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The purpose of this paper is to maximize the expected revenue of the outpatient department considering patient preferences and choices.

Patient preference refers to the preferred physician and time slot that patients hold before asking for appointments. Patient choice is the appointment decision the patient made after receiving a set of options from the scheduler. The relationship between patient choices and preferences is explored. A dynamic programming (DP) model is formulated to optimize appointment scheduling with patient preferences and choices. The DP model is transformed to an equivalent linear programming (LP) model. A decomposition method is proposed to eliminate the number of variables. A column generation algorithm is used to resolve computation problem of the resulting LP model.

Numerical studies show the benefit of multiple options provided, and that the proposed algorithm is efficient and accurate. The effects of the booking horizon and arrival rates are studies. A policy about how to make use of the information of patient preferences is compared to other naive polices. Experiments show that more revenue can be expected if patient preferences and choices are considered.

This paper proposes a framework for appointment scheduling problem in outpatient departments. It is concluded that more revenue can be achieved if more choices are provided for patients to choose from and patient preferences are considered. Additionally, an appointment decision can be made timely after receiving patient preference information. Therefore, the proposed model and policies are convenient tools applicable to an outpatient department.

As hospitals are now being designed with an increasing number of single rooms or cubicles, the individual preference of patients with respect to social contact is of great interest. The purpose of this study is to gain a better understanding of the experience of patients in an outpatient infusion center.

A total of 29 semi-structured interviews were conducted, transcribed and analyzed by using direct content analysis.

Findings showed that patients perceived a lack of acoustic privacy and therefore tried to emotionally isolate themselves or withheld information from staff. In addition, patients complained about the sounds of infusion pumps, but they were neutral about the interior features. Patients who preferred non-talking desired enclosed private rooms and perceived negative distraction because of spatial crowding. In contrast, patients who preferred talking, or had no preference, desired shared rooms and perceived positive distraction because of spatial crowding.

In conclusion, results showed a relation between physical aspects (i.e. physical enclosure) and the social environment.

The findings allow facility managers to better understand the patients’ experiences in an outpatient infusion facility and to make better-informed decisions. Patients with different preferences desired different physical aspects. Therefore, nursing staff of outpatient infusion centers should assess the preferences of patients. Moreover, architects should integrate different types of treatment places (i.e. enclosed private rooms and shared rooms) in new outpatient infusion centers to fulfill different preferences and patients should have the opportunity to discuss issues in private with nursing staff.

This study emphasizes the importance of a mix of treatment rooms, while new hospital designs mainly include single rooms or cubicles.

The provision of private health-care services by public hospitals is common in Iran. Examining factors associated with patients’ preferences to use private health services and using this knowledge in health planning and policymaking can help expand the use of such services. Thus, this study aims to investigate patients’ preferences for private health services delivered in public hospitals.

Based on a discrete choice experiment from a sample of 375 patients in a public training hospital in Qazvin, northwest city of Iran, the authors evaluated participants’ preference over the health-care attributes affecting their choice to use private health-care services delivered in the hospital. The authors also estimated the marginal willingness to pay to determine the maximum amount a patient was willing to pay for the improvement in the level of each health-care attributes.

The findings revealed that patients were 2.7 times more likely to choose private hospital services when the waiting time was reduced to less than a week. Furthermore, as patients had complimentary insurance coverage, they were over 60% more likely to receive such services from training hospitals. Finally, continuity of care and reduced health-care tariffs were significant factors that increased patients’ preference to choose private services by 52 and 37%, respectively.

Examining factors associated with patients’ preferences to use private health services and using this knowledge in policymaking can help expand such services. The findings affirmed that various incentives, including service quality factors, are required to increase the likelihood of patients choosing private services.

High quality healthcare is important to all patients. If healthcare is felt to be high quality, then patients will be satisfied, and the relationship between patients and healthcare providers will improve. Patient satisfaction is among the most commonly used service quality indicators; however, it is not fully known which factors influence satisfaction. Therefore, it is necessary to pay attention to the elements that affect both healthcare quality and patient satisfaction. Nowadays, several methods are used in health economics to assess patient preferences, prioritize them and help health policy makers improve services. Discrete choice experiment (DCE) is one method that is useful to elicit patient preferences regarding healthcare services. The purpose of this paper is to apply DCE and elicit patient preferences in medical centers to rank certain healthcare quality factors.

