Evaluation model for changes in healthcare organizations

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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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Examples include the percent of the eligible population successfully contacted with an offer of self-management support services, enrollment rates or opt-in or opt-out rates , completion rates, and drop out rates. As an example, the study of the asthma call center program described earlier reported that of the 1, member population with asthma actually enrolled in the program, and of these enrollees were stratified to the high risk subset for telephone support.

One informant emphasized that it is important for all aspects of evaluation to take into account at the beginning the number of people who are eligible for the program—that is, the opportunity for making an impact based on the total number of people the program might serve and what portion of the total the program plans to enroll or engage.

Different data sources may lead to different definitions of the total population. Programs that rely on claims data will limit the total to individuals who have been diagnosed or received some specific treatment. Yet, a significant proportion of individuals with some conditions go undiagnosed.

If the intent is to capture all people with the condition, as advocated by many proponents of the chronic care model, then health risk assessments or other screening mechanisms may be necessary to identify the total population. Who is being reached is another consideration. It is important that the people reached were, in fact, part of the target population. Another critical issue is whether the people reached are disproportionately those who already are most likely to self-manage, a difficult question to assess from most datasets.

Some evaluation research has utilized propensity scores based on analysis of predictors of program enrollment, 43 , 50 but it is not clear if propensity scores have been incorporated into program performance reporting.

In addition to reach, other program process measures assess the extent to which the self-management support interventions were implemented as intended. They essentially assess the program's performance of the self-management support processes called for in the program protocols. Examples of implementation measures reported in the program literature include the:.

An evaluation of a depression telecare program sponsored by an employee coalition, for example, reported that of eligible enrollees received at least one nurse call. They averaged The asthma program described as a call-center model regularly reported to its sponsors the percentage of patients with a care plan, but the program did not use this measure in their study. Staff also may be surveyed about the performance of self-management support processes.

One study 53 reported the results of a survey that asked staff to report the frequency with which the following self-management support processes occurred:. Alternatively, similar measures may be based on reports by patients that they have received specified self-management support services.

The DMAA Participant Satisfaction Survey, for example, includes items soliciting patients' reports of the frequency of different types of contact with program staff such as receipt of educational materials, scheduled calls, and face-to-face meetings , which lifestyle changes have been emphasized by program staff e.

The principal goal of a self-management support program is to increase patients' knowledge and self-efficacy for self-management, and it makes sense to measure its ability to do so. Measures of self-efficacy assess people's confidence in their ability to perform or adhere to specific behaviors such as exercise, diet, or stress management or to overcome obstacles to the performance of these behaviors.

Measures of knowledge and self-efficacy were used in some of the studies we reviewed. For example, when an independent delivery system implemented a 7-week, small-group self-management support program for patients with one or more chronic diseases, the evaluation included a self-efficacy measure of perceived adaptability to manage pain, fatigue, emotional distress, and other aspects of chronic illness. Self-management, problem-solving skills, and self-management barriers were also assessed.

It is important to identify measures that are validated, but it may be more difficult for this program area. More than one informant mentioned patient activation measures currently being developed and validated. According to interviewees, some programs use patient surveys to collect information on the patients' knowledge and self-efficacy. For several programs, routinely collecting such information is part of their program, e.

Patient surveys often are expensive, but relying on patients' responses to their coaches raises the potential for added bias, since patients may be less likely to be truthful with their coaches. One administrator in a primary care model program said they anonymously administer a small written survey to a portion of patients each month to measure their confidence in their ability to self-manage. They switched to the anonymous questionnaire after becoming suspicious of the very high levels of confidence reported by patients in response to in-person queries by coaches.

The American Association of Diabetes Educators AADE considers changes in patient behavior to be the outcome most sensitive to its diabetes self-management support. Diabetes educator-researchers recommend that measures of seven self-care behaviors be used to determine the effectiveness of self-management education at the individual and population levels.

These behaviors include monitoring blood glucose, problem solving, taking medicine, psychosocial adaptation, reducing risks of complications, being active, and eating. Table 4 shows the AADE's specific recommendations for measures and methods of measurement for assessing these intermediate outcomes. The DMAA recommends that disease management programs evaluate change in medication adherence and lifestyle behaviors diet, exercise, and smoking status, at a minimum.

Its recent Outcomes Guidelines Report states:. In the literature on real world programs, evaluations have measured a variety of behaviors. Examples have included measuring self-monitoring of dietary intake and physician activity, attendance at self-efficacy classes, patient-initiated telephone contact, foot care, glucose monitoring, self-management strategy use, use of self-management tools, insulin dose adjustment, medication compliance, controller use, physician visits, communication with the physician, diabetes health exams, eating and dietary behaviors, physical activity, frequency of exercise, and tobacco use.

In the example of a call center asthma program described earlier, pharmacy data were used to create measures of use of beta agonists, inhaled corticosteroids, leukotrine modifiers, and oral steroids. Most of these measures rely on pharmacy data or patient self-report.

If use of pharmacy data is feasible, then it is sensible to use these data for evaluating the program's effect on patients' medication behavior. Administrative data might provide a similar source of data on clinic visit behavior, although attention should be given to the extent of the time lag evident in the data reporting.

Reliable sources of data on other aspects of patient behavior e. As with measures of patient knowledge and self-efficacy, patient self-report may be the only feasible source of data on many of the patient behaviors targeted by the self-management support.

