Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.

Big data refers to a large complex data set that yields substantially more information when analyzed as a fully integrated data set as compared to the outputs achieved with smaller sets of the same data that are not integrated (Thew, 2016).

Benefits of using big data as part of a clinical system

The potential benefits of using big data as part of a clinical system are enormous and cannot be over-emphasized. One of them is that electronic health records (EHR) documentation which includes plans of care, physiological parameters, assessments, interventions, and progress evaluations can be shared with patients (Glassman, 2017). This will allow patients to have access to their health data and participate in their care, have autonomy, and make informed decisions. EHR can help with managing population health, inform administrative processes, and provide metrics for quality improvement to mention a few.

Likewise, the benefit of using big data as part of a clinical system is its managerial benefit like improvement of organizational performance for instance the detection of medical insurance fraud which would ultimately lead to better resource management. (Wang, Kung, & Byrd, 2018).

Challenge of using big data as part of a clinical system

Challenges of using big data as part of a clinical system include the lack of data standardization, due to varying units of data that change from one unit to another, making it extremely difficult to comprehend and difficult for industry leaders to capitalize on the great promise of big data to revolutionize the healthcare market and difficult to determine how well or badly an organization or unit is performing. They do not call the units the same thing, name them the same thing or define them the same way,” This also makes it difficult to make well-informed decisions on what to change. Having good data is key to making effective changes. (Thew, 2016).

Also, there is the management challenge. Some healthcare workers do not the knowledge of how to manage data. This can compromise the integrity of the data and /or lead to duplication of the data.

Strategy to effectively mitigate the challenges of big data

The improvement of data integrity – Data scientists and Information Technology staff that have the required skills to run the data analysis will play a major role in mitigating the challenge of data management. (Catalyst, 2018). They should be involved in data management. This will help the nurse leaders to easily analyze, synthesize, and present the data in a clearer form for decision-making.

Benefit of Big Data

Big Data refers to a large complex data set that yields substantially more information when analyzed as a fully integrated data set. (Pastorino et al., 2019).  The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers, ( 2019). The use of Big Data in healthcare helps physicians and other health providers to facilitate patients’ care and inform decision making for the executives. For example, the data retrieved from the Electronic Health Records (EHRs) provide metrics that measures past and present   patient health history, family history, lab results allergies and other useful information   that can help healthcare professionals  in decision making  for a more precise diagnosis and better organized management/treatment.( Laurette  Education,2018). It can also predict future occurrences, so that prevention can be initiated on time

Potential challenge of Big Data

Big Data may have a potential of violating principals of patient’s privacy. For example, when the patient’s health and possible behavior information, medical inventory and assets, are stored in the EHRs, the information could be accessed/visualized by other health care partners and vendors and possibly other individuals that are not supposed to get the information

Strategy to mitigate the challenge of Big Data

The strategy I will propose is for government to establish affirmative policies to secure, protect, the health data of individuals, by ensuring that the Data scientists to posses the required skills to safeguard the Data system and , Data protection committee  be appointed who would adopt  guidelines of individual personal data while processing the data.(Pastorino Et al.,).

Potential Benefits of Using Big Data as Part of a Clinical System and Why

The use of technology has become the backbone of healthcare organizations as all aspects of healthcare are delivered and accessed using technology. As a result, healthcare facilities have implemented electronic health records systems to assist with the storage and management of data (Pavithra et al.,2020). There are many examples of big data in the healthcare system, including pharmaceutical research, patient portals and medical services, medical imaging, and public records, which vary in structure and nature, leading to the development of various methods and systems for storing and analyzing data. Having all the information in big data is crucial, as it can always be referred to for further information, clarification, or confirmation if it pertains to patient information. According to Pastorino et al., (2019), one of the benefits of using big data as part of the clinical system is that EHRs, as a form of big data, have contributed significantly to the collection of patient information, enabling tests, collecting clinical data, and assisting physicians in accurately diagnosing patients. As a result, it is essential to make an accurate diagnosis so that an appropriate treatment plan can be developed for the patient, improving both the quality of care and the success of treatment. Furthermore, Pastorino et al. (2019) state that the use of big data in healthcare facilities has significantly increased operational efficiency. To analyze historical patient admission rates and staff efficiency, big data is used as an intelligence strategy.

Using predictive analytics, healthcare providers can reduce costs and provide high-quality care services, resulting in improved patient satisfaction and improved outcomes (Pavithra et al., 2020). As a result of the use of big data in healthcare facilities, medical errors are being reduced by improving financial and administrative performance, thus reducing the number of readmissions.

Potential Challenge or Risk of Using Big Data as Part of a Clinical System and Why

According to Pastorino et al., (2019), data security and privacy are two challenges or risks associated with big data in healthcare facilities. Patient information and their health history are contained within big data. Due to the highly sophisticated technologies that come with new technology, cybercrimes have increased, with more and more cases of websites and hospital systems being hacked. For patient information to be protected and kept confidential, healthcare facilities must comply with the Health Insurance Portability and Accountability Act (HIPAA). A lawsuit may be brought against a person who violates HIPAA law. A further challenge for healthcare facilities is the size of big data, which makes it difficult to analyze and store such vast and extraordinary amounts of information, as described by Pastorino et al., (2019).

Strategy Experienced, Observed, or Researched that may Effectively Mitigate the

Challenges or Risks of Using Big Data Described

Acceptance and proper management are two of the most effective strategies that healthcare facilities can use to reduce the risks associated with big data (Chmaissani, 2019). In healthcare facilities, top management may require assistance understanding big data since it is assumed that this is the responsibility of the IT department. As a result, it is essential to accept that there will always be challenges associated with big data since each department can manage the data appropriately and develop different systems to store different types of data according to their structure and nature. For example, healthcare facilities should utilize predictive analytics to determine admission rates and staff effectiveness, improving patient satisfaction, and quality of care. Additionally, Siddique et al. (2018, August) advise healthcare facilities to maintain professionalism by encrypting all passwords and changing them periodically with the rule of no exchange of passion, which will enhance cyber security and reduce fraud.

Having access to an electronic medical record is still fairly new within rural communities. One of the first hospitals I had ever worked at only switched over to electronic record five years prior which would have been 2011. With having an electronic record it has allowed facilities to accumulate large amounts of data on every patient within the system. While in most cases this has been a huge benefit, especially in management of chronic illness, all that data can be very overwhelming. To those who are unfamiliar or still learning, all of the data collected can seem very overwhelming, thus not being used to its full potential to better patient care. In fact, it was shown that 42% of hospitals use analytics and data to make major decisions but only 16% of those facilities have experienced staff to use the data to support and help determine what is beneficial (Wang et al, 2018).

The biggest barrier to using “big data” is the knowledge deficits for those who have to utilize it. In my experience as a supervisor data is collected on so many aspects of a patient interaction and there is very little time to be able to sift through it. One of the biggest issues we faced in leadership was determining reasons for readmissions.  While our data collected on the patient stated discharge teaching was done using our “teachback” and “commit to sit” techniques. That our staff was following up post- discharge with follow up phone calls. We still saw a high readmission rate.  While all this data shows we are doing the interventions appropriately per our protocols the data may not always reflect the true quality of the care given. When our rates went up it was during peak pandemic when staff was stretched thin. Discharge instructions may have been more rushed and often no vistors or “second set of ears” were present during discharge to help support patients.  But being able to use the data and dig deeper are the crucial stepping stones in improving “big data” utilization.

