Abstract
Objective
This study is aimed at calculating the magnitude of the effect of clinical practice guidelines (CPG) and supervision in inhibiting the negative impact of the COVID-19 pandemic on clinical and financial outcomes of non-COVID-19 inpatient care by pediatric residents in academic medical center (AMC) hospitals during the COVID-19 pandemic.
Methods
The cohort retrospective study was conducted. This study collected patient data from pediatric residency programs. A research cohort consisted of non-COVID-19 pediatric patients at Dr. Soetomo General Academic Hospital. This study compared the subgroup of patients treated during the pandemic with those treated before the pandemic. The results were analyzed using SPSS 26.0 and Smart-PLS.
Results
There was a 41.4% decrease in pediatric inpatients during the pandemic with an increased severity level and complexity level, a reduction of 7.46% availability of supervisors, an increase of 0.4% in readmission < 30 days, an increase of 0.31% in-hospital mortality, an increase the total costs of care, and a decrease of insurance claim profit. CPG did not moderate the effect of the COVID-19 pandemic on the clinical outcomes (β = −0.006, P = 0.083) but moderated the financial outcomes (β = −0.022, P = 0.000), by reducing the total cost of care and increasing insurance claim profit. Supervision moderated the effect of the COVID-19 pandemic on the clinical outcomes (β = 0.040, P = 0.000) by increasing aLOS and on the financial outcomes (β = −0.031, P = 0.000) by reducing the total cost of care and increasing insurance claim profit. This study model had a 24.0% variance of explanatory power for clinical outcomes and 49.0% for financial outcomes. This study's structural model effectively predicted clinical outcomes (Q2 = 0.238) and financial outcomes (Q2 = 0.413).
Conclusion
Direct supervision inhibited the negative impact of the COVID-19 pandemic on both clinical and financial outcomes of non-COVID-19 inpatient care by pediatric residents, while CPG only inhibited the negative impact on financial outcomes. Implication of This Study. In a disaster, the availability of CPG and direct supervision makes AMC hospitals able to inhibit the negative impact of disasters on clinical and financial outcomes.
1. Introduction
The COVID-19 crisis has seriously impacted the academic medical center (AMC) hospital in the clinical, financial, and health education aspects [1]. Dr. Soetomo General Academic Hospital is an AMC hospital and a national referral tertiary referral hospital with the largest number of beds (1670 beds) and the largest number of residents (1890 medical doctors) in Indonesia. In including residents to manage patients, hospitals must apply high-value, cost-consciousness care so that specialists or senior medical doctors must supervise there. High-value cost-consciousness care is easier to implement in AMC hospitals if they implement clinical practice guidelines (CPG), especially for diseases categorized as “high volume,” “high risk,” “high cost,” and “high variability” [2, 3], have an active supervisor medical doctor, and supported by other health professionals in sufficient numbers [4, 5]. The unavailability of CPG and the lack of support from other health workers, especially nurses, will threaten the high-value, cost-conscious care [6–8]. CPG availability is also highly dependent on the speed of adjustment to changes in the profile of patient visits to the hospital [4, 5] because the need for types of CPG can change in a disaster or pandemic condition [3, 9]. Changes in the profile of hospital visits are due to restrictions on admitting patients for nonemergency cases, fear of patients being infected in hospitals, and restrictions on social interaction by the government [10–18].
The quality of patient care is influenced by the presence and compliance of CPG [8, 19, 20]. The complexity of the diagnosis influences CPG availability for each diagnosis. Changes in the complexity of patients raise the need to create new CPGs. However, referring to the results of Cabana et al.'s research, it turns out that 15.7% of medical doctors do not realize that CPG is important, and 10.6% of medical doctors do not know that there is CPG for the case they are being treated in AMC hospital, and even 6.3% of medical doctors disagree that patient care is regulated by CPG [6]. CPG increases the speed of decision-making and execution by a resident when the supervisor medical doctor cannot be present in person in the AMC hospital [8, 19, 20]. Inappropriate use and availability of health service resources in hospitals are one of the significant impacts of the pandemic [21–23]. The pandemic has caused most hospitals to experience disruptions in the availability of health care resources. In AMC hospitals, care by residents without supervision can be a cause of inappropriate and inaccurate use of health care resources, such as supporting examinations and administering drugs [24]. The speed of execution of medical doctors' decisions by nurses is influenced by the direct presence of nurses in hospitals [25–27]. During the pandemic, the presence of health workers is reduced due to infection, death, and isolation [28–33].
The impact of disruption of health services on pediatric patients due to the COVID-19 pandemic in the future must be anticipated, mitigated, and minimized based on valid evidence. In addition, health care methods for pediatric patients that have been proven effective in dealing with pandemics/disasters must be applied so that we are better prepared to face another pandemic or when we face certain circumstances with insufficient resources as happened during a pandemic.
The main contribution of this research is to provide answers based on scientific evidence on whether increased supervision of residents and the existence of CPG can be a solution to disrupted play in AMC hospitals due to the scarcity of resources for specialist doctors and nurses during a disaster.
We are interested in exploring pediatric inpatient services during the COVID-19 pandemic because of the many challenging aspects of the COVID-19 pandemic in pediatric care, including reducing the number of patients with increased complexity, burden, and pressure on pediatric residents in implementing “high value and cost consciousness care” [34]. Hypothesis development: from the many references and evidence above, we propose 20 hypotheses as follows (Figure 1): Hypothesis 1 (H1): COVID-19 pandemic has a significant influence on clinical outcome; Hypothesis 2 (H2): COVID-19 pandemic has a significant impact on the financial outcome; Hypothesis 3 (H3): COVID-19 pandemic has a significantly positive impact on patient condition; Hypothesis 4 (H4): COVID-19 pandemic has a significantly negative impact on nurse adequacy; Hypothesis 5 (H5): COVID-19 pandemic has a significantly negative impact on supervision; Hypothesis 6 (H6): COVID-19 pandemic has a significantly negative impact on CPG; Hypothesis 7 (H7): the patient's condition has a significantly positive influence on supervision; Hypothesis 8 (H8): supervision has a significant effect on clinical outcome; Hypothesis 9 (H9): supervision has a significant impact on the financial outcome; Hypothesis 10 (H10): CPG has a significant impact on clinical outcome; Hypothesis 11 (H11): CPG has a significant impact on the financial outcome; Hypothesis 12(H12): the patient's condition has a significant impact on the clinical outcome; Hypothesis 13 (H13): the patient's condition has a significant impact on the financial outcome; Hypothesis 14 (H14): nurse adequacy has a significant impact on clinical outcome; Hypothesis 15 (H15): nurse adequacy has a significant impact on the financial outcome; Hypothesis 16 (H16): clinical development has a significant impact on the monetary outcome; Hypothesis 17 (H17): CPG moderated the effect of the COVID-19 pandemic on the clinical outcomes; Hypothesis 18 (H18): supervision moderated the impact of the COVID-19 pandemic on the clinical outcomes; Hypothesis 19 (H19): CPG moderated the impact of the COVID-19 pandemic on the financial outcomes; Hypothesis 20 (H20): supervision moderated the impact of the COVID-19 pandemic on the financial outcomes.
Figure 1.
Hypothesis development.
This study is aimed at calculating the magnitude of the effect of supervision and CPG in inhibiting the negative impact of the COVID-19 pandemic on clinical and financial outcomes of pediatric inpatient care by pediatric residents in academic medical center (AMC) hospitals during the COVID-19 pandemic.
In this paper, we will inform the method we used to calculate the magnitude of the effect of supervision and CPG in preventing the negative impact of the COVID-19 pandemic on pediatric residents' clinical and financial outcomes of pediatric hospitalization. In the chapter on research results and discussion, we report the impact of the COVID-19 pandemic on AMC hospital on patient care resources and outcomes in clinical and financial aspects, measurement model assessment, results of structural modeling of COVID-19 pandemic in AMC hospital, the role of CPG and supervision, implementation of CPG with supervision, the main finding, limitations, and strengths of the study.
2. Materials and Methods
2.1. Study Design, Sample Size, Data Collection, and Data Source
The cohort retrospective study was conducted. This study collected patient data from pediatric residency programs of the Faculty of Medicine Universitas Airlangga. A research cohort consisted of non-COVID-19 pediatric patients at Dr. Soetomo General Academic Hospital who were paid using National Health Insurance. This study compared the subgroup of patients treated during the pandemic with those treated before the pandemic. Data were collected retrospectively for 24 months before the pandemic (1 March 2018 to 29 February 2020) with 24 months during the pandemic (March 1, 2020, to February 29, 2022). The study was conducted according to the guidelines of the Declaration of Helsinki and approved for exempt review by the Research Ethics Committee of Dr. Soetomo General Academic Hospital, Surabaya, Indonesia (2022), with reference number 0383/KEPK/III/2022. The sample of this research is the total sample. The entire selection of non-COVID-19 hospitalized pediatric patients 24 months before and 24 months during the pandemic were 35,256 cases. When calculated statistically, which provides sufficient power (β = 90%), with a Minimum Detectable Effect (MDE) of 5%, with a level of significance (α) 5%, the minimum sample size has been reached.
