(b) Data quality

Data quality refers to all the key characteristics that describe big data, as presented in detail in the previous section. In particular, there are four categories (4 vs.), and the following challenges are encountered:

This is a purely technical issue, but one that can create difficulties if it is not respected. It is essentially about data management. In some cases, it is necessary to periodically delete or update data, and the systems available have specific capabilities. Therefore, there is a need to ensure that dynamic data management can be performed.

(d) Analytics challenges

Beyond the technical requirements, it is extremely important to "decrypt" the data, to understand it, and to develop analytical thinking and methods to create value. It is common for data to be misinterpreted by humans, leading to results that are not desired.

(e) Application of expertise

The needs of the health sector are constantly increasing through new research, observations, scientific articles, etc. However, at the same time, the technological capabilities that can help meet the needs are also increasing. Therefore, it is essential to be aware of technological developments and to intervene, if necessary, to overcome the inherent difficulties and extend the system's functionality.

(f) Prediction models

A key area of big data analytics is the generation of models that estimate and predict various situations. Specifically, in the healthcare industry, there is a need for the continuous study of data and the estimation of expected events to maximize the benefits and value of the data.

(g) Legal issues

There is a wide range of legal issues that need to be addressed, and it is necessary to keep abreast of developments in this area. System security must be ensured against unauthorized access to data by unauthorized persons. In this context, the challenges are

the same as in the area of security systems, which require a lot of time and effort. On the other hand, health information is extremely sensitive and should never be used to directly or indirectly identify individuals: a concept that is known as patient privacy. The challenge is even greater, especially in the absence of a stable and universally accepted framework.

The following figure, Figure 9, illustrates the key challenges in the healthcare sector regarding the use of big data.

**Figure 9.** Key challenges in the healthcare sector regarding the use of big data.

The need to develop tools and methods to meet all the issues raised by the use of big data in healthcare organizations requires a collective, organized, and rigorously defined effort.

#### **6. Proposed Strategies for Implementing Big Data Analytics in Healthcare for Smart City**

In the context of the Smart City concept, the integration of big data analytics in healthcare can play a critical role in improving the overall quality of life. Healthcare providers can gain a more comprehensive understanding of the community's health needs by leveraging the vast amounts of data that are generated by various sources such as wearable devices, electronic health records, and social media platforms. This can lead to more effective and targeted interventions in addressing health issues, as well as the development of proactive healthcare strategies to avoid illnesses in the first place. Furthermore, the use of big data analytics can aid in the optimization of healthcare resource allocation, lowering costs and increasing efficiency. As such, it is crucial for healthcare organizations and institutions to consider the Smart City context when developing and implementing big data analytics strategies in healthcare.

Based on the best practices in the field of big data analytics in healthcare, we provide a general framework for healthcare organizations to follow; by applying this simple strategy, health professionals can effectively leverage the potential of big data analytics to improve patient outcomes in their medical institutions.

of aggregating epidemiological, clinical, economic, and management data that can contribute to the generation of correlation information between the health of humans, economic resources, and health outcomes.

In the following figure (Figure 10), the suggested strategy for implementing big data in the healthcare industry is summarized.

**Figure 10.** Suggested strategy for implementing big data in the healthcare industry.