PLAY IT! PLUS

Participants design artificial intelligence-based solutions that answer real problems in the health sector, departing from problem statements provided directly by industrial partners, with the target of the prototype's output mature to a minimum viable product (MVP) stage.

Example problem statements:

• At the registration stage, patient waiting time needs to be reduced.
• At the history-taking (anamnesis) stage, recording complaints manually is time-consuming and sometimes leads to inconsistent note formats.
• At the diagnosis stage, patient history is scattered and hard to view as a whole.
• At the therapy stage (radiology, laboratory, physiotherapy), examination requests and results go back and forth manually and take time. Laboratory instrument results must be re-entered into the hospital information system (SIM RS), which takes extra time and risks typing errors.
• At the pharmacy stage, prescriptions take a long time, drug stock is not visible in real time, and there is a risk of stockouts.
• In support services, coordination between units still relies on paper or verbal communication, and claims/billing are delayed because the data is incomplete.
• SATUSEHAT, Mobile JKN, and the hospital information system (SIM RS) run separately, so the same data has to be entered on several platforms.
• Queues from Mobile JKN and walk-in registration are not always synchronized with the hospital information system (SIM RS).

…and other problem statements.

Participants design artificial intelligence-based solutions that answer real industrial problems in the industrialization process, departing from the problem statement provided directly by industrial partners, with the output target of the prototype mature to a minimum stage viable product (MVP) and opening a further downstream path.

Example problem statements:

• Tanker trucks queue for a long time because arrivals pile up and quality checks take time.
• Raw materials are ready, but the next process stage is not yet ready to receive them.
• Some packaging lines draw material faster than supply can keep up, causing the process to stall.
• Time is wasted on product/packaging changeovers and equipment cleaning that are poorly sequenced.
• Raw materials wait too long in tanks because the next stage is not ready — risking lower quality.
• It is hard to trace raw materials from a specific tanker truck to the finished products they end up in, because the data is scattered across many stages.

…and other problem statements.

Participants design artificial intelligence-based solutions that answer real problems in the field of food security, departing from problem statements provided directly by industrial partners, with the target of the prototype output mature to a minimum viable product (MVP) stage.

Example problem statements:

• Planting calendar and weather: extreme weather causes farmers to choose the wrong planting time, while pest attacks come without warning.
• Grain prices and farmers' bargaining position (distribution): farmers often do not know the fair price at harvest, so they are easily pressured by middlemen.
• Staple food price disparities between regions (distribution): staple food prices differ widely between regions, and residents do not know why or where to find cheaper options.
• Post-harvest losses (production): harvests are lost during drying, storage, and milling because these are done traditionally and are not monitored.
• Household and retail food waste (availability): edible food is thrown away while some residents still lack food.
• Timely and well-targeted fertilizer (management): smallholder farmers struggle to confirm their entitlement to subsidized fertilizer, when it is available, and where to redeem it.
• Farmer regeneration and farmland (production): young people are reluctant to farm, and productive land keeps shrinking without community monitoring.
• Food stocks and access at the community level (availability): residents do not know the food stock in their area or where affordable food is available. Example: SPHP rice, which Bulog has been asked to focus on distributing to areas that are not rice production centers.

…and other problem statements.