Healthcare capacity measurement

Hospital capacity management based on the queueing theory

Purpose

The purpose of this paper is to focus on the contributions of queueing theory to hospital capacity management to improve organizational performance and deal with increased demand in the healthcare sector.

Design/methodology/approach

Models were applied to six months of inpatient records from a university hospital to determine operation measures such as utilization rate, waiting probability, estimated bed capacity, capacity simulations and demand behavior assessment.

Findings

Irrespective of the findings of the queueing model, the results showed that there is room for improvement in capacity management. Balancing admissions and the type of patient over the week represent a possible solution to optimize bed and nurse utilization. Patient mixing results in a highly sensitive delay rate due to length of stay (LOS) variability, with variations in both the utilization rate and the number of beds.

Practical implications

The outcomes suggest that operational managers should improve patient admission management, as well as reducing variability in LOS and in admissions during the week.

Originality/value

The queueing theory revealed a quantitative portrait of the day-by-day reality in a fast and flexible manner which is very convenient to the task of management.

Measuring effective capacity in an emergency department

PURPOSE

The purpose of this paper is to show how elements from queueing theory can be used to obtain objective measures of effective capacity in the triage function at Skaraborg Hospital in Sweden without direct observation of the function itself.

DESIGN/METHODOLOGY/APPROACH

Approximately 30,000 patients arrived to the emergency department at Skaraborg Hospital in Sweden during 2011. The exact time of arrival and the exact time of triage were recorded for each patient on an individual level. Basic queueing theory uses arrival rates and system capacity measures to derive average queueing times. The authors use the theoretical relation between these three measures to derive system capacity measures based on observed arrival rates and observed average queueing times.

FINDINGS

The effective capacity in the triage process is not a linear function of the number of nurses. However, the management of capacity seems well adapted to the actual demand, even though service levels vary substantially during the day and night.

ORIGINALITY/VALUE

This paper uses elements from queueing theory in an innovative way to measure the effective capacity of a service process without direct observation, thereby also avoiding the potential risk of the Hawthorne effect.

Development of Balanced Scorecard for healthcare using Interpretive Structural Modeling and Analytic Network Process

Purpose

– The purpose of this paper is to develop a Balanced Scorecard-based performance measurement framework for health care system.

Design/methodology/approach

– An integrated ISM-ANP-based quantitative approach is used for the development of balance scorecard for health care system. The necessary insights are drawn from the secondary literature.

Findings

– This research identifies Key Performance Indicators and their interrelationships for health care system. An integrated approach of ISM and ANP is employed for the development of balance scorecard.

Research limitations/implications

– The findings of the research are based on insights gained from secondary literature and an analysis of five cases in health care segment and hence generalization of proposed framework is limited to the system representing some commonalities with chosen cases.

Practical implications

– Health care is the diagnosis, treatment and prevention of disease, illness, injury and other physical and mental impairments in humans. Access to health care varies across countries, groups and individuals, largely influenced by social and economic conditions as well as the health policies in place. The changing nature of today's health care organizations, including pressure to reduce costs, improve the quality of care and meet stringent guidelines, has forced health care professionals to re-examine how they evaluate their performance.

Originality/value

– The Balanced Scorecard health care expanded organization measures beyond financial analysis. It was first proposed by Robert S. Kaplan and David P. Norton in their book “The Balanced Scorecard (1996, Harvard Business School Press).” This research reports a development of Balanced Scorecard for health care using an integrated approach of Interpretive Structural Modeling and Analytic Network Process is used.

Engagement in research: an innovative three-stage review of the benefits for health-care performance

Background

There is a widely held assumption that research engagement improves health-care performance at various levels, but little direct empirical evidence.

Objectives

To conduct a theoretically and empirically grounded synthesis to map and explore plausible mechanisms through which research engagement might improve health services performance. A review of the effects on patients of their health-care practitioner's or institution's participation in clinical trials was published after submission of the proposal for this review. It identified only 13 relevant papers and, overall, suggested that the evidence that research engagement improves health-care performance was less strong than some thought. We aimed to meet the need for a wider review.

