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Original Research

Can Didactic Continuing Education Improve Clinical Decision Making and Reduce Cost of Quality? Evidence From a Case Study

MIRA VUKOVIC´ , MD, PHD; BRANISLAV S. GVOZDENOVIC´ , MD, PHD; MILENA RANKOVIC´ , MSC; BRYAN P. MCCORMICK, PHD; DANICA D. VUKOVIC´ , ECON; BILJANA D. GVOZDENOVIC´ , ECON; DRAGANA A. KASTRATOVIC´ , MD, PHD; SRDJAN Z. MARKOVIC´ , MD, MS; MIODRAG ILIC´ , MD; MIHAJLO B. JAKOVLJEVIC´ , MD, PHD Introduction: Administration of human serum albumin (HSA) solutions for the resuscitation of critically ill patients remains controversial. The objective of this study was to assess the effect of continuing medical education (CME) on health care professionals’ clinical decision making with regard to HSA administration and the costs of quality (COQ). A quasi-experimental study of time series association of CME intervention with COQ and use of HSA solution was conducted at the Surgery Department of the Hospital Valjevo, Serbia. The CME contained evidencebased criteria for HSA solution administration in surgical patients. The preintervention period was defined as January 2009 to May 2011. CME was provided in June 2011, with the postintervention period June 2011 to May 2012. Methods: Total mortality rate, the rate of nonsurgical mortality, the rate of surgical mortality, the rate of sepsis patient mortality, index of irrational use of HSA solutions, and number of hospital days per hospitalized patient were collected for each month as quality indicators. Statistical analysis was performed by multivariate autoregressive integrated moving average (MARIMA) modeling. The specification of the COQ was performed according to a traditional COQ model. Results: The CME intervention resulted in an average monthly reduction of the hospital days per hospitalized patient, the rate of sepsis patient mortality, index of irrational use of HSA solutions, and COQ for $593,890.77 per year. Discussion: Didactic CME presenting evidence-based criteria for HSA administration was associated with improvements in clinical decisions and COQ. In addition, this study demonstrates that models combining MARIMA and traditional COQ models can be useful in the evaluation of CME interventions aimed at reducing COQ. Key Words: cost/benefit–cost/effectiveness analysis, quality improvement/Six Sigma/TQM, innovative educational interventions, human serum albumin, surgery department, mortality, cost of quality, quality indicators

Introduction Disclosures: The authors report none. Dr. Vuković: Head of Education Center, Health Center Valjevo; Dr. Gvozdenović: Medical Director, Pharmacovigilance Department, PPD Serbia; Ms. Ranković: Economist, Accounting and Finance Department, Health Center Valjevo; Dr. McCormick: Professor & Chair of Department of Recreation, Park & Tourism Studies, School of Public Health–Bloomington, Indiana University; Ms. Vuković: Economist, eFront; Ms. Gvozdenović: Econimist, National Health Insurance Fund; Dr. Kastratović: Clinical Pharmacologist, Clinical Center of Serbia; Dr. Marković: Clinical Physician, Clinical Center of Serbia; Dr. Ilić: Surgeon, Institute of Cardiovascular Surgery Dedinje; Dr. Jakovljević: Head of Graduate Health Economics & Pharmacoeconomics Curricula, Associate Professor, Faculty of Medical Sciences, University of Kragujevac. Correspondence: Branislav S. Gvozdenović, 94/2, Brace Jerkovic Street, 11010 Belgrade, Serbia; e-mail: [email protected]. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council

The definition of health care cost of quality (COQ) remains ambiguous due to gaps between definitions of health care quality and health care financing models and language differences between quality professionals and health care professionals (HPs). Another cause is heterogeneous methods for recognizing, identifying, and controlling the link between health care quality and cost.1–5 This variability allowed Carlson et al.,6 for example, to conclude that health care COQ could be reduced through the development and implementation of high-quality processes, while Hussey and colleagues2 found that the association of cost and quality was small to on Continuing Medical Education, Association for Hospital Medical Education • Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/chp.21272

