The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in the insurance industry, it is rapidly adapting mathematical and machine learning models to enhance insurance services like funds prediction, customer management and get better revenue from their businesses. However, conventional computing insurance packages and premium methods are time-consuming, designation specific, and not cost-effective. During the design of insurance packages, an employee’s needs should be given more importance than his/her designation or position in an organization. The design of insurance packages in healthcare is a non-trivial task due to the employees’ changing healthcare needs; therefore, using the proposed technique employees can be moved from their existing package to another depending upon his/her need. This provides the motivation to propose a methodology in which we applied machine learning concepts for designing need-based health insurance packages rather than professional tagging. By the design of need-based packages, medical benefit optimization which is the core goal of our proposed methodology is effectively achieved. Our proposed methodology derives insurance packages that are need-based and optimal based on our defined criteria. We achieved this by first applying the clustering technique to historical medical records. Subsequently, medical benefit optimization is achieved from these packages by applying a probability distribution model on five years employees’ insurance records. The designed technique is validated on real employees’ insurance records from a large enterprise.The proposed design provides 25% optimization on medical benefit amount compared to current medical benefits amount therefore, gives better healthcare to all the employees.

Public health management can generate actionable results when diseases are studied in context with other candidate factors contributing to disease dynamics. In order to fully understand the interdependent relationships of multiple geospatial features involved in disease dynamics, it is important to construct an effective representation model that is able to reveal the relationship patterns and trends. The purpose of this work is to combine disease incidence spatio-temporal data with other features of interest in a mutlivariate spatio-temporal model for investigating characteristic disease and feature patterns over identified hotspots. We present an integrated approach in the form of a disease management model for analyzing spatio-temporal dynamics of disease in connection with other determinants. Our approach aligns spatio-temporal profiles of disease with other driving factors in public health context to identify hotspots and patterns of disease and features of interest in the identified locations. We evaluate our model against cholera disease outbreaks from 2015–2019 in Punjab province of Pakistan. The experimental results showed that the presented model effectively address the complex dynamics of disease incidences in the presence of other features of interest over a geographic area representing populations and sub populations during a given time. The presented methodology provides an effective mechanism for identifying disease hotspots in multiple dimensions and relation between the hotspots for cost-effective and optimal resource allocation as well as a sound reference for further predictive and forecasting analysis.

Communicable diseases remain a significant challenge for public health management. In particular, for resource scarce settings, it is important to understand the linkages and dynamics of multiple diseases that share common resources, space or time. We develop a framework, called Multiple Disease Management Framework (MDMF) based on machine learning approach for managing multiple diseases occurring in close space and time to identify locations that experience high disease burden rates. We use 8 water related disease incidence data in Punjab, Pakistan from year 2013 to 2019 to investigate interactions among hotspots of different diseases. However, the model is scalable and can be applied to any number of diseases. The hotspot analysis involves multi-level clustering and tagging of individual disease incidence streams that generates a distance based graph over a geographical area and is then integrated into a single stream in the framework to identify final sensitive locations called cluster alarms. The initial individual disease clustering yielded number of clusters as high as 24 clusters for each disease with up to 16 neighboring clusters of other diseases of similar sizes. The cluster tagging and multi-level clustering process was able to identify as low as 19 locations of cluster alarms across the whole province of 38 districts. The identification of high disease hotspots and their dynamics with the neighboring hotspots of multiple diseases allows identification of locations with higher need of related public health resources. This identification is very critical for national health agencies for optimal allocation of resources and devising an effective intervention programs.

The importance of a standard based interoperability framework is now widely recognized. In order to understand the challenges, feasibility and impact of outcomes of such architectures for public health improvement, it is important to implement and demonstrate the usefulness of data analytics based on pathway provided by the framework. This work provides an implementation details and results of transferring dengue data from source database to data analytical programs used by public health authorities. Dengue fever, a mosquito-borne disease that occurs in tropical and subtropical areas of the world, is considered to be a significant threat in both developing and the developed countries. This study investigates the spatio-temporal distribution of dengue in Pakistan from the years 2014-2017 and identify the most frequently affected hotspots of dengue across the the 3 provinces in the country using data mining, clustering and GIS based analysis of data. In addition, the identification of most vulnerable dengue locations has led to investigation into the potential environmental or geographical conditions that may have contributed to dengue prevalence in the area. These investigations present interesting results that shows positive correlation for temperature, humidity and population density with the dengue incidences. In addition, the results are able to identify disease hotspots over time windows that can be mapped to any spatio-temporal scales. The outcome of this research is crucial for optimal use of resources for combating dengue fever at a regional or national scale by identifying the hotspots. The hotspots identified can be use to create a sentinel surveillance network for a pandemic disease. In addition, the work serves as a useful reference for a comprehensive national level early warning and rapid disease outbreak detection framework for any disease of public health context.

Appropriate public health actions comes from data driven decision support systems. While sophisticated health information exchange framework may be costly in developing countries, the health care delivery system in place provides a promising infrastructure that spans all parts in a region. Therefore, while digital and non digital data is constantly being generated from variety of sources including public and private health sectors, the health care delivery systems remain the primary and most fundamental source for data on population health status. For low and middle income countries with minimum digitization and resource constraints, traditional existing health care delivery system can be taken advantage of, for efficient production and timely transmission and utilisation of data to identify thresholds for disease outbreak in order to improve health status and health system performance. Due to lack of appropriate universally agreed criteria for threshold, defining local thresholds for infectious disease is not only crucial but also more appropriate.In this paper, we present a low cost data driven framework called Health Data Driven Framework (HDDF) through which data generated at health care facilities may be used for threshold detection and alarm generation. We also identify a localized method based on spatio-temporal mining of available data for appropriate threshold identification.