Does the reformed regime of the Genome department of the AMC lead to higher productivity?
Bachelor Thesis January 31, 2018
M. G. M. Nieste 10776702
Economics and Business University of Amsterdam C. Ting
Abstract
The present study examined the impact of the reformed regime on productivity within the Genome department of the AMC. There were two aspects changed in the reformed regime compared to the traditional regime. First, a new target was arranged for the lead-time of all the groups working in the department. Second, the analyst group started working in pairs rather than in two large groups. However it was not clear in which degree the aspects contribute to a higher productivity. Therefore, this study collected the lead-time of each task and created theoretical regressions to confirm the impact on productivity. As I had expected, the productivity of all groups within the Genome department has significantly increased. The new target gave employees an incentive to be more productive and contributed to a shorter lead-time of tasks. The smaller groups showed that their
productivity significantly increased for tasks with a heavier workload than the larger groups from the traditional regime. The study also explained why a new target gives employees an incentive to be more productive and explained the advantages of working in smaller groups.
Statement of Originality
This document is written by Maureen Nieste, who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and it’s references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of content Introduction 5 Literature review 7 Research design 9 Results 10 Conclusion 13 Discussion 15 Reference list 17
Introduction
According to the research of Popping (2018), hospitals do not perform as well as they should. Annual reports were examined for the research, using ‘Centraal Informatiepunt Beroepen Gezondheidszorg’ or CIBG (2018). CIBG is a central information point for healthcare collected by the government. Popping (2018) stated that the high absent levels are caused by long-term, non-labor-related illness, staff shortages, work pressure and consequences of self-organization, self-management, roster problems, reorganizations and mergers. The causes of high absent levels threat the financial health of hospitals. Recent results of 2016 and 2017 obtained from CIBG (2018) showed that returns are also under pressure due to high absent levels. However, continued good care can only be achieved when the hospitals are financially healthy according to Popping (2018). There are plans in 2022 for the Genome department of the Academisch Medisch Centrum to merge with the Genome department of the VU Medical Centrum. The purpose of the merge is to increase productivity and reduce costs of the Genome departments. In the future, the merged Genome departments will implement the regime of the department that shows the highest productivity. The head of the genome department of the Academisch Medisch Centrum or AMC is under the impression that the performance of their department can be improved. She hopes in the future, the Genome department of the AMC will show a higher productivity. For that reason a reformed regime has been recently implemented with two new aspects, a new and relatively more difficult target is set for all groups and one group decreased their team size. The group used to work in two large teams and is now working in pairs. The present study researches in which degree the aspects of the reformed regime of the AMC contribute to a higher productivity. Do these aspects of the reformed regime lead to higher productivity within the Genome department of the AMC?
The information about the department is achieved by consulting the head of the Genome department and other employees. The task of the department is first explained to understand how the productivity is
measured. The information about the process of tasks of each group is important to understand how the reformed regime could impact the productivity. Afterwards the sequence of the four groups within the department is visualized (also see figure 1). The data set that is used, measures the productivity for the whole department and not each separate group. For that reason, a brief explanation is followed for the function of each group and is important to visualize the process of tasks within the department. Following, the group where the set-up is changed will be described in more detail. The process of the old and new set-up is important to understand because afterwards there will be tested if and why the changes in set-up have an effect on the productivity.
The Genome department receives a request from the hospital to investigate DNA. The productivity of the employees is measured in lead-time of the incoming request. The lead-time begins when the department receives the request for research and ends when the test results are send back to the hospital. To increase the productivity of department, the reader must bear in mind that the lead-time needs to be negatively affected, meaning; the lead-time should be reduced. The hospital receives the results of a request sooner with a reduced lead-time. A request could be to research if an unborn baby has the disease, which is carried by the parents in their genes. A gene is part of the DNA and consists of fragments; the effect of the amount of fragments in a gene will be further explained. Each request contains one gene that is requested for research. The end result of a request is, if the decease is found in the genes or not. The quality of the end result is not relevant and for that reason the quality of the end result is not taken into account.
