Kanban method in bed management: impacts on hospitalization time

Abstract

Objective  To analyze the factors that impact the hospital stay time based on the Kanban method.

Methods  This was an observational study with a quantitative, retrospective, and documentary approach. A total of 13,637 medical records of patients admitted to a university hospital in Southern Brazil were analyzed in the period from August 2018 to September 2022. Data analysis was performed using descriptive statistics, chi-square test of independence, and regression analysis using R software.

Results  It was found that the majority of hospitalizations within the ideal time are related to surgical procedures (61.0%). Clinical procedures have a higher proportion of visits with a longer than acceptable stay time (45.0%). Computed tomography was the most frequently performed examination in patients with a long hospitalization time. More than half of the patients with a time categorized as greater than the acceptable one underwent one or more computed tomography scans. Cancer, heart and lung diseases, and diabetes mellitus were the most prevalent comorbidities, all of them with a high proportion of hospitalizations lasting longer than acceptable.

Conclusion  Our results showed that the profile of patients cared for at the institution directly influences the length of hospital stay. It was possible to develop strategies for qualified care, seeking to reduce hospitalization time through monitoring.

Keywords:
Beds; Bed occupancy; Health information systems; Health profile; Inpatient Care units; Hospital administration; Epidemiology

Resumo

Objetivo  Analisar os fatores que impactam o tempo de internação hospitalar com base no método Kanban.

Métodos  Este foi um estudo observacional de abordagem quantitativa, retrospectivo e documental. Foram analisados 13.637 prontuários de pacientes internados em um hospital universitário no Sul do Brasil no período de agosto de 2018 a setembro de 2022. A análise de dados foi realizada usando estatística descritiva, teste qui-quadrado de independência e análise de regressão, a partir do software R.

Resultados  Foi verificado que a maioria das internações dentro do tempo ideal está relacionada com procedimentos cirúrgicos (61,0%). Os procedimentos clínicos têm uma proporção maior de atendimentos com tempo de internação maior que o aceitável (45,0%). A tomografia computadorizada foi o exame mais realizado nos pacientes com longo tempo de internação, na qual mais da metade dos pacientes categorizados como maior que o tempo aceitável realizaram uma ou mais tomografias computadorizadas. As comorbidades de maior prevalência foram câncer, doenças cardíacas, pulmonares e diabetes mellitus, todas com uma proporção alta de internações com tempo maior que o aceitável.

Conclusão  Os resultados demonstraram que o perfil dos pacientes atendidos na instituição influencia diretamente o tempo de internação. A partir do monitoramento do tempo de internação foi possível traçar estratégias para um atendimento qualificado, buscando sua redução.

Descritores:
Leitos; Ocupação de leitos; Sistemas de informação em saúde; Perfil de saúde; Unidades de internação; Administração hospitalar; Epidemiologia

Resumen

Objetivo  Analizar los factores que impactan en el tiempo de internación hospitalaria con base en el método Kanban.

Métodos  Se trató de un estudio observacional de enfoque cuantitativo, retrospectivo y documental. Se analizaron 13.637 historias clínicas de pacientes internados en un hospital universitario en el sur de Brasil, durante el período de agosto de 2018 a septiembre de 2022. El análisis de datos fue realizado mediante estadística descriptiva, prueba ji cuadrado de independencia y análisis de regresión, a partir del software R.

Resultados  Se verificó que la mayoría de las internaciones dentro del tiempo ideal está relacionada con procedimientos quirúrgicos (61,0 %). Los procedimientos clínicos tienen una mayor proporción de asistencia con tiempo de internación mayor que lo aceptable (45,0 %). La tomografía computarizada fue el examen más realizado en los pacientes con tiempo de internación largo, donde más de la mitad de los pacientes clasificados con mayor tiempo que lo aceptable realizó una o más tomografías computarizadas. Las comorbilidades de mayor prevalencia fueron cáncer, enfermedades cardíacas y pulmonares y diabetes mellitus, todas con una proporción alta de internaciones con mayor tiempo de lo aceptable.