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moderate in magnitude and inconsistent in direction. Additionally, the relationship of quality and cost may be influenced by changes in health care financing that simultaneously reduce funding, while mandating high-quality services. Nevertheless, evaluations of the effectiveness of continuing education for health professionals need to give greater attention to COQ, and models of how this might be done are needed in the literature. In late 2010 and early 2011, the Serbian Republic Health Insurance Fund (SRHIF) was forced to make extensive restrictions on financing health care facilities7,8 and simultaneously introduced diagnosis-related groups (DRGs) as the basis for hospital payment. DRG-based payment systems may present risks for health care quality but may also provide opportunities for quality improvements.9 A study of intensive care units (ICUs) in Europe demonstrated that patient care time and costs were poorly associated with diagnosis, as compared to other factors such as staff training, medication, and use of noninvasive ventilation.10 Of interest in the present study was SRHIF policies with regard to human serum albumin (HSA). HSA solutions are plasma expanders, which in addition to the treatment of hypovolemia, are also used for the correction of hypoalbuminemia.11 The SRHIF had previously imposed restrictions on funding for HSA solution treatments for patients with hypoalbuminemia ( 2 mL/min) of HSA may cause a sudden drop in systemic blood pressure, especially in elderly patients and those at risk of congestive heart failure.29 In addition, participants were told that HSA treatment for malignant diseases, malnutrition, terminal illness, wound healing, and trauma were irrational uses of HSA.12,13 Finally, we presented the results of a study we had conducted14 as follows: “Anticipating that patients with hypoalbuminemia < 3 g/dL consequently can have fatal outcome, for every 100 bottles of HSA used in the ICU, in 30% of our deceased patients, we only pay for the additional costs of medical controversy and our irrational anticipations.”

Activity During the CME activity, it was pointed out that (1) only hypoalbuminemia < 2 g/dL can lead to an additional disturbance of homeostasis in sepsis patients11 ; (2) rapid infusion (> 2 mL/min during 4 hours) of HSA solutions, especially in elderly sepsis patients, may cause a sudden drop in systemic blood pressure, congestive heart failure, and death13,29 ; and (3) the use of HSA solutions in the correction of hypoalbuminemia of terminal patients and/or patients with malignant disease is considered irrational. Evaluation Methods An abbreviated Modified Version30 of Kirkpatrick’s 4-level evaluation model31 was used to evaluate the intervention. We evaluated learners’ satisfaction and results (first and fourth level of Kirkpatrick’s framework). Evaluation of learners’ satisfaction was conducted at the end of the CME in the form of a 7-question evaluation. Satisfaction questions addressed the choice of topics, content, quality of presentation, practical applicability of the acquired knowledge, organization, duration, and lecturer mode of reasoning. Responses were recorded using a Likert-type ascending numerical scale of 1–5 (from lesser to greater satisfaction). Description of Resulting Variables Outcomes at Level 4—Results were assessed using the following quality indicators and calculated on a monthly basis: hospitalized patients, hospitalization days, operated patients, nonoperated patients, deceased operated patients, deceased patients without operation, total mortality rate, the rate of nonsurgical mortality, the rate of surgical mortality, the rate of mortality of sepsis patients, patients with HSA solution therapy, patients with sepsis and HSA therapy, patients with HSA therapy and malignant disease or terminal stage of disease, index of irrational HSA therapy (patients with malignant disease or terminal stage disease with HSA therapy/patients with HSA therapy), and hospital days per hospitalized patient. HSA solution utilization was expressed as the number of unit packages (bottles) consumed in the ICU. Also observed were the rate of HSA units per 100 patient deaths and HSA cost ratio (dollar value) per 100 hospitalized patients. According to the price list of the SRHIF, the unit cost of an HSA solution was $42.98, while the unit cost of a hospital day was $59.27 (direct labor, indirect labor, hotel, nutrition, and overheads). CME costs consisted of 80 hours of specialist time for data analysis and preparation of CME ($4.60/hour), rental of premises for expert meetings ($184.2/meeting), and unit cost of the CME program per listener was $5.4.