The step-by-step process that is illustrated in figure 1 holds for both of the regimes, the old and the reformed regime. The change for the reformed regime is the new target of the lead-time and the set-up of the third group, the analysts is changed. The process of the research of a request (i.e., gene) starts in the
administration department. Once the request arrives at the administration department, it is their job to deliver the request as quick as possible to the isolation group. The request is typically delivered the same day at the isolation group by the administration. The isolation group prepares the sample of DNA for the analysts group. The process of the isolation group typically takes 1 to 4 days, depending on the number of incoming requests. The number of days differs when this group is busy and relatively more requests came in. The group of analysts is the group where the set-up of the group has been changed. The analyst group researches the gene within the request and delivers the result to the following group. The final step within the Genome department is reviewing the process of the request. The task of the ‘Staf’ group is to review the work that is done and to checks if it is done properly. The ‘Staf’ group sends the results back to the hospital and only this group is authorized to put a final signature on the end results.
Figure 1. The days indicate the amount of time a group needs to finish ones process of a request, the lead-time. It could take longer or shorter depending on productivity. For the analyst group the lead-time depends on the workload of a request.
As illustrated in figure 1, there is an indication of amount of days it takes a group to finish a process of a request. The lead-time could be shorter when an employee has an incentive to be more productive. For the third group, the analysts the lead-time depends besides incentives also on the workload of the research of a request (i.e., gene). Each request contains one gene that needs to be researched. Each gene has a specific workload. The workload depends on the type of research that is necessary and the number of fragments a gene contains. Starting, a gene can demand two possible types of research actions called, MLPA and Sanger. MLPA is done on specific genes and is an action that takes a shorter amount of time then Sanger. Secondly, the more fragments a gene consists out of, longer lead-time. The process of a research for a gene with more fragments takes a longer amount of time to finish. The minimum amount of fragments a gene could have is 1 and the maximum amount of fragments could lead up to 200. The workload is heavier when the gene contains more fragments and is lighter with an MLPA type of research action. The administration, isolation and ‘Staf’ groups do not experience the same type of workload as the analyst group. The number of fragments within a gene or the type of research action is not important for the remaining three groups.
There are two aspects changed with the reformed regime to improve the productivity of the department. Both aspects of the reformed regime are implemented simultaneously. The first aspects holds for all groups, a target is set for the maximum lead-time of a finished request. The old regime had a target of 60 days and the department hopes with a reformed regime, setting the new target of 45 days is feasible. The head of the department choose the new target after observing the current average lead-time. The average lead-time is closer to 45 days instead
Administration The request arrives and after registering the request it is deliver to the next group (<1day) Isolation The group isolates the genes from DNA that are needed for research for the next group (1-4 days) Analysts The genes will be researched and the workload depends on the type of gene (1-65 days) Staf Sends the results of the request back after reviewing the research (<1 day)
of 60 days, which is the old target. The head of the department choose the new target because she knows it is feasible for the department.
The second aspect is the different set-up of the analyst group. With the old set-up, the analysts were divided in two large groups. The incoming requests were distributed per gene over the groups by their specific research. For instance there would be a period that the first group would have the requests for brain research and the second group would have the requests heart research. Every request in the old set-up was started and completed by one person, meaning an analyst would handle the request by one’s self.
The new set-up is assigning an analyst with another analyst, working in pairs. With the new set-up, the two large groups from the old regime are merged and split into pairs. The main difference with the old set-up is the size of the group. The analysts with lower ability were assigned to the analysts with a higher ability. There is a reason the new set-up is done in pairs instead of making the groups relatively larger; MLPA and Sanger researches start every week, one pair would start with MLPA and another pair would start with Sanger. The head of the department suggested pairing the analysts and not making the group larger to work together is more sufficient because the group would be to large and both researches could not start the same week. With the old set up each individual analyst started MLPA and another analyst started Sanger.
Literature review
The first aspect of the reformed regime is the new target that is set for all groups of the Genome department. Mehta and colleagues (2009) studied the relationship between student team performances and team goal orientation. The study found a positive relationship between performance and goal orientation. Wong and colleagues (2017) studies the effect of a cooperative goal within Chinese and foreign joint venture partners. The cooperative goal of the united partners improves their performance and reduces the free riding effect. Both studies suggest that groups with similar and cooperative goals are more productive.
