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.
Dialysis Centers: Automating and optimizing the workforce scheduling of patie...Einstein II
Workforce scheduling of patient care staff that include Registered
Nurses, licensed practical nurses and patient care technicians, who
provide dialysis treatments to patients is critical and complex for dialysis
centres. The recent reforms and regulatory pressures in the industry don’t
seem to help either. These regulatory reforms are forcing dialysis centres
to revisit their margins and costs in an unprecedented manner.
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.
Dialysis Centers: Automating and optimizing the workforce scheduling of patie...Einstein II
Workforce scheduling of patient care staff that include Registered
Nurses, licensed practical nurses and patient care technicians, who
provide dialysis treatments to patients is critical and complex for dialysis
centres. The recent reforms and regulatory pressures in the industry don’t
seem to help either. These regulatory reforms are forcing dialysis centres
to revisit their margins and costs in an unprecedented manner.
1
QQUALITY IMPROVEMENT STUDENT PROJECT PROPOSAL:
IMPROVING HANDOFFS IN SAN FRANCISCO GENERAL
HOSPTITAL’S EMERGENCY DEPARTMENT
TMIT Student Projects QuickStart Package ™
1. BACKGROUND
Setting: Emergency departments are “high-risk” contexts; they are over-crowded and
overburdened, which can lead to treatment delays, patients leaving without being seen by a
clinician, and inadequate patient hand-offs during changing shifts and transfers to different
hospital services (Apker et al., 2007). This project will focus on the Emergency Departments in
county hospitals, specifically San Francisco General Hospital. SFGH has the only Trauma Center
(Level 1) available for the over 1.5 million people living and working in San Francisco County
(SFGH website)
Health Care Service: This paper will focus on intershift transfers, the process of transferring a
patient between two providers at the end of a shift, which can pose a major challenge in a busy
emergency department setting.
Problem: According to the Joint Commission on Accreditation of Healthcare Organizations
(JCAHO), poor communication between providers is the root cause of most sentinel events,
medical mistakes, and ‘‘near misses.” Furthermore, a recent survey of 264 emergency
department physicians noted that 30% of respondents reported an adverse event or near miss
related to ED handoffs (Horwitz, 2008). A similar survey notes that 73.5% of hand-offs occur in
a common area within the ED, 89.5% of respondents stated that there was no uniform written
policy regarding patient sign-out, and 50.3% of those surveyed reported that physicians sign out
2
patient details verbally only (Sinha et al., 2007). At SFGH, handoffs occur in the middle of the
ED hallway, usually next to a patient’s gurney. Sign-out is dependent on the Attending and
Residents on a particular shift; thus, it is non-uniform, and hand-offs are strictly verbal.
Barriers to Quality: In a 2005 article in Academic Medicine, four major barriers to effective
handoffs were identified: (1) the physical setting, (2) the social setting, and (3) communication
barriers. Most of these barriers are present during intershift transfers at SFGH. The physical
setting is usually in a hallway, next to a whiteboard, never in private. Presentations are frequently
interrupted, and background noise is intense from the chaos of an overcrowded emergency room.
Attendings frequently communicate with each other and assume that the resident can hear them.
Solet et al. suggests that Residents are unlikely to ask questions during a handoff if the
information is coming from an Attending physician. All transfers are verbal, none are
standardized, and time pressures are well known, since sign-out involves all working physicians
in the ED at one time.
2. THE INTERVENTION
The Institute for Health Care Improvement (IHI) lays out several steps for conducting a quality
improvement ...
1
QUALITY IMPROVEMENT STUDENT PROJECT PROPOSAL:
IMPROVING HANDOFFS IN SAN FRANCISCO GENERAL
HOSPTITAL’S EMERGENCY DEPARTMENT
TMIT Student Projects QuickStart Package ™
1. BACKGROUND
Setting: Emergency departments are “high-risk” contexts; they are over-crowded and
overburdened, which can lead to treatment delays, patients leaving without being seen by a
clinician, and inadequate patient hand-offs during changing shifts and transfers to different
hospital services (Apker et al., 2007). This project will focus on the Emergency Departments in
county hospitals, specifically San Francisco General Hospital. SFGH has the only Trauma Center
(Level 1) available for the over 1.5 million people living and working in San Francisco County
(SFGH website)
Health Care Service: This paper will focus on intershift transfers, the process of transferring a
patient between two providers at the end of a shift, which can pose a major challenge in a busy
emergency department setting.
Problem: According to the Joint Commission on Accreditation of Healthcare Organizations
(JCAHO), poor communication between providers is the root cause of most sentinel events,
medical mistakes, and ‘‘near misses.” Furthermore, a recent survey of 264 emergency
department physicians noted that 30% of respondents reported an adverse event or near miss
related to ED handoffs (Horwitz, 2008). A similar survey notes that 73.5% of hand-offs occur in
a common area within the ED, 89.5% of respondents stated that there was no uniform written
policy regarding patient sign-out, and 50.3% of those surveyed reported that physicians sign out
2
patient details verbally only (Sinha et al., 2007). At SFGH, handoffs occur in the middle of the
ED hallway, usually next to a patient’s gurney. Sign-out is dependent on the Attending and
Residents on a particular shift; thus, it is non-uniform, and hand-offs are strictly verbal.
Barriers to Quality: In a 2005 article in Academic Medicine, four major barriers to effective
handoffs were identified: (1) the physical setting, (2) the social setting, and (3) communication
barriers. Most of these barriers are present during intershift transfers at SFGH. The physical
setting is usually in a hallway, next to a whiteboard, never in private. Presentations are frequently
interrupted, and background noise is intense from the chaos of an overcrowded emergency room.