JOURNAL OF CONTINUING EDUCATION IN THE HEALTH PROFESSIONS—35(2), 2015 DOI: 10.1002/chp

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Definition and Classification of Costs The classification, definition and specification of the costs of quality were performed according to a traditional COQ model1 with the following types of cost: (1) prevention cost or the cost of developing practices to minimize or eliminate errors and ensure high quality (cost of CME); (2) appraisal cost associated with activities to determine the level of quality and conformance (cost of data analysis); and (3) internal failure cost or cost of errors due to the failure of HPs (cost of irrational use of HSA, cost of HSA treatment of sepsis patients who died, cost of hospital stay; all for the average number of patients per year). Statistical Methods Data are described using measures of central tendency (mean or median) and variability by (standard deviation or range from the 10th to 90th percentiles). Analysis of learner satisfaction data was performed using the Mann-Whitney test. For a review of trends and autocorrelation of variables over time, we analyzed time series data using multivariate autoregressive integrated moving average (MARIMA), employing the Boxe-Jenkins method that finds an adequate stochastic dependence of consecutive data.32 A MARIMA model with automatic detection of outliers was constructed to determine the significance of the impact of the CME intervention on change in quality indicators, coupled with econometrics variables. Dependent time series consisted of discrete values and the presence or absence of the intervention as expressed by the appropriate category before (marked as 0) and after the intervention (marked as 1). According to the Boxe-Jenkins criteria, for each dependent variable the model fit was evaluated. It involves the following steps: (1) the first stationarity (a constant mean and variance) is checked, (2) the form of the model (p, d, q) with autocorrelation parameters (ACF) and partial autocorrelation (PACF) is identified, (3) further regression model parameters and their statistical significance is estimated, and (4) the most adequate model is chosen. Among the various models that have adequate fit, the model containing the smallest number of parameters (“most parsimonious”) is chosen. The generated coefficient R2 measures the overall fit of the regression line. The accepted level of significance was set at .05. The statistical analysis was conducted using SPSS 18 (Chicago, IL). Results During June 2012, there were 4 professional meetings in the City Hospital, with a total of 256 participants. The frequency distribution of learners by occupation was surgeons, 26; anesthesiologists, 13; infectologists, 3; transfusiologists, 2; internists, 2; and nurses, 185. Statistics on learning satis112

faction items by occupation categories with the level of significance are presented in TABLE 1. Descriptive statistics for all nonindexed quality indicators are presented per intervention period and total period in TABLE 2. Preintervention Our models also showed a preintervention transient decrease in hospital stay, because a classical hernia surgery was replaced with mesh hernioplasty (model 1 in TABLE 3). The preintervention period also showed a transient decrease in nonsurgical mortality and the index of irrational HSA use (April 2009). This time coincided with the decision of the Serbian Republic Health Insurance Fund to restrict funding for the correction of hypoalbuminemia < 3 g/dL. At the time of the abolition of this decision (February 2010), we found transient increases in the use of HSA in the ICU, the ratio of HSA bottles per 100 patient deaths, and the HSA cost ratio (dollar value) per 100 hospitalized patients (models 2, 3, and 5 in TABLE 3). Postintervention The results of MARIMA models (TABLE 3 and FIGURE 1) show that there was significantly lower HSA use in the ICU after the intervention by 48.1% compared to the preintervention period. In addition, reductions were seen in nonsurgical mortality of 1.92%, duration of admission per patient in the surgery department declined 2.27 days per patient, the ratio of HSA bottles per 100 patient deaths decreased by 1.61 bottles per 100 patients death, the HSA cost ratio (dollar value) per 100 hospitalized patients reduced by $484.90/patient, the index of irrational HSA use declined by .65 patients per month, the rate of sepsis patient mortality declined by 4.3%, and, finally, the COQ showed a reduction of $593,890.77 per year (TABLE 4). However, there were no statistically significant changes in the total mortality rate (t = .195, p = .864), or the surgical mortality rate (t = 1.337, p = 0.189) comparing post- to preintervention periods. Discussion Cervero and Gaines’s (2015) synthesis of findings of 8 systematic reviews of CME effectiveness published since 2003 concluded that CME does improve physician performance and patient health outcomes.33 Our study lends further support to the conclusions from their review. Our results suggest (models 1–5 and 7 in TABLE 3) that we correctly hypothesized that the reason for poor outcomes in the preintervention period was physicians’


Evidence From a Case Study TABLE 1. Descriptive Statistics of Learners’ Satisfaction

How are you satisfied with the CME in respect:

