Biomedical subjects

Siebren Groothuis

Publications and source records attributed to Siebren Groothuis.

5 recordsLinked to original sources

PubMed2004

Enterprise resource planning for hospitals.

Integrated hospitals need a central planning and control system to plan patients' processes and the required capacity. Given the changes in healthcare one can ask the question what type of information systems can best support these healthcare delivery organizations. We focus in this review on the potential of enterprise resource planning (ERP) systems for healthcare delivery organizations. First ERP systems are explained. An overview is then presented of the characteristics of the planning process in hospital environments. Problems with ERP that are due to the special characteristics of healthcare are presented. The situations in which ERP can or cannot be used are discussed. It is suggested to divide hospitals in a part that is concerned only with deterministic processes and a part that is concerned with non-deterministic processes. ERP can be very useful for planning and controlling the deterministic processes.

Delivery of Health Care, Integrated↗

PubMed2004

Predicting capacities required in cardiology units for heart failure patients via simulation.

The management of a department of cardiology has to plan the capacity of both elective and non-elective patients. Heart failure (HF) patients are admitted to the hospital in a non-elective way. The precision with which the capacity needed for non-elective patients can be predicted determines the degree of flexibility in planning the admission of elective patients. In this study we want to determine how accurately we can predict the bed occupancy of HF patients using a simulation model. Data of the year 2000 were used to obtain the necessary probability distribution functions. Data from the year 2001 were used for determining the prediction accuracy. The results show that the arrival of new HF patients can be adequately predicted. However, the bed occupancy by new and especially current patients is predicted less accurately. Still in 70% (90%) of the days of a 5-day-prediction interval the error is at most one (two) bed(s). The results may improve if the cardiologist is asked to predict the length of stay of the current patients.

Bed Occupancy↗

PubMed2002

Simulation studies and the alignment of interests.

Chemotherapy patients are treated with cytostatic drugs. Cytostatic drugs can be produced if ordered or they can be produced to keep in stock. The time that cytostatic drugs can be kept in stock before being spoiled is limited. It is always possible that the patient cannot receive chemotherapy because he/she is not in the right condition. A simulation model is presented which allows to calculate for each combination of patient type and cytostatic drug type the effects on waiting times and costs. In practice actors will behave not as recommended by the simulation model. The cause of the latter is analyzed and solutions are proposed.

Antineoplastic Agents↗

PubMed2002

Application of computer simulation analysis to assess the effects of relocating a hospital phlebotomy department.

BACKGROUND: This study describes a systematic approach to assess the effects of relocating a hospital department. METHODS: Using the phlebotomy service as an example, computer simulation was applied to predict changes in performance indicators, such as patient turn-around time (TAT), when planning a procedural and/or architectural redesign. RESULTS: Average patient TAT fell from 12 to 8 min, enabling the department to cope with any increase in numbers of patients. CONCLUSION: This type of study can provide useful information in assessing the consequences of future changes in the location of a hospital department.

Appointments and Schedules↗

PubMed2002

Turn-around time for chemical and endocrinology analyzers studied using simulation.

Simulation can be a means for the laboratory management to investigate the effects of proposed changes in the laboratory. Also, when specifications of analyzers are available one can investigate which analyzer should be purchased to fulfill existing needs. The potential of simulation is shown here by simulating the turn-around time of batched samples under several conditions. Three routine analyzers (DAX, AXON and Immuno-1) were modeled. The temporal pattern of the arrival of the samples at the different analyzers was determined. The impact of these patterns and the two batching methods on the turn-around time was investigated. Simulation proved to be a useful method to study the effects of batching and arrival patterns on the turn-around time.

Blood Chemical Analysis↗