Attendings frequently communicate with each other and assume that the resident can hear them.
Solet et al. suggests that Residents are unlikely to ask questions during a handoff if the
information is coming from an Attending physician. All transfers are verbal, none are
standardized, and time pressures are well known, since sign-out involves all working physicians
in the ED at one time.
2. THE INTERVENTION
The Institute for Health Care Improvement (IHI) lays out several steps for conducting a quality
improvement ...
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.
Austin Journal of Medical Oncology is an open access, peer review journal publishing original research & review articles in all the fields of Medical Oncology. Medical Oncology is the branch of medicine which deals with cancer and tumor related problems. Austin Journal of Medical Oncology provides a new platform for all researchers, scientists, scholars, students to publish their research work & update the latest research information.
Austin Journal of Medical Oncology is a comprehensive Open Access peer reviewed scientific Journal that covers multidisciplinary fields. We provide limitless access towards accessing our literature hub with colossal range of articles. The journal aims to publish high quality varied article types such as Research, Review, Short Communications, Case Reports, Perspectives (Editorials), Clinical Images.
Austin Journal of Medical Oncology supports the scientific modernization and enrichment in Medical Oncology research community by magnifying access to peer reviewed scientific literary works. Austin also brings universally peer reviewed member journals under one roof thereby promoting knowledge sharing, collaborative and promotion of multidisciplinary science.
A KNOWLEDGE BASED AUTOMATIC RADIATION TREATMENT PLAN ALERT SYSTEMijaia
In radiation therapy, preventing treatment plan errors is of paramount importance. In this paper, an alert system is proposed and developed for checking if the pending cancer treatment plan is consistent with the intended use. A key step in the development of the paper is characterization of various treatment plan fingerprints by three-dimension vectors taken from possibly thousands of variables in each treatment plan. Then three machine learning based algorithms are developed and tested in the paper. The first algorithm is a knowledge-based support vector machine method. If an incorrect treatment plan were offered, the algorithm would tell that the pending treatment plan is inconsistent with the intended use and provide a red flag. The algorithm is tested on the actual patient data sets with 100% successful rate and 0% failure rate. In addition, two algorithms based on the well-known k-nearest neighbour and Bayesian approach respectively are developed. Similar to the support vector machine algorithm, these two algorithms are also tested with 100% success rate and 0% failure rate. The key seems to pick up the right features.
HOSPITALMBA-9617Angela DiazAutomation of Hospital.docxpooleavelina
HOSPITALMBA-9617Angela Diaz
Automation of Hospital Emergency Department
Angela Diaz
Barry University
MBA-617
Industry Focus
An emergency department is a medical treatment institution or facility that focuses on the emergence of medicine acute care whereby the patient comes to the hospital by them or through the use of the ambulance. The emergency department is located in the hospital at times in the primary care center. Which is usually is operated 24 hours a day, seven days a week. The hospital emergency department had been facing a lot of challenges due to the sharp rise in the number of patients with emergencies. In most cases, many of the condition are life-threatening and as such, require immediate hospital intervention or attendance. Overcrowding and critical shortages in the Emergency Department (ED) limits access to timely emergency care in the hospital. Another common issue that arises with overcrowding is long patient wait times (Manyika, 2017). The emergency department in the country has about 80 to 85% walk in and the similar number of the patients that are sent home after treatment with medical prescriptions, the remaining 15% are usually admitted to the hospital-based on the type of ailment that they are diagnosed with (Gutherz & Baron, 2001). There is always the unintended nature of patient appearance; therefore, the hospital must deliver the primary treatment for a wide range of diseases or injuries (Manyika, 2017). That can be missed due to lack of efficiency and quality care provided by overwhelmed staff. Such challenges can only be solved by technical factors or automation. The automation process gives the physician ample time to concentrate on the quality outcome instead of receiving distractions from a disorganized emergency department system. Therefore, automation is a solution because it increases the level of productivity because machines assume roles such as registrations, dispensing of the prescription, and checkout.
Debates in differences for solutions in implementing technology to enhance efficiency within the emergency department have been discussed between organizations such as the Society for Academic Emergency Medicine as well as the American College of Emergency Physician have held several annual conferences to meet the technical solution for the ED challenge. In many ED in the country, many hospitals are overcrowded, and the leading cause is based on the hospital itself. It has been found, that safety and liability are one of the primary challenges in the emergency department sector (Manyika, 2017). It was found within the United States of America that $3.6 billion was lost due the lack of efficiency within emergency department due to multiple lawsuits. In such like state, intelligent companies might find themselves in the receiving end.
Problems Faced by the Industry
Therefore, there is critical need for increase and quality care provided to patients that can be resolved through the means of tec ...