Methods

An hourglass review was developed, consisting of three stages: (1) a planning and mapping stage; (2) a focused review concentrating on the core question of whether or not research engagement improves health care; and (3) a wider (but less systematic) review of papers identified during the two earlier stages. Studies were included inthe focused review if the concept of ‘engagementinresearch’ was an input and some measure of ‘performance’ an output. The search strategy covered the period 1990 to March 2012. MEDLINE, EMBASE, PsycINFO, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science and other relevant databases were searched. A total of 10,239 papers were identified through the database searches, and 159 from other sources. A further relevance and quality check on 473 papers was undertaken, and identified 33 papers for inclusion in the review. A standard meta-analysis was not possible on the heterogeneous mix of papers in the focused review. Therefore an explanatory matrix was developed to help characterise the circumstances in which research engagement might improve health-care performance and the mechanisms that might be at work, identifying two main dimensions along which to categorise the studies:the degree of intentionalityandthe scope of the impact.

Results

Of the 33 papers in the focused review, 28 were positive (of which six were positive/mixed) in relation to the question of whether or not research engagement improves health-care performance. Five papers were negative (of which two were negative/mixed). Seven out of 28 positive papers reported some improvement in health outcomes. For the rest, the improved care took the form of improved processes of care. Nine positive papers were at a clinician level and 19 at an institutional level. The wider review demonstrated, for example, how collaborative and action research can encourage some progress along the pathway from research engagement towards improved health-care performance. There is also evidence that organisations in which the research function is fully integrated into the organisational structure out-perform other organisations that pay less formal heed to research and its outputs. The focused and wider reviews identified the diversity in the mechanisms through which research engagement might improve health care: there are many circumstances and mechanisms at work, more than one mechanism is often operative, and the evidence available for each one is limited.

Limitations

To address the complexities of this evidence synthesis of research we needed to spend significant time mapping the literature, and narrowed the research question to make it feasible. We excluded many potentially relevant papers (though we partially addressed this by conducting a wider additional synthesis). Studies assessing the impact made on clinician behaviour by small, locally conducted pieces of research could be difficult to interpret without full knowledge of the context.

Conclusions

Drawing on the focused and wider reviews, it is suggested that when clinicians and health-care organisations engage in research there is the likelihood of a positive impact on health-care performance. Organisations that have deliberately integrated the research function into organisational structures demonstrate how research engagement can, among other factors, contribute to improved health-care performance. Further explorations are required of research networks and schemes to promote the engagement of clinicians and managers in research. Detailed observational research focusing on research engagement within organisations would build up an understanding of mechanisms.

Study registration

PROSPERO: CRD42012001990.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Discovering the impact of preceding units' characteristics on the wait time of cardiac surgery unit from statistic data

INTRODUCTION

Prior research shows that clinical demand and supplier capacity significantly affect the throughput and the wait time within an isolated unit. However, it is doubtful whether characteristics (i.e., demand, capacity, throughput, and wait time) of one unit would affect the wait time of subsequent units on the patient flow process. Focusing on cardiac care, this paper aims to examine the impact of characteristics of the catheterization unit (CU) on the wait time of cardiac surgery unit (SU).

METHODS

This study integrates published data from several sources on characteristics of the CU and SU units in 11 hospitals in Ontario, Canada between 2005 and 2008. It proposes a two-layer wait time model (with each layer representing one unit) to examine the impact of CU's characteristics on the wait time of SU and test the hypotheses using the Partial Least Squares-based Structural Equation Modeling analysis tool.

RESULTS

Results show that: (i) wait time of CU has a direct positive impact on wait time of SU (β = 0.330, p < 0.01); (ii) capacity of CU has a direct positive impact on demand of SU (β = 0.644, p < 0.01); (iii) within each unit, there exist significant relationships among different characteristics (except for the effect of throughput on wait time in SU).

CONCLUSION

Characteristics of CU have direct and indirect impacts on wait time of SU. Specifically, demand and wait time of preceding unit are good predictors for wait time of subsequent units. This suggests that considering such cross-unit effects is necessary when alleviating wait time in a health care system. Further, different patient risk profiles may affect wait time in different ways (e.g., positive or negative effects) within SU. This implies that the wait time management should carefully consider the relationship between priority triage and risk stratification, especially for cardiac surgery.