Journal of Health & Medical Informatics

One of the most important indices of defining general welfare and quality-of-life of people in the world is physical
and mental health of individuals.
Health care managers and planners therefore must make future demand for healthcare services and the need for
medicines to achieve fully and reliable supply. With the introduction of the test and treat methodology of managing HIV
patients, first line Antiretroviral drugs (ARVs) must be in adequate availability to enable facilities implement this strategy
of HIV eradication. Discontinuation of antiretroviral therapy Antiretroviral drugs (ART) due to shortages may result into
viral rebound, immune decomposition, and clinical progression of the virus, therefore there is need to plan ahead of
time to avail the most required stock for ARV drugs.
There are no proper forecasting and anticipation mechanisms of future demand for first line Antiretroviral drugs
(ARV) and this is a cross cutting problem for all the public health facilities in Mbarara District and this has led to
overstocking and understocking of these drugs leading to shortages and wastage related to expiry
This study aimed at designing a predictive model for demand of first-line ARV drugs in Mbarara district, using
data mining techniques. Using the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, the
objectives of this study were to extract and prepare dataset required for data mining, examine different methods used
in demand prediction, Design a model for predicting demand and evaluate this model.
The model was trained under the Waikato Environment for Knowledge Management (WEKA) which is a data mining
environment and predicted the demand for first line ARVs in various health facilities Mbarara district Uganda. The test
results showed that the forecasting in time series approach was more suitable and efficient for drug cycles ahead
demand forecasting. Forecast results demonstrated that the model performed remarkably well with increased number
of actual data and iterations. A regression model gave more accurate forecast results with 7.3% Mean Percentage
Error as compared to alternative methods of demand forecasting whose error was above 30%.

Healthcare institutions in developing countries are facing numerous obstacles in the digitization of patient records. Previous solutions like EHR have had disappointing results as they require more time consumption from healthcare professionals, who prefer to spend this on observing more patients. We propose a technology to assist the doctors in their daily routines and aim to reduce the gap and allow patients to get the care that they need, especially in Government hospitals of developing countries.

Pressures from high patient volumes, performing treatments once reserved for inpatient visits, and new financial programs drive departments to do more with existing resources. There is a significant body of knowledge about the challenges inherent in ambulatory care outpatient workflows setting up the need for developing, implementing, and using location-based technologies to help automate outpatient workflows on the day of service. We share the clinical value of indoor locating systems and give a simple example showing the efficacy of using location-based predictive analytics to improve the patient experience by properly managing resources and translate directly to better working conditions for the clinical staff. To make our point, we design, and model fit a simple, single predictor logistic regression model that could help reduce wait time and customize the patient experience by the staff actions to attend to the waiting patient.

Several works in healthcare diseases support systems in recent time are being inspired by a lot of semantic web technology. Specifically, there has been a rise in the number of knowledgebase system that has been developed using ontological engineering. For two decades, Sunyani Municipality records a lump number of diseases with a few such as Typhoid Fever, Malaria, Diarrhoea Diseases, Pneumonia, Anaemia, and so on being prevalent. Healthcare systems in the Municipal do not have a centralised knowledge base for these prevalent diseases, hence the need for a centralised knowledge-based system. This study proposes a knowledge-based system using ontological engineering to assist the formulation of a strong foundation for establishing a meaningful decision-making support system for the proper diagnosis and management of these diseases in the Municipality. We analysis 3,377,403 number of cases from 2013- 2017 and thereafter categorised the case into different classes of diseases. Using a threshold ratio of +1% between several cases for a particular disease (Pdc) and total number cases in its category (Cr), we characterised about thirtyfive (35) diseases as prevalent. Consequently, we designed a robust knowledge-based for the identified prevalent diseases by adopting the Cyc method, which includes three processes in connection with ontological engineering technique. The system was well rated of about 77% after staff from two primary health facilities in the municipality.