Skip to main content

Mark Lawley

Followers

5

Following

1

Public Views

Shuvro Chowdhury

University of California, Santa Barbara

Mahmoud Selim Alsanea

Al-Azhar University

Shahjalal University of Science and Technology

Mahammad Amattouch

Al-Azhar University

nafees siddiqui

Uttar Pradesh Technical University

Ambo University

Shandong University

Iosif Iulian Petrila

"Gheorghe Asachi" Tehnical University of Iasi

Interests

Uploads

Papers by Mark Lawley

Adherence to Telemonitoring Therapy for Medicaid Patients With Hypertension: Case Study

Journal of Medical Internet Research

Background Almost 50% of the adults in the United States have hypertension. Although clinical tri... more Background Almost 50% of the adults in the United States have hypertension. Although clinical trials indicate that home blood pressure monitoring can be effective in managing hypertension, the reported results might not materialize in practice because of patient adherence problems. Objective The aims of this study are to characterize the adherence of Medicaid patients with hypertension to daily telemonitoring, identify the impacts of adherence reminder calls, and investigate associations with blood pressure control. Methods This study targeted Medicaid patients with hypertension from the state of Texas. A total of 180 days of blood pressure and pulse data in 2016-2018 from a telemonitoring company were analyzed for mean transmission rate and mean blood pressure change. The first 30 days of data were excluded because of startup effects. The protocols required the patients to transmit readings by a specified time daily. Patients not transmitting their readings received an adherence re...

from the Experiences of Thirteen Indiana

Significant concerns exist over the ability of the healthcare and public health systems to meet t... more Significant concerns exist over the ability of the healthcare and public health systems to meet the surge demands that would result from an event such as an influenza pandemic. Current guid-ance for public health planners is largely based on expert opinion and may lack connection to the problems of street-level public health practice. To identify the problems of local planners and prepare a state-level planning template for increasing health care surge capacity that accounted for these issues, a study was conducted of local pandemic planning efforts in thirteen counties, finding that cognitive biases, coordination problems, institutional structures in the healthcare system, and resource shortfalls are significant barriers to preparing and implementing a surge capacity plan. In addition, local planners identify patient demand management through triage and education efforts as a viable means of ensuring adequate capacity, in contrast to guidance proposing an increased supply of care a...

Flexible routing and deadlock avoidance in automated manufacturing systems

Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)

ABSTRACT Deadlock avoidance policies guarantee deadlock free buffer space allocation to concurren... more ABSTRACT Deadlock avoidance policies guarantee deadlock free buffer space allocation to concurrent competing parts in flexible manufacturing systems. Typically, these policies require enumeration of routes that parts follow. In systems with flexible routing, where parts can choose from a set of machines at each processing step, route enumeration can be of exponential complexity. Setup and execution of deadlock avoidance constraints are rendered computationally intractable. The objective of this work is to develop a control model that allows for several processing alternatives at each step while maintaining polynomial bounds on deadlock-free operation

AdvancingtheUseofEvidence-BasedDecision-Making inLocalHealthDepartmentsWithSystemsScience Methodologies

Collaborative Management of PTSD Treatment through Smartphone Apps: What Is Available and Accessible?

Predictive model for microclimatic temperature and its use in mosquito population modeling

Scientific Reports, 2021

Mosquitoes transmit several infectious diseases that pose significant threat to human health. Tem... more Mosquitoes transmit several infectious diseases that pose significant threat to human health. Temperature along with other environmental factors at breeding and resting locations play a role in the organismal development and abundance of mosquitoes. Accurate analysis of mosquito population dynamics requires information on microclimatic conditions at breeding and resting locations. In this study, we develop a regression model to characterize microclimatic temperature based on ambient environmental conditions. Data were collected by placing sensor loggers at resting and breeding locations such as storm drains across Houston, TX. Corresponding weather data was obtained from National Oceanic and Atmospheric Administration website. Features extracted from these data sources along with contextual information on location were used to develop a Generalized Linear Model for predicting microclimate temperatures. We also analyzed mosquito population dynamics for Aedes albopictus under ambient ...

Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction

Journal of Diabetes Science and Technology, 2020

Background: Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-ti... more Background: Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures. Methods: A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake. Results: The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate d...

Impacts of diurnal temperature and larval density on aquatic development of Aedes aegypti

PLOS ONE, 2018

The increasing range of Aedes aegypti, vector for Zika, dengue, chikungunya, and other viruses, h... more The increasing range of Aedes aegypti, vector for Zika, dengue, chikungunya, and other viruses, has brought attention to the need to understand the population and transmission dynamics of this mosquito. It is well understood that environmental factors and breeding site characteristics play a role in organismal development and the potential to transmit pathogens. In this study, we observe the impact of larval density in combination with diurnal temperature on the time to pupation, emergence, and mortality of Aedes aegypti. Experiments were conducted at two diurnal temperature ranges based on 10 years of historical temperatures of Houston, Texas (21-32˚C and 26.5-37.5˚C). Experiments at constant temperatures (26.5˚C, 32˚C) were also conducted for comparison. At each temperature setting, five larval densities were observed (0.2, 1, 2, 4, 5 larvae per mL of water). Data collected shows significant differences in time to first pupation, time of first emergence, maximum rate of pupation, time of maximum rate of pupation, maximum rate of emergence, time of maximum rate of emergence, final average proportion of adult emergence, and average proportion of larval mortality. Further, data indicates a significant interactive effect between temperature fluctuation and larval density on these measures. Thus, wild population estimates should account for temperature fluctuations, larval density, and their interaction in low-volume containers.

Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management

Health Informatics Journal, 2017

The impact of infectious disease on human populations is a function of many factors including env... more The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies,...

Condition-Based Maintenance for Queues With Degrading Servers

IEEE Transactions on Automation Science and Engineering, 2019

The integration of condition monitoring with queueing systems to support decision making is not w... more The integration of condition monitoring with queueing systems to support decision making is not well explored. This work addresses the impact of condition monitoring of the server on the system level performance experienced by entities in a queueing system. The system consists of a queue with a single server subject to Markovian degradation. The model assumes a Poisson arrival process with service times and repair times according to general distributions. We develop stability conditions and perform steady state analysis to obtain performance measures (average queue length, average degradation, etc.). We propose minimizing an objective function involving four types of costs: repair, catastrophic failure, quality and holding. The queue performance measures derived from steady state analysis are bench-marked and compared to those from a discrete event simulation model. After verifying the queuing model, a sensitivity analysis is performed to determine the relationships between system performance and model parameters. Further, we explore the impact of stochastic service times on degradation and cost coefficients on optimal repair policy. Results indicate that the total cost function is convex and thus subject to an optimal repair policy. The model is sensitive to service time, quality costs, and failure costs for late-stage policy repairs decisions and sensitive to expected repair times and repair costs for early-stage policy repair decisions. The stochastic nature of service times drastically increases the likelihood of catastrophic failure of the server, and substantially increases holding costs. However, quality degrades more for constant service times while repair costs are minimally impacted.

An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums

Journal of Healthcare Engineering, 2017

Online healthcare forums (OHFs) have become increasingly popular for patients to share their heal... more Online healthcare forums (OHFs) have become increasingly popular for patients to share their health-related experiences. The healthcare-related texts posted in OHFs could help doctors and patients better understand specific diseases and the situations of other patients. To extract the meaning of a post, a commonly used way is to classify the sentences into several predefined categories of different semantics. However, the unstructured form of online posts brings challenges to existing classification algorithms. In addition, though many sophisticated classification models such as deep neural networks may have good predictive power, it is hard to interpret the models and the prediction results, which is, however, critical in healthcare applications. To tackle the challenges above, we propose an effective and interpretable OHF post classification framework. Specifically, we classify sentences into three classes: medication, symptom, and background. Each sentence is projected into an in...

