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Journal articles on the topic 'Patient allocation'

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Published: 10 December 2022
Last updated: 29 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Ruettger, K., and W. Lenz. "(P2-9) Patient Allocation to Hospitals During Mass-Casualty Incidents." Prehospital and Disaster Medicine 26, S1 (May 2011): s138. http://dx.doi.org/10.1017/s1049023x11004535.

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Due to the limited resources of specialized hospital departments, the allocation of patients to different hospitals according to the severity of their condition is an extraordinarily complex and time-critical problem. The emergency capacity was determined for all medical centers (n = 135) in the State of Hessen, for patients of the various hospitalization triage categories (red, yellow, green), for normal working hours, for weekends and nights, including logistic specifications of a potential helicopter landing. This data was entered into a state register. Using the data from the “acute-care-register”, a Ticket System was developed that allows the operations management to assign patients according to the severity of their condition, urgency and necessary specialization (e.g., neurosurgery, ophthalmology, pediatrics) to a hospital without exceeding the admission and/or treatment capacity of the hospital/facility. During a non-critical period, the order of allocations depending on the distance of the clinic to the site of the emergency is planned in advance so that no further modifications are necessary during the acute intervention phase of an emergency response. Additional notification of hospital capacities for severe casualties provided during the emergency response can be easily and immediately supplemented. Due to the relatively low frequency of such emergency responses, a cost-effective concept that is easily adaptable to the respective fields of application has been discovered. The system is a sticker set customized for the respective rescue teams. The sets will be carried permanently in the rescue equipment by the organization manager of the rescue service team. The equipment is not dependent on electronic components. The cost per sticker set is approximately US$50. Keeping track of the patient allocations is assured.
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12

Ruettger, K., and W. Lenz. "(P2-40) Patient Allocation to Hospitals During Mass-Casualty Incidents." Prehospital and Disaster Medicine 26, S1 (May 2011): s148—s149. http://dx.doi.org/10.1017/s1049023x11004845.

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Due to the limited resources of specialized hospital departments, the allocation of patients to different hospitals according to severity is an extraordinarily complex and time-critical problem. The emergency capacity was determined for all medical centers (n = 135) in the State of Hessen, Germany, for patients of various triage categories (red, yellow, green) during normal working hours, and during weekends and nights and included logistic specifications of a potential helicopter landing. These data were entered into a state register. Using the data from the “acute-care-register”, a Ticket System was developed that allows operations management to assign patients according to the severity of their condition, urgency, and specialization requirements (e.g., neurosurgery, ophthalmology, pediatrics) to a hospital without exceeding the admission and/or treatment capacity of the hospital/facility. During a non-critical period, the order of allocations depending on the distance from the clinic is planned in advance so that no further modifications are necessary during the acute intervention phase of an emergency response. Additional notification of hospital capacities for severe casualties provided during the emergency response can be easily and immediately supplemented. Due to the relatively low frequency of such emergency responses, a cost-effective concept that is easily adaptable to the respective fields of application was decided upon. The system is a sticker set customized for the respective rescue teams. The sets will be carried permanently in the rescue equipment by the organization manager of the rescue service team. The equipment is not dependent on electronic components. The cost per sticker set is approximately US$50. Keeping track of the patient allocations is assured.
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13

Addams, Joel, and Laura Stephens. "High-Volume Platelet Distribution: Who Makes the Clinical Decision?" American Journal of Clinical Pathology 154, Supplement_1 (October 2020): S3—S4. http://dx.doi.org/10.1093/ajcp/aqaa137.005.

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Abstract Anecdotally, many clinicians expect blood components to arrive in standard volumes. Blood components do not come in uniform sizes, however, and institutional experience has revealed platelet units to vary considerably in volume. Since many clinicians are not the ones examining blood products and their volumes prior to transfusion, there is a potential to transfuse a high-volume unit inadvertently to a patient at risk of transfusion-associated fluid overload (TACO), a leading cause of transfusion-related fatalities. The intent of this study was to examine the allocation practices of high-volume platelet units at an academic medical center. Over a six-month period, blood bank technical staff prospectively logged the allocation of high-volume apheresis platelet units with volumes greater than or equal to 400 mL. The staff member who issued the product logged the reason(s), if any, why he or she selected the unit for a particular patient when more than one ABO type-specific product was available. No patient identifiers or data were logged or analyzed in this study. Eighty-seven high-volume platelet units were recorded during the study period. The volumes ranged from 395 to 872 mL, with an average and standard deviation of 571 mL and 92 mL, respectively. Blood bank staff listed the following reasons for their allocation of high-volume platelet unit as follows: the product expiration date in 23 cases (26%), patient age in 13 cases (15%), patient sex in 12 cases (14%), and intraoperative use in 10 cases (11%). This study enumerated the variability in platelet unit volume, with some units containing likely double or triple the volume of the ordering clinicians expectations. Blood bank technologists made clinical decisions in the allocation of high-volume units in three-quarters of the documented cases. This underscores a need to provide clearly-defined guidelines for allocating such units, as well as a mechanism for clinicians to request lower-volume units, particularly for patients at risk of TACO.
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Melloni, Giorgio E. M., Alessandro Guida, Giuseppe Curigliano, Edoardo Botteri, Angela Esposito, Maude Kamal, Christoph Le Tourneau, et al. "Precision Trial Drawer, a Computational Tool to Assist Planning of Genomics-Driven Trials in Oncology." JCO Precision Oncology, no. 2 (November 2018): 1–16. http://dx.doi.org/10.1200/po.18.00015.

