Minimizing Population Health Loss in Times of Scarce Surgical Capacity During the Coronavirus Disease 2019 Crisis and Beyond: A Modeling Study

A decade of change towards Value-Based Health Care at a Dutch University Hospital: a complexity-informed process study

BACKGROUND

While healthcare organizations in several countries are embracing Value-Based Health Care (VBHC), there are limited insights into how to achieve this paradigm shift. This study examines the decade-long (2012-2023) change towards VBHC in a pioneering Dutch university hospital.

METHOD

Through retrospective, complexity-informed process research, we study how a Dutch university hospital's strategy to implement VBHC evolved, how implementation outcomes unfolded, and the underlying logic behind these developments. Data include the hospital's internal documents (n = 10,536), implementation outcome indicators (n = 4), a survey among clinicians (n = 47), and interviews with individuals contributing to VBHC at the hospital level (n = 20).

RESULTS

The change towards VBHC is characterized by three sequential strategies. Initially, the focus was on deep change through local, tailored implementation of multiple VBHC elements. The strategy then transitioned to a hospital-wide program aimed at evolutionary change on a large scale, emphasizing the integration of VBHC into mainstream IT and policies. Recognizing the advantages and limitations of both strategies, the hospital currently adopts a "hybrid" strategy. This strategy delicately combines deep and broad change efforts. The strategy evolved based on accumulated insights, contextual developments and shifts in decision-makers. The complexity of change was downplayed in plans and stakeholder communication. By the end of 2023, 68 (sub)departments engaged in VBHC, enabled to discuss patients' responses to Patient Reported Outcomes Measures (PROMs) during outpatient care. However, clinicians' use of PROMs data showed limitations. While pioneers delved deeper into VBHC, laggards have yet to initiate it.

CONCLUSIONS

VBHC does not lend itself to linear planning and is not easily scalable. While there appears to be no golden standard for implementation, blending local and larger-scale actions appears advantageous. Local, deep yet harmonized and system-integrated changes culminate in large scale transformation. Embracing complexity and focusing on the ultimate aims of (re)institutionalization and (re)professionalization are crucial.

The Health Impact of Waiting for Elective Procedures in the NHS in England: A Modeling Framework Applied to Coronary Artery Bypass Graft and Total Hip Replacement

INTRODUCTION

The aim of this study is to demonstrate a practical framework that can be applied to estimate the health impact of changes in waiting times across a range of elective procedures in the National Health Service (NHS) in England. We apply this framework by modeling 2 procedures: coronary artery bypass graft (CABG) and total hip replacement (THR).

METHODS

We built a Markov model capturing health pre- and postprocedure, including the possibility of exiting preprocedure to acute NHS care or self-funded private care. We estimate the change in quality-adjusted life-years (QALYs) over a lifetime horizon for 10 subgroups defined by sex and Index of Multiple Deprivation quintile groups and for 7 alternative scenarios. We include 18 wk as a baseline waiting time consistent with current NHS policy. The model was populated with data from routinely collected data sets where possible (Hospital Episode Statistics, Patient-Reported Outcome Measures, and Office for National Statistics Mortality records), supplemented by the academic literature.

RESULTS

Compared with 18 wk, increasing the wait time to 36 wk resulted in a mean discounted QALY loss in the range of 0.034 to 0.043 for CABG and 0.193 to 0.291 for THR. The QALY impact of longer NHS waits was greater for those living in more deprived areas, partly as fewer patients switch to private care.

DISCUSSION/CONCLUSION

The proposed framework was applied to 2 different procedures and patient populations. If applied to an expanded group of procedures, it could provide decision makers with information to inform prioritization of waiting lists. There are a number of limitations in routine data on waiting for elective procedures, primarily the lack of information on people still waiting.

HIGHLIGHTS

We present a modeling framework that allows for an estimation of the health impact (measured in quality-adjusted life-years) of waiting for elective procedures in the NHS in England.We apply our model to waiting for coronary artery bypass graft (CABG) and total hip replacement (THR). Increasing the wait for THR results in a larger health loss than an equivalent increase in wait for CABG.This model could potentially be used to estimate the impact across an expanded group of procedures to inform prioritization of activities to reduce waiting times.

Surgical prioritization based on decision model outcomes is not sensitive to differences between the health-related quality of life values estimates of physicians and citizens

PURPOSE

Decision models can be used to support allocation of scarce surgical resources. These models incorporate health-related quality of life (HRQoL) values that can be determined using physician panels. The predominant opinion is that one should use values obtained from citizens. We investigated whether physicians give different HRQoL values to citizens and evaluate whether such differences impact decision model outcomes.

