This document discusses using management engineering principles to analyze healthcare delivery systems. It provides an example analysis of a hospital system modeled as interdependent subsystems, including the emergency department, intensive care unit, operating rooms, and nursing units. Simulation of the mathematical model revealed important relationships between the subsystems that could inform management decisions. The conclusion advocates using objective data analysis and simulation rather than subjective opinions alone for healthcare management decisions.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
This document presents a novel approach for brain tumor classification in MRI images using feature selection and extraction. It extracts intensity, texture, and shape-based features from MRI images and applies principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction. Support vector machines (SVM) are then used to classify tumors as white matter, gray matter, CSF, abnormal or normal tissue. The technique is tested on 140 brain MRI images and achieves high classification accuracy compared to previous methods.
EMR Design as Socio-Technical Mosaic: A Multi-Lens Approach to Emergency Depa...juliahaines
This document discusses research conducted to analyze the design of electronic medical record (EMR) systems in emergency departments from various socio-technical perspectives. The researcher observed EMR use in the emergency department of a large urban hospital over several shifts. Through lenses such as activity theory, distributed cognition, and situated action, the researcher gained insights into how the EMR system both facilitated and inhibited the collaborative work of physicians, nurses and staff. The researcher found that incorporating these socio-technical perspectives could help EMR system designers better support the social and organizational aspects of emergency department work.
Understanding Construction Workers’ Risk Decisions Using Cognitive Continuum ...IJERA Editor
This document discusses a study that aimed to understand how construction workers make risk decisions when encountering hazards on the job. It draws on Cognitive Continuum Theory to develop a framework for classifying such decisions as either intuitive or deliberative, based on characteristics of the decision task. The study hypothesized that certain "decision cues" present in a hazard scenario would influence whether a worker relies more on intuition or deliberation. While the associations found in the data were modest, the study indicates further research in this area is warranted to test the theoretical predictions. The goal is to gain insights that could help reduce risk-taking and improve safety in the construction industry.
Automated segmentation and classification technique for brain strokeIJECEIAES
Difussion-Weighted Imaging (DWI) plays an important role in the diagnosis of brain stroke by providing detailed information regarding the soft tissue contrast in the brain organ. Conventionally, the differential diagnosis of brain stroke lesions is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). The type of stroke lesions consists of acute ischemic, sub-acute ischemic, chronic ischemic and acute hemorrhage. For segmentation, fuzzy c-Means (FCM) and active contour is proposed to segment the lesion’s region. FCM is implemented with active contour to separate the cerebral spinal fluid (CSF) with the hypointense lesion. Pre-processing is applied to the DWI for image normalization, background removal and image enhancement. The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). The average results for the Jaccard index, DC, FPR and FNR are 0.55, 0.68, 0.23 and 0.23, respectively. First statistical order method is applied to the segmentation result to obtain the features for the classifier input. For classification technique, bagged tree classifier is proposed to classify the type of stroke. The accuracy results for the classification is 90.8%. Based on the results, the proposed technique has potential to segment and classify brain stroke lesion from DWI images.
A comparative analysis of classification techniques on medical data setseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The document describes a decision support system designed to help physicians produce accurate prescriptions for patients with polyuria (excessive urination). The system uses an expert system shell called CLIPS to implement logical rules for polyuria treatment. The authors studied treatment rules, represented them in an antecedent/consequent model, and implemented the rules in CLIPS. They then evaluated the system using a double-blind technique and found it was able to produce prescriptions based on medical science while also allowing physician expertise and experience to be incorporated.
MR Image Segmentation of Patients’ Brain Using Disease Specific a priori Know...CSCJournals
Segmentation of high quality brain MR images using a priori knowledge about brain structures enables a more accurate and comprehensive interpretation. Benefits of applying a priori knowledge about the brain structures may also be employed for image segmentation of specific brain and neural patients. Such procedure may be performed to determine the disease stage or monitor its gradual progression over time. However segmenting brain images of patients using general a priori knowledge which corresponds to healthy subjects would result in inaccurate and unreliable interpretation in the regions which are affected by the disease. In this paper, a technique is proposed for extracting a priori knowledge about structural distribution of different brain tissues affected by a specific disease to be applied for accurate segmentation of the patients’ brain images. For this purpose, extracted a priori knowledge is gradually represented as disease specific probability maps throughout an iterative process, and then is utilized in a statistical approach for segmentation of new patients’ images. Experiments conducted on a large set of images acquired from patients with a similar neurodegenerative disease implied success of the proposed technique for representing meaningful a priori knowledge as disease specific probability maps. Promising results obtained also indicated an accurate segmentation of brain MR images of the new patients using the represented a priori knowledge, into three tissue classes of gray matter, white matter, and cerebrospinal fluid. This enables an accurate estimation of tissues’ thickness and volumes and can be counted as a substantial forward step for more reliable monitoring and interpretation of progression in specific brain and neural diseases.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
This document presents a novel approach for brain tumor classification in MRI images using feature selection and extraction. It extracts intensity, texture, and shape-based features from MRI images and applies principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction. Support vector machines (SVM) are then used to classify tumors as white matter, gray matter, CSF, abnormal or normal tissue. The technique is tested on 140 brain MRI images and achieves high classification accuracy compared to previous methods.
EMR Design as Socio-Technical Mosaic: A Multi-Lens Approach to Emergency Depa...juliahaines
This document discusses research conducted to analyze the design of electronic medical record (EMR) systems in emergency departments from various socio-technical perspectives. The researcher observed EMR use in the emergency department of a large urban hospital over several shifts. Through lenses such as activity theory, distributed cognition, and situated action, the researcher gained insights into how the EMR system both facilitated and inhibited the collaborative work of physicians, nurses and staff. The researcher found that incorporating these socio-technical perspectives could help EMR system designers better support the social and organizational aspects of emergency department work.
Understanding Construction Workers’ Risk Decisions Using Cognitive Continuum ...IJERA Editor
This document discusses a study that aimed to understand how construction workers make risk decisions when encountering hazards on the job. It draws on Cognitive Continuum Theory to develop a framework for classifying such decisions as either intuitive or deliberative, based on characteristics of the decision task. The study hypothesized that certain "decision cues" present in a hazard scenario would influence whether a worker relies more on intuition or deliberation. While the associations found in the data were modest, the study indicates further research in this area is warranted to test the theoretical predictions. The goal is to gain insights that could help reduce risk-taking and improve safety in the construction industry.
Automated segmentation and classification technique for brain strokeIJECEIAES
Difussion-Weighted Imaging (DWI) plays an important role in the diagnosis of brain stroke by providing detailed information regarding the soft tissue contrast in the brain organ. Conventionally, the differential diagnosis of brain stroke lesions is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). The type of stroke lesions consists of acute ischemic, sub-acute ischemic, chronic ischemic and acute hemorrhage. For segmentation, fuzzy c-Means (FCM) and active contour is proposed to segment the lesion’s region. FCM is implemented with active contour to separate the cerebral spinal fluid (CSF) with the hypointense lesion. Pre-processing is applied to the DWI for image normalization, background removal and image enhancement. The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). The average results for the Jaccard index, DC, FPR and FNR are 0.55, 0.68, 0.23 and 0.23, respectively. First statistical order method is applied to the segmentation result to obtain the features for the classifier input. For classification technique, bagged tree classifier is proposed to classify the type of stroke. The accuracy results for the classification is 90.8%. Based on the results, the proposed technique has potential to segment and classify brain stroke lesion from DWI images.
A comparative analysis of classification techniques on medical data setseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The document describes a decision support system designed to help physicians produce accurate prescriptions for patients with polyuria (excessive urination). The system uses an expert system shell called CLIPS to implement logical rules for polyuria treatment. The authors studied treatment rules, represented them in an antecedent/consequent model, and implemented the rules in CLIPS. They then evaluated the system using a double-blind technique and found it was able to produce prescriptions based on medical science while also allowing physician expertise and experience to be incorporated.