2.2. Variables
The independent variable was the COVID-19 pandemic. The dependent variable was the clinical outcome of care (readmission < 30 days, average length of stay, and in-hospital mortality) and financial outcome of care (total costs of care, hospital revenue, and insurance claim profit). The intervening variables were nurse adequacy, disease severity and complexity, CPG (CPG availability, CPG compliance, and CPG coverage), and supervision (supervisor availability, supervision method, and consultant supervision). This study explored the supervision and CPG moderating variable to calculate the role of supervision and CPG in inhibiting the negative impact of the COVID-19 pandemic on clinical and financial outcomes of pediatric inpatient care by pediatric residents in academic medical center (AMC) hospitals during the COVID-19 pandemic. In this study, all items of variables were in the reflective measurement [35].
Nurse adequacy was the suitability of the number of nurses in percent compared to the number of nurses who should be in the treatment room according to the number of patients and the level of emergency (low care, high care, and intensive care). Disease severity was classified into mild, moderate, and severe disease severity based on INA-CBGs (Indonesia Case-Based Groups) version 4.0 [36]. The complexity of the disease was reflected by the number of diagnoses (comorbidities) in the patient. CPG includes aspects of CPG availability which measures the percentage of CPD availability based on the patient's diagnosis (ICD X), CPG compliance which measures the percentage of resident or supervisor compliance with CPG, and CPG coverage which measures the rate of the presence of CPG for all established diagnoses. Meanwhile, the supervision assessed includes supervisor availability which measures the percentage of supervisor's presence at the hospital, supervision method, which measures the rate of direct supervisor's presence in front of the patient's bed, and the percentage of consultant supervisor's attendance which measures the attendance level of consultant medical doctors across subspecialists.
Readmission less than 30 days was the percentage of patients readmitted less than 30 days from discharge from the hospital. Length of stay (LOS) is the duration (in days) of care of the patient treated for 1 episode of treatment. In-hospital mortality is the incidence of death in hospitalization. Total costs of care based on hospital rates were the full-service costs, including nonsurgical and surgical procedures, consultations, experts, nursing services, radiological examinations, laboratory examinations, blood services, medical rehabilitation, accommodation and rooms, use of drugs (symptomatic, antibiotics, drugs, chronic disease, and chemotherapy), medical devices, consumables, and equipment rental. Hospital revenue was the hospital's total income from claim fees paid to hospitals by National Health Insurance. Insurance claim profit was a profit from insurance claims after deducting care costs.
2.3. Statistical Analysis
We used SPSS version 26 to test the normality distribution of all variables and compare values for all variables during the pandemic to before. The comparison test uses an independent T-test if the data were normally distributed and uses the Mann–Whitney test or the Kruskal-Walis test (depending on the data being numerical or categorical) if the data is not normally distributed.
Furthermore, to prove the hypotheses in our structural model (Figure 1), we use Structural Equation Modeling (SEM). If the data were normally distributed, we used Covarian Based SEM (CB-SEM) with AMOS software, and if the data was not normally distributed, we used Partial Least Squares SEM (PLS-SEM) with Smart-PLS software. We used AMOS or Smart-PLS software (the choice depends on the distribution of our research data) to test all the hypotheses in Figure 1.
Firstly, we measured Confirmatory Factor Analysis (CFA) with unidimensionality for each scale and reliability-validity of each construct. We look at the unidimensionality for each scale with factor loading, and if it shows a value less than 0.500 [37], we will remove it. Because the items from our variable (construct) were reflective, the reliability indicators were loading factors, Cronbach's alpha, composite reliability, and average extracted variance (AVE). Construct reliability was achieved when the factor loading value was greater than 0.700 [38], Cronbach's alpha, and composite reliability were equal to or greater than 0.600. The validity indicators were average extracted variance (AVE), Fornell–Larcker criteria (square root of the AVE for each variable was higher than the variance of the variable divided by other variables) [38], Heterotrait–Monotrait Ratio of Correlations by Henseler [39] for discriminant validity, and cross loading factors. Validity was achieved when the mean of extracted variance (AVE) for convergent validity was higher than 0.500 [38], Heterotrait–Monotrait Ratio of Correlations (HTMT) value was less than 0.900 [40]. Cross factor loading of items must be higher with each variable than with other variables. After that, we also had to assess whether there was a collinearity problem in the model by looking at the correlation matrix and variance inflation factor (VIF). If the value of VIF was less than 3.30 [37], there was not a collinearity problem in the model. The general threshold of VIF was 5 [38]. If there were no problems with multicollinearity, PLS-SEM was better for estimating the path coefficients among the latent variables of the structural model [41]. To assess the explanatory power of the structural model, we measured R2 values. R2 values 0.75, 0.50, and 0.25 were considered substantial, moderate, and mild, respectively [42]. To assess individual predictors and their role in the model, we measure effect size (f2). The f2 values of 0.35, 0.15, and 0.02 refer to the large, medium, and small effect of an independent variable [43]. To assess and predict relevancy and accuracy in the model, we measure Q2. Q2 value was greater than zero indicating a model relevant and accurate [42].
3. Results and Discussion
3.1. Impact of COVID-19 Pandemic on AMC Hospital
Table 1 shows that during COVID-19 pandemic (from March 1, 2020, to February 29, 2022) compared to before (from 1 March 2018 to 29 February 2020), there were 41.4% decreasing in pediatric inpatient, increase 0.44 years in the average inpatient age (P = 0.000), increase 0.08 of severity level and 0.31 of complexity level of the disease (P = 0.000), increase of medical cases by proportion (0.9%) and decrease by number (7005 cases) (P = 0.045), decrease of surgical cases both proportion (by 0.9%) and number (1730 cases) (P = 0.042), increase 2.62% of CPG availability (P = 0.000), decrease 3.99% direct supervision and decrease 7.46% availability of supervisors (P = 0.000), increase supervision by consultant (1.34%) (P = 0.000), decrease 21.9% adequacy of nurses (P = 0.000), increase 0.4% readmission < 30 days (P = 0.000), increase 0.31% in-hospital mortality (P = 0.000), increase (IDR × 1,000) 3,937.4 of the total costs of care/patient (P = 0.000), slight increase (IDR × 1,000) 524.8 of hospital revenue/patient (P = 0.000), and significant decrease (IDR × 1,000) 3,412.6 of insurance claim profit/patient (P = 0.000).
Table 1.
Characteristics of pediatric patients based on age and sex, case distribution, conditions at admission, adequacy of nurses, CPG, supervision, and outcomes.
| Variables | Distribution of pediatric inpatient (n = 35,216) | Difference | ||||
|---|---|---|---|---|---|---|
| Before pandemic (n = 22,211) | During pandemic (n = 13,005) | P | ||||
| n | (%) | n | (%) | |||
| Sex, male, n (%) | 9508 | (42.8) | 5101 | (39.2) | 0.000 | -4407 |
| Case distribution | ||||||
| Medical, n (%) | 17,676 | (79.6) | 10,671 | (80.5) | 0.045 | -7,005 |
| Mix medical-surgical, n (%) | 1097 | (4.9) | 626 | (4.9) | 0.598 | -471 |
| Surgical, n (%) | 3438 | (15.5) | 1708 | (14.6) | 0.042 | -1730 |
|
| ||||||
| Mean | SD | Mean | SD | P | Difference | |
| Age (years), mean (SD) | 6.3 | (5.85) | 6.8 | (5.92) | 0.000 | 0.44 |
| Patient's condition | ||||||
| Severity level, mean (SD) | 1.8 | (0.83) | 1.9 | (0.83) | 0.000 | 0.08 |
| Complexity level, mean (SD) | 2.9 | (2.03) | 3.2 | (2.30) | 0.000 | 0.31 |
| Clinical practice guidelines (CPG) | ||||||
| % CPG availability, mean (SD) | 26.2 | (43.95) | 28.8 | (45.27) | 0.000 | 2.62 |
| % CPG compliance, mean (SD) | 19.2 | (39.40) | 18.4 | (38.75) | 0.058 | -0.82 |
| % CPG coverage, mean (SD) | 15.2 | (30.74) | 15.9 | (30.45) | 0.060 | 0.64 |
| Supervision | ||||||
| % direct supervision method, mean (SD) | 75.1 | (43.25) | 71.1 | (45.33) | 0.000 | -3.99 |
| % supervisor availability, mean (SD) | 84.8 | (27.61) | 77.3 | (30.47) | 0.000 | -7.46 |
| % consultant supervision, mean (SD) | 85.7 | (35.01) | 87.1 | (33.57) | 0.000 | 1.34 |
| % nurse adequacy, mean (SD) | 83.6 | (9.27) | 61.7 | (18.33) | 0.000 | -21.93 |
| Clinical outcome | ||||||
| % readmission < 30 days, mean (SD) | 6.3 | (5.85) | 6.8 | (5.92) | 0.000 | 0.44 |
| Average length of stay (days), mean (SD) | 9.0 | (9.95) | 8.9 | (10.02) | 0.742 | -0.1 |
| % in hospital mortality, mean (SD) | 2.9 | (2.03) | 3.2 | (2.30) | 0.000 | 0.31 |
| Financial outcome (× 1,000 IDR) | ||||||
| Total costs of care, mean (SD) | 12.021 | (21,163.2) | 15,59.1 | (29,049.6) | 0,000 | 3,937.4 |
| Hospital revenue, mean (SD) | 12.186 | (16,647.9) | 12,711.7 | (17,306.9) | 0,005 | 524.8 |
| Insurance claim profit, mean (SD) | 165 | (15,764.3) | -3,247.4 | (22,085.8) | 0,000 | -3,412.6 |
A decrease in inpatient visits for pediatric patients (Table 1) can be caused by a reduction in the provision of health services, insecurity, restrictions on public activities, high-risk communication, and the fear of being exposed to COVID-19 infection through interactions with each other, which also makes people reluctant to leave their homes and seek routine health care [44–46]. The results of the ECIEN-2020 study also showed that the COVID-19 pandemic, especially during the lockdown, led to a decrease in the number of visits to the pediatric ER and admissions of pediatric patients to hospital inpatients an increase in the severity and complexity of respiratory illnesses [47]. The high complexity of inpatient and urgent/emergency care for non-COVID-19 patients has also been reported in many previous studies [47–51]. Several previous studies also showed that the COVID-19 pandemic affected pediatric surgery services [52–55].
