scholarly journals Three Essays in Health Economics

2021 ◽  
Author(s):  
◽  
Pranay Panta
Keyword(s):  
New Zealand ◽  
Administrative Data ◽  
Waiting Time ◽  
Elective Surgery ◽  
Waiting Times ◽  
Demand Forecasting ◽  
Clinical Severity ◽  
Publicly Funded ◽  
Model Based ◽  
Asian Patients

<p><b>This dissertation contains three essays on economic analysis of the New Zealand healthcare market particularly relating to publicly funded elective surgeries and the pharmaceutical industry.</b></p> <p>In Chapter 1, using administrative data on all patients booked for publicly funded elective surgery in 2014, we investigate whether ethnic disparities exist in waiting time for elective surgeries in New Zealand. Unlike existing studies on the topic, we extensively control for sample selection bias, clinical severity, and regional factors affecting resource supply and healthcare utilization. We find evidence of Maori, Pacific, and Asian patients waiting longer for elective surgery when com-pared to New Zealand Europeans. Our results indicate that Maori wait on average 3 percent longer for elective surgery, however, they are not consistent across all model specifications. We find that Pacific and Asian patients wait 8 - 9 percent longer for elective surgeries compared to NZE. Our results are considerably stronger for Pacific and Asian compared to Maori across all model specifications. Furthermore, these differences are pervasive across several surgical categories and are as high as 22 percent across angiography procedures. We also find evidence that waiting time in New Zealand is geographically inequitable. Although an increasing number of empirical studies find evidence of inequities in waiting time socioeconomic status, our analysis reveals no such effects once regional differences affecting waiting time are controlled for.</p> <p>In Chapter 2, using administrative data on patients who have been booked and treated for publicly funded elective surgeries over 2011-2015, we investigate how supply of and demand for elective surgeries respond to waiting times in New Zealand. Unlike existing studies, we endogenize waiting times in a system of three equations, which nests the conventional demand-and-supply model. Each structural equation is over-identified by excluded instruments, and the exclusion restriction is justified on a priori grounds while over-identifying restrictions are tested. The analysis finds that, in the case of New Zealand, the demand for elective surgery is inelastic, whereas supply is elastic. From a policy perspective, the results suggest that a long-term increase in supply will lead to a permanent reduction in waiting times and thus improving timely access to care.</p> <p>In Chapter 3, I develop an intuitive model based on the traditional newsvendor framework that enables pharmaceutical wholesalers to efficiently forecast demand and reduce inventory while adjusting for required customer service level (CSL) targets. First, using historical demand data for a major pharmaceutical wholesaler in New Zealand, I present several demand forecasting models and assess their performance in predicting demand, based on several forecast error metrics. I find that artificial neural network (ANN) models are generally stable for demand forecasting and sometimes outperform traditional demand forecasting methods. Second, using the demand fore-cast as an input, I derive a periodic review inventory model based on the newsvendor inventory framework. Numerical analysis of the proposed model shows that, compared to current practice, the model enables wholesalers to significantly reduce both inventory levels and ordering, while maintaining a very high non-stock-out probability constraint and CSL target close to 100 percent. I conduct separate analyses for both government subsidized low value-high demand drug and non-subsidized low demand-high value drug and find the results to be consistent across both types of drugs.</p>

scholarly journals Three Essays in Health Economics

2021 ◽  
Author(s):  
◽  
Pranay Panta
Keyword(s):  
New Zealand ◽  
Administrative Data ◽  
Waiting Time ◽  
Elective Surgery ◽  
Waiting Times ◽  
Demand Forecasting ◽  
Clinical Severity ◽  
Publicly Funded ◽  
Model Based ◽  
Asian Patients