The choice of topics

Occupation

Physician Nurse

Content

Physician Nurse

Quality of presentation

Physician Nurse

Practical applicability of the acquired knowledge

Physician Nurse

Organization

Physician

Duration

Physician

Nurse

Nurse Lecturer mode of reasoning

Physician Nurse

N

Percentiles 10th

Median

Percentiles 90th

p

.002

71

5.00

5.00

5.00

185

4.00

5.00

5.00

71

5.00

5.00

5.00

185

4.00

5.00

5.00

71

5.00

5.00

5.00

185

4.00

5.00

5.00

71

5.00

5.00

5.00

185

4.00

5.00

5.00

71

4.20

5.00

5.00

185

4.00

5.00

5.00

71

5.00

5.00

5.00

185

4.00

5.00

5.00

71

5.00

5.00

5.00

185

4.60

5.00

5.00

.072

.348

.001 .723

.291

.065

CME = continuing medical education.

mistaken belief that albuminemia < 3 g/dL causally leads to fatal outcomes. The observed changes in nonsurgical mortality rate and hospital stay in the surgery department after the CME were mostly explained by a reduction of irrational HSA usage for the treatment of hypoalbuminemia in terminally ill and/or patients with malignant disease in the ICU. In contrast to the significant impact of the didactic CME, activities of the Serbian Republic Health Insurance Fund did not have an impact on the mortality rate of sepsis patients (model 6 in TABLE 3 and FIGURE 1). In our study after the intervention, the monthly rate in patients with severe sepsis or septic shock corresponded with the 28-day mortality rate in the Albumin Italian Outcome Sepsis (ALBIOS) study published by Caironi et al in 2014,34 where there were no differences in terms of all observed outcomes between combined HSA and crystalloid solutions therapy with maintenance albuminemia ≥ 3 g/dL versus crystalloid solutions therapy alone. In our study, the 4.4% higher mortality rate of sepsis patients in the preintervention period compared to the postintervention period was most likely the result of inappropriate infusion rate or overdose of HSA solutions. In the case of serious outcomes, briefly summarizing the results of the systematic review by Forsetlund et al, it has been shown that using didactic CME approaches, and especially didactic CME mixed with more interactive teach-

ing techniques, could increase implementation of evidencebased practice and improve professional practice and health care outcomes for the patients.18 Throughout the whole period of this study, by automatic detection of outliers, no other significant influences (quality improvement activity in the ICU, other meetings, online resources, etc) on the resulting variables were found. Although our previous study using a moving-average model demonstrated a high association of total mortality rate and HSA use in the ICU,14 in this MARIMA model there was no change in terms of total mortality rate after the intervention compared to the preintervention period. In the previous moving average model, HSA use was 1 of 4 independent predictors of growth in the total mortality rate, while in this regressive MARIMA model HSA use was a dependent variable. We also found evidence that didactic CME can reduce the cost of quality (TABLE 4). In terms of reducing total mortality, our results can be characterized as poor. We consider the reduction in mortality of sepsis patients and irrational use of HSA, and the reduction in COQ of $593,890.77 per year are very good results. CME influence on reducing COQ is a very important issue for CME providers (especially when current political or financial interventions before CME exist) as it impacts improvements in quality and management of health care institutions, issues of importance to funders and the health care system in general.


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TABLE 2. Descriptive Statistics for Monthly Nonindexed Values of Quality Indicators per Intervention Periods and Total Period

Intervention

Hospitalized patients

Hospital days

Operated patients

Patient without operation

Deceased operated patients

Deceased patients without operation

HSA bottles used in the ICU

Patients with HSA therapy

Patients with sepsis and HSA therapy (rational use of HSA)

No. (months)

Mean

SD

29

338.34

42.08

Preintervention period Postintervention period

12

348.33

47.96

Total

41

341.27

43.51

Preintervention period

29

1861.24

328.40

Postintervention period

12

1476.25

327.97

Total

41

1748.51

369.49


29

170.90

44.38


12

164.33

28.32

Total

41

167.34

40.10


29

160.24

22.90


12

184.50

32.37

Total

41

167.34

27.93


29

5.93

2.49


12

7.75

4.24

Total

41

6.46

3.16


29

11.45

4.20


12

10.67

2.90

Total

41

11.22

3.85


29

89.72

34.42

Post-intervention period

12

45.08

20.25

Total

41

76.66

36.99


29

40.96

9.94

Post-intervention period

12

13.08

5.79

Total

41

19.66

14.78


29

13.59

4.09


12

12.08

4.98

Total

41

13.15

4.36

Patients with HSA therapy and malignant disease or


29

27.38

10.02

terminal stage of disease (irrational use of HSA)


12

1.00

1.21

Total

41

19.66

14.78


29

4.76

1.90

Deceased patients with sepsis and HSA therapy

Deceased patients with malignant disease or terminal stage of disease and HSA therapy


12

3.58

1.56

Total

41

4.42

1.87


29

7.17

3.72


12

1.58

2.15

Total

41

5.54

4.20

SD = standard deviation; HSA = human serum albumin solutions; ICU = intensive care unit.