Although there is still a deficit in education and experience in using data to intervene on the daily most providers and nurses are experts within the clinical settings.  Dr.Grant Shevhik stated how the importance of trending data can be in making adjustments in patient care. Prior to electronic records providers could only see results right in front of them and were unable to see if it was an improvement. Now with the electronic record and data we can see labs results, vitals, imaging, and other provider notes to get a better picture (Walden University, 2018).  The value of being able to trend data is so important in the care we provide. I currently work in the PACU, many of our assessments and scoring systems are based on pre-anesthesia recordings compared to post. This may only be over the course of a couple hours but being able to compare allows us to known when it is safe to transition care.

In summary, the use of “big data” is an important part of our everyday lives and the care we provide. The biggest barrier still lies on the ability to effectively interpret this data and then apply it to our care decisions, especially when going through large amounts collected over time.  Although the difficulties leadership face on data collection to improve larger interventions many bedside staff are experts at using data to their advantage. Being able to compare admission data like vitals to the current ones allow us to know if interventions are improving the patient situation or if changes need to be made.

Clinical System Big Data

My employer’s largest big data application is the electronic health record (EHR). With over 200 physicians, pathologists, nurse practitioners, and physician assistants caring for patients in the metropolitan area of Washington, DC, and the Tidewater, VA region, hundreds of thousands of patient records have accumulated over the past 15 years. The collection, storage, and retrieval of massive amounts of clinical data, such as personal and family history, medications, and pathology results, coupled with demographic data, such as gender, zip code, ethnicity, and spoken language, gives clinicians access to pertinent information helpful in making clinical decisions. It also offers endless possibilities for analytics and predictions regarding disease management, anticipated health outcomes, early intervention, and best practices for preventing colon cancer. According to the Centers for Disease Control and Prevention, nearly 52,000 people died of colon cancer in 2019 (Centers for Disease Control and Prevention, n.d.).

Risk of Using Big Data in a Clinical System

Conversely, housing enormous amounts of data in the EHR makes the practice vulnerable to breaches or inadvertent disclosures of protected health information, cyber-attacks, and unauthorized access. In a report submitted to Congress by the Office for Civil Rights, over 650 breaches affecting 500 or more individuals had occurred in 2020, a more than 60% increase from 2019 (2022). Unauthorized disclosures negatively impact patients’ trust and confidence in the practice. Notifying such occurrences is embarrassing for the company and the designated provider.

Proven Strategy to Mitigate Risk

For the past six years, the department leaders of Information Technology and I have participated in the company’s annual security risk analysis (SRA) with an outside vendor. This process gives insight into the effectiveness of current security measures, including technical safeguards, and assesses policies and procedures related to protecting patient information. The vendor provides a corrective action plan at the conclusion, which includes recommendations and suggestions based on the SRA findings. In a Canadian Press article, encrypting personal information, upgrading security systems, and multi-factor authentication are ways to address privacy and security risks (2022). All of these measures have benefited the company throughout its history, some of which were suggestions from the SRA.

Benefit of Big Data

Big Data refers to a large complex data set that yields substantially more information when analyzed as a fully integrated data set. (Pastorino et al., 2019).  The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers, ( 2019). The use of Big Data in healthcare helps physicians and other health providers to facilitate patients’ care and inform decision making for the executives. For example, the data retrieved from the Electronic Health Records (EHRs) provide metrics that measures past and present   patient health history, family history, lab results allergies and other useful information   that can help healthcare professionals  in decision making  for a more precise diagnosis and better organized management/treatment.( Laurette  Education,2018). It can also predict future occurrences, so that prevention can be initiated on time

Potential challenge of Big Data

Big Data may have a potential of violating principals of patient’s privacy. For example, when the patient’s health and possible behavior information, medical inventory and assets, are stored in the EHRs, the information could be accessed/visualized by other health care partners and vendors and possibly other individuals that are not supposed to get the information

Strategy to mitigate the challenge of Big Data

The strategy I will propose is for government to establish affirmative policies to secure, protect, the health data of individuals, by ensuring that the Data scientists to posses the required skills to safeguard the Data system and , Data protection committee  be appointed who would adopt  guidelines of individual personal data while processing the data.(Pastorino Et al.,).

The healthcare sector has advanced significantly to get to this stage, thanks to technologies like telemedicine, medical imaging, electronic health records, robotics, and more. With the aid of technology, all of this is now achievable. One of the industrial disruptors that has transformed healthcare is big data. Big data in the healthcare sector aids in reducing medical errors, preventing mass diseases, providing preventative care, modeling the spread of diseases, detecting diseases at their earliest stages, providing more accurate treatments, real-time alerting, patient personalization, forecasting the cost of treatments, identifying and assisting high-risk patients, preventing suicide and self-harm, discovering new therapies and drugs, and avoiding unnecessary trips to the emergency room (Wang et al., 2018).


The global pandemic outbreak has accelerated innovation and the adoption of digital technology, massive data, and big data analytics. However, it has exposed many weaknesses of the healthcare industry. Here we outline the benefits of big data and data analytics in healthcare and give an overview of critical applications of big data in the healthcare sector.  Big data is expected to grow faster in healthcare than in other industries like manufacturing, financial services, or media. The healthcare data is projected to see a compound annual growth rate (CAGR)


Potential challenges or risks using big data, according to Fillinger and others. The big data ecosystem was created to solve the problems of ingesting and storing a large amount of highly diverse data. Such a concept as a data lake provides the possibility to solve the problem of storing a variety of healthcare data like images, document files, and exports from old RDBMS systems (Fillinger et al., 2019). Due to the lack of standardization, sharing healthcare data between different organizations is one of the main pain points. Moreover, such sensitive information requires robust privacy protections. Under public health emergencies, and particularly the COVID-19 pandemic, it is crucial that data is shared in a timely and accurate manner and Data standardization, Data Quality, Data mining, Data visualization, Scalability, Integrating of legacy systems with the big data ecosystem, and Lack of big data skills.


The essential strategy to solve the accessibility challenges in big data sharing is the implementation of consequent security evaluations and procedures. This action could be carried out by encrypting big data and ensuring that health care professionals practice professional integrity. Many health care systems should strive to have mature EMR systems to support meaningful use and honesty. Upgrading pre-existing information systems within health facilities will enhance the ability to efficiently share health information between providers and health facilities (Perlin, 2016). I believe this strategy would aid in eliminating patient privacy breaches and the risks associated with sharing big data among providers. There is an urgent need to understand the managerial, economic and strategic impact of big data analytics, enabling healthcare practitioners to fully seize the power of big data (Wang et al., 2018).

