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
Post a description of at least one potential benefit of using
When you wake in the morning, you may reach for your cell phone to reply to a few text or email messages that you missed overnight. On your drive to work, you may stop to refuel your car. Upon your arrival, you might swipe a key card at the door to gain entrance to the facility. And before finally reaching your workstation, you may stop by the cafeteria to purchase a coffee.
From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.
As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.
To Prepare:
- Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
- Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.
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.
Required Readings
- McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
- Chapter 22, “Data Mining as a Research Tool” (pp. 537-558)
- Chapter 24, “Bioinformatics, Biomedical Informatics, and Computational Biology” (pp. 581-588)
- Glassman, K. S. (2017). Using data in nursing practice
- Links to an external site.. American Nurse Today, 12(11), 45–47. Retrieved from https://www.
americannursetoday.com/wp- content/uploads/2017/11/ant11- Data-1030.pdf - Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs
- Links to an external site.. Retrieved from https://www.
healthleadersmedia.com/ nursing/big-data-means-big- potential-challenges-nurse- execs - Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations
Links to an external site.. Technological Forecasting and Social Change, 126(1), 3–13.
Expert Answer and Explanation
Big Data Risks and Rewards
Benefit of using Big Data in a Clinical System
Big data refers to massive data sets that are often analyzed using advanced computational techniques to give information about human behaviors, trends, and patterns. In healthcare, big data helps in adopting increased speed of data processing, where individuals can gather relevant insights that aid in patient care. A primary benefit of using big data in a clinical system is that care providers can constantly and efficiently monitor vital signs of patients (Groves et al., 2016).
Healthcare givers often try to improve the health of chronic disease patients by monitoring the vitals such as temperature and blood pressure. When a patient’s health condition is constantly changing, it becomes impossible for the healthcare provider to monitor this patient because of the large volume of data generated (Groves et al., 2016). Using big data helps in computing the vitals and relate them with the progress of the patient.
Challenge of Using Big Data as Part of a Clinical System
One of the biggest challenges that face big data in its implementation in a clinical system is the inability to capture all the data. In a recent study, an ophthalmology clinic found that only 23.5% of the patient data reported is captured by electronic health records (HER), which are part of big data (Dyer et al., 2019). That is, a patient would report to have around three eye problems, but the HER would only agree to one and report it. This is a primary challenge because it means that big data cannot participate in efficient diagnosis of patient conditions.
Also, since big data is used in making and monitoring prescriptions, it is possible that the technology systems could fail to have efficient ways of capturing patient symptoms and the reaction of patients to medications (Heires, 2016). This problem is not always noticed because most of the healthcare givers using big data only focus on the small percentage of the reported data, and are oblivious of the fact that there could be other information that big data missed.
Strategy to mitigate the stated Challenge of Using Big Data in a Clinical System
One of the strategies to mitigate the challenge of capturing information in a clinical system using big data is prioritizing data types. While big data entails a situation where computers adopt human functions, it is evident that it is humans who program them to capture and record the data in specific ways (McCue & McCoy, 2017). That is, despite the fact that it is a powerful technology tool to manage data, there are human brains behind its design.
The problem of big data can be solved when these programmers ensure that they prioritize the types of data that are open for documentation, and hence reduce the probabilities of the system missing out some of the patient information. Also, clinical documentation and improvement programs that help clinicians to ensure full capturing of the data by the systems is crucial (Heires, 2016).
Such kind of education could be geared to helping the healthcare givers to have the skills to perform constant improvement and impart in them the skills to modify the system in the formats that help in relaying much of the patient information. Also, constant monitoring of the changes and implementation can help in improving the system performance using big data.
References
Dyer, B., Rao, S., Rong, Y., Sherman, C., Cho, M., Buchholz, C., & Benedict, S. (2019). 12 Clinical and cultural challenges of big data in radiation oncology. Big Data in Radiation Oncology, 181.