According to the theoretical analysis of Kandel and Lazear (1992), free riding and peer pressure affect teams differently. Due to peer pressure, the free riding effect can be reduced. Their study showed that peer pressure has an effect on free riding if two conditions are met. The first condition, a member of a team must be affected by free riding to have an incentive to pressure other team members. The second condition, other members of the team must observe the effort of one member. When a request enters the Genome department, it has to be processed by each group. If a request enters the first group and there is a delay in the process, the second group has to wait on the first group to finish. The second group is affected when the first group has not finished and is aware of the time the first group needs to finish the request. When one group is delayed, the lead-time of a request is increased. The first condition of Kandel and Lazear (1992) is met when a group is delayed because the next group is affected by the delay. The second condition is also met, one group has to wait and the next group observes the delay. Because both conditions are met, theory expects when there is a case of delay in a group, other groups will execute peer pressure.
The new target is 45 days to finish a request instead of 60 days with the old target. In an experiment created by Webb et al (2013), students participated in tests. The students with the highest productivity were the students with the easy target assigned instead of the challenging target. The head of the department set the department an easy target and that could have a positive effect on the productivity leading to a decreasing lead-time. The new target of 45 days is considered to be relatively easier for the Genome department because it was
feasible even before it was implemented according to the head of the department. Because the lead-time is expected to decrease with the new target of 45 days, the new target has a negative effect on the lead-time. A positive effect on productivity means a negative effect on the lead-time.
Hypothesis 1: The new target for the group has a negative effect on the lead-time (negative effect on lead-time equals a positive effect on productivity).
The second aspect of the reformed regime is the new set-up for the third group, the analysts. The analysts are in their old set-up working in two large groups but their task/research is performed individually. Hamilton, Nickerson and Owan (2003) found that with introducing teams, the productivity is significantly increased. With the new set up, the research is started by one of the analyst and finished by the other analyst of the team. The research of Hwang and Guynes (1994) found proof that smaller groups take a shorter amount of time to reach a final decision due to the fact that team members can communicate less and closely. The study supports the theory that the pairs are more productive due to closer teamwork and less communication. When an analyst is absent under the new set up, the other analyst can finish the research with less communication. Under the old set up, other analysts were not aware of the research of an analyst because each analyst worked individually on their research. A research is paused under the old regime when an analyst is absent. Garca-Prado and Mukesh (2006) found evidence when researching the impact of hospital management reforms on absenteeism in Costa Rica. Reducing absenteeism was an important objective when the hospitals reformed their management. The research found that the management was lacking peer pressure and therefor had a negative effect on absenteeism. According to Allen (1982), there is a larger absenteeism when the employee unit is larger. The studies suggest that smaller groups result in less absenteeism.
When group size is large, there is less distinction of contributing or not contributing, which gives an incentive of free riding to an individual (Albanese & van Fleet, 1985). Backes-Gellner, Werner and Mohnen (2015) used data sets from 214 German companies to research the effect of team-size on free riding and peer pressure. Their study found that peer pressure was stronger when the team member has strong social ties and the ties between team members were mostly strong in smaller groups.
A disadvantage of the distribution of the old set up is that one group would get genes with many fragments and the other group would have genes with fewer fragments to research. Although both groups had an equal number of genes to research, the group with more fragments per gene would have more workload. Nearchou (2007) stated in one’s research that without smoothing the workload could increase to lower
productivity in an assembly line. The study of Flores et al (2009) found that high workload and stress decreases productivity. According to these studies, productivity should increase when the workload is lighter.
The high ability analysts are paired to the low ability analysts in the new set up. Within the university of Texas, Edwards and colleagues (2006) performed an experiment with 194 individuals. The participants were randomly assigned to a partner after an ability test. The teams had a composition with only high ability
participants, low ability participants or a mix of a high ability and low ability participants. The result of the study showed that teams with a mix of ability participant performed significantly better then a team of only low ability participants.
Hypothesis 2: A research of a gene with more fragments has a reduced leading time with the introduction of the reformed regime (due to smoothing of workload).
Hypothesis 3: With the introduction of the reformed regime, the absent hours is decreased within the analyst group.
Research design
Data for research has been obtained from the head of the Genome department. I have been working there for a year and for that reason she was willing to cooperate. She provided me with a list of every request from the previous year. The start of the reformed regime was in the week of 15th of October. We decided to include all data from 24th of July until the 8th of January. The lead-time could be measured for an equal period before and after the 15th of October, the start of the reformed regime for the analysts. The head of the department recommended this period, because within this period most of the incoming requests would have been finished and send back to the hospital. We started excluding all data that was not within the chosen period from the list. The data without an end result and that has not been send back to the hospital was also excluded. The requests with an end result could only be included because then the lead-time can be measured. The number of fragments per gene is retrieved from another data set of the Genome department and was added to the main data set.
