Do waiting times reduce hospital costs?

Impact of China's digital economy development on the health of middle-aged and older people: an air pollution-based perspective

China has shown good momentum on the road of digital economy development, however, it is also rapidly entering an aging society. Exploring the health effects of the digital economy is of positive significance for realizing healthy aging in China. This paper focuses on the relationship between the digital economy and the health of middle-aged and older people using microdata from the China Health and Retirement Longitudinal Study (CHARLS) 2011-2018 and macrodata from Chinese cities. The study found that the digital economy showed a significant inverted U-shaped relationship on the health of middle-aged and older people. The results of subgroup regressions indicated heterogeneity in this effect across gender, education level, urban/rural and region. Individual health in female, highly educated, and urban groups is more closely related to the digital economy. Middle-aged and old groups in the western region are better able to enjoy the dividends of the digital economy, while middle-aged and old groups in the eastern region are more negatively affected by the digital economy. In the lead-up to the development of the digital economy, individual health can be promoted by narrowing the urban-rural income gap and increasing basic medical resources, while in the later stage of the development of the digital economy, it manifests itself in inhibiting the level of individual health by widening the urban-rural income gap and lowering the level of basic medical resources. In addition, air pollution exhibits a positive moderating effect between the digital economy and individual health, suggesting that air pollution reinforces the impact of the digital economy on health. Expansive analyses indicate that the digital economy has a negative impact on physiological health.

Reducing waiting times from 65 to under 40 days for children and adolescents receiving mental health services using a new scheduling policy

The aim of this study is to conduct an intervention that tests whether a new scheduling policy designed to reduce waiting times actually will lead to a reduction in waiting times. The new scheduling policy was developed using mixed methods. Qualitative data was gathered to fully understand current planning processes, while quantitative methods were used to model and predict future waiting times. If current planning practices are continued, waiting times will only increase. Additionally, the findings show that simulation modeling can be used to predict the capacity needed for intakes (first appointment) to reduce and maintain target waiting times over time. In our study, this meant a slight increase in capacity for intakes. This new scheduling policy led to a reduction in waiting times from 65 days in 2016, to under 40 days post-intervention in 2017. Waiting times have been held under 40 days since implementation of the new policy, 2017-2020. Our study shows that setting appropriate (weekly) intake goals, will lead to maintaining acceptable levels of variation in waiting times. This theory was tested and proven to be effective.

Referral assessment and patient waiting time decisions in specialized mental healthcare: an exploratory study of early routine collection of PROM (LOVePROM)

BACKGROUND

Norway has prioritized health services according to the principle of "severity of conditions", where waiting time reflects patients' medical urgency. We aim to investigate if the "severity-of-condition" principle performs well in the priority setting of waiting time, between and within groups of patients using community mental health services. We also aim to investigate the association between patients' diagnoses and symptom severity at the start of treatment and the corresponding waiting time.

METHODS

The study analyzed routine data from Lovisenberg electronic Patient-Reported Outcome Measurement (LOVePROM) at Lovisenberg Diaconal Hospital in Norway. We estimated patient-reported severity by using Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM), together with patients' diagnoses to identify patients' needs in general. To assess the performance of current prioritization, we compared waiting times for patients with major depressive disorder and their maximum recommended waiting time. Multivariate regression models were used to assess the association between patient-reported severity, their diagnosis, and waiting times.

RESULTS

Of the 6108 mental health disorder patients, patients with moderate to severe conditions waited seven weeks, while patients with mild conditions or below clinical cutoff waited 8 weeks. Included in the sample, 1583 were diagnosed with depression. Results indicated that patients with moderate and severe depression had a slightly shorter wait-time than patients with mild depression. However, 32.4% patients with moderate depression and 83.3% patients with severe depression, waited longer than their maximum recommended waiting time. CORE-OM identified depressive patients with risk-to-self harm, who had a 0.84 weeks shorter wait-time. These results were also applied to patients with other common mental health disorders.

