The Use of Data Analytics to Enhance Patient Safety
The current society is under immense pressure to improve patient outcomes and attain quality medical care management. However, based on medical examinations only a paltry 10% of the medical institutions use data analytics to solve medical problems. Even though the application of data analytics is not common in the medical sector, it gives medical facilities a chance to upgrade medical care leveraging additional value and comprehensions that come with data analytics (Ammouri et al., 2015). Medical data analytics facilitates quality medication through the use of real-time data to make evidence-dependent decisions hence upgrading patient safety. Subsequently, data analytics change raw data into valuable actionable information. Furthermore, medical experts can identify high risk patients thus easily formulating interventions during the medication. In the end, the outcome is valuable and acceptable. In terms of assessing community data, data analytics helps in gathering general information from the public in relation to patient groups. This way, medical users are able to define risks, gaps, and tendencies and then ultimately use the positive impact.
The Effective Adoption and Use of Data Analytics for Ensuring Patient Safety
The first step is the improving the collection of data across various medical systems. Even though various medical facilities collect data, the data does not flow into these medical institutions in a coherent or standardized manner. Health care systems’ bodies encounter numerous problems when gathering race, culture and dialectal data from patients and other types of people (Raghupathi, & Raghupathi, 2014). Clearly, stating the logic behind the data collection and then teaching workforce, management leadership and the entire community on how data collection can improve medical situation is an uphill task because people value privacy.
Nevertheless, addressing medical care inequalities needs the full support of other organizations which possess a strong reliable infrastructure for valuable measurement and improvement. Although medical institutions and health workers can play crucial roles in integrating race, cultural and linguistic data into already existing database, quality reporting influences the quality of the data collected (Mehta, & Pandit, 2018). In order to identify, the necessary phases needed to improve data collection, medical institutions should ensure that challenging barriers are eradicated from the data collecting process. Some of the ways through which medics can collect data is by asking questions related to race, ethnicity and even communication needs. Consequently, the staff can be trained on how to elicit information in an acceptable mannerism.
After collecting and standardizing information, one can then share it across the medical care system. The medical system is made up of various public and private data collection frameworks such as medical surveys, managerial registration and billing records. To some extent, medical institutions are designed in a manner that is suitable for the collection and retention of medical data from all the patients (Archenaa, & Anita, 2015). The administration and delivery of medical systems determine the impact of data analytic methods. Conventionally, medical care systems are episodic hence people only use medical institutions for the detecting symptoms or during annual medical checkups. Data analytic procedures permit an all-inclusive approach to supervising a persistent assessment of patient’s health. Thus, a patient can predict his or her own medical status based on certain population features and then take certain prevention actions in order to avoid extensive expenditure and emergency appointment to the medical institutions. Wearable devices such as smart watches, smart water bottles and virtual medical consultations give medical experts enough data on patients’ lifestyles which in turn can give medics a hint a preventive measures. More so, the creation of digital dashboards integrate medical data with other data sources hence helps in balancing different medical strategies.
The increasingly use of telehealth has helped medical systems offer providers virtual opportunities to extend medical services to various locations hence promoting competition and the collection of data for analytics. More so, data analytics has a direct correlation between patient safety and medical care delivery (Nambiar et al., 2013). It offers readily accessible data to health providers in order to enable decision making. For example, a patient medical dashboard can combine the vital information and then display it in a more interpretive manner. Therefore, medical experts find data analytics as an efficient instrument for making medical patient decisions.
Most of the times, medical institutions have information systems for collecting data and reporting on a patient progress. Thus, a medical institution’s organizational structure is designed to collect data from patients. It vital to note that effective feedback from information technology systems affect how medical experts interpret and use data for delivery of quality medical services (Priyanka & Kulennavar, 2014). In spite of the extensive advancement and execution of incident reporting in the medical sector, research claim that data analytics can be applied to formulate operational safety mechanisms. Also incident reporting allows data analytics to rectify outdated data. Thus data analytics defines the medical systems operational frameworks and then develop a basis for medical treatment within health facilities. In terms of outlining suitable medical practices, data analytics can easily identify effective from ineffective practices and set the a cause of action for properly advancing a suitable medical procedure.
References
Ammouri, A. A., Tailakh, A. K., Muliira, J. K., Geethakrishnan, R., & Al Kindi, S. N. (2015). Patient safety culture among nurses. International nursing review, 62(1), 102-110.
Archenaa, J., & Anita, E. M. (2015). A survey of big data analytics in healthcare and government. Procedia Computer Science, 50, 408-413.
Mehta, N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics, 114, 57-65.
Nambiar, R., Bhardwaj, R., Sethi, A., & Vargheese, R. (2013, October). A look at challenges and opportunities of big data analytics in healthcare. In 2013 IEEE international conference on Big Data (pp. 17-22). IEEE.
Priyanka, K., & Kulennavar, N. (2014). A survey on big data analytics in health care. International Journal of Computer Science and Information Technologies, 5(4), 5865-5868.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 3.
