Lud van der Velden

Measuring the working hours of general practitioners (GPs) is an important but complex task due to the effects of bias related to self-reporting, recall, and stress. In this paper we describe the deployment, feasibility, and implementation of an innovative method for measuring, in real time, GPs’ working time, plus the response to the study. A Short Message Service (SMS) application was developed which sent messages at random to GPs during their working week. Approximately nineteen GPs participated each week during a period of 57 weeks. The text messages asked if GPs were doing activities related to patients, directly, indirectly, or not at all, at the moment of sending. Participants were requested to reply by SMS. Approximately 27,000 messages were sent to 1051 GPs over more than one year. The SMS system was functioning 99.9% of the time. GPs replied to 94% of all the messages sent. Only a few participants dropped out of the study. The data was available in real time enabling the researchers to monitor the response and overall quality of the data each day. The SMS method offers advantages over other instruments of measurement because it allows a better response, ease of use and avoids recall bias. This makes it a feasible method to collect valid data about GPs working time.

In several countries, the number of hours worked by general practitioners (GPs) has decreased, raising concern about current and impending workforce shortages. This shorter working week has been ascribed both to the feminisation of the workforce and to a younger generation of GPs who prefer more flexible working arrangements. There is, however, limited insight into how the impact of these determinants interact. We investigated the relative importance of differences in GPs’ working hours in relation to gender, age, and employment position. An analysis was performed on real-time monitoring data collected by sending SMS text messages to 1051 Dutch GPs, who participated during a 1-week time use study. We used descriptive statistics, independent sample t-tests, and one-way ANOVA analysis to compare the working time of different GP groups. A path analysis was conducted to examine the difference in working time by gender, age, employment position, and their combinations. Female GPs worked significantly fewer hours than their male peers. GPs in their 50s worked the highest number of hours, followed by GPs age 60 and older. GPs younger than 40 worked the lowest number of hours. This relationship between working hours and age was not significantly different for women and men. As shown by path analysis, female GPs consistently worked fewer hours than their male counterparts, regardless of their age and employment position. The relationship between age and working hours was largely influenced by gender and employment position. The variation in working hours among GPs can be explained by the combination of gender, age, and employment position. Gender appears to be the most important predictor as the largest part of the variation in working hours is explained by a direct effect of this variable. It has previously been reported that the difference in working hours between male and female GPs had decreased over time. However, our findings suggest that gender remains a critical factor for variation in time use and for policy instruments such as health workforce planning.

Our research is based on a technique for time sampling, an innovative method for measuring the working hours of Dutch general practitioners (GPs), which was deployed in an earlier study. In this study, 1051 GPs were questioned about their activities in real time by sending them one SMS text message every 3 h during 1 week. The required sample size for this study is important for health workforce planners to know if they want to apply this method to target groups who are hard to reach or if fewer resources are available. In this time-sampling method, however, standard power analyses is not sufficient for calculating the required sample size as this accounts only for sample fluctuation and not for the fluctuation of measurements taken from every participant. We investigated the impact of the number of participants and frequency of measurements per participant upon the confidence intervals (CIs) for the hours worked per week. Statistical analyses of the time-use data we obtained from GPs were performed. Ninety-five percent CIs were calculated, using equations and simulation techniques, for various different numbers of GPs included in the dataset and for various frequencies of measurements per participant. Our results showed that the one-tailed CI, including sample and measurement fluctuation, decreased from 21 until 3 h between one and 50 GPs. As a result of the formulas to calculate CIs, the increase of the precision continued and was lower with the same additional number of GPs. Likewise, the analyses showed how the number of participants required decreased if more measurements per participant were taken. For example, one measurement per 3-h time slot during the week requires 300 GPs to achieve a CI of 1 h, while one measurement per hour requires 100 GPs to obtain the same result. The sample size needed for time-use research based on a time-sampling technique depends on the design and aim of the study. In this paper, we showed how the precision of the measurement of hours worked each week by GPs strongly varied according to the number of GPs included and the frequency of measurements per GP during the week measured. The best balance between both dimensions will depend upon different circumstances, such as the target group and the budget available.