Effective Use of Big Data

While the amount of data gathered for one patient within their electronic health records (EHRs) may seem overwhelming, it is critical in making sound clinical decisions for the patient (McGonigle & Matrian, 2022). In reviewing a piece of clinical data over time, a nurse can identify a previously unknown or undetected area of risk for a patient. For instance, educating patients hospitalized for congestive heart failure on taking their daily weight and continuing to monitor those daily weights while in the hospital is effective in detecting clinical interventions that may be necessary (Jaysena et al., 2016).

One potential challenge of using big data as part of a clinical system is the need for more education for many clinicians on effectively utilizing the amount of data received in their day-to-day practice. As Thew (2016) outlines, nurses receive daily financial, clinical, directional, and operational data. The key to making the data received applicable is allowing it to be organized via the technology we use into bits of data such as reports or alerts (McGonigle & Matrian, 2022). Using these alerts, nurses can alert the interdisciplinary care team that an intervention is necessary.

In my clinical practice in long-term care, I have found that a lack of education for the staff on the effective use of the EHR dashboard and daily reports created a barrier to utilizing the data gathered on patients during their shifts. Further, this education can also assist in preventing unintended misuse of the data, such as sharing information that is not necessary for clinical practice with those who do not have a “need to know” (Agris, 2016).

In their daily operations, healthcare centers use both unstructured and structured information. Structured data is vast, freeform, and available in various formats. It also has a predetermined schema. Big Data (BD), or unstructured data, on the other hand, does not adhere to the conventional structure for data processing. Big Data is an enormous collection of information sets that traditional technologies cannot handle, store, or examine. It is still kept on file but has yet to be explored. Such data can be challenging to search for and analyze because there is no explicit schema, so it needs to be turned into value using a particular technology and process (Batko & Ślęzak, 2022). Big Data analytics are methods and devices used to examine and glean information from large amounts of data. Big Data analysis outcomes can be utilized to make future predictions. They also contribute to the development of historical patterns. When it relates to healthcare, it enables the analysis of massive datasets derived from hundreds of patients, the discovery of correlations and clusters across datasets, and the creation of prediction models through data mining techniques (Batko & Ślęzak, 2022).

The output of data processing using big data analytics is suitable for data storytelling, which may help people make decisions with less risk and more evidence. Stakeholders in the healthcare industry may gain as a result. Analytics of large datasets must bring together the populations involved in data analytics and healthcare informatics to fully utilize the reasonable massive amounts of information in healthcare. Furthermore, to make sure that the proper intervention for the correct patient is appropriately timed, personalized, and potentially helpful to all sectors of the healthcare system, such as the management, payer, and patient (Wang et al., 2018). Big Data Analytics can shed light on clinical data and help decision-makers make well-informed choices on patient diagnosis and care, illness prevention, and other issues. By exploiting the value of the data, big data analytics may also increase the effectiveness of healthcare organizations.Using personalized and accurate treatment, relying on personalized information supplied in real-time and customized to particular patients, would be made possible by adopting a big data strategy. This will make it possible to make data-driven decisions and get better-personalized projections about prognosis and treatment reactions. Understand the complex factors and interactions that affect a patient’s health and the health of the healthcare system and society, and detect safety issues with drugs and devices. It will also make it possible to compare prevention, diagnosis and treatment, and treatment options more successfully (Wang et al., 2018).

The biggest challenge with Big Data is figuring out how to manage such a large amount of data and use it to make informed decisions in various areas. Another major challenge in the area of healthcare information is adjusting ample data storage, presentation of analysis, results, and inference based on them in a healthcare setting. Placing the patient at the system’s center is one of the critical components of the shifting healthcare needs to undergo. Technology needs to be improved to accomplish these goals. As a result, adjustments need to be made at the level of technology in the administration and planning of all healthcare activities and impacting service providers’ business models. Enterprises are using big data analytics more and more frequently. However, medical businesses still need to meet the demands of patients, doctors, administrators, and the creator’s policy in terms of information (Pastorino et al., 2019).

Some strategies we can employ to mitigate the challenges include advanced analytical techniques, such as deep machine learning algorithms, enabling computers to find items of interest in large amounts of unstructured data. Moreover, inferring relationships without specific models or programming instructions is urgently needed to deal with the growing diversity and unstructured data. Therefore, we anticipate that developing practical unstructured data analysis algorithms and applications will be the focus of future scientific studies (Wang et al., 2018). We must get engaged if we want to transform the vision of healthcare data into a reality and give nurses more time to spend with their patients. To do this, currently offered training and education programs for healthcare workers should incorporate data handling concerns into the curricula to ensure the acquisition of essential skills and competencies. We should ensure that all healthcare providers participate in the decision-making process when choosing new technology, offer suggestions on how to streamline workflows with technology, and share our ideas for improving patient care with technology vendors. Nurses are at the mercy of what engineers deem best without your input (Pastorino et al., 2019).


In the 21st century, technology is unavoidably present around us in everything from waking up in the morning (alarm on the phone) to freshly brewed coffee to perfectly toasted deliciousness. For nurses, we are conducting an informatics model process when we perform simple things like measuring blood pressure for your patient. Clinicians gather raw data like blood pressure, temperature, respiration rate, and heart rate from a patient and form information and knowledge that leads to forming wisdom. (Walden University, LLC. (Producer). (2018).

Big Data

Informatic technology is very effective in managing numbers. (Walden University, LLC. (Producer). (2018). For instance, when a nurse charts vital signs (blood pressure, heart rate, temperature, respiration rate), the system generates to alert clinicians to know if specific numbers are below or above average, and we use the numbers to make a judgment. Advancement in informatics has allowed clinicians to respond quickly to medical problems and prevent further damage. For example, in the case of sepsis, clinical symptoms like decreased blood pressure, increased heart rate and increased temperature allow providers to order specific lab tests such as pH in our blood and lactate level and treat the patient.

Nursing informatics help with the organization and application of data. Clinicians use these data and facts daily to make decisions for our patients. When collecting data, forming knowledge based on the data happens concordantly, leading to wisdom formation and compassionate care for our patients. (Walden University, LLC. (Executive Producer). (2012).


Big data provide enormous pools of information and create an efficient lifestyle for all of us. For example, we now have wearable technologies that monitor when we exercise, sleep and eat. In such a way, big data create personal health records and provides an opportunity for easier access to remote consultations between a patient and a provider. With more accessibility to providers, chronic and preventable diseases are managed better. Big data is also implemented in our homes with smart homes and social networks. With the beauty of efficiency that comes with big data, challenges and problems arise. Data security, data ownership between the source and the collector, and data storage remain problems. (Vinay Shanthagiri. (2014). Our goal as patient advocates should be to find a big data system that works for our healthcare system without damaging the core of it all that gave rise to big data, humans.

Big Data Risks and Rewards

The amount of data that is collected daily is extraordinary via the use of phones, social media, and through the internet.  The phrase big data is a buzzword to enforce large amount of information obtain from variety of complex areas and stored and managed (Hardy, 2018).  Big Data in healthcare can influence and predict patients results (Gleason & Dennison Himmelfarb, 2017).  Nurses are the front line, direct patient care the best recourse to obtain the data and to influence patients care (Glassman, 2017).