Cancelled Primary Care Appointments: A Prospective Cohort Study of Diabetic Patients

Journal of Medical Systems, 2017

Patients scheduled for primary care appointments often cancel or no show. For diabetic patients, ... more Patients scheduled for primary care appointments often cancel or no show. For diabetic patients, nonattendance can affect continuity of care and result in higher emergency department (ED) and hospital use. Nonattendance also impacts appointment scheduling, patient access, and clinic work load. While no show has received significant attention, little research has addressed the prevalence and impact of appointment cancellation. Data on 46,710 appointments for 7586 adult diabetic patients was used to conduct a prospective cohort study examining primary care appointment behavior. The independent variable was the status of the INDEX appointment, which was attended, cancelled, or no showed. Dependent variables included the dates of (1) the last attended appointment, (2) scheduling the NEXT appointment, (3) the next attended follow-up appointment, and (4) ED visits and hospitalizations within six months of the INDEX. Cancellation was more prevalent than no show (17.7% vs 12.2%). Of those who cancelled and scheduled a next appointment, 28.8% experienced over 30 days delay between the INDEX and NEXT appointment dates, and 59.9% delayed rescheduling until on or after the cancelled appointment date. Delay in rescheduling was associated with an 18.6% increase in days between attended appointments and a 26.0% increase in ED visits. For diabetic patients, cancellation with late rescheduling is a prevalent and unhealthy behavior. Although more work is necessary to address the health, intervention, and cost issues, this work suggests that cancellation, like no show, may be problematic for many clinics and patients.

A Consequence Mitigation Model for Water Networks Subject to Intentional Physical Attacks

Computing in Civil Engineering (2005), 2005

This paper presents a mitigation model which can reduce or minimize adverse consequences when a w... more

The impact of overbooking on primary care patient no-show

IIE Transactions on Healthcare Systems Engineering, 2013

ABSTRACT Overbooking has been widely adopted to deal with primary care’s prevalent patient no-sho... more ABSTRACT Overbooking has been widely adopted to deal with primary care’s prevalent patient no-show problem. However, there has been very limited research that analyzes the impact of overbooking on the major causes/factors of patient no-show and most importantly, its implications on patient no-show. In this paper, we take a novel approach and develop a game-theoretic framework (with queueing models) to explore the impact of overbooking on patient no-show through its effect on two important factors shown to affect no-show: appointment delay (time between a patient requesting an appointment and his actual appointment time) and office delay (the amount of time a patient waits in the office to see the doctor). While overbooking reduces appointment delay (which may positively affect patient no-show rate), it increases office delay (which may negatively affect patient no-show rate). Our results show that, considering both impacts of appointment delay and office delay, patient no-show rate always increases after overbooking. Further, there exists a critical range of patient panel size within which overbooking may also lead to lower expected profit for the clinic. Correspondingly, we propose two easy-to-implement strategies, which can increase clinic’s expected profit and reduce no-show at the same time.

Consequence Mitigation: Current Research and Future Trends

Wiley Handbook of Science and Technology for Homeland Security, 2008

Consequence mitigation focuses on the response and recovery efforts after a natural disaster or a... more Consequence mitigation focuses on the response and recovery efforts after a natural disaster or an intentional attack and related disaster/hazard management activities and command and control structures, planning, and exercises. This article provides a review of previous studies in consequence mitigation, discusses selected state-of-the-art technologies and tools, and describes measures of mitigation effectiveness. Current funding sources and research activities of consequence mitigation in the United States and other countries are also listed. This article discusses the benefits of integrating different consequence mitigation strategies and provides thoughts on future work in this domain. Keywords: consequence mitigation; disaster/hazard mitigation; disaster response and recovery; emergency; modeling and simulation

Using systems science for population health management in primary care

Journal of primary care & community health, 2014

Population health management is becoming increasingly important to organizations managing and pro... more Population health management is becoming increasingly important to organizations managing and providing primary care services given ongoing changes in health care delivery and payment systems. The objective of this study is to show how systems science methodologies could be incorporated into population health management to compare different interventions and improve health outcomes. The New York Academy of Medicine Cardiovascular Health Simulation model (an agent-based model) and data from the Behavioral Risk Factor Surveillance System were used to evaluate a lifestyle program that could be implemented in primary care practice settings. The program targeted Medicare-age adults and focused on improving diet and exercise and reducing weight. The simulation results suggest that there would be significant reductions projected in the proportion of the Medicare-age population with diabetes after the implementation of the proposed lifestyle program for a relatively long term (3 and 5 years...