Groves, P., Kayyali, B., Knott, D., & Kuiken, S. V. (2016). The’big data’revolution in healthcare: Accelerating value and innovation.
Heires, K. (2016). The risks and rewards of blockchain technology. Risk Management, 63(2), 4-7.
McCue, M. E., & McCoy, A. M. (2017). The scope of big data in one medicine: unprecedented opportunities and challenges. Frontiers in veterinary science, 4, 194.
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FAQs
What is big data in health care?
Big data in healthcare refers to the vast and complex sets of health-related data that are generated, collected, and analyzed to improve patient care, medical research, and healthcare operations. These datasets are typically characterized by their volume, variety, velocity, and sometimes, their veracity. Here’s what these characteristics mean in the context of big data in healthcare:
- Volume: Healthcare generates an enormous amount of data. This includes electronic health records (EHRs), medical imaging files (such as X-rays and MRIs), patient demographics, lab test results, and more. The volume of data is often too large to be managed and analyzed effectively using traditional methods.
- Variety: Healthcare data comes in various formats. It includes structured data, like EHRs and lab reports, as well as unstructured data, such as clinical notes, medical images, and even patient-generated data from wearable devices. These different types of data present a challenge in terms of integration and analysis.
- Velocity: Healthcare data is generated rapidly and continuously. New patient records are created, lab results are processed, and medical devices produce data in real-time. Analyzing this data in a timely manner is crucial for making informed clinical decisions and improving patient outcomes.
- Veracity: Data in healthcare must be accurate and reliable. Errors or inconsistencies in health data can have serious consequences for patient care. Ensuring data quality is a critical concern.
Big data in healthcare is collected from various sources, including hospitals, clinics, laboratories, wearable devices, and even patient-reported information. It is then processed and analyzed using advanced data analytics, machine learning, and artificial intelligence techniques to extract valuable insights and patterns. These insights can be used for a wide range of applications in healthcare, including:
- Clinical Decision Support: Big data analytics can help healthcare providers make more informed decisions about patient care, treatment plans, and diagnostics by providing access to a patient’s complete medical history and relevant research data.
- Predictive Analytics: By analyzing historical and real-time patient data, healthcare systems can predict disease outbreaks, identify patients at risk of specific health conditions, and forecast healthcare resource needs.
- Personalized Medicine: Big data allows for the customization of treatment plans based on an individual’s genetic profile, medical history, and responses to specific treatments. This tailors healthcare to the unique needs of each patient.
- Drug Discovery: Pharmaceutical companies use big data to expedite drug discovery and development processes by analyzing vast datasets related to molecular biology, clinical trials, and drug interactions.
- Population Health Management: Healthcare organizations can use big data to assess the health of populations and identify trends and disparities. This information guides public health initiatives and preventive care strategies.
- Healthcare Operations Optimization: Big data analytics can improve the efficiency of healthcare operations by optimizing resource allocation, reducing healthcare costs, and streamlining administrative processes.
Overall, big data in healthcare holds the potential to transform the industry by improving patient care, advancing medical research, enhancing operational efficiency, and ultimately leading to better health outcomes for individuals and communities. However, it also presents challenges related to data privacy, security, and ethical considerations, which must be carefully addressed to fully realize its benefits
How does access to big data have the potential to improve outcomes for patients?
Access to big data has the potential to significantly improve outcomes for patients in several ways:
- Personalized Medicine: Big data allows for the analysis of vast amounts of patient information, including genetic data, medical records, and treatment outcomes. This enables the development of personalized treatment plans tailored to an individual’s unique genetic makeup and medical history. With personalized medicine, healthcare providers can choose the most effective treatments with fewer side effects, increasing the likelihood of successful outcomes.
- Early Disease Detection: Big data analytics can detect patterns and anomalies in large datasets, which can be used to identify disease outbreaks, track the spread of diseases, and even predict the likelihood of an individual developing a particular condition. Early detection leads to earlier intervention, potentially preventing the progression of diseases and improving patient outcomes.
