Is good management the only determinant of financial results and
quality in the mentally disabled care?
A case study on J.P. van den Bent
By: Rens Boing Date: 3-6-2014
Introduction
On July 6th 2011 the Dutch Minister of Health, Welfare and Sport sent a letter to the Parliament
saying that she would conduct an experiment in order to investigate the possibility of cutting back the administrative workload in healthcare. The experiment would involve allowing all healthcare organizations to send in complaints about rules they see as unnecessary and she would investigate these rules on their necessity. After this stage five companies were chosen to conduct the experiment by ignoring the rules that were deemed unnecessary (Tweede Kamer, 2011).
In spite of the fact that the experiment is still going on the request to send in unnecessary rules had a very large response. This confirms the need for reforms in healthcare (Tweede Kamer, 2011, p. 2). On the other hand a report by the government in May 2011 states that some organizations had already reduced the administrative workload by automation of the bureaucratic process, indicating that good management is also an important factor (Rijksoverheid, 2011, p 3). There are indeed organizations that manage to report good results despite the administrative workload and cuts in the health care budget. However, the question rises if the difference in results of the separate healthcare organizations arise from automation of the administrative processes or from other factors. For example the severity of the illness of clients could explain the difference in profits. The lower quality of the care provided might be another factor.
Since 2011 the Ministry of Health, Welfare and Sport has implemented a new plan called “kwaliteitskader zorg”, a quality framework in healthcare, with the purpose of making healthcare more transparent. Now data about an organization’s financials, quality and other characteristics must be documented according to standard rules and made available to the public. With the help of these newly available data this thesis investigates whether good performance is solely due to good management or whether there are other factors involved like quality or different clientele.
Good management is defined in this thesis by two aspects, good financial management and good quality management. Good financial management is being able to report large and positive financial results. Good quality management is defined as being able to report above average quality ratings. These two aspects are investigated since an organization could have good financial results
but if they provide less quality then they’ve given profit to much priority. The primary goal of a health care organization should be to provide good care and not to make profit.
In order to come to a conclusion if there is a difference in quality first this thesis investigates possible factors that influence the quality rating and financial result. When the factors that influence quality and financial result are determined these regression equations are used in a case study of J.P van den Bent.
J.P van den Bent is chosen for the case study because it is one of the organizations that have managed to report positive results over the years and also takes part in the experiment of the Ministry of Health, Welfare and Sport. In 2012 they reported a positive result of 14.23 million and 7.36 million in 2011(J.P van den Bent, 2011 and 2012, p.3). Pijpers and Huisman have come to the conclusion that J.P van den Bent is a so called HPO, a high performance organization (2009). A HPO is an organization that outperforms other organizations over a longer period of time (Pijpers en Huisman, 2009, p. 9). J.P van den Bent was also identified as one of four good practices in a benchmark report by Price Waterhouse Coopers (Pijpers en Huisman, 2009, p. 6). For these reasons J.P van den Bent has been chosen as the organization to do a case study on, because the effects of good management should be clearly visible.
The studies by Pijpers and Huisman and Price Waterhouse Coopers were conducted some time ago and regulations surrounding the health care sector have changed since then, furthermore they only looked at J.P van den Bent’s financial results and quality and compared them to a benchmark. They didn’t take into account differences in clientele. This thesis investigates if there are differences in quality and clientele by comparing J.P van den Bent to other companies in the same sector to determine whether there are significant differences in quality or clientele.
2. Literature review
Some studies were done with respect to the relationships between profit and quality on one hand and between the severity of illness and profit or quality on the other hand. Most of these studies were conducted in the United States. O’Neill et al finds that there is a statistically significant negative relation between profit and quality in proprietary nursing homes in the United States (2003, p. 1318-9). Since nursing homes do also provide home care and the Dutch mental health organizations are also proprietary, which means not owned by the government, one could expect such a relationship in these organizations to exist as well. In a benchmark research conducted by Price Waterhouse Coopers however, no relationship between financial result and quality is to be found (2006, p. 33). Since the research done by O’Neill et al was about nursing homes in America and the research by Price Waterhouse Coopers was about the mentally disabled care sector in the Netherlands which is more suitable to this study, I assume that the research by Price Waterhouse
Coopers is externally more valid in this situation. Therefore, my first hypothesis is that financial results have no statistically significant relation with the client rating on the quality of care.