Conclusión  Los resultados demostraron que el perfil de los pacientes atendidos en la institución influye directamente en el tiempo de internación. A partir del monitoreo del tiempo de internación fue posible elaborar estrategias para una asistencia cualificada que busca su reducción.

Descriptores:
Lechos; Ocupación de camas; Sistemas de información en Salud; Perfil de salud; Unidades de internación; Administración hospitalaria; Epidemiología

Introduction

The Kanban method was used in the Toyota production system after World War II to control the production of various batches of products using colored cards to signal the pace of production.(¹,²)Its use sought to improve production, detecting failures, and analyzing situations, thus favoring decision-making for continuous improvement of results.(³,⁴)

In the hospital context, the Kanban method has been used to manage and control patient length of stay by optimizing bed usage. This control has been carried out using color-coded signaling, involving all healthcare professionals in the proper use of information by both the multidisciplinary patient care team and the hospital’s clinical protocols.(⁵,⁶)Thus, the Kanban method has emerged as a technology that helps monitor hospitalization time, being used as an indicator of management efficiency, effectiveness, and efficacy, in addition to influencing cost control.(⁷)

Thus, it was observed that efficient management of bed occupancy directly impacts the health system’s ability to provide adequate care to the population. Real-time monitoring of hospital bed occupancy has been carried out in several countries, providing an immediate view of available capacity and helping to make decisions.(⁸)The Kanban method was thus implemented in 2017 to optimize the flow of patients admitted to a hospital in southern Brazil and control their length of stay. The Kanban method also provides visibility to patients with hospitalization times above the acceptable average, facilitating the identification of the problem and contributing to its resolution. Due to the visual nature of the Kanban method, standardized colors were adopted; green color was used to flag patients with acceptable length of stay according to the SIGTAP (Management System for the Table of Procedures, Medications, and Orthoses, Prostheses, and Special Materials of the “Sistema Único de Saúde”, SUS) table (http://sigtap.datasus.gov.br/tabela-unificada/app/sec/inicio.jsp); yellow color was used for those that exceeded the ideal time but are below twice the stay time indicated by the SIGTAP table; red color was used to highlight patients who exceeded twice the hospitalization time considered acceptable; and the blue color was used to highlight patients admitted without an estimated time of stay, being classified as undetermined (tables 1 and 2).

Table 1
Tests performed during hospitalization using Kanban categories/colors

Variables	Green n = 5917	Yellow n = 3181	Red n = 4539	Value	Value	α***
	n(%)	n(%)	n(%)	p-value*	q**
Upper gastrointestinal endoscopy	36(30)	37(31)	47(39)	0.017	0.022	0.024
Colonoscopy	17(28)	23(38)	21(34)	0.022	0.026	0.023
Electrocardiogram	201(16)	219(18)	799(66)	<0.001	<0.001	0.221
Magnetic resonance imaging	102(20)	107(21)	292(58)	<0.001	<0.001	0.110
Laboratory	5.141(43)	2.828(24)	3.990(33)	<0.001	<0.001	0.051
X ray	2.586(37)	1.510(22)	2.813(41)	<0.001	<0.001	0.169
Ultrasonography	234(17)	293(21)	879(63)	<0.001	<0.001	0.222
Tomography	763(24)	687(22)	1.671(54)	<0.001	<0.001	0.248
Angiotomography	27(12)	40(17)	163(71)	<0.001	<0.001	0.108
Electrocardiogram	23(37)	17(27)	22(35)	0.66	0.66	0.008
Mammography	4(11)	3(8.1)	30(81)	<0.001	<0.001	0.053
Cholangiotomography	16(21)	11(15)	48(64)	<0.001	<0.001	0.049
Tomography	142(19)	181(24)	440(58)	<0.001	<0.001	0.138
Cholangiotomography	8(28)	5(17)	16(55)	0.036	0.039	0.022
Liquor collection	30(17)	122(70)	22(13)	<0.001	<0.001	0.120
Others	39(29)	32(24)	63(47)	<0.001	<0.001	0.032