- Predictive Analytics: By analyzing patient data, healthcare providers can predict disease risks and complications. For example, machine learning models can forecast the likelihood of a patient being readmitted to the hospital after discharge. This information can be used to implement preventive measures and interventions to reduce readmission rates and improve overall patient health.
- Treatment Optimization: Big data can help optimize treatment plans by analyzing data from a large number of patients who have undergone similar treatments. This can lead to the identification of the most effective treatment protocols, dosages, and regimens, ensuring that patients receive the best possible care based on real-world evidence.
- Drug Discovery: Pharmaceutical companies can leverage big data to identify potential drug candidates more efficiently. By analyzing vast datasets related to molecular biology and clinical trials, researchers can expedite the drug development process, leading to the discovery of new therapies that can improve patient outcomes.
- Healthcare Resource Allocation: Big data can be used to optimize the allocation of healthcare resources. Hospitals and healthcare systems can analyze data to predict patient admission rates, plan staffing levels, and manage inventory of medical supplies more effectively, ensuring that patients receive timely care and treatment.
- Patient Engagement and Education: Big data can be used to create personalized health recommendations and educational materials for patients. By analyzing a patient’s health data and preferences, healthcare providers can offer tailored guidance and information to help patients better manage their health, leading to improved outcomes.
- Remote Monitoring: With the help of wearable devices and remote sensors, big data can facilitate remote monitoring of patients’ vital signs and health conditions. This enables healthcare providers to detect and respond to changes in a patient’s health in real-time, reducing the need for frequent in-person visits and improving the overall quality of care.
- Clinical Research: Big data analytics can accelerate the pace of clinical research by identifying suitable candidates for clinical trials and analyzing trial data more efficiently. This can lead to faster development of new treatments and therapies, ultimately benefiting patients with access to cutting-edge care.
In summary, access to big data in healthcare has the potential to revolutionize patient care by enabling personalized medicine, early disease detection, predictive analytics, treatment optimization, drug discovery, resource allocation, patient engagement, remote monitoring, and clinical research. These advancements can lead to better patient outcomes, improved quality of care, and a more efficient healthcare system.
What are the challenges of using big data as part of a clinical system?
Integrating big data into a clinical system offers numerous benefits, but it also comes with several challenges that need to be addressed:
- Data Privacy and Security: Patient data is sensitive, and maintaining privacy and security is paramount. Ensuring that patient information is protected from unauthorized access or breaches is a significant challenge, especially with the increasing sophistication of cyber threats.
- Data Quality: The accuracy and reliability of the data used in clinical systems are crucial. Big data can be messy, containing errors, inconsistencies, or missing values. Cleaning and validating data to ensure its quality is a persistent challenge.
- Data Interoperability: Healthcare systems often use different standards and formats for data storage. Integrating data from various sources and making it compatible with the clinical system can be complex and time-consuming.
- Scalability: As the volume of healthcare data continues to grow, clinical systems must be scalable to handle the increasing data load effectively. Scalability challenges include hardware infrastructure, data storage, and processing capabilities.
- Data Integration: Integrating data from diverse sources, such as electronic health records (EHRs), medical devices, wearable sensors, and external databases, can be a complex task. It requires robust data integration strategies and technologies.
- Data Governance: Establishing data governance policies and procedures to manage data throughout its lifecycle is essential. This includes data collection, storage, access, sharing, and disposal. Ensuring compliance with regulations like HIPAA adds complexity.
- Ethical and Legal Concerns: Ethical considerations around data usage, informed consent, and data ownership must be addressed. Legal issues related to patient rights, liability, and compliance with healthcare regulations are significant challenges.
- Algorithmic Bias: Machine learning algorithms used for clinical decision support and predictive analytics may inherit biases present in the data. Detecting and mitigating bias to ensure fair and equitable healthcare outcomes is a critical challenge.