We will first determine what could affect the lead-time when looking at the first aspect of the reformed regime is implemented. The first aspect was the new target that is set for all groups to finish a request within 45 days. The effects of the first aspect are considered in the first regression. Afterwards, the effects of the second aspect on the lead-time will be determined. The second aspect of the reformed regime was a new set-up for the analyst group, working in pairs instead of two groups. The effects of the second aspect are considered in the second and the third regression.
The dependent variable in the first regression is the lead-time for all groups simultaneously measured in days. All variables within the regression are dummy sets. The dummy variable ‘reformed regime’ will be used when the data is observed after the 15th of October and have a value of ‘1’. When the data is observed within the old regime, the value will be ‘0’. The data needed for a bigger general research that came in and send back within the researched period is included. Because requests for a bigger general research have less priority than a regular research, it could take longer and has a positive effect on the lead-time (i.e., a negative effect on productivity). The regressor ‘General Research’ is used for requests for bigger general research and will have a value of ‘1’. The request for a ‘regular’ research for a gene will have a value of ‘0’ when there is no case of a big general research. The requests needed to be researched with urgency would also have a dummy variable within the regression and a value of ‘1’ and a value of ‘0’ when not urgent. When a request is urgent is has priority over all other researches of requests. A request could be urgent due to the fact that the hospitals request a quick result. For that reason it has a relatively low lead-time. The findings of the first regression can answer hypothesis 1, if the new target has a negative effect on the lead-time.
Regression 1
𝐿𝑒𝑎𝑑 𝑇𝑖𝑚𝑒𝑖= 𝛽0 + 𝛽1 ∗ 𝑈𝑟𝑔𝑒𝑛𝑡𝑖+ 𝛽2 ∗ 𝐺𝑒𝑛𝑒𝑟𝑎𝑙 𝑅𝑒𝑠𝑒𝑎𝑟𝑐ℎ𝑖+ 𝛽3 ∗ 𝑅𝑒𝑓𝑜𝑟𝑚𝑒𝑑 𝑅𝑒𝑔𝑖𝑚𝑒𝑖 + 𝜀𝑖 Dependent variable: Lead-time in days
Independent variables: Urgent (1), Not urgent (0); General Research (1), Regular Research (0); Reformed Regime (1), Old Regime (0)
The lead-time within the data set holds for all groups simultaneously. There cannot be measured how long every groups takes to finish one’s task of the request. By adding new variables to the regression, there can be measured
if the second aspect, the new set-up of the analysts has a different effect than the new target on the lead-time. The analysts experience workload different then the other groups by the type of possible research actions and the number of fragments. The types of research actions that are possible are MLPA and Sanger. For that reason the dummy variables ‘MLPA’ is added to the regression and has a value of ‘1’. MLPA research takes an analyst less time then Sanger. When a request is researched by using the possible research action Sanger, the value will be ‘0’. The independent variable ‘fragments’ is added to the regression because when there are more fragments in a gene, the time to finish the research is relatively longer. Both MLPA and Fragments variables are added to the regression with interaction of the Reformed regime. It is important to add these variables to test if changing the group set up has an effect on the lead-time. In the first regression there could only be measured if all groups have an effect on the lead-time. For the analysts the lead-time depends on workload (i.e., fragments) and the possible research actions and these variables do not affect the productivity of the other groups. By adding the interaction of the variables ‘MLPA’ and ‘Fragments’ with the variable ‘Reformed Regime’, the effect of the new set up can be isolated from the whole effect. Comparing the findings when adding more variables can answer Hypotheses 2.
