Development of a benchmark tool for cancer centers; results from a pilot exercise.

BACKGROUND: Differences in cancer survival exist between countries in Europe. Benchmarking of good practices can assist cancer centers to improve their services aiming for reduced inequalities. The aim of the BENCH-CAN project was to develop a cancer care benchmark tool, identify performance differences and yield good practice examples, contributing to improving the quality of interdisciplinary care. This paper describes the development of this benchmark tool and its validation in cancer centers throughout Europe. METHODS: A benchmark tool was developed and executed according to a 13 step benchmarking process. Indicator selection was based on literature, existing accreditation systems, and expert opinions. A final format was tested in eight cancer centers. Center visits by a team of minimally 3 persons, including a patient representative, were performed to verify information, grasp context and check on additional questions (through semi-structured interviews). Based on the visits, the benchmark methodology identified opportunities for improvement. RESULTS: The final tool existed of 61 qualitative and 141 quantitative indicators, which were structured in an evaluative framework. Data from all eight participating centers showed inter-organization variability on many indicators, such as bed utilization and provision of survivorship care. Subsequently, improvement suggestions for centers were made; 85% of which were agreed upon. CONCLUSION: A benchmarking tool for cancer centers was successfully developed and tested and is available in an open format. The tool allows comparison of inter-organizational performance. Improvement opportunities were successfully identified for every center involved and the tool was positively evaluated.

Exploring improvements in patient logistics in Dutch hospitals with a survey.

BACKGROUND: Research showed that promising approaches such as benchmarking, operations research, lean management and six sigma, could be adopted to improve patient logistics in healthcare. To our knowledge, little research has been conducted to obtain an overview on the use, combination and effects of approaches to improve patient logistics in hospitals. We therefore examined the approaches and tools used to improve patient logistics in Dutch hospitals, the reported effects of these approaches on performance, the applied support structure and the methods used to evaluate the effects. METHODS: A survey among experts on patient logistics in 94 Dutch hospitals. The survey data were analysed using cross tables. RESULTS: Forty-eight percent of all hospitals participated. Ninety-eight percent reported to have used multiple approaches, 39% of them used five or more approaches. Care pathways were the preferred approach by 43% of the hospitals, followed by business process re-engineering and lean six sigma (both 13%). Flowcharts were the most commonly used tool, they were used on a regular basis by 94% of the hospitals. Less than 10% of the hospitals used data envelopment analysis and critical path analysis on a regular basis. Most hospitals (68%) relied on external support for process analyses and education on patient logistics, only 24% had permanent internal training programs on patient logistics. Approximately 50% of the hospitals that evaluated the effects of approaches on efficiency, throughput times and financial results, reported that they had accomplished their goals. Goal accomplishment in general hospitals ranged from 63% to 67%, in academic teaching hospitals from 0% to 50%, and in teaching hospitals from 25% to 44%. More than 86% performed an evaluation, 53% performed a post-intervention measurement. CONCLUSIONS: Patient logistics appeared to be a rather new subject as most hospitals had not selected a single approach, they relied on external support and they did not have permanent training programs. Hospitals used a combination of approaches and tools, about half of the hospitals reported goal accomplishment and no approach seemed to outperform the others. To make improvement efforts more successful, research should be conducted into the selection and application of approaches, their contingency factors, and goal-setting procedures.

Reducing the throughput time of the diagnostic track involving CT scanning with computer simulation.