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Purpose Trials that accrue participants on the basis of genetic biomarkers are a powerful means of testing targeted drugs, but they are often complicated by the rarity of the biomarker-positive population. Umbrella trials circumvent this by testing multiple hypotheses to maximize accrual. However, bigger trials have higher chances of conflicting treatment allocations because of the coexistence of multiple actionable alterations; allocation strategies greatly affect the efficiency of enrollment and should be carefully planned on the basis of relative mutation frequencies, leveraging information from large sequencing projects. Methods We developed software named Precision Trial Drawer (PTD) to estimate parameters that are useful for designing precision trials, most importantly, the number of patients needed to molecularly screen (NNMS) and the allocation rule that maximizes patient accrual on the basis of mutation frequency, systematically assigning patients with conflicting allocations to the drug associated with the rarer mutation. We used data from The Cancer Genome Atlas to show their potential in a 10-arm imaginary trial of multiple cancers on the basis of genetic alterations suggested by the past Molecular Analysis for Personalised Therapy (MAP) conference. We validated PTD predictions versus real data from the SHIVA (A Randomized Phase II Trial Comparing Therapy Based on Tumor Molecular Profiling Versus Conventional Therapy in Patients With Refractory Cancer) trial. Results In the MAP imaginary trial, PTD-optimized allocation reduces number of patients needed to molecularly screen by up to 71.8% (3.5 times) compared with nonoptimal trial designs. In the SHIVA trial, PTD correctly predicted the fraction of patients with actionable alterations (33.51% [95% CI, 29.4% to 37.6%] in imaginary v 32.92% [95% CI, 28.2% to 37.6%] expected) and allocation to specific treatment groups (RAS/MEK, PI3K/mTOR, or both). Conclusion PTD correctly predicts crucial parameters for the design of multiarm genetic biomarker-driven trials. PTD is available as a package in the R programming language and as an open-access Web-based app. It represents a useful resource for the community of precision oncology trialists. The Web-based app is available at https://gmelloni.github.io/ptd/shinyapp.html .
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15

Bosiers, Marc, Koen Deloose, Jurgen Verbist, and Patrick Peeters. "Patient-specific treatment allocation for carotid artery disease." Interventional Cardiology 1, no. 1 (October 2009): 41–49. http://dx.doi.org/10.2217/ica.09.8.

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16

Harrison, Sarah. "Single room allocation is not reflecting patient preference." Nursing Standard 20, no. 9 (November 9, 2005): 9. http://dx.doi.org/10.7748/ns.20.9.9.s10.

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17

Jaya, Alfarika. "OPTIMIZATION MODEL FOR PATIENT ALLOCATION DURING INFECTIOUS DISEASE." International Journal of Advanced Research 6, no. 1 (January 31, 2018): 1442–46. http://dx.doi.org/10.21474/ijar01/6370.

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18

David, Guy, Evan Rawley, and Daniel Polsky. "Integration and Task Allocation: Evidence from Patient Care." Journal of Economics & Management Strategy 22, no. 3 (July 10, 2013): 617–39. http://dx.doi.org/10.1111/jems.12023.

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19

Arendts, G., K. Howard, and J. M. Rose. "Allocation decisions and patient preferences in emergency medicine." Emergency Medicine Journal 28, no. 12 (January 10, 2011): 1051–54. http://dx.doi.org/10.1136/emj.2010.099929.

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20

Hunt, Jennifer M. "Patient allocation and the nursing process at work." Nursing Standard 28, no. 19 (January 8, 2014): 34–35. http://dx.doi.org/10.7748/ns2014.01.28.19.34.s45.

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21

Handy, S., R. N. Chithiramohan, C. G. Ballard, C. Bannister, and R. Rusca. "The rationale of patient allocation for psychogeriatric assessment." International Journal of Geriatric Psychiatry 6, no. 4 (April 1991): 249–52. http://dx.doi.org/10.1002/gps.930060410.

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22

Sharma, Pratima. "Liver‐Kidney: Indications, Patient Selection, and Allocation Policy." Clinical Liver Disease 13, no. 6 (June 2019): 165–69. http://dx.doi.org/10.1002/cld.787.