<p><b>This dissertation contains three essays on economic analysis of the New Zealand healthcare market particularly relating to publicly funded elective surgeries and the pharmaceutical industry.</b></p> <p>In Chapter 1, using administrative data on all patients booked for publicly funded elective surgery in 2014, we investigate whether ethnic disparities exist in waiting time for elective surgeries in New Zealand. Unlike existing studies on the topic, we extensively control for sample selection bias, clinical severity, and regional factors affecting resource supply and healthcare utilization. We find evidence of Maori, Pacific, and Asian patients waiting longer for elective surgery when com-pared to New Zealand Europeans. Our results indicate that Maori wait on average 3 percent longer for elective surgery, however, they are not consistent across all model specifications. We find that Pacific and Asian patients wait 8 - 9 percent longer for elective surgeries compared to NZE. Our results are considerably stronger for Pacific and Asian compared to Maori across all model specifications. Furthermore, these differences are pervasive across several surgical categories and are as high as 22 percent across angiography procedures. We also find evidence that waiting time in New Zealand is geographically inequitable. Although an increasing number of empirical studies find evidence of inequities in waiting time socioeconomic status, our analysis reveals no such effects once regional differences affecting waiting time are controlled for.</p> <p>In Chapter 2, using administrative data on patients who have been booked and treated for publicly funded elective surgeries over 2011-2015, we investigate how supply of and demand for elective surgeries respond to waiting times in New Zealand. Unlike existing studies, we endogenize waiting times in a system of three equations, which nests the conventional demand-and-supply model. Each structural equation is over-identified by excluded instruments, and the exclusion restriction is justified on a priori grounds while over-identifying restrictions are tested. The analysis finds that, in the case of New Zealand, the demand for elective surgery is inelastic, whereas supply is elastic. From a policy perspective, the results suggest that a long-term increase in supply will lead to a permanent reduction in waiting times and thus improving timely access to care.</p> <p>In Chapter 3, I develop an intuitive model based on the traditional newsvendor framework that enables pharmaceutical wholesalers to efficiently forecast demand and reduce inventory while adjusting for required customer service level (CSL) targets. First, using historical demand data for a major pharmaceutical wholesaler in New Zealand, I present several demand forecasting models and assess their performance in predicting demand, based on several forecast error metrics. I find that artificial neural network (ANN) models are generally stable for demand forecasting and sometimes outperform traditional demand forecasting methods. Second, using the demand fore-cast as an input, I derive a periodic review inventory model based on the newsvendor inventory framework. Numerical analysis of the proposed model shows that, compared to current practice, the model enables wholesalers to significantly reduce both inventory levels and ordering, while maintaining a very high non-stock-out probability constraint and CSL target close to 100 percent. I conduct separate analyses for both government subsidized low value-high demand drug and non-subsidized low demand-high value drug and find the results to be consistent across both types of drugs.</p>

HOW TO REPORT AND MONITOR THE PERFORMANCE OF WAITING LIST MANAGEMENT

2002 ◽  
Vol 18 (3) ◽  
pp. 611-618
Author(s):  
Markus Torkki ◽  
Miika Linna ◽  
Seppo Seitsalo ◽  
Pekka Paavolainen
Keyword(s):  
Hallux Valgus ◽  
Waiting Time ◽  
Varicose Vein ◽  
Elective Surgery ◽  
Waiting Times ◽  
Waiting List ◽  
Mean Waiting Time ◽  
Number Of Patients ◽  
Potential Problems ◽  
Queue Discipline

Objectives: Potential problems concerning waiting list management are often monitored using mean waiting times based on empirical samples. However, the appropriateness of mean waiting time as an indicator of access can be questioned if a waiting list is not managed well, e.g., if the queue discipline is violated. This study was performed to find out about the queue discipline in waiting lists for elective surgery to reveal potential discrepancies in waiting list management. Methods: There were 1,774 waiting list patients for hallux valgus or varicose vein surgery or sterilization. The waiting time distributions of patients receiving surgery and of patients still waiting for an operation are presented in column charts. The charts are compared with two model charts. One model chart presents a high queue discipline (first in—first out) and another a poor queue discipline (random) queue. Results: There were significant differences in waiting list management across hospitals and patient categories. Examples of a poor queue discipline were found in queues for hallux valgus and varicose vein operations. Conclusions: A routine waiting list reporting should be used to guarantee the quality of waiting list management and to pinpoint potential problems in access. It is important to monitor not only the number of patients in the waiting list but also the queue discipline and the balance between demand and supply of surgical services. The purpose for this type of reporting is to ensure that the priority setting made at health policy level also works in practise.

