The analysis of healthcare systems in the various parts of the world, also known as service line analytics, is a process that involves the analysis of the drivers of performance in the given healthcare service lines to display the areas that have a high and low potential within a specific healthcare market (Storey et al., 2016). The process begins by creating patient profiles to define the consumers that are likely to make use of a particular service within a given health system. The patient profiling exercises view patients as the customers to the various healthcare institutions. The profiles are then created to display the common characteristics in their demographics and lifestyle (Storey et al., 2016). The service line analytics in the healthcare centers do not end with the profiling of the patients, however. The next step in the analysis of the service lines for the healthcare centers in the layering in of the other drivers of performance in the given structures of the service line. Some of the structures are the existing supply of the providers of the medical services in the various capacities. The other structure is the estimated demand that is given by the use of the rates at the hospitals (Storey et al., 2016). The two factors are combined in a service line model that looks into the potential for the presence of a service line in a given healthcare market. The consideration of the demand at the healthcare centers has been used as a method to determine the various medical care sites that excel in the provision of healthcare services in the different parts of the world.
The application of the various service line analytics is a helpful tool for the executives in the various healthcare institutions in the various areas that seek to re-engineer or redesign the healthcare sites and workflows to accomplish the collective dream of providing better and affordable services to the patients who are continually seeking them (Storey et al., 2016). The first step in the redesigning of the sites and workflows is to identify the opportunities in the fresh investments in the healthcare sector. After the patients' profiles are created and a potential analysis of the operating area is conducted using the service line model, opportunities for investment are created in the various capacities. The determination of the healthcare demand that has not been met is one of the ways that comes up with the new investments in the medical care industry as the hospitals aspire to provide a variety of services to the increasing number of patients with various ailments (Storey et al., 2016). The addition of new healthcare facilities in different areas, which are run by the main hospital, leads to the growth of the market share. The other step in the redesigning of the healthcare sites and workflow is to improve both the allocation of resources and the utilization of assets. The opening up of the multiple healthcare facilities does not end at that. The hospitals have to ensure that they are offering the right healthcare services at the right facilities in the various areas (Storey et al., 2016). The facility network that the healthcare centers have created has to be scored with a service line model, which leads to the identification of the points, or sites that require service lines to be added and the sites that can have various services reduced from them to suit the existing demand.
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Additionally, the healthcare institutions can also decide to allocate the resources from the facilities with low demand to facilities with high demand (Storey et al., 2016). The reallocation improves efficiency and boosts the returns on investments in the various players of the healthcare sector. Therefore, the process of service line analytics is the cornerstone of the improvement of services in the medical care institutions concerning demand and the satisfaction of the customers seeking the healthcare services at different intervals.
References
Storey, C., Cankurtaran, P., Papastathopoulou, P., & Hultink, E. J. (2016). Success Factors for
Service Innovation: A Meta‐analysis. Journal of Product Innovation Management , 33 (5), 527-548.