MR Image Segmentation of Patients’ Brain Using Disease Specific a priori Know...CSCJournals
Segmentation of high quality brain MR images using a priori knowledge about brain structures enables a more accurate and comprehensive interpretation. Benefits of applying a priori knowledge about the brain structures may also be employed for image segmentation of specific brain and neural patients. Such procedure may be performed to determine the disease stage or monitor its gradual progression over time. However segmenting brain images of patients using general a priori knowledge which corresponds to healthy subjects would result in inaccurate and unreliable interpretation in the regions which are affected by the disease. In this paper, a technique is proposed for extracting a priori knowledge about structural distribution of different brain tissues affected by a specific disease to be applied for accurate segmentation of the patients’ brain images. For this purpose, extracted a priori knowledge is gradually represented as disease specific probability maps throughout an iterative process, and then is utilized in a statistical approach for segmentation of new patients’ images. Experiments conducted on a large set of images acquired from patients with a similar neurodegenerative disease implied success of the proposed technique for representing meaningful a priori knowledge as disease specific probability maps. Promising results obtained also indicated an accurate segmentation of brain MR images of the new patients using the represented a priori knowledge, into three tissue classes of gray matter, white matter, and cerebrospinal fluid. This enables an accurate estimation of tissues’ thickness and volumes and can be counted as a substantial forward step for more reliable monitoring and interpretation of progression in specific brain and neural diseases.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
IRJET-Survey on Data Mining Techniques for Disease PredictionIRJET Journal
This document discusses using data mining techniques to predict disease, specifically focusing on heart disease. It provides an overview of different classification algorithms that can be used for disease prediction, including decision trees, Bayesian classifiers, multilayer perceptrons, and ensemble techniques. These algorithms are analyzed based on their accuracy, time efficiency, and area under the ROC curve. The document also reviews related literature applying various data mining methods like decision trees, KNN, and support vector machines to heart disease prediction. Overall, the document examines using classification algorithms and data mining to extract patterns from medical data that can help predict heart disease and other illnesses.
This document discusses strategies and technologies for recovering cognitive functions lost due to traumatic brain injury (TBI). It notes that TBI survivors can experience decades of debilitation from attention deficits, memory impairments, and executive dysfunction. While severity of injury correlates somewhat to impairment, the link is weak. Even one year post-injury, many TBI patients still have unmet cognitive needs. The document advocates strategies that both compensate for losses and recover functions, using knowledge, technology, systems, processes, retraining, stem cells, and pharmacological and learning enhancements. Computerized cognitive training alone is not a complete solution but can provide effective tools when used by clinicians. Challenges include ensuring training gains transfer to real life and addressing
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
This document compares three classification methods - artificial neural networks, decision trees, and logistic regression - for predicting malignancy in thyroid tumor patients using a clinical dataset. It describes each method and applies them to a dataset of 259 thyroid tumor patients. The artificial neural network achieved 98% accuracy on the training set and 92% on the validation set. The decision tree method used 150 cases to build a model and achieved 86% accuracy. Logistic regression analysis resulted in 88% accuracy. The artificial neural network was able to accurately predict malignancy and identified important attributes like multiple nodules and family cancer history.
IRJET- Segmentation and Visualization in Medical Image Processing: A ReviewIRJET Journal
This document provides a review of image processing, segmentation, and visualization techniques in medical imaging. It discusses how image processing aims to extract information from medical images for purposes like improving human interpretation, compressing data for storage, and enabling object detection. Segmentation involves partitioning an image into meaningful regions, which is important for feature extraction and image analysis. Visualization plays key roles in understanding and communicating medical image data through scientific visualization, data visualization, and interaction techniques. The document outlines various segmentation and visualization methods used in medical image analysis.
There has been increasing demand in improving service provisioning in hospital resources
management. Hospital industries work with strict budget constraint at the same time assures quality care.
To achieve quality care with budget constraint an efficient prediction model is required. Recently there has
been various time series based prediction model has been proposed to manage hospital resources such
ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider
the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The
issues with existing prediction are that the training suffers from local optima error. This induces overhead
and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient
inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to
evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed
model reduces RMSE and MAPE over existing back propagation based artificial neural network. The
overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in
improving the quality of health care management
The document describes a proposed clinical decision support system that uses k-means clustering and an artificial neural network with particle swarm optimization to classify patient data and determine diagnoses. It begins with background on clinical decision making and existing systems. It then outlines the proposed system, which involves clustering patient data using k-means, and training an artificial neural network using particle swarm optimization and backpropagation to classify new patient data and determine optimal treatment. The combination of these techniques is meant to improve accuracy, efficiency, time consumption and costs compared to other methods.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
Life is the most precious gift to man and safeguarding this gift is of utmost importance.With
increasing number of diseases and fast paced lives, people have less time to look after themselves and
their family members or to even visit the doctor for regular check-ups.Our E-Health patient
monitoring system can remotely monitor the health of the patients and intimate the doctor of critical
conditions without human intervention. Some of the existing E-Health systems include telemedicine
network for Francophone African countries (RAFT) and LOBIN. RAFT is implemented in java and
uses asymmetric public – private key encryption, however it is expensive, does not support mobility
and is not a context aware system. LOBIN is a hardware/software platform to locate and monitor a set
of physiological parameters and context parameters of several patients within hospital facilities.
Although it is a context aware system it cannot handle high and concurrent data traffic load.
To overcome the above flaws, our proposed system puts forward an idea of patient monitoring
using various knowledge based techniques like K-means clustering, Gaussian kernel function, ANN
and Fuzzy inference engine. In our project we intend to do remote patient health monitoring in which
we will be using three-four machines which will send various sensed health parameters to the
centralised server that will make clusters of the sensed health parameters based on criticality of the
health condition. Then depending upon clusters formed and on comparison with the threshold values
appropriate reports will be generated and send to the doctors and caretakers.
Using data from hospital information systems to improve emergency department ...Agus Mutamakin
This document summarizes 5 quality improvement projects conducted by the authors using data from hospital information systems to improve emergency department care. The projects included:
1. Providing follow-up information on patients admitted from the ED to educate staff.
2. Improving laboratory turnaround times by providing daily reports highlighting outliers.
3. Measuring ED crowding using hourly census data and its correlation with walkout rates and diversion.
4. Studying the effect of different troponin cutoff levels on positive test results and patient outcomes.
5. Developing a system to track diagnostic failures and missed foreign bodies to reduce malpractice risk.
Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network f...IJECEIAES
Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system for diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value for accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%, accuracy 86,30% and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors, symptoms and exercise ECG. The conclusion that can be drawn is that the proposed diagnosis system capable of delivering performance in the very good category, with a number of attributes that are not a lot of checks and a relatively low cost.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
View classification of medical x ray images using pnn classifier, decision tr...eSAT Journals
Abstract: In this era of electronic advancements in the field of medical image processing, the quantum of medical X-ray images so produced exorbitantly can be effectively addressed by means of automated indexing, comparing, analysing and annotating that will really be pivotal to the radiologists in interpreting and diagnosing the diseases. In order to envisage such an objective, it has been humbly endeavoured in this paper by proposing an efficient methodology that takes care of the view classification of the X-ray images for the automated annotation from their vast database, with which the decision making for the physicians and radiologists becomes simpler despite an immeasurable and ever-growing trends of the X-ray images. In this paper, X-ray images of six different classes namely chest, head, foot, palm, spine and neck have been collected. The framework proposed in this paper involves the following: The images are pre-processed using M3 filter and segmentation by Expectation Maximization (EM) algorithm, followed by feature extraction through Discrete Wavelet Transform. The orientation of X-ray images has been performed in this work by comparing among the Probabilistic Neural Network (PNN), Decision Tree algorithm and Support Vector Machine (SVM), while the PNN yields an accuracy of 75%, the Decision Tree with 92.77% and the SVM of 93.33%. Key Words: M3 filter, Expectation Maximaization, Discrete Wavelet Transformation, Probabilistic Neural Network, Decision Tree Algorithm and Support Vector Machine.
