In the healthcare industry, data is an important tool in delivering services efficiently and on time. Currently, there is little data, old data or no data which is quite a disadvantage to those in remote areas. It is seen that those who require medical assistance and make up most of the statistics of health issues in the world tend to be in developing countries. Developing countries do not have quality technology that would assist in taking data hence most physically go from place to place door to door in order to attain data. The data is then put on paper which is bulky, then the information is fed into a data system where it can be accessed before assistance is given. By the time the information is given to the intended people where assistance is to be provided, many have lost their lives due to a health issue (Groves, Kayyali, Knott & Van Kuiken, 2013). Management of data through technology in gaining information will assist to ensure that the children and women manage to gain medical attention on time. Data management systems that are cloud-based and require no training or programming will assist those on the ground to acquire data fast and efficiently. Such a system or technology will assist in making the analyzation stage easy.
There are several developments in data management that would be beneficial to the healthcare industry. The introduction of electronic medical records systems such as Magpi that assist in data collection and health information exchanges, which makes it easy to share data and data analysis that has been improved such as the enterprise data warehouses. The paper will discuss on how big data has led to great developments in healthcare through ease in an analysis, ability to follow up on patterns and trends obtained from various sources (Groves, Kayyali, Knott & Van Kuiken, 2013). Through better accessibility of data improves the way data is recorded and analyzed hence improved the healthcare industry. It is also seen to improve management decisions on how they deal with a certain medical situation.
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One of the values obtained from data management by the healthcare sector is finding and targeting the right people. Information is key when it comes to dealing with a medical situation. In a country like Kenya, in order to know how to improve the health of the nation, it is critical that they know how many children are born each day and how many lose their lives during birth. International Rescue Committee is an example of an organization that uses the Magpi system through one dollar phones by midwives to record the number of successful and unsuccessful childbirths (Raghupathi & Raghupathi, 2014). Such a platform has assisted in targeting the intended people which is the midwives who can give actual data on the ground hence gain actual first-hand data. It has assisted in finding the right people therefore help can be provided to the exact people who require efficiently and on time to avoid extreme cases.
Another example is where a country requires to know the number of children vaccinated and those that have not received the vaccination. Through platforms such as Magpi, it is easier to record as vaccination is taking place which is then saved in a cloud that can be accessed from anywhere in the world (Raghupathi & Raghupathi, 2014). Once it is saved, it is easier for the relevant authorities to identify those that have received vaccination hence targeting the right people in the right area where vaccination did not take place.
Second value added is that it will deliver the right intervention at the right time needed. Data management technology will ensure that the data recorded is accessible from any part of the world, therefore, those requiring data in order to carry out interventions will then act on time due to the availability of data. The moment data is keyed into the system those with the ability to intervene can access information and use it to support intervention. Despite intervention, data management is involved in assisting in the intervention taking place on time. Once data is keyed in while in the field, someone in the intended office gains the information and immediate solutions are created and implemented (Patel & Sharma, 2014). The most effective solution is normally presented because the information gathered is accurate and observes time. An example is the use of Magpi management system to accumulate data from the developing countries on the numbers of vaccination conducted in a population. The population is then objectively divided to ensure that all those that have not had their vaccination will participate in the vaccination process as is required. Through the data acquired in time and well accurate, it is easier for analysis will take place in order for implementation of solutions take place.
Lastly is adjusting programs where through data management systems in healthcare, one may not require one man controlled programs. According to Selanikio, one manned program is quite expensive and developing countries may not be able to afford such expensive platforms. In order to access them one will require the facilitator to train people on how to use it hence, they will require to schedule an appointment (Selanikio, 2013). They will also require to facilitate transport, accommodation, and fee in order for the person to train them on the use of the system and gain access. Therefore, the current data management systems have been made in a way that it is accessed in a cloud, requires no training and no programming. It makes it easier to operate the system individually without any restriction and can be stored and updated accordingly hence efficiency in the healthcare industry.
References
Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The ‘big data’revolution in healthcare. McKinsey Quarterly , 2 (3).
Patel, J. A., & Sharma, P. (2014, August). Big data for better health planning. In 2014 International Conference on Advances in Engineering and Technology Research (ICAETR) (pp. 1-5). Unnao: IEEE.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems , 2 (1), 3.
Selanikio, J (2013). Joel Selanikio: The surprising seeds of a big-data revolution in healthcare, TED https://www.youtube.com/watch?v=Mb8x6vLcggc&
