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Purpose – This paper seeks to look at healthcare service quality from the viewpoint of its negative impact on the industry when there is a deficiency in the delivery of service quality. To measure this impact, the potential loss of customers due to poor quality service is measured. A potential customer loss model is proposed. To address the competitive and financially driven healthcare delivery business, a three‐pillar approach, termed the Excellent Healthcare Service Model (EHSM), is introduced. This approach advocates that the healthcare industry should use a system view to deliver quality healthcare by taking into account quality, cost, and efficiency factors in a holistic manner. Design/methodology/approach – In this paper a survey questionnaire was used to gather data necessary to compute impact of deficient healthcare service. The questionnaire was administered to a sample of 400 people and the survey method used was intercept interview. Customer loss and potential customer loss due to deficient service were computed. Findings – In this paper, findings from the research indicated that, in the healthcare industry, for every 100 customers that experienced deficient service, about 70 customers would be unlikely to patronize the same organization again. In addition, for the same 100 customers who have experienced deficient service, about 75 of them will go on to tell on average nine family members and friends about their experiences. Through word of mouth from these 75 dissatisfied customers, there will eventually be about 465 persons who might have been potential customers but will probably not patronize the organization at all based on what the dissatisfied customers have told them. Practical implications – The ideas presented in this paper provide a new way of looking at service quality performance, through the impact of deficient service. With this knowledge, economic impacts of poor service quality could easily be quantified, and such economic‐based results are usually a better motivator for managers and workers to deploy quality improvement initiatives. Originality/value – The results and ideas presented in this paper are valuable for the healthcare industry. It provides an alternative approach to quantify service quality performance. The paper also proposes a system‐based approach to enhance service process performance.

Currently the healthcare industry in the US is not only contending with relentless pressures to lower costs while maintaining and increasing the quality of service but is also under a stringent timeline to become compliant with the health insurance, portability and accountability act (HIPAA) regulatory requirements. Robust healthcare information systems (HCIS) become critical to enabling healthcare organizations address these challenges. Hence, it becomes an imperative need that the information that is captured, generated and disseminated by these HCIS be of the highest possible integrity and quality as well as compliant with regulatory requirements. This paper addresses this need by proposing an integrative framework for HIPAA compliant, I*IQ HCIS. It bases this framework on an integration of the requirements for HIPAA compliance, the principles of information integrity, as well as the healthcare quality aims set forth by the Committee on the Quality of Healthcare in America.

One of the most important instruments for gathering information and processing data relating to professional and organisational quality in health systems is “healthcare processes classification”. The authors found that a typical problem of many European countries is a lack of reliable information in the field of healthcare, mainly because the development of quality instruments, including healthcare processes classification, is not a priority for medical and other health professions. Additionally, it is difficult to update this instrument coherently with organisational changes and developmental achievements. This article describes the approach used by the University Medical Centre in Ljubjana, Slovenia, to redesign its healthcare processes classification in order to improve the quality of healthcare.

The mass exodus of the professional healthcare workforce has become a cankerworm for a developing nation like Nigeria, and this worsens the already depleted healthcare systems in underdeveloped nation. This study investigated the rationale behind medical workers' brain-drain syndrome and the quality healthcare delivery in the Nigerian public healthcare sector.

To stimulate an understanding of the effect of the phenomenon called brain drain, the study adopted a diagnostic research design to survey the public healthcare personnel in government hospitals. The study administered a battery of adapted research scales of different measures to confirm the variables of interest of this study on a probability sampling strategy. The study surveyed 450 public healthcare sector employees from four government hospitals to gather pertinent data. The study used a structural equation model (SEM) and artificial neural networks (ANNs) to analyse the collected data from the medical personnel of government hospitals.

The findings of this study are significant as postulated. The study discovered that poor quality worklife experienced by Nigerian medical personnel was attributed to the brain-drain effect and poor healthcare delivery. The study further demonstrated that job dissatisfaction suffered among the public healthcare workforce forced the workforce to migrate to the international labour market, and this same factor is a reason for poor healthcare delivery. Lastly, the study discovered that inadequate remuneration and pay discouraged Nigerian professionals and allied healthcare workers from being productive and ultimately pushed them to the global market.

Practically, this study has shown three major elements that caused the mass movement of Nigerian healthcare personnel to other countries of the world and that seems novel given the peculiarity of the Nigerian labour market. The study is original and novel as much study has not been put forward in the public healthcare sector in Nigeria concerning this phenomenon.

