typical change in healthcare organizations
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Typical change in healthcare organizations

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We also control for a set of variables defined at the j , h , t cell level, such as the average number of discharges H D C j , h , t , the average age of patients A g e j , h , t and the share of male patients M a l e j , h , t.

By controlling for a set of observables over time, we control for observed differences among the treated and the control group, which allows us to reduce the imbalance of the two samples.

The heterogeneity of MDCs within hospitals also affects the heterogeneity among hospitals that a simple hospital fixed effect, such as the one used in the basic specification Eq. Hence, we also control for a set of interaction terms, which are introduced into the basic model incrementally to reach a saturated one.

The saturated model that we finally estimate can be written as follows:. By including all the possible pairwise interactions, we identify the coefficient of interest by estimating the empirical models outlined above by ordinary least squares, assuming that the remaining variation is explained by the dummy variable, which identifies the adoption of the PC model. From a methodological point of view, over-controlling in a linear regression model is similar to statistical matching e.

To account for the presence of a common random effect at the hospital level, all the models are estimated with clustered standard errors at the hospital level. We use a large administrative data set covering the full population of patients and hospitals operating in the Lombardy Health Care System.

Our data set combines information on more than They are individual records with daily frequency, but since we focus here on the average efficiency and effectiveness of MDCs in hospitals that moved to a PC organization as compared with those in hospitals that maintained the traditional organization, we consider the yearly frequency of the average HDC. The administrative data set that we use is routinely collected by hospitals for both financial and managerial purposes and is relayed regularly to the regional administration.

The main advantages of using administrative records consist of full population coverage and the significant reduction of measurement and sampling errors, with plenty of details about the diagnosis and the service provided.

Each HDC reports information regarding the patient characteristics gender, age and province of residence and the discharge characteristics e. This data set has been linked with other information, also provided by the Lombardy Health Care Department, regarding several hospital characteristics, such as ownership and geographic location.

These data are also matched with the registry office that records the deaths of all residents in the region. According to the international literature [ 38 , 39 ] , outcome indicators of hospital care essentially analyze costs in relation to some proxies for the quantity of delivered care.

Moreover, outcomes indicators have high relevance from the viewpoint of both patients and policy makers as reliable proxies for health care quality Footnote 7. Our data set allows us to define a limited number of efficiency and effectiveness outcomes. Here, as a measure of efficiency, we consider the following index:. Average days of stay in hospital : this index counts the average number of days from admission to the hospital to discharge. It provides a measure of efficiency as, by reducing the length of stay LoS a hospital would manage to reduce its costs.

As for the effectiveness measures, we consider the rate at which patients are re-hospitalized in the same major diagnostic category MDC within 30 days both in the same hospital and in different hospitals , as ceteris paribus this might signal an early discharge or unsatisfactory treatment. Related to this, we would also like to test whether patients treated in PC hospitals have different mortality rates from those treated in traditionally organized ones.

In fact, our administrative data record whether any discharged patients died at any moment after the day of discharge up to the end of , allowing us to construct a mortality rate within 30 days of discharge, which is likely to provide an accurate indication of care effectiveness [ 41 ]. We have no a priori expectation regarding how the PC organization could affect this outcome variable. It might even be that for such an important health care outcome, the PC innovation will be found to have no significant effect.

Hence, we consider three effectiveness indexes based on the available information:. Average number of readmissions within 30 days : this index measures the number of readmissions of the same patient to a Lombardy hospital within the same MDC within 30 days of discharge;. Average number of readmissions in the same hospital within 30 days : this index measures the number of readmissions of the same patient to the same hospital and to the same MDC within 30 days of discharge;.

Average mortality rates within 30 days : this index defines the mortality rate of patients within 30 days of hospital discharge. In fact, this set of indexes provides only a partial picture of efficiency and effectiveness at the hospital level.

Before using the data set to estimate the empirical models outlined above, we discarded the discharge charts belonging to patients with a province of residence outside Lombardy, discharges for hospitalizations shorter than one day and subacute hospital discharge charts Footnote 8.

Anna di Como and the Ospedale di Legnano are public and non-research-oriented hospitals, we selected only hospitals belonging to the same category. We also dropped a few other hospitals that could not be clearly ascribed to either the treated or the control group as some had started the PC model implementation before and some immediately after our observation period and those for which it was not possible to identify a clear starting point for the move to the PC model.