The descriptive, analytical study used a cross-sectional questionnaire that the authors developed. In total, 12 scenarios were chosen after applying fractional factorials. The questionnaire was completed by patients who were admitted to Kerman General Teaching Hospitals, South-East Iran in 2015. Patient preferences were identified by calculating the characteristics’ marginal effects and prioritizing them. The generalized estimation equation (GEE) model was used to determine attribute effects on patient preferences.

In total, 167 patients completed the questionnaire. Prioritizing the attributes showed that “physical examination” was the most important attribute. Other key features included “cleanliness,” “training after discharging,” “medical staff attention,” “waiting for admission” and “staff attitude.” All attributes were statistically significant (p<0.05) except staff behavior. No demographic characteristic was significant.

To increase hospital patient satisfaction, health policy makers should develop programs to enhance healthcare quality and hospital safety by increasing physical examination quality and other services.

To estimate DCE independent variables, logistic regression models are usually used. The authors used the GEE model to estimate discrete choice experiment owing the explanatory variables’ dependency.

Procedures can be categorized as certain surgeries based on their necessity and outcomes while others are classified as uncertain surgeries based on these areas. To account for this variance, policies such as the Affordable Care Act (ACA) call for health care providers to engage in shared decision making (SDM) with patients to ensure they are informed of treatment options and asked their preferences. Yet, gender may influence the decision-making process. Thus, this project examines the decision process and how gender impacts patients’ participation in decisions to undergo certain surgeries compared to uncertain surgeries.

This research project analyzed data from the National Survey of Medical Decisions 2006–2007 which surveyed the medical decisions of US residents 40 and older.

First, the data reveals that women felt more informed having uncertain surgeries compared to men. Second, patients were less likely asked their preference for surgery when undergoing certain surgeries compared to uncertain surgeries. Third, compared to men, women having uncertain surgeries were less likely to make the final decision to have surgery, compared to sharing the final decision with health care providers.

Due to the sample size, this project could not perform three-way interactions between gender, race, and surgery type.

Gender influences the level patients feel informed having uncertain surgeries. Though policy calls for SDM, health care providers are less likely to ask patients their preference for surgery regarding certain surgeries, relative to uncertain surgeries. Gender impacts the final decision-making process regarding whether patients should have uncertain surgeries.

Shared decision making (SDM) is a model of health care in which patients are involved in the decision-making process about their treatment, considering their preferences and concerns in a deliberative process with the health care provider. Many existing instruments assess the antecedents, process, or the outcomes of SDM. The purpose of this paper is to identify the SDM-related measures applied in a mental health context.

The authors performed a systematic review in several electronic databases from 1990 to October 2016. Studies that assessed quantitatively one or more constructs related to SDM (antecedents, process, and outcomes) in the field of mental health were included.

The authors included 87 studies that applied 48 measures on distinct SDM constructs. A large majority of them have been developed in the field of physical diseases and adapted or directly applied in the mental health context. The most evaluated construct is the SDM process in consultation, mainly by patients’ self-report but also by external observer measures, followed by the patients’ preferences for involvement in decision making. The most applied instrument was the Autonomy Preference Index, followed by the Observing Patient Involvement in Decision Making (OPTION) and the Control Preferences Scale (CPS). The psychometric validation in mental health samples of the instruments identified is scarce.

The bibliographic search is comprehensive, but could not be completely exhaustive. Effort should be invested in the development of new SDM for mental health tools that will reflect the complexity and specific features of mental health care.

The authors highlight several limitations and challenges for the measurement of SDM in mental health care.

Preferences of both Alzheimer patients and their spouse caregivers are related to a willingness-to-pay (WTP) measure which is used to test for the presence of mutual (rather than conventional unilateral) altruism.

Contingent valuation experiments were conducted in 2000–2002, involving 126 Alzheimer patients and their caregiving spouses living in the Zurich metropolitan area (Switzerland). WTP values for three hypothetical treatments of the demented patient were elicited. The treatment Stabilization prevents the worsening of the disease, bringing dementia to a standstill. Cure restores patient health to its original level. In No burden, dementia takes its normal course while caregiver’s burden is reduced to its level before the disease.

The three different types of therapies are reflected in different WTP values of both caregivers and patients, suggesting that moderate levels of Alzheimer’s disease still permit clear expression of preference. According to the WTP values found, patients do not rank Cure higher than No burden, implying that their preferences are entirely altruistic. Caregiving spouses rank Cure before Burden, reflecting less than perfect altruism which accounts for some 40 percent of their total WTP. Still, this constitutes evidence of mutual altruism.

The evidence suggests that WTP values reflect individuals’ preferences even in Alzheimer patients. The estimates suggest that an economically successful treatment should provide relief to caregivers, with its curative benefits being of secondary importance.