The decrease in the number of direct supervision and the adequacy of nurses (Table 1) due to COVID-19 infection is a very detrimental impact of the pandemic. This condition can be influenced by the home or hospital environment that does not create social distance and pays less attention to fundamental health problems. The results of numerical simulation research for optimal control strategies in Iran and Japan show that by creating social distance and paying attention to basic health problems, disease prevalence is controlled [56]. In a hospital setting, asymptomatic infections play an important role in developing infections, so educating all health workers on avoiding contact with these individuals [57] is necessary.
The decrease in nurse adequacy (Table 1) occurred not only during a pandemic but also in other conditions. A study informs that a reduction of the adequacy of nurses can occur outside of working hours. Of patients, more than a third was treated outside of working hours. Out-of-work care—evenings, weekends, and holidays—is characterized by a decrease in the adequacy of nurses and other staff and a reduction in the availability of certain diagnostic and support services [58]. In addition, the availability of nurses with appropriate expertise also determines the outcome of medical services in hospitals [59].
Decreasing LOS involves controlling for many factors. To reduce their caseload, residents tend to lower their LOS, with the negative impact of increasing less than 30 days of readmission. However, the facts show that patients admitted to AMC hospitals have a much shorter LOS, fewer consultants, and lower direct care costs than non-AMC hospitals. It is recognized that hospital workloads may have a negative impact on clinical and financial outcomes. However, the study at AMC hospital showed that outcomes for in-hospital mortality and less than 30 days readmission did not appear to be affected by workload [1]. There is a relationship between patient mortality at AMC hospital and the involvement of hospital leaders, including clinical department heads. In the AMC hospital, without the participation of the clinical department chair, the mortality reduction performance did not improve [58]. One of the possible reasons was the resident's natural desire to reduce their caseload. Besides, the pandemic period has made parents want their children to get out of the hospital faster [1].
3.2. Measurement Model Assessment
Because the data distribution of the dependent variable was not normally distributed, we used Partial Least Squares (PLS-SEM) with Smart-PLS software to assess the measurement model and the structural model of the conceptual framework. Items of supervisor consultant, CPG availability, readmission less than 30 days, in-hospital mortality, and hospital revenue were removed from the model (Table 2) due to the factor loading value being less than 0.700. Construct reliability was achieved because the factor loading value was more significant than 0.700 [38], and Cronbach's alpha and composite reliability were equal to or greater than 0.600 (Table 3). Validity was achieved due to the mean of extracted variance (AVE) for convergent validity was higher than 0.500, Heterotrait–Monotrait Ratio of Correlations (HTMT) value was less than 0.900 (Table 4), cross factor loading of items was higher with each variable than with other variables (Table 5), and the correlation matrix and variance inflation factor (VIF) was less than the general threshold of VIF (Table 6). Because there were no problems with multicollinearity, the structural model was better for estimating the path coefficients (Table 7). The measurement of our model meets all the criteria for a good fit and is suitable for Hypotheses testing (Table 7). As shown in Table 8, this study model had a 24.0% (R2 = 0.240) or mild variance of explanatory power for clinical outcomes and 49.0% (R2 = 0.490) or moderate variance for financial outcomes. This study's structural model effectively predicted clinical outcomes (Q2 = 0.238) and financial outcomes (Q2 = 0.413).
Table 2.
Construct and items construct descriptive statistic.
| No. | Constructs | Items of constructs | Mean | SD | 1st loading factor | 2nd loading factor |
|---|---|---|---|---|---|---|
| 1 | Pandemic status | Pandemic status | 0.37 | 0.48 | 1.000 | 1.000 |
|
| ||||||
| 2 | Patient's condition | (1) Severity | 1.84 | 0.83 | 0.816 | 0.986 |
| (2) Complexity | 3.03 | 2.14 | 0.915 | 0.989 | ||
|
| ||||||
| 3 | Supervision | (1) Supervision method (%) | 73.6 | 44.07 | 0.983 | 0.986 |
| (2) Supervisor availability (%) | 82.0 | 28.92 | 0.986 | 0.989 | ||
| (3) Supervision by consultant (%) | 86.2 | 34.48 | 0.038 | Removed | ||
|
| ||||||
| 4 | CPG | (1) CPG availability (%) | 27.1 | 44.46 | 0.521 | Removed |
| (2) CPG compliance (%) | 18.9 | 39.16 | 0.866 | 0.988 | ||
| (3) CPG coverage (%) | 15.5 | 30.63 | 0.726 | 0.827 | ||
|
| ||||||
| 5 | Nurse adequacy | Nurse adequacy (%) | 75.5 | 17.04 | 1.000 | 1.000 |
|
| ||||||
| 6 | Clinical outcome | (1) Readmission < 30 days (%) | 8.5 | 7.93 | 0.330 | Removed |
| (2) aLOS (days) | 9.0 | 9.98 | 0.875 | 1.000 | ||
| (3) In-hospital mortality (%) | 7.5 | 2.63 | 0.536 | Removed | ||
|
| ||||||
| 7 | Financial outcome (× IDR 1,000) | (1) Total cost of care | 13,475.7 | 24,447.8 | 1.000 | 0.944 |
| (2) Hospital revenue | 12,380.7 | 16,895.7 | 0.667 | Removed | ||
| (3) Insurance claim profit | -1,095.0 | 18,427.3 | -0.709 | -0.911 | ||
Table 3.
Reliability and validity results.
| Constructs | Reliability | Convergent validity (AVE) and discriminant validity (square root of AVE) | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cronbach's alpha | rho_A | Composite reliability | Average variance extracted (AVE) | Clinical outcome | Clinical practice guidelines (CPG) | Financial outcome | Nurse adequacy | Pandemic status | Pandemic∗CPG (CO) | Pandemic∗CPG (FO) | Pandemic∗ supervision (CO) | Pandemic∗supervision (FO) | Patient's condition | Supervision | |
| Clinical outcome | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||||||||||
| CPG | 0.845 | 2.112 | 0.907 | 0.831 | -0.092 | 0.911 | |||||||||
| Financial outcome | -5.231 | 0.868 | 0.704 | 0.860 | 0.672 | -0.128 | 0.928 | ||||||||
| Nurse adequacy | 1.000 | 1.000 | 1.000 | 1.000 | 0.004 | 0.006 | -0.085 | 1.000 | |||||||
| Pandemic status | 1.000 | 1.000 | 1.000 | 1.000 | -0.002 | -0.006 | 0.089 | -0.621 | 1.000 | ||||||
| Pandemic∗CPG (CO) | 1.000 | 1.000 | 1.000 | 1.000 | -0.018 | -0.018 | -0.033 | 0.005 | -0.003 | 1.000 | |||||
| Pandemic∗CPG (FO) | 1.000 | 1.000 | 1.000 | 1.000 | -0.018 | -0.018 | -0.033 | 0.005 | -0.003 | 1.000 | 1.000 | ||||
| Pandemic∗ supervision (CO) | 1.000 | 1.000 | 1.000 | 1.000 | 0.020 | 0.005 | -0.034 | 0.039 | -0.047 | -0.011 | -0.011 | 1.000 | |||
| Pandemic∗supervision (FO) | 1.000 | 1.000 | 1.000 | 1.000 | 0.020 | 0.005 | -0.034 | 0.039 | -0.047 | -0.011 | -0.011 | 1.000 | 1.000 | ||
| Patient's condition | 0.678 | 0.792 | 0.854 | 0.747 | 0.468 | -0.032 | 0.286 | -0.030 | 0.069 | -0.025 | -0.025 | -0.028 | -0.028 | 0.864 | |
| Supervision | 0.974 | 0.985 | 0.987 | 0.975 | -0.004 | -0.013 | -0.163 | 0.122 | -0.088 | 0.006 | 0.006 | 0.078 | 0.078 | 0.213 | 0.987 |
Note: CPG = clinical practice guidelines; CO = clinical outcome; FO = financial outcome.
Table 4.