The second part of my research question is if there is a relation between the kind of clientele and profit. Frank et al has modeled the American managed healthcare system to see if health care organizations had an incentive for adverse selection, if they would maximize profit (2000). Adverse selection in this case means that organizations would distort the quality of services provided to attract profitable enrollees and deter unprofitable enrollees. He comes to the conclusion that there are indeed incentives for adverse selection (Frank et al, 2000, p. 851). He also states that it is harder to prevent adverse selection when the system to determine remuneration per client is more complicated, since there are more factors to control (Frank et al, 2000, p. 831). The Dutch government has implemented a health care system in which clients are divided into one of eight categories depending on different factors like severity of illness, type of illness and other factors. This system is too complicated for a client to determine which category he’s in himself and has to be done by a specialist. Therefore one could argue that it is also complicated to check if an organization operates on the basis of adverse selection. This would make it possible for an organization to maximize profit by attracting a certain type of clientele. Higashi et al found that there is a relationship between the number of medical conditions, a person can suffer from Alzheimer and diabetes for example, and the quality of care provided (2007). They suggest that this could be because the health care organization has more contact with the client and receives more funding, With the increased funding they can provide a higher quality of care and since some of the treatments for different illnesses overlap they could make a higher profit (2007, pp. 2502-2503). An organization could be tempted to attract clients in the category in which the organization has the lowest cost/remuneration ratio. Therefore my second hypothesis is that there is a difference in clientele between J.P van den Bent and the control group.
The research done by Price Waterhouse Coopers also suggests that the size of an organization matters. They find that although there is no linear relation between size and result, organizations with a turnover between 25 and 50 million or smaller than 10 million, score on average the best on financial results and have the highest client rating on quality (2006, p. 29-30). So my third hypothesis is that the financial result and the quality rating are dependent on the size of the organization.
Pijpers and Huisman(2000) have done a large study trying to determine if J.P van den Bent’s working methods and organizational structure are effective. Although they didn’t use statistical tests to test their findings they used the benchmark research by Price Waterhouse Coopers to obtain a significant amount of data to base their conclusion on. They come to the conclusion that the working methods and culture are indeed that of an HPO. For these reasons I don’t expect that all the differences between J.P van den Bent and the other organizations comes from lower quality or
different clientele. My final hypothesis therefore is that not all difference in quality can be explained by difference in clientele or quality, but part of the difference is due to good management.
3. Method
In order to answer the question if good management is the cause of the positive financial results of J.P. van den Bent three steps have to be taken. First this thesis investigates what the determinants of quality are. This is done by taking a sample group and using ordinary least squares to find the variables that are significant and thus can predict the quality rating of J.P van den Bent.
Second this thesis investigates if there are other factors that influence financial results apart from good management. As stated earlier it is possible that an organization maximizes profit by attracting the clients with the highest remuneration/cost ratio for example.
As mentioned in the introduction the government made regulations in 2011 forcing Healthcare organizations to provide transparency about their financials, quality and other factors. Together with these new regulations they introduced quality framework healthcare where quality data must be easily available and easy to interpret. The way they have represented and indexed the quality data has changed every year for the last four years and the most recent year where client ratings on quality were published was 2011. This makes it impossible to compare differences over the years and restricts this research to data for the year 2011. Since there are only quality data of 33 organizations the sample size (the N) is relatively small. Therefore I have chosen to deem a variable significant if the p-value <0,15 since one could reasonably expect that a larger control group would have made the estimated coefficient more precise.
Almost all explanatory variables in the regressions used in step 1 and step 2 are taken from DigiMV, which was part of the initiative of the government to make health care more transparent. All organizations are required to submit their division of clientele, their capacity, their financial data and data about their organizational structure. All this data is collected and put into one file called DigiMV. The variables containing information about the clientele were found in the additional documents about the organization structure, which the organizations are required to provide.
The third step is finding the regressions that can predict the quality ratings and differing financial results best. These regressions will be used to compare J.P van den Bent to the sample averages.
Since J.P van den Bent wasn’t included in the client quality index there is now actual quality rating for J.P. van den Bent. It is possible to calculate the expected quality rating by using the regression that best predicts the quality rating. Then the expected quality rating of J.P van den Bent can be compared to the sample average.
