Health IT
Enterprise data warehousing, data mining, and data analytics capabilities
An enterprise data warehouse involves the creation of a permanent and central storage space for different sources of data so as to support the institution’s reporting and analysis (Rindfleisch, 1997). Since there is data obtained from different sources, the management in a health care institution makes decisions that are founded n sufficient data. Data warehouses can be directly applied on processes such as sales, financial management, inventory management, and market segmentation. The management is able to have access to critical data from various sources, which enhances making informed decisions on major initiatives. No time is wasted retrieving data from different sources (Chau & Hu, 2001). The management can query the data without the help of IT technicians, which saves money and time. Since data is converted into a common format, there is more confidence in data accuracy, which ensures solid decisions. The huge historical data amounts, enables analysis of various trends and time periods and enables making future predictions. More revenue is generated and more money saved.
Data mining involves finding previously unidentified trends and patterns in databases and utilizing that information for building predictive models. It can be used in treatment effectiveness, healthcare management, detecting abuse and fraud, and customer relationship management (Chau & Hu, 2001). Data mining enables collection of huge digitized patient data amounts. There is computerized and accurate documentation of data, which promotes quality health care. Previously unidentified trends can be detected from the vast data stores and this enables building of predictive models. Other benefits of data mining include maintenance scheduling, detection of frauds, and credit scoring. Healthcare institutions that are proficient in data mining might be eligible for government funding. It also enables provision of affordable and better services, identification of best practices and effective treatments, making better patient decisions, and detecting abuse and fraud (Rindfleisch, 1997).
Data analytics promotes care quality, cost savings, improved decision-making, increased research capabilities, better program and management strategies, improved care coordination, overcoming service gaps, data privacy, personalized medication, seamless care continuity, and evidence-based outcomes (Chau & Hu, 2001).
Opportunity costs
Without the use of data mining, data analytics capabilities, and enterprise data warehousing, health care institutions are unable to manage and handle their huge masses of data appropriately. Without this, there are barely any cost reductions and improvements in patient care. The quality of health care is derailed if there are no accurate and reliable data sources (Bates et al. 2001). Identifying vital healthcare trends and patterns would be difficult, similar to personalized heath care medication. Evidence-based practice and informed decision making would not be possible without advance information technology use in healthcare.
Applications of electronic data interchange (EDI) in the health care field
Electronic data interchange enables transmission of data that is used commonly between healthcare institutions through the use of phone lines and computers. It enables more accurate, faster, and cheaper performance (Rindfleisch, 1997). Providers can submit their claims electronically though EDI that is cost-effective, efficient, and faster. If performed according to the nationally recognized standards, EDI enables reduction of administrative and overhead costs in the health care industry. EDI makes auditing and tracking in healthcare easier since it is easy to track EDI transactions that cannot be found in business applications. EDI translation software offers various reports and logs that can enable tracing of transactions throughout the entire process.
References
Bates, D., Cohen, M., Leape, L., Overhage, J. M., Shabot, M. M., & Sheridan, T. (2001). Reducing the frequency of errors in medicine using information technology. Journal of the American Medical Informatics Association, 8(4), 299-308.
Chau, P. Y., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach*. Decision Sciences, 32(4), 699-719.
Rindfleisch, T. C. (1997). Privacy, information technology, and health care. Communications of the ACM, 40(8), 92-100.
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