Regression 2
𝐿𝑒𝑎𝑑 𝑇𝑖𝑚𝑒𝑖= 𝛽0 + 𝛽1 ∗ 𝑈𝑟𝑔𝑒𝑛𝑡𝑖+ 𝛽2 ∗ 𝐺𝑒𝑛𝑒𝑟𝑎𝑙 𝑅𝑒𝑠𝑒𝑎𝑟𝑐ℎ𝑖+ 𝛽3 ∗ 𝑅𝑒𝑓𝑜𝑟𝑚𝑒𝑑 𝑅𝑒𝑔𝑖𝑚𝑒𝑖+ 𝛽4 ∗ 𝑀𝐿𝑃𝐴𝑖
+ 𝛽5 ∗ 𝐹𝑟𝑎𝑔𝑚𝑒𝑛𝑡𝑠𝑖+ 𝛽6 ∗ 𝑅𝑒𝑓𝑜𝑟𝑚𝑒𝑑 𝑅𝑒𝑔𝑖𝑚𝑒𝑖+ 𝛽7 ∗ 𝑅𝑒𝑓𝑜𝑟𝑚𝑒𝑑 𝑅𝑒𝑔𝑖𝑚𝑒𝑖∗ 𝑀𝐿𝑃𝐴𝑖+ 𝛽8
∗ 𝑅𝑒𝑓𝑜𝑟𝑚𝑒𝑑 𝑅𝑒𝑔𝑖𝑚𝑒𝑖 ∗ 𝐹𝑟𝑎𝑔𝑚𝑒𝑛𝑡𝑠𝑖+ 𝜀𝑖 Dependent variable: Lead-time in days
Independent variables: Urgent (1), Not urgent (0); MPLA (1), Sanger (0); General Research (1), Regular Research (0); Reformed Regime (1), Old regime (0); Fragments: # of fragments in a gene
The absent hours of the analysts are mentioned within the logs of the department and will be compared for the period before and after the new set-up to see if there is a change and if the new set up has a positive effect on presence of the analysts. The dependent variable would be the amount of absent hours in a week. The
independent variables are the incoming requests in a week and the dummy variable is the reformed regime. The 12 weeks before and after the implementation of the new set up are taken into account. The incoming requests per week are taken into account because the amount affects the presence of the analysts. When the data is observed in the reformed regime, it will have a value of ‘1’ and ‘0’ in the old regime. The findings will answer hypothesis 3.
Regression 3
𝑦
𝑖= 𝛽
0+ 𝛽
1∗ 𝑖𝑛𝑐𝑜𝑚𝑖𝑛𝑔
𝑖+ 𝛽
2∗ 𝑅𝑒𝑓𝑜𝑟𝑚𝑒𝑑 𝑟𝑒𝑔𝑖𝑚𝑒
𝑖+ 𝜀
𝑖 Dependent variable: # absent hours within a certain weekIndependent variables: Incoming: The incoming requests in week 1 until 24 (Week 1 is the 24th of July); Reformed regime (1), Old regime
(0)
Results
The data set of the requests offered 1,013 observations. Table 1 shows the regression results from the first aspect that is changed within the Genome department. The first aspect of the reformed regime is the new target. The
target was within the old regime 60 days and within the new regime 45 days. All of the regressors from regression 1 are dummies and are significant at 1%. The variables for ‘Urgent’ have a negative effect on the lead-time meaning that the lead-time is lower when a request is urgent. The variables for ‘Research’ have a positive effect on the lead-time meaning, resulting in a higher lead-time. The reformed regime with the hard target (i.e., 45 days) leads to a decrease of 14%(=-4.4274/31.0982 found in table 1) in lead-time, meaning an increase of 14% in productivity for the Genome department.
Table 1 – Regression result from new target
Regression number Urgent General Research Reformed Regime R2
1 -16.54036 (1.319952)*** 10.13253 (3.604357)*** -4.427435 (.9349092)*** 0.1551
Notes: Standard errors are reported in parentheses below the coefficients
* Significant at 10%; ** significant at 5%; *** significant at 1% Dependent variable: Lead-time in days
Number of observations: 1,013 Constant: 31.09824
Table 2 shows the average, maximum, minimum and standard error of the lead-time for the old and the reformed regime. The table shows that the average and maximum lead-time of the reformed regime has decreased
compared to the old regime. The maximum lead-time does not satisfy the targets in both regimes. The minimum lead-time has also decreased with the reformed regime.
