Health Informatics and Surveillance Paper Example

Executive Summary

The project seeks to establish a surveillance system for cancer. Cancer surveillance has previously used the traditional surveillance system which has been ineffective due to the time required to manually process the data and also the long time needed to diagnose the disease. The project focuses on creating a syndromic disease surveillance system which is more effective in presenting real-time cancer cases and trends compared to the traditional surveillance system. The research identifies critical features in the syndromic surveillance system such as the use of an algorithm in the procession of incoming data and creating historical comparisons. The syndromic surveillance system uses digital dashboards to present the processed data into different components such as trends, geographical differences, gender ratio affected and the true scope of the disease which increases understanding of cancer prevalence and survival rates. The syndromic surveillance system is more superior compared to traditional surveillance because the syndromic surveillance system has been digitized to ease data collection and processing.

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The project establishes a syndromic surveillance evaluation process to determine the suitability and reliability of the processed data to meet the surveillance system objectives which is to reduce the overall time taken in cancer surveillance reporting. Continuous monitoring by creating performance indicators makes it easy to assess the effectivity of the syndromic surveillance system in meeting its primary objectives and also allows for constant modification and improvement by absorbing emergent technologies. The use of mobile applications such as tele-health in the syndromic surveillance system improves the ease of data collection on cancer. The project identifies the estimated cost of creating a syndromic surveillance system and its overall functionality to meet its objectives. The analysis of social, political, economic, and environmental attributes of the syndromic surveillance system establishes its feasibility as a cancer surveillance system compared to using traditional data surveillance.

History of Disease Surveillance

Public health surveillance dates back to the Egyptian first Dynasty during Pharaoh Mempses reign when an epidemic was recorded in human history (Choi, 2012). The great pestilence which was recorded in historic Egyptian records occurred in 3180 BC. The most devastating plagues Justinian, the Black Death, and Spanish Influenza have all been documented as major disease surveillance events. In the United States, the first legislation for disease surveillance was enacted in Rhode Island in 1741 when the colony required tavern keepers to report contagious diseases amongst their patrons followed by 1743 legislations requiring documentation of smallpox, yellow fever, and cholera incidences (Choi, 2012). The big data era has increased disease surveillance effectivity through new technology. In the 19th century, systematic sentinel surveillance improved systematic collection of disease data (Simonsen, Gog, Olson, & Viboud, 2016). The laboratory surveillance systems in the 20th century are still the basis of disease surveillance today. Electronic health records in the United States of America and other western countries have revolutionized modern surveillance systems. The private and internet-based surveillance systems such as the Healthmap and Global Health Intelligence Network in the US and Canada respectively has improved disease surveillance (Simonsen et al., 2016).

Surveillance Subject and Data Sources

The chosen surveillance subject is cancer which is a collective name for a group of diseases that is caused by an abnormal growth of cells. The growth of cells in cancer is uncontrollable and can spread to other parts of the body which is called metastasis (O'Keeffe et al., 2018). Lung cancer is the most prevalent form of cancer which is caused by cigarette smoking. The main sources of data in cancer surveillance is the primary and secondary sources. The Center for Disease (CDC) US Cancer Statistics Data Visualizations Tool can be a good source of cancer incidence data in the United States (CDC. 2014). The hospital registry which records all the data of cancer incidences can be a source of primary cancer incident data. The death certificates from across the country can be used to provide data on cancer mortality within a specified location or time (U.S. Cancer Statistics. 2015).

Traditional vs. Syndromic Surveillance

Syndromic surveillance uses the frequency of specific clinical features from prediagnosis data to estimate community health (Ziemann, 2015). The data is categorized into syndromes based on the symptoms and diagnoses which can be used in biologic terrorism (Abat, Chaudet, Rolain, Colson, & Raoult, 2016). Therefore, the syndromic surveillance relies on syndromic data before diagnosis are made using confirmatory laboratory data which can infer specific illness (Samaras, Garcia-Barriocanal, & Sicilia, 2017). On the other hand, the traditional surveillance is disease-specific surveillance and involves a continuous data collection and evaluation system from formal and authentic sources (Abat et al., 2016: Friesema, van Gageldonk-Lafeber, & Van Pelt, 2014). The syndromic data surveillance system has been found to be more effective compared to the traditional surveillance system because the latter takes long for data to be analyzed whereas the syndromes can easily be identified (Abat et al., 2016).