CONCLUSION

Overall, patients waited in accordance with the "severity of condition" principle, but the trend was not strong. Therefore, we advocate that there is substantial room for quality improvements in priority setting on waiting time. We suggest further research should investigate if routine collection of PROM and assessment of referral letters, can better inform specialists when deciding on waiting time.

A Cost Analysis with the Discrete-Event Simulation Application in Nurse and Doctor Employment Management

AIM

This study aimed to analyze the treatment cost of a patient, depending on the number of patients treated, patient waiting times, and the number of nurses and doctors employed in an emergency department of a private hospital.

BACKGROUND

Within health systems, changes in healthcare resources can be very costly, especially if these changes are long-term. The discrete-event simulation method described in this paper allows for the monitoring and analysis of complicated changes in real systems by using computer-based modeling.

METHOD

The discrete event simulation model was derived from 9 scenarios according to the number of nurses and doctors, and a comparison was made between the results of the scenarios and the actual results.

RESULTS

Among the scenarios, scenario 6 provided the lowest treatment cost for a patient by employing three doctors and two nurses with the best performance. The cost of treatment for a patient varies betweenŧ 9.00-ŧ 11.00 depending on the value of δ, and the daily cost of these resources to the hospital is ŧ1300.77.

CONCLUSIONS

This study provides a clear picture of a cost analysis comparison based on changes made about the actual health system in the computer-based simulated environment. Implications for Nursing Management The workforce data of nurses and doctors offers enough detail for cost analysis in healthcare settings to calculate the cost of treatment for a patient.

Does waiting times decrease or increase operational costs in short and long-term? Evidence from Portuguese public hospitals

The Portuguese National Health System is composed of all public entities offering health services. There has been a successive increase in expenditure in recent years due to various factors that have contributed to a high degree of uncertainty about the evolution of operating costs in Public Business Hospitals. This research's main objective is to study the relationship between operational costs and waiting times as well as costs with healthcare professionals and waiting times in both external consultations and hospital surgeries. Furthermore, we will empirically assess the presence of U-shaped behaviour in both of these two relationships. We have included a sample of 38 hospitals considered in the Portuguese National Health System. We also included, in our analysis, five groups of public business hospitals, according to the Administrative Central Agency of Portugal's Health Service, considering the period between January 2015 and December 2019. To validate the two relationships proposed, the Autoregressive Distributed Lag panel model was used. This study highlights that longer waiting times for external consultation and surgery significantly affect hospital costs and suggest that longer waiting times do not merely increase absence rates. The study also proves that there are long-term effects that last beyond the short-term waiting period.

Doctors' response to queues: Evidence from a Portuguese emergency department

We evaluate how doctors in an emergency department react to the number of patients waiting for treatment. Our outcomes reflect the time spent with the patient, the intensity of treatment, and discharge destination. Using visit-level data in a Lisbon-area hospital, we use a fixed effects model to exploit variation in the queue size while addressing endogeneity using the number of arrivals to the hospital in the previous 60 min as an instrumental variable. Furthermore, we estimate doctors' reactions separately for patients with different degrees of urgency, as measured by the Manchester triage system. Results show that doctors discharge patients more rapidly as queues become longer, and this effect is stronger for patients that do not have life-threatening conditions. We also find that the intensity of diagnosis/treatment procedures decreases when patients face longer queues, driven by the extensive margin. Finally, doctors are less likely to admit patients to inpatient care. We interpret the results in the light of the doctors' incentives literature, explaining how these agents behave in the context of a National Health Service, with no financial incentives.

The impact of waiting for intervention on costs and effectiveness: the case of transcatheter aortic valve replacement

OBJECTIVES

The economic crisis in Europe might have limited access to some innovative technologies implying an increase of waiting time. The purpose of the study is to evaluate the impact of waiting time on the costs and benefits of transcatheter aortic valve replacement (TAVR) for the treatment of severe aortic stenosis.

METHODS

This is a cost-utility analysis from the perspective of the Spanish National Health Service. Results of two prospective hospital registries (158 and 273 consecutive patients) were incorporated into a probabilistic Markov model to compare quality adjusted life years (QALYs) and costs for TAVR after waiting for 3-12 months, relative to immediate TAVR. We simulated a cohort of 1000 patients, male, and 80 years old; other patient profiles were assessed in sensitivity analyses.