Table 2 – Average and maximum lead-time
Regime Average Maximum Minimum Standard error
Old 29.3276 88 19 14.6419
Reformed 25.5078 55 6 11.3714
Notes: Dependent variable: Lead-time in days
Number of observations: 1,013
The second aspect that is changed in the reformed regime is the different set up for the analysts group. The variables from regression 1 appear again in regression 3. First we will look at regression 2.1 to notice the effect of the added variables under the old set up. The following dummy variables are added in regression 2.1, ‘MLPA’ and ‘Fragments. These variables are added because they determine the workload for an analyst and do not determine the workload of other groups within the department. In regression number 2.1 the dummy variable, ‘MLPA’ shows to have negative effect on the lead-time (i.e., positive effect on productivity) in the old regime and is significant at 10%. The results imply that ‘MLPA’ leads to a short lead-time. The independent variable, the number of fragments shows to have positive effect on the lead-time (i.e., negative effect on productivity) under the old regime. When more fragments are needed to be research, the lead-time increases.
In regression number 3.2, the dummy regressors ‘MLPA’ and ‘Fragments’ are interacting with the reformed regime. The reason for the added regressors is to measure if the new set up has increased the
productivity of the analysts. The ‘Reformed Regime*MLPA’ has a positive effect on the lead-time (i.e., negative effect on productivity) at 1% significance. This contradicts the findings when a MLPA research action is done under the old regime. The effect of the number of fragments within a gene in the reformed regime has negative effect on the lead-time (i.e., positive effect on productivity) with a significance of 1%. If a gene has more fragments to research then the lead-time is lower when researched in the reformed regime. The third regression shows to have a higher R2 then the second regression. For that reason the third regression gives a relatively better
explanation for the variance of the model.
To determine the total effect on productivity under the reformed regime, we look at regression 2.1. The results show a decrease in lead-time of 4.52 days. The decrease in lead-time means an increase in productivity of 14% for all groups. When the dummy variables are added in the regression number 3.2, the coefficient ‘MLPA’ drops from -1.532709 to -2.570927. Meaning that the research action MLPA increases the lead-time in the reformed regime by approximately 22% (i.e., decreasing productivity with approximately 22%). The coefficient ‘Fragments’ rises from 0.0010735 to 0.0523812. The rise means that for each fragment researched in the reformed regime, the lead-time will decrease by approximately 1%.
Table 3 – Regression results from new set up Regression number Urgent General Research Reformed Regime
MLPA Fragments Reformed Regime*MLPA Reformed Regime*Fragments R2 2.1 -16.60409 (1.321057)*** 10.43631 (3.609176)*** -4.516477 (.93641151)*** -1.532709 (.8257622)* .0010735 (.020202668) 0.1580 2.2 -16.77006 (1.317765)*** 11.04644 (3.590397)*** -4.65729 (1.268167)*** -2.570927 (.9421414)*** 0.0523812 (0.0270399)* 4.872029 (1.926663)*** -.1257455 (0.0408057)*** 0.17
Notes: Standard errors are reported in parentheses below the coefficients
* Significant at 10%; ** significant at 5%; *** significant at 1% Dependent variable: Lead-time in days
Number of observations: 1,013
Constant: 31.73328 (Regression number 2.1); 31,55288 (Regression number 2.2)
Graph 1 shows a visual and significant difference in maximum lead-time (treformed regime
=55-88/11.3714=-2.9020< tcritical-1%=2.575 found in table 2). It shows the maximum lead-time that is measured from the requests within the old and reformed regime. When the lead-time is lower, that would mean that the Genome department is more productive. For all requests within the department the researched have a lower maximum time under the reformed regime. The same holds for both types of research actions, the maximum lead-time is lower under the reformed regime. The maximum lead-time measured when the research of request is urgent is relatively comparable.
Graph 1
Table 4 shows the results from comparing the absent hours from different weeks before and after the
implementation of the reformed regime. The dependent variable in the third regression is the number of absent hours within that certain week. The independent variable ‘Incoming Requests’ has negative effect on the number of absent hours. When there are more requests for research incoming, the number of absent hours is lower. When looking the absent hours measured in a week with the reformed regime, the absent hours increase with
approximately 15%. The effect of the ‘Reformed Regime’ is not significant.