To the extent possible, care should be taken to minimize potential bias from a patient's recall difficulties or the desire to please. Not only is changing certain behavior important for many patients with chronic conditions, but sustaining behavior change is critical as well. Several experts stressed the need to evaluate whether behavior change is sustained over time. A number of the studies that we reviewed assessed programs by investigating changes in provider behavior or conformance with guidelines.

Many studies examined physicians' medication prescribing, diagnosis documentation, referrals, and rates at which they performed various procedures HbA1c tests, eye exam rates, foot exams, allergen immunotherapy, and pulmonary lab procedures. One study of an asthma program, for example, compared the performance of allergen immunotherapy, pulmonary lab procedure, ventilation and perfusion imaging, influenza immunization, and pneumococcal immunization.

To the extent that changing provider behavior is a target of the self-management support program e. Many of the measures are based on administrative data and may be readily accessible for numerous programs. Much of self-management support, however, targets patient behavior, and patient behavior alone does not determine whether these clinical processes are performed.

Researchers have used measures of HbA1c, lipids, blood pressure, weight gain, chest pain, cough, dizziness, shortness of breath, peak flow readings, asthma symptom scores, nighttime symptoms, self-reported severity of symptoms, and body mass index to assess disease or symptom control. Several of these measures also were mentioned in the interviews. Symptom control measures, along with clinical process measures, are emphasized by major national measure sets.

While patient self-report is reasonable for a number of these measures, such as chest pain or shortness of breath, it is unlikely to be reliable for other disease control measures such as cholesterol levels or other lab values. Researchers have used a variety of health outcome measures, including functional status, complications such as organ damage or lower extremity amputations, physical and mental functioning, quality of life, mortality, disability, pain, restricted activity days, days in bed, and self-reported health status.

Fewer outcome measures were mentioned by the interview respondents. These measures included global health scores, days sick at home, quality of life, and measures of physical functioning.

Improved health outcomes are unquestionably a prime goal for self-management support programs; however, a serious problem with using health outcomes for evaluation purposes is that it may take years for many of these outcomes to show the effects of improved self-management.

Measures of patient satisfaction with care and quality of life were utilized in research and mentioned by a number of interview respondents. The DMAA recently released a new assessment tool for measuring participant satisfaction with disease management.

This tool includes a number of items designed to evaluate patients' experience with the program staff, the usefulness of the services received, access to program services, and satisfaction with the information received. Measures of health care utilization included hospital admissions, emergency room visits, inpatient days, lengths of stay, outpatient visits, readmissions, and cardiac procedure rates.

As an example, the asthma call center study used inpatient admissions, inpatient bed days, emergency room visits, asthma inpatient admissions, asthma inpatient bed days, and asthma emergency room visits. With most of these measures, program success is assessed in terms of reduced utilization. However, in some cases, outpatient visits may be expected to increase from better self-management.

Utilization measures frequently are used to evaluate self-management support programs, partly because they rely on readily accessible administrative data. To the extent that the reduced utilization is expected to result from an outcome that improves over a long time period, these utilization measures will miss detecting benefits in a short followup period. Measures of productivity included days lost from work, days absent from school, and days less productive.

Patient-reported productivity items included in the DMAA Participant Satisfaction Survey, for example, focus on days missed from work and normal activities due to health problems related to the medical condition being managed and health-related limitations affecting work e.

The literature reported that various financial variables were used, including the dollar amount of claims in 1 year per patient, encounter costs, pharmacy costs, inpatient costs, outpatient costs, emergency room visit costs, radiology costs, home health care costs, charges for health care services, and costs for the program. An article on a plan's diabetes self-management support program reported per member per month paid claims, inpatient admissions per-patient per-year, inpatient days per patient per year, emergency room visits per patient per year, primary care visits per patient per year, and HEDIS scores for HbA1c tests and lipid, eye, and kidney screenings.

Stakeholders generally are interested in financial outcomes. Most interviewees focused on return on investment, and many mentioned the need for a standard methodology for calculating return on investment.

While utilization data often are used to project savings, at least one expert argued that actual changes in utilization costs should be reported. Actuarial models for evaluating cost savings from disease management programs have been utilized in the disease management field. Evaluations using short followup timeframes will miss savings that result from long-term benefits in health outcomes or utilization.

When selecting measures, it is important to consider their sensitivity to the changes targeted by the program goals. A recent review of disease management program indicators found that, in a substantial portion of studies, the indicators used did not conceptually link to the aims of the program as described in the articles. The authors recommended that selection of evaluation indicators be based on their expected sensitivity to the specific design and goals of the intervention.

For intermediate endpoints, such as patient knowledge and self-efficacy, patient behavior change, and improved disease control, indicators should be ones that might plausibly be expected to be influenced by the program components and that are associated with the expected changes in outcomes. Purchasers and providers of new programs will want to be sure that the structure of the program and the services actually provided match what the contract stipulates.

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Cancel Continue. Performance Cookies Checkbox Performance Cookies. With the largest healthcare team trained force in the world, experience indicates training and training evaluation are difficult to sustain without the support and structure provided by organizational actions of culture change. Leveraging lessons learned, a transformational change factors construct model was designed.

This heuristic systems approach to creating a culture of safety is a blueprint which remains dynamic over time. The construct, comprised of the theory of Salas training , Kirkpatrick evaluation , and Kotter culture change , provides a shared mental model for members at all levels of an organization.

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