Change Management And Contingency Planning in Transformation of Diagnostic De...Ruby Med Plus
Change Management and Contingency Planning: Case Study of Dental Hospital in implementing new Dental X-ray technology.Application of Kurt Lewin Force field Model. Kurt Lewin's three-stage model (1958) of organizational change,
Background Hospital contributes significantly tangible and intangible resources on a concurred plan by the scheduling of surgery on the OT list. Postponement decreases efficiency by declining throughput leads to wastage of resources hence burden to the nation. Patients and their family face economic and emotional implication due to the postponement. Postponement rate being a quality indicator controls check mechanism could be developed from the results. Postponement of elective scheduled operations results in inefficient use of the operating room (OR) time on the day of surgery. Inconvenience to patients and families are also caused by postponements. Moreover, the day of surgery (DOS) postponement creates logistic and financial burden associated with extended hospital stay and repetitions of pre-operative preparations to an extent of repetition of investigations in some cases causing escalated costs, wastage of time and reduced income. Methodology A cross-sectional study was done in the operation theaters of a tertiary care hospital in which total ten operation theaters of General Surgery Data of scheduled, performed and postponed surgeries was collected from all the operation theater with effect from March 1st to September 30th, 2018. A questionnaire was developed to find out the reasons for the postponement for all hospital’s stakeholders (surgeons, Anesthetist, Nursing Officer) and they were further evaluated time series analysis of scheduling of Operation Theater for moving average technique. Results Total 958 surgeries were scheduled and 772 surgeries performed were and 186 surgeries were postponed with a postponement rate of 19.42% in the cardiac surgery department during the study period. Month-wise postponement Rate exponential smoothing of time series data shows the dynamic of operating suits. To test throughput Postponement rate was plotted the postponed surgeries and on regression analysis is in a perfect linear relationship.
This assignment simulates a real-world scenario where you are a coGrazynaBroyles24
This assignment simulates a real-world scenario where you are a consultant, working collaboratively with your client to solve an organisational problem. It is based on a real-world situation observed during the course of primary research into healthcare process improvement. You will deliver a report to your client that is grounded in theory and demonstrates an understanding of the real-world challenges associated with implementing solutions that impact on organisational members.
This assignment supports you to:
· develop a sophisticated understanding of organisational functionality
· gain experience in using a key, functionalist tool
· understand the limitations of viewing organisations purely through a functionalist perspective
· understand the value of the interpretivist / social relativist perspective, and its limitations
You will be drawing on two paradigms to analyse the problem and develop your solution: the functionalist paradigm and the interpretivist / social relativist paradigm.
Assessment details
The case and your client
Your client is large, urban hospital located in Melbourne. The hospital has an Emergency Department, which is having trouble meeting government-established targets for the timely provision of emergency care. That is, patients who attend the ED are waiting too long for assessment, treatment, and discharge or admission. These delays are risky and stressful for patients, and stressful for patients' families and carers. Overcrowding and poor patient flow through the ED also creates an environment where treatment errors are more likely, and is highly stressful for hospital staff (triage nurses, doctors, nurses, management and administrative staff, porters, and the range of professional staff who run tests and x-rays). This situation is also damaging to the hospital's reputation and the morale of staff, because the hospital's performance against their targets is made public, in the interests of transparency. Staff in the ED feel stretched, under pressure, and concerned about the timeliness and quality of care for their patients.
To rectify the situation, hospital management has hired a consultancy firm that specialises in the Toyota Production System and all of its process improvement derivatives (business process reengineering, Lean thinking, Total Quality Management, Six Sigma, and so on). The consultant has worked with the hospital's Improvement Advisor, whose role is to coach medical staff in the development and implementation of process improvement techniques to solve process problems (for example, the flow of patients through the Emergency Department; waiting lists for outpatient services; discharge processes). The consultant and the improvement advisor have attempted to consult with the ED staff (doctors, nurses, administrative staff, porters, managers, etc.) but had low levels of engagement with the improvement project, which led them develop a new process effectively on their own to aid the flow of patients f ...
1
QQUALITY IMPROVEMENT STUDENT PROJECT PROPOSAL:
IMPROVING HANDOFFS IN SAN FRANCISCO GENERAL
HOSPTITAL’S EMERGENCY DEPARTMENT
TMIT Student Projects QuickStart Package ™
1. BACKGROUND
Setting: Emergency departments are “high-risk” contexts; they are over-crowded and
overburdened, which can lead to treatment delays, patients leaving without being seen by a
clinician, and inadequate patient hand-offs during changing shifts and transfers to different
hospital services (Apker et al., 2007). This project will focus on the Emergency Departments in
county hospitals, specifically San Francisco General Hospital. SFGH has the only Trauma Center
(Level 1) available for the over 1.5 million people living and working in San Francisco County
(SFGH website)
Health Care Service: This paper will focus on intershift transfers, the process of transferring a
patient between two providers at the end of a shift, which can pose a major challenge in a busy
emergency department setting.
Problem: According to the Joint Commission on Accreditation of Healthcare Organizations
(JCAHO), poor communication between providers is the root cause of most sentinel events,
medical mistakes, and ‘‘near misses.” Furthermore, a recent survey of 264 emergency
department physicians noted that 30% of respondents reported an adverse event or near miss
related to ED handoffs (Horwitz, 2008). A similar survey notes that 73.5% of hand-offs occur in
a common area within the ED, 89.5% of respondents stated that there was no uniform written
policy regarding patient sign-out, and 50.3% of those surveyed reported that physicians sign out
2
patient details verbally only (Sinha et al., 2007). At SFGH, handoffs occur in the middle of the
ED hallway, usually next to a patient’s gurney. Sign-out is dependent on the Attending and
Residents on a particular shift; thus, it is non-uniform, and hand-offs are strictly verbal.
Barriers to Quality: In a 2005 article in Academic Medicine, four major barriers to effective
handoffs were identified: (1) the physical setting, (2) the social setting, and (3) communication
barriers. Most of these barriers are present during intershift transfers at SFGH. The physical
setting is usually in a hallway, next to a whiteboard, never in private. Presentations are frequently
interrupted, and background noise is intense from the chaos of an overcrowded emergency room.