INTRODUCTION: To examine the use of computer simulation to reduce the time between the CT request and the consult in which the CT report is discussed (diagnostic track) while restricting idle time and overtime. METHODS: After a pre implementation analysis in our case study hospital, by computer simulation three scenarios were evaluated on access time, overtime and idle time of the CT; after implementation these same aspects were evaluated again. Effects on throughput time were measured for outpatient short-term and urgent requests only. CONCLUSION: The pre implementation analysis showed an average CT access time of 9.8 operating days and an average diagnostic track of 14.5 operating days. Based on the outcomes of the simulation, management changed the capacity for the different patient groups to facilitate a diagnostic track of 10 operating days, with a CT access time of 7 days. After the implementation of changes, the average diagnostic track duration was 12.6 days with an average CT access time of 7.3 days. The fraction of patients with a total throughput time within 10 days increased from 29% to 44% while the utilization remained equal with 82%, the idle time increased by 11% and the overtime decreased by 82%. The fraction of patients that completed the diagnostic track within 10 days improved with 52%. Computer simulation proved useful for studying the effects of proposed scenarios in radiology management. Besides the tangible effects, the simulation increased the awareness that optimizing capacity allocation can reduce access times.

A review on the relation between simulation and improvement in hospitals.

BACKGROUND: Simulation applications on operations management in hospitals are frequently published and claim to support decision-making on operations management subjects. However, the reported implementation rates of recommendations are low and the actual impact of the changes recommended by the modeler has hardly been examined. This paper examines: 1) the execution rate of simulation study recommendations, 2) the research methods used to evaluate implementation of recommendations, 3) factors contributing to implementation, and 4) the differences regarding implementation between literature and practice. RESULTS: Altogether 16 hospitals executed the recommendations (at least partially). Implementation results were hardly reported upon; 1 study described a before-and-after design, 2 a partial before and after design. Factors that help implementation were grouped according to 1) technical quality, of which data availability, validation/verification with historic data/expert opinion, and the development of the conceptual model were mentioned most frequently 2) process quality, with client involvement and 3) outcome quality with, presentation of results. The survey response rate of traceable authors was 61%, 18 authors implemented the results at least partially. Among these responses, evaluation methods were relatively better with 3 time series designs and 2 before-and-after designs. CONCLUSIONS: Although underreported in literature, implementation of recommendations seems limited; this review provides recommendations on project design, implementation conditions and evaluation methods to increase implementation. METHODS: A literature review in PubMed and Business Source Elite on stochastic simulation applications on operations management in individual hospitals published between 1997 and 2008. From those reporting implementation, cross references were added. In total, 89 papers were included. A scoring list was used for data extraction. Two reviewers evaluated each paper separately; in case of discrepancies, they jointly determined the scores. The findings were validated with a survey to the original authors.

Accounting for inpatient wards when developing master surgical schedules.

BACKGROUND: As the demand for health care services increases, the need to improve patient flow between departments has likewise increased. Understanding how the master surgical schedule (MSS) affects the inpatient wards and exploiting this relationship can lead to a decrease in surgery cancellations, a more balanced workload, and an improvement in resource utilization. We modeled this relationship and used the model to evaluate and select a new MSS for a hospital. METHODS: An operational research model was used in combination with staff input to develop a new MSS. A series of MSSs were proposed by staff, evaluated by the model, and then scrutinized by staff. Through iterative modifications of the MSS proposals (i.e., the assigned operating time of specialties), insight is obtained into the number, type, and timing of ward admissions, and how these affect ward occupancy. RESULTS: After evaluating and discussing a number of proposals, a new MSS was chosen that was acceptable to operating room staff and that balanced the ward occupancy. After implementing the new MSS, a review of the bed-use statistics showed it was achieving a balanced ward occupancy. The model described in this article gave the hospital the ability to quantify the concerns of multiple departments, thereby providing a platform from which a new MSS could be negotiated. CONCLUSION: The model, used in combination with staff input, supported an otherwise subjective discussion with quantitative analysis. The work in this article, and in particular the model, is readily repeatable in other hospitals and relies only on readily available data.

International benchmarking of specialty hospitals. A series of case studies on comprehensive cancer centres.