114


.031 .040

.709 −.646

22.933 −16.036

−2.130 32.391 −2.242

16.111 −3.357 10.129 −3.334 27.957 −5.426 11.408 −2.614 8.523

t

.000 .000

.040 .000 .031

.000 .002 .000 .002 .000 .000 .000 .013 .000

p

16.995 10.555 19.961

0.484 0.733 .670

.901

19.205

8.733

22.671

.588

.114

14.320

.430

R2

Ljung-Box Q (18)

Model statistics

.379

.966

.335

.912

.523

.204

.708

p (for LjungBox Q)

p (magnitude) .001 (−2.113) .000 (71.936) 0.000 (9.214) .000 (−5.573) .002 (1912.606) .002 (2257.000)

.000 (−.284)

Mar 2010 (transient) Feb 2010 (transient) Feb 2010 (transient) Apr 2009 (transient) Feb 2010 (transient) Jun 2010 (transient)

Apr 2009 (transient)

Outliers Time (type)

B = regression coefficient; SE = standard error; has = human serum albumin solution; ICU = intensive care unit. a In the model, the postintervention period is numbered as 1, while the preintervention period is numbered as 0. b Index of irrational use of human serum albumin solutions = patients with malignant disease or terminal stage of disease with HSA therapy per patients with HSA therapy. Note: All amounts were converted from euros to US dollars using a conversion rate of 1.228.

The ratio of deceased sepsis patients with HSA therapy/100 sepsis patients with HSA therapy (model 6) Index of irrational use of HSAb Constantb (model 7) Perioda

227.63 1.069 1.975

−484.90 34.615 −4.429

Perioda Constant Perioda

.504 .675 6.766 9.889 .213 .297 .490 .735 164.93

8.124 −2.268 68.530 −32.968 5.961 −1.609 5.584 −1.921 1405.71

Constant Perioda Constant Perioda Constant Perioda Constant Perioda Constant

Hospital days per hospitalized patients (model 1) HSA bottles used in the ICU (model 2) The ratio of HSA bottles/100 patient deaths (model 3) Nonoperated patients deaths/100 nonoperated patients (model 4) The HSA cost ratio ($ value)/100 hospitalized patients (model 5)

SE

Predictors

Dependent Variables

B

MARIMA model parameters

TABLE 3. Parameters of MARIMA Model with Significant Predictors and Detected Outliers for Each Dependent Variable

Evidence From a Case Study


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Vukovic´ et al. TABLE 4. Surgery Department—Traditional Cost of Quality Quantification in Postintervention Period Compared to Preintervention Period Preintervention period


Prevention cost = $0 Non.$ 0

Prevention cost = $2,119.2 Cost of CME Rental of premises = $184.2 per meeting × 4 meetings = $736.8 Cost of CME to listeners = $5.4 per listener × 256 listeners = $1382.4

Appraisal cost = $368 Cost of data analysis Cost of specialist for data analysis = $4.6 per hours × 80 hours) = $368

Appraisal cost = $368 Cost of data analysis Cost of specialist for data analysis = $4.6 per hours × 80 hours) = $368