Attendings frequently communicate with each other and assume that the resident can hear them.
Solet et al. suggests that Residents are unlikely to ask questions during a handoff if the
information is coming from an Attending physician. All transfers are verbal, none are
standardized, and time pressures are well known, since sign-out involves all working physicians
in the ED at one time.
2. THE INTERVENTION
The Institute for Health Care Improvement (IHI) lays out several steps for conducting a quality
improvement ...
1
QUALITY IMPROVEMENT STUDENT PROJECT PROPOSAL:
IMPROVING HANDOFFS IN SAN FRANCISCO GENERAL
HOSPTITAL’S EMERGENCY DEPARTMENT
TMIT Student Projects QuickStart Package ™
1. BACKGROUND
Setting: Emergency departments are “high-risk” contexts; they are over-crowded and
overburdened, which can lead to treatment delays, patients leaving without being seen by a
clinician, and inadequate patient hand-offs during changing shifts and transfers to different
hospital services (Apker et al., 2007). This project will focus on the Emergency Departments in
county hospitals, specifically San Francisco General Hospital. SFGH has the only Trauma Center
(Level 1) available for the over 1.5 million people living and working in San Francisco County
(SFGH website)
Health Care Service: This paper will focus on intershift transfers, the process of transferring a
patient between two providers at the end of a shift, which can pose a major challenge in a busy
emergency department setting.
Problem: According to the Joint Commission on Accreditation of Healthcare Organizations
(JCAHO), poor communication between providers is the root cause of most sentinel events,
medical mistakes, and ‘‘near misses.” Furthermore, a recent survey of 264 emergency
department physicians noted that 30% of respondents reported an adverse event or near miss
related to ED handoffs (Horwitz, 2008). A similar survey notes that 73.5% of hand-offs occur in
a common area within the ED, 89.5% of respondents stated that there was no uniform written
policy regarding patient sign-out, and 50.3% of those surveyed reported that physicians sign out
2
patient details verbally only (Sinha et al., 2007). At SFGH, handoffs occur in the middle of the
ED hallway, usually next to a patient’s gurney. Sign-out is dependent on the Attending and
Residents on a particular shift; thus, it is non-uniform, and hand-offs are strictly verbal.
Barriers to Quality: In a 2005 article in Academic Medicine, four major barriers to effective
handoffs were identified: (1) the physical setting, (2) the social setting, and (3) communication
barriers. Most of these barriers are present during intershift transfers at SFGH. The physical
setting is usually in a hallway, next to a whiteboard, never in private. Presentations are frequently
interrupted, and background noise is intense from the chaos of an overcrowded emergency room.
Attendings frequently communicate with each other and assume that the resident can hear them.
Solet et al. suggests that Residents are unlikely to ask questions during a handoff if the
information is coming from an Attending physician. All transfers are verbal, none are
standardized, and time pressures are well known, since sign-out involves all working physicians
in the ED at one time.
2. THE INTERVENTION
The Institute for Health Care Improvement (IHI) lays out several steps for conducting a quality
improvement ...
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.
Austin Journal of Medical Oncology is an open access, peer review journal publishing original research & review articles in all the fields of Medical Oncology. Medical Oncology is the branch of medicine which deals with cancer and tumor related problems. Austin Journal of Medical Oncology provides a new platform for all researchers, scientists, scholars, students to publish their research work & update the latest research information.
Austin Journal of Medical Oncology is a comprehensive Open Access peer reviewed scientific Journal that covers multidisciplinary fields. We provide limitless access towards accessing our literature hub with colossal range of articles. The journal aims to publish high quality varied article types such as Research, Review, Short Communications, Case Reports, Perspectives (Editorials), Clinical Images.
Austin Journal of Medical Oncology supports the scientific modernization and enrichment in Medical Oncology research community by magnifying access to peer reviewed scientific literary works. Austin also brings universally peer reviewed member journals under one roof thereby promoting knowledge sharing, collaborative and promotion of multidisciplinary science.
A KNOWLEDGE BASED AUTOMATIC RADIATION TREATMENT PLAN ALERT SYSTEMijaia
In radiation therapy, preventing treatment plan errors is of paramount importance. In this paper, an alert system is proposed and developed for checking if the pending cancer treatment plan is consistent with the intended use. A key step in the development of the paper is characterization of various treatment plan fingerprints by three-dimension vectors taken from possibly thousands of variables in each treatment plan. Then three machine learning based algorithms are developed and tested in the paper. The first algorithm is a knowledge-based support vector machine method. If an incorrect treatment plan were offered, the algorithm would tell that the pending treatment plan is inconsistent with the intended use and provide a red flag. The algorithm is tested on the actual patient data sets with 100% successful rate and 0% failure rate. In addition, two algorithms based on the well-known k-nearest neighbour and Bayesian approach respectively are developed. Similar to the support vector machine algorithm, these two algorithms are also tested with 100% success rate and 0% failure rate. The key seems to pick up the right features.
