacity allocation needs to be cyclic for schedule predictability and surgical team coordination, and also needs to satisfy surgeons' preferences. It is further complicated by the practice of surgeons sharing ORs. In this study, we propose a mathematical optimization model to coordinate capacity allocation among surgeons in order to improve the utilization of surgical capacity. We introduce the concept of capacity allocation patterns to account for schedule cyclicity and surgeons' preferences. Further, we develop a data-driven approach to coordinate OR sharing among surgeons based on their historical OR usage. The proposed methodology is applied to a case study with data from a surgical division at Mayo Clinic. Compared with the state-of-the-practice, the proposed approach shows a substantial potential in reducing the maximum number of ORs allocated daily to the division with little overtime. With a solution time of less than 0.5 s, the proposed methodology can be readily used as a decision support tool in surgical practice.

Application of Constrained Optimization Methods in Health Services Research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force.

BACKGROUND: Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity. OBJECTIVES: In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available. CONCLUSIONS: Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods.

Consequences of the 48-h rule: A lens into the psychiatric patient flow through an emergency department.

OBJECTIVE: Psychiatric patient boarding in emergency department (ED) is a severe and growing problem. In July 2013, Minnesota implemented a law requiring jailed persons committed to state psychiatric facilities be transferred within 48-h of commitment. This study aims to quantify the effect of this law on a large ED's psychiatric patient flow. METHODS: A pre- and post- comparison of 2011-2015 ED length of stay (LOS) for adult psychiatric patients was performed using electronic medical record data. Comparisons of the median LOS were assessed using a segmented regression model with time series error, and risk differences (RD) were used to determine changes in the proportion of patients with LOS ≥3 and ≥5days. Changes in patient disposition proportions were assessed using risk ratios. RESULTS: The median ED LOS for patients admitted for psychiatric care increased by 5.22h from 2011 to 2015 (95% CI: (4.33, 7.15)), while the frequency of patient encounters remained constant. Although no significant difference in the rate of ED LOS increase was found pre- and post- implementation, the proportion of adults with LOS ≥3days and ≥15days increased (RD 0.017 (95% CI: (0.013, 0.021)); 0.002 (95% CI: (0.001,0.004)), respectively). CONCLUSIONS: The proportion of ED adult psychiatric patients experiencing prolonged LOS increased following the implementation of a statewide law requiring patients committed through the criminal justice system be transferred to a state psychiatric hospital within 48h. Identifying characteristics of subsets of psychiatric patients disproportionally affected could suggest focused healthcare system improvements to improve ED psychiatric care.

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 'excessive work' and acute fatigue varied based on an organisation variable related to 'time to communicate with managers/supervisors'. 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' 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.

Constrained Optimization Methods in Health Services Research-An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force.

Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of "regular" and "severe" patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning.

Coordinating clinic and surgery appointments to meet access service levels for elective surgery.

Providing timely access to surgery is crucial for patients with high acuity diseases like cancer. We present a methodological framework to make efficient use of scarce resources including surgeons, operating rooms, and clinic appointment slots with a goal of coordinating clinic and surgery appointments so that patients with different acuity levels can see a surgeon in the clinic and schedule their surgery within a maximum wait time target that is clinically safe for them. We propose six heuristic scheduling policies with two underlying ideas behind them: (1) proactively book a tentative surgery day along with the clinic appointment at the time an appointment request is received, and (2) intelligently space out clinic and surgery appointments such that if the patient does not need his/her surgery appointment there is sufficient time to offer it to another patient. A 2-stage stochastic discrete-event simulation approach is employed to evaluate the six scheduling policies. In the first stage of the simulation, the heuristic policies are compared in terms of the average operating room (OR) overtime per day. The second stage involves fine-tuning the most-effective policy. A case study of the division of colorectal surgery (CRS) at the Mayo Clinic confirms that all six policies outperform the current scheduling protocol by a large margin. Numerical results demonstrate that the final policy, which we refer to as Coordinated Appointment Scheduling Policy considering Indication and Resources (CASPIR), performs 52% better than the current scheduling policy in terms of the average OR overtime per day under the same access service level. In conclusion, surgical divisions desiring stratified patient urgency classes should consider using scheduling policies that take the surgical availability of surgeons, patients' demographics and indication of disease into consideration when scheduling a clinic consultation appointment.

The use of patient factors to improve the prediction of operative duration using laparoscopic cholecystectomy.