Internal failure cost = $2,060,815.96

Internal failure cost = $1,464,805.99

Cost of irrational use of HSA, cost of HSA treatment sepsis patients who have died and cost of hospital stay, all for the average number of patients per year (4176 patients) Notifications: (1) The average number of patients on a monthly basis in the postintervencion period was used to calculate the number of hospitalized patients per year (see TABLE 2); (2) The average monthly cost of HSA per 100 hospitalized patients in the preinterventon period (divided by 100) was used for the cost of HSA per patient (see model 5 in TABLE 3); (3) The average monthly index of irrational use of HSA in the preinterventon period (0.71) was used for the index of irrational use of HSA (see model 7 in TABLE 3); (4) The index of rational use of HSA was obtained as 1 – index of irrational use of HSA in the preinterventon period (1 –.71 = .29); (5) The average monthly rate of deceased sepsis patients was obtained from the model 6 in TABLE 3 (the ratio of deceased sepsis patients with HSA therapy per 100 sepsis patients with HSA therapy in the preintervention period divided by 100, which is further expressed as 34.615 / 100 = .35). (6) Cost of hospital stay was calculated using the average hospital stay per patient in the preintervention period (see model 1 in TABLE 3). Cost of irrational HSA therapy = $1405.71 per 100 patients / 100 × 348 patients per month × 12 (months) × .71 (the index of irrational use of HSA) = $44,074.07 Cost of sepsis patients with HSA treatment who have died = $1405.71 per 100 patients / 100 × 348 patients per month × 12 month) × .29 (the index of rational use of HSA) × .35 deceased sepsis patients per sepsis patients = $5958.30 Cost of hospital stay = $59.27 per hospital day × 8.124 hospital days per patients × 348 patients per month × 12 months = $2,010,783.59

Cost of irrational use of HSA, cost of HSA treatment sepsis patients who have died and cost of hospital stay, all for the average number of patients per year (4176 patients) Notifications: (1) The average number of patients on a monthly basis in the postintervencion period was used to calculate the number of hospitalized patients per year (see TABLE 2); (2) The average monthly cost of HSA per 100 hospitalized patients in the postinterventon period (divided by 100) was used for the cost of HSA per patient (see model 5 in TABLE 3); (3) The average monthly index of irrational use of HSA in the postinterventon period (.709 – .646 = .06) was used for the index of irrational use of HSA (see model 7 in TABLE 3); (4) The index of rational use of HSA was obtained as 1– index of irrational use of HSA in the post-interventon period (1 – .06 = .94); (5) The average monthly rate of deceased sepsis patients was obtained from model 6 in TABLE 3 (the ratio of deceased sepsis patients with HSA therapy per 100 sepsis patients with HSA therapy in the post-intervention period divided by 100, which is further expressed as [34.615 – 4.429] / 100 = .30). (6) Cost of hospital stay was calculated using the average hospital stay per patient in the postintervention period (see model 1 in TABLE 3). Cost of irrational HSA therapy = $920.81 per 100 patients / 100 × 348 patients per month × 12 months × .06 (the index of irrational use of HSA) = $2307.18 Cost of sepsis patients with HSA treatment who have died = $920.81 per 100 patients/100 × 348 patients per month × 12 months × 0.94 (the index of rational use of HSA) × 0.30 deceased sepsis patients per sepsis patients = $10,843.75 Cost of hospital stay = $59.27 per hospital day × 5.865 hospital days per patients × 348 patients per month × 12 months = $1,451,655.06

COQ = $2,061,183.96

COQ = $1,467,293.19

Difference in the COQ between the 2 periods = $593,890.77

HSA = human serum albumin solution; COQ = cost of quality; CME = continuing medical education. Note: All amounts were converted from euros to US dollars using a conversion rate of 1.228.

The limitations of this study include inclusion of a single institution and its nonrandomized before-and-after study design, which does not exclude certain bias. In addition, the time series design is not suitable for specific separation of the study periods, such as a wash out period, and followup period. Also, severity of illness was not controlled across the timeframes of the study. Although MARIMA modelling excludes the possibility of forming a spurious regression or correlation,35 use of this method in econometrics requires a permanent multidisciplinary expertise over the interpretation 116

of results.36 In the calculation of US dollars per year, there were no adjustments for inflation. Finally, an evaluation of knowledge from the CME was not conducted, weakening the argument that the findings were attributable to the intervention. This study leads us to conclude that didactic CME of medical professionals within health care institutions can have a positive impact on health care professionals’ behavior, patient outcomes, and cost of quality.


Evidence From a Case Study

FIGURE 1. Empirical and Projected Values for Each Time Series Resulting in Significant Dependent Variables in the Entire Study Period From January 2009 to May 2012

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Lessons for Practice ●

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The didactic continuing Medical education (CME) can improve clinical decision making of health professionals in the intensive care unit during resuscitation with human serum albumin (HSA) solutions. The didactic CME can significantly reduce the cost of quality (COQ), particulary the costs of the internal failures that result from inadequate infusion rate of HSA solutions in septic patients and irrational HSA solutions therapy in patients with terminal stage of disease and/or malignant disease. CME influence on reducing COQ is a very important issue for CME providers, management of healthcare institutions, and the healthcare system in general.

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