From optimizing the performance of healthcare providers to improving the precision of diagnosis and treatment plans, data analytics has the power to revolutionize the healthcare industry. As the fields of technology and healthcare continue to weave together, data analytics professionals can provide actionable insights that can have a significant impact on patients and providers alike. This blog will explore data analytics in healthcare examples, and other ways on how data analytics is used in healthcare.
Understanding Data Analytics in Healthcare
Data is the key to optimizing and improving the healthcare industry, which is why data analytics in healthcare is critically important. The raw data collected from both patients and providers can be collected, organized and analyzed to reveal trends or uncover opportunities for improvement.
The Role of Data Analytics in Modern Healthcare
Both past and real-time data are used in the modern healthcare system to evaluate the current system and improve outcomes for patients. According to CompTIA, healthcare data analytics can be broken down into the following categories:
- Descriptive analytics
- Prescriptive analytics
- Predictive analytics
- Diagnostic analytics
Regardless of the type of data analysis being performed, data analytics in healthcare is used to reduce healthcare costs, improve efficiency and improve patient outcomes.
Key Components of Healthcare Data Analytics
Various data sets are used in healthcare analytics to evaluate current procedures, improve the qualifications of staff members, reduce costs and improve patient outcomes. Per KMS Healthcare, some of the most common types of data sets in healthcare include:
- Electronic health records
- Administrative data
- Claims data
- Health surveys
- Clinical trial data
- Genomic data
How Data Analytics Techniques Are Used in Healthcare
Data analytics techniques are used in healthcare in a variety of ways, including to reduce costs, minimize the impact of staff shortages and improve patient outcomes.
These are some of the most common data analytics in healthcare examples:
Predictive vs. Prescriptive Analytics in Healthcare
Predictive and prescriptive analytics are commonly used in healthcare to strategize and forecast for the future. While there aren’t always clear differences between predictive and prescriptive analytics, there are distinctions between the two when it comes to the use of machine learning and how data is processed:
- Predictive analytics is the process of collecting and analyzing large amounts of data to predict future trends or outcomes based on historical and current data.
- Prescriptive analysis identifies actions that can be taken in response to the forecast provided by predictive analysis – and tries to determine what the outcomes of these actions might be through simulations.
Leveraging Big Data for Health Insights
Recognizing the power and potential of big data analytics in the healthcare industry, healthcare providers and administrators now rely on a variety of sources to collect large data sets from both patients and providers. Electronic health records, wearable health technology and health surveys allow healthcare facilities to collect data, analyze trends and predict health patterns within a community.
Real-Time Monitoring and Analysis of Patient Data
Advancements and improvements in Internet of Things (IoT) devices have allowed healthcare providers to collect real-time data and continue monitoring their patients, even after they have left the office or have been discharged from the hospital. IoT devices allow for remote monitoring, which can generate continuous data streams for the provider to review and analyze. These data sets can play a critical role in early intervention, allowing doctors and nurses to act quickly in the event of an unforeseen circumstance. It also gives them an opportunity to tailor the care plan to the unique and specific needs of the individual patient.
Population Health Management Through Data Insights
Population health management is a collaborative concept that requires healthcare professionals and experts in a variety of other industries to work together to monitor and improve the health of the general population. With data analytics, healthcare providers and public health officials can identify high-risk groups, develop proactive interventions and create targeted care strategies designed to improve the health of the total population.
Predictive Modeling for Disease Diagnosis and Treatment
By collecting data not only from electronic health records but also from other sources, healthcare providers can use predictive modeling to determine an individual’s risk for a particular disease or predict the progression of a disease. Advanced algorithms have been developed that will collect and evaluate data. This allows providers to detect the signs and symptoms of a disease as early as possible and take proactive measures to improve outcomes for the patient.
Data-Driven Decision-Making for Healthcare Policies and Strategies
At a time when hospitals and clinics are facing rising costs and staff shortages, data analytics in healthcare has never been more important. Data collected and analyzed can help improve resource allocation in a facility or across an entire healthcare system and increase operational efficiency.
Fraud Detection and Prevention in Healthcare Systems
The National Health Care Anti-Fraud Association notes that healthcare systems and facilities lose tens of billions of dollars each year because of healthcare fraud, which is contributing to the rising costs of healthcare for average patients. Machine learning and data analytics can be employed to identify suspicious patterns and flag issues that may require further investigation. This ultimately reduces the amount of fraud losses that occur each year and lowering the cost of healthcare overall.