• ^*Chi-Square test of independence; ^**False discovery rate correction for multiple testing; ***Cramér’s V

Table 2
Comorbidities observed during hospitalization according to the Kanban method

Variables	Green n = 5917	Yellow n = 3377	Red n = 4345	Value	Value	α***
	n(%)	n(%)	n(%)	p-value*	q**
Anemias	6(26)	14(52)	14(52)	0.12	0.13	0.017
Cancer	144(18)	171(21)	481(60)	<0.001	<0.001	0.152
Cardiological	162(19)	202(23)	504(58)	<0.001	<0.001	0.149
Diabetes	444(25)	457(26)	863(49)	<0.001	<0.001	0.149
Gastric	8(17)	14(30)	24(52)	0.002	0.002	0.031
Hypertension	1.988(41)	1.090(23)	1.726(36)	<0.001	<0.001	0.050
Liver	85(23)	96(26)	187(51)	<0.001	<0.001	0.072
Hypothyroidism	69(27)	62(25)	120(48)	<0.001	<0.001	0.048
HIV	128(28)	90(20)	236(52)	<0.001	<0.001	0.076
Immunological	3(13)	5(21)	16(67)	0.001	0.001	0.031
Comorbidities denied	2.512(53)	1.150(24)	861(19)	<0.001	<0.001	0.175
Neurological	670(37)	413(23)	712(40)	0.001	0.001	0.057
Obesity	75(26)	77(26)	141(48)	0.001	0.001	0.056
Bones	10(16)	14(22)	39(62)	0.001	<0.001	0.044
Other comorbidities	71(40)	40(22)	67(38)	0.38	0.38	0.012
Psychiatric	90(27)	78(23)	170(50)	<0.001	<0.001	0.062
Pulmonary	205(20)	250(25)	551(55)	<0.001	<0.001	0.144
Renal	95(18)	108(21)	314(61)	<0.001	<0.001	0.125
Congenital Syphilis	31(17)	131(71)	23(12)	<0.001	<0.001	0.125
Syndromes	32(36)	27(30)	31(34)	0.32	0.33	0.013
Vascular	10(21)	10(21)	27(57)	<0.001	0.001	0.032

• * Chi-Square test of independence; ** Fixed false discovery rate for multiple testing; *** Cramér’s

The use of the Kanban method in the management of hospital beds is a promising innovation to improve management efficiency and the quality of health services. This research sought to analyze the use of the Kanban method in the management of beds in a 100% Unified Health System (SUS) hospital from August 2018 to September 2022, emphasizing the factors that impact hospital stay time. The rationale for such investigation is the need to better understand the main factors that impact hospital stay time. Therefore, the objective of this study was to analyze the factors that impact hospital stay time using the Kanban method.

Methods

This observational study with quantitative, retrospective, and documentary approaches was carried out in a university hospital (UH) in Southern Brazil. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline was used to ensure methodological quality and report the results obtained. The hospital studied has 231 beds exclusively for SUS care. It is recognized as a reference center in medium and high complexities for the Regional Health Coordination in Rio Grande do Sul (RS), covering 22 cities in the extreme south of the state, offering specialized services in traumatology and/or orthopedics, HIV/AIDS, and care for high-risk pregnant women. In addition to these specialties, the University Hospital (UH) also assists in 34 other specific areas.

The analysis of 15,579 medical records of patients treated in the period from September 2018 to September 2022 was considered as a basis. Medical records without complete information of interest to the study and those classified as undetermined (i.e., those whose diagnosis had no limit for the length of hospitalization recommended in the SIGTAP table) were excluded from the analysis. The final sample comprised 13,637 patient records, representing 87.5% of the UH records in the period studied. Age, sex, date of admission, length and outcome of hospitalization, classification according to the Kanban method, number of comorbidities, and medium and high complexity exams performed were defined as independent study variables.