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23

Bikker, Ingeborg A., Martijn R. K. Mes, Antoine Sauré, and Richard J. Boucherie. "ONLINE CAPACITY PLANNING FOR REHABILITATION TREATMENTS: AN APPROXIMATE DYNAMIC PROGRAMMING APPROACH." Probability in the Engineering and Informational Sciences 34, no. 3 (December 11, 2018): 381–405. http://dx.doi.org/10.1017/s0269964818000402.

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AbstractWe study an online capacity planning problem in which arriving patients require a series of appointments at several departments, within a certain access time target.This research is motivated by a study of rehabilitation planning practices at the Sint Maartenskliniek hospital (the Netherlands). In practice, the prescribed treatments and activities are typically booked starting in the first available week, leaving no space for urgent patients who require a series of appointments at a short notice. This leads to the rescheduling of appointments or long access times for urgent patients, which has a negative effect on the quality of care and on patient satisfaction.We propose an approach for allocating capacity to patients at the moment of their arrival, in such a way that the total number of requests booked within their corresponding access time targets is maximized. The model considers online decision making regarding multi-priority, multi-appointment, and multi-resource capacity allocation. We formulate this problem as a Markov decision process (MDP) that takes into account the current patient schedule, and future arrivals. We develop an approximate dynamic programming (ADP) algorithm to obtain approximate optimal capacity allocation policies. We provide insights into the characteristics of the optimal policies and evaluate the performance of the resulting policies using simulation.
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Kamakura, T., M. Sakamoto, T. Odaka, Y. Nose, and K. Akazawa. "Patient Registration and Treatment Allocation in Multicenter Clinical Trials Using a FAX-OCR System." Methods of Information in Medicine 33, no. 05 (1994): 530–34. http://dx.doi.org/10.1055/s-0038-1635059.

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Abstract:This article describes the design and results of implementation of an automated patient registration and freatment allocation system (RETAS) used in multicenter clinical trials. RETAS was developed using a FAX-OCR system by which handwritten Japanese and English characters, as well as numericals and forms with check boxes, are sent from participating institutions by Fax, processed using an optical character reader, and then transmitted to a host computer at a statistical center. Based on the facsimile data, RETAS can automatically review eligibility, collect patient identification data and provide a randomized treatment allocation. RETAS permits uninterrupted, unattended operation at a statistical center, 24 hours a day, 7 days a week. Therefore, it drastically decreases the workload of personnel at the statistical center needed to support central telephone registration coverage. Consequently, staff members are free to focus on patient registration, treatment allocation, and follow-up of patients. The treatment allocation procedure in this system is based on Pocock and Simon’s minimization method combined with Zelen’s method for institution balancing. By this system it was possible to balance treatment numbers for each level of various prognostic factors over an entire trial and, at the same time, balance the allocation of treatments within an institution. The system currently supports the protocol of a clinical trial for Adjuvant Chemo-Endocrine Therapy for Breast Cancer in West Japan.
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Melman, G. J., A. K. Parlikad, and E. A. B. Cameron. "Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation." Health Care Management Science 24, no. 2 (April 9, 2021): 356–74. http://dx.doi.org/10.1007/s10729-021-09548-2.

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AbstractCOVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.
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van Manen, Janine G., Helene Andrea, Ellen van den Eijnden, Anke M. M. A. Meerman, Moniek M. Thunnissen, Elisabeth F. M. Hamers, Nelleke Huson, et al. "Relationship between Patient Characteristics and Treatment Allocation for Patients with Personality Disorders." Journal of Personality Disorders 25, no. 5 (October 2011): 656–67. http://dx.doi.org/10.1521/pedi.2011.25.5.656.

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Minelli, Erin, and Bryan Liang. "Transplant Candidates and Substance Use: Adopting Rational Health Policy for Resource Allocation." University of Michigan Journal of Law Reform, no. 44.3 (2011): 667. http://dx.doi.org/10.36646/mjlr.44.3.transplant.

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Organ transplant candidates are often denied life saving organs on account of their medical marijuana drug use. Individuals who smoke medicinal marijuana are typically classified as substance abusers, and ultimately deemed ineligible for transplantation, despite their receipt of the drug under a physician's supervision and prescription. However, patients who smoke cigarettes or engage in excessive alcohol consumption are routinely considered for placement on the national organ transplant waiting list. Transplant facilities have the freedom to regulate patient selection criteria with minimal oversight. As a result, the current organ allocation system in the United States is rife with inconsistencies and results in disparities in allocation decisions. This Article reviews the history and underlying rationale of organ allocation in the United States and the National Organ Transplant Act. It then examines ill-founded policies regarding transplant candidates who present issues of substance "abuse" compared with substance "use," and the resulting disparities in waiting-list criteria. In response, a model rule for a national set of patient selection guidelines is provided. Definitions of terms, distinctions regarding proper patient classification, and protocols for a second chance policy to be used in the event of a relapse among wait-listed patients are addressed. Finally, stipulations that require designated abstention periods as well as random drug screenings in relation to subsequent relisting are also included. This policy distinguishes between candidates who present issues of substance use versus substance abuse. The use of such a model allocation policy will promote equity and scientific bases in the organ allocation process.
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Ott, Sascha, Daniel Lewin, Gaik Nersesian, Julia Stein, Isabell A. Just, Matthias Hommel, Felix Schoenrath, et al. "Improving Survival in Cardiogenic Shock—A Propensity Score-Matched Analysis of the Impact of an Institutional Allocation Protocol to Short-Term Mechanical Circulatory Support." Life 12, no. 11 (November 19, 2022): 1931. http://dx.doi.org/10.3390/life12111931.

