TITLE: Decision Support System for assessing management interventions in a Mental Health ecosystem
AUTHORS: Carlos Garcia-Alonso

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block">Here it is included the pseudocode for the Decision Support System for assessing management interventions in a Mental Health ecosystem. This pseudocode can be used to reproduce the experiments that we have carried out in Bizkaia (Basque Country, Spain).</div><div class = "text-block"><span>1: </span><span style = "font-weight:bold;">DSS procedure</span></div><div class = "text-block">2: The solution pool (SP) is cleaned</div><div class = "text-block"><span>3: </span><span style = "font-weight:bold;">For</span><span style = "font-weight:bold;font-style:italic;">j</span><span>=1 to                    </span><span style = "font-weight:bold;">do</span><span> →  being </span><span style = "font-style:italic;">j</span><span> the simulation number and  the maximum number of simulations</span></div><div class = "text-block"><span>4: </span><span style = "font-weight:bold;">For</span><span style = "font-weight:bold;font-style:italic;"> </span><span style = "font-style:italic;">i</span><span>=1 to   </span><span style = "font-weight:bold;">do</span><span> →  being </span><span style = "font-style:italic;">i</span><span> the scenario number and   the maximum number of scenarios</span></div><div class = "text-block"><span>5: </span><span style = "font-weight:bold;">For</span><span style = "font-weight:bold;font-style:italic;">k</span><span>=1 to   </span><span style = "font-weight:bold;">do</span><span> →  being </span><span style = "font-style:italic;">k</span><span> the SHA (DMU) number and   the number of SHA</span></div><div class = "text-block"><span>6: Original dataset </span><span style = "font-weight:bold;vertical-align:sub;">S</span><span style = "font-weight:bold;vertical-align:sub;">ijk</span><span> →  the Monte-Carlo simulation engine selects at random a value for each simulation, variable and SHA </span><span style = "font-style:italic;">k</span><span> according to their specific statistical distributions.</span></div><div class = "text-block"><span>7: Calculate </span><span style = "font-weight:bold;vertical-align:sub;">D</span><span style = "font-weight:bold;vertical-align:sub;">ijk</span><span> and </span><span style = "font-weight:bold;vertical-align:sub;">O</span><span style = "font-weight:bold;vertical-align:sub;">ijk</span><span> →  the fuzzy inference engine determines the day care and outpatient care fuzzy sets and membership degrees for each SHA</span></div><div class = "text-block">8: Calculate   and   →  the fuzzy inference engine determines the range for variable value interpretation according to the B-MHCCM</div><div class = "text-block"><span>9: Interpreted dataset </span><span style = "font-weight:bold;vertical-align:sub;">I</span><span style = "font-weight:bold;vertical-align:sub;">ijk</span><span> →  the original dataset </span><span style = "font-weight:bold;vertical-align:sub;">S</span><span style = "font-weight:bold;vertical-align:sub;">ijk</span><span> are interpreted by the fuzzy inference engine according to the B-MHCCM</span></div><div class = "text-block"><span>10: Calculate </span><span style = "font-weight:bold;">RTE</span><span style = "font-weight:bold;vertical-align:sub;">ijk</span><span>scores →  variable returns to scale Data Envelopment Analysis (DEA) model (both input and output-oriented) </span></div><div class = "text-block"><span>11: Save </span><span style = "font-weight:bold;">RTE</span><span style = "font-weight:bold;vertical-align:sub;">ijk</span><span> scores in the solution pool</span></div><div class = "text-block"><span>12: </span><span style = "font-weight:bold;">end for</span></div><div class = "text-block"><span>13: </span><span style = "font-weight:bold;">end for</span></div><div class = "text-block"><span>14: </span><span style = "font-weight:bold;">end for</span></div><div class = "text-block"><span>15: </span><span style = "font-weight:bold;">end procedure</span></div></div>

[STEPS]
?. DSS procedure
?. The solution pool (SP) is cleaned
?. Forj=1 to do → being j the simulation number and the maximum number of simulations
?. Fori=1 to do → being i the scenario number and the maximum number of scenarios
?. Fork=1 to do → being k the SHA (DMU) number and the number of SHA
?. Original dataset Sijk→ the Monte-Carlo simulation engine selects at random a value for each simulation, variable and small health area (SHA) k according to their specific statistical distributions
?. Calculate Dijk and Oijk → the fuzzy inference engine determines the day care and outpatient care fuzzy sets and membership degrees for each SHA
?. Calculate xleft and xright for each simulation, scenario and SHA→ the fuzzy inference engine determines the range for variable value interpretation according to the B-MHCCM
?. Interpreted dataset Iijk→ the original dataset Sijk are interpreted by the fuzzy inference engine according to the B-MHCCM
?. Calculate RTEijkscores → variable returns to scale Data Envelopment Analysis (DEA) model (both input and output-oriented)
?. Save RTEijk scores in the solution pool
?. end for
?. end for
?. end for
?. end procedure