The benefits of using big data can have enormous benefits to transform patients lives if used correctly.  The greatest benefit is using the data to improve patient’s outcome.  Type 1 diabetic patients have the support to monitor their blood sugar every 5 minutes without having to collect blood from their finger, through a continuous glucose monitoring (CGM) system.  The same data can be uploaded to their clinician to have complete access 24 hours a day 7 days a week.  This data can improve their health and help manage their care (Miller, 2020).

There is a potential for risk concerns with big data such as security issues, ethical issues and misuse of personal information.  It is important for nurses to help manage the use of this big data and it also has a significant amount of burden placed on nurses.  The amount of data that is collected from a CGM can add additional clinical time increasing the nurse’s workload (Miller, 2020).  We are in a very challenging time in healthcare and adding additional task leads to higher burnout rate.  It is important for nursing leadership to recognize that increase workload leads to burn out and to staff appropriately.

I am a single mom and my youngest child at the age of 6 was diagnosed with type one diabetes.  I had a CGM placed on him before we left the hospital.  Data/technology was the reason I was able to continue working to support my family.

The Benefit of Big data as Part of Clinical System

Big data has many benefits in the health care system, especially in nursing. One example in the hospital system where I work as a bedside nurse is the electronic medical records (EMR) that transfer patient information across the data system from each clinic and hospital within the specific hospital system. I can also access the patient’s medication prescriptions, and refills from most local pharmacies included in the hospital system. I don’t have to make an extra call to a pharmacy if I have questions regarding if a patient has been prescribed a specific dose of a prescribed opioid. Nurses can also access vaccination information for each patient within the EMR in just seconds. Big data is an extensive collection of data that expands over time, generating millions of data points daily. (McGonigle & Mastrian, 2022). The benefits of big data are convenience and time efficiency for nurses during a workday. Many bits of a patient’s information can be accessed in one area of the EMR or just a “click” away to search other information.

One challenge with big data is handling a substantial amount of information that must be stored and filed to a format that is easily accessible and readable, and high-end computer tools may be required in a clinical setting for proper functioning (Dash et al., 2019). This can be challenging because many healthcare facilities do not have sufficient funding to supply to more efficient and sophisticated big data projects, which is an expensive consequence. High volumes of digital information at high velocities in healthcare is complex and can increase medical costs for both facility and the patient involved (Wang et al., 2018-a). One way the hospital I work at has mitigated the challenges of big data and the financial expenses included was the choice of a system for documentation. The system currently in use is the same Meditech company but just upgraded from the older version that was being used, and can keep up with the data infrastructure of the facility. This system is of higher quality and efficient enough for patient care documentation. The hospital does not have the most sophisticated upgrades, such as Epic. However, Meditech offers the best reliability and affordability needed. Another way to mitigate costs to the facility with big data can include predictive analytics. Predictive analytics allows healthcare facilities to assess their current service situations to help them separate the intricate structure of clinical cost and recognize best clinical practices (Wang et al., 2018-b). Predictive analytics can make data collection and cost more productive for nurses, patients, and facilities.

There are many benefits with the use of big data. Big data “typically refers to a large complex data set that yields substantially more information when analyzed as a fully integrated data set as compared to the outputs achieved with smaller sets of the same data that are not integrated,” (Thew, 2016). One of these benefits is the ability for patients to directly interact with their provider via secured messaging. Many hospitals and health systems with mature EHRs have portals for patients to access and record their own health data (Glassman, 2017). This allows patients to remain active in their care, make their needs known and separates necessary visits from visits that can wait until a patients next visit. Additionally, this can prevent patients from making unnecessary visits to emergency rooms which tend to have long wait times and be over-crowded.

The lack of data standardization can also make it challenging for a CNE to assess how the organization or a particular unit is performing and to make well-informed decisions about what to change (Thew, 2016). This can lead to ineffective communication and even harmful miscommunication. Eliminating duplication of effort will go a long way to simplifying and streamlining nursing workflow within EHRs (Glassman, 2017). This can create a more effective workforce, but first there needs to be standards of work and lines of communication to ease these efforts. Standard flow of patient charts is also needed. Although some charting systems create a lot of flexibility for charting needs, this also creates issues in being able to quickly find the information for providers in urgent care and emergency room settings.

Creating standard flowing charting for providers, nurses and practitioners in all areas may assist in provider faster, better and more efficient care for patients. At my place of work, we use CPRS which is within the Veterans Affairs hospital system. This was one of the first EHRs created. It has a lot of flexibility but little standardization to make finding specific types of information fast and easy. This process may help to discover or uncover previously unidentified relationships among the data in a database with a focus on applications (McGonigle & Mastrian, 2022). Identifying these weak points can assist in providing good care to patients.

The Benefits of Using Big Data 

Data in healthcare organizations is a key factor in developing outcomes through the cycle of data collection, analysis, and interpretation to assess actual status, risks, and future projects to invest efforts. Advances in technology make data available faster and at a higher volume, referred to as big data (McGonigle & Mastrian, 2022). Healthcare innovations use this big data for treatment guidelines, optimization of patient care through survey data analysis, pharmaceutical drug research, etc. Big data has become a key factor in improving patient outcomes through electronic health records (Glassman, 2017). Electronic health records have been implemented in most healthcare organizations to improve the quality and safety of care, impacting cost and access. Big data in electronic health records can improve communication within the interdisciplinary team caring for a patient. Electronic health records have become an important tool for documenting and progress patient goals in the care plan (Caliebe, Leverkus, Antes, & Krawczak, 2019).

Potential Challenges for Using Using Big Data and the Interventions Thereof

The potential challenge that electronic health records encounter when providers need to communicate with other healthcare organizations is the lack of interface among different clinical systems (Farrahi et al., 2019). Nowadays, the nursing home where I work has issues getting the discharge summaries when working with facilities that use EPIC, such as an emergency room hospital within our community. Thew (2016) notes that big data challenges reside in the interactions throughout different systems (Thew, 2016). Our healthcare organization has submitted a request for our fax number to be added to an automatic table that Emergency Departments will have access to. Our nursing home will need to remind the emergency team front desk to fax the discharge summary to our office after the patient is discharged. This will be a workaround to interface with the system. The other option is to request temporary user access as a visitor, but the request is not automatic (Visconti, & Morea, 2019).

Another potential challenge for big data is security breaches (Wang et al., 2018). Big data contains confidential information. Nowadays, several business agreements are held with third-party representatives to interface data, mainly for insurance and billing purposes. Hacking and data phishing are common ways to obtain information given to other companies (Banerjee, Hemphill, & Longstreet, 2018). Healthcare organizations use SharePoint and iCloud technologies for data storage with several security layers protected by an information technology department.

I am one of those who truly thinks that “syncing data” is a big task, almost intimidating! In this modern day of technology, combined with data knowledge, digital revolution of analytics of the healthcare industry has become very complex, but beneficial. “Big data in healthcare” denotes plentiful health data accrued from many sources including electronic health records (EHRs), medical imagery, genomic sequencing, payor records, pharmaceutical research, wearables, and medical devices, just to name a few (Catalyst, 2018). Shifting to (EHRs), interoperability programs, and progressively more advanced technology means, has led to the production of huge quantities of healthcare data. Analyzing critical data has its’ challenges, however, carefully managing this data can lead to opportunities to disseminate information geared towards knew knowledge for positive healthcare outcomes and results (McGonigle et al., 2022). Just to add, I believe analyzing critical data will never reach its full potential!