Table 4 – Regression results from absent hours
Regression number Incoming Requests Reformed Regime R2
3 -.234154
(.0899957)**
4.275615 (4.469768)
0.2660
Notes: Standard errors are reported in parentheses below the coefficients
* Significant at 10%; ** significant at 5%; *** significant at 1% Dependent variable: absent hours in week 1-24
Number of observations: 24 Constant: 27.80527
Conclusion
The first aspect of the reformed regime is the new target for the four groups of the Genome department. The new target was set for 45 days instead of 60 days with the old target. Webb et al (2013) study showed that an easier target had a stronger positive effect on productivity and Mehta et al (2009) found a positive relationship between the performances of a team when there is a case of goal orientation. The theory supports the results of regression 1 and the lead-time decreases with 4,5 days in the reformed regime. The new target shows a approximately 14% increase in productivity for all the groups in the Genome Department. Table 2 and graph 1 give a better
visualization from the findings. Table 2 shows that the maximum lead-time of the reformed regime is significantly lower then the maximum time of the old regime. Graph 1 shows the same results. The lead-time is decreased with the implementation of the reformed regime. The first hypothesis is not rejected (treformed regime=-4.427435/.9349092=-4.7356845<tcritical-1%=-2.575 found in table 1).
55 54 55
26 88
78 88
27
All requests MLPA Sanger Urgent
Maximum Lead-time (in days)
The second aspect of the reformed regime is the new set up for the group of analysts. The two large groups started working in pairs under the reformed regime. To notice a difference in the productivity for the analysts in the new set up, new regressors were added in regressions 2.1 and 2.2.
The group size of the analysts is decreased to pairs and the high ability analysts were paired with low ability analysts. According to the experiment of Edwards et al (2006), the teams with a mix of ability performed better then a team with only low ability participants. Also, the finding of Hwang and Guynes (1994) suggest that smaller groups take less time communication then larger groups. According to theory, the lead-time should decrease (and productivity increase) when implementing the new set up of the analyst group. The theory contradicts the findings when research action MLPA holds. When working in pairs, the group of analysts is less productive under MLPA research action. This research action should result in a shorter lead-time because MLPA takes a shorter amount of time then Sanger. Regression 2.2 does not support the theory when looking at the interaction between ‘MLPA’ and the ‘Reformed Regime’. The interaction shows a positive relationship with the lead-time (i.e., a negative relationship with productivity). When a request is done with a MLPA research action research under the old set up, the lead-time is lower according to regression 2.1, 2.2 and theory. These results imply that the analysts are less productive with a decrease of 22% under the reformed regime with a MLPA research action. These findings are not in line with expectations. Looking at graph 1, the maximum lead-time is measured in each regime. Even though MLPA should result in a higher lead-time under the new set up according to the findings of regression 2.2, a lower maximum lead-time is measured under the new set up (found in graph 1).
However, the new set up is more productive when the workload is heavier. A reason for implementing the new set up is to evenly spread the workload over the analysts. The number of fragments determines the workload of a research. Under the old set up, regression 2.1 and 2.2 show that the number of fragments have a positive effect on the lead-time (i.e., negative effect on productivity). When the number of fragments is high, the lead-time is higher. According to Nearchou (2007), if the workload is evenly spread, productivity is increased. Thus according to theory when smoothing the workload, the productivity should increase. Results support the theory and showed that for each fragment researched in the reformed regime, the lead-time is decreased and productivity increases. For every fragment researched in the reformed regime the productivity increases with 1%. For example, when a gene has 50 fragments, the lead-time is cut in half, 50% more productive under the reformed regime (=50 fragments*1%). The second hypothesis is not rejected (treformed regime*fragments =-0.1257455/0.0408057=3.08156704>tcritical-1%=2.575 found in table 3).
The results of regression 3, table 4 show that amount of requests incoming in a week, have a negative and significant effect on the absent hours. When there are relatively more requests to process and research for the department, there are less absent hours registered. The findings contradict the theory because Backes-Gellner, Werner and Mohnen (2015) found proof for more peer pressure in smaller groups due to stronger social ties. In addition to their findings, Garca-Prado and Mukesh (2006) found proof that with more peer pressure there is less absenteeism. The new set up of the analyst group should have more peer pressure and result in less absenteeism according to theory. The third hypothesis is rejected (treformed regime=4.275615/4.469768=0.95656307< t critical-1%=2.575 found in table 4).
To summarize, the productivity has increased with 14% due to the new target. The new set up of the analyst group resulted in 22% less productivity on MLPA research type but resulted in 1% more productivity per fragment researched. When more fragments are researched, this could result in very high productiveness when working in pairs. The absenteeism in the analyst group has not been decreased with the implementation of the new set up.