The hospital information system was used to consult the information of interest and fill in a table (developed in Excel) with all the necessary data. The collection was carried out in the period from August to September 2022. The R (v. 4.3.2) software was used to process data.(⁹)At first, the variables were described based on their absolute and relative frequencies. To evaluate some variables, such as “length of hospital stay” and “age”, the mean value was calculated to compare the pre-established strata. Then, the univariate and adjusted analyses were performed, considering those with a p-value ≤0.25, thus reducing the false discovery rate.(¹⁰)In the univariate analysis, the chi-square test was used, seeking the associative effect of independence for contingency tables. In the multivariate analysis, logistic regression was performed.(¹¹)

The 95% Confidence Interval (CI) and 5% significance level were considered to identify the association of the Kanban method colors with hospitalizations and demographic characteristics of participants and thus obtain the final model presented. The study was approved by the Research Ethics Committee of the Federal University of Rio Grande (Opinion: 4,227,757; Certificate of Presentation of Ethical Appreciation: 30950420.9.0000.5324).

Results

Based on Tables 1 and 2, 61.0% of hospitalizations classified within the ideal time (green) were mainly related to surgical procedures whereas clinical procedures were 73.0% with hospitalization time longer than acceptable (yellow and red). Within these groups, several subgroups of care were observed, each with its distribution of hospitalizations between the green, yellow, and red categories.

Although most admissions involving surgical procedures were in the green category (especially in subgroups such as musculoskeletal surgery), some variations were identified. Some subgroups had a considerable proportion of hospitalizations in the red category (such as upper airway, face, head and neck, and circulatory system surgeries, and those for cancer treatment). In the green category, we could also observe clinical treatments in other specialties (1,759) with hospitalization times within the ideal range, in addition to the surgical procedures already mentioned (3,050). In the yellow category (whose time was stipulated after an institutional study; twice the time considered ideal was defined as the maximum limit: green), surgeries on the osteomuscular (419) and digestive (356) systems were again highlighted, followed by clinical treatments (1,804). Clinical treatments (other specialties), with 2,668 hospitalizations representing 58.7% of hospitalizations classified in this Kanban category, were among the most frequent procedures in the red category. We also identified that patients remained hospitalized for a longer time than the acceptable time (red) in more than 50.0% of cases in nephrology and oncology treatments. We also observed that the subgroup of surgeries on the digestive system, accessory organs, and abdominal wall was the one with the highest frequency, whereas the subgroup of diagnostics by magnetic resonance imaging was the one with the fewest procedures. After analysis, we observed that carrying out tests had some impact on the length of hospital stay. The frequency and distribution of examinations performed during hospitalization were obtained at the end of hospitalization. Therefore, the greater the number of tests performed, the longer the hospital stay. Computed tomography was the most frequently performed examination in patients who exceeded the ideal length of hospital stay (φ_c=0.248), in which more than half of the hospital stays (54.0%) were classified as red. Ultrasound (φ_c=0.222) and electrocardiogram (φ_c=0.221) were other tests that also showed a strong association. Regarding comorbidities, most showed a statistically significant association (at a 5% level) with the classification of length of hospital stay, except for anemias, syndromes, and other unspecified comorbidities. The chromosomal and congenital syndromes were the most common (84.0%). Cancer, diabetes mellitus, and cardiological and pulmonary diseases were the most prevalent comorbidities. All of them showed a high proportion of hospitalizations for a longer than acceptable time (yellow or red) (Table 2).

Table 3 shows the results of the logistic regression. The final model was obtained from the hospitalizations considered most critical and the factors that impacted the length of hospital stay. Regarding age group, children aged 0-3 years did not present an Odds Ratio (OR; for an event to occur) for prolonged hospitalization. Those aged 4-11 years had a chance of up to 1.01 times having a longer than acceptable hospital stay. People aged ≥64 years may have a chance of up to 3.15 times greater of having an acceptable length of hospital stay. For the care groups, upper airway surgery was considered as a reference. The highest OR was identified in hospitalizations for “Consultations, Care, and/or Follow-ups”: the chance of the patient remaining hospitalized for a longer than acceptable time in this type of hospitalization was up to 4.64 times greater, followed by lung problems (3.45 times greater). On the other hand, breast surgeries had the lowest OR: patients admitted to undergoing this procedure had 0.07 times the chance of remaining hospitalized for a longer than acceptable time. For all comorbidities, the absence of comorbidity was taken as a reference. Thus, patients with congenital syphilis had a 0.47 times greater chance of being hospitalized for a longer than acceptable period.

