Do maximum waiting times guarantees change clinical priorities for elective treatment? Evidence from Scotland

Codesign of health technology interventions to support best-practice perioperative care and surgical waitlist management

OBJECTIVES

This project aimed to determine where health technology can support best-practice perioperative care for patients waiting for surgery.

METHODS

An exploratory codesign process used personas and journey mapping in three interprofessional workshops to identify key challenges in perioperative care across four health districts in Sydney, Australia. Through participatory methodology, the research inquiry directly involved perioperative clinicians. In three facilitated workshops, clinician and patient participants codesigned potential digital interventions to support perioperative pathways. Workshop output was coded and thematically analysed, using design principles.

RESULTS

Codesign workshops, involving 51 participants, were conducted October to November 2022. Participants designed seven patient personas, with consumer representatives confirming acceptability and diversity. Interprofessional team members and consumers mapped key clinical moments, feelings and barriers for each persona during a hypothetical perioperative journey. Six key themes were identified: 'preventative care', 'personalised care', 'integrated communication', 'shared decision-making', 'care transitions' and 'partnership'. Twenty potential solutions were proposed, with top priorities a digital dashboard and virtual care coordination.

DISCUSSION

Our findings emphasise the importance of interprofessional collaboration, patient and family engagement and supporting health technology infrastructure. Through user-based codesign, participants identified potential opportunities where health technology could improve system efficiencies and enhance care quality for patients waiting for surgical procedures. The codesign approach embedded users in the development of locally-driven, contextually oriented policies to address current perioperative service challenges, such as prolonged waiting times and care fragmentation.

CONCLUSION

Health technology innovation provides opportunities to improve perioperative care and integrate clinical information. Future research will prototype priority solutions for further implementation and evaluation.

Intended and unintended consequences of strategies to meet performance benchmarks for rheumatologist referrals in a centralized intake system

RATIONALE

Timely assessment of a chronic condition is critical to prevent long-term irreversible consequences. Patients with inflammatory arthritis (IA) symptoms require diagnosis by a rheumatologist and intervention initiation to minimize potential joint damage. With limited rheumatologist capacity, meeting urgency wait time benchmarks can be challenging. We investigate the impact of the maximum wait time guarantee (MWTG) policy and referral volume changes in a rheumatology central intake (CI) system on meeting this challenge.

METHODS

We applied a system simulation approach to model a high-volume CI rheumatology clinic. Model parameters were based on the referral and triage data from the CI and clinic appointment data. We compare the wait time performance of the current distribution policy MWTG and when referral volumes change.

RESULTS

The MWTG policy ensures 100% of new patients see a rheumatologist within their urgency wait time benchmark. However, the average wait time for new patients increased by 51% (178-269 days). A 10% decrease in referrals resulted in a 76% decrease on average wait times (178-43 days) for new patients and an increase in the number of patients seen by a rheumatologist within 1 year of the initial visit.

CONCLUSION

An MWTG policy can result in intended and unintended consequences-ensuring that all patients meet the wait time benchmarks but increasing wait times overall. Relatively small changes in referral volume significantly impact wait times. These relationships can assist clinic managers and policymakers decide on the best approach to manage referrals for better system performance.

Better performance for right-skewed data using an alternative gamma model

BACKGROUND

The Maximum Likelihood Estimator (MLE) for parameters of the gamma distribution is commonly used to estimate models of right-skewed variables such as costs, hospital length of stay, and appointment wait times in Economics and Healthcare research. The common specification for this estimator assumes the variance is proportional to the square of the mean, which underlies estimation and specification tests. We present a specification in which the variance is directly proportional to the mean.

METHODS

We used simulation experiments to investigate finite sample results, and we used United States Department of Veterans Affairs (VA) healthcare cost data as an empirical example comparing the fit and predictive ability of the models.

RESULTS

Simulation showed the MLE based on a correctly specified alternative has less parameter bias, lower standard errors, and less skewness in distribution than a misspecified standard model. The application to VA healthcare cost data showed the alternative specification can have better R square, smaller root mean squared error, and smaller mean residuals within deciles of predicted values.

CONCLUSIONS

The alternative gamma specification can be a useful alternative to the standard specification for estimating models of right-skewed continuous variables.

