Performance Ratio Based Resource Allocation Decision-Making in Genomic Medicine

2017 ◽  
Vol 21 (2) ◽  
pp. 67-73 ◽  
Author(s):  
Vasilios Fragoulakis ◽  
Christina Mitropoulou ◽  
Daphne Katelidou ◽  
Ron H. van Schaik ◽  
Nikolaos Maniadakis ◽  
...  
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Genomic Medicine ◽  
Resource Allocation Decision ◽  
Performance Ratio ◽  
Allocation Decision

CASE STUDIES THAT ILLUSTRATE DISINVESTMENT AND RESOURCE ALLOCATION DECISION-MAKING PROCESSES IN HEALTH CARE: A SYSTEMATIC REVIEW

2013 ◽  
Vol 29 (2) ◽  
pp. 174-184 ◽  
Author(s):  
Julie Polisena ◽  
Tammy Clifford ◽  
Adam G. Elshaug ◽  
Craig Mitton ◽  
Erin Russell ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Resource Allocation ◽  
Case Studies ◽  
Resource Allocation Decision ◽  
Cochrane Library ◽  
Fee For Service ◽  
Allocation Decision ◽  
Allocation Decisions ◽  
Resource Allocation Decisions

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.

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

2018 ◽  
Vol 30 (4) ◽  
pp. 164-174 ◽  
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.

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

2016 ◽  
Vol 20 (2) ◽  
pp. 246-264 ◽  
Author(s):  
Nathaniel D. Bastian ◽  
Tahir Ekin ◽  
Hyojung Kang ◽  
Paul M. Griffin ◽  
Lawrence V. Fulton ◽  
...  
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Health Systems ◽  
Resource Allocation Decision ◽  
Allocation Decision ◽  
Multi Objective ◽  
Fixed Input

TESTING MULTI-CRITERIA DECISION ANALYSIS FOR MORE TRANSPARENT RESOURCE-ALLOCATION DECISION MAKING IN COLOMBIA

2016 ◽  
Vol 32 (4) ◽  
pp. 307-314 ◽  
Author(s):  
Hector Eduardo Castro Jaramillo ◽  
Mireille Goetghebeur ◽  
Ornella Moreno-Mattar
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Decision Analysis ◽  
Health Technology Assessment ◽  
Technology Assessment ◽  
Social Protection ◽  
Health Technology ◽  
Resource Allocation Decision ◽  
Allocation Decision ◽  
Multi Criteria Decision Analysis

Objectives: In 2012, Colombia experienced an important institutional transformation after the establishment of the Health Technology Assessment Institute (IETS), the disbandment of the Regulatory Commission for Health and the reassignment of reimbursement decision-making powers to the Ministry of Health and Social Protection (MoHSP). These dynamic changes provided the opportunity to test Multi-Criteria Decision Analysis (MCDA) for systematic and more transparent resource-allocation decision-making.Methods: During 2012 and 2013, the MCDA framework Evidence and Value: Impact on Decision Making (EVIDEM) was tested in Colombia. This consisted of a preparatory stage in which the investigators conducted literature searches and produced HTA reports for four interventions of interest, followed by a panel session with decision makers. This method was contrasted with a current approach used in Colombia for updating the publicly financed benefits package (POS), where narrative health technology assessment (HTA) reports are presented alongside comprehensive budget impact analyses (BIAs).Results: Disease severity, size of population, and efficacy ranked at the top among fifteen preselected relevant criteria. MCDA estimates of technologies of interest ranged between 71 to 90 percent of maximum value. The ranking of technologies was sensitive to the methods used. Participants considered that a two-step approach including an MCDA template, complemented by a detailed BIA would be the best approach to assist decision-making in this context. Participants agreed that systematic priority setting should take place in Colombia.Conclusions: This work may serve as the basis to the MoHSP on its interest of setting up a systematic and more transparent process for resource-allocation decision-making.

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