Implementation by simulation; strategies for ultrasound screening for hip dysplasia in the Netherlands

Background: Implementation of medical interventions may vary with organization and available capacity. The influence of this source of variability on the cost-effectiveness can be evaluated by computer simulation following a carefully designed experimental design. We used this approach as part of a national implementation study of ultrasonographic infant screening for developmental dysplasia of the hip (DDH). Methods: First, workflow and performance of the current screening program (physical examination) was analyzed. Then, experimental variables, i.e., relevant entities in the workflow of screening, were defined with varying levels to describe alternative implementation models. To determine the relevant levels literature and interviews among professional stakeholders are used. Finally, cost-effectiveness ratios (inclusive of sensitivity analyses) for the range of implementation scenarios were calculated. Results: The four experimental variables for implementation were: 1) location of the consultation, 2) integrated with regular consultation or not, 3) number of ultrasound machines and 4) discipline of the screener. With respective numbers of levels of 3,2,3,4 in total 72 possible scenarios were identified. In our model experimental variables related to the number of available ultrasound machines and the necessity of an extra consultation influenced the cost-effectiveness most. Conclusions: Better information comes available for choosing optimised implementation strategies where organizational and capacity variables are important using the combination of simulation models and an experimental design. Information to determine the levels of experimental variables can be extracted from the literature or directly from experts

Capacity management of nursing staff as a vehicle for organizational improvement

<p>Abstract</p> <p>Background</p> <p>Capacity management systems create insight into required resources like staff and equipment. For inpatient hospital care, capacity management requires information on beds and nursing staff capacity, on a daily as well as annual basis. This paper presents a comprehensive capacity model that gives insight into required nursing staff capacity and opportunities to improve capacity utilization on a ward level.</p> <p>Methods</p> <p>A capacity model was developed to calculate required nursing staff capacity. The model used historical bed utilization, nurse-patient ratios, and parameters concerning contract hours to calculate beds and nursing staff needed per shift and the number of nurses needed on an annual basis in a ward. The model was applied to three different capacity management problems on three separate groups of hospital wards. The problems entailed operational, tactical, and strategic management issues: optimizing working processes on pediatric wards, predicting the consequences of reducing length of stay on nursing staff required on a cardiology ward, and calculating the nursing staff consequences of merging two internal medicine wards.</p> <p>Results</p> <p>It was possible to build a model based on easily available data that calculate the nursing staff capacity needed daily and annually and that accommodate organizational improvements. Organizational improvement processes were initiated in three different groups of wards. For two pediatric wards, the most important improvements were found to be improving working processes so that the agreed nurse-patient ratios could be attained. In the second case, for a cardiology ward, what-if analyses with the model showed that workload could be substantially lowered by reducing length of stay. The third case demonstrated the possible savings in capacity that could be achieved by merging two small internal medicine wards.</p> <p>Conclusion</p> <p>A comprehensive capacity model was developed and successfully applied to support capacity decisions on operational, tactical, and strategic levels. It appeared to be a useful tool for supporting discussions between wards and hospital management by giving objective and quantitative insight into staff and bed requirements. Moreover, the model was applied to initiate organizational improvements, which resulted in more efficient capacity utilization.</p

An intelligent real-time scheduler for out-patient clinics: A multi-agent system model

Scheduling of resources and patients are crucial in outpatient clinics, particularly when the patient demand is high and patient arrivals are random. Generally, outpatient clinic systems are push systems where scheduling is based on average demand prediction and is considered for long term (monthly or bimonthly). Often, planning and actual scenario vary due to uncertainty and variability in demand and this mismatch results in prolonged waiting times and under-utilization of resources. In this article, we model an outpatient clinics as a multi-agent system and propose an intelligent real-time scheduler that schedules patients and resources based on the actual status of departments. Two algorithms are implemented: one for resource scheduling that is based on predictive demand and the other is patient scheduling which performs path optimization depending on the actual status of departments. In order to match resources with stochastic demand, a coordination mechanism is developed that reschedules the resources in the outpatient clinics in real time through auction-bidding procedures. First, a simulation study of intelligent real-time scheduler is carried out followed by implementation of the same in an outpatient clinic of Aravind Eye Hospital, Madurai, India. This hospital has huge patient demand and the patient arrivals are random. The results show that the intelligent real-time scheduler improved the performance measures like waiting time, cycle time, and utilization significantly compared to scheduling of resources and patients in isolation. By scheduling resources and patients, based on system status and demand, the outpatient clinic system becomes a pull system. This scheduler transforms outpatient clinics from open loop system to closed-loop system. </jats:p