Algorithms in Automated Disease Surveillance System

Algorithms incorporation in the surveillance of diseases has improved the quality of the results by making it possible for public health organizations to process large quantities of data (Spreco & Timpka, 2016). The outbreak detection algorithms which has been used by pioneer health organizations in Britain such as the Public Health England has improved the ability to process data within the shortest time possible and create credible disease outlook regarding the population affected and the disease trend (Paterson & Durrheim, 2013). HPA algorithms have been useful in creating a linear pattern of a disease which takes account of seasonality and creates credible possible outbreak identification (Enki, Garthwaite, Farrington, Noufaily, Andrews, & Charlett, 2016).

Digital Dashboard

Digital dashboards primary role in disease surveillance is the improved presentation of the disease trends using infographics which makes it easy to understand the disease trends over time (Hopkins et al., 2017). The digital dashboard is an electronic interface that uses KPIs to represent different diseases or an individual disease at a different point in time as well as during different statistical figures (Sarikaya, Correll, Bartram, Tory, & Fisher, 2018). The digital dashboard creates timely situation awareness, and multiple partners can assess one database presenting similar data visualization in different locations which promotes partnership and cooperation in diseases outbreak surveillance. In this case, the use of dashboards has improved cancer control and research because it is a disease that affects different areas and countries requiring great cooperation in research and implementation of controls (Tieu, Cigsar, Ahmed, Ng, Diller, Millar, ... & Hodgson, 2014).

System Comparison

There are different cancer surveillance systems at the local, state, regional, and national levels which collect cancer incidence data. In the state of Alabama, cancer surveillance takes place at the county levels by the Alabama Statewide Cancer Registry (ASCR) which collect and present cancer surveillance data in the State of Alabama (Alabama Department of Public Health. 2017). Cancer cases reporting are coordinated by the organization from the pathology registry, physicians, and the hospital's cancer registry where it is consolidated in a common central registry (Alabama Department of Public Health. 2017). In the national level, cancer data surveillance is carried out by Surveillance Epidemiology and End Results Program (SEER) that operates under the National Cancer Institute (American Cancer Society, 2018). The program gathers data on new cancer cases, prevalence and survival rates. The program has been critical in cancer data surveillance in the United States covering 30% of the national cancer data and representing 28% of the US population (American Cancer Society, 2018: Duggan, Anderson, Altekruse, Penberthy, & Sherman, 2016).

Tele-Health

Tele-health is a new application in disease surveillance using an application as a large number of hospitals are using the application to communicate with patients with an estimated 65% healthcare facilities use of mobile devices which raises the need to incorporate mobile applications such as tele-health in cancer surveillance (Forbes. 2018; Baldwin, Singh, Sittig, & Giardina, 2017). The application increases the speed of disease data delivery and can help to improve data submission convenience (Beyene, Asfaw, Getachew, Tufa, Collins, Beyi, & Revie, 2018).

System Evaluation Proposal

Evaluation of Automated Disease Surveillance System

The evaluation of the automated surveillance systems is critical because it increases effectivity, accuracy and the ability of the surveillance system to serve its core purpose (Calba, Goutard, Hoinville, Hendrikx, Lindberg, Saegerman, & Peyre, 2015). Public cancer automated surveillance system should be able to generate information that can be used to create actionable decisions. The information should be of sufficient quality, timeliness and resolution that meets the primary data objective. These can be ensured through system evaluation and constant monitoring to ensure outcomes-focused research and health information management (Groseclose & Buckeridge, 2017). An automated cancer surveillance system is created to establish the prevalence, survival rate and identifying new cases. Regular evaluation of the automated surveillance system which uses algorithms to process cancer data and present it in digital dashboards using creative infographics can be instrumental in ensuring effectivity (...