Health Information Systems Essay

It is worth mentioning that a Health Information System (HIS) is any system that is used to capture, store, manage, or transmit data that is health-related within the health sector. In the recent past, health care has greatly improved in efficiency, safety, and quality due to advancement in technology. Moreover, patient care has become increasingly complex thus there is a need for an improved health information system in the health sector. As a result, HIS has become one of the major issues that the healthcare sector is focusing on. Therefore, the e-healthcare system is becoming a new trend that health agencies and nations globally are working towards.

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In essence, with an increase in e-healthcare systems that focuses on meeting the patients' health needs just right where they are, there is need to be good and stable health information systems put in place. The health IS requires a health information technology that can record, analyze and share patients' health information. Health IT is significant since it helps support recording of patients' data in order to improve delivery of healthcare. It also allows healthcare practitioners, health agencies, and the ministry of health to easily analyze information that is recorded in the systems during healthcare delivery (Bowman, 2013). Health IT also enables accuracy which in return reduces medical errors and helps practitioners understand the medical histories of different patients better.

However, despite health IT being such a significant part of healthcare, it is essential for the information systems to be simple and sustainable so that they do not overstretch the staff during delivery of healthcare. Therefore, the health IS should be easy to uses and sustainable. They should have the ability to hold lots of data for long periods. It is also a system that ensures that the system can analyze critical information and statistical data. The HIS chosen should ensure that the aggregate patient data collected at different points of service delivery is made available to improve quality and use of all the health information of every patient (Chen et al., 2014). The diagram below illustrates how IT technologies can be implemented in the development of e-healthcare systems.

Before the implementation of health information systems, there must be planning strategies on them in order to be able to implement a fully functional health IS. Before the implementation, it is also vital to consider the regulative and legislative frameworks that are essential for the health information system to be effective. All these are the HIM resources that are required to ensure an effective health information system is put in place. More resources such as personnel, logistics, financing, and communication technology are also important at this point. The resources are primarily to coordinate mechanisms during and after the implementation. At this point, it is possible to identify the kind of programming that could be most effective for the HIM depending on the kind of data.

For instance, since healthcare involves a large database, it is vital to get a programming that will be able to hold the large sets of data effectively. One of such programming is the Map Reduce Paradigms that is designed to allow parallel distributed processing of a lot of information. It then goes further to convert the distributed data into smaller sets of tuples and further combines to reduce the already formed tuples into even smaller tuples. In other terms, this programming is designed to take big data and break it into regular-sized data by distributing it equally. The Map-Reduce Paradigms can work together with the Hadoop System. The Hadoop system is almost similar to the distributed system and different in that it is different in that it is a write-once-read-many model (Padhy, 2013). As a result, it helps to simplify data consistency and also enables high data access.

The second step will involve setting up the indicators to be included in the HIM. It should include the basic set of indicators that are common in healthcare such as name, symptoms, prescription among others. It is also essential to include other related target areas such as the input, output, results, health status, and a comment section. It is then followed by a recording of the source of information. The reason for including a data source is because some of the information does not fall in the categories previously presented in the indicators and could be important information that is not available in one of the common sources (Chen et al., 2014). It could include data collected during survey and researcher and is relevant to healthcare.

Data management is important in health information technology. Data is essential and should be carefully handled right from the time it is collected to the time it is analyzed. It is important that the data that is recorded in the health information system is quality and it flows for easier processing and compiling. The data is then transformed into information that is classified and clustered into the different categories as a basis for evidence and so that it is easily accessible whenever required (Cresswell, Bates & Sheikh, 2013). Lastly, the data in the health information system is ready for used by health practitioners or health agencies. It can be used as predictive analysis where it is used to understand the future or as a prescriptive analytics which is used to inform on possible outcomes in healthcare.

References

Bowman, S. (2013). Impact of electronic health record systems on information integrity: quality and safety implications. Perspectives in Health Information Management, 10(Fall).

Chen, H., Hailey, D., Wang, N., & Yu, P. (2014). A review of data quality assessment methods for public health information systems. International journal of environmental research and public health, 11(5), 5170-5207.

Cresswell, K. M., Bates, D. W., & Sheikh, A. (2013). Ten key considerations for the successful implementation and adoption of large-scale health information technology. Journal of the American Medical Informatics Association, 20(e1), e9-e13.

Padhy, R. P. (2013). Big data processing with Hadoop-MapReduce in cloud systems. International Journal of Cloud Computing and Services Science, 2(1), 16.