Improvement of Data Quality - Essay Example

Improvement of Data Quality Institution Name Date Issues of Data Quality 1. Missing data Missing data mainly occur at the point of data collection. It can be due to negligence, mistakes or failure to know what kind of data is required (Kmietowicz, 2004). The first initiative in preventing incidences of missing data is training all the officers who are involved in data collection. This training should be aimed at helping them understand the nature of data that is required for each case. At the data collection points, there should be specific data elements which are defined in a uniform manner for every patient (Kerr, 2006). This means that each of those elements must be collected and recorded for every patient. These details should be availed in a manual either in soft or hard copy that will act as reference information for every data collection procedure. This information may be different for every data collection point depending on the intended purpose. Those involved in collection of data should be dedicated members of staff since failure to do it properly can result in poor diagnosis. Therefore health institutions should have well defined procedures on what data needs to be collected for every patient. Before this data is used or saved in the data base, it should be cross checked to ensure that there is no missing data. 2. Database errors These are errors that occur when the data is being stored in the databases. Once an error has been done on this data, it can be propagated to other systems. One of the methods in which such errors can be prevented is by having a system that will give a feedback when the data is incomplete, has missing entries, values that are out of range or inconsistent entries. This feedback will be sent to the person entering the data into the database and will be able to correct the error immediately. Data audits into the database can also help in minimizing database errors. This can be done on entries that are highly subjective to errors. Data quality assurance procedures are also important and must be observed by the data entry staff. Training these staff on the procedures of data entry can also help in minimizing these errors. Once the database has been created, it is important to protect that data from unauthorized access. This is because some people may access that data and make intentional errors on the data and this may create errors in the database. Protection of databases is therefore important in ensuring that intentional errors are not made on the data (Berndt et al., 2001). 3. Inaccuracy of data Inaccuracy of data results during initial data entry, due to data decay or during data movement. To prevent inaccuracy during data entry, it is important to ensure that those involved in data entry have adequate typing skills. They should also avoid rush when entering data. It is also important to ensure that there are minimal or no distractions during data entry and that the exercise is carried out in a comfortable place. Data decay happens where values may be accurate when they are being entered but they become inaccurate with time. These are data such as contacts, marital status and number of dependents among others. To avoid this, it is important to keep re-verifying them every time a patient visits to ensure their continued accuracy. Inaccuracy during data movement occurs during extracting, transforming and loading data into another source. This happens if the system is too complex for the users (Shankaranarayan, Ziad & Wang, 2003). To avoid this, it is important to ensure that those involved in these data handling processes have adequate knowledge of the systems and also to verify the data before using it. Transmission of inaccurate data to the users may result in wrong decisions which may affect the end results. 4. Modified data One of the methods that can prevent modification of the stored data is to ensure limited access of that data. This can be achieved by ensuring that each user of that data has an individual account through which he or she will log into so that he or she can make changes into the stored data. After the changes, the person should log out to ensure that no other person accesses that data. Those authorized to access and change this data should only work with their passwords which they should not share with any other person. These passwords should also be changed at different intervals to ensure their security. Another measure for preventing data modification is to conduct data audit. This involves keeping track of all the changes made to the data in the electronic records. The audits will be to confirm whether it is only the authorized deletions, additions or alterations that have been made on the records. Computer generated audits can be able to capture the modifications that were made on the data (Bowen, Fuhrer & Guess, 1998). Another method would be to ensure that the date and time on the computer systems is always correct and signals any changes made to the date and time. This is because people may make changes to the data and back date the time of those changes to show that they were out of duty at that particular time. Basically a tight security system should be maintained on the stored data. 5. Dirty data This is incomplete, inaccurate and erroneous data. Dirty data originates from data entry processes and is most of it is caused by human error. This can