Relevant bibliographies by topics / Patient allocation

Academic literature on the topic 'Patient allocation'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Patient allocation"

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Ford, Pauline. "Positive Results Of patient Allocation." Nursing Standard 2, no. 3 (October 17, 1987): 36–37. http://dx.doi.org/10.7748/ns.2.3.36.s79.

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Kerr, Rhonda, and Delia V. Hendrie. "Is capital investment in Australian hospitals effectively funding patient access to efficient public hospital care?" Australian Health Review 42, no. 5 (2018): 501. http://dx.doi.org/10.1071/ah17231.

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Objective This study asks ‘Is capital investment in Australian public hospitals effectively funding patient access to efficient hospital care?’ Methods The study drew information from semistructured interviews with senior health infrastructure officials, literature reviews and World Health Organization (WHO) reports. To identify which systems most effectively fund patient access to efficient hospitals, capital allocation systems for 17 Organisation for Economic Cooperation and Development (OECD) countries were assessed. Results Australian government objectives (equitable access to clinically appropriate, efficient, sustainable, innovative, patient-based) for acute health services are not directly addressed within Australian capital allocation systems for hospitals. Instead, Australia retains a prioritised hospital investment system for institutionally based asset replacement and capital planning, aligned with budgetary and political priorities. Australian systems of capital allocation for public hospitals were found not to match health system objectives for allocative, productive and dynamic efficiency. Australia scored below average in funding patient access to efficient hospitals. The OECD countries most effectively funding patient access to efficient hospital care have transitioned to diagnosis-related group (DRG) aligned capital funding. Measures of effective capital allocation for hospitals, patient access and efficiency found mixed government–private–public partnerships performed poorly with inferior access to capital than DRG-aligned systems, with the worst performing systems based on private finance. Conclusion Australian capital allocation systems for hospitals do not meet Australian government standards for the health system. Transition to a diagnosis-based system of capital allocation would align capital allocation with government standards and has been found to improve patient access to efficient hospital care. What is known about the topic? Very little is known about the effectiveness of Australian capital allocation for public hospitals. In Australia, capital is rarely discussed in the context of efficiency, although poor built capital and inappropriate technologies are acknowledged as limitations to improving efficiency. Capital allocated for public hospitals by state and territory is no longer reported by Australian Institute of Health and Welfare due to problems with data reliability. International comparative reviews of capital funding for hospitals have not included Australia. Most comparative efficiency reviews for health avoid considering capital allocation. The national review of hospitals found capital allocation information makes it difficult to determine ’if we have it right’ in terms of investment for health services. Problems with capital allocation systems for public hospitals have been identified within state-based reviews of health service delivery. The Productivity Commission was unable to identify the cost of capital used in treating patients in Australian public hospitals. Instead, building and equipment depreciation plus the user cost of capital (or the cost of using the money invested in the asset) are used to estimate the cost of capital required for patient care, despite concerns about accuracy and comparability. What does this paper add? This is the first study to review capital allocation systems for Australian public hospitals, to evaluate those systems against the contemporary objectives of the health systems and to assess whether prevailing Australian allocation systems deliver funds to facilitate patient access to efficient hospital care. This is the first study to evaluate Australian hospital capital allocation and efficiency. It compares the objectives of the Australian public hospitals system (for universal access to patient-centred, efficient and effective health care) against a range of capital funding mechanisms used in comparable health systems. It is also the first comparative review of international capital funding systems to include Australia. What are the implications for practitioners? Clinical quality and operational efficiency in hospitals require access for all patients to technologically appropriate hospitals. Funding for appropriate public hospital facilities, medical equipment and information and communications technology is not connected to activity-based funding in Australia. This study examines how capital can most effectively be allocated to provide patient access to efficient hospital care for Australian public hospitals. Capital investment for hospitals that is patient based, rather than institutionally focused, aligns with higher efficiency.
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Carson, Rachel C., Brian Forzley, Sarah Thomas, Nina Preto, Gaylene Hargrove, Alice Virani, John Antonsen, et al. "Balancing the Needs of Acute and Maintenance Dialysis Patients during the COVID-19 Pandemic." Clinical Journal of the American Society of Nephrology 16, no. 7 (February 8, 2021): 1122–30. http://dx.doi.org/10.2215/cjn.07460520.

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The COVID-19 pandemic continues to strain health care systems and drive shortages in medical supplies and equipment around the world. Resource allocation in times of scarcity requires transparent, ethical frameworks to optimize decision making and reduce health care worker and patient distress. The complexity of allocating dialysis resources for both patients receiving acute and maintenance dialysis has not previously been addressed. Using a rapid, collaborative, and iterative process, BC Renal, a provincial network in Canada, engaged patients, doctors, ethicists, administrators, and nurses to develop a framework for addressing system capacity, communication challenges, and allocation decisions. The guiding ethical principles that underpin this framework are (1) maximizing benefits, (2) treating people fairly, (3) prioritizing the worst-off individuals, and (4) procedural justice. Algorithms to support resource allocation and triage of patients were tested using simulations, and the final framework was reviewed and endorsed by members of the provincial nephrology community. The unique aspects of this allocation framework are the consideration of two diverse patient groups who require dialysis (acute and maintenance), and the application of two allocation criteria (urgency and prognosis) to each group in a sequential matrix. We acknowledge the context of the Canadian health care system, and a universal payer in which this framework was developed. The intention is to promote fair decision making and to maintain an equitable reallocation of limited resources for a complex problem during a pandemic.
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Hoffman, Rob, Sally Costar, Tass Kostopoulos, Justine Little, Aaron Livingstone, Fiona McAlinden, Paul Newland, Jacinta Re, Dina Watterson, and Terry P. Haines. "Guardianship in hospitals: a collaborative pilot project." Australian Health Review 44, no. 2 (2020): 322. http://dx.doi.org/10.1071/ah19019.