Heterotrait–Monotrait Ratio (HTMT).
| Constructs | Clinical outcome | ClPG | Financial outcome | Nurse adequacy | Pandemic status | Pandemic∗CPG (CO) | Pandemic∗CPG (FO) | Pandemic∗supervision (CO) | Pandemic∗supervision (FO) | Patient's condition |
|---|---|---|---|---|---|---|---|---|---|---|
| Clinical outcome | ||||||||||
| Clinical practice guidelines | 0.085 | |||||||||
| Financial outcome | 0.726 | 0.113 | ||||||||
| Nurse adequacy | 0.004 | 0.009 | 0.091 | |||||||
| Pandemic status | 0.002 | 0.012 | 0.098 | 0.621 | ||||||
| Pandemic∗CPG (CO) | 0.018 | 0.020 | 0.036 | 0.005 | 0.003 | |||||
| Pandemic∗CPG (FO) | 0.018 | 0.020 | 0.036 | 0.005 | 0.003 | 1.000 | ||||
| Pandemic∗supervision (CO) | 0.020 | 0.008 | 0.037 | 0.039 | 0.047 | 0.011 | 0.011 | |||
| Pandemic∗supervision (FO) | 0.020 | 0.008 | 0.037 | 0.039 | 0.047 | 0.011 | 0.011 | 1.000 | ||
| Patient's condition | 0.539 | 0.153 | 0.327 | 0.035 | 0.081 | 0.030 | 0.030 | 0.032 | 0.032 | |
| Supervision | 0.004 | 0.040 | 0.177 | 0.124 | 0.086 | 0.006 | 0.006 | 0.079 | 0.079 | 0.279 |
Note: CPG = clinical practice guidelines; CO = clinical outcome; FO = financial outcome; aLOS = average length of stay.
Table 5.
Cross loadings.
| Constructs | Pandemic status | Patient's condition | Clinical practice guidelines | Supervision | Nurse adequacy | Clinical outcome | Financial outcome | Pandemic∗CPG (CO) | Pandemic∗supervision (CO) | Pandemic∗CPG (FO) | Pandemic∗supervision (FO) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Pandemic status | 1.000 | 0.069 | -0.006 | -0.088 | -0.621 | -0.002 | 0.089 | -0.003 | -0.047 | -0.003 | -0.047 |
| Severity | 0.046 | 0.929 | -0.088 | 0.239 | -0.021 | 0.283 | 0.119 | -0.018 | -0.017 | -0.018 | -0.017 |
| Complexity | 0.070 | 0.794 | 0.008 | 0.155 | -0.029 | 0.488 | 0.332 | -0.025 | -0.029 | -0.025 | -0.029 |
| CPG_percent_coverage | 0.010 | -0.148 | 0.988 | -0.066 | -0.007 | -0.046 | -0.027 | -0.016 | 0.009 | -0.016 | 0.009 |
| CPG_compliance | -0.010 | 0.001 | 0.827 | 0.002 | 0.009 | -0.099 | -0.148 | -0.017 | 0.004 | -0.017 | 0.004 |
| Supervisor_availability | -0.124 | 0.210 | -0.013 | 0.986 | 0.125 | -0.007 | -0.165 | 0.004 | 0.091 | 0.004 | 0.091 |
| Supervision_method | -0.044 | 0.210 | -0.014 | 0.989 | 0.117 | -0.001 | -0.157 | 0.007 | 0.062 | 0.007 | 0.062 |
| Nurse_adequacy | -0.621 | -0.030 | 0.006 | 0.122 | 1.000 | 0.004 | -0.085 | 0.005 | 0.039 | 0.005 | 0.039 |
| aLOS | -0.002 | 0.468 | -0.092 | -0.004 | 0.004 | 1.000 | 0.672 | -0.018 | 0.020 | -0.018 | 0.020 |
| Total_cost_of_care | 0.078 | 0.367 | -0.102 | -0.179 | -0.089 | 0.685 | 0.944 | -0.033 | -0.036 | -0.033 | -0.036 |
| Claim profit | -0.089 | -0.141 | 0.140 | 0.119 | 0.066 | -0.549 | -0.911 | 0.027 | 0.027 | 0.027 | 0.027 |
| Pandemic status ∗ clinical practice guidelines (CO) | -0.003 | -0.025 | -0.018 | 0.006 | 0.005 | -0.018 | -0.033 | 1.000 | -0.011 | 1.000 | -0.011 |
| Pandemic status ∗ supervision (CO) | -0.047 | -0.028 | 0.005 | 0.078 | 0.039 | 0.020 | -0.034 | -0.011 | 1.000 | -0.011 | 1.000 |
| Pandemic status ∗ clinical practice guidelines (FO) | -0.003 | -0.025 | -0.018 | 0.006 | 0.005 | -0.018 | -0.033 | 1.000 | -0.011 | 1.000 | -0.011 |
| Pandemic status ∗ supervision (FO) | -0.047 | -0.028 | 0.005 | 0.078 | 0.039 | 0.020 | -0.034 | -0.011 | 1.000 | -0.011 | 1.000 |
Note: CPG = clinical practice guidelines; CO = clinical outcome; FO = financial outcome.
Table 6.
Collinearity statistics (VIF) inner VIF values.
| Constructs | Patient's condition | Clinical practice guidelines | Supervision | Nurse adequacy | Clinical outcome | Financial outcome |
|---|---|---|---|---|---|---|
| Pandemic status | 1.000 | 1.000 | 1.005 | 1.000 | 1.638 | 1.640 |
| Patient's condition | 1.005 | 1.060 | 1.381 | |||
| Clinical practice guidelines | 1.001 | 1.010 | ||||
| Supervision | 1.074 | 1.093 | ||||
| Nurse adequacy | 1.641 | 1.641 | ||||
| Clinical outcome | 1.315 | |||||
| Financial outcome | ||||||
| Pandemic∗CPG (CO) | 1.001 | |||||
| Pandemic∗supervision (CO) | 1.010 | |||||
| Pandemic∗CPG (FO) | 1.001 | |||||
| Pandemic∗supervision (FO) | 1.012 |
Note: CPG = clinical practice guidelines; CO = clinical outcome; FO = financial outcome.
Table 7.
Path coefficients, mean, SD, T values, P values, and decision of hypotheses.
| Hypotheses | Path | Beta | Mean | SD | T values | P values | 95% CI | Decision |
|---|---|---|---|---|---|---|---|---|
| 1 | Pandemic status ⟶ clinical outcome | -0.041 | -0.041 | 0.006 | 6.786 | 0.000 | -0.054 to -0.030 | Supported |
| 2 | Pandemic status ⟶ financial outcome | 0.055 | 0.055 | 0.006 | 9.839 | 0.000 | 0.044 to 0.065 | Supported |
| 3 | Pandemic status ⟶ patient's condition | 0.069 | 0.070 | 0.006 | 12.421 | 0.000 | 0.058 to 0.080 | Supported |
| 4 | Pandemic status ⟶ nurse adequacy | -0.621 | -0.621 | 0.004 | 139.974 | 0.000 | -0.631 to -0.613 | Supported |
| 5 | Pandemic status ⟶ supervision | -0.103 | -0.102 | 0.005 | 19.314 | 0.000 | -0.113 to -0.092 | Supported |
| 6 | Pandemic status ⟶ clinical practice guidelines | -0.006 | -0.006 | 0.005 | 1.123 | 0.262 | -0.017 to 0.004 | Rejected |
| 7 | Patient's condition ⟶ supervision | 0.220 | 0.220 | 0.005 | 46.335 | 0.000 | 0.211 to 0.229 | Supported |
| 8 | Supervision ⟶ clinical outcome | -0.118 | -0.118 | 0.005 | 24.855 | 0.000 | -0.128 to -0.109 | Supported |
| 9 | Supervision ⟶ financial outcome | -0.150 | -0.151 | 0.007 | 21.976 | 0.000 | -0.163 to -0.136 | Supported |
| 10 | Clinical practice guidelines ⟶ clinical outcome | -0.078 | -0.078 | 0.003 | 24.550 | 0.000 | -0.084 to -0.072 | Supported |
| 11 | Clinical practice guidelines ⟶ financial outcome | -0.069 | -0.069 | 0.004 | 19.508 | 0.000 | -0.076 to -0.062 | Supported |
| 12 | Patient's condition ⟶ clinical outcome | 0.494 | 0.494 | 0.006 | 86.868 | 0.000 | 0.482 to 0.506 | Supported |
| 13 | Patient's condition ⟶ financial outcome | -0.002 | -0.001 | 0.007 | 0.263 | 0.793 | -0.015 to 0.010 | Rejected |
| 14 | Nurse adequacy ⟶ clinical outcome | 0.007 | 0.007 | 0.006 | 1.145 | 0.253 | -0.004 to 0.018 | Rejected |
| 15 | Nurse adequacy ⟶ financial outcome | -0.034 | -0.033 | 0.007 | 4.737 | 0.000 | -0.047 to -0.020 | Supported |
| 16 | Clinical outcome ⟶ financial outcome | 0.667 | 0.666 | 0.019 | 34.340 | 0.000 | 0.622 to 0.699 | Supported |
| 17 | Pandemic∗ CPG (CO) ⟶ clinical outcome | -0.006 | -0.006 | 0.003 | 1.735 | 0.083 | -0.012 to 0.001 | Rejected |
| 18 | Pandemic∗supervision (CO) ⟶ clinical outcome | 0.040 | 0.041 | 0.005 | 8.825 | 0.000 | 0.032 to 0.049 | Supported |
| 19 | Pandemic∗CPG (FO) ⟶ financial outcome | -0.022 | -0.022 | 0.002 | 9.376 | 0.000 | -0.027 to -0.017 | Supported |
| 20 | Pandemic∗supervision(FO) ⟶ financial outcome | -0.031 | -0.031 | 0.005 | 6.386 | 0.000 | -0.041 to-0.023 | Supported |
Note: CPG = clinical practice guidelines; CO = clinical outcome; FO = financial outcome.