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Temporary mechanical circulatory support (tMCS) is a life-saving treatment option for patients in cardiogenic shock (CS), but many aspects such as patient selection, initiation threshold and optimal modality selection remain unclear. This study describes a standard operating procedure (SOP) for tMCS allocation for CS patients and presents outcome data before and after implementation. Data from 421 patients treated for CS with tMCS between 2018 and 2021 were analyzed. In 2019, we implemented a new SOP for allocating CS patients to tMCS modalities. The association between the time of SOP implementation and the 30-day and 1-year survival as well as hospital discharge was evaluated. Of the 421 patients included, 189 were treated before (pre-SOP group) and 232 after implementation of the new SOP (SOP group). Causes of CS included acute myocardial infarction (n = 80, 19.0%), acute-on-chronic heart failure in patients with dilated or chronic ischemic heart failure (n = 139, 33.0%), valvular cardiomyopathy (n = 14, 3.3%) and myocarditis (n = 5, 1.2%); 102 patients suffered from postcardiotomy CS (24.2%). The SOP group was further divided into an SOP-adherent (SOP-A) and a non-SOP-adherent group (SOP-NA). The hospital discharge rate was higher in the SOP group (41.7% vs. 29.7%), and treating patients according to the SOP was associated with an improved 30-day survival (56.9% vs. 38.9%, OR 2.21, 95% CI 1.01–4.80, p = 0.044). Patient allocation according to the presented SOP significantly improved 30-day survival.
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Drovetta, Megan, Emily Cramer, Alaina Linafelter, Jordan Sevart, and Michele Maddux. "Impact of Perceived Barriers on Patient Engagement and Attitudes towards Transition and Transfer." Children 9, no. 9 (August 24, 2022): 1273. http://dx.doi.org/10.3390/children9091273.

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Objective: This study is a preliminary evaluation of how perceived barriers towards transition might impact patient attitudes towards their own readiness and ability to transition, self-efficacy towards their IBD, and the allocation of treatment responsibility. Methods: A sample of 81 young adults with IBD were seen for standard care in a Young Adult Clinic (YAC). Patients completed questionnaires on perceived transition barriers; perceived confidence, importance, motivation, and readiness towards transition and transfer; IBD self-efficacy; and allocation of treatment responsibility. Path model analyses were conducted. Results: Not knowing how and who to transfer to and not understanding insurance details were the most commonly endorsed perceived barriers to transition. A significant relationship was found between the attitude toward transition and allocation of treatment responsibility, but no meaningful indirect effects were found from perceived barriers to the allocation of treatment responsibility, using attitudes toward transition as an intervening variable. The relationship between perceived barriers and allocation of treatment responsibility was at least partially explained by examining the intervening effects of attitudes toward transfer and self-efficacy. Conclusions: The study findings carry important implications for targets of clinical intervention to assist young adults with IBD in engaging in their health care and ultimately transferring into adult care.
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Lin, Carrie Ka Yuk, Teresa Wai Ching Ling, and Wing Kwan Yeung. "Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization." Journal of Healthcare Engineering 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/9034737.

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This paper studies the real-life problems of outpatient clinics having the multiple objectives of minimizing resource overtime, patient waiting time, and waiting area congestion. In the clinic, there are several patient classes, each of which follows different treatment procedure flow paths through a multiphase and multiserver queuing system with scarce staff and limited space. We incorporate the stochastic factors for the probabilities of the patients being diverted into different flow paths, patient punctuality, arrival times, procedure duration, and the number of accompanied visitors. We present a novel two-stage simulation-based heuristic algorithm to assess various tactical and operational decisions for optimizing the multiple objectives. In stage I, we search for a resource allocation plan, and in stage II, we determine a block appointment schedule by patient class and a service discipline for the daily operational level. We also explore the effects of the separate strategies and their integration to identify the best possible combination. The computational experiments are designed on the basis of data from a study of an ophthalmology clinic in a public hospital. Results show that our approach significantly mitigates the undesirable outcomes by integrating the strategies and increasing the resource flexibility at the bottleneck procedures without adding resources.
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Cody, Erin E., Joe Feinglass, Barbara Farrell, and James R. Webster. "Patient age as a determinant of housestaff time allocation." American Journal of Medicine 98, no. 5 (May 1995): 515–17. http://dx.doi.org/10.1016/s0002-9343(99)80358-1.