BACKGROUND: Reliable prediction of operative duration is essential for improving patient and care team satisfaction, optimizing resource utilization and reducing cost. Current operative scheduling systems are unreliable and contribute to costly over- and underestimation of operative time. We hypothesized that the inclusion of patient-specific factors would improve the accuracy in predicting operative duration. METHODS: We reviewed all elective laparoscopic cholecystectomies performed at a single institution between 01/2007 and 06/2013. Concurrent procedures were excluded. Univariate analysis evaluated the effect of age, gender, BMI, ASA, laboratory values, smoking, and comorbidities on operative duration. Multivariable linear regression models were constructed using the significant factors (p < 0.05). The patient factors model was compared to the traditional surgical scheduling system estimates, which uses historical surgeon-specific and procedure-specific operative duration. External validation was done using the ACS-NSQIP database (n = 11,842). RESULTS: A total of 1801 laparoscopic cholecystectomy patients met inclusion criteria. Female sex was associated with reduced operative duration (-7.5 min, p < 0.001 vs. male sex) while increasing BMI (+5.1 min BMI 25-29.9, +6.9 min BMI 30-34.9, +10.4 min BMI 35-39.9, +17.0 min BMI 40 + , all p < 0.05 vs. normal BMI), increasing ASA (+7.4 min ASA III, +38.3 min ASA IV, all p < 0.01 vs. ASA I), and elevated liver function tests (+7.9 min, p < 0.01 vs. normal) were predictive of increased operative duration on univariate analysis. A model was then constructed using these predictive factors. The traditional surgical scheduling system was poorly predictive of actual operative duration (R 2 = 0.001) compared to the patient factors model (R 2 = 0.08). The model remained predictive on external validation (R 2 = 0.14).The addition of surgeon as a variable in the institutional model further improved predictive ability of the model (R 2 = 0.18). CONCLUSION: The use of routinely available pre-operative patient factors improves the prediction of operative duration during cholecystectomy.

Physician, Interrupted: Workflow Interruptions and Patient Care in the Emergency Department.

BACKGROUND: It is unclear how workflow interruptions impact emergency physicians at the point of care. OBJECTIVES: Our study aimed to evaluate interruption characteristics experienced by academic emergency physicians. METHODS: This prospective, observational study collected interruptions during attending physician shifts. An interruption is defined as any break in performance of a human activity that briefly requires attention. One observer captured interruptions using a validated tablet PC-based tool that time stamped and categorized the data. Data collected included: 1) type, 2) priority of interruption to original task, and 3) physical location of the interruption. A Kruskal-Wallis H test compared interruption priority and duration. A chi-squared analysis examined the priority of interruptions in and outside of the patient rooms. RESULTS: A total of 2355 interruptions were identified across 210 clinical hours and 28 shifts (means = 84.1 interruptions per shift, standard deviation = 14.5; means = 11.21 interruptions per hour, standard deviation = 4.45). Physicians experienced face-to-face physician interruptions most frequently (26.0%), followed by face-to-face nurse communication (21.7%), and environment (20.8%). There was a statistically significant difference in interruption duration based on the interruption priority, χ2(2) = 643.98, p < 0.001, where durations increased as priority increased. Whereas medium/normal interruptions accounted for 53.6% of the total interruptions, 53% of the interruptions that occurred in the patient room (n = 162/308) were considered low priority (χ2 [2, n = 2355] = 78.43, p < 0.001). CONCLUSIONS: Our study examined interruptions over entire provider shifts and identified patient rooms as high risk for low-priority interruptions. Targeting provider-centered interventions to patient rooms may aid in mitigating the impacts of interruptions on patient safety and enhancing clinical care.

Prolonged length of stay in ED psychiatric patients: a multivariable predictive model.

BACKGROUND: We aimed to evaluate factors associated with prolonged emergency department (ED) length of stay (LOS) among psychiatric patients and to develop a multivariable predictive model to guide future interventions to reduce ED LOS. METHODS: Electronic health records of ED patients receiving a psychiatric consultation and providing research authorization were reviewed from September 14, 2010, through September 13, 2013, at an academic hospital with approximately 73000 visits annually. Prolonged LOS was defined as ≥8 hours. RESULTS: We identified 9247 visits among 6335 patients; median LOS was 4.1 hours, with 1424 visits (15%) with prolonged LOS. In the multivariable model, characteristics associated with an increased risk of a prolonged LOS included patient age 12 to 17 years (odds ratio [OR], 2.43; P<.001) or ≥65 years (OR, 1.46; P=.007); male gender (OR, 1.24; P=.002); Medicare insurance coverage (OR, 1.34; P=.008); use of restraints (OR, 2.25; P=.006); diagnoses of cognitive disorder (OR, 4.62; P<.001) or personality disorder (OR, 3.45; P<.001); transfer to an unaffiliated psychiatric hospital (OR, 22.82; P<.001); ED arrival from 11 pm through 6:59 am (OR, 1.53; P<.001) or on a Sunday (OR, 1.76; P<.001); or ED evaluation in February (OR, 1.59; P=.006), April (OR, 1.66; P=.002), and May (OR, 1.54; P=.007). CONCLUSIONS: Many psychiatric patients had a prolonged ED LOS. Understanding the multiple, patient-specific, ED operational, and seasonal factors that predict an increased LOS will help guide allocation of resources to improve overall ED processes and patient care.