There are a number of beneficial gains from embracing Big Data, including the focus of moving to a value based care model, instead of the pay for service model. In comparison, the latter mentioned involves financially rewarding caregivers based on patient health populations, while the pay for service model rewards caregivers for performing procedures. Another benefit of embracing Big Data, allows the dissemination of evidence based information that will gradually increase healthcare efficacy. In addition, patients can now access their own health records and can actively participate in their own interventions and treatment (Thew, 2016). One challenge of Big Data management is the lack of data standardization. This particular element of Big Data insufficiency limits the accessibility of specific organizational performance, which also decreases chances of making educated and well informed decisions on what adjustments need to be made to produce better healthcare outcomes. When professionals fail to identify how data interacts throughout systems, this limits the types of data analytic creation (Thew, 2016). In order to mitigate challenges of embracing Big Data,  good, efficient, and reliable data has to be produced. This will allow the healthcare industry to move forward in many healthcare aspects. Data management is critical. This helps nurse leaders, analysts, and other professionals effectively synch multiple sources of information to create positive healthcare outcomes. (Catalyst, 2018).


According to the Online Journal of Nursing Informatics, big data “usually refers to a vast, complicated data collection that gives much more information when evaluated as a fully integrated data set as opposed to the outputs produced with smaller sets of the same data not combined.

Big data analytics has been called for due to the necessity of efficiently integrating an organization’s IT assets to provide a superior patient experience, boost operational efficiency, and even create novel data-driven business models. (Wang, Kung, & Byrd, 2018)

Big data analytics, generated from business intelligence and decision support systems, enables healthcare companies to examine large volumes, types, and velocities of data across several healthcare networks to support evidence-based decision-making and action.



Big data enhances the quality and accuracy of clinical choices by processing many health records in a matter of seconds. Still, the absence of data standards makes it difficult.

An executive may need help to evaluate the performance of the company or a specific unit and to make well-informed judgments about what to do. Having accurate information is essential for creating successful adjustments. (n.d.)



Teaching key personnel about big data analytics:

Managers and employees need critical thinking and interpretation skills to use significant data analytics results effectively. Because incorrectly reading reports may lead to substantial errors and questionable conclusions. Consequently, healthcare organizations must educate their employees in core statistics, data mining, and business intelligence to support the increasingly information-rich work environment. According to a survey conducted by the American Management Association in 2013, mentoring, cross-functional team-based training, and self-study help employees develop their practical data analysis skills. Additionally, healthcare organizations may recruit people with analytical skills. (Wang, Kung, & Byrd, 2018) This is precisely what my organization did, and there was a vast improvement in my unit.

Big data refers to vast amounts of information that, when properly analyzed, can do great things. For the past two decades, due to its enormous untapped potential, it has been a subject that has generated much attention as a matter of particular interest. Big data is being developed, stored, and analyzed across various businesses in the public and private sectors to improve the services offered (Dash et al. 2019). In this case, I will discuss how big data relates to health care.

Benefits of using big data as part of a clinical system

Big data can come from a variety of sources in the healthcare business. Some sources include hospital records, patients’ medical records, the results of medical examinations, medical records of patients, the results of medical tests, and devices connected to the internet of things (Dash et al. 2019). This has helped patients’ outcomes and safe practices where doctors can share information due to access to the internet. For patients taking medications requiring therapeutic blood levels, prescribers can access lab results, and patients can receive the correct dosages of medicines promptly. An example is for patients that are on clozapine, they get blood work completed, and pharmacists may have a standing order depending on the outcome of blood work. Patients on coumadin require INR results and other medications and treatments that need care coordination and information to be shared promptly. Care coordination and patient safety rely heavily on the nurses who write most electronic health records (EHR). This documentation includes care plans, physiological parameters, assessments, interventions, and progress evaluations. Patients can access and record their health data through portals offered by many hospitals and health systems that have developed EHRs. The meaningful use of electronic health records (EHRs) is supported by sharing this data within the confines of the Health Insurance Portability and Accountability Act (HIPAA) (Glassman, 2017).


Challenge of using big data in health care

Regarding the communications, processing, and storage of data in cyber-physical systems, cybersecurity, and data privacy are among the most critical factors. (Javid, 2020). These days, mobile phones, sensors, patients, hospitals, researchers, providers, and organizations are all contributing to the production of massive amounts of data about healthcare. The real challenge facing healthcare systems is figuring out how to locate, collect, analyze, and manage information to make people’s lives easier and healthier. This information contributes to understanding new diseases and treatments, predicting earlier stages’ outcomes, and making real-time decisions (Asri et al. 2015). When discussing the use of big data in healthcare, there is always the possibility that confidential information could be made public. Because of this, everyone involved should consider the risk-benefit ratio seriously to preserve the confidentiality of patients.

Potential challenges and risk

I work in a home care setting, and we use electronic health records to administer medications. This company does not use epics like most hospitals in the area and doctors’ offices. Therefore, when medication changes are made and information is given to the patients, most of the time, changes are not made in a timely manager due to a lack of connected EHR data technology that shares information. The challenges and risks are especially when the patient is taking high-risk medications like insulin, dosage changes could be made during an MD visit, and the nurse administering that medication in the community does not receive information timely. I would advocate for a data system with shared information to avoid medication errors.

Knowledge and data are very important to have as a nurse in order to help us make on-the-spot life make or break decisions.  Some of this data could and has literally saved lives.  Thousand of evidenced practice research and randomized trials have been completed and continue to be done in order to give professionals more knowledge and provide safer practice.  But how much is too much?!  Are we drowning in data?  Does it even make sense anymore?  Data is knowledge and knowledge is a success but there needs to be a standardized way to analyze data so it is understandable to everyone (Thew, 2016).

One downfall in the data that is collected is that we cannot account for all possible variables.  For instance, I just did some research about the benefits of using informatics and technology to remind people of adhering to their prescribed medication regimen.  Although the technology sounds amazing and evolved greatly over the years, the data was unable to account for the number of people that will acknowledge their medication reminders but still do not take their medications.

The benefits of big data are that large volumes of research and data from across the population and healthcare networks are able to be analyzed and broken down into practical and beneficial information to help healthcare professionals to do better at providing safe, quality care.  One example that I can think of is the EMR electronic medical record system that all nurses in the 21st century are familiar with.  Once upon a time, nurses did all paper charting.  Everything was written by hand.  There were lots of check marks made in little, tiny boxes.  Now, we use computer software, information technology, and big data to store all the patient information that we collect.  This electronic medical record system allows providers to see real-time patient information and has improved the efficiency and quality of care that we are able to provide.  As technology evolves, we have seen more people wearing smartwatches that can store their personal information in an application regarding their health, sleep patterns, activity levels, heart rate, oxygen saturation levels, etc.  Telehealth is also becoming a more popular and available option to access healthcare (Wang, et al., 2018).