RESULTS

As waiting time increased, costs decreased at the expense of lower survival and loss of QALYs, leading to incremental cost-effectiveness ratios for eliminating waiting lists of about 12,500 € per QALY. In subgroup analyses prioritization of patients for whom higher benefit was expected led to a smaller loss of QALYs. Concerning budget impact, long waiting lists reduced spending considerably and permanently.

CONCLUSIONS

A shorter waiting time is likely to be cost-effective (considering commonly accepted willingness-to-pay thresholds in Europe) relative to 3 months or longer waiting periods. If waiting lists are nevertheless seen as unavoidable due to severe but temporary budgetary restrictions, prioritizing patients for whom higher benefit is expected appears to be a way of postponing spending without utterly sacrificing patients' survival and quality of life.

The effect of waiting times on demand and supply for elective surgery: Evidence from Italy

Waiting times are a major policy concern in publicly funded health systems across OECD countries. Economists have argued that, in the presence of excess demand, waiting times act as nonmonetary prices to bring demand for and supply of health care in equilibrium. Using administrative data disaggregated by region and surgical procedure over 2010-2014 in Italy, we estimate demand and supply elasticities with respect to waiting times. We employ linear regression models with first differences and instrumental variables to deal with endogeneity of waiting times. We find that demand is inelastic to waiting times while supply is more elastic. Estimates of demand elasticity are between -0.15 to -0.24. Our results have implications on the effectiveness of policies aimed at increasing supply and their ability to reduce waiting times.

The intertwined relationship between patient education, hospital waiting times and hospital utilization

Background Hospital waiting times are established instruments to ration healthcare when resources are scarce. However, higher educated patients may be better able to influence access to, and exit from, hospital care when waiting times are long. Methods Based on a representative sample of 11 European countries from the Survey of Health, Ageing and Retirement in Europe (SHARE) collected in 2004/2005, this paper investigates whether the relationship between individual educational background and hospital utilization depends on the prevalent hospital waiting times in a country. Logistic regression with interaction effects between individual education and average waiting times per country are conducted. Results Primary education is significantly associated with a lower probability of visiting a hospital overnight (OR = 0.88) compared to secondary and tertiary education. Patients in countries with long waiting times had shorter stays (OR = 0.92), and the significant interaction effect indicates that lower educated patients have longer hospital stays than higher educated patients in countries where waiting times tend to be long (OR = 1.06). Conclusions While the findings imply that educational differences exist with regard to hospital care, future research should investigate potential underlying mechanisms, i.e. patients' perceived access barriers and the perceived quality of hospital treatment.

Impact of the single point of access referral system to reduce waiting times and improve clinical outcomes in an assistive technology service

The introduction of a single point referral system that prioritises clients depending on case complexity and overcomes the need for re-admittance to a waiting list via a review system has been shown to significantly reduce maximum waiting times for a Posture and Mobility (Special Seating) Service from 102.0 ± 24.33 weeks to 19.2 ± 8.57 weeks (p = 0.015). Using this service model linear regression revealed a statistically significant improvement in the performance outcome of prescribed seating solutions with shorter Episode of Care completion times (p = 0.023). In addition, the number of Episodes of Care completed per annum was significantly related to the Episode of Care completion time (p = 0.019). In conclusion, it is recommended that it may be advantageous to apply this service model to other assistive technology services in order to reduce waiting times and to improve clinical outcomes.

Do waiting times affect health outcomes? Evidence from coronary bypass

Long waiting times for non-emergency services are a feature of several publicly-funded health systems. A key policy concern is that long waiting times may worsen health outcomes: when patients receive treatment, their health condition may have deteriorated and health gains reduced. This study investigates whether patients in need of coronary bypass with longer waiting times are associated with poorer health outcomes in the English National Health Service over 2000-2010. Exploiting information from the Hospital Episode Statistics (HES), we measure health outcomes with in-hospital mortality and 28-day emergency readmission following discharge. Our results, obtained combining hospital fixed effects and instrumental variable methods, find no evidence of waiting times being associated with higher in-hospital mortality and weak association between waiting times and emergency readmission following a surgery. The results inform the debate on the relative merits of different types of rationing in healthcare systems. They are to some extent supportive of waiting times as an acceptable rationing mechanism, although further research is required to explore whether long waiting times affect other aspects of individuals' life.