Table 8.
Discriminant validity R square, f square, and Q square.
| Constructs | R 2 adjusted | Q 2 | f 2 | |||||
|---|---|---|---|---|---|---|---|---|
| Clinical outcome | Clinical practice guidelines | Financial outcome | Nurse adequacy | Patient condition | Supervision | |||
| Clinical outcome | 0.240 | 0.238 | 0.240 | 0.663 | ||||
| Clinical practice guidelines | 0.000 | 0.000 | 0.000 | 0.009 | ||||
| Financial outcome | 0.490 | 0.413 | 0.490 | |||||
| Nurse adequacy | 0.386 | 0.384 | 0.386 | 0.001 | ||||
| Pandemic status | 0.001 | 0.000 | 0.004 | 0.628 | 0.005 | 0.011 | ||
| Pandemic∗CPG (CO) | 0.000 | |||||||
| Pandemic∗CPG (FO) | 0.001 | |||||||
| Pandemic∗supervision (CO) | 0.002 | |||||||
| Pandemic∗supervision (FO) | 0.002 | |||||||
| Patient's condition | 0.005 | 0.003 | 0.303 | 0.000 | 0.051 | |||
| Supervision | 0.056 | 0.054 | 0.017 | 0.040 | ||||
Note: CPG = clinical practice guidelines; CO = clinical outcome; FO = financial outcome.
3.3. Results of Structural Modeling of COVID-19 Pandemic in AMC Hospital
About the COVID-19 pandemic, from the structural model (Figure 2 and Table 7), our study showed that COVID-19 pandemic had the following: (1) direct effect on a pediatric patient's condition (β = 0.069, P = 0.000) increases severity and complexity; (2) direct effect on decreasing nurse adequacy (β = −0.621, P = 0.000); (3) no direct effect on CPG (β = −0.006, P = 0.262); (4) direct effect on supervision (β = −0.103, P = 0.000), by decreasing direct supervision and supervisor availability; (5) indirect effects on clinical outcome (β = −0,041, T = 8.825, P = 0.000) by decreasing aLOS; and (6) the indirect effect on financial outcomes (β = 0.069, P = 0.000) by increasing the total cost of care and decreasing the insurance claim profit.
Figure 2.
Result of the structural modeling.
This finding (Figure 2 and Table 7) implies that residents need to be made aware that runaway medical costs will threaten the accessibility and affordability of the health care system. Thus, insight into high-value, cost-conscious care should also be emphasized in residents who, in the past, focused solely on clinical outcomes [59]. There has been little emphasis on high-value, cost-consciousness care in the resident education process in the past. To enhance learning, residents should feel free to ask their supervisors critical questions and discuss dilemmas and differing beliefs regarding high-value, cost consciousness care. For this reason, both the resident education program and current departmental policies must contribute to the high-value, cost consciousness care learning [59].
The supervisor is responsible for controlling 80% of health expenditures. Our study showed that supervision had a significant impact on the financial outcome (β = −0.150, P = 0.000) by reducing the total cost of care and increasing insurance claim profit; therefore, the supervision of residents plays a significant role in achieving a waste reduction in hospitals. Residents should be trained in high-value, cost-conscious care earlier to reduce waste in the future. Waste in health services reaches 20%. Reducing waste will increase profits [59]. It is known that residents are often unaware of the actual costs of care, and few residency programs have a formal curriculum in high-value, cost-conscious care. The data show that the extravagant behavior of the residents persists, and it results from the practice environment in the hospital.
3.4. The Role of Clinical Practice Guidelines (CPG)
About CPG, from the structural model (Figure 2 and Table 7), our study showed that (1) COVID-19 pandemic had no direct effect on CPG (β = −0.006, P = 0.262); (2) CPG significantly impacted clinical outcomes (β = −0.078, P = 0.000) by decreasing aLOS; (3) CPG significantly impacted the financial outcome (β = −0.069, P = 0.000) by reducing the total cost of care and increasing insurance claim profit; (4) CPG did not moderate the effect of the COVID-19 pandemic on the clinical outcomes (β = −0.006, T = 1.735, P = 0.083); (5) CPG moderated the impact of the COVID-19 pandemic on the financial outcomes (β = −0.022, T = 9.376, P = 0.000) by reducing the total cost of care and increasing insurance claim profit; and (6) CPG had a significant effect only on financial outcomes (f2 = 0.009). Preparation of CPG is also associated with improved patient satisfaction and increased implementation of surgical procedures by residents with low complication rates. CPG implementation includes more about procedural actions, the equipment used, and the cost of care [60]. Feedback on CPG implementation and resident performance will improve CPG compliance. A previous study found that CPGs did not significantly affect clinical outcomes [61]. This finding is similar to our result (Figure 2 and Table 7), showing that CPG did not moderate the effect of the COVID-19 pandemic on the clinical outcomes (β = −0.006, P = 0.083). However, CPG still governs the best practice safety process measures.
Complications due to medical procedures are the second most common cause of side effects among hospitalized pediatric patients. To increase the safety of the action procedure, CPG is required. However, formal procedural resident supervision by experienced supervisors performing bedside medical procedures is required. The results of previous studies inform the medical education community that the development of the CPG did not ensure that residents' chances of gaining hands-on procedural experience would increase. For this reason, simulation training is to complement (not replace) bedside training with direct supervision so that procedural skills are better and safer [61]. CPGs help medical doctors determine the best a patient treatment for a particular medical condition but are not designed to take each patient's unique needs into account. CPG standardizes medical practice according to scientific principles or the best available evidence of effectiveness. By reducing uncertainty, CPG minimizes variation in medical practice, thereby improving outcomes in patient care. In two systematic reviews of the literature, it was found that lack of knowledge and belief in CPG was a significant cause of nonadherence [62].
Our study found that the effect of the COVID-19 pandemic on increasing total cost of care and decreasing insurance claim profit can be inhibited by CPG (Figure 2 and Table 7, hypothesis 19); once again, residents must be made aware that uncontrolled medical expenses will threaten the health of the pocket home business. Insights into high-value, cost-conscious care should also be emphasized to residents. Education residents today are faced with the difficult task of producing medical doctors who are high-value, cost-conscious care-oriented [59]. Exactly why experts are more likely than beginners to deviate from CPG is unclear. One possibility is that, as medical doctors become more skilled, medical doctors move from an analytical approach to an experience-based process. Educational strategies need to be differentiated for medical doctors who are experts and beginners. For specialists, judicious deviation from CPG concerning individual patient circumstances is not only acceptable but also to be expected. [62].
Although CPG is evidence-based, clinical management decisions by clinicians may be influenced by other contextual factors. A drawback of CPG is that in the patient context, particularly aspects of the context that are personal or “nonmedical” (i.e., social) receive relatively less attention than aspects of scientific evidence. Interventions that integrate patient contextual factors into clinical management decisions in resident education can increase cognitive load and result in less-than-optimal learning. The CPG was developed to assist clinicians in determining the best treatment for a patient. However, it was not designed to consider the individual patient's unique needs. One study showed that clinicians were less likely to follow CPG recommendations in the presence of contextual factors than without contextual factors (56% versus 80%, respectively; odds ratio [OR] = 0.32, 95% [CI] = [0.17 − 0.53], P < 0,001). Contextual factors include patient learning activities, proximity to care, treatment expectations, and factors related to home life [62].
Effective collaboration among health care team members was crucial; residents often felt pressured following perceived low-value recommendations from consultants [63]; in the absence of contextual factors, expert physicians and novice physicians were equally likely to adhere to the CPG (80% for expert versus 79% for novice physician, OR = 1.05, 95% CI = [0.59, 1.8], P = 0.85). A patient's context influences how physicians manage care, even when CPGs are available and known. The previous study does not support the hypothesis that adherence is related primarily to physicians' knowledge of CPGs. Despite this demonstrated familiarity, management decisions deviated from CPGs more often in the presence of a contextual factor. These observations imply that the “lack of adherence” among physicians may not be a function of their level of CPG knowledge but their sensitivity to each patient's unique needs. While based on the best available evidence, clinical management decisions are tailored according to the physician's perception of potential benefits given the whole experience of the patient [62].
3.5. The Role of Supervision
Regarding supervision from the structural model (Figure 2 and Table 7), our study showed the following: (1) COVID-19 pandemic significantly impacts supervision (β = −0.103, P = 0.000); (2) supervision significantly impacted clinical outcomes (β = −0.118, P = 0.000) by decreasing the average LOS; (3) supervision significantly impacted the financial outcome (β = −0.150, P = 0.000) by reducing the total cost of care and increasing insurance claim profit; (4) supervision moderated the effect of the COVID-19 pandemic on the clinical outcomes (β = 0.040, T = 8.825, P = 0.000) by increasing the average LOS; (5) supervision moderated the impact of the COVID-19 pandemic on the financial outcomes (β = −0.031, T = 6.386, P = 0.000) by reducing the total cost of care and increasing insurance claim profit; (6) supervision had a strong effect only on clinical outcomes (f2 = 0.017); (7) the impact of the COVID-19 pandemic on reducing average LOS was complementary to other variables (patient's condition and supervision); and (8) the severity and complexity of the disease increased direct supervision (β = 0.220, P = 0.000).