Using Patient Flow to Examine Hospital Operations

Principles and Applications

Adopting an admission-to-discharge patient flow perspective has the potential to improve hospital... more Adopting an admission-to-discharge patient flow perspective has the potential to improve hospital operations. Flow paths provide insight regarding patient care needs, support resource allocation and capacity planning decisions, and improve the operational performance of the hospitals. Studying patient flow through systems engineering tools and applications can help decision makers assess and improve care delivery. This chapter presents current research and techniques used to describe, measure, and model inpatient flow. We formally define patient flow from an operational standpoint and discuss why it is crucial for operational decisions. Systems engineering techniques, which describe and analyze inpatient flow, are introduced. The chapter concludes with a discussion of emerging approaches to capture patient flow.

No-Show Modeling for Adult Ambulatory Clinics

ABSTRACT Patient no-show is a pervasive problem in outpatient clinics. This chapter provides a li... more ABSTRACT Patient no-show is a pervasive problem in outpatient clinics. This chapter provides a literature review and discussion on how to develop statistical no-show models. The literature review is a structured and representative selection of research studies from a variety of medical areas. The literature is grouped into four classes. The first class covers self-reported reasons for no-show. The most common self-reported reasons are forgetting, conflicts, transportation, scheduling system problems, and physical or mental illness. The second class discusses the effect of no-show interventions such as appointment reminders, patient education, and changes in scheduling systems on no-show behavior. The third class develops statistical models of no-show behavior in a variety of settings. Several patient, provider, and clinic characteristics are considered in developing these models. The last class of literature considers the impact of no-shows on health outcomes, which illustrates the importance of no-show modeling. The second part of the chapter explains how statistical no-show models can be developed. The data requirements, determination of significant factors, development of logistic regression models, and model validation are explained in detail. An example no-show model is provided to illustrate the modeling and validation process. The chapter concludes with summarizing thoughts and a discussion of future research opportunities.

Mitigating shortage and distribution costs in damaged water networks

Socio-Economic Planning Sciences, 2012

ABSTRACT Recent events have sparked renewed interest in disaster mitigation for public infrastruc... more ABSTRACT Recent events have sparked renewed interest in disaster mitigation for public infrastructures. Presidential Decision Directive 63 identifies water distribution as being among the most vital and vulnerable of our large-scale infrastructures. Water distribution networks are vulnerable to threats such as chemical and biological contamination, cyber attacks on computer-based management systems, and physical destruction from acts of nature and intentional attack. This research develops methods for configuring the undamaged portion of the water network to mitigate the consequences of physical destruction. The approach is to find a hydraulically feasible residual network that can be pressurized to meet the demand of a subset of demand sectors. Demand sectors not pressurized then receive water through truck distribution from pressurized sectors. The objective is to minimize weighted water shortage and water truck distribution costs by identifying sectors to pressurize along with an assignment of unpressurized sectors to pressurized sectors for water delivery by truck. The paper develops an optimization model, describes a solution method, and presents computational results for three example networks.

Assigning patients to nurses in neonatal intensive care

Journal of the Operational Research Society, 2002

An intensive care nursery provides health care for critically ill newborn infants. During a typic... more An intensive care nursery provides health care for critically ill newborn infants. During a typical shift, infants range from those needing only occasional care to those requiring constant attention. At the beginning of each shift, the head nurse groups the patients for assignment to staff nurses. Typically each nurse cares for one group of infants throughout the shift. The large variation in infant conditions along with several complicating side constraints makes it difficult to develop balanced nurse work loads. We develop a mathematical programming approach for achieving better workload balance. We first develop a detailed neonatal acuity system that quantifies the nursing workload of each patient. We then develop an integer linear program that assigns patients to nurses while balancing nurse workloads. Because this model is computationally intractable, we develop a heuristic that exploits the fact that most nurseries are divided into a number of physical zones. We use ten case studies taken from a major university hospital to benchmark the performance of this heuristic. We also perform a designed experiment using randomly generated problems that examines the effect of nursery parameters on heuristic performance.

