Dataset Information

Managing waiting times in diagnostic medical imaging.

ABSTRACT: This paper aims to analyse the variation in the delivery of diagnostic imaging services in order to suggest possible solutions for the reduction of waiting times, increase the quality of services and reduce financial costs.This study provides a logic model to manage waiting times in a regional context. Waiting times measured per day were compared on the basis of the variability in the use rates of CT and MRI examinations in Tuscany for the population, as well as on the basis of the capacity offered with respect to the number of radiologists available. The analysis was performed at the local health authority level to support the decision-making process of local managers.Diagnostic imaging services, in particular the CT and MRI examinations. The study involved all the 12 local health authorities that provide services for 3.7 million inhabitants of the Italian Tuscany Region.the study uses regional administrative data on outpatients and survey data on inpatient diagnostic examinations in order to measure productivity.The study uses the volumes per 1000 inhabitants, the days of waiting times and the number of examinations per radiologist. Variability was measured using the traditional SD measures.A significant variation in areas considered homogeneous in terms of age, gender or mortality may indicate that the use of radiological services is not optimal and underuse or overuse occurs and that there is room for improvement in the service organisation.Considering that there is a high level of variability among district use rates and waiting times, this study provides managers with a specific tool to find the cause of the problem, identify a possible solution, assess the financial impact and initiate the eventual reduction of waste.

SUBMITTER: Nuti S

PROVIDER: S-EPMC3533122 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Managing waiting times in diagnostic medical imaging.

Nuti Sabina S Vainieri Milena M

BMJ open 20121214 6

<h4>Objective</h4>This paper aims to analyse the variation in the delivery of diagnostic imaging services in order to suggest possible solutions for the reduction of waiting times, increase the quality of services and reduce financial costs.<h4>Design</h4>This study provides a logic model to manage waiting times in a regional context. Waiting times measured per day were compared on the basis of the variability in the use rates of CT and MRI examinations in Tuscany for the population, as well as ...[more]

PMID: 23242480

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Project description:BACKGROUND:Waiting times for elective treatments, including elective surgery, are a source of public concern and therefore are on policy makers' agenda. The long waiting times have often been tackled through the allocation of additional resources, in an attempt to reduce them, but results are not straightforward. At the same time, researchers have reported wide geographical variations in the provision of elective care not driven by patient needs or preferences but by other factors. The paper analyses the relationship between waiting times and treatment rates for nine high-volume elective surgical procedures in order to support decision making regarding the availability of these services for the citizens. Using the framework already proposed for the diagnostic services, we identify different patterns that can be followed to align the supply with patient needs in the Italian context. METHODS:After measuring the waiting times and the treatment rates for nine procedures in the 34 districts in Tuscany, we performed correlation analyses. Then, we plotted the results in a matrix cross-checking waiting times and rates. By doing so, we identified four different contexts that require a second step analysis to tackle unwarranted geographical variations and ensure timely care to patients. Finally, for each district and elective surgical procedure, we measured the economic impact of the different treatment rates in order to evaluate whether there are any supply criticalities and eventually some room for maneuver. We also included active and passive mobility of patients. RESULTS:The results show a high degree of variation both in treatment rates and waiting times, especially for the orthopaedic procedures: knee replacement, knee arthroscopy and hip replacement. The analysis performed for the nine interventions shows that the 34 districts are in varying positions in the waiting time-treatment rate matrix, suggesting that there is no straightforward relationship between rates and waiting times. Each combination in the matrix may have different determinants that require healthcare managers to adopt diversified strategies. The decision making process needs to be supported by a two-level analysis: the first one to put in place the matrix that cross-checks waiting times and treatment rates, the second one to analyse the characteristics of each quadrant and the improvement actions that can be proposed. CONCLUSIONS:In Italy, waiting times in elective surgical services are a main policy issue with a relevant geographical variation. Our analysis reveals that this variation is due to multiple elements. In order to avoid simplistic approaches that do not solve the problem but often lead to increased expenditure, policy makers and healthcare managers should follow a two-step strategy firstly identifying the type of context and secondly analysing the impact of elements such as resource productivity, resource availability, patients' preferences and care appropriateness. Only in some cases it is required to increase the service supply.