Table 3
Logistic regression model for hospitalization category using the Kanban method

Characteristics	OR (95% CI)	p-value
Age range (years)
0 - 3	—
4 - 11	0.82 (0.66; 1.01)	0.065
12 - 18	1.58 (1.16; 2.14)	0.003
19 - 42	1.71 (1.38; 2.12)	<0.001
43 - 63	2.18 (1.77; 2.69)	<0.001
≥64	2.55 (2.08; 3.15)	<0.001
Biological sex
Male	—
Female	1.01 (0.93; 1.09)	0.80
Service subgroup
Surgery of the upper airway, face, head, and neck	—
Endocrine gland surgery	0.15 (0.05; 0.45)	0.001
Breast surgery	0.07 (0.01; 0.27)	<0.001
Circulatory system surgery	0.61 (0.28; 1.33)	0.21
Surgery of the digestive system, accessory organs, and abdominal wall	0.36 (0.19; 0.69)	0.002
Genitourinary system surgery	0.32 (0.16; 0.62)	<0.001
Surgery of the central and peripheral nervous systems	0.20 (0.07; 0.54)	0.002
Surgery of the musculoskeletal system	0.25 (0.13; 0.49)	<0.001
Oncological surgery	0.31 (0.13; 0.73)	0.008
Obstetric surgery	1.18 (0.59; 2.39)	0.65
Reconstructive surgery	0.10 (0.02; 0.41)	0.005
Thoracic surgery	0.50 (0.20; 1.21)	0.13
Material collection	0.82 (0.36; 1.88)	0.63
Consultations / Services / Follow-ups	4.64 (1.85; 12.1)	0.001
Diagnosis by endoscopy	1.54 (0.21; 13.4)	0.67
Other surgeries	0.94 (0.46; 1.97)	0.87
Childbirth and birth	0.76 (0.15; 3.27)	0.73
Minor surgeries and surgeries of the skin, subcutaneous tissue, and mucosa	1.35 (0.54; 3.36)	0.52
Treatment of injuries, poisonings, and others resulting from external causes	0.63 (0.31; 1.30)	0.20
Nephrology treatment	2.26 (1.17; 4.45)	0.016
Oncology treatment	0.75 (0.39; 149)	0.41
Clinical treatments (other specialties)	0.86 (0.45; 1.65)	0.64
Anemias	1.52 (0.65; 3.60)	0.34
Cancer	2.21 (1.85; 2.65)	<0.001
Cardiological	1.58 (1.35; 1.84)	<0.001
Diabetes	1.31 (1.17; 1.48)	<0.001
Gastric	1.56 (0.83; 2.95)	0.16
Systemic arterial hypertension	0.96 (0.85; 1.08)	0.45
Liver	1.04 (0.84; 1.30)	0.70
Hypothyroidism	0.92 (0.70; 1.21)	0.56
Immunological	2.78 (1.17; 7.10)	0.024
Comorbidities denied	0.77 (0.67; 0.89)	<0.001
Neurological	1.67 (1.44; 1.94)	<0.001
Obesity	1.37 (1.06; 1.76)	0.015
Bones	2.21 (1.29; 3.88)	0.005
Others	1.59 (1.13; 2.23)	0.007
Psychiatric	1.41 (1.11; 1.78)	0.005
Pulmonary (COPD 39.5%, upper respiratory tract diseases (28.3%) others (40.23)	3.45 (2.89; 4.13)	<0.001
Renal	1.22 (1.0; 1.49)	0.057
Congenital syphilis	0.47 (0.29; 0.74)	0.002
Syndromes	0.91 (0.57; 1.43)	0.68
Vascular	1.70 (0.92; 3.16)	0.091