Since the actual profit per client for J.P van den Bent is known this thesis uses the regression that best predicts the financial result to calculate the expected financial result per client. The expected financial result per client will then be compared to the actual result per client of J.P van den Bent and the sample average.
These comparisons will show if there is indeed good management in J.P van den Bent or that some or all of the difference can be explained by other factors.
4. Variables
In order to investigate the relationship between quality, clientele and financial results three regressions have been done. The first two regressions are to find the determinants of quality. Both use the same explanatory variables but different measures for quality. The method of ordinary least squares (OLS) has been used In order to establish the determinants of quality. The formulas for the first two regressions are:
(1) Grade Client on organization = α+ β1* quality employees+ β2*PersonnelFTE+
β3*Capacitybeds+ β4*Extramural+ β5*ratiopatienttotalcosts+β6*ratioseverelessseverepatient
(2) Gradeclientofcare = α+ β1* quality employees+ β2*PersonnelFTE+ β3*Capacitybeds+
β4*Extramural+ β5*ratiopatienttotalcosts+β6*ratioseverelessseverepatient
In the regressions the dependent variables, grade client on organization and grade client on
care, are measured by the Client quality index of the Ministry of Health, Welfare and Sport. The
grade client on the organization measures the indexed grade the respondents gave on the organization and the grade client of care measures the indexed grade the respondents gave on the care provided. Indexed means that the average grade is already controlled for several variables. These control variables are different for three categories: Clients staying in a home, clients living at home, and representatives. For the client in a home the variables are length of stay in the home, age, highest level of finished education and self-reported health. For the clients living at home these variables are the time that care is provided, age, highest finished level of education, self-reported health and if they needed help from a representative to fill in the questionnaire. If the questionnaire had to be filled in by a representative the control variables were relationship with the client (son, daughter, etc.), highest completed level of education, time of stay of the client in the organization, age and self-reported health (Actiz et al, 2011, p. 10). Seven variables have been added which have a possible influence on quality where the α is a constant.
The first explanatory variable is the quality of the employees. The quality of employees is measured by the average indexed grade the clients gave the employees of an organization and is also
measured by the client quality index in 2011. Scotti et al found that there is a statistically significant relationship between perceived quality of the employees and the customer satisfaction(2007, p. 116). Because of this reason it is reasonable to assume that the coefficient on quality of employees is positive, large and significant, and because the clients interact with the employees the most. So if the cleints are satisfied with the employees it is plausible that they’re also satisfied with the care provided.
The second variable is Personnel (FTE), which is the total number of working hours of the entire personnel. O’Neill et al found in his study a significant positive relationship between nursing staff and quality (2003, p. 1324), so it is expected that there is a positive relationship between these two variables in this regression.
The third variable is the Capacity, which is a measure of the total number of beds an organization has. This variable is added to control for the size of an organization and to see if larger organizations have indeed lower quality ratings. Shippee et al found in their studies that clients in larger nursing homes, where size is measured by the number of beds, have a lower quality of life index( 2013, p.8). Thus in this regression capacity it can be expected has a negative influence on the quality rating of clients.
The fourth variable is the number of extramural clients, which means the number of clients who are living at home but receive care of an organization. This variable is added because the benchmark study by Price Waterhouse Coopers suggests that extramural patients give, on average, higher quality scores (2007, p. 49). This means that a positive relationship between the number of extramural clients and the quality scores is to be expected.
The fifth variable is the ratio patient related costs to total costs. The benchmark study by Price Waterhouse Coopers (2007) has found that the more personnel are assigned to work directly with the clients, the higher the quality scores will be. Since we already have a variable personnel the chance on multicolinearity is high, therefore the ratio patient related to total costs is added. Where patient related costs are costs made doing the physical activities of the organizations (Intrakoop, 2009, p. 9). These physical activities are basic things like changing diapers more often or transport costs of the clients. Since the benchmark study found a positive relationship with quality, the same relationship is expected to be found in this regression.