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Objectives This paper describes the development, implementation and preliminary results of a collaborative pilot project aimed at reducing the time hospital-based patients with cognitive impairments spend waiting for the allocation of legally appointed Advocate Guardian decision makers from the Office of the Public Advocate (OPA). The aim of the study was to investigate the effect of increased availability of public advocate guardians on guardian allocation waits, patient discharge outcomes and healthcare system demand. Methods A multi-institutional pilot program created a dedicated hospital guardian team within OPA, funded by the health networks, to reduce the time to guardian allocation for patients within each network. A multisite, quasi-experimental historical control group design was used, with initial data collection over 12 months, followed by study of 12-month post-implementation cohorts. Results Under the pilot program, the time from guardianship order lodgement to guardian allocation decreased significantly from 46.5 to 22.9 days, halving the average time hospital-based patients spend waiting for a guardian (difference –23.55 days, two-sample t(154) = –6.575, P < 0.0001, 95% confidence interval [–30.65, –16.48].). Mean total length of stay decreased from 163.2 to 148.5 days. The estimated value of the reduction in allocation wait time was A$15473 per patient, or A$5 of resources released per A$1 spent on increased staffing. Conclusions Direction of a small amount of resources from health services to staff within OPA appears to have created much greater savings for the health services involved. The pilot program has reduced the period of time vulnerable patients spend waiting in hospital for a guardian. What is known about the topic? Guardianship resources are under increasing stress, with demand outstripping funding and hospital-based applicants deprioritised due to assumptions of lower risk, leading to extensive wait times for guardian allocation. What does this paper add? The paper quantifies the impact of greater guardianship resourcing on access to both guardianship and healthcare resources, highlighting benefits for vulnerable patient groups, healthcare system sustainability and access to both guardianship and healthcare resources for the broader community. What are the implications for clinicians? Improving patient flow through healthcare systems may involve allocating resources to services that are managed outside the healthcare system where ‘bottlenecks’, such as wait times for guardian allocation, have been identified.
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CHERKASSKY, LISA. "Does the United States Do It Better? A Comparative Analysis of Liver Allocation Protocols in the United Kingdom and the United States." Cambridge Quarterly of Healthcare Ethics 20, no. 3 (May 20, 2011): 418–33. http://dx.doi.org/10.1017/s0963180111000107.

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NHS Blood and Transplant (NHSBT) is responsible for the procurement and allocation of human organs in the United Kingdom. Its main role is to “ensure that organs donated for transplant are matched and allocated to patients in a fair and unbiased way.” NHSBT’s liver allocation policies are underpinned by the National Liver Transplant Standards, a document published by the Department of Health in 2005 to oversee patient care, patient assessment, liver allocation and transplantation, education and training, and research and development. NHSBT has developed its own liver allocation protocols under the powers assigned to it by the Department of Health, which include a “super-urgent” liver allocation policy, a Liver Allocation Sequence, and pediatric candidate liver allocation protocols.
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Jönsson, Bengt. "Improving Patient Care: Consequences for Resource Allocation." Cardiology 84, no. 6 (1994): 420–26. http://dx.doi.org/10.1159/000176434.

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VISSERS, JAN M. H. "Patient flow based allocation of hospital resources." Mathematical Medicine and Biology 12, no. 3-4 (1995): 259–74. http://dx.doi.org/10.1093/imammb/12.3-4.259.

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Vermeulen, Ivan B., Sander M. Bohte, Sylvia G. Elkhuizen, Han Lameris, Piet J. M. Bakker, and Han La Poutré. "Adaptive resource allocation for efficient patient scheduling." Artificial Intelligence in Medicine 46, no. 1 (May 2009): 67–80. http://dx.doi.org/10.1016/j.artmed.2008.07.019.

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Sidani, Souraya, Mary Fox, and Laura Collins. "Towards Patient-Centered Clinical Trial Designs." European Journal for Person Centered Healthcare 5, no. 3 (September 26, 2017): 300. http://dx.doi.org/10.5750/ejpch.v5i3.1308.

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Rationale, aims and objectives: Evidence shows a trend towards low enrollment in randomized clinical trials (RCTs), which negatively affect validity of conclusions. Low enrollment is associated with different factors, but has recently been attributed to an increasing proportion of patients expressing concerns about randomization. In this paper, we summarize the evidence on reasons for non-enrollment, and we propose preference-based and shared decision-making as alternative methods for allocating patients to treatments in effectiveness and comparative effectiveness trials.Methods: This paper is a narrative review of available literature.Results: Converging findings of quantitative and qualitative studies revealed three interrelated and frequently mentioned reasons for declining enrollment in RCTs: 1) concerns about randomization related to the lack of understanding of equipoise, lack of appreciation of the scientific merits of randomization, and unfavorable perceptions of randomization as not reflecting methods of treatment selection used in practice; 2) preferences for treatments under evaluation, which contribute to unwillingness to be randomized; and 3) desires for involvement in treatment decision-making, which are not respected with randomization.Conclusions: Alternative methods for treatment allocation are needed to make effectiveness and comparative effectiveness trials attractive to patients. Preference-based and shared decision-making are viable methods that respectively represent the informed choice and the collaborative choice styles of treatment selection commonly used in practice. The extent to which these two methods of treatment allocation enhance enrollment should be further investigated.
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Wen, Jianpei, Hanyu Jiang, and Jie Song. "A Stochastic Queueing Model for Capacity Allocation in the Hierarchical Healthcare Delivery System." Asia-Pacific Journal of Operational Research 36, no. 01 (February 2019): 1950005. http://dx.doi.org/10.1142/s0217595919500052.

