scholarly journals A Multi-Faceted Strategy for Evidence Translation Reduces Healthcare Waiting Time: A Mixed Methods Study Using the RE-AIM Framework

2021 ◽  
Vol 2 ◽  
Author(s):  
Katherine E. Harding ◽  
Annie K. Lewis ◽  
David A. Snowdon ◽  
Bridie Kent ◽  
Nicholas F. Taylor

Background: Waiting lists are often thought to be inevitable in healthcare, but strategies that address patient flow by reducing complexity, combining triage with initial management, and/or actively managing the relationship between supply and demand can work. One such model, Specific Timely Appointments for Triage (STAT), brings these elements together and has been found in multiple trials to reduce waiting times by 30–40%. The next challenge is to translate this knowledge into practice.Method: A multi-faceted knowledge translation strategy, including workshops, resources, dissemination of research findings and a community of practice (CoP) was implemented. A mixed methods evaluation of the strategy was conducted based on the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework, drawing on an internal database and a survey of workshop and CoP participants.Results: Demonstrating reach, at July 2020 an internal database held details of 342 clinicians and managers from 64 health services who had participated in the workshop program (n = 308) and/or elected to join an online CoP (n = 227). 40 of 69 (58%) respondents to a survey of this population reported they had adopted the model, with some providing data demonstrating that the STAT model had been efficacious in reducing waiting time. Perceived barriers to implementation included an overwhelming existing waiting list, an imbalance between supply and demand and lack of resources.Conclusion: There is high quality evidence from trials that STAT reduces waiting time. Using the RE-AIM framework, this evaluation of a translation strategy demonstrates uptake of evidence to reduce waiting time in health services.

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ali Alowad ◽  
Premaratne Samaranayake ◽  
Kazi Ahsan ◽  
Hisham Alidrisi ◽  
Azharul Karim

PurposeThe purpose of this paper is to systematically investigate the patient flow and waiting time problems in hospital emergency departments (EDs) from an integrated voice of customer (VOC) and voice of process (VOP) perspective and to propose a new lean framework for ED process.Design/methodology/approachA survey was conducted to better understand patients' perceptions of ED services, lean tools such as process mapping and A3 problem-solving sheets were used to identify hidden process wastes and root-cause analysis was performed to determine the reasons of long waiting time in ED.FindingsThe results indicate that long waiting times in ED are major concerns for patients and affect the quality of ED services. It was revealed that limited bed capacity, unavailability of necessary staff, layout of ED, lack of understanding among patients about the nature of emergency services are main causes of delay. Addressing these issues using lean tools, integrated with the VOC and VOP perspectives can lead to improved patient flow, higher patient satisfaction and improvement in ED capacity. A future value stream map is proposed to streamline the ED activities and minimize waiting times.Research limitations/implicationsThe research involves a relatively small sample from a single case study. The proposed approach will enable the ED administrators to avoid the ED overcrowding and streamline the entire ED process.Originality/valueThis research identified ED quality issues from the integration of VOC and VOP perspective and suggested appropriate lean tools to overcome these problems. This process improvement approach will enable the ED administrators to improve productivity and performance of hospitals.


Author(s):  
Rebecca Bisanju Wafula (BSCN, MSCHSM) ◽  
Dr. Richard Ayah (MBCHB, MSC, PHD)

Background: Long waiting time in outpatient clinics is a constant challenge for patients and the health care providers. Prolonged waiting times are associated with poor adherence to treatment, missed appointment and failure or delay in initiation of treatment and is a major factor towards the perception of the patient towards the care received. Objective: To determine the waiting time and associated factors among out patients attending staff clinic at University of Nairobi health services. Method: A cross-sectional study design was used and data collected from 384 ambulatory patients over a period of four weeks using an interviewer administered pretested structured exit questionnaire with a time-tracking section. Simple random sampling was used to select respondents in a walk- in outpatient clinic set up. Data was cleaned and analysed using Statistical Package for Social Sciences (SPSS) 20. Analysis of variance (ANOVA), and cross tabulation was used to establish associations between the independent variable and dependent variables. Results: In total 384 patients were tracked and interviewed. The average patient waiting time was 55.3mins.Most respondents (52%) suggested that improving availability of staff at their stations would help to reduce patient waiting time. In this study, gender (P=0.005) and availability of doctors (p=0.000) were found to affect patient waiting time with women waiting longer than the male patients. Conclusion: Majority of the patients spent about an hour at the facility to be served. Inadequate number of health workers was the main cause of long waiting time.