The final variable is the ratio severe/less severe clients. In the documents every organization was required to submit for the quality framework healthcare in 2011, there was a table that summarized which category of severity of the handicap the respondents were suffering from. As mentioned in the introduction there are eight categories, one to eight. The exact indication is too complicated to account for in this regression, but Pijpers and Huisman provide basic guidelines in their study (2009, p. 90). According to these guidelines the higher the number of the category, the
more care for the client is needed. Therefore the categories are divided into two groups, the group less severe which contains categories one through four and the group severe which contains the categories five through eight. Intramural clients are the only clients that need to be indicated, so this variable measures the ratio severe less severe patients in the homes of the organizations. Higashi et al found in their study that one reason that the number of medical conditions is positively related to the quality of care provided is that there is more contact between patients and employees (2003). So it is to be expected that the coefficient off the variable severe/less severe patients is positive since the more care is needed for a patient the more contact there will be between patients and employees.
The third regression is to find factors that could contribute to a positive financial result and to see if there is a relationship between profit and quality. O’Neill et al states that putting nursing staff and financial variables in one regression would lead to over identification because nursing staff has a high correlation with total costs, turnover and profits (2003, p. 1320). Therefore financial variables couldn’t be included in the first regression. The regression that is done to investigate other possible factors that influence profits is as follows:
(3) Financial result/total clients= α + β1* ratio severe/less severe patients+ +β2*ratio extramural
clients/ total clients+ β3*ratio general costs/total clients+ β4*grade client on care
The dependent variable is the ratio financial result/total clients. The dependent variable is a ratio because financial result needs to be controlled for by size since a large organization could have a larger financial result but it could still be relatively lower than a small organization. Price Waterhouse Coopers has found that there is a relationship between an organization’s size and financial result. Since this relationship isn’t linear, a regression with variables that control for size isn’t significant. Therefore a ratio is used with the total clients as measure for size.
The first variable is a constant. The second is the variable that measures the ratio severe/less
severe, which is the ratio between the more severe handicapped patients and the less handicapped
patients. This variable is also used in the first regression because it could have a relationship with quality but it could also influence profit. Frank et al found in their study that organizations do have incentives for adverse selection if they maximize profit (2000, p. 851). Since the remuneration policy of the Dutch health care system is complicated organizations could get away with attracting the “most profitable” clients. The exact remuneration differs per client so it is difficult to form expectations about the effect of this variable on the dependent variable.
The third variable is the ratio extramural/total clients, the percentage of extramural clients an organization has, measured as the number of extramural clients divided by the total clients. The
last decennia there has been a trend to more extramural care (Rijksoverheid, 2012, p. 21). Part of the reason for this shift is that the government policy has, in some cases stimulated, and in each case accommodated this by letting go the tight supply control and stimulating accessibility of care by accommodating a strong production driven compensation policy (Rijksoverheid, 2012, p. 33). A shift to “more light” forms of care is a direct result of that. Therefore one would expect that the amount of extramural care has a positive influence on the financial result.
The fourth variable is ratio general costs/beds, where general costs are costs for communication, administration and memberships (Intrakoop, 2010, p. 7), so they are a large part of the overhead costs. These costs must also be controlled for by size, since larger organizations have also larger general costs. Price Waterhouse Coopers have found that companies with relatively larger overhead costs score on average better on financial results (2007). The main reason is that smaller companies usually have higher overhead costs but have better financial results. Therefore it is expected that the ratio general costs/beds has a positive coefficient.
Since part of my research question is if there is a relationship between quality provided and financial result, the variable quality of care is added into this regression. The variable quality of care is, just like in regression 1, measured by the average indexed grade of clients on the organization. There have been different studies which have investigated the relationship between quality and profit. O’Neil et al have found that the group of organizations in their study with the highest profit levels also had the highest number of deficiencies (2003, p. 1323). Price Waterhouse Coopers didn’t find a systematic relation between profit and quality (2007, p. 32). Since the benchmark research was done in 2007 and the number of extramural patients and low care patients have risen even more (Rijksoverheid, 2012, p. 33) one could expect that the positive relationship between extramural patients and profit together with the expected positive relationship between extramural patients and quality, there is also a positive relationship between profit and quality.
5. Results
Table 1 depicts the estimated coefficients of the variables of the first regression. The variable
Capacity (beds) is used as a control variable to control for the size of the organization. The coefficient
on quality employees is large and significant at the five percent level, which is to be expected, because if the client is satisfied with quality of the people they come in contact with the most, there is a large chance that they are also satisfied about the organization.
Table 1.
