A first aspect arising from the survey that is worth considering is the extent of application of disinvestment policies across Europe. If the goals of disinvestment are clear, the definition of a disinvestment policy, its framework and methods are less shared and standardized. According to the well-known definition of Elshaug et al.
Disinvestment is about resource re-allocation, not merely the promotion of the appropriate use of a health technology. In this respect, it can be misleading to consider disinvestment policies as aimed at cost-containing. Budget concerns are a public health issue, and healthcare systems worldwide are facing the issue of wasteful health spending.
Our survey showed waste is a big concern also in cancer care. It should be considered that oncology is an area of fast increasing costs. But how to translate it into practice? The evidence offers two valuable approaches. This program, aimed to the development of guidelines focused on the improvement of standard of care, gives the opportunity to discuss not only new investments but also disinvestments in healthcare.
Among the clinical guidelines produced by the program, there are also disinvestment recommendations, i. The authors concluded the adoption of this framework for the prioritization activity of the CDF would determine disinvestment from cost-ineffective drugs refundable by CDF. The paper shows most programs are implemented outside Europe, namely Australia and Canada. There are several papers addressing unnecessary, i.
The agreement on fields of waste and disinvestment policies sheds lights on how to pursue resource re-allocation in cancer care. Nonetheless, operational methods to put disinvestment in place are lacking, even though the scientific community is recognizing Health Technology Assessment HTA as a suitable approach, 2 first of all because it allows the evaluation of several criteria 12 at the same time and, second, because it employs a systematic approach.
Third, HTA is a policy-oriented process born and used to support decision-making and it has been shown that the presence of an HTA agency is associated to the probability of having disinvestment programs in the country. In the latter, political, social and clinical challenges may play an important role.
Because of the latter characteristic, its multi-disciplinary and evidence-based approach, HTA should be considered a pillar for investing and for disinvesting in health. Our work has some limits. First, the survey was performed on an opportunistic sample of representatives from European countries with a low response rate. This aspect might limit the transferability of results to the whole of Europe.
Second, the questionnaire used to carry out the survey was not validated, limiting the generalizability of results and it only assessed personal knowledge and attitudes of responders, with no check on institutional documents. Nevertheless, to the best of our knowledge, our work is the first to address views and opinions on disinvestment across Europe and responders came back from all the European regions, allowing to get a first overview of the perspectives on this challenging topic.
The project is a part of the activities funded under the Italian National Centre for Disease Prevention and Control to support the Ministry of Health in co-ordinating and implementing the workpackage 5 of the European Joint Action CanCon. Key points The economic and financial sustainability of health systems and the optimal allocation of resources are central public health issues and there is a growing interest towards approaches to optimize the use of health technologies, while disinvesting from those of little or no value.
Because of high costs of health technologies and rapid innovation, disinvestment from clinical interventions with lower performance is crucial in Oncology to ensure healthcare quality. Because of its multi-disciplinary, multi-stakeholders and evidence-based approach, HTA should be considered a pillar for investing and disinvesting in health.
Rational disinvestment. Google Scholar. Reversals of established medical practices: evidence to abandon ship. JAMA ; : 37 — 8. Guiding the process of health technology disinvestment. Health Policy ; 98 : — Pearson S , Littlejohns P. Reallocating resources: how should the National Institute for Health and Clinical Excellence guide disinvestment efforts in the National Health Service?
J Health Serv Res Policy ; 12 : — 5. Challenges in Australian policy processes for disinvestment from existing, ineffective health care practices. Aust New Zealand Health Policy ; 4 : Department of Human Services. Victoria, Australia : Department of Human Services , Google Preview. Health Technology Assessment International. Health technology reassessment: the art of the possible.
Eagle D. The cost of cancer care: part I. Oncology Williston Park ; 26 : — 21, Rotily M , Roze S. What is the impact of disease prevalence upon health technology assessment? Best Pract Res Clin Gastroenterol ; 27 : — Chabot I , Rocchi A. Oncology drug health technology assessment recommendations: canadian versus UK experiences. Clinicoecon Outcomes Res ; 6 : — Goodman CS. Soc Sci Med ; : — Tackling Wasteful Spending on Health. Eliminating waste in US health care.