Diagnosis of rheumatoid arthritis using an ensemble learning approachcsandit
Rheumatoid arthritis is one of the diseases that it
s cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnos
is and treatment of the disease. In this
study, a predictive model is suggested that diagnos
es rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients
referred to rheumatology clinic. For each
patient a record consists of several clinical and d
emographic features is saved. After data
analysis and pre-processing operations, three diffe
rent methods are combined to choose proper
features among all the features. Various data class
ification algorithms were applied on these
features. Among these algorithms Adaboost had the h
ighest precision. In this paper, we
proposed a new classification algorithm entitled CS
-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboos
t algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of
Adaboost in predicting of Rheumatoid
Arthritis.
How to establish and evaluate clinical prediction models - StatsworkStats Statswork
A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and health education. Despite the positive effects of clinical prediction models on practice, prediction modeling is a difficult process that necessitates meticulous statistical analysis and sound clinical judgments. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following always on Time, outstanding customer support, and High-quality Subject Matter Experts.
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Propose a Enhanced Framework for Prediction of Heart DiseaseIJERA Editor
This document proposes a new framework for predicting heart disease using machine learning techniques. It first discusses techniques like artificial neural networks and Naive Bayes classification that can be used for classification. It also discusses feature selection techniques like principal component analysis and information gain that can reduce the number of attributes before classification. The proposed framework would take a dataset, apply feature selection to reduce attributes, then use two classification algorithms (ANN and Naive Bayes) on the reduced dataset to select important attributes for heart disease prediction. This is intended to help identify key attributes and predict heart disease symptoms more efficiently.
IRJET - Chronic Kidney Disease Prediction using Data Mining and Machine LearningIRJET Journal
This document discusses predicting chronic kidney disease through data mining and machine learning techniques. It examines using KNN, SVM, and ensemble models on a dataset of 400 patient records with 24 attributes related to chronic kidney disease. For data mining, SVM with an RBF kernel achieved 87% accuracy. For machine learning, KNN and SVM ensemble achieved over 92% accuracy. The document reviews several related studies applying classification algorithms like decision trees, neural networks, and Naive Bayes to chronic kidney disease prediction and their limitations. It then describes the KNN algorithm and its application to this problem in more detail.
The aim of this paper is to use Text mining(TM) concepts in the field of Health care System. We no that now days decision making in health care involves number of opinions given by the group of medical experts for specific disease in the form of decisions which will be presented in medical database in the form of text. These decisions are then mined from database with the help of Data Mining techniques. Text document clustering is considered as tool for performing information based operations. For clustering normally K-means clustering technique is used. In this paper we use Bisecting K-means clustering technique and it is better compared to traditional K-means technique. The objective is to study the revealed
groupings of similar opinion-types associated with the likelihood of physicians and medical experts.
Effect Of Interdependency On Hospital Wide Patient FlowAlexander Kolker
This document discusses using simulation modeling to analyze the impact of interdependencies between key departments in a hospital system, including the emergency department (ED), intensive care unit (ICU), operating rooms (OR), and nursing units. It summarizes how modeling each department individually can identify factors influencing performance, such as patient length of stay in the ED and scheduling of elective surgeries in the ICU. The document also provides examples of operational performance criteria used to evaluate the OR and potential simulation models analyzing the impact of changes like adding OR capacity.
This document summarizes a research paper on developing a web-based health care management system. It discusses the need for digitizing hospital management processes to increase efficiency. The proposed system would allow patients to book appointments online, give doctors access to patient records and histories, and help hospital administrators manage daily operations and record keeping in a centralized digital manner. It outlines the objectives, research gaps, literature review on similar systems, proposed system methodology using web technologies, and expected features and results of the new management system. The system aims to streamline hospital management through a unified digital platform.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
ICU PATIENT DETERIORATION PREDICTION: A DATA-MINING APPROACHcscpconf
A huge amount of medical data is generated every day, which presents a challenge in analysing
these data. The obvious solution to this challenge is to reduce the amount of data without
information loss. Dimension reduction is considered the most popular approach for reducing
data size and also to reduce noise and redundancies in data. In this paper, we investigate the
effect of feature selection in improving the prediction of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a subset of features would mean choosing the
most important lab tests to perform. If the number of tests can be reduced by identifying the
most important tests, then we could also identify the redundant tests. By omitting the redundant
tests, observation time could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deterioration using the medical lab results. We
apply our technique on the publicly available MIMIC-II database and show the effectiveness of
the feature selection. We also provide a detailed analysis of the best features identified by our
approach.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
IRJET-Survey on Data Mining Techniques for Disease PredictionIRJET Journal
This document discusses using data mining techniques to predict disease, specifically focusing on heart disease. It provides an overview of different classification algorithms that can be used for disease prediction, including decision trees, Bayesian classifiers, multilayer perceptrons, and ensemble techniques. These algorithms are analyzed based on their accuracy, time efficiency, and area under the ROC curve. The document also reviews related literature applying various data mining methods like decision trees, KNN, and support vector machines to heart disease prediction. Overall, the document examines using classification algorithms and data mining to extract patterns from medical data that can help predict heart disease and other illnesses.
This document discusses strategies and technologies for recovering cognitive functions lost due to traumatic brain injury (TBI). It notes that TBI survivors can experience decades of debilitation from attention deficits, memory impairments, and executive dysfunction. While severity of injury correlates somewhat to impairment, the link is weak. Even one year post-injury, many TBI patients still have unmet cognitive needs. The document advocates strategies that both compensate for losses and recover functions, using knowledge, technology, systems, processes, retraining, stem cells, and pharmacological and learning enhancements. Computerized cognitive training alone is not a complete solution but can provide effective tools when used by clinicians. Challenges include ensuring training gains transfer to real life and addressing
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
This document compares three classification methods - artificial neural networks, decision trees, and logistic regression - for predicting malignancy in thyroid tumor patients using a clinical dataset. It describes each method and applies them to a dataset of 259 thyroid tumor patients. The artificial neural network achieved 98% accuracy on the training set and 92% on the validation set. The decision tree method used 150 cases to build a model and achieved 86% accuracy. Logistic regression analysis resulted in 88% accuracy. The artificial neural network was able to accurately predict malignancy and identified important attributes like multiple nodules and family cancer history.
IRJET- Segmentation and Visualization in Medical Image Processing: A ReviewIRJET Journal
This document provides a review of image processing, segmentation, and visualization techniques in medical imaging. It discusses how image processing aims to extract information from medical images for purposes like improving human interpretation, compressing data for storage, and enabling object detection. Segmentation involves partitioning an image into meaningful regions, which is important for feature extraction and image analysis. Visualization plays key roles in understanding and communicating medical image data through scientific visualization, data visualization, and interaction techniques. The document outlines various segmentation and visualization methods used in medical image analysis.
There has been increasing demand in improving service provisioning in hospital resources
management. Hospital industries work with strict budget constraint at the same time assures quality care.