be avoided by accurately defining the data entry requirements. These are requirements such as what data is to be collected, where it is coming from and how often it should be collected. During transformation of data, there should be procedures in place for validating that data and for reporting any error in it. Then the data should be securely stored with limited access to ensure that it is not altered. Establishing effective data management policies and procedures at every data handling point is also very important (Fletcher, 2004). Every person handling data should be responsible for any action on it. This could help prevent duplication of records and missing data. Critical control means should be put at every data entry point to ensure that data is clean before it enters the database from where it circulates to the intended users. Data cleansing can also be adopted which will be routinely conducted in time intervals. This involves cross checking all the records both manual and electronic to ensure that no data is missing, incomplete or erroneous. It may be a costly undertaking but it protects the patients from wrong diagnosis. 6. Poor classification of data To ensure that data is properly classified, the hospital should establish a data classification scheme that will be applied in the entire organization depending on how critical and sensitive the data is. For example there may be public, confidential or top secret data. For electronic records, classification of data can be automated where there is a system that will automatically classify that data according to the level of sensitivity assigned to that data. High skills are also required for data classification. Data classification staff should be well trained on how to assign levels to data and they should be committed to achieve proper data classification (Institute of Medicine, 2000). For example hospital data may be classified according to age into the class of infants, school going age and adults. It may also be classified depending on the residence areas of the patients. This therefore requires collaboration between data storage administrators and the end user on the classification criteria. The doctors who are the end users should state how they would like the data to appear for use and agree with the system administrators. This will ensure that the users receive data in the correct format which easy for them to use. Once classified, data should also be protected from unauthorized access to prevent alterations which may end up mixing it up. 7. Poor data framework and security Data quality framework is a tool for assessing the quality of data in an organization (Wang et al.. 1996). The main purpose of this framework is to identify areas of poor quality or inefficiencies which may affect organizational performance (Kerr, Norris & Stockdale, 2007). To ensure an effective data quality framework, the hospital must have a data quality policy, data quality management and a data quality control. The data quality policy should state the direction of the hospital in regard to quality of data products and should be formally expressed by top management. Data quality management should be a management function whose responsibility is to determine and implement the data quality policy. The data quality control involves techniques and activities required in attaining the required quality of data. Such a system setting offers an effective framework for ensuring data quality (Wang, Veda, Christopher, 1995). Data security can be achieved by having a system for monitoring data movement to ensure that the data is in safe use and is not being used by unauthorized users. Every data user should however take responsibility of ensuring safe handling of the data and maintaining its privacy and security. The data handlers should ensure privacy of their log in details and ensure that they log out after every use. References Kerr, K., Norris, T. & Stockdale, R. (2007). Data Quality Information and Decision Making: A Healthcare Case Study. Proceedings of the 18th Australian Conference on Information Systems (ACIS 2007), Toowoomba, Queensland, Australia Kerr, K. (2006). The institutionalization of Data Quality in the New Zealand Health Sector. Unpublished PhD Thesis, University of Auckland. Wang, R., Strong, D. and Guarascio, L. (1996). Beyond accuracy: what data quality means to data consumers. Journal of management information systems, vol 12, no 4, pp. 5-33. Shankaranarayan, G., Ziad, M. and Wang, R. (2003). Managing data quality in dynamic decision environments: an information product approach. Journal of data management, vol 14, no. 4, pp. 14-32. Wang, R., Veda, C. and Christopher, P. (1995). A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering, Vol. 7, No, 4, Berndt, D. J., Fisher, J. W., Hevner, A. R., & Studnicki, J. (2001). Health care data warehousing and quality assurance. Computer, 34(12), 56-65. Bowen, P., Fuhrer, D. & Guess, F. (1998). Continuously improving data quality in persistent databases. Data Quality, 4(1). Fletcher, D. (2004). Achieving data quality: new data from a pediatric health information system earns trust of its users. Journal of AHIMA, 75(10), 22-26. Institute of Medicine. (2000). To Err is Human. Building a Safer Health System. Washington: National Academy Press. Kmietowicz, Z. (2004). Data collection is poor because staffs don’t see the point. British Medical Journal, 328,786. Read More