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We provide a continuation of the existing Activity Table Modeling methodology with a modular spreadsheets simulation. The simulation model developed is comprised of 28 modeling elements for the abdominal surgery cycle process. The simulation of a two-week patient flow in an abdominal clinic with 75 beds demonstrates the applicability of the methodology. The simulation does not include macros, thus programming experience is not essential for replication or upgrading the model. Unlike the existing methods, the proposed solution employs a modular approach for modeling the activities that ensures better readability, the possibility of easily upgrading the model with other activities, and its easy extension and connectives with other similar models. We propose a first-in-first-served approach for simulation of servicing multiple patients. The uncertain time duration of the activities is modeled using the function “rand()”. The patients movements from one activity to the next one is tracked with nested “if()” functions, thus allowing easy re-creation of the process without the need of complex programming. [ABSTRACT FROM AUTHOR]

• We developed data-driven and expert-driven computer-based DSS models for medication change of Parkinson's disease patients • The DSS models cover the whole space of input attributes' values, i.e., any combination of motor and non-motor symptoms, and any epidemiologic characterization of a patient • Expert-driven DSS model resembles well the decisions made by physicians and outperforms the data-driven model in terms of accuracy. • The accuracy results indicate that the constructed models are sufficiently adequate and fit for the purpose of making suggestions to DSS users. Parkinson's disease (PD) is a degenerative disorder of the central nervous system for which currently there is no cure. Its treatment requires long-term, interdisciplinary disease management, and usage of typical medications, including levodopa, dopamine agonists, and enzymes, such as MAO-B inhibitors. The key goal of disease management is to prolong patients' independence and keep their quality of life. Due to the different combinations of motor and non-motor symptoms from which PD patients suffer, in addition to existing comorbidities, the change of medications and their combinations is difficult and patient-specific. To help physicians, we developed two decision support models for PD management, which suggest how to change the medication treatment. The models were developed using DEX methodology, which integrates the qualitative multi-criteria decision modelling with rule-based expert systems. The two DEX models differ in the way the decision rules were defined. In the first model, the decision rules are based on the interviews with neurologists (DEX expert model), and in the second model, they are formed from a database of past medication change decisions (DEX data model). We assessed both models on the Parkinson's Progression Markers Initiative (PPMI) and on a questionnaire answered by 17 neurologists from 4 European countries using accuracy measure and the Jaccard index. Both models include 15 sub-models that address possible medication treatment changes based on the given patients' current state. In particular, the models incorporate current state changes in patients' motor symptoms (dyskinesia intensity, dyskinesia duration, OFF duration), mental problems (impulsivity, cognition, hallucinations and paranoia), epidemiologic data (patient's age, activity level) and comorbidities (cardiovascular problems, hypertension and low blood pressure). The highest accuracy of the developed sub-models for 15 medication treatment changes ranges from 69.31 to 99.06 %. Results show that the DEX expert model is superior to the DEX data model. The results indicate that the constructed models are sufficiently adequate and thus fit for the purpose of making "second-opinion" suggestions to decision support users. [ABSTRACT FROM AUTHOR]