The coefficient on capacity is also significant at the five percent level, which is in line with the expectation formulated in part three that the estimated coefficient for capacity would be negative.
The variable ratio patient related cost/ total cost isn’t significant in this regression. This variable was added to see if more expenses were made doing the core activities of the organization would result in higher quality.
The variable extramural was added because of the argument of Price Waterhouse Coopers that extramural clients give a better quality rating (2007, 49), but in this regression the coefficient on Extramural is negative and significant.
The ratio severe less severe was added because it could be expected that organizations with more severe handicapped patients would get a higher quality rating. As can be seen in table 1 the coefficient on extramural has a statistically significant negative effect on quality.
The coefficient on personnelFTE is also positive and significant, which means that the higher the number of personnel, the more satisfied the clients are. Personnel (FTE) stands for all the personnel in an organization so it doesn’t immediately mean more nursing staff. This means that more administrative staff could also have a positive influence. However since the nursing staff is the largest expense of an organization as indicated by O’Neill et al (2003, p. 1320) and they also find that more nursing staff has a positive influence on quality (O’Neill et al, 2003, p. 1324), it is reasonable to assume that the main reason for this coefficient to be positive is that more personnel means a better quality rating.
The second regressions have the same explanatory variables as the first regression but the dependent variable quality has a different measure, the client rating of the care provided, instead of the client rating on the organization. The regression results are depicted in table 2.
_cons 1.024454 3.130085 0.33 0.747 -5.466946 7.515853 PersonellFTE .0019471 .0008016 2.43 0.024 .0002847 .0036094 ratioseverelessseverepatient -.5630521 .3538703 -1.59 0.126 -1.296934 .1708299 extramuralclients -.0001584 .0000582 -2.72 0.012 -.0002791 -.0000378 ratiopatienttotalcosts .9936047 .7275769 1.37 0.186 -.5152975 2.502507 Capacitybeds -.0024475 .0010033 -2.44 0.023 -.0045283 -.0003667 qualityemployees 2.095524 .9839361 2.13 0.045 .0549651 4.136082 gradeclientonorganisation Coef. Std. Err. t P>|t| [95% Conf. Interval]
Table 2
Compared to the first regression, some results are slightly more significant and others slightly less except for the coefficients on ratio patient/total costs and ratio severe/less severe. In regression 1 the coefficient on the ratio patient/total costs wasn’t significant and the coefficient on ratio severe/ less severe was, now it’s the other way around. A possible explanation could be that the patient related costs are made doing the core physical activities of an organization. Things like changing a diaper more often or transportation costs of clients are cost drivers for the patient related costs (Intrakoop, 2007, p.7). These things are more likely to have a more positive influence on the grade of the client on the care provided than on the grade on the organization.
The coefficient on the ratio patient related/ general costs is now positive and significant at the 15 percent level. As explained above the result is plausible since the patient related costs are a result from the direct care for the patient.
In regression 3 the ratio severe/ less severe patients isn’t significant at 5, 10 or 15 percent level, so having more or less patients with a severe disability has no significant effect on financial result per client. Since there could be multicolinearity between the ratio severe/ less severe patients, the grade of the client on the organization and the ratio extramural clients/ total clients, as can be seen in the results of regression 1, the results of the regression without the variable severe/less severe patients is depicted in table 4.
Table 4
Now the ratio extramural clients/total clients is significant at the 5 percent level indicating that the number of extramural patients has a negative influence on the profit per client. This is different from the expectation formulated in part 3.
_cons 1.480025 2.752023 0.54 0.596 -4.227321 7.187371 PersonellFTE .001478 .0007048 2.10 0.048 .0000164 .0029396 ratioseverelessseverepatient -.4456264 .3111286 -1.43 0.166 -1.090868 .1996148 extramuralclients -.0001343 .0000511 -2.63 0.015 -.0002404 -.0000283 ratiopatienttotalcosts 1.051838 .6396977 1.64 0.114 -.2748136 2.37849 Capacitybeds -.0017883 .0008822 -2.03 0.055 -.0036178 .0000412 qualityemployees 1.986412 .8650929 2.30 0.032 .1923193 3.780505 gradeclientofcare Coef. Std. Err. t P>|t| [95% Conf. Interval] _cons -1772.269 3268.001 -0.54 0.591 -8437.401 4892.862 gradeclientonorganisation 624.181 386.8381 1.61 0.117 -164.7806 1413.143 Ratiogeneralcostsclients .2663756 .1520892 1.75 0.090 -.0438124 .5765636 ratioextratotal -3349.197 1464.351 -2.29 0.029 -6335.759 -362.6338 resultclients Coef. Std. Err. t P>|t| [95% Conf. Interval]
The coefficient on the ratio general costs/total costs is positive and significant, which is in line with my expectations formulated in part four.