scholarly journals Analysis of waiting time for elective surgical procedures in neurosurgery department at a tertiary care teaching hospital in NCT, India

2017 ◽  
Vol 5 (10) ◽  
pp. 4538
Author(s):  
Sapna Ramani Sardana ◽  
Shakti Kumar Gupta ◽  
D. K. Sharma ◽  
Aarti Vij ◽  
S. S. Kale
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Tertiary Care ◽  
Operating Time ◽  
Healthcare Providers ◽  
Publicly Funded ◽  
Clinical Criteria ◽  
Elective Surgical Procedures ◽  
Patient Prioritization ◽  
A Prospective Study

Background: Reported increases in waiting times for publicly-funded elective surgeries have intensified the need to decrease wait by healthcare providers and hence the study.Methods: Descriptive study done in neurosurgery department, to ascertain waiting times for its elective surgeries, included a retrospective analysis of admitted post-surgical patients and a prospective study using interviews with relevant stakeholders to do a process mapping.Results: Median time from decision of surgery to actual date of surgery was found to be 110.5 days. It was calculated that for optimum utilization of present available OTs, 19 extra beds are required and to address the existing load of patients waiting for their respective surgeries there is a need of 63 additional beds with 2 additional OTs functioning per day.Conclusions: The most common cause of waiting time was unavailability of vacant beds due to mismatch in demand-supply. The reason for postponement of surgery after admission was found to be lack of availability of theatre time followed by patient not being fit for surgery. Shortage of operating time was due to delayed start of operation theatre time. The study recommends improving admission process, restricting OPD time, standardized patient prioritization depending on relevant clinical criteria.

scholarly journals Waiting time information services: how well do different statistics forecast a patient's wait?

2002 ◽  
Vol 25 (6) ◽  
pp. 75 ◽  
Author(s):  
David A. Cromwell ◽  
David A. Griffths
Keyword(s):  
Waiting Time ◽  
Public Hospital ◽  
Elective Surgery ◽  
Waiting Times ◽  
Information Services ◽  
Waiting List ◽  
Web Based ◽  
Time Information ◽  
Sydney Australia ◽  
Patient Waiting

This study investigates how accurately the waiting times of patients about to join a waiting list are predicted by the types of statistics disseminated via web-based waiting time information services. Data were collected at a public hospital in Sydney, Australia, on elective surgery activity and waiting list behaviour from July 1995 to June 1998.The data covered 46 surgeons in 10 surgical specialties. The accuracy of the tested statistics varied greatly, being affected more by the characteristics and behaviour of a surgeon's waiting list than by how the statistics were derived. For those surgeons whose waiting times were often over six months, commonly used statistics can be very poor at forecasting patient waiting times.

scholarly journals P-EGS14 From Gallstone Disease to Cholecystectomy: An audit looking at the waiting times for cholecystectomy in a small District General Hospital

2021 ◽  
Vol 108 (Supplement_9) ◽  
Author(s):  
Jacob Mewse ◽  
Virginia Ledda ◽  
Ellie Connor ◽  
Peter Frank Mason
Keyword(s):  
Laparoscopic Cholecystectomy ◽  
Waiting Time ◽  
Negative Impact ◽  
Elective Surgery ◽  
Waiting Times ◽  
Gallstone Disease ◽  
Gastrointestinal Surgery ◽  
Emergency Operation ◽  
Biliary Colic ◽  
Emergency General Surgery

