Withholding Medical Interventions and Ageism During a Pandemic: A Model for Resource Allocation Decision Making

Objective:Technological change accounts for approximately 25 percent of health expenditure growth. To date, limited research has been published on case studies of disinvestment and resource allocation decision making in clinical practice. Our research objective is to systematically review and catalogue the application of frameworks and tools for disinvestment and resource allocation decision making in health care.Methods:An electronic literature search was executed for studies on disinvestment, obsolete and ineffective technologies, and priority healthcare setting, published from January 1990 until January 2012. Databases searched were MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, Embase, The Cochrane Library, PubMed, and HEED.Results:Fourteen case studies on the application of frameworks and tools for disinvestment and resource allocation decisions were included. Most studies described the application of program budgeting and marginal analysis (PBMA), and two reports used health technology assessment (HTA) methods for coverage decisions in a national fee-for-service structure. Numerous healthcare technologies and services were covered across the studies. We describe the multiple criteria considered for decision making, and the strengths and limitations of these frameworks and tools are highlighted.Conclusions:Disinvestment and resource allocation decisions require evidence to ensure their transparency and objectivity. PBMA was used to assess resource allocation of health services and technologies in a fixed budget jurisdiction, while HTA reviews focused on specific technologies, principally in fee-for-service structures. Future research can review the data requirements and explore opportunities to increase the quantity of available evidence for disinvestment and resource allocation decisions.

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Construction and Application of Resource Allocation Decision-making Model for Innovation Management

2010 3rd International Conference on Information Management, Innovation Management and Industrial Engineering ◽

10.1109/iciii.2010.51 ◽

2010 ◽

Author(s):

Xiuhui Yu ◽

Shoulian Tang

Keyword(s):

Decision Making ◽

Resource Allocation ◽

Innovation Management ◽

Resource Allocation Decision ◽

Allocation Decision ◽

Decision Making Model

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Withholding Medical Care & Ageism During a Pandemic: A Model for Resource Allocation Decision-Making (SCI959)

Journal of Pain and Symptom Management ◽

10.1016/j.jpainsymman.2021.01.119 ◽

2021 ◽

Vol 61 (3) ◽

pp. 701-702

Author(s):

Susan Lowey

Keyword(s):

Decision Making ◽

Resource Allocation ◽

Medical Care ◽

Resource Allocation Decision ◽

Allocation Decision

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Resource allocation decision making in an uncertain environment

Acta Psychologica ◽

10.1016/0001-6918(87)90015-1 ◽

1987 ◽

Vol 66 (1) ◽

pp. 1-19 ◽

Cited By ~ 17

Author(s):

Jerome R. Busemeyer ◽

In Jae Myung

Keyword(s):

Decision Making ◽

Resource Allocation ◽

Resource Allocation Decision ◽

Allocation Decision ◽

Uncertain Environment

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Case Manager Resource Allocation Decision-Making for Adult Home Care Clients: With Comparisons to a High Needs Pediatric Home Care Clients

Home Health Care Management & Practice ◽

10.1177/1084822318779371 ◽

2018 ◽

Vol 30 (4) ◽

pp. 164-174 ◽

Cited By ~ 1

Author(s):

Kimberly Fraser ◽

Garland Baird Lisa ◽

Daley Laing ◽

Jonathan Lai ◽

Neelam Saleem Punjani

Keyword(s):

Decision Making ◽

Resource Allocation ◽

Data Collection ◽

Home Care ◽

Quality Care ◽

Case Manager ◽

Care Program ◽

Ethnographic Study ◽

Resource Allocation Decision ◽

Allocation Decision

Home care programs have become integral parts of the overall health service system in Canada and in many other developed nations. Resource allocation decision-making by home care case managers (CM) is a complex task where CMs are challenged to meet the dual responsibilities for clients, in order that they achieve high quality care, and to the system to contain costs. The purpose of this study was to extend what is known about resource allocation decision-making factors identified in a previous systematic literature review and ethnographic study within a high needs pediatric context conducted by the principal investigator in Western Canada. Spradley’s ethnoscience method was used in this research. The study sample consisted of 17 home care CMs, professional practice leads, and their managers from two separate home care offices. All participating CMs had assigned caseloads and were involved in the assessment and implementation of care planning for clients. Purposive sampling methods were employed. In keeping with Spradley’s ethnoscience approach, data collection occurred in three distinct phases or rounds. The first round of data collection began with a series of one-on-one interviews with card sorts, the second round of data collection was another series of one-on-one interviews with CMs who were not interviewed in the prior round, and the third and final round of data collection was a focus group to accomplish further refinement and verification of our established categories. Participants identified five categories of factors that effected their resource allocation decision-making. The categories were related to one of five main areas: the client, the CM, the home care program, community resources, or the health care system. The findings of this study reinforced the complexity of CM resource allocation decision-making in home care. This study provides new insights into CM resource allocation decision-making based on multidisciplinary, integrated home care teams caring for adults, the majority of whom are 65 years and older. This study also provides the comparison of taxonomy that differs between pediatric and adult home care populations that influence resource allocation decision-making.

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