Discussion
The result of the study shows that I was successful in evaluating the effect of the two aspect of the reformed regime. The data set of the Genome department of AMC consisted of 1,013 observations. Bases on this, there can be concluded when the research is repeated, the results will be equivalent. The results of the study imply higher productivity when set a more difficult target. Working in smaller groups has a more positive effect on productivity when the workload is heavier. In the future when the Genome departments of the AMC and the VU merge, the reformed system of the Genome department of the AMC could be implemented in the new merged department to increase productivity.
The following information contains the limitations in the data set provided by the Genome department of the AMC. This study is unable to encompass all details of the data set because the data set did not provide information about the specific details of the processes of each request. Thence, the data set of incoming requests of the Genome department should be improved. It was difficult to measure the amount of time one group needs to process a request. In figure 1 there is an indication stated for the lead-time in days per group but if one group took a longer amount of time then necessary to process a request, the reason and effect of a delay could not be observed. When a new system for observing the lead-time for each group and each request would be
implemented, there can be research done which group has a specific effect on the lead-time of a request. The fragments within a request can be researched by robot or by hand. There is one robot in their department and there is a possibility that it would be occupied or when a request is urgent then that request would have priority over any other requests. If an employee wants to use the robot, one needs to register the request into the schedule. According to the analysts, if one is too late with registering that could lead to a maximum delay of one day to finish the request. Within the data set there is no report made when the robot was occupied. The delay is not the fault of an analyst but is observed as a decrease in productivity and increase in lead-time. A delay and ones related reason should be registered and observable within the data to increase accuracy. If registered, the variable could be taken into account and the effect could be measured in the regression.
A reason for a delay in a result for the request is that the analysts could request more DNA to research. When a research of a gene needs to be redone due to failure or a shortage of DNA, the added process results in a delay. The data set does not register when a research has failed or has a shortage of DNA. The request for more DNA is submitted to the hospital. The research of the request is paused until the analysts receive more DNA from the hospital. The request for more DNA takes time and because the reason for the delay is not registered, it is taken into account as less productive. These outliers are within the data and are taken into account because from the data set, the specifics of the delay cannot be acknowledged.
The results of regression 2.2 show that the theory is not supported when looking at the interaction between ‘MLPA’ and the ‘Reformed Regime’. The interaction shows a positive relationship with the lead-time
(i.e., a negative relationship with productivity). A reason for the contradicting results, when working in pairs and the research action MLPA, the research fails more often. The research could fail for example due to poor communication. If so, DNA needs to be requested from the hospital, resulting in a delay of lead-time and low productivity. Because the data set does not observe a research that needs to be redone, the effect could not be taken into account in the regression. If in the future, this is taken into account in the data, the effect could be studied.
The absent hours could only be measured by using a system where the employees register the absent hours. According to the head of the department, each employee does not register the absent hours accurately. The limitation of the data set of absent hours could have resulted in non-accurate results. The results implied that the absent hours increased under the new set up. A reason for this finding could be that the employees did not register the absent hours accurately before the new set up. With the new set up the employees could be pressured to register the absent hours more accurately.
The reader should bear in mind that each gene requested for research is different. Although my knowledge of DNA is not optimal, I have tried to take the greatest differences between the genes into account within the study of the thesis. But there are many differences between the genes that are not taken into account. The reason for these differences not being observed is due to too little knowledge and time pressure. Some genes have a special action that is needed, resulting in a higher lead-time. The general differences in special actions were Sanger and MLPA. If more interactions between the reformed regime and variables could be added, then the study would have a more relatively better explanation for the variance of the model. Due to time pressure and lack of knowledge, the analysts could not give me further information about the research of the differences in genes.
For further research within the Genome department of the AMC, it is important that the data sets are improved. There must be a system introduced for each group per request within the department to follow the actions. With the new system there must be a lead-time considered per gene. Each gene must have a specific lead-time for the needed types of research actions and the number of fragments. When a request is delayed in a process of a group, the reason for delay must be registered. The new system could offer a way of mutual monitoring; the ability of team members to observe each other’s actions. Towry (2003) findings suggest that mutual monitoring results in a stronger level of team-identity and higher effectiveness. A suggestion for further research could be to observe the productivity when a new system for monitoring the lead-time is implemented.
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