The shadows of waiting and care: on discourses of waiting in the history of the British National Health Service

Waiting is at the centre of experiences and practices of healthcare. However, we know very little about the relationship between the subjective experiences of patients who wait in and for care, health practitioners who 'prescribe' and manage waiting, and how this relates to broader cultural meanings of waiting. Waiting features heavily in the sociological, managerial, historical and health economics literatures that investigate UK healthcare, but the focus has been on service provision and quality, with waiting (including waiting lists and waiting times) drawn on as a key marker to test the efficiency and affordability of the NHS. In this article, we consider the historical contours of this framing of waiting, and ask what has been lost or occluded through its development. To do so, we review the available discourses in the existing literature on the NHS through a series of 'snapshots' or key moments in its history. Through its negative imprint, we argue that what shadows these discourses is the idea of waiting and care as phenomenological temporal experiences, and time as a practice of care. In response, we begin to trace the intellectual and historical resources available for alternative histories of waiting - materials that might enable scholars to reconstruct some of the complex temporalities of care marginalized in existing accounts of waiting, and which could help reframe both future historical accounts and contemporary debates about waiting in the NHS.

Socioeconomic inequality, waiting time initiatives and austerity in Scotland: an interrupted time series analysis of elective hip and knee replacements and arthroscopies

OBJECTIVES

National Health Service (NHS) waiting times have long been a political priority in Scotland. In 2002, the Scottish government launched a programme of investment and reform to reduce waiting times. The effect on waiting time inequality is unknown as is the impact of subsequent austerity measures.

DESIGN

An interrupted time series analysis between the most and least socioeconomically deprived population quintiles since the introduction of waiting time initiative 1 July 2002 and austerity measures 1 April 2010.

SETTING

All NHS-funded elective primary hip replacement, primary knee replacement and arthroscopy patient data in Scotland from 1 April 1997 to 31 March 2019.

PARTICIPANTS

NHS Scotland funded patients treated in Scotland.

MAIN OUTCOME MEASURES

Trends and changes in mean waiting time.

RESULTS

There were 135,176, 122,883 and 173,976 NHS funded hip replacement, knee replacement and arthroscopy patients, respectively, in Scotland between 1 April 1997 and 31 March 2019. From 1 July 2002 to 31 March 2010, waiting time inequality between the most and least deprived patients fell and increased thereafter. For hip replacements before 1 July 2002, waiting time inequality increased 1.07 days per quarter; this changed at 1 July 2002 with significant slope change of -2.32 (-3.53, -1.12) days resulting in a decreasing rate of inequality of -1.26 days per quarter. On 1 April 2010 the slope changed significantly by 1.84 (0.90, 2.78) days restoring increasing inequality at 0.58 days per quarter. Knee replacements and arthroscopies had similar results.

CONCLUSIONS

The waiting time initiative in Scotland is associated with a reduction in waiting time inequality benefiting the most socioeconomically deprived patients. Austerity measures may be reversing these gains.

Patient prioritisation methods to shorten waiting times for elective surgery: A systematic review of how to improve access to surgery

BACKGROUND

Concern about long waiting times for elective surgeries is not a recent phenomenon, but it has been heightened by the impact of the COVID-19 pandemic and its associated measures. One way to alleviate the problem might be to use prioritisation methods for patients on the waiting list and a wide range of research is available on such methods. However, significant variations and inconsistencies have been reported in prioritisation protocols from various specialties, institutions, and health systems. To bridge the evidence gap in existing literature, this comprehensive systematic review will synthesise global evidence on policy strategies with a unique insight to patient prioritisation methods to reduce waiting times for elective surgeries. This will provide evidence that might help with the tremendous burden of surgical disease that is now apparent in many countries because of operations that were delayed or cancelled due to the COVID-19 pandemic and inform policy for sustainable healthcare management systems.

METHODS

We searched PubMed, EMBASE, SCOPUS, Web of Science, and the Cochrane Library, with our most recent searches in January 2020. Articles published after 2013 on major elective surgery lists of adult patients were eligible, but cancer and cancer-related surgeries were excluded. Both randomised and non-randomised studies were eligible and the quality of studies was assessed with ROBINS-I and CASP tools. We registered the review in PROSPERO (CRD42019158455) and reported it in accordance with the PRISMA statement.

RESULTS

The electronic search in five bibliographic databases yielded 7543 records (PubMed, EMBASE, SCOPUS, Web of Science, and Cochrane) and 17 eligible articles were identified in the screening. There were four quasi-experimental studies, 11 observational studies and two systematic reviews. These demonstrated moderate to low risk of bias in their research methods. Three studies tested generic approaches using common prioritisation systems for all elective surgeries in common. The other studies assessed specific prioritisation approaches for re-ordering the waiting list for a particular surgical specialty.

CONCLUSIONS

Explicit prioritisation tools with a standardised scoring system based on clear evidence-based criteria are likely to reduce waiting times and improve equitable access to health care. Multiple attributes need to be considered in defining a fair prioritisation system to overcome limitations with local variations and discriminations. Collating evidence from a diverse body of research provides a single framework to improve the quality and efficiency of elective surgical care provision in a variety of health settings. Universal prioritisation tools with vertical and horizontal equity would help with re-ordering patients on waiting lists for elective surgery and reduce waiting times.