To achieve quality care with budget constraint an efficient prediction model is required. Recently there has
been various time series based prediction model has been proposed to manage hospital resources such
ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider
the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The
issues with existing prediction are that the training suffers from local optima error. This induces overhead
and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient
inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to
evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed
model reduces RMSE and MAPE over existing back propagation based artificial neural network. The
overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in
improving the quality of health care management
The document describes a proposed clinical decision support system that uses k-means clustering and an artificial neural network with particle swarm optimization to classify patient data and determine diagnoses. It begins with background on clinical decision making and existing systems. It then outlines the proposed system, which involves clustering patient data using k-means, and training an artificial neural network using particle swarm optimization and backpropagation to classify new patient data and determine optimal treatment. The combination of these techniques is meant to improve accuracy, efficiency, time consumption and costs compared to other methods.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
Life is the most precious gift to man and safeguarding this gift is of utmost importance.With
increasing number of diseases and fast paced lives, people have less time to look after themselves and
their family members or to even visit the doctor for regular check-ups.Our E-Health patient
monitoring system can remotely monitor the health of the patients and intimate the doctor of critical
conditions without human intervention. Some of the existing E-Health systems include telemedicine
network for Francophone African countries (RAFT) and LOBIN. RAFT is implemented in java and
uses asymmetric public – private key encryption, however it is expensive, does not support mobility
and is not a context aware system. LOBIN is a hardware/software platform to locate and monitor a set
of physiological parameters and context parameters of several patients within hospital facilities.
Although it is a context aware system it cannot handle high and concurrent data traffic load.
To overcome the above flaws, our proposed system puts forward an idea of patient monitoring
using various knowledge based techniques like K-means clustering, Gaussian kernel function, ANN
and Fuzzy inference engine. In our project we intend to do remote patient health monitoring in which
we will be using three-four machines which will send various sensed health parameters to the
centralised server that will make clusters of the sensed health parameters based on criticality of the
health condition. Then depending upon clusters formed and on comparison with the threshold values
appropriate reports will be generated and send to the doctors and caretakers.
Using data from hospital information systems to improve emergency department ...Agus Mutamakin
This document summarizes 5 quality improvement projects conducted by the authors using data from hospital information systems to improve emergency department care. The projects included:
1. Providing follow-up information on patients admitted from the ED to educate staff.
2. Improving laboratory turnaround times by providing daily reports highlighting outliers.
3. Measuring ED crowding using hourly census data and its correlation with walkout rates and diversion.
4. Studying the effect of different troponin cutoff levels on positive test results and patient outcomes.
5. Developing a system to track diagnostic failures and missed foreign bodies to reduce malpractice risk.
Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network f...IJECEIAES
Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system for diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value for accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%, accuracy 86,30% and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors, symptoms and exercise ECG. The conclusion that can be drawn is that the proposed diagnosis system capable of delivering performance in the very good category, with a number of attributes that are not a lot of checks and a relatively low cost.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
View classification of medical x ray images using pnn classifier, decision tr...eSAT Journals
Abstract: In this era of electronic advancements in the field of medical image processing, the quantum of medical X-ray images so produced exorbitantly can be effectively addressed by means of automated indexing, comparing, analysing and annotating that will really be pivotal to the radiologists in interpreting and diagnosing the diseases. In order to envisage such an objective, it has been humbly endeavoured in this paper by proposing an efficient methodology that takes care of the view classification of the X-ray images for the automated annotation from their vast database, with which the decision making for the physicians and radiologists becomes simpler despite an immeasurable and ever-growing trends of the X-ray images. In this paper, X-ray images of six different classes namely chest, head, foot, palm, spine and neck have been collected. The framework proposed in this paper involves the following: The images are pre-processed using M3 filter and segmentation by Expectation Maximization (EM) algorithm, followed by feature extraction through Discrete Wavelet Transform. The orientation of X-ray images has been performed in this work by comparing among the Probabilistic Neural Network (PNN), Decision Tree algorithm and Support Vector Machine (SVM), while the PNN yields an accuracy of 75%, the Decision Tree with 92.77% and the SVM of 93.33%. Key Words: M3 filter, Expectation Maximaization, Discrete Wavelet Transformation, Probabilistic Neural Network, Decision Tree Algorithm and Support Vector Machine.
Diagnosis of rheumatoid arthritis using an ensemble learning approachcsandit
Rheumatoid arthritis is one of the diseases that it
s cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnos
is and treatment of the disease. In this
study, a predictive model is suggested that diagnos
es rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients
referred to rheumatology clinic. For each
patient a record consists of several clinical and d
emographic features is saved. After data
analysis and pre-processing operations, three diffe
rent methods are combined to choose proper
features among all the features. Various data class
ification algorithms were applied on these
features. Among these algorithms Adaboost had the h
ighest precision. In this paper, we
proposed a new classification algorithm entitled CS
-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboos
t algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of
Adaboost in predicting of Rheumatoid
Arthritis.
How to establish and evaluate clinical prediction models - StatsworkStats Statswork
A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and health education. Despite the positive effects of clinical prediction models on practice, prediction modeling is a difficult process that necessitates meticulous statistical analysis and sound clinical judgments. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following always on Time, outstanding customer support, and High-quality Subject Matter Experts.
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Propose a Enhanced Framework for Prediction of Heart DiseaseIJERA Editor
This document proposes a new framework for predicting heart disease using machine learning techniques. It first discusses techniques like artificial neural networks and Naive Bayes classification that can be used for classification. It also discusses feature selection techniques like principal component analysis and information gain that can reduce the number of attributes before classification. The proposed framework would take a dataset, apply feature selection to reduce attributes, then use two classification algorithms (ANN and Naive Bayes) on the reduced dataset to select important attributes for heart disease prediction. This is intended to help identify key attributes and predict heart disease symptoms more efficiently.
IRJET - Chronic Kidney Disease Prediction using Data Mining and Machine LearningIRJET Journal
This document discusses predicting chronic kidney disease through data mining and machine learning techniques. It examines using KNN, SVM, and ensemble models on a dataset of 400 patient records with 24 attributes related to chronic kidney disease. For data mining, SVM with an RBF kernel achieved 87% accuracy. For machine learning, KNN and SVM ensemble achieved over 92% accuracy. The document reviews several related studies applying classification algorithms like decision trees, neural networks, and Naive Bayes to chronic kidney disease prediction and their limitations. It then describes the KNN algorithm and its application to this problem in more detail.
The aim of this paper is to use Text mining(TM) concepts in the field of Health care System. We no that now days decision making in health care involves number of opinions given by the group of medical experts for specific disease in the form of decisions which will be presented in medical database in the form of text. These decisions are then mined from database with the help of Data Mining techniques. Text document clustering is considered as tool for performing information based operations. For clustering normally K-means clustering technique is used. In this paper we use Bisecting K-means clustering technique and it is better compared to traditional K-means technique. The objective is to study the revealed
groupings of similar opinion-types associated with the likelihood of physicians and medical experts.
Effect Of Interdependency On Hospital Wide Patient FlowAlexander Kolker
This document discusses using simulation modeling to analyze the impact of interdependencies between key departments in a hospital system, including the emergency department (ED), intensive care unit (ICU), operating rooms (OR), and nursing units. It summarizes how modeling each department individually can identify factors influencing performance, such as patient length of stay in the ED and scheduling of elective surgeries in the ICU. The document also provides examples of operational performance criteria used to evaluate the OR and potential simulation models analyzing the impact of changes like adding OR capacity.
This document summarizes a research paper on developing a web-based health care management system. It discusses the need for digitizing hospital management processes to increase efficiency. The proposed system would allow patients to book appointments online, give doctors access to patient records and histories, and help hospital administrators manage daily operations and record keeping in a centralized digital manner. It outlines the objectives, research gaps, literature review on similar systems, proposed system methodology using web technologies, and expected features and results of the new management system. The system aims to streamline hospital management through a unified digital platform.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
ICU PATIENT DETERIORATION PREDICTION: A DATA-MINING APPROACHcscpconf
A huge amount of medical data is generated every day, which presents a challenge in analysing
these data. The obvious solution to this challenge is to reduce the amount of data without
information loss. Dimension reduction is considered the most popular approach for reducing
data size and also to reduce noise and redundancies in data. In this paper, we investigate the
effect of feature selection in improving the prediction of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a subset of features would mean choosing the
most important lab tests to perform. If the number of tests can be reduced by identifying the
most important tests, then we could also identify the redundant tests. By omitting the redundant
tests, observation time could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deterioration using the medical lab results. We
apply our technique on the publicly available MIMIC-II database and show the effectiveness of
the feature selection. We also provide a detailed analysis of the best features identified by our
approach.
