Predicting Hospital Patients\u27 Admission to Reduce Emergency Department Boarding

Emergency Department (ED) boarding - the inability to transfer emergency patients to inpatient beds- is a key factor contributing to ED overcrowding. This paper presents a novel approach to improving hospital operational efficiency and, therefore, to decreasing ED boarding. Using the historic data of 15,000 patients, admission results and patient information are correlated in order to identify important admission predictor factors. For example, the type of radiology exams prescribed by the ED physician is identified as among the most important predictors of admission. Based on these factors, a real-time prediction model is developed which is able to correctly predict the admission result of four out of every five ED patients. The proposed admission model can be used by inpatient units to estimate the likelihood of ED patients\u27 admission, and consequently, the number of incoming patients from ED in the near future. Using similar prediction models, hospitals can evaluate their short-time needs for inpatient care more accurately

Automatic knowledge extraction from EHRs

Increasing efforts in the collection, standardization, and maintenance of large scale longitudinal elec- tronic health care records (EHRs) across the world provide a promising source of real world medical data with the potential of providing major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper builds upon the existing and intensifying efforts at using machine learning to provide predictions on future diagnoses likely to be experienced by a particular individual based on the person’s existing diagnostic history. The specific model adopted as the baseline predictive framework is based on the concept of a binary diagnostic history vector representation of a patient’s diagnostic medical record. The technical novelty introduced herein concerns the manner in which transitions between diagnostic history vectors are learnt. We demonstrate that the proposed change prima fasciae enables greater learning specificity. We present a series of experiments which demon- strate the effectiveness of the proposed techniques, and which reveal novel insights regarding the most promising future research directions.Postprin

Improving Emergency Department Patient Flow Through Near Real-Time Analytics

ABSTRACT IMPROVING EMERGENCY DEPARTMENT PATIENT FLOW THROUGH NEAR REAL-TIME ANALYTICS This dissertation research investigates opportunities for developing effective decision support models that exploit near real-time (NRT) information to enhance the operational intelligence within hospital Emergency Departments (ED). Approaching from a systems engineering perspective, the study proposes a novel decision support framework for streamlining ED patient flow that employs machine learning, statistical and operations research methods to facilitate its operationalization. ED crowding has become the subject of significant public and academic attention, and it is known to cause a number of adverse outcomes to the patients, ED staff as well as hospital revenues. Despite many efforts to investigate the causes, consequences and interventions for ED overcrowding in the past two decades, scientific knowledge remains limited in regards to strategies and pragmatic approaches that actually improve patient flow in EDs. Motivated by the gaps in research, we develop a near real-time triage decision support system to reduce ED boarding and improve ED patient flow. The proposed system is a novel variant of a newsvendor modeling framework that integrates patient admission probability prediction within a proactive ward-bed reservation system to improve the effectiveness of bed coordination efforts and reduce boarding times for ED patients along with the resulting costs. Specifically, we propose a cost-sensitive bed reservation policy that recommends optimal bed reservation times for patients right during triage. The policy relies on classifiers that estimate the probability that the ED patient will be admitted using the patient information collected and readily available at triage or right after. The policy is cost-sensitive in that it accounts for costs associated with patient admission prediction misclassification as well as costs associated with incorrectly selecting the reservation time. To achieve the objective of this work, we also addressed two secondary objectives: first, development of models to predict the admission likelihood and target admission wards of ED patients; second, development of models to estimate length-of-stay (LOS) of ED patients. For the first secondary objective, we develop an algorithm that incorporates feature selection into a state-of-the-art and powerful probabilistic Bayesian classification method: multi-class relevance vector machine. For the second objective, we investigated the performance of hazard rate models (in particual, the non-parametric Cox proportional hazard model, parametric hazard rate models, as well as artificial neural networks for modeling the hazard rate) to estimate ED LOS by using the information that is available at triage or right after as the covariates in the models. The proposed models are tested using extensive historical data from several U.S. Department of Veterans Affairs Medical Centers (VAMCs) in the Mid-West. The Case Study using historical data from a VAMC demonstrates that applying the proposed framework leads to significant savings associated with reduced boarding times, in particular, for smaller wards with high levels of utilization. For theory, our primary contribution is the development of a cost sensitive ward-bed reservation model that effectively accounts for various costs and uncertainties. This work also contributes to the development of an integrated feature selection method for classification by developing and validating the mathematical derivation for feature selection during mRVM learning. Another contribution stems from investigating how much the ED LOS estimation can be improved by incorporating the information regarding ED orderable item lists. Overall, this work is a successful application of mixed methods of operation research, machine learning and statistics to the important domain of health care system efficiency improvement