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Furnham, A., C. Thomas, and K. V. Petrides. "Patient characteristics and the allocation of scarce medical resources." Psychology, Health & Medicine 7, no. 1 (February 2002): 99–106. http://dx.doi.org/10.1080/13548500120101595.

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Kappes, Andreas, Hazem Zohny, Julian Savulescu, Ilina Singh, Walter Sinnott-Armstrong, and Dominic Wilkinson. "Race and resource allocation: an online survey of US and UK adults’ attitudes toward COVID-19 ventilator and vaccine distribution." BMJ Open 12, no. 11 (November 2022): e062561. http://dx.doi.org/10.1136/bmjopen-2022-062561.

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ObjectiveThis study aimed to assess US/UK adults’ attitudes towards COVID-19 ventilator and vaccine allocation.DesignOnline survey including US and UK adults, sampled to be representative for sex, age, race, household income and employment. A total of 2580 participated (women=1289, age range=18 to 85 years, Black American=114, BAME=138).InterventionsParticipants were asked to allocate ventilators or vaccines in scenarios involving individuals or groups with different medical risk and additional risk factors.ResultsParticipant race did not impact vaccine or ventilator allocation decisions in the USA, but did impact ventilator allocation attitudes in the UK (F(4,602)=6.95, p<0.001). When a racial minority or white patient had identical chances of survival, 14.8% allocated a ventilator to the minority patient (UK BAME participants: 24.4%) and 68.9% chose to toss a coin. When the racial minority patient had a 10% lower chance of survival, 12.4% participants allocated them the ventilator (UK BAME participants: 22.1%). For patients with identical risk of severe COVID-19, 43.6% allocated a vaccine to a minority patient, 7.2% chose a white patient and 49.2% chose a coin toss. When the racial minority patient had a 10% lower risk of severe COVID-19, 23.7% participants allocated the vaccine to the minority patient. Similar results were seen for obesity or male sex as additional risk factors. In both countries, responses on the Modern Racism Scale were strongly associated with attitudes toward race-based ventilator and vaccine allocations (p<0.0001).ConclusionsAlthough living in countries with high racial inequality during a pandemic, most US and UK adults in our survey allocated ventilators and vaccines preferentially to those with the highest chance of survival or highest chance of severe illness. Race of recipient led to vaccine prioritisation in cases where risk of illness was similar.
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Ahmed, Shamsuddin. "Simulation Method to Improve Hospital Service Quality." International Journal of Information Systems in the Service Sector 6, no. 3 (July 2014): 96–117. http://dx.doi.org/10.4018/ijisss.2014070106.

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This article presents the results of a simulation model designed to reduce patient waiting time in the emergency department of a hospital in the United Arab Emirates. The process-oriented simulation model shows how the resources in the hospital are inter-related. The model depicts the hospital operating system and its performance and management issues with regards to allocation of human and material resources. Based on results of the simulation, optimized response surfaces are developed to explain patient waiting time and the total time a patient spends in the hospital for treatment. Results of the study can be used by hospital management to reduce patient waiting time and improve service quality by using a mix of operational strategies and resource allocations.
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Bramstedt, Katrina A. "Age-based health care allocation as a wedge separating the person from the patient and commodifying medicine." Reviews in Clinical Gerontology 11, no. 2 (May 2001): 185–88. http://dx.doi.org/10.1017/s0959259801011297.

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Agism in the form of age-based health care allocation fosters the separation of the physiological part of a patient from the person who is the patient. It does so by ignoring the holistic best interests of the patient and instead focuses on providing certain procedures or therapies only when the patient’s age is less than or equal to a specified value (the allocation limit). Certainly not all forms of clinically relevant care and treatment are age-restricted in the scheme of aged-based health care allocation, but it is clear that this scheme functions on the arbitrary, and patients may miss out on optimal therapy presumably because it will be ranked as too expensive or too rare to provide to older people. Arbitrarily chosen age limits or those chosen based upon an estimation of humans’ natural lifespan have the effects of minimizing the patient’s clinical choices, devaluing the experiential skills and knowledge of the medical team, weakening the doctor-patient relationship, and commodifying medicine. Policies of this nature do not solve our current health care dilemma, rather they are an economic bandage over the still present (and unattended to) root cause.
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Hantel, Andrew, Fay J. Hlubocky, Michael Quinn, Sang Mee Lee, Mark Siegler, and Christopher Daugherty. "The practical ethics of medication shortages: Understanding patient preferences for allocation, decision making, and disclosure through narrative inquiry." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e18323-e18323. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e18323.

