Programmaleider Arbeids- en Organisatievraagstukken in de Gezondheidszorg, bijzonder hoogleraar 'Arbeids- en organisatievraagstukken in de zorg', Radboud Universiteit
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The accuracy of General Practitioner workforce projections.
Greuningen, M. van, Batenburg, R., Velden, L. van der. The accuracy of General Practitioner workforce projections.: , 2013. 162 p. Abstract. In: Abstractbook EHMA Annual Conference 2013 'What healthcare can we afford? Better, quicker, lower cost health services'. 26-28 juni 2013, Milaan.
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Context: Health workforce projections are important to prevent imbalances in the health workforce. Matrix
Insight provided an overview of health workforce planning in the EU, which shows that 13 countries are engaged in model-based workforce planning using workforce projections. However, in most cases, workforce projections are not evaluated. Consequently, it is difficult to assess whether workforce planning has been successful and projections were accurate. As in the Netherlands health workforce projections have been executed since 2000 to support health workforce planning, the following key question can be addressed: what has been the accuracy of these projections, so far. Methods: We back tested the Dutch workforce projection model by comparing (ex-post) the projected number of GPs with the actual number of GPs between 1998 and 2011. All data and assumptions used in the projections are based on historical data, but the current model and the actual inflow in training were used. As the required training inflow is the key result of the workforce planning model, that has actually determined past adjustments of training inflow, the accuracy of the model is back tested using the actual training inflow. The accuracy of projections was analysed by different lengths of projection horizon and base period, i.e. 5, 10 and 15 years. By comparing the results of the projections with the actual number of GPs, the mean absolute percentage errors (MAPE) were calculated. The MAPE is a summarizing measure to express the projection error during a certain period of time regardless the direction of error. Results: The back test results show that the errors of the projection model were relatively small. The mean absolute percentage errors range from 1.9% to 14.9%, with the projections being more accurate in more recent years. As can be expected, projections with a shorter projection horizon have a higher accuracy than projections with a longer horizon. Unexpectedly however, projections with a shorter base period have a higher accuracy than those with a longer base period. In conjunction it appeared that, the accuracy is highest for projections with both the shortest rojection horizon and the shortest base period. Discussion: The results imply that it is recommendable to execute health workforce projections frequently, to minimize errors in projections with a longer horizon. This should be considered against the feasibility to execute projections with a shorter horizon and the unintended outcome that dramatic fluctuations in yearly training inflow are needed to match supply and demand. The results also show that projections are done best with data based on relatively short periods. From a data availability perspective this implies that there is significant scope for more countries to engage in model-based health workforce planning. However, the successful application of any workforce projection model is dependent on the type of health care system of a country. Hence, future research is needed to investigate which type of health workforce planning fits with which type of health care system and to evaluate the accuracy of projection models in other countries and for other medical occupations.
Insight provided an overview of health workforce planning in the EU, which shows that 13 countries are engaged in model-based workforce planning using workforce projections. However, in most cases, workforce projections are not evaluated. Consequently, it is difficult to assess whether workforce planning has been successful and projections were accurate. As in the Netherlands health workforce projections have been executed since 2000 to support health workforce planning, the following key question can be addressed: what has been the accuracy of these projections, so far. Methods: We back tested the Dutch workforce projection model by comparing (ex-post) the projected number of GPs with the actual number of GPs between 1998 and 2011. All data and assumptions used in the projections are based on historical data, but the current model and the actual inflow in training were used. As the required training inflow is the key result of the workforce planning model, that has actually determined past adjustments of training inflow, the accuracy of the model is back tested using the actual training inflow. The accuracy of projections was analysed by different lengths of projection horizon and base period, i.e. 5, 10 and 15 years. By comparing the results of the projections with the actual number of GPs, the mean absolute percentage errors (MAPE) were calculated. The MAPE is a summarizing measure to express the projection error during a certain period of time regardless the direction of error. Results: The back test results show that the errors of the projection model were relatively small. The mean absolute percentage errors range from 1.9% to 14.9%, with the projections being more accurate in more recent years. As can be expected, projections with a shorter projection horizon have a higher accuracy than projections with a longer horizon. Unexpectedly however, projections with a shorter base period have a higher accuracy than those with a longer base period. In conjunction it appeared that, the accuracy is highest for projections with both the shortest rojection horizon and the shortest base period. Discussion: The results imply that it is recommendable to execute health workforce projections frequently, to minimize errors in projections with a longer horizon. This should be considered against the feasibility to execute projections with a shorter horizon and the unintended outcome that dramatic fluctuations in yearly training inflow are needed to match supply and demand. The results also show that projections are done best with data based on relatively short periods. From a data availability perspective this implies that there is significant scope for more countries to engage in model-based health workforce planning. However, the successful application of any workforce projection model is dependent on the type of health care system of a country. Hence, future research is needed to investigate which type of health workforce planning fits with which type of health care system and to evaluate the accuracy of projection models in other countries and for other medical occupations.