The effectiveness of different patient referral systems to shorten waiting times for elective surgeries: systematic review

BACKGROUND

Long waiting times for elective surgery are common to many publicly funded health systems. Inefficiencies in referral systems in high-income countries are more pronounced than lower and middle-income countries. Primary care practitioners play a major role in determining which patients are referred to surgeon and might represent an opportunity to improve this situation. With conventional methods of referrals, surgery clinics are often overcrowded with non-surgical referrals and surgical patients experience longer waiting times as a consequence. Improving the quality of referral communications should lead to more timely access and better cost-effectiveness for elective surgical care. This review summarises the research evidence for effective interventions within the scope of primary-care referral methods in the surgical care pathway that might shorten waiting time for elective surgeries.

METHODS

We searched PubMed, EMBASE, SCOPUS, Web of Science and Cochrane Library databases in December-2019 to January-2020, for articles published after 2013. Eligibility criteria included major elective surgery lists of adult patients, excluding cancer related surgeries. Both randomised and non-randomised controlled studies were eligible. The quality of evidence was assessed using ROBINS-I, AMSTAR 2 and CASP, as appropriate to the study method used. The review presentation was limited to a narrative synthesis because of heterogeneity. The PROSPERO registration number is CRD42019158455.

RESULTS

The electronic search yielded 7543 records. Finally, nine articles were considered as eligible after deduplication and full article screening. The eligible research varied widely in design, scope, reported outcomes and overall quality, with one randomised trial, two quasi-experimental studies, two longitudinal follow up studies, three systematic reviews and one observational study. All the six original articles were based on referral methods in high-income countries. The included research showed that patient triage and prioritisation at the referral stage improved timely access and increased the number of consultations of surgical patients in clinics.

CONCLUSIONS

The available studies included a variety of interventions and were of medium to high quality researches. Managing patient referrals with proper triaging and prioritisation using structured referral formats is likely to be effective in health systems to shorten the waiting times for elective surgeries, specifically in high-income countries.

How the design and implementation of centralized waiting lists influence their use and effect on access to healthcare - A realist review

CONTEXT

Many health systems have centralized waiting lists (CWLs), but there is limited evidence on CWL effectiveness and how to design and implement them.

AIM

To understand how CWLs' design and implementation influence their use and effect on access to healthcare.

METHODS

We conducted a realist review (n = 21 articles), extracting context-intervention-mechanism-outcome configurations to identify demi-regularities (i.e., recurring patterns of how CWLs work).

RESULTS

In implementing non-mandatory CWLs, acceptability to providers influences their uptake of the CWL. CWL eligibility criteria that are unclear or conflict with providers' role or judgement may result in inequities in patient registration. In CWLs that prioritize patients, providers must perceive the criteria as clear and appropriate to assess patients' level of need; otherwise, prioritization may be inconsistent. During patients' assignment to service providers, providers may select less-complex patients to obtain CWLs rewards or avoid penalties; or may select patients for other policies with stronger incentives, disregarding the established patient order and leading to inequities and limited effectiveness.

CONCLUSION

These findings highlight the need to consider provider behaviours in the four sequential CWL design components: CWL implementation, patient registration, patient prioritization and patient assignment to providers. Otherwise, CWLs may result in limited effects on access or lead to inequities in access to services.

Factors affecting the performance of public out-patient services

Objective The delivery of public out-patient services is an essential part of complex healthcare systems, but the contribution of public out-patient services is often ill defined and poorly evaluated. The aim of this study was to identify and better understand those factors that may affect the performance of out-patient services to provide health service managers, clinicians and executives with a conceptual framework for future decision-making processes. Methods The present qualitative research involved five exploratory case studies. These case studies were conducted across two specialties at hospitals in the Metro North Hospital and Health Service in Queensland. Data were obtained from 38 interviews and 15 focus groups, and were analysed to identify common themes. Further analysis helped identify the most significant factors and build a conceptual framework for understanding the relationships between those factors and their effect on performance. Results Across both specialties there were 10 factors (scheduling, performance, service framework, categorisation or prioritisation of patients, internal and external stakeholders, resources, service demand, culture, system challenges and medical stakeholders) identified that may affect the performance of out-patient services. These factors were condensed into five core domains: culture, stakeholders, resources, demand and system reform. Conclusion Strategies to address the five core domains identified may provide a framework for sustainable improvement in the delivery of out-patient services. What is known about the topic? The provision of specialist out-patient services is an essential element of health service delivery. Access to specialist services in the public sector is challenging because of the escalating demand associated with an increasing and aging demographic. The factors that may affect the delivery of out-patient services need to be addressed for long-term sustainable improvement. What does this paper add? This paper provides a conceptual framework grounded in rigorous qualitative data analysis for understanding the internal and external factors that affect waiting times for specialist out-patient services. The results of this qualitative research indicate that there are five core domains that may influence waiting times in the public out-patient setting. When these domains are addressed at the strategic, tactical and operational levels, they have the potential to provide significant improvement in the delivery of out-patient services. What are the implications for practitioners? This paper guides the attention of relevant stakeholders towards the five core domains identified (culture, stakeholders, resources, demand and system reform) that influence the performance of waiting times at the operational, tactical and strategic levels within the public hospital setting.