HOSPITALMBA-9617Angela DiazAutomation of Hospital.docxpooleavelina
HOSPITALMBA-9617Angela Diaz
Automation of Hospital Emergency Department
Angela Diaz
Barry University
MBA-617
Industry Focus
An emergency department is a medical treatment institution or facility that focuses on the emergence of medicine acute care whereby the patient comes to the hospital by them or through the use of the ambulance. The emergency department is located in the hospital at times in the primary care center. Which is usually is operated 24 hours a day, seven days a week. The hospital emergency department had been facing a lot of challenges due to the sharp rise in the number of patients with emergencies. In most cases, many of the condition are life-threatening and as such, require immediate hospital intervention or attendance. Overcrowding and critical shortages in the Emergency Department (ED) limits access to timely emergency care in the hospital. Another common issue that arises with overcrowding is long patient wait times (Manyika, 2017). The emergency department in the country has about 80 to 85% walk in and the similar number of the patients that are sent home after treatment with medical prescriptions, the remaining 15% are usually admitted to the hospital-based on the type of ailment that they are diagnosed with (Gutherz & Baron, 2001). There is always the unintended nature of patient appearance; therefore, the hospital must deliver the primary treatment for a wide range of diseases or injuries (Manyika, 2017). That can be missed due to lack of efficiency and quality care provided by overwhelmed staff. Such challenges can only be solved by technical factors or automation. The automation process gives the physician ample time to concentrate on the quality outcome instead of receiving distractions from a disorganized emergency department system. Therefore, automation is a solution because it increases the level of productivity because machines assume roles such as registrations, dispensing of the prescription, and checkout.
Debates in differences for solutions in implementing technology to enhance efficiency within the emergency department have been discussed between organizations such as the Society for Academic Emergency Medicine as well as the American College of Emergency Physician have held several annual conferences to meet the technical solution for the ED challenge. In many ED in the country, many hospitals are overcrowded, and the leading cause is based on the hospital itself. It has been found, that safety and liability are one of the primary challenges in the emergency department sector (Manyika, 2017). It was found within the United States of America that $3.6 billion was lost due the lack of efficiency within emergency department due to multiple lawsuits. In such like state, intelligent companies might find themselves in the receiving end.
Problems Faced by the Industry
Therefore, there is critical need for increase and quality care provided to patients that can be resolved through the means of tec ...
Change Management And Contingency Planning in Transformation of Diagnostic De...Ruby Med Plus
Change Management and Contingency Planning: Case Study of Dental Hospital in implementing new Dental X-ray technology.Application of Kurt Lewin Force field Model. Kurt Lewin's three-stage model (1958) of organizational change,
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An Operations Management Approach for Radiology Services.pdf
1. An Operations Management Approach for
Radiology Services
Diego Falsini 1, Arianna Perugia,
Massimiliano M. Schiraldi
Department of Enterprise Engineering, “Tor Vergata” University of Rome,
Via del Politecnic, 00133 Rome, Italy
1
Correspondig Author: diego.falsini@uniroma2it, +39.06.7259.7164
Abstract: this paper focus on the application of Operations Management techniques in the context of
radiological and diagnostic imaging services provision. More specifically, the outpatient appointment
scheduling problem for MRI diagnostic imaging services in a radiology clinics is approached and solved
taking into account set-up time minimization. This is pursued trough the design of an innovative system for
the on-line assignment of appointments for specific diagnostic imaging scans. An appointment rule, a patient
classification and an heuristic procedure for the booking process are defined in order to better manage
uncertainty and improve system performance. The proposed approach was validated on the case of a
diagnostic centre of Alliance Medical, a primary multinational company in the field of diagnostic imaging
services.
Keywords: Service Operations Management, Healthcare, Radiology, Appointment Scheduling.
1. Introduction
Nowadays, given the extraordinary developments in the
field of radiology, the important role of images in modern
clinical practice on top of the cost increase of diagnostic
imaging services, a competitive and efficient radiology
department should necessarily focus on improving
process and resource management, mainly in terms of
operations efficiency. Computerized Axial Tomography
(CAT) and Magnetic Resonance Imaging (MRI) represent
strategic assets in radiological departments:
reimbursement rates are high while service processing
times are generally long. Consequently, the goal of
improving these areas should be considered as a priority.
Operations Management techniques may help to achieve
this goal through the analysis of processes, service levels
and quality standards increase, thus supporting the
hospital manager in taking the best operative and strategic
decisions.
Professionals in radiological sciences are often
inexperienced in process reengineering and optimization;
they may be persuaded that many critical issues in their
departments can be solved through the application of
rough-cut solutions which, on the contrary, tend to cause
negative side effects: it is not advisable to reduce costs
without a deep understanding of the problem that cause
the inefficiency; the introduction of newer information
systems on top of those already existing increases
management complexity; a simple top-down obligation to
increase service level is not efficient since this goal is
reached only through staff coordination and
encouragement.
The aim of this paper is to investigate the potentialities of
Operations Management techniques in the context of
health services provision and, more specifically, with
reference to the radiological and diagnostic imaging field.
The literature review shows a growing attention, in recent
years, on the application of Operations Management
techniques in healthcare, specifically regarding business-
oriented management of public health facilities. It is
primarily on hospitals, indeed, that the implementation of
Service Operations Management is focused (see, for
instance, Butler et al., 1996; Li et al., 2002; Vos et al.,
2007) even if the activities of private radiology clinics,
which provide diagnostic imaging services to outpatients,
certainly represent an interesting target for processes
optimization. Indeed, in this paper the latter are described
and analyzed. Actually, these healthcare services are
becoming more and more competitive because of the
great number of new private diagnostic centers growing
up. In order to improve key performance indicators, lean
approaches were occasionally suggested in literature (see
Workman-Germann & Hagg, 2007 or Lodge & Bamford,
2008), but more structured approaches are required in
order to achieve radical improvements in the way medical
departments operate.