A Greybox Hospital Information System in the Medical Center Tobruk Libya base...IOSR Journals
This document presents a study on developing a greybox hospital information system for the Medical Center in Tobruk, Libya based on the Three-layer Graph-based Model (3LGM). The study aims to model the current information system and propose improvements using 3LGM. It describes modeling the main functions, logical and physical layers, use cases, and databases for patient, doctor, and clinical documentation data. Tables compare 3LGM to other models. Figures illustrate the domain layers, tools layers, use cases, and database tables. The conclusion is that all tasks were successfully completed to develop and implement an information system model to support management of patient, doctor, and clinical data using 3LGM.
Usability evaluation of a discrete event based visual hospital management sim...hiij
Hospital Management is a complex and dynamic organisational challenge. Hospital managers (HMs)
are responsible for the effective use of valuable resources and assets, which is a significant issue in
healthcare. Due to factors such as the increase in health care costs and political pressure, HMs have
been compelled to examine new ways to improve efficiency and reduce healthcare delivery costs whilst
improving patient satisfaction. Healthcare managers require tools that will allow them to review the
current system or identify areas of improvement and quantify the possible changes.
This paper covers an evaluation of a hospital simulator developed by the authors. A usability test of the
simulator was carried out with hospital managers to provide real-world feedback on the simulator. This
has provided lessons to be applied in the development and use of such a tool. For instance, use of traffic
light colours in assisting management of hospital areas and Sensitivity Analysis supporting multiple or
more complex scenarios.
Severity of illness scoring systems have been developed to evaluate delivery of care and provide prediction of outcome of groups of critically ill patients who are admitted to the intensive care units. This prediction is achieved by collating routinely measured data specific to the patient. This article reviews the various commonly used ICU scoring systems, the characteristics of the ideal scoring system, the various methods used for validating the scoring systems.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
Dynamic Rule Base Construction and Maintenance Scheme for Disease Predictionijsrd.com
Business and healthcare application are tuned to automatically detect and react events generated from local are remote sources. Event detection refers to an action taken to an activity. The association rule mining techniques are used to detect activities from data sets. Events are divided into 2 types' external event and internal event. External events are generated under the remote machines and deliver data across distributed systems. Internal events are delivered and derived by the system itself. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used in the event detection process. The new event discovery process is designed to support uncertain data environment. Uncertain derivation of events is performed on uncertain data values. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. Selectability filters events that are irrelevant to derivation by some rules. Selectability algorithm is applied to extract new event derivation. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. A sampling algorithm is used for efficient approximation of new event derivation. Medical decision support system is designed with event detection model. The system adopts the new rule mapping mechanism for the disease analysis. The rule base construction and maintenance operations are handled by the system. Rule probability estimation is carried out using the Apriori algorithm. The rule derivation process is optimized for domain specific model.
An excellent article that uses predictive and optimization methods to reduce hospital readmissions.
Another great article, "Reducing hospital readmissions by integrating empirical prediction with resource optimization" (Helm, Alaeddini, Stauffer, Bretthaur, and Skolarus, 2016) describes how Machine Learning modeling tools were used to determine the root-causes and individualized estimation of readmissions. The post-discharge monitoring schedule and workplans were then optimized to patient changes in health states.
Model guided therapy and the role of dicom in surgeryKlaus19
1. Model-guided therapy uses patient-specific models to complement image-guided therapy, bringing treatment closer to precise diagnosis, accurate prognosis assessment, and individualized planning and validation of therapy.
2. TIMMS is an IT system that facilitates model-guided therapy through interoperability of data, images, models, and tools to support the therapeutic intervention.
3. Patient-specific models in TIMMS must represent multidimensional and multiscale patient data, interface various system components, and link model components meaningfully while maintaining model accuracy over time.
Evaluation of a clinical information system (cis)nikita024
This power point presentation provides an overview of a clinical information system (CIS). It discusses what a CIS is, how CIS have evolved, and the key players involved in designing CIS. It also examines the electronic health record component of a CIS and discusses the eight basic components that make up an EHR. Additional topics covered include clinical decision making systems, safety, costs, and education regarding CIS. The presentation was created by four students with each student covering specific slides and aspects of the topic.
DETECTION OF LIVER INFECTION USING MACHINE LEARNING TECHNIQUESIRJET Journal
This document discusses using machine learning techniques to detect liver infections. It provides an overview of various machine learning methods that have been applied to medical data related to the liver, including supervised learning algorithms like naive Bayes classifiers, k-nearest neighbors, and support vector machines. Deep learning techniques like deep neural networks are also mentioned. The goal is to automatically predict liver diseases early based on complex data from electronic health records, images, genomics and other sources to help doctors and improve patient care and outcomes.
This document describes a patient management system project for a university. The system aims to automate a hospital's manual patient record keeping system. It will computerize patient, doctor, and hospital details to make record keeping more efficient. The system will allow scheduling appointments, tracking medical bills and patient rooms. It will generate reports on patient information and utilize databases to store records. Diagrams including data flow diagrams and entity-relationship diagrams are provided to illustrate the system's design and data structure.
Recent research states that using new and emerging
technologies in the areas of telecommunications are widely
used in healthcare sector. The system Intelligent Electornic
Patient Record Management System (IEPRMS) is a
centralized database contains the in-patient record. It was
implemented using PHP & MYSQL combination. The
database record contains the patient personal info, department
lies-in, physician, tours, ,treatment and lab results. Since the
patient enters the hospital the workflow starts as the reception
user creates new record by entering the personal info and
sends the record to assigned department; at this stage the nurse
starts update the record by entering the physician comments,
required treatment, and sends lab test when it is required. The
procedure continues as long as the patient still in the hospital.
At last when the patient recovered or died the International
Classsification of Diseases(ICD) inserted to the record and out
or died date. In addition there are many supported tables that
can be updated manually through independent pages by IT
administrator. These tables like Physician names, medicines,
lab tests, users and ICDs. As the system consists of different
users and different user permissions. Also there are advance
search that can help to make statistical reports and researches
for the physicians. The system is considered time and cost
effective to healthcare.
Rick MacCornack, PhD
Chief Systems Integration Officer
Northwest Physicians Network
CEO
Rainier Health Network
Closing Presentation "ACOs and Health IT: True Delivery System Reform or Another Round of Unintended Consequences?"
A fundamental component of the Affordable Care Act is support for the creation of so-called Accountable Care Organizations. Health care information technology will play a critical role in the reform process, perhaps in ways which are not yet well understood. Using the framework and early experience of a local CMS appointed ACO, this session is intended to ask questions and provide examples for how IT efforts might contribute to healthy, disruptive change in improving medical care delivery.
Learning Objectives:
∙ Consider the unintended consequences of the current IT trajectory in supporting medical care delivery in relation to the mandates of the
Affordable Care Act. Consider some opportunities for future IT contributions and what will need to occur for these opportunities to be tapped.
∙ Reflect on the historical contributions of IT in health and how there will necessarily be a shift in IT development in the future in support of
medical care delivery reform.
Expert Systems vs Clinical Decision Support SystemsAdil Alpkoçak
The document discusses expert systems and clinical decision support systems. It provides an overview of key concepts including:
- Medical artificial intelligence aims to perform diagnosis and make therapy recommendations using symbolic models of disease.
- Clinical decision support systems directly assist health professionals with decision making, though there is scope for ambiguity in inputs like patient history.
