Towards Data Driven Spatio-Temporal Threshold Identification Based on Cost Effective Public Health Information Management Framework

Muhammad Nazakat,Nadeem Ahsan,Fatima Khalique,Shoab Ahmed Khan

doi:10.1109/access.2022.3149349

Abstract

Appropriate public health action 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 may provide 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.

Highlights

H EALTH data collection, analysis and transmission is conducted to achieve public health targets of population health improvements
As a first step towards threshold identification, we performed spatial clusteing of Malaria disease based on patient address using k-means
In order to determine the value of number of clusters, we plotted the variance in data as function of k and identified the value that flattens the curve through Elbow Method

Summary

Introduction

H EALTH data collection, analysis and transmission is conducted to achieve public health targets of population health improvements. Data collection and analysis cannot be designed to consume resources if action does not follow. The health problem definition is realized through quality health related data. Public health dynamics are misinterpreted, resulting in policies and programs that do not adequately address the problem leading to an appropriate allocation of resources. The pathway through which data reaches public health authorities defines the data quality. An effective and functional information system provide a sound foundation towards ensuring that programs designed based on received data meet their goals and resources allocated are not biased given the underlying health situation [2].

Methods

Results

Discussion

Conclusion