More in detail, aiming at a significant increase in efficiency
and in service level, the paper suggests to approach the
problem through a radical re-engineering of outpatient
appointments scheduling process. “The objective of outpatient
scheduling is to find an appointment system for which a particular
measure of performance is optimized in a clinical environment”
(Cayirli & Veral, 2003). It is at this stage, indeed, that the
first resource allocation is defined, mainly in terms of
matching between exams/patients and diagnostic
machines. Correct scheduling of examinations is surely
crucial in a radiology center: the activity planning helps to
prevent overcrowding, avoid interference between
urgencies management and standard exams programming,
allows a better assignment of responsibilities among the
various functions and helps preventing staff demotivation.
Also, especially for public hospitals, a proper scheduling
of exams avoids oversizing of departments resources,
reduces overtimes and, simultaneously, the extension of
hospitalization (see Tattoni et al., 2009). This paper aims
to tackle the problem of radiological exams scheduling
and to propose an heuristic solution; this has been already
Proceedings of the Conference on “Sustainable Development: Industrial Practice, Education & Research”, Monopoli, Bari (Italy), 14-18 September 2010
D. Falsini, A. Perugia, M.M. Schiraldi (2010)
2. tested on the case of a leading Italian private company in
the field of diagnostic imaging service provision.
2. Literature review
Outpatient appointment scheduling problem was
approached for the first time by Welch and Bailey (1952)
while a comprehensive literature review was provided by
Cayirli & Veral (2003) and recently updated by Gupta &
Denton (2008). These studies showed that the greates part
of scientific contributes are concentrated on an off-line
“static” version of the problem, where the scheduling of
all examinations is determined before the first arrival.
Only a few number of works focused on the dynamic
appointment scheduling problem, where on-line decision-
making process still represents one of the most critical
complication of the problem (Gupta & Denton, 2008). In
the dynamic outpatient appointment scheduling problem,
patient requests arrive dinamically over time and, for each
request, an appointment has to be prompty determined
(Sickinger & Kolish, 2009). Relevant works in this sense
are those of Klassen & Rohleder (1996, 2004) and of
Rohleder & Klassen (2000, 2002). As described by Cayirli
and Veral (2003), in both static and dynamic appointment
scheduling problem, performance measurements tipically
depend on patients waiting/flow time, doctors idle
time/overtime, number of patients in the queue/system
and very few works seem to deal with the minimization of
the time dedicated to complete machines setup.
Indeed, every time a MRI equipment focus on a different
part of the patient body, the operator must change the coil
and reconfigure the machine; this may take a few or
several minutes. Analogously to what happens in
industrial companies or in manufacturing environments, it
is evident that in the absence of an appropriate procedure
that consider set-up minimization in order to reduce
downtime on the machine, scheduling is inefficient.
Furthermore, given the relatively high tariffs of diagnostic
imaging examinations, recovering slack times may lead to
increase the number of service provided, which results in
a significant impact on turnover and service levels. On the
other hand, other benefits could be achieved in terms of
the reduction of overtimes and used coils.
Ivanov et al. (2009) applied the classical scheduling model
with sequence dependent setup times to Magnetic
Resonance Imaging scan processing, in order to minimize
the total production time; however, their approach cannot
be directly used in the dynamic version of the problem:
their two-stages model – in which, in a first stage all the
patients requests are collected and then, in the second
stage, the optimal scheduling is announced later on – is
not compatible with the need of directly fixing the
appointments during the patient request. This paper focus
specifically on this issue, proposing a re-design of the
appointment system for a radiology clinic in a dynamic
context. Stochastic inconveniences such as walks-in, no-
shows and emergencies are not considered.
3. Problem description
Here the outpatient appointment scheduling problem for
the diagnostic imaging department of a radiology clinics is
analyzed. More specifically, the problem concerns the on-
line assignment of appointments to outpatient requests
for specific diagnostic imaging scans.
As previously stated, this problem differs from the
traditional manufacturing two-stages scheduling problem:
the scheduling must be performed real-time and
appointment needs to be confirmed immediately when
requested by the patients; the scheduling, however, must
be appropriate so that patients do not suffer
inconveniences from long waiting times or delays in their
processing on the day scheduled for their examination.
Workarounds such as implementing a recall process in the
call-center in order to propose appointment shifting
cannot be considered because usually the volumes of daily
processed patients is too high.
In order to properly assess the benefits arising from
improvements on the service process it is at first necessary
to identify a set of performance indicators which are
obviously consistent with the strategic priorities of the
company (service cost, service level, machine availability,
etc.). In the specific case of a radiology centre that
performs diagnostic imaging examinations, the number of
patients processed every day (i.e. productivity, given a
fixed number of working hours) may be taken into
account as an indicator of service level; note that this
indicator is directly linked to Company’s revenue. All
examinations that have been scheduled must be
performed and it is easy to understand how the
accumulation of inefficiencies during the day leads to
overtime, which is another element that should be
monitored and minimized. With these goals in the
reengineering of diagnostic imaging service provision, the
minimization of set-ups of diagnostic machines results as
one of the most effective intervention in order to aim to
an increase in efficiency.
Examinations are performed on parallel machines, each
with a number of coils to be used on different areas of the
body. Now, if the number of machines was equal to the
number of examination types it would be trivial to
dedicate each machine to an examination of an area of the
body, effectively nullifying the number of coil change.
Real-life experiences demonstrate that this would be an
inappropriate solution, unless the capacity of the service
provider was infinite: there would be an evident problem
of load balancing; the number of examinations usually
differs from type to type and this would inevitably lead to
an overload for some machines and excess of idle time for
others. Therefore it is correct to accept the practice that,
over the working day, all machines have to run any kind
of examination, in order to balance the workload on
diagnostic equipment and to avoid blockage situations due
to unexpected downtime.
