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Biomedical subjects

G G van Merode

Publications and source records attributed to G G van Merode.

7 recordsLinked to original sources
PubMed2004

The Health Technology Assessment-disease management instrument reliably measured methodologic quality of health technology assessments of disease management.

OBJECTIVE: Systematic reviews aim to summarize the evidence in a particular topic area, giving attention to the identified methodologic quality of published research. Because research in a specific area may be susceptible to specific biases, it is assumed that the methodologic quality of Health Technology Assessment (HTA) of disease management cannot properly be measured with the existing methodologic quality assessment instruments. The purpose of this study was to describe to what extent existing instruments are useful in assessing the methodologic quality of HTA of disease management. STUDY DESIGN AND SETTING: An inventory was made of the problems that arise when assessing the methodologic quality of six HTAs of disease management with three different instruments. Based on these findings, a new instrument is proposed and validated. RESULTS: Problems mainly concern the items related to the study design, criteria for selection and restriction of patients, baseline and outcome measures, blinding of patients and providers, and the description of (co)-interventions. CONCLUSION: With its more specific characteristics, the HTA-DM addresses the problems mentioned. The HTA-DM is a reliable instrument for methodologic quality assessment of HTA of disease management in comparison with the other three instruments.

Disease Management↗
PubMed2001

Simulation as decision tool for capacity planning.

In this paper we demonstrate how discrete event simulation technique can be used to optimise the use of catheterization capacity. The patient flow at the catheterization room is described. A simulation model of the current situation was built in MedModel, a discrete event simulation package, and the model was validated. A short presentation of MedModel is given. To investigate alternative ways to optimise the use of the catheterization room three experiments were formulated, modelled and simulated. Two different scheduling strategies were applied to the current situation and the three experimental situations. The number of patients that can be treated and the duration of a working day were determined as measures of performance. The results of the simulation experiments are discussed. The results of these experiments give the management of the catheterization room valuable information how to optimise the use of the catheterization room.

Cardiac Catheterization↗
PubMed2000

Discrete event simulation in the health policy and management program.

Students in Health Policy and Management at the Faculty of Health Sciences of Maastricht University must learn to analyze and design workflow processes in health care. To attain this, a discrete event simulation training, using MedModel is used. This paper describes the training in two consecutive years. In both years the training was evaluated systematically. The evaluation results demonstrate that the simulation training increased the students' knowledge about analyzing and designing workflow processes in healthcare. Students considered the training as a very important part of their program.

Computer Simulation↗
PubMed2000

Impact of insurance coverage type on laboratory test ordering behaviour of general practitioners.

BACKGROUND: There exists much variation between GP's in the use of laboratory tests. Although the requesting pattern of GPs has been extensively described in the literature, little is still known of the factors which influence the GP's test ordering behaviour. AIM: This study aimed to determine whether the payment scheme according to which general practitioners are reimbursed influences the laboratory test ordering behaviour. METHOD: The laboratory test ordering behaviour of the general practitioners of Tilburg, a town with 180,000 citizens in the south of The Netherlands, was studied during a four month period, in relation to the type of insurance coverage of the patients. Two types of insurance were considered: voluntary and compulsory. The data were collected from the laboratory administration and coupled with information obtained from two, interview rounds. RESULTS: Two findings support the hypothesis that the type of insurance coverage of the patient, has an impact on the test ordering behaviour of the physician: The ratio between laboratory requests for sickness fund patients and patients with a private health insurance was found to depend on the fraction of persons with a private health insurance within the family practice. This was tested with multiple linear regression analysis. General practices were divided into two subgroups, those with many > 29%) and few (< 29%), voluntarily insured patients. Where a patient was privately insured it was found that relatively more tests were ordered. In case of general practices with many voluntarily insured patients this distinction disappears. The relative proportion of voluntarily insured patients was found to be an important variable in explaining the test ordering behaviour of general practice physicians in Tilburg.

Aged↗
PubMed1996

Advanced management facilities for clinical laboratories.

As part of the OpenLabs (AIM 2028) programme a decision support system (DSS) for laboratory capacity management has been developed. This DSS contains a simulation module for determining the performance of planning rules given the equipment and staffing of the clinical laboratory and the demand for laboratory services. User requirements are discussed and a method is developed to (re-)define rules concerning various planning decisions. To show the functionality of the simulation module some simulation experiments are described.

Clinical Laboratory Information Systems↗
PubMed1995

Decision support for clinical laboratory capacity planning.

The design of a decision support system for capacity planning in clinical laboratories is discussed. The DSS supports decisions concerning the following questions: how should the laboratory be divided into job shops (departments/sections), how should staff be assigned to workstations and how should samples be assigned to workstations for testing. The decision support system contains modules for supporting decisions at the overall laboratory level (concerning the division of the laboratory into job shops) and for supporting decisions at the job shop level (assignment of staff to workstations and sample scheduling). Experiments with these modules are described showing both the functionality and the validity.

Clinical Laboratory Information Systems↗