People need to not be so scared of the changes that we face in technology and the direction healthcare is headed.  Education and training are imperative for healthcare staff to be more efficient and provide safer care.  I believe that healthcare professionals need to not only continue education on healthcare but there also needs to be in-services, seminars, and free classes for healthcare professionals to stay up to date on all the different information technologies that are available and continuously evolving.

Benefits of Big Data

Big data is essentially the collection of data that is collected from all different places and can be gathered into sets and analyzed to identify patterns and draw conclusions (McGonigle & Mastrian, 2022, p. 478). Big data can be useful in general as it is a way to identify problems which may not be easily seen. In healthcare specifically, this can be a way to identify places where errors occur, where medications are not scanned and why, which processes are cumbersome and take more time etc. One potential benefit of using big data within a clinical system is to be able to track a multitude of different items at once. This could include how many patients come into a certain clinic, how many return, what they come in for, and the number of times they get referred to the ED all gathered from information which is readily available. Big data also allows the electronic healthcare record to be organized and easily accessible (Wang, Kung, & Byrd, 2018).

Challenges of Big Data

One potential challenge to big data due to the vast amount of data which can be collected. A problem presents when one cannot sift through all of the data that is collected to make meaningful analyses (Threw, 2016). With the use of technology there is also the possibility that the information could be hacked, and in healthcare this presents a privacy problem. (Househ et. al., 2017).

Strategy to Mitigate Challenges or Risks of Big Data

            One strategy to mitigate the leak of information is to educate staff on proper technological habits and safety regarding patient information. By teaching staff which information is protected and how to protect it, the possibility of information leaking decreases. One way my institution does this is by requiring yearly training and refresher courses on phishing, malware, ransomware, and how to report any suspicious activity. This is reinforced by sending fake ransomware and phishing emails which have links to click that say its harmful and what should have given it away, or the recipient gets a congratulations for reporting it to spam. I believe that this is a simple way to reinforce the teachings without overwhelming the staff.

Many different applications of big data are now in use in healthcare systems. Big data is being utilized and documented in various healthcare-related contexts, from electronic health records to prescription administration and monitoring systems. Incorporating big data into a mental health healthcare system may open up new avenues for experimentation, which is a positive outcome. For instance, the gold standard of randomized controlled trials can only be undertaken adequately in some cases and circumstances owing to insufficient sample numbers or durations of follow-up. Data collected from an entire population may be used for extensive data analysis, allowing for closer scrutiny of aspects like these. Using this case in particular, researchers in Denmark were able to collect data on the prevalence of suicide and the impact of psychosocial intervention on suicide prevention (Schofield & Das-Munshi, 2018).

Problems arise when big data is used in healthcare IT systems. There needs to be more oversight over the data collection process is one area where the use of big data for mental health might be problematic. Developments in the fields of mental health and ethnic studies are two good examples of this. In some instances, ethnicity was not recorded in EHRs. The availability of these statistics has allowed researchers to look for links between psychosis and the African American population. The issue’s root is the need for further classifications and potential threats. The research does not contain or keep tabs on demographic information such as the participant’s sexual orientation, socioeconomic position, marital status, etc. (Schofield, 2018).

As an illustrative case, this highlights a possible difficulty in implementing big data in a healthcare setting (Thew, 2016). This ensures that everyone involved in the research is on the same page and is cognizant of the possibility that other variables may have contributed to the findings, despite the study’s decision to isolate the effects of the one being studied. Combining extensive data analysis with other research is another approach that may assist in reducing dangers or difficulties. When used to supplement other research, big data may assist in compensating for their shortcomings (Schofield & Das-Munshi, 2018).

The benefit of using big data as part of a clinical system

With the rising adoption of digitalized records and physician notes in healthcare, analytics for generated Big Data is very important. Big Data is of enormous value to organizations and data mining researchers because better results are obtained from a more significant volume of data. According to J.Gantz, “Big Data technologies describe a new generation of technologies and architectures, designed to economically extract value from massive volumes of a wide variety of data, by enabling high-velocity capture, discovery, and analysis. (Reinsel – Gantz, 2011). Big data generally captures what is easy to entangle, data that are openly expressed like swiped, scanned, sensed, people’s actions and behavior which it takes at face value. For example, Electronic healthcare records (EHRs), which use big data analyticsLinks to an external site. for major evaluations of diseases and performance of epidemiological analyses, can be regarded as a breakthrough in medical information management (Hännikäinen, 2017Links to an external site.Perera et al., 2016Links to an external site.). Where we can ensure that a complete health record is available to an authorized healthcare provider at the point of care when needed. This record may contain information from various providers, such as family physicians, specialists, social workers, pharmacists, radiologists, dieticians, physiotherapists, and nurses.

While big data offers many opportunities, it poses several challenges for human geography. One challenge of using big data is cost-effectively leveraging the evolving technology and communications infrastructure. The time and cost of application of big medical data are regarded as the significant causes of failure in establishing a big data warehouse in the healthcare industry (Chute et al., 2013Links to an external site.Jee & Kim, 2013bLinks to an external site.Jonathon Northover, 2014Links to an external site.Raghupathi & Raghupathi, 2014Links to an external site.Shapiro, Mostashari, Hripcsak, Soulakis, & Kuperman, 2011Links to an external site.Wills, 2014Links to an external site.). IT systems that support nursing practice are revolutionizing the way in which nursing care is structured, operationalized, implemented, and assessed (Dreyfus, 2001). Big Data stored in databases grow massively and becomes difficult to capture, form, store, manage, share, analyze and visualize via specific database software tools.

The competency of medical employees is also believed to be the key factor in successfully implementing medical big data systems. Some scholars state that resistance from adopters or users is an essential source of innovation failure (Sheth & Ram, 1987Links to an external site.). Also needed are managers and analysts with excellent insight into how big data can be applied. Companies must accelerate employment programs while making significant investments in the education and training of key personnel. 

Big data has altered how people lead and use data in many fields, particularly in healthcare. The good news is that we can transform the healthcare sector with big data. One type of method for assessing all types of data gathered from various sources is healthcare data management. It enables the healthcare sector to treat patients with greater accuracy and care. Similar to how variety, brevity, honesty, merit, exactness, justice, and illustration are of more concern in medical applications, big data is an impending manifestation in its body of legislation and assumption. (Belle et al., 2015).

Benefits of Using Big Data

Utilizing big data as a technology and collecting data to enhance the healthcare system have several advantages. An illustration is in the area of making wise decisions. Big data provides decision-makers with crucial analytical insight into the operation and interaction of many healthcare components. Searching through massive amounts of data may be boring and uninteresting for clinical practitioners like nurses. Big data analytics, however, provides a platform for a thorough evaluation of correlations and therefore enhances the outputs of analytical processes (Sielemann et al., 2020). It is also significant and given the sheer amount of data produced by each individual subject, it is implausible that a human subject could effectively execute data analysis jobs at the same level as big data analytics. Big data provides healthcare with a chance to enhance outcomes by utilizing data that might otherwise be insignificant.