- Expert systems are interactive programs that enhance decision making through a knowledge base and rules. They are used to solve problems typically addressed by human experts.
- Key components of expert systems include the knowledge base containing rules, a working memory, and an inference engine to derive conclusions from rules and data.
Case Based Medical Diagnosis of Occupational Chronic Lung Diseases From Their...CSCJournals
The clinical decision support system using the case based reasoning (CBR) methodology of Artificial Intelligence (AI) presents a foundation for a new technology of building intelligent computer aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. In this paper, we have used the Case Based Reasoning methodology to develop a clinical decision support system prototype for supporting diagnosis of occupational lung diseases. 127 cases were collected for 14 occupational chronic lung diseases, which contains 26 symptoms. After removing the duplicated cases from the database, the system has trained set of 47 cases for Indian Lung patients. Statistical analysis has been done to determine the importance values of the case features. The retrieval strategy using nearest-neighbor approaches is investigated. The results indicate that the nearest neighbor approach has shown the encouraging outcome, used as retrieval strategy. A Consultant Pathologist’s interpretation was used to evaluate the system. Results for Sensitivity, Specificity, Positive Prediction Value and the Negative Prediction Value are 95.3%, 92.7%, 98.6% and 81.2% respectively. Thus, the result showed that the system is capable of assisting an inexperience pathologist in making accurate, consistent and timely diagnoses, also in the study of diagnostic protocol, education, self-assessment, and quality control. In this paper, clinical decision support system prototype is developed for supporting diagnosis of occupational lung diseases from their symptoms and signs through employing Microsoft Visual Basic .NET 2005 along with Microsoft SQL server 2005 environment with the advantage of Object Oriented Programming technology
The purpose of this presentation is providing an overview of the main approaches in using big data: data focus vs. business analytics focus. The following topics will be covered:
- Why getting data should not be a starting point in business analytics, and why more data not always result in more accurate predictions
- The simulation analytics methodology in comparison to machine learning and data science approach
- Examples of two business cases:
(i) Healthcare: Pediatric Triage in a Severe Pandemic-Maximizing Population Survival by Establishing Admission Thresholds
(ii) Banking & Finance: Analysis of the staffing and utilization of a team of mutual fund analysts for timely producing ‘buy-sell’ reports
Many resources discuss machine learning and data analytics from a technology deployment perspective. From the business standpoint, however, the real value of analytics is in the methodology for solving some systemic holistic problems, rather than a specific technology or platform.
In this presentation, the focus is shifted from the technology deployment to the analytics methodology for solving some holistic business problems. Two examples will be covered in detail:
(i) Analysis of the performance and the optimal staffing of a team of doctors, nurses, and technicians for a large local hospital unit using discrete event simulation with a live demonstration. This simulation methodology is not included in most Machine Learning algorithms libraries.
(ii) Identifying a few factors (or variables) that contribute most to the financial outcome of a local hospital using principal component decomposition (PCD) of the large observational dataset of population demographic and disease prevalence.
DEA is a technique that measures the efficiency of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. It defines an efficiency score for each DMU as a weighted sum of outputs divided by a weighted sum of inputs, with all scores restricted to a range of 0 to 1. DEA calculates efficiency scores by choosing input/output weights that maximize each DMU's score, presenting it in the best possible light relative to its peers. Strengths of DEA include its ability to handle multiple inputs/outputs without assuming a functional form and directly compare DMUs against peers or combinations of peers.
This document describes a study conducted at Froedtert Hospital to develop a predictive model of emergency department operations and the effect of patient length of stay on ED diversion. The study analyzed patient length of stay data, developed an ED simulation model, and used the model to test scenarios with different upper limits on length of stay. The model predicted that ED diversion could be reduced to around 0.5% by limiting discharged patients' length of stay to 5 hours and admitted patients' length of stay to 6 hours.
This document describes using process modeling simulation to analyze the effect of daily leveling of elective surgeries on ICU diversion rates at a hospital. The simulation models the patient flow through different units like the ICU, OR, and ED. Currently, elective surgeries are scheduled without considering ICU capacity, leading to periods of high utilization and ICU diversion. The simulation analyzes scenarios where elective case limits are set each day, smoothing out utilization across days and reducing ICU diversion times. Initial results show imposing daily caps of 5 cases for one unit and 4 for another reduces scheduling variability by around 20-28% compared to the current practice.
This document provides an outline and overview of a course on healthcare administration and delivery systems. It discusses the following key points:
- The course will introduce quantitative decision-making methods in healthcare management and apply techniques like forecasting, optimization, and simulation to address challenges in the healthcare system.
- Traditional management has relied on intuition but incorporating quantitative methods can help address problems in a systematic way.
- The roles and responsibilities of healthcare managers have become more visible and important given issues around costs, access, and quality in the system.
- A background in both healthcare and business administration is valuable for medical and health services managers.
This document provides details about a graduate course on healthcare administration and delivery systems, including its objectives, topics, assignments, and evaluation criteria. The course uses lectures, discussions, and exercises to teach students how to apply quantitative techniques like forecasting, optimization, simulation, and analytics to decision-making in healthcare. The goal is to help students develop skills in using data-driven methods for planning, managing, and evaluating healthcare programs and organizations. The course meets weekly and includes a midterm and final exam that evaluate students' problem-solving abilities and understanding of operational challenges in healthcare settings.
This document discusses various frameworks for optimizing healthcare staffing levels with variable patient demand. It begins by outlining different approaches including the newsvendor framework, linear optimization, and discrete event simulation. The newsvendor framework is then explained in more detail, showing how to calculate optimal staffing levels by balancing the costs of over- and under-staffing based on historical demand data. Key points are that the optimal level may be higher or lower than the average depending on costs, and it provides a trade-off between having too many or too few nurses on staff at a given time.
The document discusses using discrete event simulation (DES) to analyze capacity and plan renovations for a hospital's surgical suite. It provides an example where DES was used to simulate different scenarios for renovating the Children's Hospital of Wisconsin's surgical facilities. The simulation analyzed patient wait times and resource needs under each scenario. The output recommended scenario 3 and reallocating beds to meet performance criteria for wait times.
The document discusses data science, data analytics, and their application in hospital operations management. It states that data science and analytics strive to transform raw data into actionable business decisions using quantitative methods. Various types of analytics are described like descriptive, predictive, and prescriptive analytics. Examples of applying different analytical methods to common business problems in healthcare are provided, such as using simulation for capacity planning and optimization for resource allocation. The key is integrating analytics into decision-making processes to create value for customers.
Primary care clinics-managing physician patient panelsAlexander Kolker
OUTLINE
• Traditional scheduling and the advanced
access at a primary care clinic
• Uncertainties that should be considered when
patients are scheduled
• Decisions that need to be made for designing an
appointment system
• Practice on using the panel size calculator
•Emerging Trends in Primary Care:
Staffing with variable demand in healthcare settingsAlexander Kolker
Outline
Main Concept and Some Definitions.
The “newsvendor” framework approach.
Staffing a nursing unit with variable census (demand)
Linear optimization framework approach.
Minimizing staffing cost subject to variable constraints
Discrete event simulation framework approach.
Staffing a unit with cross-trained staff
Key Points and Conclusions
Staffing Decision-Making Using Simulation ModelingAlexander Kolker
The use of Management Engineering methodology for
staffing decision-making.
• Part 1 - Quality and Cost: Outpatient Flu Clinic.
• Part 2 - Quality and Cost : Optimal PACU Nursing
Staffing.
• Summary of Fundamental Management Engineering
1) The Child Protection Center (CPC) evaluated children who may have been abused and aimed to reduce patient wait times which were perceived to be due to staff shortages.
2) A discrete event simulation model was developed to analyze current patient flow and identify bottlenecks. It found the sexual abuse exam room and medical assistants were causing most delays.