The coefficient on the client rating on the organization is also positive and significant at the 15 percent level. This means that a higher quality provided has a positive influence on the profit per client.
6. Validity of the results
Now that the determinants of quality and financial results per client have been established, it is important to discuss if some of the results that had a different sign than hypothesized are still valid. This is because it is necessary to check if the results, that were different from the hypothesized influence described in part 4, are accurate predictors of the quality and the results per clients. In the first regression the coefficient on severe less severe was negative instead of the positive value predicted. An explanation for the negative coefficient could be that the chance that the representatives filled in the questionnaires is higher. Bobinac et al have investigated the effect of a person’s illness on the happiness of so-called care givers, people in one’s direct surroundings who take care of the client, using data from the Dutch healthcare system (2010). They found a positive relationship between the severity of illness and the happiness of the care giver (Bobinac et al, 2010, p. 553). Together with the findings of Kahanpaa et al that family members are more critical and give the lowest assessments of quality on all aspects of quality of care (2006, p. 5), it could explain the negative coefficient on the ratio severe less severe.
The second variable where the coefficient had a different sign than hypothesized was extramural. An explanation is that there has been a tradeoff to the larger volume of the extramural care production in recent decennia (Rijksoverheid, 2012, p. 21) and the quality of the care provided.
In the third regression the coefficient on the ratio extramural/total clients had a different sign than hypothesized. An explanation is that a large volume of extramural production can be profitable because it is easier to increase extramural production than intramural production. This is because in order to increase extramural production one only needs a larger amount of personnel since the extramural patients are living at home, however for an increase in intramural production large investments in real estate have to be done in order to accompany the increased clientele. This regression measured the influence on the percentage of extramural patients on the profit per client so even though there is a trend of increasing extramural production, the profit margin on intramural patients could still be higher. Price Waterhouse Coopers for example have found that extramural patients require higher overhead costs (2007, p. 69).
So there have been other studies done that can explain the positive or negative signs of the coefficients in the regression, that were different than hypothesized. Therefore I assume that the regressions done are still valid and can be used to compare J.P van den Bent with the control group.
7. Application results to J.P. van den Bent
Now that the factors that have a significant influence on quality and financial result per client are determined the research question of this thesis can be answered. Recall that the research question was:
Are there differences in quality and clientele between the organizations in the mentally disabled care sector that can explain the differing financial results?
This question is answered by comparing J.P van den Bent to other companies in the same sector to determine whether there are significant differences in quality or clientele. The first part of the question is if there are differences in the quality provided. When the client quality index was measured in 2011 they didn’t include J.P van den Bent but we can calculate the expected grades on organization and on care provided with the help of regression equations one and two. Table five depicts the regression coefficients and the value of the variables for J.P van den Bent to find the expected grade on quality of care provided.
Table 5
Variable Coefficient Value
Variable Coefficient* Value variable Constant 1,480025 quality employees 1,986412 3,364548 6,632855126 capacity(beds) -0,0017883 1236 -2,2103388
ratio patient related costs/ total cost 1,051838 0,004050934 0,004260927 ratio severe/less severe patients -0,4456264 0,821138211 -0,365920865
extramural clients -0,0001343 1455,00 -0,1954065
Personnel(FTE) 0,001478 1125,7 1,6637846
grade client on care 7,059782939
Recall that all the variables in this regression for J.P. van den Bent were taken from DigiMV,. As told in part three the data on quality employees is measured in the client quality index in which J.P van den Bent isn’t represented. Therefore in order to make the calculation I assume that the quality of J.P van den Bent is the average of the fourth percentile of the grades given on the quality of the employees. This is because J.P van den Bent was voted the best employer in 2012 (Intermediair and effectory, 2012) and Scotti et al found that there is a statistically significant relationship between perceived quality of the employees and the employee satisfaction (2007, p. 116). Futhermore scotti et al found that there is a statistically significant relationship between perceived quality of the
employees and the employees’ perceptions on being able to provide high quality service(2007, p. 116). Pijpers and Huisman have found in their studies that employees of J.P van den Bent score higher in comparison to the benchmark in the employee survey on autonomy and their input (2009, P. 15). So one can expect that quality of employees' score will be higher for J.P van den Bent, since employees have more input and autonomy. These two arguments support the assumption that J.P van den Bent is indeed in the top percentile of scores on the quality of employees.