- Resource Constraints: Implementing and maintaining a big data clinical system can be costly and resource-intensive. Healthcare organizations may face budget limitations, and there might be a shortage of skilled data professionals.
- Change Management: Introducing big data into clinical systems often requires changes in workflows and practices. Healthcare professionals may need training and support to adapt to these changes effectively.
- Data Access and Governance: Balancing the need for data access by clinicians, researchers, and administrators with strict governance and security policies can be challenging. Striking the right balance between accessibility and control is crucial.
- Data Storage and Retention: Managing large volumes of healthcare data over time can be complex. Decisions regarding data retention periods, archiving, and purging must align with regulatory requirements and clinical needs.
- Data Standardization: Establishing common data standards and vocabularies across different healthcare systems is essential for meaningful data exchange and analytics. Achieving consensus on these standards can be difficult.
- Patient Consent and Trust: Gaining patient consent for data use and maintaining trust in healthcare institutions are ongoing challenges. Patients must be informed about how their data is used and have confidence in its protection.
- Data Analytics Expertise: Healthcare organizations may lack the expertise needed to effectively analyze and derive insights from big data. Recruiting or training data scientists and analysts is crucial for leveraging big data’s potential.
Addressing these challenges requires a multidisciplinary approach, involving healthcare professionals, data scientists, IT experts, legal advisors, and policymakers. Furthermore, ongoing collaboration, research, and investment are essential to harness the full potential of big data in clinical systems while ensuring patient safety, privacy, and quality care.
What are the potential benefits of using big data as part of a clinical system?
Leveraging big data as part of a clinical system offers numerous potential benefits that can significantly enhance patient care, streamline healthcare operations, and drive advancements in medical research and practice. Here are some of the key potential benefits:
- Personalized Medicine: Big data analytics enable the development of personalized treatment plans based on an individual’s genetic makeup, medical history, and real-time health data. This approach can lead to more effective and targeted treatments, minimizing adverse effects and improving patient outcomes.
- Early Disease Detection: By analyzing large datasets, clinical systems can detect patterns and anomalies indicative of disease outbreaks or individual health risks. Early detection allows for timely intervention and preventive measures, potentially saving lives and reducing the burden on healthcare systems.
- Predictive Analytics: Big data can predict disease risks and patient outcomes with greater accuracy. Healthcare providers can use these predictions to proactively manage patients’ health, reduce hospital readmissions, and optimize resource allocation.
- Treatment Optimization: Clinical systems can analyze data from diverse sources to identify the most effective treatment protocols, dosages, and interventions. This optimization can improve patient responses to therapy and minimize trial-and-error approaches.
- Drug Discovery: Big data accelerates the drug discovery process by analyzing vast datasets related to molecular biology, clinical trials, and patient outcomes. This leads to the development of new drugs and therapies more quickly, expanding treatment options for patients.
- Healthcare Resource Allocation: Data-driven insights enable healthcare systems to allocate resources efficiently, ensuring that the right resources are available at the right time and in the right place. This can lead to reduced wait times, improved access to care, and better patient experiences.
- Remote Monitoring: Wearable devices and remote sensors connected to clinical systems enable real-time monitoring of patients’ vital signs and health conditions. Healthcare providers can intervene promptly in case of emergencies and provide ongoing care, especially for chronic conditions.
- Clinical Research: Big data facilitates faster and more efficient clinical research by identifying suitable candidates for clinical trials and analyzing trial data. This expedites the development of new treatments and therapies, benefiting patients with access to cutting-edge care.
- Patient Engagement: Clinical systems can use big data to engage patients actively in their healthcare. Personalized health recommendations, educational materials, and telemedicine options can empower patients to manage their health more effectively.
- Cost Reduction: Through data analytics, healthcare organizations can identify cost-saving opportunities, such as reducing unnecessary tests and hospital readmissions. This can lead to lower healthcare costs for both patients and providers.