Should I stay or should I go? Hospital emergency department waiting times and demand

In the absence of the price mechanism, hospital emergency departments rely on waiting times, alongside prioritisation mechanisms, to restrain demand and clear the market. This paper estimates by how much the number of treatments demanded is reduced by a higher waiting time. I use variation in waiting times for low-urgency patients caused by rare and resource-intensive high-urgency patients to estimate the relationship. I find that when waiting times are higher, more low-urgency patients are deterred from treatment and leave the hospital during the waiting period without being treated. The waiting time elasticity of demand for low-urgency patients is approximately -0.25 and is highest for the lowest-urgency patients.

Reasons doctors provide futile treatment at the end of life: a qualitative study

OBJECTIVE

Futile treatment, which by definition cannot benefit a patient, is undesirable. This research investigated why doctors believe that treatment that they consider to be futile is sometimes provided at the end of a patient's life.

DESIGN

Semistructured in-depth interviews.

SETTING

Three large tertiary public hospitals in Brisbane, Australia.

PARTICIPANTS

96 doctors from emergency, intensive care, palliative care, oncology, renal medicine, internal medicine, respiratory medicine, surgery, cardiology, geriatric medicine and medical administration departments. Participants were recruited using purposive maximum variation sampling.

RESULTS

Doctors attributed the provision of futile treatment to a wide range of inter-related factors. One was the characteristics of treating doctors, including their orientation towards curative treatment, discomfort or inexperience with death and dying, concerns about legal risk and poor communication skills. Second, the attributes of the patient and family, including their requests or demands for further treatment, prognostic uncertainty and lack of information about patient wishes. Third, there were hospital factors including a high degree of specialisation, the availability of routine tests and interventions, and organisational barriers to diverting a patient from a curative to a palliative pathway. Doctors nominated family or patient request and doctors being locked into a curative role as the main reasons for futile care.

CONCLUSIONS

Doctors believe that a range of factors contribute to the provision of futile treatment. A combination of strategies is necessary to reduce futile treatment, including better training for doctors who treat patients at the end of life, educating the community about the limits of medicine and the need to plan for death and dying, and structural reform at the hospital level.

Norwegian Priority Setting in Practice - an Analysis of Waiting Time Patterns Across Medical Disciplines

BACKGROUND

Different strategies for addressing the challenge of prioritizing elective patients efficiently and fairly have been introduced in Norway. In the time period studied, there were three possible outcomes for elective patients that had been through the process of priority setting: (i) high priority with assigned individual maximum waiting time; (ii) low priority without a maximum waiting time; and (iii) refusal (not in need for specialized services). We study variation in priority status and waiting time of the first two groups across different medical disciplines.

METHODS

Data was extracted from the Norwegian Patient Register (NPR) and contains information on elective referrals to 41 hospitals in the Western Norway Regional Health Authority in 2010. The hospital practice across different specialties was measured by patient priority status and waiting times. The distributions of assigned maximum waiting times and the actual ones were analyzed using standard Kernel density estimation. The perspective of the planning process was studied by measuring the time interval between the actual start of healthcare and the maximum waiting time.

RESULTS

Considerable variation was found across medical specialties concerning proportion of priority patients and their maximum waiting times. The degree of differentiation in terms of maximum waiting times also varied by medical discipline. We found that the actual waiting time was very close to the assigned maximum waiting time. Furthermore, there was no clear correspondence between the actual waiting time for patients and their priority status.

CONCLUSION

Variations across medical disciplines are often interpreted as differences in clinical judgment and capacity. Alternatively they primarily reflect differences in patient characteristics, patient case-mix, as well as capacity. One hypothesis for further research is that the introduction of maximum waiting times may have contributed to push the actual waiting time towards the maximum. The finding that the actual waiting time was very close to the maximum waiting time supports this. The lack of clear correspondence between the actual waiting time for patients and their priority status may imply that urgency, described in the referral letter, and severity of illness, according to guidelines, are two separate entities.