Sample Research paper on Supporting Health Informatics Initiatives

Data Standardization

One of the greatest difficulties in using a data dictionary in the healthcare sector relates to data standardization. The healthcare sector is characterized by high data volumes and similarly high data traffic. Moreover, healthcare enterprises have a variety of departments across which communication has to be done and information sharing has to be accomplished. Data dictionary maintenance and standardization is faced by various challenges. In the healthcare sector as in many other sectors, the key challenge in data dictionary standardization and maintenance relates to the nature of the data entailed. The complexity of the data structures makes it difficult to handle effectively. Systems developed for the implementation of different data dictionaries have to incorporate several data types which are also handled by several key personnel. At times, coordination and effective definition of data can be challenging. Furthermore, the diverse data sources also make standardization all the more difficult due to the ambiguity in data comprehension across different sources.

The extraction process in data dictionary maintenance and standardization is made difficult by the variety in data sources in that some of the data to be sourced are a result of the interaction of more than one department. In particular, the standardization process can be very challenging where the sources are in constant communication and where data changes are rapid (Cai & Zhu, 2015). In addition to the diverse sources, the data volumes also pose a challenge to the composition of the data dictionary. For instance, one of the most pertinent characteristics of data in the data dictionary is the data quality. Where the data volumes are significantly high, it can be difficult to judge the data quality within a limited time frame. This can result in delays in the ETL process.  At the same time, where data changes are rapid, making informed decisions can be difficult. The conclusions made following rapid data changes can be misleading. Effective operations in data dictionary standardization require high speed processors and higher data handling capacities.

Another challenge in the maintenance and standardization process is the lack of unification in the data standardization procedures. According to Cai and Zhu (2015), the data dictionary management process does not have universal standards as like the ISO standards for product qualities. Consequently, it is difficult to unify beliefs and findings in terms of data quality. Based on this, communicating across different enterprise departments can be challenging especially where there are conflicting beliefs. For instance, the knowledge held by the finance department about a particular concept may be different from that held by the marketing or the customer service departments.

Challenges with Data Information and File Structures

Structuring health data to address the needs of the user can be very challenging based on the nature of the data involved, the users of the data and the frequency of data updates. Because of these reasons, personnel in the healthcare department often find it difficult to implement electronic health recording. The major challenge when it comes to data information and file structuring relates to the infrastructures involved. Accessing health data may not be challenging, especially when the users are healthcare personnel. However, the infrastructures required to be able to effectively manage the EHR process are costly. The hardware components of electronic health recording are many and also costly. Because of this, managers in the sector often find it challenging to implement EHR. Furthermore, they are discouraged by other data management practices in the industry.

According to Sivarajah et al (2017), electronic health recording faces a challenge in data mining due to the perceived ethical considerations in the sector. Ethical practices require great confidentiality in handling client information. For instance, the use of disease rather than patient information in advertisement is a mandatory ethical practice in healthcare service delivery. On the other hand, electronic health recording results in information sharing across different departments and among different people in the departments, which can result in ethical violations. Another challenge mentioned by Sivarajah et al (2017) is on data privacy issues. According to the authors, information security and governance are key concerns in electronic health recording. While factors such as confidentiality of information may be adhered to in the traditional data recording procedures, the same level of security cannot be guaranteed in the use of EHR. This is due to the exposure of electronic data to the internet, where the potential for corruption is high. Factors such as data modification, theft and encryption are all inevitable in electronic data handling hence the need to have stringent security measures.

Other challenges that may be faced include process challenges and data interpretation. Process challenges entail data capturing, transformation of data to suit the specific enterprise needs and integration of data from various sources into a unified information bank. Handling such processes can be a serious challenge especially when combined with the massive data volumes. Data interpretation can also be challenging due to the different perceptions across departments. Unlike in data base managements, data dictionaries can raise questions regarding ambiguity of the source data and the differences in recognized data characteristics (Sivarajah et al., 2017).

