balancing an integrated eye care training program with service needs and available resources

balancing an integrated eye care training program with service needs and available resources

 A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

See reference in diagram notes. WIP for Environment part of primary care regional model. GP centric calibration by JPS at  IM-14117  See also  IM-3126  for Regional Health Service Use Context
See reference in diagram notes. WIP for Environment part of primary care regional model. GP centric calibration by JPS at IM-14117 See also IM-3126 for Regional Health Service Use Context
 Overview of choice modelling as part of value, effectiveness and motivation series of insights about wants, needs and demands related to health care (regional) model See also  IM-4043

Overview of choice modelling as part of value, effectiveness and motivation series of insights about wants, needs and demands related to health care (regional) model See also IM-4043

Protein conformance change based on Ed Gallaher and Jim Rogers 2021 Forrester Award Lecture ISDC
Protein conformance change based on Ed Gallaher and Jim Rogers 2021 Forrester Award Lecture ISDC
 Causal loop diagram based on Jack  Homer's  Worker burnout: a dynamic model with implications  for prevention and control See  IM-333  for simulation model and IM-641 for  Rich Picture CLD  
 System Dynamics Review 1985 1(1)42-62 
  

Causal loop diagram based on Jack  Homer's  Worker burnout: a dynamic model with implications  for prevention and control See IM-333 for simulation model and IM-641 for Rich Picture CLD

System Dynamics Review 1985 1(1)42-62

 

 Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA.  
 Understanding diabetes population dynamics through simulation modeling  
 and experimentation. American Journal of Public Health 2006;96(3):488-494. 
  http://ajph.aphapublications.org/cgi/content/abstract/96/3/488

Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA.

Understanding diabetes population dynamics through simulation modeling

and experimentation. American Journal of Public Health 2006;96(3):488-494.

http://ajph.aphapublications.org/cgi/content/abstract/96/3/488

WIP integrating Epidemiology Systems Science and Policy making, mainly based on books and AJE articles by Keyes and Galea
WIP integrating Epidemiology Systems Science and Policy making, mainly based on books and AJE articles by Keyes and Galea
This is a high level system dynamics model which is built to determine the dynamic relationships of the FSA and Followups capacity. Therefore, it can help clinicians to find out the optimistic method in order to reduce the waiting list. At past clinicians were seeing more FSA patients, however, afte
This is a high level system dynamics model which is built to determine the dynamic relationships of the FSA and Followups capacity. Therefore, it can help clinicians to find out the optimistic method in order to reduce the waiting list. At past clinicians were seeing more FSA patients, however, after few months, the followups patients overwhelmed the clinics. Therefore waiting list has been built up again. By running this model, clinicians can find out the balanced leverage point(s).

Authors: Ashish Taneja, Keming Wang and Daniel Wong
 Causal Loop Rich Picture unfolding from Repenning, N. and J. Sterman (2002). Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement. Administrative Science Quarterly, 47: 265 - 295. http://jsterman.scripts.mit.edu/docs/Repenning-2002-CapabilityTraps.pdf

Causal Loop Rich Picture unfolding from Repenning, N. and J. Sterman (2002). Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement. Administrative Science Quarterly, 47: 265 - 295. http://jsterman.scripts.mit.edu/docs/Repenning-2002-CapabilityTraps.pdf

 Modelling prevalence of cardiovascular disease within a population using agent based modelling. The initial population is defined within Tools->Variables and macros.     This is a partial model that is not yet complete.
Modelling prevalence of cardiovascular disease within a population using agent based modelling. The initial population is defined within Tools->Variables and macros.

This is a partial model that is not yet complete.
WIP example of Services oriented multiscale computable narrative synthesis focussed on Coping carefully with diabetes
WIP example of Services oriented multiscale computable narrative synthesis focussed on Coping carefully with diabetes
 Simplified version of  IM-852   Erythropoiesis Stimulating Agents (ESA) Dosing in Anemia due to Renal Failure from Jim Rogers See Stock Flow Map   Insight 810  

Simplified version of IM-852  Erythropoiesis Stimulating Agents (ESA) Dosing in Anemia due to Renal Failure from Jim Rogers See Stock Flow Map  Insight 810 

Based on Chris Argyris 2010 Book Organizational Traps Oxford University Press, built around  Insight 619  on single and double loop learning
Based on Chris Argyris 2010 Book Organizational Traps Oxford University Press, built around Insight 619 on single and double loop learning
 Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.  We add simple containment meassures that affect two paramenters, the Susceptible population and the rate to become infected.  The initial parametrization is based on the su

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

We add simple containment meassures that affect two paramenters, the Susceptible population and the rate to become infected.

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

The questions that we want to answer in this kind of models are not the shape of the curves, that are almost known from the beginning, but, when this happens, and the amplitude of the shapes. This is crucial, since in the current circumstance implies the collapse of certain resources, not only healthcare.

The validation process hence becomes critical, and allows to estimate the different parameters of the model from the data we obtain. This simulation approach allows to obtain somethings that is crucial to make decisions, the causality. We can infer this from the assumptions that are implicit on the model, and from it we can make decisions to improve the system behavior.

Yes, simulation works with causality and Flows diagrams is one of the techniques we have to draw it graphically, but is not the only one. On https://sdlps.com/projects/documentation/1009 you can review soon the same model but represented in Specification and Description Language.

 Replaced by  IM-752  Causal Loop Rich Picture unfolding from Repenning, N. and J. Sterman (2002). Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement. Administrative Science Quarterly, 47: 265 - 295. http://jsterman.scripts.mit.edu/docs/Repenning-2002-Capa

Replaced by IM-752 Causal Loop Rich Picture unfolding from Repenning, N. and J. Sterman (2002). Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement. Administrative Science Quarterly, 47: 265 - 295. http://jsterman.scripts.mit.edu/docs/Repenning-2002-CapabilityTraps.pdf