2021 ◽  
Vol 0 (4) ◽  
pp. 137-158
Author(s):  
Anna Akhmetova ◽  
◽  
Elena Shevchenko ◽  
Taras Sharamko ◽  
Tatyana Aleshina ◽  
...  

The imbalance between the demand for health services and their supply leads to a decrease in the availability of health care. The aim of the study is to analyze the key mechanisms of the policy on reducing the waiting time for planned medical care. The issues of ensuring the guarantee for maximum time limits are studied; the foreign experience of managing waiting times for medical care is reviewed, the possibility of applying it in Russian practice is analyzed; the possibilities of reducing waiting times at the level of medical organizations are considered. The review of foreign experience shows a purposeful state policy to reduce waiting times, and allows us to determine the most effective measures. In Russia, the guaranteed maximum patient waiting times are shorter than in most of the countries reviewed, however, state resources do not support these guarantees; there is no unified state approach for monitoring, and no well-thought-out mechanism for their regulation, based on both system capabilities and social needs. Taking into account the studied international and Russian experience, the recommendations for creating a system for managing the waiting time for planned medical care in Russia are proposed.


2019 ◽  
Vol 8 (3) ◽  
pp. e000542 ◽  
Author(s):  
Alexandra von Guionneau ◽  
Charlotte M Burford

BackgroundLong waiting times in accident and emergency (A&E) departments remain one of the largest barriers to the timely assessment of critically unwell patients. In order to reduce the burden on A&Es, some trusts have introduced ambulatory care areas (ACAs) which provide acute assessment for general practitioner referrals. However, ACAs are often based on already busy acute medical wards and the availability of clinical space for clerking patients means that these patients often face long waiting times too. A cheap and sustainable method to reducing waiting times is to evaluate current space utilisation with the view to making use of underutilised workspace. The aim of this quality improvement project was to improve accessibility to pre-existing clinical spaces, and in doing so, reduce waiting times in acute admissions.MethodsData were collected retrospectively from electronic systems and used to establish a baseline wait time from arrival to having blood taken (primary outcome). Quality improvement methods were used to identify potential implementations to reduce waiting time, by increasing access to clinical space, with serial measurements of the primary outcome being used to monitor change.ResultsData were collected over 54 consecutive days. The median wait time increased by 55 min during the project period. However, this difference in waiting time was not deemed significant between the three PDSA cycles (p=0.419, p=0.270 and p=0.350, Mann-Whitney U). Run chart analysis confirmed no significant changes occurred.ConclusionIn acute services, one limiting factor to seeing patients quickly is room availability. Quality improvement projects, such as this, should consider facilitating better use of available space and creating new clinical workspaces. This offers the possibility of reducing waiting times for both staff and patients alike. We recommend future projects focus efforts on integration of their interventions to generate significant improvements.


Author(s):  
Naidhia Alves Soares Ferreira ◽  
Jean Henri Maselli Schoueri ◽  
Isabel Cristina Esposito Sorpreso ◽  
Fernando Adami ◽  
Francisco Winter dos Santos Figueiredo

Brazilian law requires that treatment for breast cancer begin within 60 days of diagnosis. This waiting time is an indicator of accessibility to health services. The aim of this study was to analyze which factors are associated with waiting times between diagnosis and treatment of breast cancer in women in Brazil between 1998 and 2012. Information from Brazilian women diagnosed with breast cancer between 1998 and 2012 was collected through the Hospital Registry of Cancer (HRC), developed by the National Cancer Institute (INCA). We performed a secondary data analysis, and found that the majority of women (81.3%) waited for ≤60 days to start treatment after being diagnosed. Those referred by the public health system, aged ≥50 years, of nonwhite race, diagnosed at stage I or II, and with low levels of education waited longer for treatment to start. We observed that only 18.7% experienced a delay in starting treatment, which is a positive reflection of the quality of the care network for the diagnosis and treatment of breast cancer. We also observed inequalities in access to health services related to age, region of residence, stage of the disease, race, and origin of referral to the health service.