Triagem de pedidos de assistência médica

Nesta dissertação foi avaliada a capacidade de efectuar a triagem de pedidos de assistência médica recorrendo a técnicas de Data Mining. Com base na revisão da literatura decidiu-se seguir a metodologia de Cios et. al (2000), tendo-se explorado diversas abordagens. Uma das principais razões para a escolha desta metodologia foi o facto de se verificar que é a mais utilizada em estudos na área da saúde. Os dados utilizados consistem em 2.070.227 pedidos de assistência médica com as variáveis Ano, Mês, Dia, Dia da Semana, Hora, Distrito, Concelho, Prioridade, Tipo de Ocorrência, Faixa Etária e Sexo, sendo a variável Prioridade o nível de triagem atribuído, podendo este assumir um de quatro valores Emergentes, Urgente, Pouco-urgente e Nãourgente. O tratamento de dados médicos exige cuidados que vão além dos requisitos habituais neste tipo de trabalhos. Para além da dificuldade na obtenção de dados por questões de confidencialidade, é importante que o resultado seja transparente e perceptível e cuidadosamente avaliado. Nesse sentido, foram aplicados os algoritmos árvores de decisão (J48), o Naïve Bayes e Máquinas de Vectores de Suporte (SMO e LibSVM) considerando a escala real de quatro níveis (Emergente, Urgente, Pouco-urgente e Não-urgente). Foi igualmente considerada uma escala de dois níveis, derivada a partir da escala real. As medidas de avaliação utilizadas foram a taxa de acerto, sensibilidade e especificidade. Os resultados mostram que as técnicas de Data Mining são mais eficazes a efectuar a triagem considerando apenas dois níveis. Igualmente se demonstrou nas diferentes abordagens que as Máquinas de Vectores de Suporte são mais eficazes que as restantes técnicas utilizadas.In this dissertation was evaluated the ability to perform the screening of medical assistance requests using Data Mining techniques. Based on the literature review it was decided to follow the methodology of Cios et. al (2000), and several approaches have been explored. One of the main reasons for choosing this methodology was the fact that it is used most frequently in healthcare studies. The data consists of 2,070,227 requests of medical assistance and it features the following variables: Year, Month, Day, Day of the Week, Hour, District, County, Priority, Type of Occurrence, Age Group and Gender. The variable for Priority is the level of triage attributed, which may assume one of four values: Emergent, Urgent, Less Urgent and Nonurgent. The processing of medical data demands a supplementary degree of caution when comparing to other kinds of data. In addition to the difficulties of obtaining sensitive and confidential information, it is important that the results are transparent, perceptible and carefully evaluated. In this regard, the following algorithms are applied: Decision Tree (J48), the Naïve Bayes and Support Vector Machines (SMO and LibSVM), considering the four-levels of the real scale: Emergent, Urgent, Less Urgent and Nonurgent. A two-level scale was also derived from the original scale. The evaluation measures used were: Accuracy, Sensitivity and Specificity. The results show that Data Mining techniques are more effective performing triage considering only two levels. It has also been demonstrated in the different approaches investigated that the Support Vector Machines are more effective than the other techniques analyzed

Data-Driven Quality of Service Improvements in Hospitals

In recent years, there has been an increasing interest in developing novel methods for effective and efficient healthcare service delivery and data analytics has widely been recognized as being essential for decision-making at various healthcare service settings. This dissertation consists of three research projects each aiming to improve decisions at three different healthcare settings with one being related to critical care delivery and the other two being related to inpatient patient flow management. Each project combines statistical analysis of hospital data with techniques and methodologies from operations research. The first project concerns care delivery in the cardiac intensive care unit (CICU). We analyze a prospective study to describe admissions and care practices within the CICU of the UNC Hospital, and also evaluate the influence of an open versus closed model of care on patient outcomes and resource consumption. The second and third projects study management of patient flow from emergency department (ED) to hospital internal wards (IWs). Both projects develop effective inpatient flow management policies with the objective of reducing ED boarding time, which is defined as the time between the decision for admission for an ED patient and the time the patient is physically admitted to an IW. Delayed admission to IWs has been identified as a key factor for ED overcrowding and is a big challenge for many hospitals. We approach the problem from two different angles: early discharging patients to free up beds in IWs, and early requesting beds for ED patients based on early prediction of need for IW beds. In both projects, we develop relevant statistical models for analyzing the detailed hospital patient flow data and build mathematical decision models to develop new methods. We also build simulation models and use these models to investigate the benefits of the proposed methods. Simulation studies suggest significant potential improvements in various performance measures of interest.Doctor of Philosoph