Best Practices in Data Mining and Exploration

Data mining and exploration can be conducted effectively using the database querying approach. The approach involves giving commands for the transformation and/ or filtration of the data and subsequently getting the desired results. For this process to be effective, Van den Eynden et al (2011) propose a series of best practices. The first is to double check the observation and response codes. This can help to align the data to departmental sections and to avoid ambiguity and double coding of data and responses. Furthermore, double checking also enhances data accuracy, which is a component of data quality. Double checking the codes also goes hand in hand with confirming the data completeness. All elements of any data must be checked for satisfactory entry into the system.

Upon effective data entry, data verification is an essential practice in the management of data mining and exploration activities. This has to be done periodically to ensure that all the data in the data dictionary is up to date. Due to the large volumes of data in the dictionary, verification cannot be performed on all the data in the system hence the need to carry out random sampling of the digital data in the system. The random samples picked can then be compared with actual, hard copy data to confirm its accuracy and timeliness (Van den Eynden et al., 2011). When creating file names, it is important to adhere to best practices such as choice of short yet meaningful names for files. Long file names cause confusion and enhance the difficulties in file handling procedures. Furthermore, when the file names are not meaningful or have no relationship with the file contents, it can be a challenge to determine the required file among others where information of a particular nature is needed. The objective of such kind of file naming is to ensure that the labeling is clear and organized and that it can be easily located. Physical accessibility of files is an imperative part of data dictionary management practices and clarity in storage is the key to accomplishing this accessibility. Once the data has been efficiently and clearly named, it is also recommended that data storage should be conducted in multiple storage systems. For instance, in a case where data is not stored in clouds, back-up storage has to be planned for and also implemented (Van den Eynden et al., 2011).

Challenges with the Information management Process

From the report given by Ngafeeson (2015), the adoption of information management processes in the healthcare sector has faced some resistance from the stakeholders. This resistance is reportedly based on the challenges experienced in the process. For instance, technological challenges such as lack of standardization and absence of effective health information exchange systems are prevalent in the sector. Such challenges are reported mostly by the health personnel in charge of information management. Lack of standardization causes misunderstanding and possible misinterpretation of data in the EHR. From the management perspective, the costs of implementation pose the greatest challenge in the information management process. The probability that the systems would result in lack of returns is challenging to implementation managers since the costs of infrastructures are high. Investors in the sector are also not keen on electronic information management.

In addition to the management and technological challenges, electronic information management is also challenging in terms of the healthcare setting. The privacy and security issues in the sector result in fear among the potential system users due to the probability of revealing sensitive information. Best practices in the use of electronic data systems require that electronic health records should focus on the diseases and their respective control measures rather than on the patients and their characteristics. While this may be achieved, it depends on the system users who also pose an additional barrier to the implementation process. The system users offer significant resistance to the implementation of electronic health recording process. The potential users of the EHR systems have to be trained in order to enhance their dedication to system implementation. With slow response rates, the training process can be strenuous and costly on the part of the management.

The regulatory environment in healthcare can also be challenging to the information management process. The healthcare environment is faced with a variety of regulatory standards in terms of different parameters that have to be satisfied. For instance, hygiene, infection and re-infection preventive measures, disease control mechanisms such as isolation of infectious disease cases are all requirements in the healthcare sector and must be adhered to in full. The healthcare personnel, while using the EHR systems have to determine the degree of compliance with various regulations and subsequently before updating information on the EHR systems. To the user, this can be a challenge where the work conditions do not favor frequent updates of information. In conclusion, the information management system in the healthcare setting has to be handled effectively for benefits to be accrued.


Cai, L. & Zhu, Y., (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal. 14, p. 2. Retrieved from

Ngafeeson, M. (2014). Healthcare Information Systems: Opportunities and Challenges. In Khosrow- Pour Encyclopedia of Information Science and Technology, 3rd Ed. Pp. 352- 367. Retrieved from

Sivarajah, U., Kamal, M.M., Irani, Z. and Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263- 286. Retrieved from

Van den Eynden, V., Corti, L., Woollard, M., Bishop, L. and Horton, L. (2011). Managing and sharing data: best practice for researchers. UK Data Archive. Retrieved from