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We use the capacity allocation as a demand management tool to optimize the patient flow distribution on a hierarchical healthcare delivery system, which is a mixture of patient choice and gatekeeping. Capacity allocation for such service system can be challenging because of the inherent stochastic referral process and patients’ heterogeneous delay sensitivities. In this research, a stochastic queueing-based model is proposed to find the optimal allocation of the limited service capacity of the second level of experts. Considering the impact of the deficiency of the skill level and the amount of gatekeepers, the stochastic referral process is modeled with a tandem queue. By solving a fixed-point problem, we show that there is an unique optimal allocation and corresponding equilibrium demand. We carry out numerical studies and find that providing two alternatives for patients can be better than gatekeeper system, when the capacity of the gatekeeper is moderate compared to patients’ potential demand. Results also indicate that the optimal allocation is robust in terms of the referral rate and the mistreatment rate when two rates are less than corresponding thresholds.
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Dissertations / Theses on the topic "Patient allocation"

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Walts, Lynn Maddox Walker George M. "Patient classification system : an integrated method for measuring nursing intensity and optimizing resource allocation /." See options below, 1992. http://proquest.umi.com/pqdweb?did=745208811&sid=2&Fmt=2&clientId=68716&RQT=309&VName=PQD.

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Lin, Di. "Wireless health monitoring: patient arrival models, resource allocation and decision support systems." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=121438.

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Overcrowding in the emergency department is a worldwide problem impairing the ability of hospitals to offer emergency care within a reasonable time frame. Not merely a problem of patient satisfaction, the problem of overcrowding is leading to an increased number of waiting room death cases, which refer to the death of patients while staying in a hospital's waiting room due to a lack of sufficient medical care, and this problem underscores the significance of improving healthcare quality. As a potential way of improving healthcare quality, a wireless healthcare monitoring system (HMS) could help healthcare staff monitor the condition of patients by automatically sending alert messages to a doctor device (e.g. a smartphone, a personal digital assistant, or a laptop) once emergent conditions occur.From a network design perspective, a wireless HMS should be capable of supporting the number of patients that will be using the system; being able to assess the network's capability to serve a given number of patients (defined as network patient capacity) is a critical factor in promoting adoption of such systems. This thesis investigates schemes for enhancing the network patient capacity within a HMS. The major objective is to explore the tradeoff between the network patient capacity and the Quality-of-Service (QoS) requirements of each patient, so that a fairly good network capacity is achieved subject to the constraints of QoS requirements within real-world transmission scenarios.In the first part of this thesis, we develop novel methods to estimate the average waiting time of a patient to access the Emergency Department (ED) of a hospital, showing why developing a HMS and allocating its limited wireless resources are important to improve the quality of medical care. The following part of this thesis presents various schemes for resource allocation within a HMS, in view of several factors that need to be taken into account in a real scenario, including different QoS requirements, Electromagnetic Interference (EMI) on medical equipments, as well as imperfect channel state information. We propose three novel techniques for improving the network patient capacity within a HMS, including a statistical multiplexing scheme, a channel prediction based scheme, and a medical decision support based scheme. The last part of this thesis focuses on the performance evaluation of a decision support system, a result that is important to assess the validity and acceptability of the decision support based resource allocation scheme proposed above.
La surpopulation dans les urgences est un problème très répandu qui peut incommoder les hôpitaux à promulguer des soins urgents dans des délais raisonnables. Au-delà de la satisfaction du patient, le problème de surpopulation entraine une augmentation du nombre de décès en salle d'attente, référant à la mort de patient durant l'attente d'une prise en charge, laquelle est causée par manque de soins médicaux. Ce problème met en évidence l'importance d'améliorer la qualité des soins médicaux, c'est pourquoi un système de surveillance médicale (SSM) pourrait aider le personnel médical à contrôler l'état des patients, en envoyant automatiquement des messages d'alerte aux appareils des médecins (ex. un téléphone intelligent, un assistant numérique personnel ou un ordinateur portable) dès qu'une condition médicale urgente se déclare. Dans une perspective de conception réseau, un SSM sans fil devrait être capable de supporter le nombre de patients qui utiliseront le système, et le fait de pouvoir évaluer l'aptitude du réseau à traiter un certain nombre de patients (défini comme la capacité en patients du réseau) est un facteur important dans la promotion de tels systèmes. Cette thèse étudie les procédés pour augmenter la capacité en patients du réseau dans un SSM. L'objectif principal est de trouver un compromis entre la capacité en patients du réseau et les exigences de la Qualité de Service (QdS) pour chaque patient, de sorte à avoir une capacité de réseau raisonnable en dépit des contraintes de la QdS dans des scénarios de transmissions réelles. Dans la première partie de cette thèse, nous développons de nouvelles méthodes pour estimer la durée moyenne d'attente d'un patient pour accéder au Département des Urgences (DU) d'un hôpital. Ainsi nous montrons pourquoi le développement d'un SSM et l'allocation de ses ressources sans fil limitées sont importants pour améliorer la qualité des soins médicaux. La suite de cette thèse présente plusieurs plans pour l'allocation de ressources dans un SSM, en considérant plusieurs facteurs qui ont besoin d'être pris en compte dans le cas d'un scénario réel, ce qui comprend les différentes exigences de la QdS, les interférences électromagnétiques (IEM) sur les équipements médicaux, ainsi que l'information imparfaite des états du canal. Nous proposons trois nouvelles techniques pour améliorer la capacité du réseau en patients dans un SSM, ce qui inclut une partie sur le multiplexage statistique, une partie basée sur la prédiction du canal et une partie basée sur la décision médical à l'appui.La dernière partie de cette thèse se concentre autour de l'évaluation des performances pour un système d'aide à la décision, qui est un résultat important pour évaluer la validité et l'acceptabilité de la décision d'aide basée sur le schéma d'allocation des ressources proposé précédemment.
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Burgwin, Drew H. "Comparison of dispatch call evaluation to patient acuity and the resulting resource allocation in emergency medical services." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ54581.pdf.