Challenge of Using Big Data

One of the primary issues with employing big data analytics is incompatibility. Lack of a single language, or distinct terminologies, causes compatibility problems amongst EHRs, claim. Human subjects may be able to identify synonyms and similarities based on prior knowledge and expectations, whereas computer algorithms need exact phrasing or the recognition of synonyms to classify data into the same categories. Data transport is also impacted by compatibility problems in addition to terminology. Different codes might make it more difficult to share data, which is essential for big data analytics. For example, PDF and JPEG file formats are incompatible with Microsoft Word documents (the.doc and.docx formats). Despite being able to read the text in both formats, people could find it difficult to switch between them. These problems make large data analytics more difficult, which limits the full breadth of data analytics across many platforms (Macieira et al., 2017).

Strategies for alleviating the Challenge of Big Data.

The standardized language was suggested as the optimal solution to challenging diversity concerns in an effort to increase the usability of big data and increase the sample size for big data analytics (Macieira et al., 2017). Given that nursing is the largest field in terms of personnel in healthcare, standardizing nursing language would considerably enhance the uniformity of information storage (Thorstad & Wolff, 2018). The problems with incompatibility between software and hardware would also be adequately resolved by standardized programming. Standardization would establish a foundation for uniform learning during training and practice. Nursing would also benefit more from it in other words, with standardized terminology and computer programming, many nurses would be adequately prepared to practice in any context (Thorstad & Wolff, 2018).

Benefits of Using Big Data

               The term “big data” was used for the first time in 1997 by Michael Cox and David Ellsworth. The benefits of using big data in your organization cuts your costs in operation, increase efficiency in the workplace, offers fraud protection, increased productivity, patient satisfaction, and better decision making. Big data has four components such as volume which refers to how much data is actually collected, veracity refers to how reliable data is, velocity refers to how fast data can be generated, gathered, and analyzed, and variety. Big data is high volume, velocity, and variety that requires new technology and techniques to capture and store that is used to enhance decision making. In our workplace, utilizing the EHR, we as clinicians can share information across different health systems for example, the patient portal has the current information regarding medical history, diagnosis, medications, treatment plans, immunizations, allergies, and laboratory results readily available which allows providers to make decisions about the patient’s care. The EHR can be created and managed by authorized providers in a digital format that is capable of being shared. Big data strengthen patient relationships and create personalized engagements that drive health outcomes. Big data reduce costs by having electronic records, tracking data to improve the healthcare system, and shortening the time a patient spends in the hospital. Big data can have a positive impact on medical and healthcare functions.

Challenges of Using Big Data

             The lack of data standardization can make it difficult to assess how the organization or unit is performing. Information Technology presents challenges such as inadequate integration of healthcare systems and poor healthcare information management. The systems can be hacked when transmitting sensitive information to a third-party, halting the system which refers to poor internet connection, limited connectivity, especially rural areas, the towers need to be upgraded. The high volume of information that is generated at higher velocities and varieties in healthcare add challenges to the healthcare system. By having these challenges such as increase in medical costs, and time for patients and healthcare providers. The lack of appropriate infrastructure for data storage are critical that might endanger a big data healthcare. The government that uses “Big Data” in the health sector needs to establish affirmative policies to protect the health data of individuals.

Strategies to Effectively Mitigate the Challenges of Big Data

Organizations are implementing efficient technology systems to provide comprehensive and quality training data, developing quality tools while protecting patient privacy, ensuring providers trust and support analytical tools. Big Data have demonstrated in enhancing multiple areas of care, from medical imaging and chronic disease management to population health and medicine. By utilizing an automated system, the healthcare organization can gather more data from images used to train machine models, and synthesizing a massive dataset of distinct training examples. To remove bias from big data, developers can work with providers to understand what clinical measures are important.

Data, information, and knowledge work hand in hand. Data is a collection of information that helps us to identify a particular problem and find a solution. Information has a huge impact on problem-solving. Without information, problems cannot be solved, and a piece of information is knowledge.  all data are important to healthcare. In our day-to-day activities, we continue to gather a piece of vital information that either impact our lives or the life of our patient. In our workplace, we collect both subjective and objective data that when analyzed, will be used to plan for safety and quality care for our patients.  To make informed practice decisions, nurses need access to aggregate data about their patients and the impact of their care, and they need to know how to interpret that data (Glassman, 2017).


                                      One potential benefit of using big data as part of a clinical system

One of the potential benefits of using big data can be seen in the use of electronic health records in caring for our patients. Nurses, who do most of the EHR documentation (including plans of care, physiological parameters, assessments, interventions, and progress evaluations) in hospitals, are critical to care integration and patient safety (Glassman, 2017). Electronic health records are user-friendly and have a portal that allows patients access to and adds their health data. It helps us to eliminate duplication and medication errors. s. Patient care devices (such as cardiac monitors, vital sign monitors, and I.V. infusion pumps) can be linked with the electronic health record. According to Thaw (2016), the utilization of electronic health records has helped them to create alerts t avoid medication errors, and vaccine administration errors, a culture of reporting that not only covers lapses in care and errors but also promotes reporting of near misses.


  Challenge of using big data as part of a clinical system    

Some of the challenges of using big data as a part of a clinical system are the system upgrade that appears from time-to-time delaying activities in the system, Downtown, when the IT or system management is doing some maintenance, and work within the system, the worker has to wait for some time. According to Thaw, (2016) dealing with big data can understandably be challenging for the chief nurse executives

Strategy to effectively mitigate the challenges of big data

While the constantly growing body of academic research on big data analytics is mostly technology oriented, a better understanding of the strategic implication of big data is urgently needed (wang, Kung, & Byrd,2018). Be part of the selection process for new technology, provide feedback about

technology support to improve workflows, and communicate with technology companies about what will improve patient care. (Thaw,2016).

A considerable benefit of big data in the clinical system is how transferable the information is to patients and other healthcare-related companies necessary for patient care. Patient’s have the ability to access their test results, medications, and upcoming appointments through their healthcare systems’ patient portals once uploaded in the electronic health record (EHR). This allows them to keep up to date on their health status, communicate with their provider, and receive reminders about preventative care. This same documentation in the EHR informs insurance companies, such as Medicare, about specific publicly measured data (Glassman, 2017). Capturing big data allows for more research to be performed in order to better aid specific patient populations.

A potential challenge of using big data as part of a clinical system is the high influx of information and how to organize it all. This coincides with sifting through and picking the most relevant data. Key characteristics of pertinent data should be that it is findable, accessible, interoperable, and reusable (Shilo, Rossman, & Regal, 2020). A lack of standardization of data organization can slow down the information review process. The consequences of this are increased costs in healthcare services and the time of both patients and providers.

One strategy to effectively mitigate the challenge of information overload is to incorporate the use of machine learning algorithms. Machine learning is a program that learns to make a decision or perform a task automatically from a collection of data (Beam & Kohane, 2018). The advantages of machine learning include the assimilation and evaluation of large amounts of healthcare data. The ability to analyze diverse data types such as demographic information, imaging, laboratory results, and free-text notes can be incorporated into predictions for diagnosis, disease risk, appropriate treatments, and prognosis (Ngiam & Khor, 2019). The elimination of this big data analysis allows healthcare providers more time with their patients and acts as a resource for better clinical decision making.

