Awareness and Utilisation of Primary Healthcare to Reduce Emergency Department Overcrowding in Saudi Arabia

Background Patients seeking emergency department (ED) care for non-acute conditions are a major contributor to ED overcrowding, which results in longer wait times. Method This was a cross-sectional study, conducted using an online survey among the Saudi population to assess their awareness about primary healthcare clinics (PHCCs) and urgent care clinics (UCCs), their role, and their scope of practice. Results A total of 565 participants were included in this study. Most of the respondents (81.1%) reported lengthy waiting times in the ED. Moreover, most (81.6%) stated that they had never visited a family doctor, yet they (92.6%) favoured having one for follow-up care. Close to half of the participants (50.3%) reported attending PHCCs without an appointment, and the majority of them (69.2%) said that PHCCs were overcrowded. Finally, most participants (92.4%) had not heard aboutUCCs. Conclusion ED overcrowding and prolonged waiting times remain a public concern. PHCCs and UCCs are underutilised, and this is attributed to the lack of awareness about their scope and their services.  

Harnessing Big Data for Official Statistics in Pakistan

Official Statistics is the main source to track economic, demographic and social conditions in any country/region. Reliable and timely statistics provide the basis for evidence-based planning and decision making, helps the governments to address the real needs of citizens. Due to rapid technological advancements, government organizations are moving towards digitalization instead of manual data recordings. Which is producing more and more digital data, required modern tools for data processing with statistical care. Administrative data’s importance for the production of official statistics is undeniable. If these sources fall under the definition of Big Data, it becomes a treasure trove for the National Statistical Organizations. Without having proper statistical and data processing human resource with advanced computational skills, it is awful to optimally utilize these sources. This research is designed to address the issues related to the use of Big Data source in production of official statistics, especially in case of Pakistan. The theoretical framework of this dissertation is divided into three parts (a) Review, (b) Assess and (c) Measure. Review part is based on the first two chapters. In Chapter 1, the core elements of the official statistics and Big Data are briefly explained. Its sources and administrative data’s importance in production of official statistics have been discussed. Furthermore, we have briefly discussed the Big Data sources, definitions, classifications and its importance for official statistics and the National Statistical System of Pakistan. At the end of this chapter, the research problem is defined and the research objectives and the theoretical framework of this dissertation is demonstrated. Chapter 2 is based on the review of current developments, tools and techniques in the field of Big Data for official statistics. Here, we have reviewed the current advancements in the use of administrative Big Data and non-administrative Big Data source in production of official statistics world widely as well as in the case of Pakistan. Assessment part of this research is covered in Chapter 3 and 4. In Chapter 3, several aspects of production of official statistics and Big Data use in production of official statistics in public-sector organizations have been covered. For sake of this a survey titled “Survey of Official Statistics Production (SOS-Pak)” has been conducted at national level. It was a censusbased survey conducted thorough post mail inquiry. 171 public sector organizations from Federal and Punjab government participated in this survey. Key descriptive statistics from the survey are discussed here in this chapter. In Chapter 4, a major contribution of the research is incorporated related to the x assessment of unreported Administrative and Big Data sources in public-sector organizations of Pakistan. It is hypothesized that a little portion of digital data sources produced under the departments are being reported and utilized in production of official statistics whereas most of these sources left unreported. We have tried to validate the statement and to uncover these sources to link/merge it with the existing data sources for production of official statistics with more accuracy, timeliness and relevancy. Measurement part of this thesis is based on further two chapters. In Chapter 5 we have made a second major contribution by the development of Official Statistics and Big Data processing capability indicators. These indicators can be used to explore and compare the available potential and weak sectors/organizations need consideration and capacity building with respect to their weak measures. Both, the Official Statistics and Big Data Processing capability indicators are the compound indicators based on various dimensions. While each dimension is based on further various measures related to official statistics production and Big Data processing of publicsector organization. In Chapter 6, the Statistical cadre and data processing human resource are rationalized with respect to both the Federal and Punjab government organizations. The job function/activities are analyzed with different angles. The conclusions are drawn and recommendations are given aside with at the end of each chapter instead of presenting it separately. In the last Chapter 7, some data merging techniques for the merging of multiple data sources are discussed as a future roadmap. As the quality and reliable official statistics can only be produced by merging the Administrative Big Data sources with the existing Official Statistics produced under the National Statistical Institutions.