Modeling hospital infrastructure by optimizing quality, accessibility and efficiency via a mixed integer programming model

BACKGROUND: The majority of curative health care is organized in hospitals. As in most other countries, the current 94 hospital locations in the Netherlands offer almost all treatments, ranging from rather basic to very complex care. Recent studies show that concentration of care can lead to substantial quality improvements for complex conditions and that dispersion of care for chronic conditions may increase quality of care. In previous studies on allocation of hospital infrastructure, the allocation is usually only based on accessibility and/or efficiency of hospital care. In this paper, we explore the possibilities to include a quality function in the objective function, to give global directions to how the ‘optimal’ hospital infrastructure would be in the Dutch context. METHODS: To create optimal societal value we have used a mathematical mixed integer programming (MIP) model that balances quality, efficiency and accessibility of care for 30 ICD-9 diagnosis groups. Typical aspects that are taken into account are the volume-outcome relationship, the maximum accepted travel times for diagnosis groups that may need emergency treatment and the minimum use of facilities. RESULTS: The optimal number of hospital locations per diagnosis group varies from 12-14 locations for diagnosis groups which have a strong volume-outcome relationship, such as neoplasms, to 150 locations for chronic diagnosis groups such as diabetes and chronic obstructive pulmonary disease (COPD). CONCLUSIONS: In conclusion, our study shows a new approach for allocating hospital infrastructure over a country or certain region that includes quality of care in relation to volume per provider that can be used in various countries or regions. In addition, our model shows that within the Dutch context chronic care may be too concentrated and complex and/or acute care may be too dispersed. Our approach can relatively easily be adopted towards other countries or regions and is very suitable to perform a ‘what-if’ analysis

The use of quality information by general practitioners: does it alter choices? A randomized clustered study

Background: Following the introduction of elements of managed competition in the Netherlands in 2006, General Practitioners (GPs) and patients were given the role to select treatment hospital using public quality information. In this study we investigate to what extent hospital preferences of GP's are affected by performance indicators on medical effectiveness and patient experiences. We selected three conditions: breast cancer, cataract surgery, and hip and knee replacement. Methods. After an inquiry 26 out of 226 GPs in the region signed up to participate in our study. After a 2:1 randomization, we analyzed the referral patterns in the region using three groups of GPs: GPs (n=17) who used the report cards and received personal clarification, GPs that signed up for the study but were assigned to the control group (n=9), and the GPs outside the study (n=200).We conducted a difference in differences analysis where the choice for a particular hospital was the dependent variable and time (2009 or 2010), the sum score of the CQI, the sum score of the PI's and dummy variables for the individual hospitals were used as independent variables. Results: The analysis of the conditions together and cataract surgery and hip and knee replacement separately, showed no significant relationships between the scores on the report cards and the referral patterns of the GPs. For breast cancer our analysis revealed that GPs in the intervention group refer 1.0% (p=0.01) more to hospitals that score one percent point better on the indicators for medical effectiveness. Conclusion: Our study provides empirical evidence that GP referral patterns were unaffected by the available quality information, except for the outcome indicators for breast cancer care that were presented. This finding was surprising since our study was designed to identify changes in hospital preference (1) amongst the most motivated GP's, (2) that received personal clarification of the performance indicators, and (3) selected indicators/conditions from a large set of indicators that they believed were most important. This finding may differ when quality information is based on outcome indicators with a clinically relevant difference, as shown by our indicators for breast cancer treatment. We believe that the current set of (largely process) hospital quality indicators do not serve the GP's information needs and consequently quality plays little role in the selection of hospitals for treatment. © 2013 Ikkersheim and Koolman; licensee BioMed Central Ltd

Comparative Performance Information Plays No Role in the Referral Behaviour of GPs

Comparative performance information (CPI) about the quality of hospital care is information used to identify high-quality hospitals and providers. As the gatekeeper to secondary care, the general practitioner (GP) can use CPI to reflect on the pros and cons of the available options with the patient and choose a provider best fitted to the patient’s needs. We investigated how GPs view their role in using CPI to choose providers and support patients. Method: We used a mixed-method, sequential, exploratory design to conduct explorative interviews with 15 GPs about their referral routines, methods of referral consideration, patient involvement, and the role of CPI. Then we quantified the qualitative results by sending a survey questionnaire to 81 GPs affiliated with a representative national research network. Results: Seventy GPs (86% response rate) filled out the questionnaire. Most GPs did not know where to find CPI (87%) and had never searched for it (94%). The GPs reported that they were not motivated to use CPI due to doubts about its role as support information, uncertainty about the effect of using CPI, lack of faith in better outcomes, and uncertainty about CPI content and validity. Nonetheless, most GPs believed that patients would like to be informed about quality-of- care differences (62%), and about half the GPs discussed quality-of-care differences with their patients (46%), though these discussions were not based on CPI. Conclusion: Decisions about referrals to hospital care are not based on CPI exchanges during GP consultations. As a gatekeeper, the GP is in a good position to guide patients through the enormous amount of quality information that is available. Nevertheless, it is unclear how and whether the GP’s role in using information about quality of care in the referral process can grow, as patients hardly ever initiate a discussion based on CPI, though they seem to be increasingly more critical about differences in quality of care. Future research should address the conditions needed to support GPs’ ability and willingness to use CPI to guide their patients in the referral process

Modeling the optimal hospital infrastructure by optimizing quality, accessibility and efficiency via a Mixed Integer Programming model

Background The majority of curative health care is organized in hospitals. As in most other countries, the current 94 hospital locations in the Netherlands offer almost all treatments, ranging from rather basic to very complex care. Recent studies show that concentration of care can lead to substantial quality improvements for complex conditions and that dispersion of care for chronic conditions may increase quality of care. In previous studies on allocation of hospital infrastructure, the allocation is usually only based on accessibility and/or efficiency of hospital care. In this paper, we explore the possibilities to include a quality function in the objective function, to give global directions to how the ‘optimal’ hospital infrastructure would be in the Dutch context. Methods To create optimal societal value we have used a mathematical mixed integer programming (MIP) model that balances quality, efficiency and accessibility of care for 30 ICD-9 diagnosis groups. Typical aspects that are taken into account are the volume-outcome relationship, the maximum accepted travel times for diagnosis groups that may need emergency treatment and the minimum use of facilities. Results The optimal number of hospital locations per diagnosis group varies from 12-14 locations for diagnosis groups which have a strong volume-outcome relationship, such as neoplasms, to 150 locations for chronic diagnosis groups such as diabetes and chronic obstructive pulmonary disease (COPD). Conclusions In conclusion, our study shows a new approach for allocating hospital infrastructure over a country or certain region that includes quality of care in relation to volume per provider that can be used in various countries or regions. In addition, our model shows that within the Dutch context chronic care may be too concentrated and complex and/or acute care may be too dispersed. Our approach can relatively easily be adopted towards other countries or regions and is very suitable to perform a ‘what-if’ analysis