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Burbidge, Nancy M. "Organ allocation and patient responsibility, re-examining the concept of responsibility in light of the thought of Emmanuel Levinas." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ66129.pdf.

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Göransson, Katarina. "Registered nurse-led emergency department triage : organisation, allocation of acuity ratings and triage decision making." Doctoral thesis, Örebro University, Department of Health Sciences, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-732.

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Successful triage is the basis for sound emergency department (ED) care, whereas unsuccessful triage could result in adverse outcomes. ED triage is a rather unexplored area in the Swedish health care system. This thesis contributes to our understanding of this complex nursing task. The main focus of this study has been on the organisation, performance, and decision making in Swedish ED triage. Specific aims were to describe the Swedish ED triage context, describe and compare registered nurses’ (RNs) allocation of acuity ratings, use of thinking strategies and the way they structure the ED triage process.

In this descriptive, comparative, and correlative research project quantitative and qualitative data were collected using telephone interviews, patient scenarios and think aloud method. Both convenience and purposeful sampling were used when identifying the participating 69 nurse managers and 423 RNs from various types of hospital-based EDs throughout the country.

The results showed national variation, both in the way triage was organised and in the way it was conducted. From an organisational perspective, the variation emerged in several areas: the use of various triageurs, designated triage nurses, and triage scales. Variation was also noted in the accuracy and concordance of allocated acuity ratings. Statistical methods provided limited explanations for these variations, suggesting that RNs’ clinical experience might have some affect on the RNs’ triage accuracy. The project identified several thinking strategies used by the RNs, indicating that the RNs, amongst other things, searched for additional information, generated hypotheses about the fictitious patients and provided explanations for the interventions chosen. The RNs formed relationships between their interventions and the fictitious patients’ symptoms. The RNs structured the triage process in several ways, beginning the process by searching for information, generating hypotheses, or allocating acuity ratings. Comparison of RNs’ use of thinking strategies and the structure of the triage process based on triage accuracy revealed only slight differences.

The findings in this dissertation indicate that the way a patient is triaged, and by whom, depends upon the particular organisation of the ED. Moreover, the large variation in RNs triage accuracy and the inter-rater agreement and concordance of the allocated acuity ratings suggest that the acuity rating allocated to a patient may vary considerably, depending on who does the allocation. That neither clinical experience nor the RNs’ decision-making processes alone can explain the variations in the RNs triage accuracy indicates that accuracy might be influenced by individual and contextual factors. Future studies investigating triage accuracy are recommended to be carried out in natural settings.

In conclusion, Swedish ED triage is permeated by diversity, both in its organisation and in its performance. The reasons for these variations are not well understood.

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Martins, Sara Vanessa Teixeira. "Apuramento de custos por utente nos cuidados de saúde primários." Master's thesis, Universidade Nova de Lisboa. Escola Nacional de Saúde Pública, 2012. http://hdl.handle.net/10362/10192.