Waiting time distribution in public health care: empirics and theory

Excessive waiting times for elective surgery have been a long-standing concern in many national healthcare systems in the OECD. How do the hospital admission patterns that generate waiting lists affect different patients? What are the hospitals characteristics that determine waiting times? By developing a model of healthcare provision and analysing empirically the entire waiting time distribution we attempt to shed some light on those issues. We first build a theoretical model that describes the optimal waiting time distribution for capacity constraint hospitals. Secondly, employing duration analysis, we obtain empirical representations of that distribution across hospitals in the UK from 1997-2005. We observe important differences on the 'scale' and on the 'shape' of admission rates. Scale refers to how quickly patients are treated and shape represents trade-offs across duration-treatment profiles. By fitting the theoretical to the empirical distributions we estimate the main structural parameters of the model and are able to closely identify the main drivers of these empirical differences. We find that the level of resources allocated to elective surgery (budget and physical capacity), which determines how constrained the hospital is, explains differences in scale. Changes in benefits and costs structures of healthcare provision, which relate, respectively, to the desire to prioritise patients by duration and the reduction in costs due to delayed treatment, determine the shape, affecting short and long duration patients differently. JEL Classification I11; I18; H51.

Heart Surgery Waiting Time: Assessing the Effectiveness of an Action

BACKGROUND

Waiting time is an index assessing patient satisfaction, managerial effectiveness and horizontal equity in providing health care. Although heart surgery centers establishment is attractive for politicians. They are always faced with the question of to what extent they solve patient's problems.

OBJECTIVES

The objective of this study was to evaluate factors influencing waiting time in patients of heart surgery centers, and to make recommendations for health-care policy-makers for reducing waiting time and increasing the quality of services from this perspective.

PATIENTS AND METHODS

This cross-sectional study was performed in 2013. After searching articles on PubMed, Elsevier, Google Scholar, Ovid, Magiran, IranMedex, and SID, a list of several criteria, which relate to waiting time, was provided. Afterwards, the data on waiting time were collected by a researcher-structured checklist from 156 hospitalized patients. The data were analyzed by SPSS 16. The Kolmogorov Smirnov and Shapiro tests were used for determination of normality. Due to the non-normal distribution, non-parametric tests, such as Kruskal-Wallis and Mann-Whitney were chosen for reporting significance. Parametric tests also used reporting medians.

RESULTS

Among the studied variables, just economic status had a significant relation with waiting time (P = 0.37). Fifty percent of participants had diabetes, whereas this estimate was 43.58% for high blood pressure. As the cause of delay, 28.2% of patients reported financial problems, 18.6% personal problem and 13.5% a delay in providing equipment by the hospital.

CONCLUSIONS

It seems the studied hospital should review its waiting time arrangements and detach them, as far as possible, from subjective and personal (specialists) decisions. On the other hand, ministries of health and insurance companies should consider more financial support. It is also recommend that hospitals should arrange preoperational psychiatric consultation for increasing patients' emotionally readiness.

Competition and quality in health care markets: a differential-game approach

We investigate the effect of competition on quality in health care markets with regulated prices taking a differential game approach, in which quality is a stock variable. Using a Hotelling framework, we derive the open-loop solution (health care providers set the optimal investment plan at the initial period) and the feedback closed-loop solution (providers move investments in response to the dynamics of the states). Under the closed-loop solution competition is more intense in the sense that providers observe quality in each period and base their investment on this information. If the marginal provision cost is constant, the open-loop and closed-loop solutions coincide, and the results are similar to the ones obtained by static models. If the marginal provision cost is increasing, investment and quality are lower in the closed-loop solution (when competition is more intense). In this case, static models tend to exaggerate the positive effect of competition on quality.