METHODS

A two-round Delphi study was conducted. Citizens estimated HRQoL of pre- and post-operative health states for ten surgeries using a visual analogue scale. These values were compared using Bland-Altman analysis with HRQoL values previously obtained from physicians. Impact on decision model outcomes was evaluated by calculating the correlation between the rankings of surgeries established using the physicians' and the citizens' values.

RESULTS

A total of 71 citizens estimated HRQoL. Citizens' values on the VAS scale were - 0.07 points (95% CI - 0.12 to - 0.01) lower than the physicians' values. The correlation between the rankings of surgeries based on citizens' and physicians' values was 0.96 (p < 0.001).

CONCLUSION

Physicians put higher values on health states than citizens. However, these differences only result in switches between adjacent entries in the ranking. It would seem that HRQoL values obtained from physicians are adequate to inform decision models during crises.

Prioritization of surgical patients during the COVID-19 pandemic and beyond: A qualitative exploration of patients' perspectives

INTRODUCTION

During the COVID-19 pandemic, prioritizing certain surgical patients became inevitable due to limited surgical capacity. This study aims to identify which factors patients value in priority setting, and to evaluate their perspective on a decision model for surgical prioritization.

METHODS

We enacted a qualitative exploratory study and conducted semi-structured interviews with N = 15 patients. Vignettes were used as guidance. The interviews were transcribed and iteratively analyzed using thematic analysis.

RESULTS

We unraveled three themes: 1) general attitude towards surgical prioritization: patients showed understanding for the difficult decisions to be made, but demanded greater transparency and objectivity; 2) patient-related factors that some participants considered should, or should not, influence the prioritization: age, physical functioning, cognitive functioning, behavior, waiting time, impact on survival and quality of life, emotional consequences, and resource usage; and 3) patients' perspective on a decision model: usage of such a model for prioritization decisions is favorable if the model is simple, uses trustworthy data, and its output is supervised by physicians. The model could also be used as a communication tool to explain prioritization dilemmas to patients.

CONCLUSION

Support for the various factors and use of a decision model varied among patients. Therefore, it seems unrealistic to immediately incorporate these factors in decision models. Instead, this study calls for more research to identify feasible avenues and seek consensus.

Estimating the health impact of delayed elective care during the COVID -19 pandemic in the Netherlands

BACKGROUND

The COVID-19 pandemic had a major impact on the continuity of healthcare provision. Appointments, treatments and surgeries for non-COVID patients were often delayed, with associated health losses for patients involved.

OBJECTIVE

To develop a method to quantify the health impact of delayed elective care for non-COVID patients.

METHODS

A model was developed that estimated the backlog of surgical procedures in 2020 and 2021 using hospital registry data. Quality-adjusted life years (QALYs) were obtained from the literature to estimate the non-generated QALYs related to the backlog. In sensitivity analyses QALY values were varied by type of patient prioritization. Scenario analyses for future increased surgical capacity were performed.

RESULTS

In 2020 and 2021 an estimated total of 305,374 elective surgeries were delayed. These delays corresponded with 319,483 non-generated QALYs. In sensitivity analyses where QALYs varied by type of patient prioritization, non-generated QALYs amounted to 150,973 and 488,195 QALYs respectively. In scenario analyses for future increased surgical capacity in 2022-2026, the non-generated QALYs decreased to 311,220 (2% future capacity increase per year) and 300,710 (5% future capacity increase per year). Large differences exist in the extent to which different treatments contributed to the total health losses.

CONCLUSIONS

The method sheds light on the indirect harm related to the COVID-19 pandemic. The results can be used for policy evaluations of COVID-19 responses, in preparations for future waves or other pandemics and in prioritizing the allocation of resources for capacity increases.

Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input

OBJECTIVES

A previously developed decision model to prioritize surgical procedures in times of scarce surgical capacity used quality of life (QoL) primarily derived from experts in one center. These estimates are key input of the model, and might be more context-dependent than the other input parameters (age, survival). The aim of this study was to validate our model by replicating these QoL estimates.

METHODS

The original study estimated QoL of patients in need of commonly performed procedures in live expert-panel meetings. This study replicated this procedure using a web-based Delphi approach in a different hospital. The new QoL scores were compared with the original scores using mixed effects linear regression. The ranking of surgical procedures based on combined QoL values from the validation and original study was compared to the ranking based solely on the original QoL values.