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RESUMO - Introdução: A ausência de um plano de contabilidade analítica para os Cuidados de Saúde Primários é um problema para a realização da contabilidade interna, fundamental para a gestão de qualquer instituição de saúde. Sem linhas orientadoras para a uniformização dos critérios de imputação e distribuição dos custos/proveitos, torna-se complicado obter dados analíticos para que haja um controlo de gestão mais eficaz, que permita a utilização dos recursos de uma forma eficiente e racional, melhorando a qualidade da prestação de cuidados aos utentes. Objectivo: O presente projecto de investigação tem como principal objectivo apurar o custo por utente nos Cuidados de Saúde Primários. Metodologia: Foi construída uma metodologia de apuramento de custos com base no método Time-Driven Activity-Based Costing. O custo foi imputado a cada utente utilizando os seguintes costs drivers: tempo de realização da consulta e a produção realizada para a imputação dos custos com o pessoal médico; produção realizada para a imputação dos outros custos com o pessoal e dos custos indirectos variáveis; número total de utentes inscritos para a imputação dos custos indirectos fixos. Resultados: O custo total apurado foi 2.980.745,10€. O número médio de consultas é de 3,17 consultas por utente inscrito e de 4,72 consultas por utente utilizador. O custo médio por utente é de 195,76€. O custo médio por utente do género feminino é de 232,41€. O custo médio por utente do género masculino é de 154,80€. As rubricas com mais peso no custo total por utente são os medicamentos (40,32%), custo com pessoal médico (22,87%) e MCDT (17,18%). Conclusão: Na implementação de um sistema de apuramentos de custos por utente, é fulcral que existam sistemas de informação eficientes que permitam o registo dos cuidados prestados ao utente pelos vários níveis de prestação de cuidados. É importante também que a gestão não utilize apenas os resultados apurados como uma ferramenta de controlo de custos, devendo ser potenciada a sua utilização para a criação de valor ao utente.
ABSTRACT - Introduction: The lack of a cost accounting plan for the primary health care program is an issue for the implementation of internal accounting, which is fundamental to the management of any health care institution. Without guidelines to standardize the criteria for allocation and distribution of costs/income, it becomes difficult to obtain the necessary analytical data to a more effective management control, allowing the use of resources in an efficient and rational way and delivering an improved healthcare service to the patients. Objectives: The aim of this study is to determine the cost per patient in primary healthcare. Methods: The cost evaluation study was based on the Time-Driven Activity-Based Costing method. The cost was allocated to each patient using the following cost drivers: duration of the consultation and undertaken production for the allocation of costs with medical staff; undertaken production for the allocation of other costs concerning staff and indirect variables; total number of patients registered for the allocation of indirect fixed costs. Results: The total cost calculated was €2.980.745, 10. The average number of consultations is 3,17 per registered patient and 4,72 per user patient. The average cost per patient is €195,76. The female population has an average cost per patient of €232,41. As for the male population, the average cost per patient is €154,80. The main cost categories contributing to the total cost per patient are medications (40,32%), medical costs (22,87%) and both diagnostics and therapeutics (17,18%). Conclusion: Within the implementation of a cost allocation system per patient, it is crucial to have efficient information systems to record the several patient healthcare services provided through the different levels of care. In addition to using the results as a cost control tool, it is also important for managers to use it as an instrument to create value for patients.
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Pike, Kenneth Charles. "Allocating life : the selection of liver transplant patients /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/8917.

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GREGOIRE, YVES AMBROISE. "La place de l'allocation aux adultes handicapes dans la prise en charge psychiatrique : etude faite au sein d'une population de patients suivis par une meme equipe soignante." Aix-Marseille 2, 1988. http://www.theses.fr/1988AIX20093.

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Tsang, K., and 曾光. "Prioritization preferences for corneal transplantation allocation in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B31972226.

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Stocken, Deborah Dawn. "Statistical modelling for the prognostic classification of patients with pancreatic cancer for optimisation of treatment allocation." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/1303/.

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Pancreatic cancer is a common cause of cancer death and is difficult to diagnose and treat. A prognostic index can be used in clinical practice to predict survival. Thirty six prognostic factor studies were identified but size and statistical methods were inappropriate. Continuous variables are often simplified incorrectly i) assuming linear relationships between predictors and log-hazard or ii) using dichotomisation. Non-linearity is addressed for the first time in this disease site using restricted cubic spline and fractional polynomial functions. Multivariable models containing non-linear transformations gave a substantially better fit. Important effects of some covariates were unrecognised under simplistic assumptions. The fitted functions generated by the two methods were similar. A direct comparison of these strategies was based on assessing the difference in the AIC values by calculating a sampling distribution in multiple bootstrap resamples. Model validation is also addressed for the first time in this disease and suggested minimal over-fitting with reproducible prognostic information when fitted to external data. This thesis provides the first validated prognostic tool in advanced pancreatic cancer developed using appropriate statistical methodology. Risk-sets identified by the model could help clinicians target treatments to patients more appropriately and have an impact on future trial design and analysis.
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Books on the topic "Patient allocation"

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David, Guy. Integration and task allocation: Evidence from patient care. Cambridge, MA: National Bureau of Economic Research, 2011.

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Brown, Roswyn Ann. The social organisation of work in two paediatric wards: In relation to patient and task allocation. [s.l.]: typescript, 1986.

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Frederic, Kilner John, Orr Robert D. 1941-, Shelly Judy Allen, and Center for Bioethics and Human Dignity., eds. The changing face of health care: A Christian appraisal of managed care, resource allocation, and patient-caregiver relationships. Grand Rapids, Mich: William B. Eerdmans Pub., Paternoster Press, 1998.

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The limits of principle: Deciding who lives and what dies. Westport, Conn: Praeger, 1998.

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Office, General Accounting. VA health care: Allocation of resources to medical facilities in the Sun Belt : report to congressional requesters. Washington, D.C: The Office, 1986.

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Office, General Accounting. VA health care: Resource allocation methodology has had little impact on medical centers' budgets : report to the Committee on Veterans' Affairs, U.S. Senate. Washington, D.C: The Office, 1989.

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Zivin, Joshua Graff. AIDS treatment and intrahousehold resource allocations: Children's nutrition and schooling in Kenya. Cambridge, Mass: National Bureau of Economic Research, 2006.

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Life on the line: Ethics, aging, ending patients' lives, and allocating vital resources. Grand Rapids, Mich: W.B. Eerdmans Pub. Co., 1992.

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Putting patients first, increasing organ supply for transplantation: Hearing before the Subcommittee on Health and Environment of the Committee on Commerce, House of Representatives, One Hundred Sixth Congress, first session, April 15, 1999. Washington: U.S. G.P.O., 1999.

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United States. Congress. Senate. Committee on Labor and Human Resources., ed. Putting patients first: Resolving allocation of transplant organs : joint hearing before the Subcommittee on Health and Environment of the Committee on Commerce, House of Representatives, and the Committee on Labor and Human Resources, U.S. Senate, One Hundred Fifth Congress, second session, June 18, 1998. Washington: U.S. G.P.O., 1998.