Context: Health workforce projections are important to prevent imbalances in the health workforce. Matrix
Insight provided an overview of health workforce planning in the EU, which shows that 13 countries are engaged in model-based workforce planning using workforce projections. However, in most cases, workforce projections are not evaluated. Consequently, it is difficult to assess whether workforce planning has been successful and projections were accurate. As in the Netherlands health workforce projections have been executed since 2000 to support health workforce planning, the following key question can be addressed: what has been the accuracy of these projections, so far. Methods: We back tested the Dutch workforce projection model by comparing (ex-post) the projected number of GPs with the actual number of GPs between 1998 and 2011. All data and assumptions used in the projections are based on historical data, but the current model and the actual inflow in training were used. As the required training inflow is the key result of the workforce planning model, that has actually determined past adjustments of training inflow, the accuracy of the model is back tested using the actual training inflow. The accuracy of projections was analysed by different lengths of projection horizon and base period, i.e. 5, 10 and 15 years. By comparing the results of the projections with the actual number of GPs, the mean absolute percentage errors (MAPE) were calculated. The MAPE is a summarizing measure to express the projection error during a certain period of time regardless the direction of error. Results: The back test results show that the errors of the projection model were relatively small. The mean absolute percentage errors range from 1.9% to 14.9%, with the projections being more accurate in more recent years. As can be expected, projections with a shorter projection horizon have a higher accuracy than projections with a longer horizon. Unexpectedly however, projections with a shorter base period have a higher accuracy than those with a longer base period. In conjunction it appeared that, the accuracy is highest for projections with both the shortest rojection horizon and the shortest base period. Discussion: The results imply that it is recommendable to execute health workforce projections frequently, to minimize errors in projections with a longer horizon. This should be considered against the feasibility to execute projections with a shorter horizon and the unintended outcome that dramatic fluctuations in yearly training inflow are needed to match supply and demand. The results also show that projections are done best with data based on relatively short periods. From a data availability perspective this implies that there is significant scope for more countries to engage in model-based health workforce planning. However, the successful application of any workforce projection model is dependent on the type of health care system of a country. Hence, future research is needed to investigate which type of health workforce planning fits with which type of health care system and to evaluate the accuracy of projection models in other countries and for other medical occupations.
Insight provided an overview of health workforce planning in the EU, which shows that 13 countries are engaged in model-based workforce planning using workforce projections. However, in most cases, workforce projections are not evaluated. Consequently, it is difficult to assess whether workforce planning has been successful and projections were accurate. As in the Netherlands health workforce projections have been executed since 2000 to support health workforce planning, the following key question can be addressed: what has been the accuracy of these projections, so far. Methods: We back tested the Dutch workforce projection model by comparing (ex-post) the projected number of GPs with the actual number of GPs between 1998 and 2011. All data and assumptions used in the projections are based on historical data, but the current model and the actual inflow in training were used. As the required training inflow is the key result of the workforce planning model, that has actually determined past adjustments of training inflow, the accuracy of the model is back tested using the actual training inflow. The accuracy of projections was analysed by different lengths of projection horizon and base period, i.e. 5, 10 and 15 years. By comparing the results of the projections with the actual number of GPs, the mean absolute percentage errors (MAPE) were calculated. The MAPE is a summarizing measure to express the projection error during a certain period of time regardless the direction of error. Results: The back test results show that the errors of the projection model were relatively small. The mean absolute percentage errors range from 1.9% to 14.9%, with the projections being more accurate in more recent years. As can be expected, projections with a shorter projection horizon have a higher accuracy than projections with a longer horizon. Unexpectedly however, projections with a shorter base period have a higher accuracy than those with a longer base period. In conjunction it appeared that, the accuracy is highest for projections with both the shortest rojection horizon and the shortest base period. Discussion: The results imply that it is recommendable to execute health workforce projections frequently, to minimize errors in projections with a longer horizon. This should be considered against the feasibility to execute projections with a shorter horizon and the unintended outcome that dramatic fluctuations in yearly training inflow are needed to match supply and demand. The results also show that projections are done best with data based on relatively short periods. From a data availability perspective this implies that there is significant scope for more countries to engage in model-based health workforce planning. However, the successful application of any workforce projection model is dependent on the type of health care system of a country. Hence, future research is needed to investigate which type of health workforce planning fits with which type of health care system and to evaluate the accuracy of projection models in other countries and for other medical occupations.