Abstract Background Gallstone-related disease accounts for a third of emergency general surgery admissions and referrals. The average waiting time for acute gallstone presentations to laparoscopic cholecystectomy is about 7 days in England. This audit aims to identify emergency admissions and compare local management to the Association of Upper Gastrointestinal Surgery of Great Britain and Ireland (AUGIS) guidelines standards with a focus on waiting times for laparoscopic cholecystectomy (LC). Where AUGIS standards were not met, number of re-admissions and complications were identified. A cost analysis was also completed looking at the overall costs of delayed treatment. Methods We identified all patients admitted as an emergency between September 2019 and September 2020 with gallstone-related pathology. Patients not referred to the surgical team, with negative Ultrasound Scans (USS) or known HPB malignancy were excluded. The patients were divided into a pre- COVID -19 and during COVID-19 category (respectively before and after March 2020), to identify whether the cancellation to non- urgent elective surgery (due to COVID-19) had caused further delays or complications. Each patient’s management was compared to AUGIS guidelines depending on their diagnosis at presentation (biliary colic, cholecystitis, cholangitis, gallstone-related pancreatitis), focusing on the timing between presentation and LC. Results A total of 99 patients were identified. Of the patients presenting with biliary colic (n = 9 pre-COVID, n = 5 during COVID), none underwent LC within 72 hours from presentation as recommended by AUGIS. Of the patients presenting with cholecystitis (n = 20 pre-COVID and n = 16 during COVID), none had LC within the recommended 72 hours. 5 patients in each COVID group had LC, with a significantly longer waiting time compared to the pre-COVID group. Re-admissions and complications were similar for the cholecystitis patients in both COVID groups. In the gallstone-related pancreatitis group, only 1 patient underwent LC within the recommended 2 weeks. Conclusions This audit showed that locally we are failing to meet AUGIS guidelines for LC within 72 hrs, 2 weeks or 6 weeks both pre and during COVID. This has caused re-admissions of patients with cholecystitis, pancreatitis and perforated gallbladders. Factors that cause delay are limited access to USS, limited staff and theatre availability. To improve outcomes, it is necessary to implement a hot gallbladder service with dedicated theatre slots. A change in the overall perception of LC is also needed: this is should be considered an emergency operation as its delay has a significant negative impact on patients’ outcomes.

scholarly journals How a government strategy of active performance management has influenced District Health Boards' delivery of publicly funded elective services

2021 ◽  
Author(s):  
◽  
Corinne Gower
Keyword(s):  
New Zealand ◽  
Health System ◽  
Performance Management ◽  
Management Practices ◽  
Waiting Times ◽  
Institutional Logics ◽  
Performance Model ◽  
Publicly Funded ◽  
District Health ◽  
The Government

<p>New Zealand, like most countries, is limited in the amount of publicly funded non-emergency (elective) medical and surgical services that it can provide to its population. In 2000, the ‘Reduced Waiting Times for Public Hospital Elective Services: Government Strategy’ outlined the systematic approach New Zealand would take with elective service waiting time management. The approach included the Government’s use of active performance management, namely, the setting of accountability and clear performance expectations; the ongoing monitoring, measurement, and reporting of performance; and the management of system change using facilitative networks. Since 2001, District Health Boards (DHBs) have been accountable for implementing government electives policy.  The thesis examines how the Government’s strategic use of active performance management has influenced DHBs in their delivery of publicly funded elective services. In order to better understand and evaluate elective service delivery outcomes, (in particular that equity of service access has been achieved), and to evaluate the improvement of health service decision-making, there is a need to understand how decision-makers at the macro, meso, and micro levels of the health system are influenced by performance management practices. The research has examined influence from a multi-stakeholder and performance management system perspective.  Methods include interviews with DHB and government stakeholders, review of Nationwide Service Framework and government policy documents, and the analysis of ten years of publicly available DHB performance reports to understand compliance patterns. The research narrative synthesised from study data is interpreted using a blend of neo-institutional meta-theories and institutional logics.  The research found the government uses two performance models: an administrative control performance model which relies on information collection, control logic and performance feedback, and a professional services performance model which relies on the management of change using networks. Each DHB has established organisational practices in response to active performance management which are largely concerned with the promotion of DHB legitimacy. The influence of the two performance models and the interests of multiple DHB stakeholders is explained by considering the interplay between fifteen organisational practices, the government institutional logics of Active Performance Management and Service Improvement and the organisational field-level institutional logics of Population Health Management, Service Management, Medical Professional, and Integrated Care.  Overall, the research concludes that ‘Active Performance Management’ has made a significant contribution reducing public hospital waiting times. It focuses the attention of DHB service managers who are concerned with mitigating risks of financial penalties and loss of leadership legitimacy. However, there are different ‘supply’ decision-making agendas and criteria operating at different levels of the health system. In particular, it is difficult to lock in appropriate accountability arrangements with primary care, and the strategic use of active performance management has led to tensions between DHB management and hospital specialists. If New Zealand wishes to expand its evaluation of health service delivery to take into account outcomes measures, there needs to be a better understanding of the aggregated impact of performance management practices on the health system.</p>