4. Solution approach
As specified by Cayirli & Veral (2003), the design of an
appointment scheduling system should deal with three
fundamental aspects:
the appointment rule;
the patient classification;
Proceedings of the Conference on “Sustainable Development: Industrial Practice, Education & Research”, Monopoli, Bari (Italy), 14-18 September 2010
D. Falsini, A. Perugia, M.M. Schiraldi (2010)
3. the adjustments for reducing the disruptive
effects of uncertainty.
4.1. Appointment rule
An appointment rule is the combination of three
variables: block-size (ni), that is the number of outpatients
scheduled to the i-th block; begin-block (n1), that is the
number of outpatients scheduled to the first block;
appointment interval (ai), that is the time interval between
two successive appointments.
Considering that issues regarding walk-ins, no-shows and
emergency patients are not in the scope of the present
paper, this appointment system does not comply with
overbooking or safety capacity/time, differently from the
approach in Tattoni et al., 2009. Block-size and begin-
block can be easily fixed equal to the number of parallel
machines (servers) that can perform diagnostic imaging
scans.
The appointment interval can be determined considering
the partition of working day in a number of slots,
eventually derived from the analysis of historical data and
relative to the number of examinations of each type that
are usually performed on every type of diagnostic
machine. Variability of examinations duration implies a
variability in appointment interval.
Referring to the classification provided by Cayirli & Veral
(2003) the appointment rule taken into account in the
present paper is called multiple-block/variable-interval rule
without an initial block. According to literature review, this
kind of rule seems not to have been applied in real cases
up to now.
4.2. Patient classification
In an appointment system, patient classification plays a
crucial role especially when appointments have to be
assigned at booking time. Having a preliminary
classification of patient in a certain number of groups,
these are in turn assigned to pre-marked slots, which helps
to increase performance of a real-time scheduling.
With regards to applications in radiology departments, a
relevant work was realized by Walter in 1973: he divided
patient with similar exam times into different session.
However this rules goes exaclty in the opposite direction
of minimizing the total setup time. Thus, for the proposed
appointment system, a better patient partitioning seems to
be the one based on grouping together patients with
similar exam type (i.e. that request the same coil for the
scan).
At this point, taking into account that daily each machine
has to perform all the typologies of exams, it is possible to
preemptively assign to each machine a fixed number of
macro-slots, equal to the number of patient groups (i.e.
different exam type). The number of slots per each
macro-slots can be determined on the basis of statistical
data and is considered the same for all the machines.
4.3. Adjustments for reducing the disruptive effects of uncertainty
No-shows, walk-ins, urgent patients and emergencies are
typical inconveniences in a health-care system. However,
as previously stated, these elements are not considered in
the present paper: the real-time nature of the dynamic
problem already forces the system to cope with
uncertainty. This derives from the unpredictability of the
request type, of the arrival time and of the number of
requests during the day.
An heuristic procedure is proposed for the booking
process, in order to better manage the described
uncertainty and improve appointment system
performance.
4.4. An heuristic procedure for appointment scheduling process
Being k the number of outpatient groups (corresponding
to the number of macro-slots per each machine) and m
the number of diagnostic imaging parallel machines, ij
S
is defined as the availability slots previously assigned to
the machine i, i = 1,…, m, for the execution of the exam
type j, j = 1,…, n, in a certain day. ij
S is comprised
between 0 and j
S , that is the number of slots assigned
to each machine per each day for performing the exam
type j, j = 1,..., n. All these slots are available at the
moment the daily planning starts.
The following procedure is proposed for a real-time
appointment planning in a diagnostic imaging department,
per each day in agenda:
Step 0 Set ij
S = j
S per each i = 1,…, m, j = 1,…, n.
Step 1 An incoming call of type j occurs.
Step 2 If there is at least a machine i for which ij
S > 0
then go to step 3, else go to step 4
Step 3 Among the set of machines with available slots for
exams of type j, assign the incoming call to the
machine with the minimum number of assigned
slots for exams of type j (if two or more machines
have the same available capacity, choose the
machine on the basis of a previously defined
numerical order).
Set ij
S = ij
S – 1 and go to step 5.
Step 4 If there are any machines with an available slot
immediately following the macro-slot dedicated to
exam of type j, among them assign the call to the
machine with the minimum number of total
assigned slots (if two or more machines have the
same available capacity, choose the machine on the
basis of a previously defined numerical order). Set
1
ij
S = 1
ij
S – 1.
Else if there are any machines with an available
slot immediately preceding the macro-slot
Proceedings of the Conference on “Sustainable Development: Industrial Practice, Education & Research”, Monopoli, Bari (Italy), 14-18 September 2010
D. Falsini, A. Perugia, M.M. Schiraldi (2010)
4. dedicated to exam of type j, proceed analogously
to the previous way. Set 1
ij
S = 1
ij
S – 1.
Else if there are other available slots, assign the call
randomly to one of them. Set hk
S = hk
S – 1,
where h is the selected machine and k is the exam
type assigned to the selected slot
Else Stop and check the availability in an another
day in agenda, following the same procedure.
Step 5 If there are still any machines with available
capacity, go back to step 1. Else Stop and carry on
the procedure for the next day in agenda.