Hence, we collapsed the data set by major diagnostic categories MDCs Footnote 9 , hospitals and year and dropped all the cells produced by the collapse with fewer than 30 discharges to preserve an acceptable level of precision Footnote Eventually, we obtained a panel of 25 MDCs belonging to 86 hospitals over at most 9 years from to , with a total size of nearly 13 thousand observations.

Table 2 shows some summary statistics of the total sample, showing that in the average MDC the average age is Table 3 shows some descriptive statistics of the efficiency and effectiveness outcomes for the PC and functional hospitals before and after the organizational change that took place at the end of The average number of days in hospital of average MDCs increased by 0.

As we observe the full population of Lombardy hospitals, we can also observe the case of patients who needed re-hospitalization for the same MDC but decided to change hospital, possibly because they did not appreciate the treatment received in the first one. As our administrative data are matched with registry office data recording people who passed away, we can also make a clear estimate of the mortality rate of patients after being discharged by a hospital.

The differences in the changes between pre- and post-treatment periods of average MDCs in the control and treated groups for the considered measures of efficiency and effectiveness suggest that some improvement might have been produced by the switch to the PC organizational model, but for a proper statistical assessment of their significance we need the estimation of the empirical model outlined above.

At the core of our difference-in-difference identification strategy lies the so-called parallel trends assumption. A graphical representation of the parallel trend assumption is provided in Fig. However, as in some cases the graphical representation is not conclusive, we also tested the internal validity of our identification strategy by checking whether there is any evidence rejecting the assumption of parallel trends for the period before the treatment of PC and traditionally organized hospitals.

The results are presented in Table 4 , showing that there is no evidence to reject the parallel trends assumption Footnote 11 , hence we proceed presenting our main results. Table 5 shows our main results. Each coefficient estimate comes from different regressions, in which only the estimate of our coefficient of interest, its standard error in brackets and the total number of observations are presented.

This offers us an immediate analysis of the overall effect on the average MDC of adopting PC organization in health care in the outcome analyzed. Column 1 presents the results for the basic model Eq. In column 2 we add the interactions between hospitals and MDC fixed effects and the number of discharges to capture effects that could be hospital-specific, MDC-specific or size-specific. All the models are estimated with cluster-corrected standard errors at the hospital level.

Column 1 of Table 5 shows that, on the one hand, there is no evidence that PC hospitals deal with higher levels of efficiency their coefficient are not statistically different from zero , while, on the other hand, we find significant evidence of higher levels of effectiveness of PC hospitals in terms of the re-hospitalization rate in the same MDC.

However, once we control for the interaction of MDCs, year dummies and number of discharges in each cell column 2 and eventually reach the fully saturated model column 4 , all the coefficients become statistically significant, suggesting that, taking into account the average heterogeneity among MDCs, the PC organizational model has an effect on both the selected efficiency and the selected effectiveness outcomes.

These results suggest the following conclusions. However, in addition to the predictable higher level of efficiency associated with the PC model, one should also expect an impact in terms of effectiveness, looking at the average re-hospitalization rate within 30 days of discharge for the same MDC and for the same MDC and hospital and on the mortality rate at 30 days.

We find no statistically significant reduction of the mortality rate the estimated coefficient is 0 but a relatively more important reduction in both the re-hospitalization rates of discharged patients. This is a relevant drop, which immediately affects the welfare of discharged patients.

There are, however, some caveats that should be stressed. First, there is the role of possibly confounding factors, which could bias our estimates. For instance, the transition to a PC model from a traditional organizational model involves changing incentives, for medical doctors, for nurses and for managers, but to account for them we should have access to detailed information about the composition of the hospital workforce and its remuneration and incentive policies. This is something that unfortunately we cannot address with the available data.

Second, there is the issue of the external validity of our results. We provide here an empirical analysis using recent data on public hospitals operating in the Italian national health care system. Our results are likely to be relevant to public hospitals operating in national health care systems i. However, we are unable to say whether our estimated effects would be confirmed in countries where there is no similar system.

Our evaluation analysis could be criticized for not allowing the capture of all the complexities and articulations of the PC model or the specificities of each and every implementation of the general framework of the model. In fact, we claim that our quantitative approach does not substitute but complements more qualitative analyses based, for instance, on ethnographic approaches or case study analyses [ 17 , 32 , 42 ].

Our approach allows one to gain an assessment of the overall average change of a set of outcomes, controlling for a large range of confounding factors, and to measure the overall effect of the switch to the PC model exploiting the time variation of treated and untreated units and the heterogeneity among MDCs and hospitals. As we mentioned above the adoption of the PC organizational model is not an immediate process but often requires a preparation period as well as a period of adaptation to the new organizational standards.