scholarly journals How a government strategy of active performance management has influenced District Health Boards' delivery of publicly funded elective services

2021 ◽  
Author(s):  
◽  
Corinne Gower
Keyword(s):  
New Zealand ◽  
Health System ◽  
Performance Management ◽  
Management Practices ◽  
Waiting Times ◽  
Institutional Logics ◽  
Performance Model ◽  
Publicly Funded ◽  
District Health ◽  
The Government

<p>New Zealand, like most countries, is limited in the amount of publicly funded non-emergency (elective) medical and surgical services that it can provide to its population. In 2000, the ‘Reduced Waiting Times for Public Hospital Elective Services: Government Strategy’ outlined the systematic approach New Zealand would take with elective service waiting time management. The approach included the Government’s use of active performance management, namely, the setting of accountability and clear performance expectations; the ongoing monitoring, measurement, and reporting of performance; and the management of system change using facilitative networks. Since 2001, District Health Boards (DHBs) have been accountable for implementing government electives policy.  The thesis examines how the Government’s strategic use of active performance management has influenced DHBs in their delivery of publicly funded elective services. In order to better understand and evaluate elective service delivery outcomes, (in particular that equity of service access has been achieved), and to evaluate the improvement of health service decision-making, there is a need to understand how decision-makers at the macro, meso, and micro levels of the health system are influenced by performance management practices. The research has examined influence from a multi-stakeholder and performance management system perspective.  Methods include interviews with DHB and government stakeholders, review of Nationwide Service Framework and government policy documents, and the analysis of ten years of publicly available DHB performance reports to understand compliance patterns. The research narrative synthesised from study data is interpreted using a blend of neo-institutional meta-theories and institutional logics.  The research found the government uses two performance models: an administrative control performance model which relies on information collection, control logic and performance feedback, and a professional services performance model which relies on the management of change using networks. Each DHB has established organisational practices in response to active performance management which are largely concerned with the promotion of DHB legitimacy. The influence of the two performance models and the interests of multiple DHB stakeholders is explained by considering the interplay between fifteen organisational practices, the government institutional logics of Active Performance Management and Service Improvement and the organisational field-level institutional logics of Population Health Management, Service Management, Medical Professional, and Integrated Care.  Overall, the research concludes that ‘Active Performance Management’ has made a significant contribution reducing public hospital waiting times. It focuses the attention of DHB service managers who are concerned with mitigating risks of financial penalties and loss of leadership legitimacy. However, there are different ‘supply’ decision-making agendas and criteria operating at different levels of the health system. In particular, it is difficult to lock in appropriate accountability arrangements with primary care, and the strategic use of active performance management has led to tensions between DHB management and hospital specialists. If New Zealand wishes to expand its evaluation of health service delivery to take into account outcomes measures, there needs to be a better understanding of the aggregated impact of performance management practices on the health system.</p>

Waiting time information services: what are the implications of waiting list behaviour for their design?

