Qualitative study of patients' choice between public and private hospital emergency departments

Factors associated to patients' referral to public or private Covid-19 healthcare centers in Tabriz, Iran

BACKGROUND

In Iran, tracking of patients and its associated data recording in private healthcare centers are poor, and thus a majority of patients suffering from Covid-19 are treated without any control on the isolation and quarantine processes. The present study aims to investigate the factors contributed to referral to private or public healthcare centers that provide Covid-19 care services.

METHODS

This cross-sectional study was conducted from November 2021 to January 2022 in Tabriz, Iran. We invited a total of 258 and 202Covid-19 patients from governmental and private healthcare centers, respectively, to participate in the study by convenient sampling method. Applying a self-administered questionnaire, we collected data on the reason of referring to the healthcare centers, patient's waiting time, quality of healthcare services received by the patients, patients' level of satisfaction, accessibility, insurance coverage, perceived severity of the disease, and the level of staff compliance from health protocols. Logistic regression model was used for data analysis by using SPSS-26 software.

RESULTS

Adjusted for other variables, higher socio-economic status (AOR (Adjusted Odds Ratio) = 6.64), older age (AOR = 1.02), referral of friends and family members (AOR = 1.52), shorter waiting time (AOR = 1.02) and higher satisfaction (AOR = 1.02) were contributed to referral to private centers. Better accessibility (AOR = 0.98) and wider insurance coverage (AOR = 0.99) were also contributed to referral to governmental centers.

CONCLUSION

Providing more appropriate insurance coverage by private healthcare centers, and promoting their level of accessibility seems to promote patients' referral to such centers. Moreover, establishing an accurate system for recording patients' information and follow up in private centers might promote the role of private healthcare centers in managing the overload of patients on healthcare system during such epidemics.

Patient preferences and choices as a reflection of trust-A cluster analysis comparing postsurgical perceptions in a private and a public hospital

Background

Active participation of patients in managing their medical treatment is a major component of the patient empowerment process and may contribute to better clinical outcomes. Patient perceptions and preferences affect the patient–physician encounter in a variety of dimensions, such as patient autonomy, freedom of choice and trust in the healthcare system. The Israeli healthcare system is mostly publicly funded, with additional private healthcare services for surgery and other medical treatments. The aim of this study was to compare the perceptions and preferences of patients in the public and private hospitals in Israel.

Methods

A cross‐sectional study among 545 individuals who had surgical procedures at two hospitals in Israel (one public and one private). A structured questionnaire comprising 23 items was used to collect perceptions via personal telephone interviews. The responses were categorized into five clusters and compared by type of health services provider (public vs. private) and sociodemographic characteristics (gender, age and education level).

Results

A hierarchical cluster analysis methodology identified five conceptual groups: trust, concern towards medical errors, dialogue between medical staff and the patient/patient's family, confidentiality and staff bias towards more informed patients, or those with supportive families. Four main themes that highlight patients' preferences were found: physical conditions, personal empowerment and perceived autonomy, patient experience and patient–provider encounter communication. Significant differences between the private and the public healthcare systems were found in four clusters: trust and patient care, patient's concerns, the extent of explanation and medical staff's commitment. Differences secondary to sociodemographic parameters were noticed: patients treated at the private hospital scored significantly higher items of trust, medical staff caring and the importance of choosing their treating surgeon, while patients treated at the public hospital scored higher staff commitment to the patient than those treated at the private hospital.

Conclusions

The study revealed the perceptions underlying the decisions of patients to undergo surgical procedures in public or private hospitals. Mutual learning could pave the way to better patient–physician encounters.

Patient or Public Contribution

Patients from the two hospitals were involved in this study by responding to the questionnaire. The data presented is based on the patient's responses.

Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

OBJECTIVE

Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments.

METHODS

Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020).

RESULTS

There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period.

CONCLUSIONS

Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

Australian private emergency departments can assist ambulance services by taking public emergency patients during surge and disasters

We describe a novel ambulance diversion programme, piloted in Victoria. This article discusses creating increased emergency capacity during surge or disasters by utilising private EDs, tested during a recent thunderstorm asthma disaster and an influenza epidemic. Public hospitals and EDs often run at or over capacity during normal operations. This leaves limited ability to manage surges in demand, resulting in suboptimal outcomes for patients, public ED staff and ambulance services. It is feasible to create surge capacity in private EDs for public ambulance patients. Other states could consider this option to help manage health disasters.

Patients' choice between public and private hospital emergency departments: a cross-sectional survey

OBJECTIVE

The aim of this study was to understand what factors influence patients' choice between public and private hospital ED and the relative weight of those factors among adult patients with private health insurance in Australia.

METHODS

A survey of 280 patients was conducted in four public and private hospitals' EDs in Brisbane between May and August 2015. The survey included information about respondent's demographics, nature of illness, decision-making, attitudes and choice. Independent t-test and Pearson's χ²-test were used to identify binary associations, and logistic regression was used to determine what factors influence patients' choice.

RESULTS

Patients who agreed that 'long waiting time is a barrier to access public hospital ED' were twice as likely to choose private hospitals (odds ratio [OR] 2.172, P = 0.001). Alternatively patients who did not consider that 'there were long waiting times in public hospital ED' were less likely to access private hospitals (OR 0.200, P = 0.003). More public hospital patients (70.7%) than private hospital patients (56.4%) (P = 0.015) agreed that 'out-of-pocket payment is a barrier to accessing private hospital ED'. Patients attending private hospitals rated the quality of service higher than those attending public hospitals (OR 1.26, P = 0.001).

CONCLUSION

Longer waiting times in public EDs is the principal issue considered by patients choosing private EDs and the out-of-pocket payment for accessing private EDs is the principal issue considered by public ED patients. The study suggests that addressing the out-of-pocket payments will attract more patients with private health insurance to access private EDs.