2021 ◽  
Vol 2 (3) ◽  
pp. 412-417
Author(s):  
S. Assetzadeh

Patient flow analysis was used to assess the waiting time of patients referred to a large paediatric outpatient department, and also the lengths of the consultations of the paediatricians and interns. The average waiting times to see the paediatricians and interns were 77 minutes and 7.8 minutes, respectively. The average lengths of consultation for the paediatricians and interns were 3.4 minutes, and 7.7 minutes, respectively


Author(s):  
Daniel McIntyre ◽  
Clara K. Chow

As pressure increases on public health systems globally, a potential consequence is that this is transferred to patients in the form of longer waiting times to receive care. In this review, we overview what waiting for health care encompasses, its measurement, and the data available in terms of trends and comparability. We also discuss whether waiting time is equally distributed according to socioeconomic status. Finally, we discuss the policy implications and potential approaches to addressing the burden of waiting time. Waiting time for elective surgery and emergency department care is the best described type of waiting time, and it either increases or remains unchanged across multiple developed countries. There are many challenges in drawing direct comparisons internationally, as definitions for these types of waiting times vary. There are less data on waiting time from other settings, but existing data suggest waiting time presents a significant barrier to health care access for a range of health services. There is also evidence that waiting time is unequally distributed to those of lower socioeconomic status, although this may be improving in some countries. Further work to better clarify definitions, identify driving factors, and understand hidden waiting times and identify opportunities for reducing waiting time or better using waiting time could improve health outcomes of our health services.


2018 ◽  
Vol 17 (3) ◽  
pp. 120-120
Author(s):  
MNT (Marjolein) Kremers ◽  
◽  
Prabath WB Nanayakkara ◽  

In recent years we indeed have witnessed an increasing demand on healthcare services coupled with spiraling healthcare costs forcing us towards identifying factors and interventions leading to greater healthcare efficiency. The case mix of our ED patients is changing with an increase in the number of the elderly needing acute (hospital) care, often suffering from multiple comorbidities leading to simple problems becoming easily complex and demanding admission. Partly due to this changing case mix, acute bed capacity is under serious pressure leading to ED stagnation and increased waiting times internationally. When the ED is at its capacity, acute physicians have to make choices how to divide the few available beds. Are we able to predict who needs a bed the most and make justified decisions? Which patient can wait at the ED before admission and which ones can’t? The study of Byrne et al. in this issue focused on the association between ED waiting times and clinical outcomes in Ireland, measured by 30 days mortality, using patient data of admitted acute medical patients collected from 2002 until 2017. High Risk Score patients with a longer waiting time at the ED, appear to have an increased risk on mortality. It is therefore necessary to identify these patients early and prioritize their hospital admission. However, to our knowledge, the used risk score isn’t implemented in daily practice. In 2012 the National Early Warning Score (NEWS) has been broadly implemented and it would be of interest to know whether the used retrospective Risk Score using laboratory data accord to the NEWS. Curiously, in this study, patients in all three MTS urgent categories with <4 hours waiting time, have a higher risk on mortality than patients experiencing a longer waiting time. What’s the cause of this effect? Are patients so severely ill that urgent treatment and admission can’t change the adverse outcome? Or is it possible that all three urgent MTS categories identify patients who are sicker with a higher chance of dying? Intriguingly, in Ireland the mortality amongst admitted acute medical patients decreased since 2002 by 1.3%. An important question remained unanswered by Byrne et al: why has this mortality decreased? Has the severity of the diseases by urgently admitted patients diminished? Has the treatment for acute medical patients been improved? Don’t severely ill patients come to the ED anymore, due to proper advanced care planning? In contrast to the decreased mortality, the median waiting times >6 hours have increased by 50%, from 10 to 15 hours. What caused this increase? What happened in the acute care in Ireland? Have other European countries experienced the same effect or can’t the Irish results not being extrapolated to other European acute care systems? For example, in the Netherlands, the total number of patients being seen at the ED has decreased and stabilised in the last years, although the number of acute medical patients, especially elderly, is increasing. During the last flu season patients we were faced with ED closures, long length of stay and overnight ED stays due to the lack of beds in-hospital. However, waiting hours >12 hours at the ED are rare in Dutch EDs. A key factor in constraining the patient flow to the ED is the well-functioning primary care system with adequate out of office hours care by GP-posts. When a GP post is placed at an ED, GPs treat 75% of the self-referred patient, which is safe and cost-effective. Due to this the ED`s can concentrate on the sick patients who need urgent care. Despite the decreased patient flow to the ED in the Netherlands, the organisation of the acute care has gained much attention of policy makers, media and health care professionals due to frequent ED closures and stagnation in some regions in the Netherlands. Recently, a prediction model for hospital admission in a mixed ED population has been established by using data directly available after triage, aiming to use for shortening the Length of Stay (LOS) at the ED. A computerised tool calculates admission probability for any patient at the time of triage by using age, triage category, arrival mode and main symptom. It demonstrates that different European countries are facing the same issues and are trying to optimize the acute care with some overlapping focus. We believe that at a time where the demand on acute care is increasing, it’s essential to pay attention to the organisation of acute care so that high-quality care is guaranteed and the available resources should be handled efficiently. Studies such as executed by Byrne et al. contribute to this topic and provide lessons which can be learned internationally. We need tools to identify sick patients who need properly care on time and acute physicians can play a central role in developing these tools.