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e18323 Background: Hospital medication shortages (HMS) are pervasive throughout the U.S. healthcare system. Current management mechanisms are heterogeneous but routinely include the use of alternatives and the rationing of medications between patients. Little is known about oncology patient preferences for decision-makers, ethical allocation systems, or thresholds for disclosure during HMS. Methods: Oncology patients previously hospitalized for inpatient care within the last 24 months underwent qualitative interviews supplemented with validated instruments measuring: trust (Trust in Oncologist Scale), therapeutic alliance (Human Connection Scale), and shared decision-making (Shared Decision-Making Questionnaire). Qualitative data underwent Framework Analysis for thematic identification. Results: To date, 16 patients have been interviewed: median age 61y (31-77); 44% female; 56% married; 56% > college education; median number of treatment regimens 2 (1-6), days in hospital 18 (3-66), number of hospitalizations 2(1-8). All patients (100%) reported extremely high levels of trust, therapeutic alliance, and shared decision-making with their oncologist. Two patients (13%) reported personal experiences with HMS, 43% reported knowledge of HMS within the U.S., and no patients reported knowledge of local hospital HMS. Framework Analysis revealed that virtually all patients preferred that their oncologist act as the primary decision-maker during allocation/rationing and favored pharmacist and ethicist involvement. Most patients preferred allocation systems that prioritized efficacy, age, and degree of illness. No patients desired the use of a lottery or reciprocity-based decisions. Virtually all patients favored disclosure of shortages if alternatives were used, independent of the level of difference in efficacy/toxicity, and in the case of both chemotherapeutics and supportive medications. Conclusions: Despite ubiquitous HMS in oncology, patients are generally unaware of local HMS and prefer: multi-disciplinary decision-makers during HMS allocation, prioritized allocation schemes, and more frequent HMS disclosure than presently occurs. Study recruitment is ongoing.
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Boardman-Pretty, Theo, John Tweed, Camilla Day, Lucy Stephenson, Jalon Quinn, and Larry Rifkin. "Restructuring Patient Review and Allocation in a South London Home Treatment Team." BJPsych Open 8, S1 (June 2022): S88. http://dx.doi.org/10.1192/bjo.2022.282.

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AimsLambeth Home Treatment Team (LHTT) provides short-term intensive community psychiatric care to a diverse South London population. The high turnover of patients requires a streamlined process to review and discuss their progress. We aimed to discuss patients in more frequent, targeted and shorter meetings, and to improve continuity of medical care using a ‘named doctor’ system. We assessed impact on length of stay with LHTT, on staff time as well as on both patient and staff satisfaction.MethodsThe system of once-weekly day-long discussions of entire caseload was replaced by twice-weekly discussions of new and concerning patients only. The system of medical reviews was changed from ad hoc to MDT-agreed allocation to a specific doctor for the duration of LHTT stay.Data on duration of treatment and caseload size were taken from regular LHTT statistical reports. Staff and patient questionnaires assessed impact on satisfaction and time spent in review discussions.ResultsQualitative reports of staff experience revealed that the new system was felt to provide better continuity of care, better time efficiency (less time spent learning about new patients) and improved learning experiences for doctors in training. Downsides included lack of ‘automatic second opinion’ when a patient was reviewed by a different doctor, felt to be mitigated by more frequent discussions in MDT reviews when needed.Patient feedback showed no significant change was noted in overall experience of LHTT, although patients were more likely to feel involved in their care (88% said ‘definitely’ compared to 68% before the change).Time spent discussing patients in clinical review meetings reduced from an average of 38.5 to 28.5 person-hours per week.Average caseload reduced from 57 to 42. However, duration of treatment increased from 18.8 days to 20.4 days.ConclusionThe reduction in staff time in reviews suggests that the system had been appropriately streamlined. While caseload size reduced, duration of stay slightly increased, so the new system was not found to have had a significant impact on objective measures of patient care.Staff feedback was generally favourable, highlighting continuity of care and time efficiency. Patient feedback, while good both before and after our change, suggested a greater feeling of involvement in their care, possibly due to clearer communication and discussion of plan from the start of LHTT care.
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Teixeira, Andreza Correa, Fernanda Fernandes Souza, Gustavo de Assis Mota, Ana de Lourdes Candolo Martinelli, Ajith Kumar Sankarankutty, and Orlando de Castro e Silva. "Liver transplantation: expectation with MELD score for liver allocation in Brazil." Acta Cirurgica Brasileira 21, suppl 1 (2006): 12–14. http://dx.doi.org/10.1590/s0102-86502006000700003.

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Liver transplantation represents the most effective therapy for patients suffering from chronic end-stage liver disease. Until very recently, in Brazil, liver allocation was based on the Child-Turcotte-Pugh score and the waiting list followed a chronological criterion. In February 2002 the Model for End-stage Liver Disease (MELD) score was adopted for the allocation of donor livers in the US. After that change, an increased number of patients with more severe liver disease was observed, although there was no difference in 1-year patient and graft survival. A reduction in waiting-list mortality was also observed. In Brazil, the MELD score was adopted on May 31st, 2006. Good results are expected regarding the new criterion for allocation.
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Goh, Mien Li, Emily N. K. Ang, Yiong-Huak Chan, Hong-Gu He, and Katri Vehviläinen-Julkunen. "Patient Satisfaction Is Linked to Nursing Workload in a Singapore Hospital." Clinical Nursing Research 27, no. 6 (June 13, 2017): 692–713. http://dx.doi.org/10.1177/1054773817708933.