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.

Intelligent Emergency Department: Validation of Sociometers to Study Workload.

Sociometers are wearable sensors that continuously measure body movements, interactions, and speech. The purpose of this study is to test sociometers in a smart environment in a live clinical setting, to assess their reliability in capturing and quantifying data. The long-term goal of this work is to create an intelligent emergency department that captures real-time human interactions using sociometers to sense current system dynamics, predict future state, and continuously learn to enable the highest levels of emergency care delivery. Ten actors wore the devices during five simulated scenarios in the emergency care wards at a large non-profit medical institution. For each scenario, actors recited prewritten or structured dialogue while independent variables, e.g., distance, angle, obstructions, speech behavior, were independently controlled. Data streams from the sociometers were compared to gold standard video and audio data captured by two ward and hallway cameras. Sociometers distinguished body movement differences in mean angular velocity between individuals sitting, standing, walking intermittently, and walking continuously. Face-to-face (F2F) interactions were not detected when individuals were offset by 30°, 60°, and 180° angles. Under ideal F2F conditions, interactions were detected 50 % of the time (4/8 actor pairs). Proximity between individuals was detected for 13/15 actor pairs. Devices underestimated the mean duration of speech by 30-44 s, but were effective at distinguishing the dominant speaker. The results inform engineers to refine sociometers and provide health system researchers a tool for quantifying the dynamics and behaviors in complex and unpredictable healthcare environments such as emergency care.

Applying dynamic simulation modeling methods in health care delivery research-the SIMULATE checklist: report of the ISPOR simulation modeling emerging good practices task force.

Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic simulation modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic simulation modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event simulation, and agent-based modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. This report assists researchers and decision makers in deciding whether these simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these simulation modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other modeling approaches used typically in health technology assessment applications.

Selecting a dynamic simulation modeling method for health care delivery research-part 2: report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force.

In a previous report, the ISPOR Task Force on Dynamic Simulation Modeling Applications in Health Care Delivery Research Emerging Good Practices introduced the fundamentals of dynamic simulation modeling and identified the types of health care delivery problems for which dynamic simulation modeling can be used more effectively than other modeling methods. The hierarchical relationship between the health care delivery system, providers, patients, and other stakeholders exhibits a level of complexity that ought to be captured using dynamic simulation modeling methods. As a tool to help researchers decide whether dynamic simulation modeling is an appropriate method for modeling the effects of an intervention on a health care system, we presented the System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence (SIMULATE) checklist consisting of eight elements. This report builds on the previous work, systematically comparing each of the three most commonly used dynamic simulation modeling methods-system dynamics, discrete-event simulation, and agent-based modeling. We review criteria for selecting the most suitable method depending on 1) the purpose-type of problem and research questions being investigated, 2) the object-scope of the model, and 3) the method to model the object to achieve the purpose. Finally, we provide guidance for emerging good practices for dynamic simulation modeling in the health sector, covering all aspects, from the engagement of decision makers in the model design through model maintenance and upkeep. We conclude by providing some recommendations about the application of these methods to add value to informed decision making, with an emphasis on stakeholder engagement, starting with the problem definition. Finally, we identify areas in which further methodological development will likely occur given the growing "volume, velocity and variety" and availability of "big data" to provide empirical evidence and techniques such as machine learning for parameter estimation in dynamic simulation models. Upon reviewing this report in addition to using the SIMULATE checklist, the readers should be able to identify whether dynamic simulation modeling methods are appropriate to address the problem at hand and to recognize the differences of these methods from those of other, more traditional modeling approaches such as Markov models and decision trees. This report provides an overview of these modeling methods and examples of health care system problems in which such methods have been useful. The primary aim of the report was to aid decisions as to whether these simulation methods are appropriate to address specific health systems problems. The report directs readers to other resources for further education on these individual modeling methods for system interventions in the emerging field of health care delivery science and implementation.