Supervisors must be better prepared to teach and set an example in high-value, cost-conscious care to educate residents better. Unfortunately, the data show that preparation for this is lacking. We must intervene to educate future medical doctors on the cost of care during residency education [1]. Most of the supervision in clinical education is directed at identifying the patient's condition and providing the “best” therapy for the patient's clinical condition. At the same time, CPG tends to strengthen the rationale for choosing a treatment. On the one hand, supervision supports “patient-centered care” and involves the patient in decision-making; on the other hand, optimal treatment is a direct consequence of scientific evidence implemented in the CPG [62].
For over a century, direct supervision (a bedside round process) in which supervisors and residents discuss inpatient conditions and treatment plans in front of patients has been the ideal learning method in hospitals. A 2009 study showed that only 17% of resident supervision occurred in patients' rooms at the bedside. Restrictions on resident working hours, and increasing reliance on technology, have caused medical doctors and senior residents to seek other forms of learning that are considered more efficient, namely in the conference room. Despite the rhetorical and theoretical merits of direct supervision (a bedside round process), recent research has shown that resident supervision occurring at the bedside in patient rooms is decreasing. Yet, since the mid-20th century, the bedside round process was the norm. Today, experts and leaders want clinical learning methods to return to the bedside round process. Direct supervision (bedside round process) allows residents to develop their skills, practice teamwork, and communicate with patients and all hospital staff for patient-centered care [64].
3.6. Implementation of CPG with Supervision
Implementation of CPG alone had no impact on clinical outcomes. Still, the implementation of CPG by residents with supervision by senior medical doctors had a much lower procedural failure rate (4.2% vs. 7.2%, P = 0.020) [60]. With supervised CPG implementation, appropriate ultrasound guidance was higher (96.8% vs. 90.0%, P = 0.004) and lower femoral CVC rates (10.5% vs. 17.3%, P = 0.020). The direct supervision performed at AMC hospital on pediatric residents is safer than previously thought. Cost savings occurred with a greater opportunity when performing a medical procedure based on the CPG supervisor was present (26.6% vs. 7.6%, P < 0.0001) [61]. Feedback on CPG implementation and residents' performance will increase residents' compliance with CPG [65]. In implementing CPG, performing bedside medical procedures without supervision by a senior medical doctor did not improve clinical outcomes but can still enhance financial outcomes [61]. In other words, as we found from our research (Figure 2 and Table 7), supervision and CPG reduce the total costs of care that increased during the COVID-19 pandemic and increase insurance claim profit that decreased during the COVID-19 pandemic. This study proves that if the supervision of residents occurs at the bedside in the patient's room, high-value cost-consciousness care is easier to achieve (Tables 1 and 7 and Figure 2).
On the other hand, some residents also questioned the benefits of the bedside round learning process and whether bedside rounds could continue to be considered a standard of educational practice. However, almost all residents believe that the learning process at the bedside positively influences resident behavior in providing health care in hospitals [64]. The supervisor was an effective role model for residents in making complex clinical decisions and allowed them to pursue their preferred plans [59]. In several academic hospitals, the implementation of supervision and CPG was developed through a bedside round process to improve patient safety and the quality of resident education [61]. Therefore, the introduction of CPG and supervised bedside rounds should be given up front during resident orientation before working in AMC hospitals, including diagnostic and treatment algorithms [1].
3.7. The Main Finding
The most important result of this paper is to find out how big the role of supervision and CPG is when health human resources are scarce, especially doctors and nurses, during a pandemic. The main finding of this study is that direct supervision inhibited the negative impact of the pandemic on both clinical and financial outcomes of non-COVID-19 inpatient care by pediatric residents. In contrast, CPG was only inhibited on financial outcomes. Based on the facts, our research has proven that the COVID-19 crisis has seriously impacted the academic medical center (AMC) hospital in the clinical, financial, and health education aspects (Table 1, Figure 2, and Table 7). This research's novelty is quantifying the effect of CPG and supervision on medical services in AMC hospitals by pediatric residents on clinical outcomes and financial outcomes in times of resource scarcity. Previous literatures, which also discussed Supervision and/or CPG [1, 60–64, 66–72] have not calculated how big the “effect size” of Supervision and CPG is on clinical outcomes and financial outcomes in conditions of resource scarcity.
The COVID-19 pandemic also has triggered significant changes in medical education. Effective learning designs need to be developed and implemented to address COVID-19-related barriers in resident education to help educators turn the COVID-19 crisis into opportunities for positive and sustainable change. Supervision with a practical design enables residents to make complex clinical decisions and develop their competence in the high-value, cost-conscious care [69]. It is not surprising that today's academic lecturers have difficulty teaching high-value, cost consciousness care, as they were previously untrained [59]. The results of this study imply that, in a disaster, the availability of CPG and direct supervision makes AMC hospitals able to inhibit the negative impact of disasters on clinical and financial outcomes.
What is not available in previous papers [1, 60–64, 66–72], is that this paper provides information on the right solution for AMC hospitals forced to experience resource scarcity. The impact of disruption of health services on pediatric patients due to the COVID-19 pandemic in the future must be anticipated, mitigated, and minimized based on valid evidence. In addition, health care methods for pediatric patients that have been proven effective in dealing with pandemics or disasters must be studied and applied so that we are better prepared when facing another pandemic or certain circumstances with insufficient resources similar to the pandemic.
3.8. Limitations and Strengths of the Study
The limitations of this research are the following: (1) the design used is retrospective; there are limitations to this retrospective study; (2) the research subjects only came from one hospital (the largest AMC hospital in Indonesia); and (3) data is only taken from pediatric patients financed by national health insurance. To consolidate our findings, further prospective multicenter studies should include more hospitals. The strengths of this research are the following: (1) the number of subjects observed was quite large, comparing the pandemic conditions with the previous one with a long observation period (24 months) before the pandemic and 24 months during the pandemic; (2) use path analysis with a structural equation model (SEM) to explore the relationship between research variables. PLS-SEM is an appropriate, robust, and powerful tool [73, 74]; and (3) our study found that the Q2 value was more significant than zero (Table 8). Information in Table 8 indicating the relevance and accuracy of path model predictions is acceptable for certain variables. Therefore, this model effectively predicts clinical outcomes (Q2 = 0.238) and financial outcomes (Q2 = 0.413).
4. Conclusions
This study proves that direct supervision contributes to inhibiting the negative impact of the COVID-19 pandemic on the clinical and financial outcomes of non-COVID-19 inpatient care by pediatric residents. On the other hand, CPG only contributes to financial results.
We recommend a multicenter study on the impact of supervision and clinical practice guidelines on clinical and financial outcomes at AMC hospital to continue this research. In addition, we recommend further research to evaluate the impact of supervision and clinical practice guidelines on conditions where AMC hospital's resources routinely experience a decline in services outside of working days and outside working hours. Information from this research can improve the quality and safety of services under these conditions.
Acknowledgments
We would like to express special gratitude to our subjects and their parents and advisers who allowed us to do this research. We would like to thank Budi Santoso as Dean of Faculty of Medicine Universitas Airlangga and Joni Wahyuhadi as Director of Dr. Soetomo General Academic Hospital for their support in finishing this manuscript.
Data Availability
The authors confirm that the data supporting the findings of this study are available within the article.
Conflicts of Interest
The authors declare no conflicts of interest in this work.
