Health Systems最新文献

Patients diagnosed with rheumatoid arthritis require lifelong monitoring by a rheumatologist. Initiation of the disease-modifying anti-rheumatic drug therapy within twelve weeks of the onset of symptoms is crucial to prevent joint damage and functional disability. We examine the impact of the engagement of alternate care providers (ACP) in alleviating delay due to limited rheumatologist capacity. Using queueing theory and discrete-event simulation, we model rheumatologist-only and rheumatologist-with-ACP system configurations as closed, multi-class queueing networks with class switching.Using summary data from an actual rheumatology clinic for illustration, we analyze various parameter conditions to aid clinic managers and policymakers in decisions concerning capacity allocations and feasible patient panel size that impact timeliness of care and resource utilization.Results not only confirm that a substantial increase in RA patient panel size with an ACP involved in the care of follow-up patients but also demonstrates the boundaries for feasible panel sizes and workload allocation.

During intra-hospital transfers, multiple clinicians perform coordinated tasks that leave patients vulnerable to undesirable outcomes. Communication has been established as a challenge to care transitions, but less is known about the organisational complexities within which transfers take place. We performed a qualitative assessment that included various professions to capture a multi-faceted understanding of intra-hospital transfers. Ethnographic observations and semi-structured interviews were conducted with clinicians and staff from the Medical Intensive Care Unit, Emergency Department, and general medicine units at a large, urban, academic, tertiary medical centre. Results highlight the organisational factors that stakeholders view as important for successful transfers: the development, dissemination, and application of protocols; robustness of technology; degree of teamwork; hospital capacity; and the ways in which competing hospital priorities are managed. These factors broaden our understanding of the organisational context of intra-hospital transfers and informed the development of a practical guide that can be used prior to embarking on quality improvement efforts around transitions of care.

Health systems are critical to the realisation of Universal Health Coverage. There has been insufficient attention to the evaluation of priority setting for health system strengthening within low income countries, including evaluation of the local capacity to implement priorities. This study evaluated the extent to which health system strengthening was prioritized in Uganda. The Kapiriri & Martin framework was used to evaluate health system priority setting from 2005-2015. A document analysis was triangulated with interview data (n = 67) from global, national and subnational stakeholders and analysed using content analysis. Health system strengthening was perceived to be circumvented by a lack of resources as well as influential actors with disease focused, rather than system-oriented, interests. There were defined processes with explicit criteria for identifying priorities and evidence was highly valued. But sub-optimal transparency and weak accountability often compromised the integrity of priority setting and contributed to stalling progress on health system strengthening and achieving health system outcomes. The strengths in the current planning processes should be harnessed. In addition, a systematic approach to priority setting, potentially through the establishment of an independent body, and stronger oversight mechanisms, would strengthen health system planning in this setting.

On 11 March 2020, the World Health Organisation (WHO) declared COVID-19 a pandemic. Early epidemiological estimates show that COVID-19 is highly transmissible, infecting populations across the globe in a short amount of time. WHO has recommended widespread clinical testing in order to contain COVID-19. However, mass testing in emergency department (ED) settings may result in crowded EDs and increase transmission risk for healthcare staff and other ED patients. Drive-through COVID-19 testing sites are an effective solution to quickly collect samples from suspected cases with minimal risk to healthcare personnel and other patients. Nevertheless, there are many logistical and operational challenges, such as shortages of testing kits, limited numbers of healthcare staff and long delays for collecting samples. Solving these problems requires an understanding of disease dynamics and epidemiology, as well as the logistics of mass distribution. In this position paper, we provide a conceptual framework for addressing these challenges, as well as some insights from prior literature and experience on developing decision support tools for public health departments.

Large-scale immunisation programmes against seasonal influenza are characterised by logistical challenges related to the need for vaccinating large cohorts of people in a short amount of time. Careful operational planning of resources is essential for a successful implementation of such programmes. We focused on the process of child vaccination in schools and analysed the staffing and workflow aspects of a school-aged children vaccination programme in England. Our objectives were to document vaccination processes and analyse times and costs associated with different models deployed across England. We collected data through direct non-participatory observations. Statistical data analysis enabled us to identify potential factors influencing vaccine delivery time and informed the development of a tool to simulate vaccination sessions. Using this tool, we carried out scenario analyses and explored trade-offs between session times and costs in different settings. Our work ultimately supported the local implementation of school-based vaccination.

This paper presents a framework for understanding and improving the process of simulation model building involving a group of domain experts, classifying the different roles the model may play at various stages of its development. The framework consists of four different "object roles", defined along two dimensions: a functional dimension (boundary object vs. representational object) and a knowledge dimension (epistemic object vs. technical object). A model can take different roles during the development process, e.g. for facilitating communication, for gaining insight into the real-world system, or for experimentation and policy evaluation. The use of the framework is illustrated by two case studies in healthcare. Its relevance and applicability are examined through a survey on model use. The survey was conducted among a group of modelling consultants with experience of using both discrete-event simulation and system dynamics within the NHS, and indicated the potential usefulness of the framework.

Structured data formats are gaining momentum in electronic health records and can be leveraged for decision support and research. Nevertheless, such structured data formats have not been explored for clinical coding, which is an essential process requiring significant manual workload in health organisations. This article explores the extent to which fully structured clinical data can support assignment of clinical codes to inpatient episodes, through a methodology that tackles high dimensionality issues, addresses the multi-label nature of coding and optimises model parameters. The methodology encompasses transformation of raw data to define a feature set, build a data matrix representation, and testing combinations of feature selection methods with machine learning models to predict code assignment. The methodology was tested with a real hospital dataset and showed varying predictive power across codes, while demonstrating the potential of leveraging structuring data to reduce workload and increase efficiency in clinical coding.

Over the last decade, chemotherapy treatments have dramatically shifted to outpatient services such that nearly 90% of all infusions are now administered outpatient. This shift has challenged oncology clinics to make chemotherapy treatment as widely available as possible while attempting to treat all patients within a fixed period of time. Historical data from a Veterans Affairs chemotherapy clinic in the United States and staff input informed a discrete event simulation model of the clinic. The case study examines the impact of altering the current schedule, where all patients arrive at 8:00 AM, to a schedule that assigns patients to two or three different appointment times based on the expected length of their chemotherapy infusion. The results identify multiple scheduling policies that could be easily implemented with the best solutions reducing both average patient waiting time and average nurse overtime requirements.

Users with cognitive impairments use an assistive technology email system, CogLink, for socialisation and help in their activities of daily living. As users interact with the AT email client, the logged stream of events is monitored and analysed to aid decision-making. When caregivers receive monitor notifications, they know that the user has had a significant change in her emailing behaviour. Consequently, caregivers select adaptations to the email client that can challenge the user to gain still more emailing skills. The monitor in this emailing system analyzes user data to recognise when significant changes in user behaviour warrants caregiver attention. Moreover, the monitor can distinguish newly learned skills from new, but transient behaviours. This article summarises the CogLink assistive technology monitoring techniques, introduces the learning likelihood algorithm, which distinguishes transient from learned behaviour, and provides lessons learnt from a decade of monitoring CogLink users.