In healthcare, choices have real impacts on the sick and society.
The ability to collect and evaluate complete, detailed data helps decision-makers determine medication or surgery options, forecasting the course of significant health incidents and making long-term plans.
Several problems affect the healthcare industry, ranging from recent disease outbreaks to maintaining optimum operational quality (Zalani et al., 2016).
Data analytics helps manage these problems by obtaining practical insights to enhance the industry’s operating efficiency with the available data such as financial, clinical, administration, operational data, etc.
But what is data analytics?
According to Manogaran (2017), data analytics analyzes quantitative data to disclose qualitative knowledge, address concerns, and identify patterns.
You can analyze data manually or with the help of software and algorithms. It can help visualize data by making graphs and charts to design presentations and display trends.
How to incorporate data analytics in health decision making
Practitioners’ assessment and development
It is possible to analyze data collected from patients regarding their healthcare professionals’ experiences to uncover areas that need improvement.
For instance, just like most companies ask customers to rate their services, the sick can anonymously rate their doctors and other health professionals who attend to them while giving reasons for their feedback.
This way, the practitioners can be trained or counseled towards accommodating patients as this contributes to patient’s recovery through love and support.
Sensing Irregularities in Scanners
Healthcare workers can use Machine-learning to leverage data analytics in healthcare services (Wiens, Jenna & Erica, 2018).
With the correct use, algorithms can analyze data faster and more effectively than human beings.
For example, Doctors can use algorithms to tell dissimilarities between some medical images like scans as they learn according to the information given, which they use to identify the differences between them within a short time.
However, research has shown that total dependence on algorithms with zero human guidance is impossible.
It requires health professional’s intervention and knowledge to save lives and time.
Data analytics may forecast patterns in the spread of diseases (Thapen et al., 2016), enabling physicians, hospitals, colleges, and individuals to plan well, aiming at mitigation.
For instance, disaster forecasting sometimes uses past data to calculate the likelihood of hazard happenings, making preparedness and mitigation possible.
With the present coronavirus pandemic, information can be gathered and analyzed to foretell future occurrences and actions taken to save lives and prepare psychologically and physically.
How to mitigate pandemic or epidemics disasters using data analytics
Epidemiologists can use advanced data analytics to analyze past and present disaster or epidemic occurrences (Li et al., 2017).
They can then use this information to predict future vulnerabilities, therefore, providing early warning systems and calling policymakers and the society to work towards preparedness, mitigation, and recovery.
Moreover, with this timely information and warnings, actions can be taken on time before pandemics or epidemics occur to provide resilience.
For instance, curfews and movement restrictions alongside vaccinations can be introduced earlier to reduce or prevent vulnerability.
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Three significant benefits of adopting statistical data analysis in making healthcare decisions.
- Counteracting potential harm
Healthcare personnel can apply advanced data analytics to help healthcare organizations avoid causing suffering on patients by preventing errors during and after treatment.
They can analyze Electronic Health Records to detect patterns of common mistakes and controls put in place to avoid them.
- Disease Prevention
It can provide trends and patterns in disease prognosis.
From the patients’ data, healthcare workers can note interventions that worsen the patients’ condition, leading to complications and those that improve patients’ condition.
From the clinical data analysis, clinicians can confirm which interventions are suitable to prevent a particular disease.
- Reduction on expenses
A lot of money is spent in hospitals to manage finances (Tsofa et al., 2017).
Since inadequate or excess staff might be the leading cause of this issue, data analytics can effectively ensure that there is the exact number of staff needed to accommodate the sick’s needs.
This can promote savings on the healthcare facilities and benefit patients by reducing the waiting time, ensuring enough beds, and quick attendance.
As illustrated in this article, managers can use advanced data analytics to improve revenue, operations, safety, and efficiency in healthcare.
The secret of data visualization in healthcare
Data visualization focuses on the most significant takeaways within the health industry.
According to Wang et al. (2018), it helps people recognize the trends and associations, which boosts data analysis effectiveness.
Moreover, when making critical judgments concerning the sick’s care, physicians refer to health data visualization, enabling professionals to quickly address key trends and data using charts, graphs, and perhaps other illustrations that tell or display the vital information.
Data analysis offers a lot.
When thoroughly incorporated, it will revolutionize the health industry’s potential to optimize care delivery, lower costs, minimize mistakes, and anticipate potential health threats.
Now is the time to start thinking about taking advantage of advanced data analytics in your organization.
Delaying implementation means you are lagging in finding solutions to your challenges.
Find out here how you can benefit from the data you have in your organization.
However, healthcare organizations should ensure that they use their data appropriately for informed decision-making.
Data should be secured and only accessed by the right workers upon verification.
Zalani, G. S., Bayat, M., Shokri, A., Mirbahaeddin, S. E., Rasi, V., Alirezaei, S., & Manafi, F. (2016). Affecting factors on the performance of community health workers in Iran’s rural areas: a review article. Iranian journal of public health, 45(11), 1399
Manogaran, G., Lopez, D., Thota, C., Abbas, K. M., Pyne, S., & Sundarasekar, R. (2017). Big data analytics in healthcare Internet of Things. In Innovative healthcare systems for the 21st century (pp. 263-284). Springer, Cham.
Wiens, J., & Shenoy, E. S. (2018). Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases, 66(1), 149-153.
Thapen, N., Simmie, D., Hankin, C., & Gillard, J. (2016). DEFENDER: detecting and forecasting epidemics using novel data analytics for enhanced response. PloS one, 11(5), e0155417.
Tsofa, B., Molyneux, S., Gilson, L., & Goodman, C. (2017). How does decentralization affect health sector planning and financial management? A case study of early effects of devolution in Kilifi County, Kenya. International journal for equity in health, 16(1), 1-12.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.