Adherence to Telemonitoring Therapy for Medicaid Patients With Hypertension: Case Study

Journal of Medical Internet Research

Background Almost 50% of the adults in the United States have hypertension. Although clinical tri... more Background Almost 50% of the adults in the United States have hypertension. Although clinical trials indicate that home blood pressure monitoring can be effective in managing hypertension, the reported results might not materialize in practice because of patient adherence problems. Objective The aims of this study are to characterize the adherence of Medicaid patients with hypertension to daily telemonitoring, identify the impacts of adherence reminder calls, and investigate associations with blood pressure control. Methods This study targeted Medicaid patients with hypertension from the state of Texas. A total of 180 days of blood pressure and pulse data in 2016-2018 from a telemonitoring company were analyzed for mean transmission rate and mean blood pressure change. The first 30 days of data were excluded because of startup effects. The protocols required the patients to transmit readings by a specified time daily. Patients not transmitting their readings received an adherence re...

from the Experiences of Thirteen Indiana

Significant concerns exist over the ability of the healthcare and public health systems to meet t... more Significant concerns exist over the ability of the healthcare and public health systems to meet the surge demands that would result from an event such as an influenza pandemic. Current guid-ance for public health planners is largely based on expert opinion and may lack connection to the problems of street-level public health practice. To identify the problems of local planners and prepare a state-level planning template for increasing health care surge capacity that accounted for these issues, a study was conducted of local pandemic planning efforts in thirteen counties, finding that cognitive biases, coordination problems, institutional structures in the healthcare system, and resource shortfalls are significant barriers to preparing and implementing a surge capacity plan. In addition, local planners identify patient demand management through triage and education efforts as a viable means of ensuring adequate capacity, in contrast to guidance proposing an increased supply of care a...

Flexible routing and deadlock avoidance in automated manufacturing systems

Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)

ABSTRACT Deadlock avoidance policies guarantee deadlock free buffer space allocation to concurren... more ABSTRACT Deadlock avoidance policies guarantee deadlock free buffer space allocation to concurrent competing parts in flexible manufacturing systems. Typically, these policies require enumeration of routes that parts follow. In systems with flexible routing, where parts can choose from a set of machines at each processing step, route enumeration can be of exponential complexity. Setup and execution of deadlock avoidance constraints are rendered computationally intractable. The objective of this work is to develop a control model that allows for several processing alternatives at each step while maintaining polynomial bounds on deadlock-free operation

AdvancingtheUseofEvidence-BasedDecision-Making inLocalHealthDepartmentsWithSystemsScience Methodologies

Collaborative Management of PTSD Treatment through Smartphone Apps: What Is Available and Accessible?

Predictive model for microclimatic temperature and its use in mosquito population modeling

Scientific Reports, 2021

Mosquitoes transmit several infectious diseases that pose significant threat to human health. Tem... more Mosquitoes transmit several infectious diseases that pose significant threat to human health. Temperature along with other environmental factors at breeding and resting locations play a role in the organismal development and abundance of mosquitoes. Accurate analysis of mosquito population dynamics requires information on microclimatic conditions at breeding and resting locations. In this study, we develop a regression model to characterize microclimatic temperature based on ambient environmental conditions. Data were collected by placing sensor loggers at resting and breeding locations such as storm drains across Houston, TX. Corresponding weather data was obtained from National Oceanic and Atmospheric Administration website. Features extracted from these data sources along with contextual information on location were used to develop a Generalized Linear Model for predicting microclimate temperatures. We also analyzed mosquito population dynamics for Aedes albopictus under ambient ...

Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction

Journal of Diabetes Science and Technology, 2020

Background: Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-ti... more Background: Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures. Methods: A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake. Results: The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate d...