RESULTS

The overall mean difference in QoL estimates between the validation study and the original study was - 0.11 (95% CI:  -0.12 - -0.10). The model output (DALY/month delay) based on QoL data from both studies was similar to the model output based on the original data only: The Spearman's correlation coefficient between the ranking of all procedures before and after including the new QoL estimates was 0.988.

DISCUSSION

Even though the new QoL estimates were systematically lower than the values from the original study, the ranking for urgency based on health loss per unit of time delay of procedures was consistent. This underscores the robustness and generalizability of the decision model for prioritization of surgical procedures.

COVID-19 and the forgone health benefits of elective operations

BACKGROUND AND AIM

The first SARS-CoV-2 pandemic wave in Germany involved a tradeoff between saving the lives of COVID-19 patients by providing sufficient intensive care unit (ICU) capacity and foregoing the health benefits of elective procedures. This study aims to quantify this tradeoff.

METHODS

The analysis is conducted at both the individual and population levels. The analysis calculates quality-adjusted life years (QALYs) to facilitate a comparison between the health gains from saving the lives of COVID-19 patients in the ICU and the health losses associated with postponing operative procedures. The QALYs gained from saving the lives of COVID-19 patients are calculated based on both the real-world ICU admissions and deaths averted from flattening the first wave. Scenario analysis was used to account for variation in input factors.

RESULTS

At the individual level, the resource-adjusted QALY gain of saving one COVID-19 life is predicted to be 3 to 15 times larger than the QALY loss of deferring one operation (the average multiplier is 9). The real-world QALY gain at the population level is estimated to fall within the range of the QALY loss due to delayed procedures. The modeled QALY gain by flattening the first wave is 3 to 31 times larger than the QALY loss due to delayed procedures (the average multiplier is 17).

CONCLUSION

During the first wave of the pandemic, the resource-adjusted health gain from treating one COVID-19 patient in the ICU was found to be much larger than the health loss from deferring one operation. At the population level, flattening the first wave led to a much larger health gain than the health loss from delaying operative procedures.

Minimising population health loss in times of scarce surgical capacity: a modelling study for surgical procedures performed in nonacademic hospitals

BACKGROUND

The burden of the COVID-19 pandemic resulted in a reduction of available health care capacity for regular care. To guide prioritisation of semielective surgery in times of scarcity, we previously developed a decision model to quantify the expected health loss due to delay of surgery, in an academic hospital setting. The aim of this study is to validate our decision model in a nonacademic setting and include additional elective surgical procedures.

METHODS

In this study, we used the previously published three-state cohort state-transition model, to evaluate the health effects of surgery postponement for 28 surgical procedures commonly performed in nonacademic hospitals. Scientific literature and national registries yielded nearly all input parameters, except for the quality of life (QoL) estimates which were obtained from experts using the Delphi method. Two expert panels, one from a single nonacademic hospital and one from different nonacademic hospitals in the Netherlands, were invited to estimate QoL weights. We compared estimated model results (disability adjusted life years (DALY)/month of surgical delay) based on the QoL estimates from the two panels by calculating the mean difference and the correlation between the ranks of the different surgical procedures. The eventual model was based on the combined QoL estimates from both panels.

RESULTS

Pacemaker implantation was associated with the most DALY/month of surgical delay (0.054 DALY/month, 95% CI: 0.025-0.103) and hemithyreoidectomy with the least DALY/month (0.006 DALY/month, 95% CI: 0.002-0.009). The overall mean difference of QoL estimates between the two panels was 0.005 (95% CI -0.014-0.004). The correlation between ranks was 0.983 (p < 0.001).

CONCLUSIONS

Our study provides an overview of incurred health loss due to surgical delay for surgeries frequently performed in nonacademic hospitals. The quality of life estimates currently used in our model are robust and validate towards a different group of experts. These results enrich our earlier published results on academic surgeries and contribute to prioritising a more complete set of surgeries.

How should ICU beds be allocated during a crisis? Evidence from the COVID-19 pandemic

Background

The first wave of the COVID-19 pandemic overwhelmed healthcare systems in many countries, and the rapid spread of the virus and the acute course of the disease resulted in a shortage of intensive care unit (ICU) beds. We studied preferences of the public in the Netherlands regarding the allocation of ICU beds during a health crisis.