Making sense of delays in outpatient specialty care: A system perspective

Objectives: To assess whether delays to outpatient specialty care can be solved by improving the way supply and demand are matched, without adding capacity. Methods: A systematic review of the interventions applied by 18 clinics using the model of 'advanced access' and a statistical analysis of the effects of the interventions on their delays. Results: The clinics applied different combinations of interventions aimed at improving the way they match supply and demand, improving the efficiency of the way supply is organised and at reducing unnecessary demand. Fourteen clinics show statistically significant improvements. Two probably significantly improved and two clinics did not. Their access reduced on average 55%, from 47 to 21 days. Conclusions: It seems that delays in outpatient specialty care can be solved to a large extend by improving the way supply and demand are matched. Policy makers should analyse whether delays are caused by capacity problems or matching problems. For the latter, it appears more effective to invest in the ability to react then the ability to plan. Policy makers should create incentives for clinics to keep access short and remove incentives that stimulate delays

Making sense of delays in outpatient specialty care: A system perspective

Objectives To assess whether delays to outpatient specialty care can be solved by improving the way supply and demand are matched, without adding capacity.Methods A systematic review of the interventions applied by 18 clinics using the model of 'advanced access' and a statistical analysis of the effects of the interventions on their delays.Results The clinics applied different combinations of interventions aimed at improving the way they match supply and demand, improving the efficiency of the way supply is organised and at reducing unnecessary demand. Fourteen clinics show statistically significant improvements. Two probably significantly improved and two clinics did not. Their access reduced on average 55%, from 47 to 21 days.Conclusions It seems that delays in outpatient specialty care can be solved to a large extend by improving the way supply and demand are matched. Policy makers should analyse whether delays are caused by capacity problems or matching problems. For the latter, it appears more effective to invest in the ability to react then the ability to plan. Policy makers should create incentives for clinics to keep access short and remove incentives that stimulate delays.Hospital outpatient clinics Access to healthcare

Principal agent relationships and the efficiency of hospitals

Principal agent relationships, Efficiency, Hospitals, Stochastic frontier estimation, D2, I1,

A critical evaluation and framework of business process improvement methods

The redesign of business processes has a huge potential in terms of reducing costs and throughput times, as well as improving customer satisfaction. Despite rapid developments in the business process management discipline during the last decade, a comprehensive overview of the options to methodologically support a team to move from as-is process insights to to-be process alternatives is lacking. As such, no safeguard exists that a systematic exploration of the full range of redesign possibilities takes place by practitioners. Consequently, many attractive redesign possibilities remain unidentified and the improvement potential of redesign initiatives is not fulfilled. This systematic literature review establishes a comprehensive methodological framework, which serves as a catalog for process improvement use cases. The framework contains an overview of all the method options regarding the generation of process improvement ideas. This is established by identifying six key methodological decision areas, e.g. the human actors who can be invited to generate these ideas or the information that can be collected prior to this act. This framework enables practitioners to compose a well-considered method to generate process improvement ideas themselves. Based on a critical evaluation of the framework, the authors also offer recommendations that support academic researchers in grounding and improving methods for generating process improvement ideas. Next to the framework and its critical evaluation, this review investigates the research procedures of the studies that were used to create the framework. Related to this investigation, academic researchers can find additional guidance regarding procedures for building and evaluating new methods