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Book chapters on the topic "Patient allocation"

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Schäfer, Fabian, Manuel Walther, and Alexander Hübner. "Patient-Bed Allocation in Large Hospitals." In Springer Proceedings in Mathematics & Statistics, 299–300. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66146-9_28.

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Smith, Melanie S. "Nursing Aspects of Inappropriate Patient Care." In ICU Resource Allocation in the New Millennium, 293–95. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3866-3_36.

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Friedman, Eli A. "Donor Kidney Allocation to High Risk Patient." In Legal and Ethical Concerns in Treating Kidney Failure, 167–75. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4355-4_20.

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Freebody, Jane. "The Patient Workers Inside Hospital." In Mental Health in Historical Perspective, 261–97. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-13105-9_8.

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AbstractFreebody explores how a patient’s class, gender, age, physical health and mental condition influenced the allocation of occupation in French and English institutions. Class was an important consideration since manual labour was considered unsuitable for the middle classes, particularly for women, despite its alleged benefits as a therapy. Whether a patient’s condition was perceived as curable or incurable made a difference to the type of occupation prescribed in England, and to whether it was prescribed in France. The reasons why curable patients at the acute stage of their illness were more likely to be prescribed occupational therapy in England, or unoccupied and treated biologically in France, are examined. In both countries, incurable patients, and those whose condition had deteriorated into chronicity, were allocated work around the hospital, provided they were physically fit, for the benefit of both institution and patient. Freebody compares the material conditions of English and French, rural and metropolitan institutions, that provided the context for patient work, and the varied approaches towards offering incentives to work.
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Cummings, Nancy Boucot. "Allocation of scarce resources: justice and rationing of health care." In Nephrology and Urology in the Aged Patient, 597–603. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1822-4_64.

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Gibson, Jennifer L. "Resource Allocation in Paediatric Patient and Family-Centred Care." In Paediatric Patient and Family-Centred Care: Ethical and Legal Issues, 35–54. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0323-8_3.

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Siparsky, Nicole, David Axelrod, and Richard B. Freeman. "Organ Allocation in Liver Transplantation: Ethics, Organ Supply, and Evidencebased Practice." In Medical Care of the Liver Transplant Patient, 75–87. Oxford, UK: Wiley-Blackwell, 2012. http://dx.doi.org/10.1002/9781444398441.ch7.

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Kittleson, Michelle, Jon Kobashigawa, and Minh Luu. "Listing, Donor Allocation and Optimization of the Pre-transplant Patient." In Clinical Guide to Heart Transplantation, 37–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43773-6_4.

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Sitek, Paweł, and Jarosław Wikarek. "Resource-Constrained Model of Optimizing Patient-to-Hospital Allocation During a Pandemic." In Computational Collective Intelligence, 176–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_14.

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Freebody, Jane. "Conclusions." In Mental Health in Historical Perspective, 335–51. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-13105-9_10.

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AbstractThe comparison of patient occupation in French and English mental institutions has highlighted different attitudes towards the causation and treatment of mental disorder and towards different models of care. It has also emphasised the influence of factors external to institutions, such as poverty, welfare provision, local employment, war and financial crises, on patient occupation. The comparison has revealed how the perceived need by one group of medical professionals (in this case, psychiatrists) for a particular treatment (occupational therapy) can stimulate the growth of a new profession and the infrastructure to support it. It has demonstrated how old ideas and practices can be re-imagined and brought back into use, becoming the hallmark of a modern hospital once again. While the precise nature of the activity and the justifications for its allocation have changed in emphasis over time and in different settings, patient occupation has remained a constant in institutions for the mentally disordered since the early nineteenth century.
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Conference papers on the topic "Patient allocation"

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Budai, Istvan, Balázs Kocsi, and László Pusztai. "New approach for resource allocation in digital healthcare 4.0." In CARPE Conference 2019: Horizon Europe and beyond. Valencia: Universitat Politècnica València, 2019. http://dx.doi.org/10.4995/carpe2019.2019.10280.

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The examination and automation opportunities in healthcare processes, which aims at reducing patient journey and their waiting time, while increasing the utilization of medical equipment as well as monitoring patients. Waiting times are playing a significant role in the total process time of patient care. One of the main reasons is the insufficient resource allocation. This research presents a methodological improvement which supports decision making in digital health processes. The current research provides a methodology that makes weekly human resource scheduling more efficient than before. With the combination of process mining and operations research, we developed a weighted forecast for the probable number of patients. During the research we processed historical data as well as we identified the bottlenecks in the examined health process. Furthermore, we took the causality into account. In today’s fast-paced societies, IT-based solutions are more and more frequently used in healthcare, with the aim of reducing risks and increase patient satisfaction. The method created by us offers a fast, precise and efficient solution to decision making in digital health processes.
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Choi, Chung-ho, and William K. Cheung. "A multi-agent resource allocation framework for patient journey shortening." In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2010. http://dx.doi.org/10.1109/bibmw.2010.5703849.

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Schooley, Ben, Akanksha Singh, Sarah Floyd, Stephan Pill, and John Brooks. "Direct Weighting Interactive Design of Patient Preferences for Shared Decision Making in Orthopaedic Practice." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002105.