Impacts of diurnal temperature and larval density on aquatic development of Aedes aegypti

PLOS ONE, 2018

The increasing range of Aedes aegypti, vector for Zika, dengue, chikungunya, and other viruses, h... more The increasing range of Aedes aegypti, vector for Zika, dengue, chikungunya, and other viruses, has brought attention to the need to understand the population and transmission dynamics of this mosquito. It is well understood that environmental factors and breeding site characteristics play a role in organismal development and the potential to transmit pathogens. In this study, we observe the impact of larval density in combination with diurnal temperature on the time to pupation, emergence, and mortality of Aedes aegypti. Experiments were conducted at two diurnal temperature ranges based on 10 years of historical temperatures of Houston, Texas (21-32˚C and 26.5-37.5˚C). Experiments at constant temperatures (26.5˚C, 32˚C) were also conducted for comparison. At each temperature setting, five larval densities were observed (0.2, 1, 2, 4, 5 larvae per mL of water). Data collected shows significant differences in time to first pupation, time of first emergence, maximum rate of pupation, time of maximum rate of pupation, maximum rate of emergence, time of maximum rate of emergence, final average proportion of adult emergence, and average proportion of larval mortality. Further, data indicates a significant interactive effect between temperature fluctuation and larval density on these measures. Thus, wild population estimates should account for temperature fluctuations, larval density, and their interaction in low-volume containers.

Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management

Health Informatics Journal, 2017

The impact of infectious disease on human populations is a function of many factors including env... more The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies,...

Condition-Based Maintenance for Queues With Degrading Servers

IEEE Transactions on Automation Science and Engineering, 2019

The integration of condition monitoring with queueing systems to support decision making is not w... more The integration of condition monitoring with queueing systems to support decision making is not well explored. This work addresses the impact of condition monitoring of the server on the system level performance experienced by entities in a queueing system. The system consists of a queue with a single server subject to Markovian degradation. The model assumes a Poisson arrival process with service times and repair times according to general distributions. We develop stability conditions and perform steady state analysis to obtain performance measures (average queue length, average degradation, etc.). We propose minimizing an objective function involving four types of costs: repair, catastrophic failure, quality and holding. The queue performance measures derived from steady state analysis are bench-marked and compared to those from a discrete event simulation model. After verifying the queuing model, a sensitivity analysis is performed to determine the relationships between system performance and model parameters. Further, we explore the impact of stochastic service times on degradation and cost coefficients on optimal repair policy. Results indicate that the total cost function is convex and thus subject to an optimal repair policy. The model is sensitive to service time, quality costs, and failure costs for late-stage policy repairs decisions and sensitive to expected repair times and repair costs for early-stage policy repair decisions. The stochastic nature of service times drastically increases the likelihood of catastrophic failure of the server, and substantially increases holding costs. However, quality degrades more for constant service times while repair costs are minimally impacted.

An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums

Journal of Healthcare Engineering, 2017

Online healthcare forums (OHFs) have become increasingly popular for patients to share their heal... more Online healthcare forums (OHFs) have become increasingly popular for patients to share their health-related experiences. The healthcare-related texts posted in OHFs could help doctors and patients better understand specific diseases and the situations of other patients. To extract the meaning of a post, a commonly used way is to classify the sentences into several predefined categories of different semantics. However, the unstructured form of online posts brings challenges to existing classification algorithms. In addition, though many sophisticated classification models such as deep neural networks may have good predictive power, it is hard to interpret the models and the prediction results, which is, however, critical in healthcare applications. To tackle the challenges above, we propose an effective and interpretable OHF post classification framework. Specifically, we classify sentences into three classes: medication, symptom, and background. Each sentence is projected into an in...