The expected client grade on the care provided is a 7,1 (rounded to first decimal). The average grade of the sample is a 7,5, which means that the expected score of J.P van den Bent is below the sample average. This would mean that there is indeed a difference in the quality of care provided and it could explain the difference in quality.
Table 6 depicts the calculation of the expected grade of the clients on the organization.
Table 6
Variable Coefficient Value variable Coefficient* value variable
Constant 1,024454
quality employees 2,09524 3,364548 6,99624417
capacity(beds) -0,0024475 1236 -3,02511
extramural clients -0,0001584 1455,00 -0,230472 ratio patient related costs/ total cost 0,9936047 0,004050934 0,004025027 ratio severe/less severe patients -0,5630521 0,821138211 -0,462343594
Personnel(FTE) 0,0019471 1125,7 2,19185047
grade client on organization 6,551939513
The expected client grade on the organization is now a 6,6. The average of the sample group is a 7,3, which means that J.P. van den Bent also scores lower on the grade of the client on the organization. With the help of regression four we can calculate the expected financial result per client for J.P van den Bent. Since we have the actual financial result per client for J.P van den Bent the difference between the expected and actual financial result per client is the result of good management. Table seven depicts the estimated coefficients of regression 4, the value of the variables and the estimated coefficients of regression 4 multiplied by the value of the variables. The sum of the last column is the expected financial result per client.
Table 8
Variable Coeffficient Value variable Coefficient* value variable
Constant -1772,269
ratio extramural clients/total clients -3349,197 0,571036107 -1912,512416 ratio general cost/total clients 0,2663756 1361,541209 362,6813564 grade client on organization 624,181 6,551939513 4056,332653
Financial result/total clients 767,4960978
As can be seen from table 8 the expected result per client is 767.49 euro, much lower than the actual 2732.6 reported in 2011.
8. Conclusion
This thesis has established the factors that influence the quality rating of the clients in the sector that provide care for the handicapped. With these factors an expectation of the quality rating on J.P van den Bent could be formed and it was below the sample average. This means that one possible explanation for the higher financial result could be lower quality provided. However in the financial regression the quality rating was also included, but since the difference between the expected and actual financial result per client was so large, the chance of omitted variable bias is large. So aside from the fact that the influence off quality on financial result isn’t fully found, it is likely that not all of the good financial result can be attributed to good management since the possibility that the lower quality provided could be a factor.
Policy implications
The negative significant relationship between the percentage of extramural clients and the plan of the government to achieve a shift to even more extramural care could have implications for the sector. The VGN, the branche organization of the handicapped sector, published a factsheet in 2012 explaining how the government wants to extramuralize the care even more(2012). From 2013 on the lowest two categories for the severity of handicapedness(?) are lapsed and from 2015 it is expected that also categories 3 and 4 are lapsed (VGN, 2012, p. 2-3). This means that these categories will be replaced with extramural care. Since the effect on extramural care on profit per client is negative this could mean that it will be harder in the years to come to report positive results. The government will also continue with trying to make the healthcare sector more transparent and the next quality ratings will be published next year. Since this thesis also found a negative
relationship between quality and the number of extramural patients this could mean that the larger number of extramural patients due to the policy changes will lower the quality rating of the
organizations. It is therefore recommended that health care organizations focus more on making the extramural production more efficient while focusing on how to improve the quality of extramural care provided.
9. Discussion
In 2008, when the cq index for the mentally disabled care was being developed, NIVEL did a study about the efficiency of the index and came to the conclusion that it is not always possible to apply it in the sector of the care for the disabled. In order to make it possible to do the questionnaires different ways of questioning the clients have to be found, but these ways could form a treat to the standardization (NIVEL, 2008, p.8). So the cq index of the mentally disabled care isn’t always reliable because of the extra measures that have to be taken in order to insure a response that’s high enough. Examples of these measures are for example only selecting people that can fill in such a questionnaire which would make the sample selection not random.