References
- 1.Perez J. A., Awar M., Nezamabadi A., et al. Comparison of direct patient care costs and quality outcomes of the teaching and nonteaching hospitalist services at a large academic medical center. Academic Medicine . 2018;93(3):491–497. doi: 10.1097/ACM.0000000000002026. [DOI] [PubMed] [Google Scholar]
- 2.Qaseem A., Forland F., Macbeth F., et al. Guidelines international network: toward international standards for clinical practice guidelines. Annals of Internal Medicine . 2012;156(7):525–531. doi: 10.7326/0003-4819-156-7-201204030-00009. [DOI] [PubMed] [Google Scholar]
- 3.Lydiksen N., Greve J., Jakobsen M., Kristensen S. R. Using national clinical guidelines to reduce practice variation - the case of Denmark. Health Policy . 2021;125(6):793–798. doi: 10.1016/j.healthpol.2021.03.002. [DOI] [PubMed] [Google Scholar]
- 4.Holliman C. J., Wuerz R. C., Meador S. A. Decrease in medical command errors with use of a "standing orders" protocol system. The American Journal of Emergency Medicine . 1994;12(3):279–283. doi: 10.1016/0735-6757(94)90138-4. [DOI] [PubMed] [Google Scholar]
- 5.Sacchetti A., Carraccio C., Harris R. H. Resident management of emergency department patients: is closer attending supervision needed? Annals of Emergency Medicine . 1992;21(6):749–752. doi: 10.1016/S0196-0644(05)82797-0. [DOI] [PubMed] [Google Scholar]
- 6.Cabana M. D., Rand C. S., Powe N. R., et al. Why don't physicians follow clinical practice guidelines?: a framework for improvement. Journal of the American Medical Association . 1999;282(15):1458–1465. doi: 10.1001/jama.282.15.1458. [DOI] [PubMed] [Google Scholar]
- 7.Miller J., Thomson L. J., Stewart L. S., et al. Implementation of a risk mitigating COVID-adapted colorectal cancer pathway. BMJ Open Quality . 2021;10(1, article e001135) doi: 10.1136/bmjoq-2020-001135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Evans-Lacko S., Jarrett M., McCrone P., Thornicroft G. Facilitators and barriers to implementing clinical care pathways. BMC Health Services Research . 2010;10(1):1–6. doi: 10.1186/1472-6963-10-182. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Indonesia, Perhimpunan Rumah Sakit Seluruh. Pedoman Penyusunan Panduan Praktik Klinis dan Clinical Pathways dalam Asuhan Terintegrasi sesuai Standar Akreditasi Rumah Sakit 2012 . Edisi I.; 2015. [Google Scholar]
- 10.Elmore R., Schmidt L., Lam J., et al. Risk and protective factors in the COVID-19 pandemic: a rapid evidence map. Frontiers in Public Health . 2020;8 doi: 10.3389/fpubh.2020.582205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Tonna J. E., Hanson H. A., Cohan J. N., et al. Balancing revenue generation with capacity generation: case distribution, financial impact and hospital capacity changes from cancelling or resuming elective surgeries in the US during COVID-19. BMC Health Services Research . 2020;20(1):1–7. doi: 10.1186/s12913-020-05975-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hashmi P., Fahad S., Khan H. N., Zahid M., Sadruddin A., Noordin S. Covid-19 pandemic: economic burden on patients with musculoskeletal injuries in a tertiary care hospital of LMIC; retrospective cross sectional study. Annals of Medicine and Surgery . 2020;60:5–8. doi: 10.1016/j.amsu.2020.09.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kaye A. D., Okeagu C. N., Pham A. D., et al. Economic impact of COVID-19 pandemic on healthcare facilities and systems: International perspectives. Best Practice & Research Clinical Anaesthesiology . 2021;35(3):293–306. doi: 10.1016/j.bpa.2020.11.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Qian X. The impact of COVID-19 pandemic on insurance demand: the case of China. The European Journal of Health Economics . 2021;22(7):1017–1024. doi: 10.1007/s10198-021-01344-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Chen Y., Cai M., Li Z., Lin X., Wang L. Impacts of the COVID-19 pandemic on public hospitals of different levels: six-month evidence from Shanghai, China. Risk Management and Healthcare Policy . 2021;Volume 14:3635–3651. doi: 10.2147/RMHP.S314604. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ahuja G., Verma M., Patkar D. Financial impact of COVID-19 on radiology practice in India. The Indian Journal of Radiology & Imaging . 2021;31(Suppl 1):S31–S37. doi: 10.4103/ijri.IJRI_305_20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.da Silva Etges A. P., Cardoso R. B., Marcolino M. S., et al. The economic impact of COVID-19 treatment at a hospital-level: investment and financial registers of Brazilian hospitals. Journal of Health Economics and Outcomes Research . 2021;8(1):36–41. doi: 10.36469/jheor.2021.22066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Waitzberg R., Quentin W., Webb E., Glied S. The structure and financing of health care systems affected how providers coped with COVID-19. The Milbank Quarterly . 2021;99(2):542–564. doi: 10.1111/1468-0009.12530. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Rotter T., Kinsman L., James E. L., et al. Clinical pathways: effects on professional practice, patient outcomes, length of stay and hospital costs. Cochrane Database of Systematic Reviews . 2010;3 doi: 10.1002/14651858.CD006632.pub2. [DOI] [PubMed] [Google Scholar]
- 20.Evans S., Taylor C., Antoniou A., et al. Implementation of a clinical pathway for the surgical treatment of colorectal cancer during the COVID-19 pandemic. Colorectal Disease . 2020;22(9):1002–1005. doi: 10.1111/codi.15247. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Köppen J., Hartl K., Maier C. B. Health workforce response to Covid-19: what pandemic preparedness planning and action at the federal and state levels in Germany? The International Journal of Health Planning and Management . 2021;36(S1):71–91. doi: 10.1002/hpm.3146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Verma P., Dumka A., Bhardwaj A., Ashok A., Kestwal M. C., Kumar P. A statistical analysis of impact of COVID19 on the global economy and stock index returns. SN Computer Science . 2021;2(1):p. 27. doi: 10.1007/s42979-020-00410-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Panteli D., Maier C. B. Regulating the health workforce in Europe: implications of the COVID-19 pandemic. Human Resources for Health . 2021;19(1):80–86. doi: 10.1186/s12960-021-00624-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Alami H., Lehoux P., Fleet R., et al. How can health systems better prepare for the next pandemic? lessons learned from the management of COVID-19 in Quebec (Canada) Frontiers in Public Health . 2021;9:p. 696. doi: 10.3389/fpubh.2021.671833. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Blumberg M. L., Joseph A. M., Freeman J. R. A strategy for improving the supervision and performance of moonlighting residents. Academic Medicine: Journal of the Association of American Medical Colleges . 1995;70(2):155–157. doi: 10.1097/00001888-199502000-00022. [DOI] [PubMed] [Google Scholar]
- 26.Ereso A. Q., Garcia P., Tseng E., et al. Live transference of surgical subspecialty skills using telerobotic proctoring to remote general surgeons. Journal of the American College of Surgeons . 2010;211(3):400–411. doi: 10.1016/j.jamcollsurg.2010.05.014. [DOI] [PubMed] [Google Scholar]
- 27.Finlayson A., Bartolucci G., Streiner D. L. Deployment, Supervision and Decision-Making of Residents in an Emergency Psychiatric Service . 3. Vol. 24. Los Angeles, CA: SAGE Publications Sage CA; 1979. [DOI] [PubMed] [Google Scholar]
- 28.Pines J. M., Zocchi M. S., Black B. S., et al. The effect of the COVID-19 pandemic on the economics of United States emergency care. Annals of Emergency Medicine . 2021;78(4):487–499. doi: 10.1016/j.annemergmed.2021.04.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Godley B. F., Lawley T. J., Rubenstein A., Pizzo P. A. How academic medical centers can navigate the pandemic and its aftermath: solutions for 3 major issues. Academic Medicine: Journal of the Association of American Medical Colleges . 2021;96(11):1529–1533. doi: 10.1097/ACM.0000000000004155. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Gómez-Ochoa S. A., Franco O. H., Rojas L. Z., et al. COVID-19 in health-care workers: a living systematic review and meta-analysis of prevalence, risk factors, clinical characteristics, and outcomes. American Journal of Epidemiology . 2021;190(1):161–175. doi: 10.1093/aje/kwaa191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Buonafine C. P., Paiatto B. N. M., Leal F. B., et al. High prevalence of SARS-CoV-2 infection among symptomatic healthcare workers in a large university tertiary hospital in São Paulo, Brazil. BMC Infectious Diseases . 2020;20(1):917–918. doi: 10.1186/s12879-020-05662-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Kunz M., Strasser M., Hasan A. Impact of the coronavirus disease 2019 pandemic on healthcare workers: systematic comparison between nurses and medical doctors. Current Opinion in Psychiatry . 2021;34(4):413–419. doi: 10.1097/YCO.0000000000000721. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Bandyopadhyay S., Baticulon R. E., Kadhum M., et al. Infection and mortality of healthcare workers worldwide from COVID-19: a systematic review. BMJ Global Health . 2020;5(12, article e003097) doi: 10.1136/bmjgh-2020-003097. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Blankenburg R., Gonzalez del Rey J., Aylor M., et al. The impact of the COVID-19 pandemic on pediatric graduate medical education: lessons learned and pathways forward. Academic Medicine . 2022;97(3S):S35–S39. doi: 10.1097/ACM.0000000000004532. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Ramayah T., Cheah J., Chuah F., Ting H., Memon M. A. Partial least squares structural equation modeling (PLS-SEM) using smart PLS 3.0. An Updated Guide and Practical Guide to Statistical Analysis . 2018.
- 36.Satibi S., Andayani T. M., Endarti D., Suwantara I. P. T., Wintariani N. P., Agustini N. P. D. Comparison of real cost versus the Indonesian case base groups (INZ-CBGs) tariff rates among patients of high- incidence cancers under the national health insurance scheme. Asian Pacific Journal of Cancer Prevention: APJCP . 2019;20(1):117–122. doi: 10.31557/APJCP.2019.20.1.117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Kock N. Common method bias in PLS-SEM. International Journal of e-Collaboration . 2015;11(4):1–10. doi: 10.4018/ijec.2015100101. [DOI] [Google Scholar]
- 38.Hair J. F., Jr., Babin B. J., Krey N. Covariance-based structural equation modeling in thejournal of advertising: review and recommendations. Journal of Advertising . 2017;46(1):163–177. doi: 10.1080/00913367.2017.1281777. [DOI] [Google Scholar]
- 39.Henseler J., Ringle C. M., Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science . 2015;43(1):115–135. doi: 10.1007/s11747-014-0403-8. [DOI] [Google Scholar]
- 40.Gold A. H., Malhotra A., Segars A. H. Knowledge management: an organizational capabilities perspective. Journal of Management Information Systems . 2001;18(1):185–214. doi: 10.1080/07421222.2001.11045669. [DOI] [Google Scholar]
- 41.Avkiran N. K. An in-depth discussion and illustration of partial least squares structural equation modeling in health care. Health Care Management Science . 2018;21(3):401–408. doi: 10.1007/s10729-017-9393-7. [DOI] [PubMed] [Google Scholar]
- 42.Sarstedt M., Ringle C., Hair J. Handbook of Market Research . 2017.