• ^* OR: Odds ratio; CI: Confidence interval; McFadde’s Pseudo-R²: n=0.13

Discussion

Understanding the profile of hospital admissions helps identify the main reasons why users seek care as well as their characteristics. In turn, knowing this data helps to develop and implement new policies to promote equity in the health system, reduce the waste of economic resources as well as improve the efficiency and quality of hospital care provided to the population.(¹²)

The World Health Organization has established that hospital networks are fundamental for a well-articulated health system to be able to provide curative and preventive health care to the population. Thus, managers in the health area seek to implement resources, strategies, and tools for greater efficiency concerning patient hospitalization. In this sense, knowing the profile of hospitalizations can help managers take measures to improve efficiency.(¹³)

In this research, it was observed that the highest number of hospitalizations occurred for clinical and surgical reasons. Clinical reasons corresponded to 54.5% whereas surgical reasons reached {sugiro corresponded} to 46.3%. In the category of surgical hospitalizations, it was observed that musculoskeletal (43.5%), digestive (27.7%), and genitourinary (13.8%) surgeries were the most recurrent. Similar findings pointed to the same reality in other Brazilian hospitals.(¹⁴,¹⁵)A study showed that the most recurrent surgeries were the same as those found above, followed by circulatory system surgeries and neoplasms, agreeing with these results.(¹⁵)

When analyzing the relationship between kanban color and its categories in the hospitalizations analyzed, a higher frequency was identified in the classification of patients within the ideal length of stay for surgical hospitalizations. They presented a high percentage of classification in the green Kanban category (61.0%), different from the inpatient clinics that represented 27.0% of only green classification in this study. However, a more detailed analysis showed that some surgical subcategories presented yellow and red Kanban classifications, highlighting patients with an acceptable length of stay or longer than the acceptable limit.

The length of hospital stay was determined by various elements, including the severity of the patient’s clinical conditions, the excellence of the care provided, and the effectiveness of the clinical management adopted in each case.(¹⁶)In the analysis carried out, medium and high-complexity exams were shown to be directly linked to the length of stay greater than the acceptable limit, which may also be associated with the presence of comorbidities. Thus, we emphasize that this information allows for establishing care flows focused on the users’ needs, improving patients’ conditions. These conditions are significant for public health as they represent risk factors and can lead to serious health complications.(¹⁵)

A recent study showed that the longest delay in hospital discharges is linked to the delay in carrying out tests as well as the times for compensation of patients’ clinical condition and waiting for specialist evaluation.(¹⁷)Expert opinions and assessments are low-density technology resources. They seek support in the opinion of other professionals and/or specialists to clarify the diagnosis and determine the conduct to be adopted by the health team.(¹⁸)However, this ends up having a major impact on the increase in hospital stay time.

In complex hospitalizations, the high number of tests can cause greater expenses for the hospital, patients, and health systems. Furthermore, the long wait to perform tests can impact the quality of care and patient safety, as the tests performed help to detect complications early and outline the treatment plan as necessary.

When analyzing the probability of hospitalizations with a stay length longer than the acceptable limit with basis on the profile of the hospitalized patients, we noticed that the age group with the highest probability of long hospitalizations was people aged >64 years. The main reasons for hospitalization of elderly people are related to the decompensation of chronic diseases or complications of acute diseases, both due to their comorbidities and to the circumstances of hospitalization associated with complications resulting from advanced age.(¹⁹)

Hospitalizations for diagnosis and monitoring, as well as lung problems, were shown to be the most likely causes of long hospitalizations. Among them, chronic obstructive pulmonary diseases (COPD) and those of the upper airways are the most recurrent in the findings of this research. These results agree with research that has shown that COPD and other lung problems are the leading causes of long-term hospitalization. They are in third place in the world in illness, hospital admissions, and mortality, thus being largely responsible for the burden on public health.(²⁰,²¹)COPD is a disease that can be treated and prevented, as smoking is its main cause. Furthermore, difficulties found in primary care and low patient adherence to treatments contribute to the high rates of patients with COPD.(²²)

In this study, hospitalizations corresponding to “consultations and follow-ups” appeared with a high probability of length of stay (red Kanban group). In Brazil, the waiting period for care, surgeries, as well as exams, and diagnoses is one of the biggest causes of dissatisfaction among SUS users. Furthermore, such waiting may affect the progression of cases as to both life expectancy and life quality of patients with serious illnesses.(²³)

This study was conducted at a single institution and this was a limitation. However, our results may provide support to better understand the epidemiological profile of surgical and clinical hospitalizations. Furthermore, they can contribute to the effective development of public policies as well as better planning and management aimed at achieving greater bed turnover.