This is the reason why we defined the PC dummy variable for these three hospitals as equal to one for the years and only and equal to zero for all the other years. Hence, we tested the robustness of the results by simultaneously dropping both the years and , which allows for an adjustment period and for a preparation period respectively towards the PC model Table 6.

The results show that the main findings for both efficiency and effectiveness of the PC model are broadly confirmed, showing only a slightly larger effect of the PC innovation on the average length of hospital stay. Also results on effectiveness show the overall robustness of results to the exclusion of the years Table 6.

Finally, observing that our sample size is affected by the fact that many MDC-year cells present fewer than 30 HDCs per year and that small denominators MDCs with very few patients in any one year may introduce statistical noise into our outcome indicators - and for these reasons have been dropped from the analysis - we estimate the same empirical models allowing for different minimum cell sizes.

The results are presented in Tables 7 and 8 and again produce evidence of overall strong robustness of our estimates. One can notice that the effects on re-hospitalization rates both the same MDC and the same hospital-MDC are largely unaffected by the different cell sizes. The signs do not change and the statistical significance of these indicators is roughly constant, between 20 and 40 minimum cell sizes, and equal to the baseline selection of Table 5.

As for the size of the reduction in mortality and the length of the hospital stay, it is positively correlated with the cell size, suggesting that the higher the restriction, the stronger and more significant is the estimated effect, implying that the adoption of a PC organizational model has stronger effects in relatively larger MDCs.

This profound shift can be traced to a Institute of Medicine report [ 43 ] that identified a focus on Patient-Centered care as one factor constituting high-quality care. This solidified the Patient-Centered care approach not only as a way of creating a more appealing patient experience, but also as a fundamental practice for the provision of high-quality care, with direct implication on hospital organizational models and processes [ 44 ].

In this paper we took advantage of the fortunate coincidence of a quasi-experimental setting regarding all the MDCs in three hospitals of an important region of Italy and of the availability of a unique administrative data set to develop an ex post evaluation of an innovation from a traditional functional model to a PC organizational model in hospitals.

We suggested a quantitative framework for overcoming some of the current challenges in the evaluative policies of hospital organizational models for a similar approach to policy analysis in health care see [ 45 ]. To the best of our knowledge, this is the first quantitative assessment of such an important and frequently found organizational setting in hospitals. We managed to estimate difference-in-difference models that support some of the theoretical claims of the PC model as a whole.

In particular, the PC model seems to have an effect on effectiveness, which is a relevant dimension of the quality of health care services. The strongest effects are found in the efficiency variable measuring the duration of hospitalization. These results are in line with the theoretical framework outlined in the Empirical Model subsection, which suggested increased efficiency and effectiveness of PC hospitals.

In particular, the increase in efficiency emerges from the reduction of the hospitalization duration. As for efficacy, our results, showing a reduction in re-hospitalization, suggest an increased level of efficacy of hospitals that switched to a PC organization.

The lack of statistical significance of mortality rates suggests that this organizational innovation is unlikely to have any impact on such an outcome. First, this paper fills the quantitative assessment gap related to the PC hospital model with a specific focus on efficiency and effectiveness. Such an organizational change towards the PC model can be a costly process, implying a rebalancing of responsibilities and power among hospital personnel, affecting inter-disciplinary and inter-professional relations e.

Nevertheless, our results confirm the effect of these hospital innovations on efficiency [ 11 ] , adding some robust results, thus suggesting that a change to the PC model can be worthwhile.

This evidence can be used to inform and sustain hospital managers and policy makers in their hospital design efforts, and to communicate the innovation advantages within the hospital organizations, among the personnel and in the public debate. With these data analysis, we believe that this health care innovation can be regarded as an actual improvement to meet the needs of the community, contrasting the possible perception that it may have been driven by managerial, international or political trends.

As suggested by McKee and Healy [ 36 ] , all that we can be certain of is that the hospital of the future will be different from the hospital of today and the PC model is an interesting innovation, which, however, requires a proper evaluation. Second, this research exercise can be also considered as a guiding example for ex-post evaluation of broad interventions.

This is a complicated task, although worthwhile as it provides fundamental suggestions to policy makers engaged in important future and complex innovations [ 46 ]. This study refers to the long-standing tradition of program evaluation, which may be used when the real-world provides data to support testing hypothesis with a counterfactual approach.