The overall healthcare system gains a lot from the usage of big data in healthcare settings. One advantage is that it makes it easier to give patients individualized care. A steady stream of data is produced by sensors that monitor and record various healthcare processes. This data can help healthcare workers provide individualized treatment to address the specific needs of patients, management, and other relevant stakeholders. Technology-enabled medical equipment provides information that can be used to create treatment programs that are incredibly effective. Data consistency aids in the early detection of complex medical diseases, making it easier to alter therapies when conditions change (McGonigle & Mastrian, 2015). The timely flow of data that may be established with patient-specific thresholds to alert healthcare providers of changes can be beneficial for both outpatient and inpatient situations, allowing them to serve more patients while also providing more effective and high-quality healthcare for everyone.

The fact that big data improves efficiency in the clinical system is another advantage. Big data offers healthcare personnel the chance to improve operational efficiency, which will improve patient care (McGonigle & Mastrian, 2015). Big data enables healthcare organizations to comprehend how staff, equipment, and patient data are used together, enabling the identification of opportunities to improve operations through better resource utilization, automation, and ways to take advantage of new capabilities like connected healthcare systems.

Despite the benefits noted, there are significant disadvantages to using big data in clinical systems, including concerns about data security because patient information is kept in data centers with variable levels of security. Patients’ data may be extremely vulnerable to a variety of technical measures used by healthcare institutions to protect patient data in their systems in the face of growing dangers to stored data posed by high-profile breaches, hacking, and ransomware incidents. Due to the development of technology and the preference of most organizations for convenience over time-consuming software updates, this includes the use of highly effective anti-virus software, the installation of firewalls, the encryption of sensitive data, and the use of multi-factor authentication.

Nurses are in an optimal position to identify opportunities for data use to improve health care outcomes, decrease costs, and increase patient satisfaction. There is an overwhelming amount of data available to nurses. Extraction of meaningful data is paramount to gaining new insights that impact healthcare decisions and practice improvements (Gibson et al., 2022).

Auditing of nursing documentation is a quality improvement measure that helps ensure consistent quality care. Clinical audits of big data will provide perspective on the interactions between nurses and patients and assist in identifying practice gaps (Ramukumba & El Amouri, 2019).

A potential challenge of using big data as part of a clinical system is the amount of data available. Not all data is created equal. Nurses are often inundated with loads of data and must spend vast amounts of time determining which data is of value. It’s important for nurses to determine how to best use the data received to create value (Mylod & Lee, 2022). Data is initially unstructured and requires organization to improve its functionality and usability. To manage, analyze, and learn from big data in healthcare, we must have a way to systematically organize the enormous amount of data in a meaningful way (Dash et al., 2019).

The goal is to provide nurses with the right data at the right time to convert information into action. They need benchmarking data to visualize their progress in performance and achievement of goals. Dashboards provide systematic extraction of data that allows for analytical review and conversion to actionable knowledge (Mylod & Lee, 2022).

As both healthcare and technology as a whole continue evolve, the two become increasingly intertwined with each other. While health records used to be maintained in stacks of notes and forms, data is now being stored, analyzed and retrieved electronically. The Online Journal of Nursing Informatics defines big data as “a large complex data set that yields substantially more information when analyzed as a fully integrated data set as compared to the outputs achieved with smaller sets of the same data that are not integrated” (Thew, 2016).


With the relatively new ability to amass large amounts of patient data, from across thousands of cases, through the use of electronic health records (EHRs) healthcare facilities now have the ability to compare outcomes, treatment efficacy, patient safety data, demographics, market trends, and countless other parameters (Wang, Kung, & Byrd, 2018). These allow for not only more accurate record keeping, but the ability to modify the way in which are is delivered, to anticipate which interventions are going to lead to the best outcome for the patient. Applying simple statistics can take this vast sea of data and yield an incredible amount of useful information. The more technology advances, the more integrated everything becomes. New technologies are allowing patient care devices, location tracking devices, communication devices, and countless other devices to all work together within the same informational framework (Glassman, 2017).


This is not to say that utilizing big data in healthcare has been without its hurdles. The early stages of transitioning from paper records to EHRs and computerization were described by many as being nothing short of disastrous. Financial services would utilize one software system, while inpatient services would utilize another, and laboratory services would utilize another, and surgery would utilize another, and plant operations would utilize another, and on and on, to the point where there were numerous systems being used, but none of them had the ability to “talk to” one another. Now that EHRs are no longer in their infancy, there is a big push for data standardization and compatibility, because when data is not usable across the continuum of care, it is far less useful than it would be in a standardized form (Thew, 2016). At my own facility, I have seen a massive effort put in to integrating as many areas of the hospital on to the same software system as possible. Billing, radiology, laboratory services, physician records, and nurse location tracking are just a few areas which have been pulled on to the same software system. This standardization has recently spread not only within the facility itself, but also to the freestanding emergency departments that are operated under the same company designation as our hospital. Having quick access to these records has shown to significantly increase the ease with which information is communicated between departments and facilities and has greatly improved patient safety and continuity of care.

It is readily recognized that nurses are the most populous of healthcare professions; therefore, they have increased access to inputting and retrieving information in electronic health systems, which end up influencing decision-making in healthcare organizations (Glassman, 2017: McGonigle & Mastrian, 2022). A major advantage of big data is that it facilitates safety by allowing healthcare professionals to track medication side effects and adverse reactions. An example is the brand name Vioxx, which was marketed as having lesser incidences of gastrointestinal bleeding (GIB); however, the manufacturers did not include an increased risk of cardiovascular events (CVE) (Parkinson et al., 2011). Through multiple smaller health organizations reporting CVE and the continued risk of GIB, regulatory boards such as the Food and Drug Administration were able to find sufficient data to halt the sale of Vioxx.

Data storage using computers comes with a unique set of challenges, such as data breaches and computer hacking. It is reported that the incidences of data breaches are rising, and so is the cost of ameliorating these breaches (Fleury-Charles et al., 2022). Employees of an organization can unintentionally cause a breach of protected health information by sending or faxing to the wrong recipient email or fax, but more commonly, data breaches result from third parties phishing or using technology attacks such as cyber-attack or social engineering attacks (Fleury-Charles et al., 2022).   During the pandemic, Fresenius Health was hit by ransomware, with the attackers requesting cryptocurrency for negotiation (Zmudzinski, n.d.).

Data protection requires a concerted effort by all employees of an organization and particularly end users of computers. Machine learning facilitates the recognition of patterns and is therefore applicable in biometrics, such as fingerprints or iris patterns, to secure data access (McGonigle & Mastrian, 2022). Additionally, semi-annual refresher courses on the type of attacks and how to safeguard data should be implemented as part of learning modules. The Information Technology (IT) Department should also create and periodically send phishing-like emails to employees to keep them alert and encourage reporting of concerns. Lastly, healthcare organizations should invest in modern antivirus software, spyware, and firewall systems.


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