3) The best scenario found was adding 0.6 full-time equivalent medical assistant in the afternoon and changing the exam room configuration to one exam room and two sexual abuse exam rooms. This significantly reduced total patient wait times.
SHS_ASQ 2010 Conference: Poster The Use of Simulation for Surgical Expansion ...Alexander Kolker
Children's Hospital of Wisconsin is planning a major expansion and renovation of its surgical suite to increase capacity. Computer simulation models were developed to analyze three expansion scenarios and determine the optimal design. Model 3 was selected as the best option, as it would separate gastroenterology and pulmonary services into their own area with 2-3 procedure rooms and 8-11 pre/postoperative beds, while meeting all performance criteria for patient wait times and OR utilization through 2013. The simulations accounted for patient volume flow, limited system capacity, and the balance needed between these factors for efficient patient throughput.
SHS ASQ 2010 Conference Presentation: Hospital System Patient FlowAlexander Kolker
The document discusses using systems engineering principles to improve healthcare delivery. It describes modeling a hospital as interconnected subsystems like the emergency department, intensive care unit, operating rooms, and medical units. The emergency department is analyzed in depth as a case study. A simulation model of patient flow through the emergency department is created to predict how limiting patient length of stay would reduce times when the emergency department must be closed to new patients due to capacity issues. The document advocates applying mathematical modeling and analysis to make more informed management decisions compared to traditional intuitive approaches.
Advanced Process Simulation Methodology To Plan Facility RenovationAlexander Kolker
This document summarizes a case study on using simulation modeling to plan for a surgical suite renovation at Children's Hospital of Wisconsin. The hospital needed to increase surgical capacity to meet growing demand. A project team used simulation to evaluate options for allocating operating rooms and beds across services. Their model found that separating gastroenterology and pulmonary services into their own area with 2-3 procedure rooms and 8-11 beds would best meet goals of minimizing wait times while staying within budget. The renovation is projected to increase patient satisfaction and yield a positive return on investment within 15 years. Ongoing simulation will evaluate the new process over time.
Here is a high-level layout of the PACU simulation model:
- Inputs:
- Historical daily OR schedule with planned start/end times of surgeries
- Distributions of surgery durations
- Distributions of PACU length of stay for different surgery types
- Process:
- Simulate surgeries based on schedule and duration distributions
- Patients enter PACU after surgery based on OR schedule
- Patients spend time in PACU based on PACU length of stay distributions
- Patients discharge from PACU over time
- Outputs:
- PACU census (number of patients) tracked over time
- Staffing requirements calculated to maintain target nurse-to-patient ratios
The model simulates patient flows
1. SYSTEM ENGINEERING AND MANAGEMENT SCIENCE FOR
HEALTHCARE: EXAMPLES AND FUNDAMENTAL PRINCIPLES
Alexander Kolker, Children’s Hospital and Health System, Milwaukee, WI 53226
Abstract The objective of this paper is to illustrate the
predictive and analytical power of management
Relatively little technical and intellectual resources engineering applied to a typical hospital-wide system that
have been devoted to process engineering and analysis of consists of a set of interdependent subsystems.
overall healthcare delivery as an integrated system that Fundamental management engineering principles for
should provide high quality care for many thousands of effective managerial decision-making in healthcare
patients in an economically sustainable way. settings are summarized in the Conclusions.
A real long-term impact on quality of care and
efficiency of healthcare as an integrated system can be What is Management?
achieved only by using fundamental principles of
management engineering. Probability theory, optimization, There are many possible definitions of management.
computer simulation are scientific and technical For the purpose of this paper, management is defined as
foundations for such an approach. controlling and leveraging available resources (material,
This paper includes a quantitative analysis of a financial and human) aimed at achieving a system’s
typical entire hospital system represented as a set of performance objectives.
interdependent subsystems. It is demonstrated that local Traditional healthcare management is based on past
improvement of one subsystem does not necessarily result experience, feeling, intuition, educated guess and/or static
in improvement of the entire system. pictures or simple linear projections.
In conclusion, fundamental management science/ In contrast, management engineering is the discipline
engineering principles are summarized. The main take- of building mathematical models of real systems and their
away is that hospital/clinic managerial operational analysis for the purpose of developing justified managerial
decisions are most effective if based on objective data decisions. Management decisions for leveraging resources
analysis and process simulation rather than subjective that best meet system performance objectives are based on
opinion, intuition and past experience. outcomes of valid mathematical models.
The underlying foundation of a management
Introduction engineering approach is that analysis of a valid
mathematical model leads to better justified decisions
Modern medicine has achieved great progress in rather than traditional ‘common sense’ decision making
treating individual patients. This progress is based mainly such as educated guesses, past experiences and/or simple
on life science (molecular genetics, biophysics, linear extrapolations.
biochemistry) and development of medical devices and A system is defined as a set of interrelated elements
imaging technology. (subsystems) that form a complex whole that behaves in
However, relatively little resources and technical ways that these elements acting alone would not. Models
talent have been devoted to the proper functioning of of a system enable one to study the impact of alternative
overall health care delivery as an integrated system in ways of running the system, i.e. alternative designs,
which access to efficient care should be delivered to many different configurations and management approaches.
thousands of patients in an economically sustainable way. System models enable one to experiment with systems in
According to the joint report published by Institute of ways that cannot be used with real systems.
Medicine and National Academy of Engineering, a real Large systems are usually deconstructed into smaller
impact on quality, efficiency and sustainability of the subsystems using natural breaks in the system. The
health care system can be achieved only by using health subsystems are modeled and analyzed separately, but they
care delivery engineering (Reid et al., 2005). should be reconnected back in a way that recaptures the
A systematic way of developing effective managerial most important interdependency between them.
decisions using information technologies and predictive Analysis of a complex system is usually incomplete
design of the process of delivery and organizational and can be misleading without taking into account
operations is the scope of what is called healthcare systems subsystems’ interdependencies. Analysis of a
engineering. mathematical model using analytic or computer
algorithmic techniques reveals important hidden and
2. critical relationships in the system that allows leveraging treated, stabilized and released home. ED patients admitted
them to find out how to influence the system’s behavior into the hospital (ED output) form an inpatient input flow
into desired direction. into ICU, OR and/or NU. Length of stay distribution best
Management engineering decisions are often fit was identified separately for patients released home and
counterintuitive compared to traditional management patients admitted to the hospital (Kolker, 2008). About
decisions. There are two main reasons for this. First, most 60% of admitted patients are taken into operating rooms
managerial decisions are being made in an uncertain (OR) for emergency surgery, about 30% of admitted
environment with large variability. It is a general human patients move into ICU, and about 10% of patients
tendency to avoid the complications of incorporating admitted from ED into floor nursing units.
uncertainty into the decision making by ignoring it or The OR suite has 12 interchangeable operating
turning it into certainty. For example, average time or rooms used both for emergent and scheduled surgeries.
average numbers of procedures are typically treated as if There are four daily scheduled OR cases at 6 am, 9 am, 12
they are fixed values ignoring the effect of variability pm and 3 pm, Monday to Friday (there are no scheduled
around these averages. This practice often results in surgeries on weekends). Scheduled cases form a separate
erroneous conclusions made by traditional management OR admissions flow, as indicated on Figure 1.
decision-making (the so-called ‘flaw of averages’). Elective surgery duration depends on surgical service
Second, non-linear scaling effect (size effect) of most type, such as general surgery, orthopedics, neuro-surgery,
healthcare systems makes direct benchmarking difficult. etc. For the simplicity of this particular model elective
Large capacity systems can function at a much higher surgery duration was weighted by each service percentage,
utilization level and have lower patient waiting time than and the best statistical distribution fit was identified.
smaller capacity systems even if the patient arrival rate About 30% of post surgery patients are admitted
relative to their size is the same (Kolker, 2009b). Only from OR into ICU (direct ICU admission) while 70% are
mathematical models (computer simulation models) offer a admitted into floor NU. However some patients (about
means of incorporating the variability and scaling into the 5%) are readmitted from floor NU back to ICU (indirect
effective decision making. ICU admission from OR). ICU length of stay (LOS) is
assumed to be from 1 day to 3 days with most likely 1.5
Hospital System Description days represented by a triangle distribution. Kolker (2009a)
developed a detailed ICU simulation model and analysis.