Competition rules maximise consumer welfare by promoting efficient use of scarce resource and thus high output, low prices, high quality, varied services, innovation, production and distribution. European courts consider doctors and hospital staff as undertakings (any entity that performs economic activities), so that if they enter into agreements then they have to comply with competition rules. This paper's objective is to determine whether competition law, which applies to undertakings, can in fact be applied to different healthcare‐sector players and whether specific rules are needed regarding competition between healthcare undertakings.

Data were selected from relevant European and national case law, European institution legal documents (such as regulations, guidelines and communications) and healthcare competition law literature, and then examined.

The paper finds that competition rules are applicable to healthcare players considering the consequences if competition rules are applied to the healthcare market. For market processes to result in the appropriate cost, quality and output, competition law must be proactive. In other words, quality must be fully factored into the competitive mix, allowing consumers to weigh healthcare price and non‐price characteristics.

Countries have different healthcare system and competition rules (although similar), competition rule impact is different for each country. Some healthcare systems are more regulated and there will be less opportunity for healthcare players to compete.

Efficiently applying competition law to healthcare players means that several challenges need facing, such as healthcare quality complexity and court scepticism.

This article points out the challenges when competition law is applied to the healthcare sector and how these challenges are faced in certain countries such as The Netherlands.

Current health care quality performance indicators appear to be inadequate to inform the public to make the right choices. The aim of this paper is to define a framework and an organizational setting in which valid and reliable healthcare information can be produced to inform the general public about healthcare quality.

To improve health care quality information, the paper explores the analogy between financial accounting, which aims to produce valid and reliable information to support companies informing their shareholders and stakeholders, and healthcare aiming to inform future patients about healthcare quality. Based on this analogy, the authors suggest a measurement framework and an organizational setting to produce healthcare information.

The authors suggest a five‐quality element framework to structure quality reporting. The authors also indicate the best way to report each type of quality, comparing performance indicators with certification/accreditation. Health gain is the most relevant quality indicator to inform the public, but this information is the most difficult to obtain. Finally, the organizational setting, comparable to financial accounting, required to provide valid, reliable and objective information on healthcare quality is described.

Framework elements should be tested in quantitative studies or case studies, such as a performance indicator's relative value compared to accreditation/ certification. There are, however, elements that can be implemented right away such as third party validation of healthcare information produced by healthcare institutions.

Given the money spent on healthcare worldwide, valid and reliable healthcare quality information's value can never be overestimated. It can justify delivering “expensive” healthcare, but also points the way to savings by stopping useless healthcare. Valid and reliable information puts the patient in the driver's seat and enables him or her to make the right decision when choosing their healthcare provider.

The aim of this paper is to build a comprehensive conceptual model to understand and measure variables affecting patient satisfaction‐based healthcare quality.

A total of 24 articles from international journals were systematically reviewed for factors determining patient satisfaction and healthcare quality.

Patient satisfaction is a multi‐dimensional healthcare construct affected by many variables. Healthcare quality affects patient satisfaction, which in turn influences positive patient behaviours such as loyalty. Patient satisfaction and healthcare service quality, though difficult to measure, can be operationalized using a multi‐disciplinary approach that combines patient inputs as well as expert judgement.

The paper develops a conceptual model that needs to be confirmed empirically. Also, most research pertains to developed countries. Findings are presented that may not be generalized to developing nations, which may be quite different culturally.

The paper has direct implications for health service providers. They are encouraged to regularly monitor healthcare quality and accordingly initiate service delivery improvements to maintain high levels of patient satisfaction.

The paper collates and examines recent healthcare quality study findings. It presents a comprehensive, conceptual model encompassing research work and a holistic view of various aspects affecting patient satisfaction and healthcare quality. Although a large amount of healthcare quality research has been done, each studying a particular service, this paper comprehensively brings together various research findings.

This article initially aims to explore the factors of every quality construct of the 5Qs model of service quality and, second, identify the significant factors affecting the total quality of e-healthcare services and its association with consumer satisfaction using a multidimensional hierarchical 5Qs model of e-healthcare service quality.

Questionnaire-oriented research was performed at three public hospitals of Punjab and Chandigarh. In total, 53 variables were covered in all quality constructs for data collection from the designated public hospitals. The respondents who agreed to have knowledge regarding e-Healthcare services and were availing these services were included in the study. The analysis comprised structural equation modeling technique using AMOS 21.