- Quality Improvement: Continuous data analysis allows healthcare institutions to identify areas for quality improvement. This leads to higher standards of care, reduced medical errors, and better patient outcomes.
- Population Health Management: Clinical systems can analyze data at a population level to identify health trends and disparities. This information helps public health officials and healthcare organizations implement targeted interventions to improve the health of communities.
- Efficient Decision-Making: Data-driven insights support clinical decision-making, enabling healthcare professionals to make informed choices about patient care, resource allocation, and treatment strategies.
- Streamlined Workflows: Big data can automate administrative tasks and streamline clinical workflows, reducing administrative burdens on healthcare staff and allowing them to focus more on patient care.
- Data Sharing and Collaboration: Clinical systems equipped with big data capabilities facilitate data sharing and collaboration among healthcare providers, researchers, and institutions, leading to better-coordinated care and accelerated medical advancements.
In summary, incorporating big data into clinical systems holds the promise of transforming healthcare by improving patient outcomes, enhancing preventive care, accelerating research and drug development, optimizing resource allocation, and ultimately making healthcare more efficient and patient-centered.
What challenges face healthcare in regards to managing healthcare information?
Healthcare faces several challenges when it comes to managing healthcare information. These challenges encompass various aspects of information management, including data collection, storage, sharing, security, and accessibility. Here are some of the key challenges:
- Data Privacy and Security: Protecting patient data from unauthorized access, breaches, and cyberattacks is a top concern. Healthcare organizations must comply with regulations like the Health Insurance Portability and Accountability Act (HIPAA) to safeguard patient information, but security threats continue to evolve.
- Data Quality and Accuracy: Ensuring that healthcare data is accurate, complete, and up-to-date is critical for patient safety and effective decision-making. Inaccurate data can lead to medical errors and compromised care.
- Data Integration: Healthcare data comes from various sources, including electronic health records (EHRs), medical devices, and external systems. Integrating these disparate sources to provide a comprehensive patient record can be complex.
- Interoperability: Many healthcare systems use different standards and formats for data, making it difficult to share information seamlessly between providers and organizations. Interoperability challenges can hinder care coordination.
- Data Governance: Establishing clear policies and procedures for data collection, access, sharing, and retention is essential. Effective data governance helps maintain data quality, security, and compliance.
- Ethical and Legal Considerations: Managing healthcare information involves ethical considerations, such as informed consent for data use and data ownership. Legal issues related to patient rights and liability are complex and must be navigated carefully.
- Health Information Exchange: Ensuring the secure exchange of patient information across different healthcare organizations and systems is a logistical challenge. Establishing common standards for data exchange is necessary for interoperability.
- Data Access Control: Balancing the need for healthcare professionals to access patient data with strict access controls is challenging. Ensuring that only authorized individuals can view and modify patient records is crucial for privacy and security.
- Data Storage and Retention: Managing the vast amount of healthcare data over time, including archiving and purging records, requires careful planning to comply with regulatory requirements and operational needs.
- Data Analytics Expertise: Many healthcare organizations lack the expertise to effectively analyze and derive insights from the wealth of data they collect. Recruiting or training data scientists and analysts is essential to leverage data for improvement.
- Patient Engagement: Encouraging patients to actively participate in managing their health information, such as updating records and sharing data from wearable devices, can be challenging but is increasingly important for personalized care.
- Cost Constraints: Healthcare organizations often have budget limitations, which can affect their ability to invest in advanced information management systems and data security measures.
- Legacy Systems: Transitioning from older, legacy systems to modern EHRs and information management solutions can be a significant challenge. It requires careful planning, training, and data migration.
- Data Exchange with Research: Sharing patient data with researchers while protecting privacy and complying with regulations presents challenges. Striking the right balance between data sharing and privacy is crucial for advancing medical research.
- Patient Consent and Trust: Gaining patient consent for data use and maintaining trust in healthcare institutions are ongoing challenges. Patients need to be informed about how their data is used and assured of its protection.