2021 ◽  
Vol 04 (01) ◽  
Author(s):  
Owin Bambang Wijanarko ◽  

Background: Outpatient services are a reflection of hospital services.As a form of health service facility that organizes health efforts, hospitals often experience difficulties in managing information for both internal and external needs. One form of application is through service systems by utilizing information technology through the use of computer-based on information systems.The Lean Hospital concept, which has been successfully implemented in several hospitals, is expected to eliminate waste and add value added activity which will ultimately increase patient satisfaction. Purpose: The purpose of this study was to calculate patient waiting time with the application of information technology in the outpatient polyclinic of RSU Islam Klaten. Research methods: This type of research uses a descriptive analytic method. This research meth-od used a cross sectional approach. The sample in this study amounted to 81 respondents with the sampling technique using purposive sampling. The analysis in research using the t-test. The re-search instruments used included literature studies, interviews and direct observation of medical record officers, nurses of polyclinic nurses, registration departments, and patients at RSU Islam Klaten. Result: There is a significant relationship between waiting time and patient satisfaction p = 0.001. Patients with long waiting times were more dissatisfied (60.0%), while fast waiting times were more very satisfied (73.9%). Conclusion: The success of health services is seen from the patient's waiting time and patient satisfaction. Waiting time is the time used by patients to get health services from the registration point to getting in the doctor's examination room. Overall information technology shortens patient waiting time in parts of registration-polyclinic and Pharmacy.


Author(s):  
A. K. Warps ◽  
◽  
M. P. M. de Neree tot Babberich ◽  
E. Dekker ◽  
M. W. J. M. Wouters ◽  
...  

Abstract Purpose Interhospital referral is a consequence of centralization of complex oncological care but might negatively impact waiting time, a quality indicator in the Netherlands. This study aims to evaluate characteristics and waiting times of patients with primary colorectal cancer who are referred between hospitals. Methods Data were extracted from the Dutch ColoRectal Audit (2015-2019). Waiting time between first tumor-positive biopsy until first treatment was compared between subgroups stratified for referral status, disease stage, and type of hospital. Results In total, 46,561 patients were included. Patients treated for colon or rectal cancer in secondary care hospitals were referred in 12.2% and 14.7%, respectively. In tertiary care hospitals, corresponding referral rates were 43.8% and 66.4%. Referred patients in tertiary care hospitals were younger, but had a more advanced disease stage, and underwent more often multivisceral resection and simultaneous metastasectomy than non-referred patients in secondary care hospitals (p<0.001). Referred patients were more often treated within national quality standards for waiting time compared to non-referred patients (p<0.001). For referred patients, longer waiting times prior to MDT were observed compared to non-referred patients within each hospital type, although most time was spent post-MDT. Conclusion A large proportion of colorectal cancer patients that are treated in tertiary care hospitals are referred from another hospital but mostly treated within standards for waiting time. These patients are younger but often have a more advanced disease. This suggests that these patients are willing to travel more but also reflects successful centralization of complex oncological patients in the Netherlands.


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