Cancelled Primary Care Appointments: A Prospective Cohort Study of Diabetic Patients

Journal of Medical Systems, 2017

Patients scheduled for primary care appointments often cancel or no show. For diabetic patients, ... more Patients scheduled for primary care appointments often cancel or no show. For diabetic patients, nonattendance can affect continuity of care and result in higher emergency department (ED) and hospital use. Nonattendance also impacts appointment scheduling, patient access, and clinic work load. While no show has received significant attention, little research has addressed the prevalence and impact of appointment cancellation. Data on 46,710 appointments for 7586 adult diabetic patients was used to conduct a prospective cohort study examining primary care appointment behavior. The independent variable was the status of the INDEX appointment, which was attended, cancelled, or no showed. Dependent variables included the dates of (1) the last attended appointment, (2) scheduling the NEXT appointment, (3) the next attended follow-up appointment, and (4) ED visits and hospitalizations within six months of the INDEX. Cancellation was more prevalent than no show (17.7% vs 12.2%). Of those who cancelled and scheduled a next appointment, 28.8% experienced over 30 days delay between the INDEX and NEXT appointment dates, and 59.9% delayed rescheduling until on or after the cancelled appointment date. Delay in rescheduling was associated with an 18.6% increase in days between attended appointments and a 26.0% increase in ED visits. For diabetic patients, cancellation with late rescheduling is a prevalent and unhealthy behavior. Although more work is necessary to address the health, intervention, and cost issues, this work suggests that cancellation, like no show, may be problematic for many clinics and patients.

A Consequence Mitigation Model for Water Networks Subject to Intentional Physical Attacks

Computing in Civil Engineering (2005), 2005

This paper presents a mitigation model which can reduce or minimize adverse consequences when a w... more

The impact of overbooking on primary care patient no-show

IIE Transactions on Healthcare Systems Engineering, 2013

ABSTRACT Overbooking has been widely adopted to deal with primary care’s prevalent patient no-sho... more ABSTRACT Overbooking has been widely adopted to deal with primary care’s prevalent patient no-show problem. However, there has been very limited research that analyzes the impact of overbooking on the major causes/factors of patient no-show and most importantly, its implications on patient no-show. In this paper, we take a novel approach and develop a game-theoretic framework (with queueing models) to explore the impact of overbooking on patient no-show through its effect on two important factors shown to affect no-show: appointment delay (time between a patient requesting an appointment and his actual appointment time) and office delay (the amount of time a patient waits in the office to see the doctor). While overbooking reduces appointment delay (which may positively affect patient no-show rate), it increases office delay (which may negatively affect patient no-show rate). Our results show that, considering both impacts of appointment delay and office delay, patient no-show rate always increases after overbooking. Further, there exists a critical range of patient panel size within which overbooking may also lead to lower expected profit for the clinic. Correspondingly, we propose two easy-to-implement strategies, which can increase clinic’s expected profit and reduce no-show at the same time.

Consequence Mitigation: Current Research and Future Trends

Wiley Handbook of Science and Technology for Homeland Security, 2008

Consequence mitigation focuses on the response and recovery efforts after a natural disaster or a... more Consequence mitigation focuses on the response and recovery efforts after a natural disaster or an intentional attack and related disaster/hazard management activities and command and control structures, planning, and exercises. This article provides a review of previous studies in consequence mitigation, discusses selected state-of-the-art technologies and tools, and describes measures of mitigation effectiveness. Current funding sources and research activities of consequence mitigation in the United States and other countries are also listed. This article discusses the benefits of integrating different consequence mitigation strategies and provides thoughts on future work in this domain. Keywords: consequence mitigation; disaster/hazard mitigation; disaster response and recovery; emergency; modeling and simulation

Using systems science for population health management in primary care

Journal of primary care & community health, 2014

Population health management is becoming increasingly important to organizations managing and pro... more Population health management is becoming increasingly important to organizations managing and providing primary care services given ongoing changes in health care delivery and payment systems. The objective of this study is to show how systems science methodologies could be incorporated into population health management to compare different interventions and improve health outcomes. The New York Academy of Medicine Cardiovascular Health Simulation model (an agent-based model) and data from the Behavioral Risk Factor Surveillance System were used to evaluate a lifestyle program that could be implemented in primary care practice settings. The program targeted Medicare-age adults and focused on improving diet and exercise and reducing weight. The simulation results suggest that there would be significant reductions projected in the proportion of the Medicare-age population with diabetes after the implementation of the proposed lifestyle program for a relatively long term (3 and 5 years...