Conclusion

The length of hospital stay is affected by several factors, including elements internal to the hospital as well as the individual characteristics of patients. The delay in the evaluation of other specialties and the performance of tests appears as one of the main reasons for prolonged hospitalizations, as the evaluations contribute to the diagnosis and the procedures to be adopted in the treatment and discharge of patients. However, it has been shown that the long length of hospital stays can be reduced by implementing appropriate strategies, with the help of tools such as the Kanban method. It encourages daily discussions by the multidisciplinary team about the individual needs of patients and, thus, the adoption of clinical protocols and guidelines to accelerate discharges without compromising the quality of treatment. Continuous improvement in healthcare management is essential to accelerate the scheduling and performance of exams, as well as early starting planning for hospital discharge during hospitalization. For an effective discharge, improving the dialogue among health professionals, managers, and patients is essential. Such measures aim to ensure the quality of services provided, reduce costs, and minimize risks not only to reduce prolonged hospitalization but also to avoid the patient’s premature return to the hospital.

References

• ¹ Silva JB, Anastácio FAM. Método Kanban como ferramenta de controle de gestão. ID on Line. Rev. Psicol. 2019;13(43):1018-27.
• ² Lanza-León P, Sanchez-Ruiz L, Cantarero-Prieto D. Kanban system applications in healthcare services: A literature review. Int J Health Plann Manage. 2021;36(5):1397-13.
• ³ Depetris MC, Azevedo BC. A aplicação do método kanban no controle de pacientes de covid-19. Revista científica eletrônica de ciências aplicadas da FAIT. 2021, 27:1-16. Disponível em: https://revista.fait.edu.br/cloud/artigos/2024/04/20240426200828-0177.pdf
  » https://revista.fait.edu.br/cloud/artigos/2024/04/20240426200828-0177.pdf
• ⁴ Krishnaiyer K, Chen FF, Bouzary H. Cloud Kanban framework for service operations management. Procedia Manufacturing. 2018; 17:531-8.
• ⁵ Carvalho EE, Arrais DJ, Aguiar YA, Mendes M, Santana LC. Kanban system in bed management: evaluation of hospital indicators in a reference maternity hospital. RSD. 2022;11(1):e2311123926.
• ⁶ Pinto JS, Tessmann M, Lux E, Zilli MG. Gerenciamento de projetos na gestão em saúde. Inova Saúde. 2024;14(4):115-31.
• ⁷ Brasil. Ministério da Saúde. Secretaria de Ciência, Tecnologia, Inovação e Insumos Estratégicos em Saúde Departamento de Ciência e Tecnologia: Síntese de evidências para políticas de saúde Congestão e superlotação dos serviços hospitalares de urgências. Brasília (DF); Ministério da Saúde; 2020 [citado 2024 dez 4]. Disponível em: Shttps://docs.bvsalud.org/biblioref/2020/04/1087521/13-sintesecongestaosuperlotacaofinal31mar2020.pdf
  » https://docs.bvsalud.org/biblioref/2020/04/1087521/13-sintesecongestaosuperlotacaofinal31mar2020.pdf
• ⁸ Coelho CG, Pilecco FB. Indicadores de saúde e testagem para a Covid-19. In: Barreto ML, Pinto Junior EP, Aragão E, Barral-Netto M, organizadores. Construção de conhecimento no curso da pandemia de Covid-19: aspectos biomédicos, clínico-assistenciais, epidemiológicos e sociais. Salvador: Edufba; 2020.