The availability of administrative data, which is increasing in all developed countries and is characterised by little measurement error and high detail of information, makes the opportunity for sound quantitative assessments, offering evidence that turns useful in the planning of innovation initiatives and their policy implications for the overall society. This paper provides a quantitative estimation of efficiency and effectiveness changes following the implementation of the PC hospital model in a major region of Italy.

Taking advantage of a quasi-experimental setting and a detailed administrative dataset, we perform an ex-post evaluation of innovating the hospital organization by switching from a traditional functional model to a PC organizational one.

We provide robust evidence, at the average MDC, of a statistically significant and positive effect of the introduction of the PC model on both effectiveness and efficiency.

In particular, the increase in efficiency emerges from the reduction of the average length of stay, while for efficacy, our results, show a reduction in re-hospitalization rates of hospitals that switched to a PC organization. These results are in line with our theoretical framework which suggests an increase in efficiency and effectiveness of PC hospitals and provides a sound example of a quantitative evaluation of an organizational intervention adopting a counterfactual approach. MDC codes are internationally recognized thanks to their adoption in the United States medical care reimbursement system.

They are formed mapping all the DRG codes into 25 mutually exclusive diagnosis areas. We estimate log-linear models of the outcome means considering that the outcomes that we use are strictly non-negative e. This is clearly equivalent to including a standard interaction term between the treatment variable and a post-reform dummy. Also notice that there is no need to include a treatment dummy, as we have the full set of hospital fixed effects, or a post-reform dummy variable, as we have the full set of year fixed effects.

Individual HDC records are not publicly available under the Italian privacy law. The Health Care Department of the Lombardy Region must be contacted to discuss the provision of the data. The diagnosis-related group DRG code is a standard classification [ 48 ] adopted in the Lombardy Region of Italy since In fact, HDC data trace the department that is in charge of each patient and record the total number of departmental transfers of each HDC, but not whether a transfer is in fact a bed change within the same hospital or, more simply, a change of the administratively responsible department.

An important efficiency measure that we do not observe is the cost of single HDCs as we have no information on the composition and cost of the physical and human resources used. In fact, we are provided with the cost of reimbursement by the Lombardy Health Care System to hospitals for each HDC, but this variable is unsuitable for use as a cost measure as it is affected by DRG up-coding practices, discretionality of the regional policy makers in deciding the price of the duration and the DRG of each HDC, allowing for strategic behaviour of hospital managers.

For an extensive analysis of the reimbursement mechanism adopted in the Lombardy Health Care System, see [ 49 ].

The main reason for dropping the HDCs of patients with residence outside Lombardy is because they might be occasional users of the Lombardy Health Care System and we lack relevant information about them regarding their possible re-hospitalization and death. For instance, as we know the date of death of Lombardy residents only, including non-Lombardy patients would bias the average mortality rate of patients downward by an unpredictable amount.

We also dropped one-day-long and subacute HDCs due to comparability issues. A similar approach was used by [ 50 ]. Some robustness checks assessing the relevance of this selection rule are provided in Tables 7 and 8. We developed this test results in Table 4 for all the models that we estimated in Table 5 columns 1 to 4 , starting from the basic equation Eq. First, we computed each outcome variable of interest after partialling out the contribution of all the independent variables except for P C h , t.

Hence, we regressed each of them on a fourth-degree polynomial time trend, allowing all the coefficients to differ between the PC and the traditionally organized hospitals unrestricted model , and we regressed the same dependent variable on a fourth-degree polynomial time trend in which only the intercept is allowed to differ between the two groups considered.

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Building blocks. The present study poses the question: what characterizes successful organizational changes in health care? The aim was to investigate the characteristics of changes of relevance for the work of health care professionals that they deemed successful.

Methods: The study was based on semi-structured interviews with 30 health care professionals: 11 physicians, 12 registered nurses and seven assistant nurses employed in the Swedish health care system. An inductive approach was applied using questions based on the existing literature on organizational change and change responses.

The questions concerned the interviewees' experiences and perceptions of any changes that they considered to have affected their work, regardless of whether these changes were "objectively" large or small changes. The interviewees' responses were analysed using directed content analysis. Results: The analysis yielded three categories concerning characteristics of successful changes: having the opportunity to influence the change; being prepared for the change; valuing the change.

The interviewees emphasized the importance of having the opportunity to influence the organizational changes that are implemented.

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Applying Change Management in Health Care

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