BACKGROUND: Benchmarking is one of the methods used in business that is applied to hospitals to improve the management of their operations. International comparison between hospitals can explain performance differences. As there is a trend towards specialization of hospitals, this study examines the benchmarking process and the success factors of benchmarking in international specialized cancer centres. METHODS: Three independent international benchmarking studies on operations management in cancer centres were conducted. The first study included three comprehensive cancer centres (CCC), three chemotherapy day units (CDU) were involved in the second study and four radiotherapy departments were included in the final study. Per multiple case study a research protocol was used to structure the benchmarking process. After reviewing the multiple case studies, the resulting description was used to study the research objectives. RESULTS: We adapted and evaluated existing benchmarking processes through formalizing stakeholder involvement and verifying the comparability of the partners. We also devised a framework to structure the indicators to produce a coherent indicator set and better improvement suggestions. Evaluating the feasibility of benchmarking as a tool to improve hospital processes led to mixed results. Case study 1 resulted in general recommendations for the organizations involved. In case study 2, the combination of benchmarking and lean management led in one CDU to a 24% increase in bed utilization and a 12% increase in productivity. Three radiotherapy departments of case study 3, were considering implementing the recommendations.Additionally, success factors, such as a well-defined and small project scope, partner selection based on clear criteria, stakeholder involvement, simple and well-structured indicators, analysis of both the process and its results and, adapt the identified better working methods to the own setting, were found. CONCLUSIONS: The improved benchmarking process and the success factors can produce relevant input to improve the operations management of specialty hospitals.

Exploring types of focused factories in hospital care: a multiple case study.

BACKGROUND: Focusing on specific treatments or diseases is proposed as a way to increase the efficiency of hospital care. The definition of "focus" or "focused factory", however, lacks clarity. Examples in health care literature relate to very different organizations.Our aim was to explore the application of the focused factory concept in hospital care, including an indication of its performance, resulting in a conceptual framework that can be helpful in further identifying different types of focused factories. Thus contributing to the understanding of the diversity of examples found in the literature. METHODS: We conducted a cross-case comparison of four multiple-case studies into hospital care. To cover a broad array of focus, different specialty fields were selected. Each study investigated the organizational context, the degree of focus, and the operational performance. Focus was measured using an instrument translated from industry. Data were collected using both qualitative and quantitative methods and included site visits. A descriptive analysis was performed at the case study and cross-case studies level. RESULTS: The operational performance per specialty field varied considerably, even when cases showed comparable degrees of focus. Cross-case comparison showed three focus domains. The product domain considered specialty based focused factories that treated patients for a single-specialty, but did not pursue a specific strategy nor adapted work-designs or layouts. The process domain considered delivery based focused factories that treated multiple groups of patients and often pursued strategies to improve efficiency and timeliness and adapted work-designs and physical layouts to minimize delays. The product-process domain considered procedure based focused factories that treated a single well-defined group of patients offering one type of treatment. The strategic focusing decisions and the design of the care delivery system appeared especially important for delivery and procedure based focused factories. CONCLUSIONS: Focus in hospital care relates to limitations on the patient group treated and the range of services offered. Based on these two dimensions, we identified three types of focused factories: specialty based, delivery based, and procedure based. Focus could lead to better operational performance, but only when clear strategic focusing decisions are made.

Improving the efficiency of a chemotherapy day unit: applying a business approach to oncology.

AIM: To improve the efficiency of a hospital-based chemotherapy day unit (CDU). METHODS: The CDU was benchmarked with two other CDUs to identify their attainable performance levels for efficiency, and causes for differences. Furthermore, an in-depth analysis using a business approach, called lean thinking, was performed. An integrated set of interventions was implemented, among them a new planning system. The results were evaluated using pre- and post-measurements. RESULTS: We observed 24% growth of treatments and bed utilisation, a 12% increase of staff member productivity and an 81% reduction of overtime. CONCLUSIONS: The used method improved process design and led to increased efficiency and a more timely delivery of care. Thus, the business approaches, which were adapted for healthcare, were successfully applied. The method may serve as an example for other oncology settings with problems concerning waiting times, patient flow or lack of beds.