Health workforce projections are important instruments to prevent imbalances in the health workforce. For both the tenability and further development of these projections, it is important to evaluate the accuracy of workforce projections. In the Netherlands, health workforce projections have been done since 2000 to support health workforce planning. What is the accuracy of the techniques of these Dutch general practitioner workforce projections? We backtested the workforce projection model by comparing the ex-post projected number of general practitioners with the observed number of general practitioners between 1998 and 2011. Averages of historical data were used for all elements except for inflow in training. As the required training inflow is the key result of the workforce planning model, and has actually determined past adjustments of training inflow, the accuracy of the model was backtested using the observed training inflow and not an average of historical data to avoid the interference of past policy decisions. The accuracy of projections with different lengths of projection horizon and base period (on which the projections are based) was tested. The workforce projection model underestimated the number of active Dutch general practitioners in most years. The mean absolute percentage errors range from 1.9% to 14.9%, with the projections being more accurate in more recent years. Furthermore, projections with a shorter projection horizon have a higher accuracy than those with a longer horizon. Unexpectedly, projections with a shorter base period have a higher accuracy than those with a longer base period. According to the results of the present study, forecasting the size of the future workforce did not become more difficult between 1998 and 2011, as we originally expected. Furthermore, the projections with a short projection horizon and a short base period are more accurate than projections with a longer projection horizon and base period. We can carefully conclude that health workforce projections can be made with data based on relatively short base periods, although detailed data are still required to monitor and evaluate the health workforce.

The high cost of training and the relatively long period of training for physicians make it beneficial to stimulate physicians to retire later. Therefore, a better understanding of the link between the factors influencing the decision to retire and actual turnover would benefit policies designed to encourage later retirement. This study focuses on actual GP turnover and the determining factors for this in the Netherlands. The period 2003–2007 saw fewer GPs retiring from general practice than the period 1998–2002. In addition, GPs’ retirement age was higher in 2003–2007. For these two periods, we analysed work perception, objective workload and reasons for leaving, and related these with the probability that GPs would leave general practice at an early age.

In many countries, health-care labour markets are constantly being challenged by an alternation of shortage and oversupply. Avoiding these cyclic variations is a major challenge. In the Netherlands, a workforce planning model has been used in health care for ten years.

Primary care nurses play an important role in diabetes care, and were introduced in GP-practice partly to shift care from hospital to primary care. The aim of this study was to assess whether the referral rate for hospital treatment for diabetes type II (T2DM) patients has changed with the introduction of primary care nurses, and whether these changes were related to the number of diabetes-related contacts in a general practice.

For the further development of palliative care, it is relevant to gain insight into trends in non-acute mortality. The aim of this article is twofold: (a) to provide insight into ten-year trends in the characteristics of patients who died from cancer or other chronic diseases in the Netherlands; (b) to show how national death statistics, derived from physicians' death certificates, can be used in this type of investigations.

To describe the development to date of the percentage of female physicians and to predict the situation in 2027, including a calculation of the number of physicians needed. Retrospective data collection, description and analysis. Using historical data from Statistics Netherlands and the Dutch certification boards ofgeneral practitioners, clinical specialists, and public and occupational health physicians, the percentage ofwomen among medical students, residents and specialists was calculated for the period between 1950 and 2007. For the prediction of the percentage of women among clinical specialists in 2027, model calculations prepared by The Netherlands Institute for Health Services Research (NIVEL) on behalf of the national committee set up to determine current and future specialist training capacity were used, taking into account the effect of part-time jobs. In 1987 about 20% of all physicians and 15% of all specialists were female. In 2007 this had risen to 40% of all physicians and 34% of all specialists. In 2027 approximately 66% of all physicians and 55% of all specialists will be female. Due to the higher percentage of women 6% more practicing specialists will be needed. This requires an increase of 11% in the number of specialists and physicians in training. The percentage of women among physicians and medical students indicates that, numerically, medicine is indeed 'feminising' and that this process will continue in the coming years. The implications in terms of the number of specialists needed and training capacity are smaller than generally assumed. Other developments, such as an ageing population, will have a greater impact on the demand for physicians.