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Patients need the ability to accurately and efficiently communicate their preferences across outcome domains to their healthcare providers.1-7 No existing system provides an efficient and timely approach to collect and communicate patient preferences across outcome domains to support shared decision making (SDM) in orthopaedic practice.2-4,8-19 The overarching goal of this research is to design, build, and test an app that collects baseline patient preferences and health status across orthopaedic outcomes and reports this information to the provider for use in patient care. A core component of the app is a Direct-Weighting (DW) preference assessment approach, originated from our prior research, and applied in a touchscreen based interactive design. It is envisioned that patients will use the app after scheduling a first visit to a surgeon for a new orthopaedic condition. Direct weighting (DW) approaches calculate patient-specific preference weights across outcomes by asking patients to disperse portions of a hypothetical “whole” across outcomes in a manner that reflects a patient’s preferences.20 DW has low respondent burden but it requires respondents to make “implicit” comparisons which may be difficult to conceptualize.20 However, the DW approach has become generally accepted in the quality-of-life literature and it has been shown that patients dividing up pieces of a “pie” across quality-of-life domains yields valid representations of patient preferences across the domains.20-22 However, the DW approach has not been validated with specific clinical scenarios using a clinically focused set of outcomes or by using a mobile software app. Drawing on prior research, we iteratively design and develop the app with input from prior DW research, informaticians, and clinicians. We use a qualitative approach to pilot test the app with 20 first-time visit patients presenting with joint pain and/or function deficiency. Participants were interviewed about their outcome preferences for care, used the app to prioritize outcome preferences, answered interview questions about their experience using the app, and completed a mHealth App Usability Questionnaire (MAUQ). Interview questions focused on the utility and usability of the mobile app for communicating with their provider, and capability of the app to capture their outcome preferences. Results validated five core preference domains, with most users dividing their 100-point allocation across 1-3 domains. The tool received moderate to high usability scores. Patients with older age and lower literacy found the DW approach more difficult in terms of allocating 100 points across 5 domains. Suggestions for DW interface interaction improvement included instantiation of a token/points oriented DW preference scoring methodology rather than a 1-10 sliding scale approach for improved preference weighting cognition and SDM with a provider. As more patient reported outcome (PRO) apps hit the marketplace across a broad spectrum of health conditions, these results provide evidence for a DW approach and interactive design for patients to communicate their treatment preferences to their providers.References:1.Baumhauer JF, Bozic KJ. Value-based Healthcare: Patient-reported Outcomes in Clinical Decision Making. Clin Orthop Relat Res. 2016;474(6):1375-1378.2. Slim K, Bazin JE. From informed consent to shared decision-making in surgery. J Visc Surg. 2019;156(3):181-184.3. Damman OC, Jani A, de Jong BA, et al. The use of PROMs and shared decision-making in medical encounters with patients: An opportunity to deliver value-based health care to patients. J Eval Clin Pract. 2020;26(2):524-540.4. Sorensen NL, Hammeken LH, Thomsen JL, Ehlers LH. Implementing patient-reported outcomes in clinical decision-making within knee and hip osteoarthritis: an explorative review. BMC Musculoskelet Disord. 2019;20(1):230.5. Kamal RN, Lindsay SE, Eppler SL. Patients Should Define Value in Health Care: A Conceptual Framework. J Hand Surg Am. 2018;43(11):1030-1034.6. Charles C, Gafni A, Whelan T. Decision-making in the physician-patient encounter: revisiting the shared treatment decision-making model. Social Science & Medicine. 1999;49(5):651-661.7. Niburski K, Guadagno E, Mohtashami S, Poenaru D. Shared decision making in surgery: A scoping review of the literature. Health Expect. 2020.8. Selten EM, Geenen R, van der Laan WH, et al. Hierarchical structure and importance of patients' reasons for treatment choices in knee and hip osteoarthritis: a concept mapping study. Rheumatology (Oxford). 2017;56(2):271-278.9. Kannan S, Seo J, Riggs KR, Geller G, Boss EF, Berger ZD. Surgeons' Views on Shared Decision-Making. J Patient Cent Res Rev. 2020;7(1):8-18.10. Briffa N. The employment of Patient-Reported Outcome Measures to communicate the likely benefits of surgery. Patient Relat Outcome Meas. 2018;9:263-266.
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Japarova, Damira. "Allocation and Use of Financial Resources in Health Care in Kyrgyzstan." In International Conference on Eurasian Economies. Eurasian Economists Association, 2017. http://dx.doi.org/10.36880/c08.01830.

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TThe distribution of the limited financial resources in the state hospitals in Kyrgyzstan is uneven. The problems associated with the current method of distribution of resources: the poor quality of services at the level of polyclinics and high hospitalization rates that require an evaluation of the budget allocation of healthcare organizations operating in the Single Payer system. In order to improve the efficiency of resource use it is suggested to review the principles of allocation of resources to the primary level of patient care.
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Balzer, Felix, Martin Dittmar, Olaf Ahlers, and Niels Pinkwart. "Validating Algorithmic Optimization of Patient Allocation at Medical Schools: Which Patient is the Best Fit for Undergraduate Training?" In 2015 IEEE 15th International Conference on Advanced Learning Technologies (ICALT). IEEE, 2015. http://dx.doi.org/10.1109/icalt.2015.52.

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Schinstock, Emma, Alex Deakyne, Tinen Iles, Andrew Shaffer, and Paul A. Iaizzo. "Lung Allocation Pipeline: Machine Learning Approach to Optimized Lung Transplant." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9030.

