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Resilient hospitals have the vital role in reducing mortality, severity of injuries by providing required emergency services during accidents and disasters. This study aims to identify and prioritize key indicators on hospital resilience.

This cross-sectional study was conducted in 2019. The draft of the indicators obtained from the systematic review of the previous study was finalized, with three expert panel sessions and 14 experts in resilience fields. The outputs of these sessions were divided into three domains including constructive resilience, infrastructural resilience and administrative resilience, 17 sub-domains and 71 indicators. Then fuzzy analytic network process method was used to weight and prioritize the final indicators of hospital disaster resilience.

Administrative resilience, logistic and financial management and strategic outsourcing agreement allocated the highest weight as domain, sub-domains and indicators, respectively. The weight of each sub-domain and indicator was also determined.

Investigating the weight of domains, sub-domains and indicators shows the importance of managerial and operational issues in hospital resilience. By using the indicators and relative weights, a tool for measuring hospital disaster resilience can be created in further studies. The output of these assessments is effective in promoting safety and increasing awareness of hospital managers and health policymakers.

Strategic planning is the best tool for managers seeking an informed presence and participation in the market without surrendering to changes. Strategic planning enables managers to achieve their organizational goals and objectives. Hospital goals, such as improving service quality and increasing patient satisfaction cannot be achieved if agreed strategies are not implemented. The purpose of this paper is to investigate the factors affecting strategic plan implementation in one teaching hospital using interpretive structural modeling (ISM).

The authors used a descriptive study involving experts and senior managers; 16 were selected as the study sample using a purposive sampling method. Data were collected using a questionnaire designed and prepared based on previous studies. Data were analyzed using ISM.

Five main factors affected strategic plan implementation. Although all five variables and factors are top level, “senior manager awareness and participation in the strategic planning process” and “creating and maintaining team participation in the strategic planning process” had maximum drive power. “Organizational structure effects on the strategic planning process” and “Organizational culture effects on the strategic planning process” had maximum dependence power.

Identifying factors affecting strategic plan implementation is a basis for healthcare quality improvement by analyzing the relationship among factors and overcoming the barriers.

The authors used ISM to analyze the relationship between factors affecting strategic plan implementation.

Measuring dental clinic service quality is the first and most important factor in improving care. The quality provided plays an important role in patient satisfaction. The purpose of this paper is to identify factors affecting dental service quality from the patients’ viewpoint.

This cross-sectional, descriptive-analytical study was conducted in a dental clinic in Tehran between January and June 2014. A sample of 385 patients was selected from two work shifts using stratified sampling proportional to size and simple random sampling methods. The data were collected, a self-administered questionnaire designed for the purpose of the study, based on the Parasuraman and Zeithaml’s model of service quality which consisted of two parts: the patients’ demographic characteristics and a 30-item questionnaire to measure the five dimensions of the service quality. The collected data were analysed using SPSS 21.0 and Amos 18.0 through some descriptive statistics such as mean, standard deviation, as well as analytical methods, including confirmatory factor.

Results showed that the correlation coefficients for all dimensions were higher than 0.5. In this model, assurance (regression weight=0.99) and tangibility (regression weight=0.86) had, respectively, the highest and lowest effects on dental service quality.

The Parasuraman and Zeithaml’s model is suitable to measure quality in dental services. The variables related to dental services quality have been made according to the model.

This is a pioneering study that uses Parasuraman and Zeithaml’s model and CFA in a dental setting. This study provides useful insights and guidance for dental service quality assurance.