4. The Case of Alliance Medical
The proposed procedure was validated with the data of
Alliance Medical case, a primary multinational company
leader in European and Italian sector of diagnostic
imaging services. The present case study regards the
activities of one of the most important diagnostic centres
of Alliance Medical in Italy, the “Istituto Andrea
Cisalpino”. The centre, located in Terontola (Arezzo,
Tuscany), started the operations 20 years ago and at the
moment employs nearly 50 people. Its service area
includes Tuscany, Umbria, Marche and Latium regions.
The centre perform diagnostic imaging services trough
MRI and CAT machines, servicing private and local health
authority patients. The case study focuses on the former
type of service, for which four machine are dedicated:
three of them can be considered as parallel machines, and
the last is excluded from the analysis due to its particularly
long processing times (it is a specific machine only used
for claustrophobic patients). The centre operates 15 hours
per day from Monday to Friday and 11 hours on Saturday,
performing on average about 70.000 MRI exams per year.
The following table shows the number of exams for each
type with and without the use of Contrast Agents (CA),
referring to the period coming from April 2008 to March
2009. CA utilization requires the presence of an
anesthetist.
According to the proposed approach, exams (and relative
patient requests) were classified in five typologies, on the
basis of the type of used coil. The following table shows
this classification, with the indication of the percentage on
the total of the exams included in each type.
Type Exams Perc.
1 Neck angiography, skull angiography,
cholangiography, neck, neck + CA,
skull, skull + CA, facial skeleton,
facial skeleton + CA, hypophysis,
hypophysis + CA, orbits, orbits +
CA, parotid + CA.
18%
2 Temporomandibular joint, cervical,
cervical + CA, latissimus dorsi,
latissimus dorsi + CA, lumbosacral,
41%
lumbosacral+CA,
3 Ankle, thigh, thigh + CA, leg, leg +
CA, knee, knee + CA, hand, hand +
CA, foot, foot + CA, wrist, wrist +
CA.
24%
4 Arm, elbow, shoulder, shoulder +
CA
10%
5 Superior abdomen angiography,
superior abdomen, superior
abdomen + CA, pelvis, pelvis + CA,
bilateral breast, bilateral breast + CA,
mono-lateral breast, thorax, thorax +
CA.
7%
Table 1: exam classification
The values of exams and set-up time were assumed equal
to the average ones, that are respectively 18 and 2
minutes. This means a theoretical capacity of 50 exams
per day from Monday to Friday and about 36 exams per
day on Saturday.
According to the proposed approach and described , a 50-
slots daily agenda was considered for each machine. Slots
were divided on the basis of the percentage of exams of
each type on the total. Thus, from Monday to Friday, 9
slots were dedicated to type 1, 20 to type 2, 12 to type 3, 5
to type 4 and 4 to type 5. Slots were assigned on Saturday
analogously.
Applying the proposed heuristic procedure to real data
regarding incoming calls at Medical Alliance diagnostic
centre from April 2008 to March 2009, it resulted that, as
an average, only the 4% of the total calls were not directly
assigned to dedicated slots (step 3) and had to be
reallocated (step 4). The average number of set-up per day
was equal to 14, against the 60 setups per day that were
calculated on the basis of the real agenda, planned by
Medical Alliance dedicated operator.
A reduction of 46 setups is equivalent to an increase in
available capacity of about 1,5 hours, that means 5 more
exams per day. The increase in available capacity
corresponds to a potential increase in the annual turnover
of about 170.000 euro.
5. Conclusion and Further Research
Literature on Outpatient Appointment Scheduling
Problem seems to be very widen as far as it concerns
results deriving from managing uncertain events, such as
no-shows, walk-ins and emergency patients through
Operations Management techniques. However, all
approaches seem to show an important gap regarding
setup time and cost consideration. Focusing on the
particular case of diagnostic imaging services in radiology
centers, setups significantly impact on total production
time, generating a reduction of clinic capacity.
The present work succeeded in approaching the problem
from this original point of view, proposing a quick and
Proceedings of the Conference on “Sustainable Development: Industrial Practice, Education & Research”, Monopoli, Bari (Italy), 14-18 September 2010
D. Falsini, A. Perugia, M.M. Schiraldi (2010)
5. flexible heuristic procedure for a real-time booking
appointments in a diagnostic imaging center.
The model validation on the real case of Alliance Medical
has led to the quantification of the benefits that would
come from a correct scheduling in booking process. This
has proved the opportunity to dramatically reduce the
number of coil changes and increase available capacity.
Further research should concentrate on integrating this
new kind of approach, that evidently can be refined for
better performance, with the state-of-the-art approaches
of safety capacity management and overbooking. For
instance, the experience in Alliance Medical showed
evident opportunities on improving the designed
appointment system with an overbooking and recall
system. This could further improve the performance of
proposed model in terms of efficiency and service level.
_____________________________________
Acknowledgments: the authors wish to thank dr. Vito
Cantisani, staff attending of the Diagnostic Imaging Operative
Unit of the “Umberto I” Hospital, “Sapienza” University of
Rome, President of the Section of Contrast Agents in Echography
in the Italian Medical and Biological Ultrasound Society, for
stimulating discussion and for his suggestions.
References
Bailey, N. (1952). A study of queues and appointment
systems in hospital outpatient. Journal of the Royal Statistical
Society. Series B (Methodological) , 14 (2), 185-199.
Butler, T., Leong, G., & Everett, L. (1996). The
operations management role in hospital strategic planning.
Journal of Operations Management , 14, 137-156.
Cayirli, T., & Veral, E. (2003). Outpatient Scheduling in
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