Limits...
Can modeling of HIV treatment processes improve outcomes? Capitalizing on an operations research approach to the global pandemic.

Xiong W, Hupert N, Hollingsworth EB, O'Brien ME, Fast J, Rodriguez WR - BMC Health Serv Res (2008)

Bottom Line: Mathematical modeling has been applied to a range of policy-level decisions on resource allocation for HIV care and treatment.Optimization of clinical and logistical processes through modeling may improve outcomes, but successful OR-based interventions will require contextualization of response strategies, including appreciation of both existing health care systems and limitations in local health workforces.Increasing the number of cross-disciplinary collaborations between engineering and public health will help speed the appropriate development and application of these tools.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Public Health, Weill Medical College, Cornell University, New York, NY, USA. wmz2001@med.cornell.edu

ABSTRACT

Background: Mathematical modeling has been applied to a range of policy-level decisions on resource allocation for HIV care and treatment. We describe the application of classic operations research (OR) techniques to address logistical and resource management challenges in HIV treatment scale-up activities in resource-limited countries.

Methods: We review and categorize several of the major logistical and operational problems encountered over the last decade in the global scale-up of HIV care and antiretroviral treatment for people with AIDS. While there are unique features of HIV care and treatment that pose significant challenges to effective modeling and service improvement, we identify several analogous OR-based solutions that have been developed in the service, industrial, and health sectors.

Results: HIV treatment scale-up includes many processes that are amenable to mathematical and simulation modeling, including forecasting future demand for services; locating and sizing facilities for maximal efficiency; and determining optimal staffing levels at clinical centers. Optimization of clinical and logistical processes through modeling may improve outcomes, but successful OR-based interventions will require contextualization of response strategies, including appreciation of both existing health care systems and limitations in local health workforces.

Conclusion: The modeling techniques developed in the engineering field of operations research have wide potential application to the variety of logistical problems encountered in HIV treatment scale-up in resource-limited settings. Increasing the number of cross-disciplinary collaborations between engineering and public health will help speed the appropriate development and application of these tools.

Show MeSH
Schematic diagram of simulation model of HIV clinic operations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2533310&req=5

Figure 1: Schematic diagram of simulation model of HIV clinic operations.

Mentions: In 2005–6, we created a stochastic simulation model to assist capacity planning for HIV treatment clinics, diagramed in Figure 1[37,38]. This simulation model was designed to capture patient characteristics (WHO stage, CD4 count distribution, attrition), disease progression (CD4 decline), treatment protocols, human resource utilization, and the competition for limited resources in a single clinic. We modeled the clinic as a set of interconnected work stations representing different working cadres (e.g., clerk, nurse, doctor). An enrollment plan of the clinic is used as one input to the simulation. Patients scheduled by the enrollment plan, as well as random arrivals, are created by the simulation program and are routed to the clinic, visiting different stations in based on health status (which the model assigns to each patient based on inputted values and evidence-based assumptions) and country-specific treatment protocols (based on health ministry publications). Clinical details ranging from the treatment process, logistics, staffing, to the demand for drugs, are considered in the clinic visit. After each visit, patients may come back or be lost to follow-up visits. During this time, each individual patients disease progression or biological reactions to treatment are modeled. In this way, the simulation program can be used to represent aggregate patient visit dynamics to determine maximal enrollment and visit capacity under steady-state clinic operations in the setting of user-manipulated resource constraints.


Can modeling of HIV treatment processes improve outcomes? Capitalizing on an operations research approach to the global pandemic.

Xiong W, Hupert N, Hollingsworth EB, O'Brien ME, Fast J, Rodriguez WR - BMC Health Serv Res (2008)

Schematic diagram of simulation model of HIV clinic operations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2533310&req=5

Figure 1: Schematic diagram of simulation model of HIV clinic operations.
Mentions: In 2005–6, we created a stochastic simulation model to assist capacity planning for HIV treatment clinics, diagramed in Figure 1[37,38]. This simulation model was designed to capture patient characteristics (WHO stage, CD4 count distribution, attrition), disease progression (CD4 decline), treatment protocols, human resource utilization, and the competition for limited resources in a single clinic. We modeled the clinic as a set of interconnected work stations representing different working cadres (e.g., clerk, nurse, doctor). An enrollment plan of the clinic is used as one input to the simulation. Patients scheduled by the enrollment plan, as well as random arrivals, are created by the simulation program and are routed to the clinic, visiting different stations in based on health status (which the model assigns to each patient based on inputted values and evidence-based assumptions) and country-specific treatment protocols (based on health ministry publications). Clinical details ranging from the treatment process, logistics, staffing, to the demand for drugs, are considered in the clinic visit. After each visit, patients may come back or be lost to follow-up visits. During this time, each individual patients disease progression or biological reactions to treatment are modeled. In this way, the simulation program can be used to represent aggregate patient visit dynamics to determine maximal enrollment and visit capacity under steady-state clinic operations in the setting of user-manipulated resource constraints.

Bottom Line: Mathematical modeling has been applied to a range of policy-level decisions on resource allocation for HIV care and treatment.Optimization of clinical and logistical processes through modeling may improve outcomes, but successful OR-based interventions will require contextualization of response strategies, including appreciation of both existing health care systems and limitations in local health workforces.Increasing the number of cross-disciplinary collaborations between engineering and public health will help speed the appropriate development and application of these tools.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Public Health, Weill Medical College, Cornell University, New York, NY, USA. wmz2001@med.cornell.edu

ABSTRACT

Background: Mathematical modeling has been applied to a range of policy-level decisions on resource allocation for HIV care and treatment. We describe the application of classic operations research (OR) techniques to address logistical and resource management challenges in HIV treatment scale-up activities in resource-limited countries.

Methods: We review and categorize several of the major logistical and operational problems encountered over the last decade in the global scale-up of HIV care and antiretroviral treatment for people with AIDS. While there are unique features of HIV care and treatment that pose significant challenges to effective modeling and service improvement, we identify several analogous OR-based solutions that have been developed in the service, industrial, and health sectors.

Results: HIV treatment scale-up includes many processes that are amenable to mathematical and simulation modeling, including forecasting future demand for services; locating and sizing facilities for maximal efficiency; and determining optimal staffing levels at clinical centers. Optimization of clinical and logistical processes through modeling may improve outcomes, but successful OR-based interventions will require contextualization of response strategies, including appreciation of both existing health care systems and limitations in local health workforces.

Conclusion: The modeling techniques developed in the engineering field of operations research have wide potential application to the variety of logistical problems encountered in HIV treatment scale-up in resource-limited settings. Increasing the number of cross-disciplinary collaborations between engineering and public health will help speed the appropriate development and application of these tools.

Show MeSH