A case study hospital system includes the following Patient LOS in NU is assumed to range between 2
interdependent high-level subsystems: (i) subsystem 1 - days to 10 days with the most likely 5 days represented by
Emergency Department (ED), capacity 30 beds; (ii) a triangle distribution. At the simulation start ED, ICU and
subsystem 2 - Intensive Care Unit (ICU), capacity 51 beds; NU were pre-filled with midnight census 15, 46 and 350
(iii) subsystem 3 - Operating Rooms (OR), capacity 12 patients, respectively.
OR; (iv) subsystem 4 - Regular Nursing Units (NU),
capacity 380 beds. A high-level flow map (layout) of the Simulation Results
entire hospital system is shown on Figure 1. When the ED
is full, a diversion status on ambulance is declared. Simulation results are summarized in Table 1. There
Patients who waited longer than 2 hours to be admitted are seven performance metrics (95% Confidence Intervals-
into the ED leave without being seen. Some patients are CI) indicated in column 1.
Figure 1. A high-level typical hospital flow map
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3. Baseline (current state) results are presented in Otherwise, even if the ED reports a significant
column 2. Aggressive improvement efforts in ED result in progress in its patient LOS reduction program, this
reducing LOS for patients admitted into the hospital to less progress will not translate into improvement of the overall
than 6 hours compared to the current state 20 - 24 hours hospital system patient flow (do not ‘over-improve’
(from ED registration to ED discharge). However, locally). Of course, many other scenarios could be
because of the interdependency of the downstream units, analyzed using a simulation model to find out how to
three out of seven metrics became worse (column 4). The improve the entire hospital system patient flow rather than
ED bottleneck just moved downstream into the OR and each separate hospital subsystem/department.
ICU because of their inability to handle increased patient
volume from ED. Conclusions
Thus, aggressive process improvement in one
subsystem (ED) results in a worsening situation in other Improvement of separate subsystems (local
interrelated subsystems (OR and ICU). Rather than using optimization or local improvement) should not be confused
an aggressive ED LOS reduction, if a less aggressive with the improvement of the entire system that consists of
improvement is implemented, e.g. LOS not more than 10 the interdependent subsystems. A system of local
hours for patients admitted to the hospital, then none of improvements is not the best system; it could be very
seven metrics become worse than the current state inefficient. Analysis of an entire complex system is usually
(columns 5 and 6). While in this case ED performance is incomplete and can be misleading without taking into
not as good as it could locally be, it is still better than it is account subsystems’ interdependency.
at the current state level. At the same time, this less There are fundamental management engineering
aggressive local ED improvement does not, at least, have a principles that govern behavior of most complex
negative impact on the ICU, OR and floor NU. healthcare systems. These principles have been illustrated
Thus, from the entire hospital system standpoint the both by examples presented in this paper and examples
primary focus of process improvement should be on the published elsewhere (Kolker, 2009b). Knowledge and
ICU because of its highest percent diversion followed by understanding of these fundamental principles alone would
ED and OR. At the same time, the ED patient target LOS help making right managerial decisions even without
reduction program should not be too aggressive, and it building a full blown simulation model.
should be closely coordinated with that for OR and ICU.
Table 1. Summary of simulation results
1 2 3 4 5 6
Too aggressive ED Downstream Less aggressive Downstream
Current
improvement: Units: Better ED improvement: Units: Better or
Performance Metrics State
patients admitted or worse than patients admitted words than current
Baseline
within 6 hours current state? within 10 hours state?
95% CI of the number of
patients waiting to get to 25 – 27 8 – 10 Better 17 – 19 Better
ED (ED in)
95% CI of the number of
patients waiting hospital 57 – 62 64 – 69 Worse 57 – 62 Neutral
admissions (ED out)
Number of patients left
not seen (LNS) after
waiting more than 2 23 – 32 0 Better 0–3 Better
hours
95% CI for % ED
diversion 22% – 23% 0.4% – 0.5% Better 6.8% – 7.3% Better
95% CI for % ICU
diversion 28% – 32% 30% – 34% Worse 28% – 32% Neutral
95% CI for % OR
diversion 12% – 13% 13% – 15% Worse 12% – 13% Neutral
95% CI for % floor NU
diversion 11% – 12% 11% – 12% Neutral 11% – 12% Neutral
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4. • Overall, systems behave differently than a
combination of independent subsystems. Reid, P., Compton, W, Grossman, J., Fanjiang, G., 2005.
• All other factors being equal, interchangeable Building a better delivery system: A new engineering /
resources are, in most cases, more efficient than Healthcare partnership. Committee on Engineering and
specialized (dedicated) resources with the same total the Health Care System, Institute of Medicine and National
capacity. Academy of Engineering. Washington, DC. National
• Scheduling appointments (jobs) in the order of their Academy Press.
increased duration variability (from lower to higher
variability) results in a lower overall cycle time. Biographical Sketch
• Size matters. Large units with the same arrival rate
(relative to its size) always have a significantly lower Alexander Kolker, PhD, ASQ CRE, Six Sigma Black Belt
waiting time. Large units can also function at a much
higher utilization level than small units with about the Alex holds a PhD in applied mathematics. He is both
same patient waiting time. an American Society for Quality Certified Reliability
• Work load leveling (smoothing) of elective procedures Engineer (CRE) and a certified Six Sigma Black Belt.
schedule is an effective strategy to reduce waiting Alex has extensive practical expertise in quantitative
time and improve patient flow. methods for healthcare management, such as hospital
• Because of variability of patient arrivals and service capacity expansion analysis, system-wide patient flow
time, a reserved capacity (sometimes up to 30%) is optimization, staffing planning, forecasting trends and
usually needed to avoid regular operational problems market expansion analysis. He widely applies process
due to excessive waiting time and long lines. simulation methodology to analyze different scenarios for
• Capacity, staffing and financial projections based on allocation of resources that result in the most effective
average input values usually result in significant errors operational solutions.
(the flaw of averages). Alex actively publishes in peer reviewed journals,
• Generally, the higher utilization levels of the resource published book chapters and speaks at national
(good for the organization) the longer the waiting time conferences in the area of discrete event simulation and
to get this resource (bad for patient). Utilization levels management engineering applications in healthcare
higher than 80%-85% result in a significant increase settings. He serves on the Review Boards of Healthcare
in waiting time for random patient arrivals and Management Science and Journal of Medical Systems.
random service time.
• In a series of dependent activities, only a bottleneck
defines the throughput of the entire system. A
bottleneck is a resource (or activity) whose capacity is
less than or equal to demand placed on it.
• Reduction of process variability is the key to patient
flow improvement, increasing throughput and
reducing delays.
References
Kolker, A., 2008. Process Modeling of Emergency
Department Patient Flow: Effect of patient Length of Stay
on ED diversion. Journal of Medical Systems, 32(5), pp.
389-401.
Kolker, A., 2009a. Process Modeling of ICU Patient Flow:
Effect of Daily Load Leveling of Elective Surgeries on ICU
Diversion. Journal of Medical Systems, 33(1), pp.27-40.
Kolker, A., 2009b. Queuing Theory and Discrete Events
Simulation for Health Care: from basic processes to
complex systems with interdependencies. Chapter 20. In:
Handbook of Research on Discrete Event Simulation
Technologies and Applications. Ed: Abu-Taieh, E., El
Sheik, A., IGI-press Global, pp.443-483.
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