The outcomes suggest that the 5Qs model is more comprehensive and can be used to evaluate service quality perceptions using e-Healthcare services. The research identified 11 sub-dimensions for the five quality constructs of the 5Qs model, representing total quality, which is primary to consumer satisfaction. “Overall objectivity” and “technical objectivity” defined the quality of object. The quality of process of e-Healthcare services was characterized by “functionality,” “timeliness” and “responsiveness.” Quality of infrastructure was defined by “technical infrastructure,” “physical infrastructure,” “manpower skills” and “organizational infrastructure.” “Manner of interaction” and “timely interaction” defined the quality of interaction. The atmosphere was represented by only one factor. The results also suggest that quality of infrastructure, quality of interaction and quality of atmosphere play the most significant role in total quality leading to consumer satisfaction.

Theoretical implications: The multidimensional hierarchical model will help the researchers study the e-Healthcare service quality in a more organized manner, and the outcomes of this study can be linked with that of future studies for more generalized application in other public hospitals. The sub-dimensions of each quality construct of the 5Qs model can be applied in private hospitals, and the hierarchical model can be tested in different industries to measure service quality perceptions of the consumer

The outcomes of the study can be applied in various public sector hospitals to redesign the e-Healthcare services based on consumers' perception for better consumer satisfaction and quality services. This paper identifies the role of each quality construct in e-Healthcare services for improvement in the total quality, which in turn will lead to higher satisfaction for the consumers.

In this study, the original 5Qs model has been used for the first time in a new instrument to understand better and design quality e-Healthcare services. The paper explores the sub-factors of each quality construct and its significance in measuring the total quality.

This paper synthesises the literature on the issues related to the older patient, health service quality and its measurement. It discusses the need to consider these perspectives in the definition and assessment of quality of a community‐focused aged healthcare programme, and critically examines the existing evaluation of quality in healthcare, contrasting the patient's role and impact on the quality of the service and its outcome. The paper then reviews the documented problems associated with using satisfaction as an indicator of the patient's view of quality. An alternate validated approach to measuring the patient's perception of the quality of the service is identified in the services literature; this multidimensional hierarchical tool and scale, which specifically measures the patient's view of quality, is presented. The tool covers nine sub‐dimensions, four dimensions and the global perspective of quality as perceived by the patient. An adaptation of this tool is presented to measure the patient's view of quality using the relatively new Transition Aged Care programme as an example, and make the argument for the holistic measurement of transitional aged care quality, using a validated and reliable patient‐specific tool. Importantly, the paper proposes that the identification of the patient view of service quality will offer information that could specifically assist with service improvement.

Healthcare networks are becoming ubiquitous, yet it is unclear how hospitals with varying quality capabilities would fare by being affiliated with large healthcare networks. The purpose of this paper is to first consider the deductive configuration perspective and distinguish high and low quality hospitals by using clinical and experiential quality as two dimensions of quality capability. Next, it examines the impact of healthcare network size on operating costs of hospitals. Additionally, the paper investigates the interaction effect of hospital demand and healthcare network size on operating costs.

The paper uses a dataset that was created by combining five separate sources. Cluster analysis technique is used to classify hospitals into four groups – holistic quality leaders (high clinical and experiential quality capability), experiential quality focusers (low clinical quality capability and high experiential quality capability), clinical quality focusers (high clinical capability and low experiential quality capability), and quality laggards (low clinical and experiential quality capability). The authors test the research hypotheses by means of regression analyses after controlling for several contextual characteristics.

The results show that affiliation with large healthcare networks reduces operating costs for quality laggards, but increases these costs for experiential quality focusers and clinical quality focusers. The hypothesized positive relationship between healthcare network size and costs is not supported for holistic quality leaders. The authors find that clinical quality focusers and holistic quality leaders can complement higher utilization levels in their operations due to increased demand and healthcare network size to reduce their operating costs per day.

There has been increasing evidence suggesting that hospitals must carefully manage both clinical and experiential quality. By focusing on both clinical and experiential quality, unlike experiential quality focusers and clinical quality focusers, holistic quality leaders are not adversely affected by the size of their network. The results suggest that experiential quality focusers and clinical quality focusers should either embrace holistic quality management or restrict the size of their networks to maintain their quality level and to reduce coordination costs.