This paper presents a framework for public healthcare data acquisition and management model based on standard protocol for its easy adoption by any country or international health organizations. The model assumes basic digitization of electronic health record (EHR) at basic health facilities. Thus far, the models in the literature have utilized EHR in multiple secondary contexts; however, there is a gap in developing an integrated and comprehensive framework that addresses the use of EHR in a standardized way for public health, privacy issue by anonymizing patient specific information, fusing multiple records with slight changes in the same information, augmenting a broad spectrum of contextual data, and so on. We present a framework that can be used in the context for acquisition and transmission of EHR from multiple sources as an evidence base for addressing public health-related activities, including surveillance, registries, and immunization record keeping while addressing all the gaps we have identified in the literature that are critical for developing countries. In addition, EHR data are also effectively processed to serve as a knowledge base for building artificial intelligence-based research models. We, in our model, utilize Health Level Seven (HL7) as an interoperability health standard and recommend creation of specialized data marts to support public health and research-related knowledge bases. The proposed framework in its adoption provides a very effective platform for generating alerts and alarms along with providing statistics for better planning of healthcare-related issues at national, district, or at any level of administrative hierarchy. It is applicable to any country even when there is no standard EHR and has hospitals working in silos with limited digitalization. We have validated this framework for its mapping to a national level public health hierarchy in Pakistan. INDEX TERMS Electronic health records (EHR), evidence-based policy, health data interoperability, health information exchange (HIE), Health Level 7 (HL7), public health framework, public health surveillance.
Unlike conventional mobile ad hoc networks, tactical networks, which provide communication of software-defined radios (SDRs) in mission critical and time-sensitive applications, require cognitive functions across the TCP/IP stack to encounter strict constraints while providing smooth incorporation with IP-based applications. The tactical applications are mission-critical and thus pose unique requirements for the network, including decentralized control and mission specific latency bounds for end-to-end data delivery. This paper presents a mathematical model for a cross-layer design, which optimizes trade-offs among different configurations of the SDRs to achieve maximum performance in terms of energy efficiency, reliable packet delivery at an appropriate data rate and within affordable latency bounds in multi-hop tactical networks. The proposed model is used in a number of mission-critical network scenarios to demonstrate enhanced performance, where SDRs effectively adapt to the dynamic environment.
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.
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.
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