JAMA ; : — 6. Evidence for overuse of medical services around the world. Lancet ; : — Availabale at : www. Comparing cancer care, outcomes, and costs across health systems: charting the course. J Natl Cancer Inst Monogr ; : — Economic burden of cancer across the European Union: a population-based cost analysis. Lancet Oncol ; 14 : — Drummond M. Clinical guidelines: a NICE way to introduce cost-effectiveness considerations? Value Health ; 19 : — Leigh S , Granby PA. Tale of two thresholds: a framework for prioritization within the cancer drugs fund.
Health technology disinvestment worldwide: overview of programs and possible determinants. Cervical smears—an opportunity for disinvestment? Br J Gen Pract ; 46 : — 8. Estimating the direct costs of bowel cancer services provided by the National Health Service in England. Value Health ; 18 : — 5. Sustainability in health care by allocating resources effectively SHARE 3: examining how resource allocation decisions are made, implemented and evaluated in a local healthcare setting.
Case studies that illustrate disinvestment and resource allocation decision-making processes in health care: a systematic review. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search. Search Menu.
Skip Nav Destination Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Article Navigation. Oxford Academic. Giuseppe La Torre. Allied health services such as physical therapy or social work are now commonly provided on weekends in hospitals internationally, though not to the same extent as during the week [ 6 — 10 ]. However, the efficacy of these services has not been established.
A recent, methodologically inclusive systematic review of these services amongst acute joint arthroplasty patients found some benefit in improving length of stay and function [ 16 ]. However, this finding was largely driven by findings of observational studies, and no randomised trials examining the efficacy of weekend allied health services in acute settings were identified.
Links between allied health staffing levels and the weekend effect have not been established, and some have questioned the very existence of the weekend effect [ 17 , 18 ]. The aim of this study was to establish the impact of disinvesting from provision of allied health services on weekends across acute medical and surgical hospital wards. Human Research Ethics Committee approval for this project was provided by the Monash Health approval ref B and Melbourne Health approval number: We have previously proposed an approach for use in the context of disinvestment from a service that has unknown effectiveness [ 4 ].
This approach is centred on simultaneous disinvestment from the health service in question while also generating the evidence examining the effectiveness of this service. In this research, we conducted 2 stepped-wedge cluster randomised controlled trials across each of 2 tertiary, metropolitan teaching hospitals, Dandenong Hospital and Footscray Hospital, in Victoria, Australia.
Stepped-wedge trials are a form of cluster randomised trial with unidirectional crossover that are increasingly being used for health service evaluations [ 19 ]. It contained examination of both non-inferiority and superiority hypotheses for each primary outcome examined, and superiority hypotheses for each secondary outcome examined.
Testing non-inferiority hypotheses does not preclude testing of superiority hypotheses and can be done without statistical penalty [ 20 ]. This reintroduction of weekend allied health services in Trial 2 was considered necessary in the present context given the incremental and evolving development of the current weekend service delivery models that had taken place at the participating sites combined with heterogeneity in delivery models of these services internationally. The newly developed model that was reintroduced was intended to be a complex intervention that used a reproducible procedure for development.
Its development involved gathering feedback from medical, nursing, and allied health staff working on the targeted wards. This feedback was reviewed by and discussed with local allied health managers in charge, who made the final decision as to the make-up of the newly developed model. These data include approved clinical exceptions during no weekend service periods. There were 3 deviations from the analysis plan and 1 change to the outcome measures described in our study protocol S1 Text.
First, a pre-planned meta-analysis that combined data from both sites and trials was not performed due to discordance in findings between Trial 1 and Trial 2 [ 21 ]. Third, analyses without adjustment for monthly outcome data for the previous 2 years were undertaken. These analysis changes were inspired by peer-reviewers. The secondary outcome of staff absenteeism could not be extracted in clinical units relevant for this trial, so was abandoned. This research took place across 6 acute medical or surgical wards at Dandenong Hospital and 6 at Footscray Hospital, Victoria, Australia.
