In Wait et al. (2017) published article, the authors focus on examining the positive implications that data application and management has, with emphasis on cancer care. Overall, Wait and colleagues explain that with cancer risk, applying innovations, and especially timely data collection/analytics helps supplement cancer care, lowers avoidable inefficiencies for better patient outcomes. Of the varied implications, the authors recognize how beneficial data analytics is, in supporting early detections, and contributing in improving survival rates among cancer patients.
Research Question (Question can only be inferred, as the Authors do not state it verbatim): Does collecting and reporting transparent patient outcome data contribute to improved efficiency?
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Dataset Used : In this article, they use multiple data, some entailing chemotherapy data, patient records, costs incurred during treatment and operational data, e.g., patient waiting times.
Data Source : Secondary data was used, primarily from existing health records or registries.
Position Explanation On Big Data Use In Health Care
The personal position supports big data use in healthcare, just as Wait et al. (2017) shows the benefits of data analytics measures have played a role in healthcare. Hence, with data innovations, this approach has varied benefits. By applying big data concepts in healthcare, this process relies on collecting massive data using existing digital technologies, for service improvement (Dash et al., 2019). And through these processes, there are positive outcomes, one being improving care, as there is timely collection and analysis of patient data. Overall, upon data analysis, and applying managerial tools, there is improving hospital operations and performance.
How Privacy, Data Security Concerns And Data Misuse Outweigh Advantages
On one side, there are many concerns associated with the increased application of big data within the healthcare sector. As healthcare professionals, these concerns are valid, which makes dealing with them pivotal in assuring faith among patients and other associated stakeholders. Privacy is essential, and with collecting patient information through currently existing electronic health records (EHR), violating the privacy demands, or using the data for unwanted practices is detrimental. Despite these concerns, big data analytics measures have major advantages, not only for the patients but also for the overall healthcare system. First, through data collection and the follow-up analysis, there is defining effective measures improving care processes, precision during caring and raise success during disease detection (Dash et al., 2019). For example, in Wait et al. (2017), the authors explained that data collection was key in improving survival rates for cancer patients. Through big data analytics, there is an improvement in disease detection and planning, allowing for easier and timely response measures. Through these measures, there is realizing better patient outcomes (Dash et al., 2019), and treatment success within the major healthcare system.
Using Secondary Data Sources By Local Health Authorities
As health authorities, improving decision-making and patient outcomes is paramount, which makes data collection (secondary) pivotal in supporting these outcomes. For example, within the hospital setting, some useful secondary data entails patient registry information, past illnesses, locations and major complaints reported, which as noted by Dash et al. (2019), are vital in healthcare planning processes. Second, with EHR, data stored is pivotal in planning for inventory and staffing measures, by considering areas that need more demand. With secondary data on patient’s insurance plans and medical care, health authorities have a better chance in planning, and advocating for policies that will improve community health. This information is also vital in hospital planning and requesting for funding for a given financial year.
References
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data , 6 (1), 54. https://doi.org/10.1186/s40537-019-0217-0
Wait, S., Han, D., Muthu, V., Oliver, K., Chrostowski, S., Florindi, F., ... & Wierinck, L. (2017). Towards sustainable cancer care: Reducing inefficiencies, improving outcomes—A policy report from the All. Can initiative. Journal of Cancer Policy , 13 , 47-64. https://doi.org/10.1016/j.jcpo.2017.05.004