Project description:BackgroundAntenatal care (ANC) provides a range of critical health services during pregnancy that can improve maternal and neonatal health outcomes. In Mozambique, only half of women receive four or more ANC visits, which are provided for free at public health centers by maternal and child health (MCH) nurses. Waiting time has been shown to contribute to negative client experiences, which may be a driver of low maternity care utilization. A recent pilot study of a program to schedule ANC visits demonstrated that scheduling care reduces waiting time and results in higher rates of complete ANC. This study aims to explore client experiences with waiting time for ANC in standard practice and care and after the introduction of appointment scheduling.MethodsThis study uses a series of qualitative interviews to unpack client experiences with ANC waiting time with and without scheduled care, in order to better understand the impact of waiting time on client experiences. Thirty-eight interviews were collected in May to June 2017 at three pilot study clinics in southern Mozambique, with a focus on two paired intervention and comparison facilities sharing similar facility characteristics. Data were analyzed using inductive thematic analysis methods using NVivo software.ResultsClients described strong motivations to seek ANC, pointing to the need to address convenience of care, and highlighted direct and indirect costs of seeking care that were exacerbated by long waiting times. Direct costs include time and transport costs of going to the clinic, while indirect costs include being unable to fulfill household and work obligations. Other barriers to complete ANC utilization of four or more visits include transport costs, negative provider experiences, and delayed ANC initiation, which limit the potential number of clinic contacts.ConclusionsFindings demonstrate that the scheduling intervention improves the client experience of seeking care by allowing women to both seek ANC and fulfill other productive obligations. Innovation in healthcare delivery should consider adapting models that minimize waiting times.

Project description:BackgroundGlobally, reduction of patient waiting time has been identified as one of the major characteristics of a functional health system. In South Africa, 83% of the general population visiting primary healthcare (PHC) facilities must contend with long waiting times, overcrowding, staff shortages, poor quality of care, an ineffective appointment booking system, and a lack of medication. These experiences may, in turn, affect how patients view service quality.MethodsThis scoping review was guided by Arksey and O'Malley methodological framework. The primary literature search of peer-reviewed and review articles was achieved through PubMed/MEDLINE, Google Scholar, Science Direct, and World Health Organization (WHO) library databases, using waiting times, outpatient departments, factors, interventions, and primary healthcare facilities as keywords. Two independent reviewers screened abstracts and full articles, using the set inclusion and exclusion criteria. We used NVIVO® version 10 software to facilitate thematic analysis of the results from included studies.ResultsFrom the initial 250 records screened, nine studies were eligible for inclusion in this scoping review. Seven papers identified the factors contributing to waiting time, and five papers mentioned effective interventions implemented to reduce waiting times within PHC facilities. Our analysis produced three (patient factors, staff factors, and administrative systems) and two (manual-based waiting time reduction systems and electronic-based waiting time reduction systems) main themes pertaining to factors contributing to long waiting times and interventions to reduce waiting times, respectively.ConclusionOur results revealed that the patients, staff, and administrative systems all contribute to long waiting times within the PHC facilities. Patient waiting times recorded a wider and more evenly spread patient arrival pattern after the identified interventions in our study were implemented. There is a need to constantly strategize on measures such as implementing the use of an electronic appointment scheduling system and database, improving staff training on efficient patient flow management, and regularly assessing and optimizing administrative processes. By continuously monitoring and adapting these strategies, PHC facility managers can create a more efficient and patient-centered healthcare experience.

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Managing waiting times in diagnostic medical imaging.

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Managing waiting times in diagnostic medical imaging.

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