Patient allocation to wards was driven by patient specialty requirements and bed availability. Patients who were exposed to the no weekend allied health service condition as well as either the current or newly developed weekend allied health service conditions were excluded to avoid research-design-induced contamination. The requirement to collect individual patient-level consent for researchers to access the primary and secondary outcomes reported in this paper was waived during the ethical approval process.
Random allocation of wards at each site to starting position in the trial design was undertaken at public meetings at each site. Investigators D. Mitchell and EHS developed a list of pseudonyms to represent each ward and then provided these to another investigator TPH who was blinded to the meaning of each pseudonym. This investigator then used a random number generator command in Microsoft Excel to allocate wards to starting positions in the stepped-wedge trial design.
From this point, staff and patients were not blinded to group allocations due to the practical need to notify staff when the weekend allied health service on their ward would cease. A detailed description of weekend allied health service models investigated in both of these trials including TIDiER checklist [ 22 ] and the process used to develop the newly designed model in Trial 2 was provided in our protocol [ 5 ].
Briefly, we undertook extensive consultation with relevant stakeholders medical, nursing, and allied health staff and managers on participating wards. These staff were not asked to say which professional discipline they wanted to be employed on the weekends, rather, to identify and prioritise the tasks that they believe to be most important for allied health to perform on weekends in terms of improving patient health outcomes, improving patient flow, and reducing readmissions.
They were also asked to reflect on the strengths and limitations of the current model of care, suggest areas for improvement, and examine patient incident and clinical exception data gathered during Trial 1 to inform their decisions. Allied health managers were provided with this list of tasks and other feedback gathered, so they could propose the new stakeholder-driven model of weekend allied health service they felt would work best.
A Delphi meeting was used to facilitate this process separately at each hospital site [ 23 ]. Services delivered under the original Trial 1 and newly developed models Trial 2 at each site are presented in S3 and S4 Texts, which present the amount of service provided by each discipline group when the service was fully operational first month of Trial 1 and last month of Trial 2.
The no weekend service condition entailed delivery of no allied health services on weekends to wards affected, unless criteria for a clinical exception had been met. A safety mechanism specified in our trial protocol was that a process be established whereby the trial protocol could be violated for an individual patient if specific, pre-planned criteria set by local clinicians and approved by local administrators were met S5 Text. The primary outcomes were indicators of the domains of patient flow through the hospital length of stay measured in days, proportion of patients staying longer than their diagnosis-related group average inlier length of stay , failures in discharge planning proportion of patients with unplanned readmissions within 28 days of discharge , and failures in patient care proportion of patients experiencing any of the following adverse events: in-hospital fall, Code Blue call, Medical Emergency Team call, pulmonary embolus, deep vein thrombosis, death, hospital-acquired pressure area, or intensive care unit admission from the ward.
We used 2 indicators of patient flow due to the inherent limitations of using the intuitively attractive indicator length of stay measured in days in the context of our stepped-wedge design. The results of a stepped-wedge trial can be biased if certain hospital wards, but not others in the same trial, change the types of patients particularly those with greater or lesser lengths of stay they tend to admit over time to cope with seasonal demands.
Hospital data coders classify patients into these groupings that are based on similarity of conditions and usage of hospital resources, using information in the hospital morbidity record such as the diagnoses, procedures, and demographic characteristics of the patient [ 24 ]. The primary outcomes were collected through hospital administrative data systems and checked daily by research assistants interviewing ward representatives and checking handover documentation.
Secondary outcomes collected across all participants included the proportion of patients discharged to residential aged care facilities, the cost in Australian dollars per patient to the healthcare system per admission, the proportion of patients discharged on a Saturday or Sunday, and the number of compliments and complaints total and allied health specific.
These outcomes were extracted from hospital administrative data systems at completion of the study. The cost of inpatient treatment per patient was extracted from hospital-based clinical costing systems in August to allow finalisation of hospital costing processes. It should be noted here that clinical costing data are largely driven by length of stay, and hence this outcome has the same limitations as described above for the length of stay outcome.
Process measures collected included the occasions of weekend allied health service delivery i. Occasions of allied health service delivery were recorded by allied health staff, collected through routine hospital administrative data systems, and extracted at the end of the study.