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No studies have examined the association between patient satisfaction and the allocation of nursing care hours using a workload management system. The aim of this study is to examine the correlation between inpatients’ perceived satisfaction with nursing care and nursing workload management in a Singapore hospital. A secondary data analysis was performed based on the results of 270 patients’ perceived satisfaction measured by the Revised Humane Caring Scale and nursing workload management data extracted from the TrendCare Patient Acuity System. Data were collected from March to October 2013. There were weak positive ( rs = .212 to rs = .120) and negative ( rs = −.120 to rs = −.196) correlations between patient satisfaction and nursing workload. Nursing leaders should build positive work environment through maximizing efficient resource allocation and adequate staffing to deliver safe patient care. Future studies could involve other patient outcomes such as incidences of fall and pressure ulcer.
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40

Paige, Danielle L. "Allocation of Responsibility for Medication Errors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 10 (September 2002): 905–9. http://dx.doi.org/10.1177/154193120204601006.

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This study was designed to assess perceptions of responsibility for consumer safety while using prescription medication. Twenty-five university students were presented with four scenarios depicting an adverse outcome due to negligence involving the administration of a prescription medication. Responsibility could be assigned to the physician, the pharmacist, or the consumer (patient). Scenarios were framed either with no information regarding who committed the error, a physician error, or a patient error. The consumer was given significantly more responsibility overall, mean = 54.59 percent for consumer, compared to 34.48 percent for physician. The percent responsibility allocated to the pharmacist was not a focus of this study as its mean allocation was small, mean = 10.92, and did not vary with experimental manipulations. The shift in responsibility assigned to the consumer when the scenario highlighted consumer error was significantly greater than the corresponding shift in responsibility assigned to the physician in the physician condition.
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Rosenberg-Yunger, Zahava R. S., and Ahmed M. Bayoumi. "EVALUATION CRITERIA OF PATIENT AND PUBLIC INVOLVEMENT IN RESOURCE ALLOCATION DECISIONS: A LITERATURE REVIEW AND QUALITATIVE STUDY." International Journal of Technology Assessment in Health Care 33, no. 2 (2017): 270–78. http://dx.doi.org/10.1017/s0266462317000307.

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Objectives: We developed specific evaluation criteria to assess patient and public involvement in resource allocation decisions in health care.Methods: We reviewed the literature from health and other sectors relevant to stakeholder involvement and conducted twenty-seven key informant interviews with stakeholders knowledgeable about patient and public involvement in Canadian drug resource allocation decisions. We used an inductive qualitative thematic approach to analyze the interviews with codes and categories developed directly from individuals’ interview transcripts.Results: Integrating respondents’ comments and the literature review, we identified nine evaluation criteria of patient and the public involvement in healthcare resource allocation decision making: clarity regarding rationale and roles of patient and public members, sufficient support, adequate representation of relevant views, fair decision-making processes, legitimacy of committee processes, adequate opportunity for participation, meaningful degree of participation, noticeable effect on decisions, and considerations of the efficiency of patient and public involvement.Conclusions: Our results will help to develop methods to evaluate patient and public involvement in healthcare decision making.
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Garcia, B. L., R. Bekker, R. D. van der Mei, N. H. Chavannes, and N. D. Kruyt. "Optimal patient protocols in regional acute stroke care." Health Care Management Science 24, no. 3 (February 23, 2021): 515–30. http://dx.doi.org/10.1007/s10729-020-09524-2.

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AbstractIn acute stroke care two proven reperfusion treatments exist: (1) a blood thinner and (2) an interventional procedure. The interventional procedure can only be given in a stroke centre with specialized facilities. Rapid initiation of either is key to improving the functional outcome (often emphasized by the common phrase in acute stroke care “time=brain”). Delays between the moment the ambulance is called and the initiation of one or both reperfusion treatment(s) should therefore be as short as possible. The speed of the process strongly depends on five factors: patient location, regional patient allocation by emergency medical services (EMS), travel times of EMS, treatment locations, and in-hospital delays. Regional patient allocation by EMS and treatment locations are sub-optimally configured in daily practice. Our aim is to construct a mathematical model for the joint decision of treatment locations and allocation of acute stroke patients in a region, such that the time until treatment is minimized. We describe acute stroke care as a multi-flow two-level hierarchical facility location problem and the model is formulated as a mixed integer linear program. The objective of the model is the minimization of the total time until treatment in a region and it incorporates volume-dependent in-hospital delays. The resulting model is used to gain insight in the performance of practically oriented patient allocation protocols, used by EMS. We observe that the protocol of directly driving to the nearest stroke centre with special facilities (i.e., the mothership protocol) performs closest to optimal, with an average total time delay that is 3.9% above optimal. Driving to the nearest regional stroke centre (i.e., the drip-and-ship protocol) is on average 8.6% worse than optimal. However, drip-and-ship performs better than the mothership protocol in rural areas and when a small fraction of the population (at most 30%) requires the second procedure, assuming sufficient patient volumes per stroke centre. In the experiments, the time until treatment using the optimal model is reduced by at most 18.9 minutes per treated patient. In economical terms, assuming 150 interventional procedures per year, the value of medical intervention in acute stroke can be improved upon up to € 1,800,000 per year.
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Wu, Jingna, Bo Chen, Danping Wu, Jianqiang Wang, Xiaodong Peng, and Xia Xu. "Optimization of Markov Queuing Model in Hospital Bed Resource Allocation." Journal of Healthcare Engineering 2020 (December 8, 2020): 1–11. http://dx.doi.org/10.1155/2020/6630885.

