Do patients' preferences prevail in hospital selection?: a comparison between discrete choice experiments and revealed hospital choice

The elicitation of patient and physician preferences for calculating consumer-based composite measures on hospital report cards: results of two discrete choice experiments

PURPOSE

The calculation of aggregated composite measures is a widely used strategy to reduce the amount of data on hospital report cards. Therefore, this study aims to elicit and compare preferences of both patients as well as referring physicians regarding publicly available hospital quality information METHODS: Based on systematic literature reviews as well as qualitative analysis, two discrete choice experiments (DCEs) were applied to elicit patients' and referring physicians' preferences. The DCEs were conducted using a fractional factorial design. Statistical data analysis was performed using multinomial logit models RESULTS: Apart from five identical attributes, one specific attribute was identified for each study group, respectively. Overall, 322 patients (mean age 68.99) and 187 referring physicians (mean age 53.60) were included. Our models displayed significant coefficients for all attributes (p < 0.001 each). Among patients, "Postoperative complication rate" (20.6%; level range of 1.164) was rated highest, followed by "Mobility at hospital discharge" (19.9%; level range of 1.127), and ''The number of cases treated" (18.5%; level range of 1.045). In contrast, referring physicians valued most the ''One-year revision surgery rate'' (30.4%; level range of 1.989), followed by "The number of cases treated" (21.0%; level range of 1.372), and "Postoperative complication rate" (17.2%; level range of 1.123) CONCLUSION: We determined considerable differences between both study groups when calculating the relative value of publicly available hospital quality information. This may have an impact when calculating aggregated composite measures based on consumer-based weighting.

Dynamic resectorization to improve utility of healthcare systems

Balancing is an essential challenge in healthcare systems that requires effective strategies. This study aims to address this crucial issue by suggesting a practical approach. We show the potential of balancing a regional healthcare system to improve its utility. We consider a regional healthcare system comprising multiple hospitals with different sizes, capacities, quality of service, and accessibility. We define a utility function for the system based on the sectorization concept, which endeavors to form a balance between hospitals in terms of essential outputs such as waiting times and demands. The dynamic nature of the system means that this balance degrades over time, necessitating periodic sectorization, which is called resectorization. Our methodology stands out for incorporating resectorization as a dynamic strategy, enabling more flexible and responsive adaptations to continuously changing healthcare needs. Unlike previous studies, based on a system-oriented approach, our resectorization scenarios include the periodic closure of some hospitals. This enables us to enhance both the capacity and quality of healthcare facilities. Furthermore, in contrast to other studies, we investigate the states of diminishing demand throughout the resectorization process. To provide empirical insights, we conduct a simulation using data from a real-world case study. Our analysis spans multiple time periods, enabling us to dynamically quantify the utility of the healthcare system. The numerical findings demonstrate that substantial utility improvements are attainable through the defined scenarios. The study suggests a practical solution to the critical challenge of balancing issues in regional healthcare systems.

Choosing a maternity hospital: a matter of travel distance or quality of care?

BACKGROUND

The choice of a hospital should be based on individual need and accessibility. For maternity hospitals, this includes known or expected risk factors, the geographic accessibility and level of care provided by the hospital. This study aims to identify factors influencing hospital choice with the aim to analyze if and how many deliveries are conducted in a risk-appropriate and accessible setting in Bavaria, Germany.

METHODS

This is a cross-sectional secondary data analysis based on all first births in Bavaria (2015-18) provided by the Bavarian Quality Assurance Institute for Medical Care. Information on the mother and on the hospital were included. The Bavarian Index of Multiple Deprivation 2010 was used to account for area-level socioeconomic differences. Multiple logistic regression models were used to estimate the strength of association of the predicting factors and to adjust for confounding.

RESULTS

We included 195,087 births. Distances to perinatal centers were longer than to other hospitals (16 km vs. 12 km). 10% of women with documented risk pregnancies did not deliver in a perinatal center. Regressions showed that higher age (OR 1.03; 1.02-1.03 95%-CI) and risk pregnancy (OR 1.44; 1.41-1.47 95%-CI) were associated with choosing a perinatal center. The distances travelled show high regional variation with a strong urban-rural divide.

CONCLUSION

In a health system with free choice of hospitals, many women chose a hospital close to home and/or according to their risks. However, this is not the case for 10% of mothers, a group that would benefit from more coordinated care.

Who Serves Where: A Geospatial Analysis of Access to Endocrine Surgeons in the United States and Puerto Rico

BACKGROUND

The association between surgical volume and patient outcome is well established, with higher case volume associated with a lower risk of complications. We hypothesized that the geographic distribution of endocrine/head and neck surgeons with an endocrine focus in the United States and Puerto Rico may limit access to many potential patients, particularly in rural areas.

METHODS

We used web-based directories from the American Association of Endocrine Surgeons, American Head and Neck Society, and the American Academy of Otolaryngology-Head and Neck Surgery to identify endocrine surgery specialists in the United States and Puerto Rico. Using geographic coordinates and OpenStreetMap and Valhalla software, we calculated the areas within a 60-, 90-, or 120-minute driving distance from specialist offices. We used 2020 U.S. Census Data to calculate census tract populations inside or outside the accessible areas.

RESULTS

Excluding duplicate providers across organizations, we geocoded 603 specialist addresses in the United States and Puerto. We found that 23.76% (78.3 million) of Americans do not have access to a society-affiliated endocrine/head and neck surgeon with an endocrine focus within a 60-minute drive, 14.37% (47.4 million) within a 90-minute drive, and 8.38% (27.6 million) within a 120-minute drive. We observed that the areas of coverage are primarily focused on metropolitan areas.

CONCLUSION

Nearly one-third of Americans do not have access to a society-affiliated endocrine/head and neck surgeon with an endocrine focus within a 1-hour drive, highlighting a concerning geographic barrier to care. Further work is needed to facilitate patient access and mitigate disparities in quality care.

Preferences of patients with diabetes mellitus for primary healthcare institutions: a discrete choice experiment in China

OBJECTIVES

To quantify the preference of patients with diabetes mellitus (DM) for primary healthcare (PHC) institutions in China to redirect the patient flow and improve health outcomes.

DESIGN

Cross-sectional study. Discrete choice experiment (DCE) surveys asked patients with DM to choose between hypothetical institutions that differed in the medical service capacity, out-of-pocket (OOP) medical costs per month, travel time, the attitude of medical staff and the availability of diabetes drugs.

SETTING

Shandong province, China.

PARTICIPANTS

The participants were 887 patients with DM from 36 urban communities and 36 rural villages in Shandong province. One participant did not provide any DCE answers and a further 57 patients failed the internal consistency test. 829 fully completed surveys were included in the final data analysis.

MAIN OUTCOMES AND MEASURES

A mixed logit model was used to calculate the willingness to pay and predict choice probabilities for PHC institution attributes. Preference heterogeneity was also investigated.

RESULTS

All five attributes were associated with the preferences of patients with DM. The OOP medical costs and the medical service capacity were the most influential attributes. Improvements simultaneously in the attitude of medical staff, drug availability and travel time increased the likelihood of a patient's PHC institution choice. Preferences differed by region, annual household income and duration of diabetes.

CONCLUSIONS

Our patient preference data may help policymakers improve health services and increase acceptance of choosing PHC institutions. The OOP medical costs and medical service capacity should be regarded as a priority in decision-making.