Another factor that influences the results of the regressions is that the sample group was only 34 companies. This is because the cq index of 2011 didn’t include all the organizations in the sector. The low N causes the estimates to be less precise.
The healthcare sector is also not very transparent. There are different types of remunerations for different clients in the form of AWBZ, subsidies, WMO and ZVW’s. All for different forms of care provided and although most of the remunerations in this sector come from the AWBZ, organizations also provide different types of care which have different kind of remunerations. This thesis however only compared the financial result, which contains each type of remuneration, with the kind of care that falls under the AWBZ. So even though a very large part of the turnover comes from the AWBZ the comparison between financial results and intra- and extramural care could have a measurement error.
Another problem with the regressions done to determine the factors that influence quality and the regression with the financial variables is that there can be much more factors that could influence the dependent variable. This problem is called omitted variable bias and the chance on omitted variable bias is the largest for the regression that determines the factors that influence financial result per client. Variables like competition, geographic location and number of registered nurses could also have an influence for example. Unfortunately these variables aren’t yet documented in the public documents.
Finally organizations can report costs and gains in different ways, which could make it hard to estimate the financial explanatory variables.
10. References
Actiz., Branchebelang thuiszorg Nederland., Zorgverzekeraars Nederland., 2011. ‘Handle with Care’; Een handreiking voor het gebruik van de resultaten van het kwaliteitskader Verantwoorde zorg. Higashi, T., Wenger, N., Adams, J., Fung, C., Roland, M., McGlynn, E., Reeves, D., Asch, S., Kerr, E., Shekelle, P., 2007. Relationship between Number of Medical Conditions and Quality of Care. New England Journal of Medicine (352), 2496-2504.
Frank, R., Glazer, G., McGuire, T., 2000. Measuring adverse selection in managed health care. Journal of Health Economics 19, 829-854.
Kahanpӓӓ., Perӓlӓ, M., Rӓikkӧnen, O., 2006. Consistency of quality assessments in long-term care by the clients, family members and named nurses. Scandinavian Journal of Caring Sciences,
Volume 4(20), 375–385.
O'Neill, C., Harrington, C., Kitchener, M., Saliba, D., 2003. Quality of Care in Nursing Homes: An Analysis of Relationships among Profit, Quality, and Ownership. Medical Care 41 (12), 1318-1330. Pijpers, V., Huijsman, R., 2009. Individueel vraaggericht werken in kleinschalige voorzieningen in de verstandelijke gehandicaptenzorg: Goed voor cliënten, medewerkers, organisatie en stelsel? Eindrapportage iBMG.
Price Waterhouse Coopers., 2007. Weten voor Beter, Brancherapport benchmark gehandicaptenzorg.
Rijksoverheid., 2006. ‘Meer tijd voor de cliënt’.
Rijksoverheid., 2012. Naar beter betaalbare zorg; rapport taskforce beheersing Zorguitgaven. Scotti, D., ., Driscoll, A., Harmon, J., Behson, S., 2007. Links Among High-Performance
Work Environment, Service Quality, and Customer Satisfaction: An Extension to the Healthcare Sector. Journal of Healtcare management 52 (2), p. 109-124).
Shippee, T., Henning-Smith, C., Kane, R., Lewis, T., 2013. Resident- and Facility-Level Predictors of Quality of Life in Long-Term Care. The Gerontologist, 10(148), p. 1-13.
Tweede Kamer., 2006. Brief van de staatssecretaris van Volksgezondheid, Welzijn en Sport, Nr. 50. Wijngaarden, B. van., Kok, I., Sixma, H., 2008. De bruikbaarheid van een CQ-index voor de ggz, verslavingszorg en gehandicaptenzorg: eerste bevindingen. Tijdschrift voor
Gezondheidswetenschappen. 86(8), 463-470.
VGN., 2012. Factsheet extramuralisering zorgwaartepakketten gehandicaptenzorg.
Zhang, N., Wan, T., 2007. Effect on institutional mechanisms on nursing home quality. Journal of health and human services administration 29 (4), 380-408.
Intermediar., Effectory., 2012. De beste werkgevers van 2012.