The frequency and reason for clinical exceptions taking place were recorded by site investigators KM, TC, and MS who had local responsibility for approving these at the time of the exception being granted. Non-patient-related outcomes and qualitative, economic, subsample, and meta-regression analyses described in our protocol were reserved for further publications.
An audit was conducted prior to the trial to establish the average amount of weekend service per ward and the financial costs of the overall service. During Trial 1, weekend services were titrated down according to pre-trial audit amounts. If a ward received 4 hours of physical therapy over a weekend prior to trial commencement, it is this amount that was removed when this service was ceased on this ward.
For Trial 2, the service was reintroduced on a pro rata basis. At the Dandenong Hospital site, managers reallocated their funds to increase the overall number of allied health hours provided by moving some hours to a Friday afternoon and Monday morning. This was intended to facilitate discharge planning prior to the weekend and immediately following and to maximise the total number of allied health hours.
At the Footscray Hospital site, managers changed the personnel who provided the weekend allied health service from casual staff to allied health staff working in the intensive care unit and the Immediate Response Service, whose usual responsibility was to screen and assess presentations to the emergency department to prevent inappropriate admissions and facilitate discharge planning. Nursing staff managers on each ward were engaged prior to study commencement to identify which tasks usually performed by allied health staff on weekends could be modified, accelerated to a Friday , delayed to a Monday , or transferred to other staff who were present on weekends.
They were also provided with the criteria for clinical exceptions on their wards. Study stopping rules and non-inferiority margins were also developed prior to trial commencement by hospital administrators and allied health managers at participating sites [ 5 ]. Project research assistant data collectors were present 7 days per week during the study period.
They collected data through medical record review and interviews with hospital staff, enabling crosschecking of data collected between routine hospital administrative data systems and direct data collection approaches [ 25 , 26 ]. These analyses were conducted by a statistician SM independent to the research locations and blinded to ward allocation status through use of 6 mock codes representing different ward allocation patterns. An intention-to-treat analysis approach was employed. Models were initially constructed using patient-level data nested within ward nested within hospital site.
Patient admissions were coded against the first study ward of their admission and calendar month of their first admission to a study ward. Log natural transformation of length of stay data was planned due to the anticipated skewed distribution [ 5 ]. Effect estimates were generated using ward-month-level data if effect estimates could not be calculated using patient-level data on account of inability to ascertain starting values for models with binary outcomes.
All analyses were adjusted for study month and ward as categorical fixed factors in line with recommendations for analysis of stepped-wedge designs [ 19 ]. Adjustment was also made for monthly outcome data from the previous 2 years for primary outcomes to account for potential seasonal fluctuations that are consistent from year to year. Analyses were undertaken using Stata MP v. Three post hoc exploratory analyses were undertaken. The first investigated potential differences in outcomes between the 2 weekend allied health service delivery models current versus newly developed directly.
This analysis was considered important as there was no guarantee that the newly developed model would be superior to the current model despite this being the intent of redesigning the weekend allied health service. The second examined whether the results under the no weekend service condition were comparable between the 2 trials.
Previous studies have reported substantial changes over time within health services in outcomes examined in this trial e. Adjustment for study month was not used in these exploratory analyses as this would have led to collinearity with the independent variables of interest. The third post hoc analysis was a sensitivity analysis for Trail 1 whereby a 1-month washout period was applied to each ward following their transition to the no weekend allied health service condition.
This meant that the month of data immediately following the transition in model of care was excluded from the analysis for each ward and that the final month included in the analysis of Trial 1 became September for the Dandenong Hospital site and November for the Footscray Hospital site. This analysis was important as changes in practice can potentially affect hospital practices and processes in a way that is different to what happens once the change has had more time to become imbedded in practice.
Data were monitored by a committee drawn from senior clinical staff at participating sites who were not members of the study investigative team. Trial dates are provided Fig 1 , along with deviations from the registered trial protocol Fig 2 ; inability to commence at 1 site and closure of 1 ward during Trial 2. Study data used in these analyses are provided S1 and S2 Data. Study power analysis calculations are provided S6 Text and were described in our protocol paper [ 5 ].