- 43.Cohen J. Statistical Power Analysis for the Behavioral Sciences . Routledge; 2013. [DOI] [Google Scholar]
- 44.Shadmi E., Chen Y., Dourado I., et al. Health equity and COVID-19: global perspectives. International Journal for Equity in Health . 2020;19(1):1–16. doi: 10.1186/s12939-020-01218-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Kruizinga M. D., Peeters D., van Veen M., et al. The impact of lockdown on pediatric ED visits and hospital admissions during the COVID19 pandemic: a multicenter analysis and review of the literature. European Journal of Pediatrics . 2021;180(7):2271–2279. doi: 10.1007/s00431-021-04015-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Chanchlani N., Buchanan F., Gill P. J. Addressing the indirect effects of COVID-19 on the health of children and young people. CMAJ . 2020;192(32):E921–E927. doi: 10.1503/cmaj.201008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Ramos-Lacuey B., Herranz Aguirre M., Calderón Gallego C., et al. ECIEN-2020 study: the effect of COVID-19 on admissions for non-COVID-19 diseases. World Journal of Pediatrics . 2021;17(1):85–91. doi: 10.1007/s12519-020-00406-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Perondi B., Miethke-Morais A., Montal A. C., Harima L., Segurado A. C., Hospital das Clinicas COVID-19 Crisis Management Committee Setting up hospital care provision to patients with COVID-19: lessons learnt at a 2400-bed academic tertiary center in Sao Paulo, Brazil. Brazilian Journal of Infectious Diseases . 2020;24(6):570–574. doi: 10.1016/j.bjid.2020.09.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Kushner L. E., Schroeder A. R., Kim J., Mathew R. “For COVID” or “with COVID”: classification of SARS-CoV-2 hospitalizations in children. Hospital Pediatrics . 2021;11(8):e151–e156. doi: 10.1542/hpeds.2021-006001. [DOI] [PubMed] [Google Scholar]
- 50.McGlacken-Byrne S. M., Drew S. E., Turner K., Peters C., Amin R. The SARS-CoV-2 pandemic is associated with increased severity of presentation of childhood onset type 1 diabetes mellitus: a multi-Centre study of the first COVID-19 wave. Diabetic Medicine . 2021;38(9, article e14640) doi: 10.1111/dme.14640. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Brisca G., Vagelli G., Tagliarini G., et al. The impact of COVID-19 lockdown on children with medical complexity in pediatric emergency department. American Journal of Emergency Medicine . 2021;42:225–227. doi: 10.1016/j.ajem.2020.11.066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Zhou Y., Xu H., Li L., Ren X. Management for patients with pediatric surgical disease during the COVID-19 epidemic. Pediatric Surgery International . 2020;36(6):751–752. doi: 10.1007/s00383-020-04656-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Davenport M., Pakarinen M. P., Tam P., Laje P., Holcomb G. W. From the editors: the COVID-19 crisis and its implications for pediatric surgeons. Journal of Pediatric Surgery . 2020;55(5):785–788. doi: 10.1016/j.jpedsurg.2020.04.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Wulkan M. L. Pediatric Surgery in the Time of COVID-19 . Mary Ann Liebert, Inc.; 2020. [DOI] [PubMed] [Google Scholar]
- 55.Idham Y., Paramita V. M. W., Fauzi A. R., Dwihantoro A., Makhmudi A. The impact of COVID-19 pandemic on pediatric surgery practice: a cross- sectional study. Annals of Medicine and Surgery . 2020;59:96–100. doi: 10.1016/j.amsu.2020.09.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Mohammadi H., Rezapour S., Jajarmi A. On the fractional SIRD mathematical model and control for the transmission of COVID-19: the first and the second waves of the disease in Iran and Japan. ISA Transactions . 2022;124:103–114. doi: 10.1016/j.isatra.2021.04.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Baleanu D., Abadi M. H., Jajarmi A., Vahid K. Z., Nieto J. A new comparative study on the general fractional model of COVID-19 with isolation and quarantine effects. Alexandria Engineering Journal . 2022;61(6):4779–4791. doi: 10.1016/j.aej.2021.10.030. [DOI] [Google Scholar]
- 58.Behal R., Finn J. Understanding and improving inpatient mortality in academic medical centers. Academic Medicine . 2009;84(12):1657–1662. doi: 10.1097/ACM.0b013e3181bf4e8b. [DOI] [PubMed] [Google Scholar]
- 59.Stammen L., Slootweg I., Stalmeijer R., et al. The struggle is real: how residents learn to provide high-value, cost-conscious care. Teaching and Learning in Medicine . 2019;31(4):402–411. doi: 10.1080/10401334.2019.1583566. [DOI] [PubMed] [Google Scholar]
- 60.Spangler H., Stephens J. R., Sturkie E., Dancel R. Implementation of an academic hospital medicine procedure service: 5-year experience. Hospital Practice . 2021;49(3):209–215. doi: 10.1080/21548331.2021.1890958. [DOI] [PubMed] [Google Scholar]
- 61.Tukey M. H., Wiener R. S. The impact of a medical procedure service on patient safety, procedure quality and resident training opportunities. Journal of General Internal Medicine . 2014;29(3):485–490. doi: 10.1007/s11606-013-2709-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Mercuri M., Sherbino J., Sedran R. J., Frank J. R., Gafni A., Norman G. When guidelines don’t guide: the effect of patient context on management decisions based on clinical practice guidelines. Academic Medicine . 2015;90(2):191–196. doi: 10.1097/ACM.0000000000000542. [DOI] [PubMed] [Google Scholar]
- 63.Seltz L. B., Nathaniel E., Ball A., Jimenez S., Tchou M. Pediatric residents' experiences with high-value care at an academic children's hospital. Journal of Graduate Medical Education . 2022;14(1):80–88. doi: 10.4300/JGME-D-21-00665.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Ratelle J. T., Gallagher C. N., Sawatsky A. P., et al. The effect of bedside rounds on learning outcomes in medical education: a systematic review. Academic Medicine: Journal of the Association of American Medical Colleges . 2022;97(6):923–930. doi: 10.1097/ACM.0000000000004586. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Akins R. B., Handal G. A. Utilizing quality improvement methods to improve patient care outcomes in a pediatric residency program. Journal of Graduate Medical Education . 2009;1(2):299–303. doi: 10.4300/JGME-D-09-00043.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Young J. Q., Friedman K. A., Thakker K., et al. Supervision and care quality as perceived by redeployed attendings, fellows, and residents during a COVID-19 surge: lessons for the future. Academic Medicine . 2022;97(3S):S28–S34. doi: 10.1097/ACM.0000000000004529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Celebi N., Tsouraki R., Engel C., Holderried F., Riessen R., Weyrich P. Does doctors’ workload impact supervision and ward activities of final-year students? a prospective study. BMC Medical Education . 2012;12(1):1–7. doi: 10.1186/1472-6920-12-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Ilgen J. S., de Bruin A. B., Teunissen P. W., Sherbino J., Regehr G. Supported independence: the role of supervision to help trainees manage uncertainty. Academic Medicine . 2021;96(11S):S81–S86. doi: 10.1097/ACM.0000000000004308. [DOI] [PubMed] [Google Scholar]
- 69.Thakur A., Soklaridis S., Crawford A., Mulsant B., Sockalingam S. Using rapid design thinking to overcome COVID-19 challenges in medical education. Academic Medicine . 2021;96(1):56–61. doi: 10.1097/ACM.0000000000003718. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Russell S. W., Ahuja N., Patel A., O'Rourke P., Desai S. V., Garibaldi B. T. Peabody's paradox: balancing patient care and medical education in a pandemic. Journal of Graduate Medical Education . 2020;12(3):264–268. doi: 10.4300/JGME-D-20-00251.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Tomlinson J. Using clinical supervision to improve the quality and safety of patient care: a response to Berwick and Francis. BMC Medical Education . 2015;15(1):1–8. doi: 10.1186/s12909-015-0324-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Subramaniam A., Silong A. D., Uli J., Ismail I. A. Effects of coaching supervision, mentoring supervision and abusive supervision on talent development among trainee doctors in public hospitals: moderating role of clinical learning environment. BMC Medical Education . 2015;15(1):1–9. doi: 10.1186/s12909-015-0407-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Becker N. B., de Jesus S. N., Viseu J. N., Stobäus C. D., Guerreiro M., Domingues R. B. Depresion y calidad de vida en adultos mayores: mediacion de la calidad del sueño. International Journal of Clinical and Health Psychology . 2018;18(1):8–17. doi: 10.1016/j.ijchp.2017.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Uzir M. U., Bukari Z., Jerin I., Hasan N., Abdul Hamid A. B., Ramayah Impact of COVID-19 on psychological distress among SME owners in Ghana: partial least square–structural equation modeling (PLS-SEM) approach. Journal of Community Psychology . 2022;50(3):1282–1314. doi: 10.1002/jcop.22716. [DOI] [PubMed] [Google Scholar]
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Data Availability Statement
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