Methods

We distributed a cross-sectional online survey at the end of March 2020 to a representative sample of the adult population in the Netherlands. We collected preferences regarding the allocation of ICU beds, both in terms of who should be involved in the decision-making and which rationing criteria should be considered. We conducted Probit regression analyses to investigate associations between these preferences and several characteristics and opinions of the respondents.

Results

A total of 1,019 respondents returned a completed survey. The majority favored having physicians (55%) and/or expert committees (51%) play a role in the allocation of ICU beds and approximately one-fifth did not favor any of the proposed decision-makers. Respondents preferred to assign higher priority to vulnerable patients and patients who have the best prospect of full recovery. They also preferred that personal characteristics, including age, play no role.

Conclusion

“Our findings show that current guidelines for allocating ICU beds that include age as an independent criterion may not be consistent with societal preferences. Age may only play a role indirectly, in relation to the vulnerability of patients and their prospect of full recovery. Allocation of ICU beds during a health crisis requires a multivalue ethical framework.”

Development of a decision analytical framework to prioritise operating room capacity: lessons learnt from an empirical example on delayed elective surgeries during the COVID-19 pandemic in a hospital in the Netherlands

OBJECTIVE

To develop a prioritisation framework to support priority setting for elective surgeries after COVID-19 based on the impact on patient well-being and cost.

DESIGN

We developed decision analytical models to estimate the consequences of delayed elective surgical procedures (eg, total hip replacement, bariatric surgery or septoplasty).

SETTING

The framework was applied to a large hospital in the Netherlands.

OUTCOME MEASURES

Quality measures impacts on quality of life and costs were taken into account and combined to calculate net monetary losses per week delay, which quantifies the total loss for society expressed in monetary terms. Net monetary losses were weighted by operating times.

RESULTS

We studied 13 common elective procedures from four specialties. Highest loss in quality of life due to delayed surgery was found for total hip replacement (utility loss of 0.27, ie, 99 days lost in perfect health); the lowest for arthroscopic partial meniscectomy (utility loss of 0.05, ie, 18 days lost in perfect health). Costs of surgical delay per patient were highest for bariatric surgery (€31/pp per week) and lowest for arthroscopic partial meniscectomy (-€2/pp per week). Weighted by operating room (OR) time bariatric surgery provides most value (€1.19/pp per OR minute) and arthroscopic partial meniscectomy provides the least value (€0.34/pp per OR minute). In a large hospital the net monetary loss due to prolonged waiting times was €700 840 after the first COVID-19 wave, an increase of 506% compared with the year before.

CONCLUSIONS

This surgical prioritisation framework can be tailored to specific centres and countries to support priority setting for delayed elective operations during and after the COVID-19 pandemic, both in and between surgical disciplines. In the long-term, the framework can contribute to the efficient distribution of OR time and will therefore add to the discussion on appropriate use of healthcare budgets. The online framework can be accessed via: https://stanwijn.shinyapps.io/priORitize/.

Impact of COVID-19 pandemic on diagnostic pathology in the Netherlands

BACKGROUND

The COVID-19 pandemic has a huge impact on healthcare provided. The nationwide pathology registry of the Netherlands, PALGA, offers an outstanding opportunity to measure this impact for diseases in which pathology examinations are involved.

METHODS

Pathology specimen numbers in 2020 were compared with specimen numbers in 2019 for 5 periods of 4 weeks, representing two lockdowns and the periods in between, taking into account localization, procedure and benign versus malignant diagnosis.

RESULTS

The largest decrease was seen during the first lockdown (spring 2020), when numbers of pathology reports declined up to 88% and almost all specimen types were affected. Afterwards each specimen type showed its own dynamics with a decrease during the second lockdown for some, while for others numbers remained relatively low during the whole year. Generally, for most tissue types resections, cytology and malignant diagnoses showed less decrease than biopsies and benign diagnoses. A significant but small catch-up (up to 17%) was seen for benign cervical cytology, benign resections of the lower gastro-intestinal tract, malignant skin resections and gallbladder resections.

CONCLUSION

The COVID-19 pandemic has had a significant effect on pathology diagnostics in 2020. This effect was most pronounced during the first lockdown, diverse for different anatomical sites and for cytology compared with histology. The data presented here can help to assess the consequences on (public) health and provide a starting point in the discussion on how to make the best choices in times of scarce healthcare resources, considering the impact of both benign and malignant disease on quality of life.