Trial dates of commencement and completion along with occasions of allied health service are presented Fig 1. There were 28 approved clinical exceptions in total 14 in each trial during the no weekend allied health periods, of which the most common justification was a post-fall mobility review by a physiotherapist The remaining occasions of service provision were not approved clinical exceptions during this period.
Data for primary and secondary outcomes for the full trial sample are presented Table 2. Effect size estimates for each trial are presented Table 3. The p -values presented in Table 3 reflect the probability of the findings observed when the superiority null hypothesis is true, and were derived from multilevel, mixed-effects generalised linear model analyses. Length of stay was greater for the no weekend service condition in Trial 1, but shorter in Trial 2.
The proportion of patients staying longer than their diagnosis-related group average inlier length of stay was lower for the no weekend service condition in Trial 2 compared to the newly developed weekend allied health service condition. It was also superior for the proportion of patients staying longer than expected and the proportion who had 1 or more adverse event outcomes. Examination of site-by-intervention interaction effects identified an interaction for the proportion of patients with an unplanned readmission in Trial 1.
Examination of superiority hypotheses for secondary outcomes demonstrated that there was difference between conditions in the proportion of patients discharged to residential aged care facilities. However, the cost to the healthcare system per admission was greater for the no weekend service condition in Trial 1, but less in Trial 2.
This finding should be viewed in the context that length of stay was longer when a weekend allied health service was present in that trial. Exploratory analyses Table 4 indicated that patients in the no weekend service condition in Trial 1 had better outcomes across multiple domains than those in the no weekend service condition in Trial 2.
Patients exposed to the original weekend allied health service delivery model did not experience different outcomes than those exposed to the newly developed model for the primary outcomes of the proportion staying in hospital longer than expected, the proportion who had an unplanned readmission within 28 days, and the proportion experiencing any adverse event when these patient groups were compared directly.
There was a difference for mean length of stay log transformed and total cost favouring the original weekend allied health service delivery model, though these outcomes did not account for differences in patient diagnosis categories between phases. The other outcomes were largely unaffected in this sensitivity analysis. Analyses without adjustment for monthly outcome data for the previous 2 years had some inconsistencies with main analyses, where the adjustment was made S7 Text. Differences in the proportion of patients staying longer than expected and with any adverse event were no longer significant between the no weekend service and newly developed weekend allied health service conditions in Trial 2.
There were also site-by-intervention interaction effects for the log-transformed length of stay Trial 1 and proportion of patients with any adverse event Trial 2 outcomes. The result for the mean length of stay outcome from Trial 1 was sensitive to whether a 1-month washout period was applied in the analysis.
When the washout period was applied, the no weekend allied health service condition was found to be non-inferior to the current weekend allied health service model. In Trial 2, the no weekend allied health condition was non-inferior to the newly developed weekend allied health condition across all primary outcomes, and superior for the proportion of patients staying longer than expected, proportion experiencing any adverse event, and mean length of stay.
The findings of this study were somewhat discordant with the only previously published randomised trial of the effect of weekend allied health services on patient and health service outcomes. However, this trial did identify small benefits of the service in terms of improved functional independence and health-related quality of life attributable to the intervention, though these benefits were arguably below clinically meaningful thresholds [ 30 — 32 ].
It is difficult to directly compare these results given the differences in ward types and patient populations involved, the fact that the earlier study focused only on a physical therapy service, and the differences in the activities undertaken by allied health staff between acute and rehabilitation settings.
Our study was the first to our knowledge to use this particular disinvestment research design to simultaneously disinvest from a routinely provided service with uncertain effectiveness while also developing evidence that had previously been missing as to the effectiveness of the intervention.
It should be noted that the models of care we examined in Trial 1 were limited to those in place at the outset of the trial at the study locations. However, the model of care in Trial 2 was a complex intervention, where the process used could be reproduced in other settings to develop models that are similarly tailored to local conditions. A strength of these two trials in addressing the research context area was their size, which led to narrow confidence intervals in our analyses and a high degree of certainty in the results.
Our choice to investigate both the current weekend allied health service and a newly developed service also enhanced the generalisability of our findings to real world settings. However, these trials did not investigate the breadth of all possible permutations and combinations of weekend allied health service delivery models.