Toward Precision Medicine in Intensive Care: Leveraging Electronic Health Records and Patient Similarity

The growing adoption of Electronic Health Record (EHR) systems has resulted in an unprecedented amount of data. This availability of data has also opened up the opportunity to utilize EHRs for providing more customized care for each patient by considering individual variability, which is the goal of precision medicine. In this context, patient similarity (PS) analytics have been introduced to facilitate data analysis through investigating the similarities in patients’ data, and, ultimately, to help improve the healthcare system. This dissertation is presented in six chapters and focuses on employing PS analytics in data-rich intensive care units. Chapter 1 provides a review of the literature and summarizes studies describing approaches for predicting patients’ future health status based on EHR and PS. Chapter 2 demonstrates the informativeness of missing data in patient profiles and introduces missing data indicators to use this information in mortality prediction. The results demonstrate that including indicators with observed measurements in a set of well-known prediction models (logistic regression, decision tree, and random forest) can improve the predictive accuracy. Chapter 3 builds upon the previous results and utilizes these missing indicators to reveal patient subpopulations based on their similarity in laboratory test ordering being used for them. In this chapter, the Density-based Spatial Clustering of Applications with Noise method, was employed to group the patients into clusters using the indicators generated in the previous study. Results confirmed that missing indicators capture the laboratory-test-ordering patterns that are informative and can be used to identify similar patient subpopulations. Chapter 4 investigates the performance of a multifaceted PS metric constructed by utilizing appropriate similarity metrics for specific clinical variables (e.g. vital signs, ICD-9, etc.). The proposed PS metric was evaluated in a 30-day post-discharge mortality prediction problem. Results demonstrate that PS-based prediction models with the new PS metric outperformed population-based prediction models. Moreover, the multifaceted PS metric significantly outperformed cosine and Euclidean PS metric in k-nearest neighbors setting. Chapter 5 takes the previous results into consideration and looks for potential subpopulations among septic patients. Sepsis is one of the most common causes of death in Canada. The focus of this chapter is on longitudinal EHR data which are a collection of observations of measurements made chronologically for each patient. This chapter employs Functional Principal Component Analysis to derive the dominant modes of variation in septic patients’ EHR's. Results confirm that including temporal data in the analysis can help in identifying subgroups of septic patients. Finally, Chapter 6 provides a discussion of results from previous chapters. The results indicate the informativeness of missing data and how PS can help in improving the performance of predictive modeling. Moreover, results show that utilizing the temporal information in PS calculation improves patient stratification. Finally, the discussion identifies limitations and directions for future research

Using prediction to facilitate patient flow in a health care delivery chain

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 163-178).A health care delivery chain is a series of treatment steps through which patients flow. The Emergency Department (ED)/Inpatient Unit (IU) chain is an example chain, common to many hospitals. Recent literature has suggested that predictions of IU admission, when patients enter the ED, could be used to initiate IU bed preparations before the patient has completed emergency treatment and improve flow through the chain. This dissertation explores the merit and implications of this suggestion. Using retrospective data collected at the ED of the Veterans Health Administration Boston Health Care System (VHA BHS), three methods are selected for making admission predictions: expert opinion, naive Bayes conditional probability and linear regression with a logit link function (logit-linear regression). The logit-linear regression is found to perform best. Databases of historic data are collected from four hospitals including VHA BHS. Logit-linear regression prediction models generated for each individual hospital perform well based on multiple measures. The prediction model generated for the VHA BHS hospital continues to perform well when predictive data are collected and coded prospectively by nurses. For two weeks, predictions are made on each patient that enters the VHA BHS ED. This data is then summarized and displayed on the VHA BHS internet homepage. No change was observed in key ED flow measures; however, interviews with hospital staff exposed ways in which the prediction information was valuable: planning individual patient admissions, personal scheduling, resource scheduling, resource alignment, and hospital network coordination. A discrete event simulation of the system shows that if IU staff emphasizes discharge before noon, flow measures improve as compared to a baseline scenario where discharge priority begins at 1pm. Sharing ED crowding or prediction information leads to best patient flow performance when using specific schedules dictating IU response to the information. This dissertation targets the practical and theoretical implications of using prediction to improve flow through the ED/IU health care delivery chain. It is suggested that the results will have impact on many other levels of health care delivery that share the delivery chain structure.by Jordan Shefer Peck.Ph.D