The purpose of this paper is to evaluate the performance on standardizing appointment slot length in a primary care clinic to understand the impact of providers’ preferences and practice differences.

The treatment time data were collected for each provider. There were six patient types: emergency/urgent care (ER/UC), follow-up patient (FU), new patient, office visit (OV), physical exam, and well-child care. Simulation model was developed to capture patient flow and measure patient wait time, provider idle time, cost, overtime, finish time, and the number of patients scheduled. Four scheduling scenarios were compared: scheduled all patients at 20 minutes; scheduled ER/UC, FU, OV at 20 minutes and others at 40 minutes; scheduled patient types on individual provider preference; and scheduled patient types on combined provider preference.

Standardized scheduling among providers increase cost by 57 per cent, patient wait time by 83 per cent, provider idle time by five minutes per patient, overtime by 22 minutes, finish time by 30 minutes, and decrease patient access to care by approximately 11 per cent. An individualized scheduling approach could save as much as 14 per cent on cost and schedule 1.5 more patients. The combined preference method could save about 8 per cent while the number of patients scheduled remained the same.

The challenge is to actually disseminate the findings to medical providers and adjust scheduling systems accordingly.

This paper concluded standardization of providers’ clinic preference and practice negatively impact clinic service quality and access to care.

This paper aims to explore how tensions and alignments between different actors’ needs in a transformative services network affect balanced centricity, which is an indicator of well-being. Balanced centricity describes a situation in which all network actors’ interests and needs are fulfilled simultaneously. In such cases, all actors are better off, which increases both individual actors’ and overall actor-network well-being.

The empirical study takes place in nursing homes in which in-bed baths represent co-created service encounters that affect the well-being of focal actors (i.e. patients), frontline service employees (i.e. nurses) and transformative service mediators (i.e. family members), who have potentially competing needs. Using a qualitative, phenomenological approach, the study inductively explores and deductively categorizes actors’ personal experiences to gain deep, holistic insights into the service network and its complex web of actor interdependencies.

The resulting conceptual model of balanced centricity identifies actors’ lower-order needs as different manifestations of the psychological needs for autonomy, competence and relatedness. If actors’ needs are aligned, their psychological needs can be satisfied, which facilitates balanced centricity. If actors exhibit competing needs though, balanced centricity is impeded.

This study establishes actors’ psychological needs as the origin of tensions/alignments in multi-actor networks that impede/contribute to balanced centricity. Transformative service providers should try to address all actors’ psychological needs when co-creating services to achieve network well-being.

This study adopts a novel, multi-actor perspective and thereby presents a conceptual model that contributes to the understanding of balanced centricity. Future research could test this model in other transformative service settings.

As healthcare continues to become more expensive and complex, considering the voice of the patient in the design and operation of healthcare practices is important. Wound care and rural healthcare scenarios pose additional complexities for providers and patients. This study sought to identify key determinants of patient service quality in wound care.

Patients at the wound care/ostomy clinic (WOC) in a rural hospital were surveyed using the Kano model. The Kano model enables the categorization of quality attributes based on the attributes' contribution to the subject's overall satisfaction (and dissatisfaction). Chi-square goodness-of-fit testing, multinomial analysis and power analysis were then used to determine the Kano categories for each satisfaction-related attribute.

The analyses resulted in 14 one-dimensional attributes and 3 indeterminable attributes. For the one-dimensional attributes, customer satisfaction is directly proportional to the level of performance for that attribute. The one-dimensional attributes included providing correct care on the first, provision of necessary supplies for care, appropriately qualified medical staff and confidence in care provided by medical staff, among others. Understanding the attributes important to the patient drive patient-centered care, which improves positive patient outcomes and recovery. These attributes can then be used by healthcare professionals to design patient-centric processes and services. This research provides a framework for incorporating the voice of the patient into healthcare services.

While the research methodology can be used in other healthcare settings, the findings are not generalizable to other wound care clinics. This research was conducted in one small, rural hospital. In addition, the sample size was small due to the size of the wound clinic; therefore, an analysis of the differences between demographics could not be performed.

Considering the perspectives of rural wound care patients is important, as the patients are an under-served population with unique challenges related to patient care. The research findings detail rural patients' expectations during wound care treatments, which enable the clinic to focus on improving patient satisfaction. This research contributes to understanding the factors that are important to patient satisfaction in wound care. Further, the methodology presented can be applied to other healthcare settings.

While studies exist using the Kano model in healthcare and the literature is sparse in rural healthcare, this is the first case study using the Kano model in wound care to understand patient preferences.