Rather, we focused on the pragmatic scenario of what was currently being allocated and the budgetary envelope of this. Our study was limited in that we were unable to proceed with this research on 1 ward at 1 of our sites in Trial 2 due to closure of that clinical unit. We were also unable to proceed at a third site due to local opposition to participation in the study.
This highlights some of the difficulties that can be encountered in conducting disinvestment research of this nature over an extended period of time. This study could have been further strengthened by considering the healthcare costs consumed by patients after they were discharged from these acute wards. The scope of the clinical implications of our findings should be clearly defined. The study locations did not have all types of specialty wards e.
The finding of no effect of weekend allied health services overall should not be extrapolated to weekday services. It is possible that variation in staffing profile and the lack of availability of community-based services on weekends that support patients upon discharge are an important difference. We also did not withhold weekend allied health services from those who met our clinical exception criteria.
The low frequency of these exceptions may indicate that using staff from other areas that do have a weekend allied health service e. This study reported discordant findings for the mean length of stay and total cost outcomes between Trials 1 and 2. This can be explained by 5 potential mechanisms. First, these changes could be attributed to background variation in patient case mix not accounted for by these outcomes. Second, it may be that the current weekend allied health service delivery model was superior to the newly developed model.
Our exploratory analyses directly comparing these models did not support this explanation as the log-transformed length of stay outcome favoured the newly developed service when these conditions were directly compared. Third, the current service model could be argued to have had an unfair advantage in a direct comparison with the newly developed service model. The current service model had several years of refinement locally and integration into usual care before being subjected to this evaluation, whereas the newly developed service model was evaluated as soon as it was introduced and did not have the same opportunity to be refined and integrated.
Fourth, the patient cohort or standard of usual care provided at study sites could have changed within the no weekend service periods across Trials 1 and 2. Our exploratory analyses identified multiple outcomes where differences favoured those exposed to the no weekend service condition during Trial 2 compared to Trial 1, which would support this hypothesis. The fifth potentially concurrent explanation is that both withdrawing the weekend allied health service model and installing a new one required an accommodation period for staff to adapt to the new service settings.
This hypothesis would also be supported by the differences observed between Trials 1 and 2 within the no weekend service periods. Models of organisational lag have been formally investigated since the s [ 34 ] and may indicate that our original research design should have included a washout period following the transition to the new model of care.
Our sensitivity analyses, where we introduced a 1-time-period washout to Trial 1, identified that the length of stay in the no weekend service condition was non-inferior to the current weekend service condition though other results were not substantially changed. There is potential that naturally occurring change over time could confound the results of these stepped-wedge trials.
Naturally occurring change over time maturation is a potential confounder in every longitudinal interventional research design. The important concern is whether this may have biased the intervention effect size estimates calculated from each trial. In a stepped-wedge design, this problem is prominent given the unidirectional crossover employed.
This problem was first described in detail by Hussey and Hughes [ 35 ]. Their solution to this problem was to explicitly model the effect of each time period to eliminate this bias from the intervention effect estimate. We used this approach in every analysis presented in Table 3. Thus, our estimated effect sizes calculated from within Trial 1 and Trial 2 are statistically independent of the potentially confounding effects of change over time that occurred across the organisations involved, and can be considered to be free of bias from this source.
The analyses that were not independent of these effects are the comparisons between Trial 1 and Trial 2 that are reported in Table 4. We could not use this approach in the comparisons between the 2 control periods, and in the direct comparisons of the current and the newly developed weekend services in Table 4 as these were not comparisons based on a stepped-wedge design.
Rather, these were pre- versus post-intervention design comparisons with no overlap in time periods between the conditions being compared thus a time covariate would directly confound the covariate of interest, which in this case was Trial 1 versus Trial 2. The washout sensitivity analysis model in Table 4 did use the approach described by Hussey and Hughes as this was a stepped-wedge design. The key implication of this research is that resources being used to support weekend allied health service delivery to acute medical and surgical wards similar to those involved in this study could potentially be put to better use elsewhere in the healthcare system.
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