2002 ◽  
Vol 25 (4) ◽  
pp. 40 ◽  
Author(s):  
David Cromwell ◽  
David Griffiths
Keyword(s):  
Waiting Time ◽  
Elective Surgery ◽  
Waiting Times ◽  
Waiting Lists ◽  
Information Services ◽  
Waiting List ◽  
Surgical Units ◽  
Time Information ◽  
Over Time ◽  
Sydney Australia

In some countries, patients requiring elective surgery can access comparative waiting time information for various surgical units. What someone can deduce from this information will depend upon how the statistics are derived, and how waiting lists behave. However, empirical analyses of waiting list behaviour are scarce. This study analysed three years of waiting list data collected at one hospital in Sydney, Australia. The results highlight various issues that raise questions about using particular waiting time statistics to make inferences about patient waiting times. In particular, the results highlight the considerable variation in behaviour that can exist between surgeons in the same specialty, and that can occur over time.

COST-UTILITY OF WAITING TIME IN TOTAL JOINT REPLACEMENTS: A RANDOMIZED CLINICAL TRIAL

2013 ◽  
Vol 29 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Ulla Tuominen ◽  
Harri Sintonen ◽  
Pasi Aronen ◽  
Johanna Hirvonen ◽  
Seppo Seitsalo ◽  
...  
Keyword(s):  
Waiting Time ◽  
Elective Surgery ◽  
Waiting Times ◽  
High Volume ◽  
Developed Countries ◽  
The United States ◽  
Cost Utility ◽  
Joint Replacements ◽  
Total Joint Replacements ◽  
Mean Waiting Time

In many Western countries, long waiting times for elective surgery are a concern. Major joint replacement is an example of a type of surgery with a high volume of demand and relatively long waiting periods for patients. As populations get older, the prevalence of slowly progressive diseases, such as osteoarthritis (OA) in hip and knee joints, is increasing. Over three-quarters of a million total hip and knee replacement surgeries are done in the United States annually (1). Furthermore, according to March et al. (1997), the costs of OA have been estimated to account for up to 1–2.5 percent of the Gross National Product (GNP) in several developed countries (2). In Finland, a total of 11,104 total joint replacements (TJRs) were performed in 2004 (hip 6,600 and knee 5,905), with the median waiting time of 181 days for the surgery (hip 153 and knee 209 days). Until 2007, the number of TJRs was 17,334 (hip 7,698 and knee 9,636), with a median waiting time of 120 and 142 days, respectively (3;4). The mean waiting time for elective surgical procedures is approximately 3 months in several countries and the maximum waiting times can stretch into years.

scholarly journals Waiting Time as an Indicator for Health Services Under Strain: A Narrative Review

2020 ◽  
Vol 57 ◽  
pp. 004695802091030
Author(s):  
Daniel McIntyre ◽  
Clara K. Chow
Keyword(s):  
Health Care ◽  
Socioeconomic Status ◽  
Health Services ◽  
Waiting Time ◽  
Health Care Access ◽  
Elective Surgery ◽  
Waiting Times ◽  
Developed Countries ◽  
Policy Implications ◽  
Emergency Department Care

As pressure increases on public health systems globally, a potential consequence is that this is transferred to patients in the form of longer waiting times to receive care. In this review, we overview what waiting for health care encompasses, its measurement, and the data available in terms of trends and comparability. We also discuss whether waiting time is equally distributed according to socioeconomic status. Finally, we discuss the policy implications and potential approaches to addressing the burden of waiting time. Waiting time for elective surgery and emergency department care is the best described type of waiting time, and it either increases or remains unchanged across multiple developed countries. There are many challenges in drawing direct comparisons internationally, as definitions for these types of waiting times vary. There are less data on waiting time from other settings, but existing data suggest waiting time presents a significant barrier to health care access for a range of health services. There is also evidence that waiting time is unequally distributed to those of lower socioeconomic status, although this may be improving in some countries. Further work to better clarify definitions, identify driving factors, and understand hidden waiting times and identify opportunities for reducing waiting time or better using waiting time could improve health outcomes of our health services.

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