• ⁹ R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.
• ¹⁰ Creswell JW, Creswell JD. Projeto de pesquisa: Métodos qualitativo, quantitativo e misto. 5ª ed. Porto Alegre: Penso; 2021.
• ¹¹ Gonçalves A, Soares SV. Emprego de métodos quantitativos em pesquisas sobre gestão de riscos de acidentes de trabalho. In: ADM 2020 - Congresso Internacional de Administração, 2020. Anais ADMPG; 2020.
• ¹² Castro GG, Leite MA, Martins Junior G, Silva KR, Reis Junior AG. Perfil das internações hospitalares em município de Minas Gerais. REFACS. 2018; 6(1):45-52.
• ¹³ Brasil. Ministério da Saúde. Relatório de gestão 2022. Brasília (DF); Ministério da Saúde; 2022 [citado 2024 Apr 5]. Disponível em: https://bvsms.saude.gov.br/bvs/publicacoes/relatorio_gestao_2022.pdf
  » https://bvsms.saude.gov.br/bvs/publicacoes/relatorio_gestao_2022.pdf
• ¹⁴ Oliveira MF, Zanchim MC. NutriDia Brasil: retrato dos cuidados nutricionais em um hospital de alta complexidade do Rio Grande do Sul. BRASPEN J. 2020; 35(3) 216-21.
• ¹⁵ Luna AA, Paixão CM, Caldas SA, Silva NC, Souza PA, Fassarella CS. Perfil epidemiológico do paciente cirúrgico no Brasil. Rev Recien. 2022;12(38):32-41.
• ¹⁶ Costa MF, Perez EI, Ciosak SI. Practices of hospital nurses for continuity of care in primary care: an exploratory study. Texto Contexto Enferm. 2021; 30:20200401.
• ¹⁷ Beserra JV, Mendes MC, Cunha IM, Carvalho EM. Fatores que contribuem para o aumento do tempo de permanência na clínica médica de um hospital público. Rev JRG Estud Acad. 2023; 6(12):184-98.
• ¹⁸ Farias GB, Fajardo AP. A interconsulta em serviços de atenção primária à saúde. Rev Gestão & Saúde. 2015; 6(Supl 3):2075-93.
• ¹⁹ Teixeira JJ, Bastos GC, Souza AC. Perfil de internação de idosos. Rev Soc Bras Clin Med (Goiânia). 2017;15(1):15-20.
• ²⁰ Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2021 Report. USA; Global Initiative for Chronic Obstructive Lung Disease; 2020 [cited 2024 Nov 2]. Available from: https://goldcopd.org/wp-content/uploads/2020/11/GOLD-REPORT-2021-v1.0-11Nov20_WMV.pdf
  » https://goldcopd.org/wp-content/uploads/2020/11/GOLD-REPORT-2021-v1.0-11Nov20_WMV.pdf
• ²¹ Cruz MM, Pereira M. Epidemiology of chronic obstructive pulmonary disease in Brazil: a systematic review and meta-analysis. Ciênc Saúde Colet. 2020;25(11):4547-57.
• ²² Moreira AT, Pinto CR, Lemos AC, Assunção-Costa L, Souza GS, Martins Netto E. Evidence of the association between adherence to treatment and mortality among patients with COPD monitored at a public disease management program in Brazil. J Bras Pneumol. 2022;48(1):e20210120.
• ²³ Nascimento EY, Araújo KQ, Campos SM, Mendonça AE, Andrade FB. Tempo de espera no atendimento e internação: satisfação dos usuários e a perspectiva do planejamento e avaliação em saúde. Rev Ciênc Plural. 2024;10(2):1-15.

Edited by

• Associate Editor:
  Alexandre Pazetto Balsanelli (https://orcid.org/0000-0003-3757-1061) Escola Paulista de Enfermagem, Universidade Federal de São Paulo, São Paulo, SP, Brazil

Publication Dates

• Publication in this collection
  07 Nov 2025
• Date of issue
  2025

History

• Received
  12 Nov 2024
• Accepted
  23 Apr 2025