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Abstract Lung donation is the most risky transplant procedures. With low survival rates, and poor acceptance of donated lungs, those in need of a lung transplant are at high risk of dying. One reason for poor outcomes is the lack of optimal match between donor and recipient when it comes to lung size and shape. Lungs that do not properly fit in the recipient’s chest cavity can fail to inflate fully and quickly start to deteriorate. In such patients, lung contusions can form, edema occurs in healthy lung tissue, and overall lung function declines. To improve patient outcomes after lung transplant, we describe here a developed a computational pipeline which enables donor lungs to be properly matched to recipients. This tool uses CT scans from both the donor and potential recipients to calculate how anatomically different the sets of lungs are, and therefore provide improved matches in both size and shape for the donor lungs.
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Chen, Dan, Huabo Zhu, and Xinggang Luo. "The fairness of patient routing allocation policy with heterogeneous servers in outpatient department." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162242.

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Tan, Kar Way, Wei Hao Tan, and Hoong Chuin Lau. "Improving patient length-of-stay in emergency department through dynamic resource allocation policies." In 2013 IEEE International Conference on Automation Science and Engineering (CASE 2013). IEEE, 2013. http://dx.doi.org/10.1109/coase.2013.6653988.

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Kim, Yeongin, Mehmet Ayvaci, Srinivasan Raghunathan, and Bekir Tanriover. "Repairing the Digital Divide Can Increase the Service Divide: The Effects of Patient Portals on Kidney Allocation." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2019. http://dx.doi.org/10.24251/hicss.2019.481.

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Alelaiwi, Abdulhameed. "Resource Allocation Management in Patient-to-Physician Communications Based on Deep Reinforcement Learning in Smart Healthcare Services." In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2020. http://dx.doi.org/10.1109/icmew46912.2020.9105982.

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Reports on the topic "Patient allocation"

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David, Guy, Evan Rawley, and Daniel Polsky. Integration and Task Allocation: Evidence from Patient Care. Cambridge, MA: National Bureau of Economic Research, September 2011. http://dx.doi.org/10.3386/w17419.

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Rieder, Karen A., and Susan S. Jackson. An Evaluative Study of the Navy Medical Department's Patient Classification System and Staffing Allocation System. (The Workload Management System for Nursing). Fort Belvoir, VA: Defense Technical Information Center, December 1985. http://dx.doi.org/10.21236/ada170377.

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Collington, Rosie, and William Lazonick. Pricing for Medicine Innovation: A Regulatory Approach to Support Drug Development and Patient Access. Institute for New Economic Thinking Working Paper Series, January 2022. http://dx.doi.org/10.36687/inetwp176.

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The United States represents the world’s largest market for pharmaceutical drugs. It is also the only advanced economy in the world that does not regulate drug prices. There is no upper threshold for the prices of medicines in the United States. List prices are instead set by manufacturers in negotiation with supply-chain intermediaries, though some federal programs have degrees of discretion in price determinations. In practice, this deregulated system means that drug prices in the United States are generally far higher than in other advanced economies, adversely affecting patient accessibility and system affordability. In this paper, we draw on the “theory of innovative enterprise” to develop a framework that provides both a critique of the existing pricing system in the United States and a foundation for developing a new model of pricing regulation to support safety and effectiveness through drug development as well as accessibility and affordability in the distribution of approved medicines to patients. We introduce a regulatory approach we term “Pricing for Medicine Innovation” (PMI), which departs dramatically from the market-equilibrium assumptions of conventional (neoclassical) economics. The PMI approach recognizes the centrality of collective investments by government agencies and business firms in the productive capabilities that underpin the drug development process. PMI specifies the conditions under which, at the firm level, drug pricing can support both sustained investment in these capabilities and improved patient access. PMI can advance both of these objectives simultaneously by regulating not just the level of corporate profit but also its allocation to reinvestment in the drug development process. PMI suggests that although price caps are likely to improve drug affordability, there remain two potential issues with this pricing approach. Firstly, in an innovation system where a company’s sales revenue is the source of its finance for further drug development, price caps may deprive a firm of the means to invest in innovation. Secondly, even with adequate profits available for investment in innovation, a firm that is run to maximize shareholder value will tend to use those profits to fund distributions to shareholders rather than for investment in drug innovation. We argue that, if implemented properly, PMI could both improve the affordability of medicines and enhance the innovative performance of pharmaceutical companies.
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Morris, Andrew M., Sally Bean, Chaim M. Bell, Martin Betts, Jennifer Gibson, Christopher Graham, Rebecca Greenberg, et al. Strategies to Manage Tocilizumab Supply During the COVID-19 Pandemic. Ontario COVID-19 Science Advisory Table, April 2021. http://dx.doi.org/10.47326/ocsat.2021.02.22.1.0.

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Tocilizumab is an anti-inflammatory medication that acts by inhibiting interleukin-6 (IL-6) and is shown to improve outcomes including mortality in patients hospitalized with COVID-19 requiring supplemental oxygen. Ontario supply of tocilizumab is limited, and tocilizumab demand in Ontario might exceed supply in the near future. A strategy that includes using a fixed, single intravenous dose of 400 mg for eligible patients will help extend available supply and is likely effective in treatment of COVID-19. Sarilumab, another IL-6 inhibitor, can be considered as a substitute. Additional options to consider to optimize tocilizumab use include the use of a provincial dashboard to help monitor and allocate use and estimating supply-to-demand adequacy. Likewise, a centralized allocation lottery system could be employed as soon as predicted demand exceeds supply to help ensure fair allocation. However, other issues may need to be taken into account for allocation decisions, as appropriate.
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