Using Patient Flow to Examine Hospital Operations

Principles and Applications

Adopting an admission-to-discharge patient flow perspective has the potential to improve hospital... more Adopting an admission-to-discharge patient flow perspective has the potential to improve hospital operations. Flow paths provide insight regarding patient care needs, support resource allocation and capacity planning decisions, and improve the operational performance of the hospitals. Studying patient flow through systems engineering tools and applications can help decision makers assess and improve care delivery. This chapter presents current research and techniques used to describe, measure, and model inpatient flow. We formally define patient flow from an operational standpoint and discuss why it is crucial for operational decisions. Systems engineering techniques, which describe and analyze inpatient flow, are introduced. The chapter concludes with a discussion of emerging approaches to capture patient flow.

No-Show Modeling for Adult Ambulatory Clinics

ABSTRACT Patient no-show is a pervasive problem in outpatient clinics. This chapter provides a li... more ABSTRACT Patient no-show is a pervasive problem in outpatient clinics. This chapter provides a literature review and discussion on how to develop statistical no-show models. The literature review is a structured and representative selection of research studies from a variety of medical areas. The literature is grouped into four classes. The first class covers self-reported reasons for no-show. The most common self-reported reasons are forgetting, conflicts, transportation, scheduling system problems, and physical or mental illness. The second class discusses the effect of no-show interventions such as appointment reminders, patient education, and changes in scheduling systems on no-show behavior. The third class develops statistical models of no-show behavior in a variety of settings. Several patient, provider, and clinic characteristics are considered in developing these models. The last class of literature considers the impact of no-shows on health outcomes, which illustrates the importance of no-show modeling. The second part of the chapter explains how statistical no-show models can be developed. The data requirements, determination of significant factors, development of logistic regression models, and model validation are explained in detail. An example no-show model is provided to illustrate the modeling and validation process. The chapter concludes with summarizing thoughts and a discussion of future research opportunities.

Mitigating shortage and distribution costs in damaged water networks

Socio-Economic Planning Sciences, 2012

ABSTRACT Recent events have sparked renewed interest in disaster mitigation for public infrastruc... more ABSTRACT Recent events have sparked renewed interest in disaster mitigation for public infrastructures. Presidential Decision Directive 63 identifies water distribution as being among the most vital and vulnerable of our large-scale infrastructures. Water distribution networks are vulnerable to threats such as chemical and biological contamination, cyber attacks on computer-based management systems, and physical destruction from acts of nature and intentional attack. This research develops methods for configuring the undamaged portion of the water network to mitigate the consequences of physical destruction. The approach is to find a hydraulically feasible residual network that can be pressurized to meet the demand of a subset of demand sectors. Demand sectors not pressurized then receive water through truck distribution from pressurized sectors. The objective is to minimize weighted water shortage and water truck distribution costs by identifying sectors to pressurize along with an assignment of unpressurized sectors to pressurized sectors for water delivery by truck. The paper develops an optimization model, describes a solution method, and presents computational results for three example networks.

Assigning patients to nurses in neonatal intensive care

Journal of the Operational Research Society, 2002

An intensive care nursery provides health care for critically ill newborn infants. During a typic... more An intensive care nursery provides health care for critically ill newborn infants. During a typical shift, infants range from those needing only occasional care to those requiring constant attention. At the beginning of each shift, the head nurse groups the patients for assignment to staff nurses. Typically each nurse cares for one group of infants throughout the shift. The large variation in infant conditions along with several complicating side constraints makes it difficult to develop balanced nurse work loads. We develop a mathematical programming approach for achieving better workload balance. We first develop a detailed neonatal acuity system that quantifies the nursing workload of each patient. We then develop an integer linear program that assigns patients to nurses while balancing nurse workloads. Because this model is computationally intractable, we develop a heuristic that exploits the fact that most nurseries are divided into a number of physical zones. We use ten case studies taken from a major university hospital to benchmark the performance of this heuristic. We also perform a designed experiment using randomly generated problems that examines the effect of nursery parameters on heuristic performance.