This paper introduces a novel framework which combines the outputs from Radio Frequency Identification (RFID) technology, the automated outpatient feedback survey form, Hospital Management Information System (HMIS) and sensors to develop an automated patient experience management system (PEMS) using a Genetic algorithm (GA). The output from the RFID tag is the time spent by a patient at various stations in the hospital. While the output from the automated survey is an overall satisfaction index (OSI), which is the overall experience (in the form of a number) a patient has during his/her stay in the hospital. HMIS has details regarding the structure of the hospital; this includes details about doctors, nurses, rooms, location of various departments, etc. In addition, environmental conditions (temperature and humidity) from installed sensors are used to capture the physical context of the patient's experience. To develop an automated PEMS GA is used for computing the patient experience. The collected data (timing information, HMIS and sensor data) is given as input so that the GA generates optimized weights which are then applied to the final PEMS to automatically produce the overall satisfaction index best matching with OSI. This proposed framework reduces the time taken by manual statistics by automating the complete interaction of patient and hospital staff at all stations. The experiments are performed using the developed tool, in a local hospital, and the results demonstrate an accuracy of 80.3%. This accuracy gives a good indication to hospital management in real-time to take measures in areas where the patient experience is going relatively low. INDEX TERMSGenetic Algorithm (GA), Hospital management information system (HMIS), Overall satisfaction index (OSI), Patient experience management index (PEMI), Radio Frequency Identification device (RFID)
Heart diseases and strokes are considered as number one killer as they account for around 35 to 40 per cent of the total disease burden in Pakistan. The ratio of heart patients is increasing day by day, which is an alarming condition for the country. This situation needs a detailed analysis which can show the geographical distribution of heart patients and also the city wise attributes (age, weight, income etc) that are aggregating more in the heart disease. A Threshold Based Inference Engine is designed which infers the knowledge base by generating the association rules on each city. These rules infer the clustered data to extract the city wise more risk increasing attributes, and the common disease in that city. Automated Minnesota code is used for the verification of the collected ECGs. The results show that Threshold based Inference Engine successfully and efficiently generates a detailed report of each city including more diseased people and highlights the attributes increasing the risk factor.
This paper proposes a decision support framework for defining planning parameters for national crop production. The proposed framework addresses the gaps in policymaking, the role of all stakeholders, and uses historical data of crop production in different sectors of land in optimizing the profits that shall meet the defined constraints including national requirement, and export demand of different crops. There are many agro-economy-based countries where agriculture is the main contributor to their GDP, while there are others who always struggle to meet their national need while optimizing the agro-economy component of their earnings. Crops are not always produced with keeping the demand and production balance. Rather, the crops are mostly produced according to the farmer's ease and last year's prices for profit maximization leading to excessive production of one type of crop, lowering of profit margins, and shortage of some key crops for national need. This paper presents a decision aiding tool that can be used for farmer's awareness for crop production planning that meets the national and export needs while maximizing farmer's earnings. The model is tested on historical data of different segments of cultivated lands in Pakistan for validation with the first experiment performed on single farmland with multi-fields while the second performed for multi-locations and multi-fields. The results are promising and provide estimates of net profit, expected production against the demand for each crop, and analysis that aids in crop planning, before and after the application of our model while meeting all necessary constraints.
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