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Bed resources are the platform in which most medical and health resources in the hospital play a role and carry the core functions of the health service system. How to improve the efficiency of the use of bed resources through scientific management measures and methods and ultimately achieve the optimization of overall health resources is the focus of hospital management teams. This paper analyzes the previous research models of knowledge related to queuing theory in medical services. From the perspective of the hospital and the patient, several indicators such as the average total number of people, the utilization rate of bed resources, the patient stop rate, and the patient average waiting time are defined to measure the performance of the triage queue calling model, which makes the patient queue more reasonable. According to the actual task requirements of a hospital, a Markov queuing strategy based on Markov service is proposed. A mathematical queuing model is constructed, and the process of solving steady-state probability based on Markov theory is analyzed. Through the comparative analysis of simulation experiments, the advantages and disadvantages of the service Markov queuing model and the applicable scope are obtained. Based on the theory of the queuing method, a queuing network model of bed resource allocation is established in principle. Experimental results show that the queuing strategy of bed resource allocation based on Markov optimization effectively improves resource utilization and patient satisfaction and can well meet the individual needs of different patients. It does not only provide specific optimization measures for the object of empirical research but also provides a reference for the development of hospital bed resource allocation in theory.
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Marinho da Silva, Márcia Elizabeth, Eduardo R. Santos, and Denis Borenstein. "Implementing Regulation Policy in Brazilian Health Care Regulation Centers." Medical Decision Making 30, no. 3 (December 29, 2009): 366–79. http://dx.doi.org/10.1177/0272989x09344748.

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The regulation of specialist medical appointments represents one of the problematic areas of the Brazilian Public Health System. In this regulation process, 2 issues stand out: 1) which patients should have the highest attendance priority, and 2) which service suppliers can best resolve the specific health problem of a patient? Based on the consideration of the existing Brazilian context in the field of medical assistance, this study proposes a model designed to aid regulation centers deal with the decisions related to the process of allocating specialist medical appointments. The model integrates multicriteria decision analysis and linear programming for the specialist medical appointment allocation, in which the allocation of patients is defined as a function of the relative significance of a set of criteria related to the notion of effectiveness of the specialist medical care and the capability of the accredited specialist health care units. The integrated model was implemented in a computer-based system and validated using cardiology and vein surgery data from the regulation center in Porto Alegre, Brazil. The validated computational system was applied to mammography services in another regulation center. The system successfully implemented a prioritization scheme, decreasing significantly the examination waiting time of severe cases.
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Boas-Knoop, S., T. Mehlitz, P. Neuhaus, and A. Pascher. "Development of Patient Characteristic in the MELD-Based Liver Allocation." Transplantation 98 (July 2014): 735. http://dx.doi.org/10.1097/00007890-201407151-02507.

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46

Wiseman, David. "Medical Resource Allocation as a Function of Selected Patient Characteristics." Journal of Applied Social Psychology 36, no. 3 (March 30, 2006): 683–89. http://dx.doi.org/10.1111/j.0021-9029.2006.00024.x.

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Su, Xuanming, and Stefanos A. Zenios. "Patient Choice in Kidney Allocation: A Sequential Stochastic Assignment Model." Operations Research 53, no. 3 (June 2005): 443–55. http://dx.doi.org/10.1287/opre.1040.0180.

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Hogan, Andrew J., and David W. Smith. "Patient classification and resource allocation in Veterans Administration nursing homes." Advances in Nursing Science 9, no. 3 (April 1987): 56–71. http://dx.doi.org/10.1097/00012272-198704000-00013.

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Vissers, Jan M. H. "Patient flow-based allocation of inpatient resources: A case study." European Journal of Operational Research 105, no. 2 (March 1998): 356–70. http://dx.doi.org/10.1016/s0377-2217(97)00242-7.

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Mangus, Richard S. "Liver‐Intestine/Multivisceral Perspective: Indications, Patient Selection, and Allocation Policy." Clinical Liver Disease 14, no. 4 (October 2019): 142–45. http://dx.doi.org/10.1002/cld.848.

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