Examining Failure in a Dynamic Decision Environment: Strategies for Treating Patients with a Chronic Disease

Ramsey, Gregory; Johnson, Paul E.; O’Connor, Patrick J.; Sperl-Hillen, JoAnn M.; Rush, William A.; Biltz, George R.

doi:10.1007/978-3-319-23294-2_5

Cited by 1 publication

(5 citation statements)

References 27 publications

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“…Physicians who treat the disease are engaged in a processcontrol task where they obtain information on patient states, make decisions and take actions to move a patient toward clinical goals, obtain new information to determine the effects of actions taken, and repeat the process . These processes of information seeking and action taking were represented in agent-based computational models (Ramsey, 2010). As defined, these models delivered care to the patient model in the same manner as would be delivered by idealised physicians.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Conditionality of these perturbations resulted in decision agents only committing (i.e., generating) errors under specific environmental conditions. Use and application of hypothesised process perturbations as error sources were tested in a prior study (Ramsey, 2010).…”

Section: Datasetmentioning

confidence: 99%

“…A combination of validation and verification methods was used with individual physician models and with the multi-agent clinic model (Balci, 1994;Klugl, 2008). In a prior study (Ramsey, 2010) the individual physician agent models were subjected to a rigorous validation and verification process. Validity checks included desk checking model processes against peer-reviewed and committee formulated guidelines for the treatment of patients with T2DM.…”

Section: Model Validationmentioning

confidence: 99%

“…A two-step process was used to model physician behaviour (Ramsey, 2010). First, each physician's simulation data from each case was scored on a visit-byvisit basis to identify errors of omission and commission.…”

Section: Datasetmentioning

confidence: 99%

“…The physician models generated three types of actions based on information obtained from the patient model (Ramsey, 2010):…”

Section: Literature Reviewmentioning

confidence: 99%

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Evaluating policies using agent-based simulations: investigating policies for continuity of care

Ramsey

2014

IJSPM

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Anticipating effects of proposed clinical policies is a difficult task. This study investigates the usefulness of agent-based simulations for evaluating clinical policies. Two policies for continuity of care for patients with type 2 diabetes are investigated using an agentbased simulation. Computational models of a dynamic decision environment were simulated to determine aggregated effects of individual care-providing agents acting to achieve clinical goals. The simulated policies were: 1) continuous care (CC), where each patient was randomly assigned a specific physician model for care across visits; 2) opportunistic care (OC), where each patient on each visit was randomly assigned to a physician model for treatment. These policy scenarios are at the crux of a debate as to whether continuity of care needs to be administered by a single provider or by a single organisation (e.g., clinic). The study determines under which conditions CC and OC policies result in favourable patient outcomes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%