The Impact of Data Analytics on Patient Care
While many healthcare administrators rely heavily on data analytics to reduce costs and improve operations at their facilities, these same machine learning techniques can be used to improve both patient care and outcomes. For example, predictive analytics can be used to monitor the progress of a disease and reduce the risk of hospitalization. This then allows the patient to receive superior care along the way and avoid a hospital stay.
Enhancing Patient Outcomes Through Predictive Analytics
Predictive analytics offers actionable insights to providers, allowing them to enhance patient care and improve outcomes. For instance, predictive models can be used to:
- Identify an individual’s risk for cancer, allowing doctors to monitor the patient more closely and take advantage of early detection screening opportunities.
- Monitor the progress of a disease, ensuring that the patient receives timely intervention and reduces the risk of prolonged complications from the illness.
Case Studies: Real-World Success Stories
Data analytics is making healthcare more affordable, accessible and impactful for patients around the world. These are a few of the top data analytics in healthcare success stories:
- Carolinas Healthcare System used data analytics to reduce inappropriate emergency room use. This provided patients with the information and resources they needed to seek care outside the emergency room when possible. That ultimately helped to reduce the strain on staff and improve patient outcomes.
- Beth Israel Medical Center used real-time data collected from ICU patients to predict outcomes within the ICU and improve risk assessment in this critical care unit.
Overcoming Challenges in Healthcare Data Analytics
While data analytics in healthcare is the driving force behind the industry’s revitalization efforts, it’s not without its challenges. Challenges, mistakes and unintended consequences are part of the learning process. That said, healthcare providers will need to work to overcome these challenges as they integrate data analytics into their healthcare facilities.
Addressing Privacy and Security Concerns
One of the most complex challenges associated with data analytics in healthcare is privacy and security concerns. HIPAA regulations require healthcare providers to always maintain patient privacy, and data analytics professionals must make sure that all data collection and analysis techniques are in line with these regulations. Data security is not only essential for patient trust, but it’s also critical for operations, as a data breach has the potential to halt operations at healthcare facilities.
Navigating the Complexities of Data Integration
Each individual healthcare system has its own electronic health records system, and integrating those systems can be difficult, particularly when patients see specialists or providers at different healthcare facilities. Data standardization can help providers and administrators navigate the complexities of data integration. Data standardization can help:
- Improve workflows at healthcare facilities.
- Ensure patients receive timely, prompt care.
- Improve precision with billing.
- Optimize imaging studies.
The Future of Healthcare: Predictions and Trends
Data analytics will continue to play a pivotal role in the future of healthcare. Advancements in technology are already changing the way that data is harvested, stored, analyzed and used, enabling more accurate diagnoses, personalized treatment plans, and efficient healthcare management. As these technologies evolve, the potential for data analytics to enhance medical research, predict disease outbreaks, and streamline hospital operations will only grow, making it an indispensable tool in modern healthcare.
The Role of AI and Machine Learning in Healthcare Analytics
Experts predict that artificial intelligence and machine learning will revolutionize the field of healthcare analytics. AI and machine learning technology can handle larger datasets and uncover complex patterns that may not be able to be identified by humans alone. In the end, AI and machine learning technology will help improve the precision of the actionable insights that result from healthcare analytics.
Emerging Technologies Shaping the Future
There are several emerging technologies on the horizon that will continue to shape the future of data analytics in healthcare. As healthcare professionals continue to learn how to harness the power of augmented reality and virtual reality technology, updated treatment options and staff training experiences will be available. In addition, industry insiders anticipate that blockchain technology will play a critical role in data security. With these technologies on the horizon, the future is bright for those interested in a data analytics career.
Best Practices for Successful Implementation of Data Analytics
The best practices for implementing data analytics in healthcare include:
- Improving the quality of data collected by eliminating bias.
- Providing staff members with ongoing training in data analytics.
- Protecting patient privacy at all costs and investing in increased security measures to protect and store data.
How to Evaluate the Effectiveness of Data Analytics Initiatives
In addition to implementing data analytics techniques, healthcare professionals will need to develop a process to evaluate the effectiveness of these initiatives. By outlining success metrics in advance and tracking those metrics along the way, data analytics techniques and strategies can be monitored and refined.
The Importance of Data Literacy for Healthcare Professionals
While data analysts are the experts at collecting and analyzing data, all healthcare professionals should receive training to become data literate. This is important because all healthcare providers and administrators will need to be able to understand and interpret the data to provide patients with the best possible care.
Learn How Data Analytics is Used in Healthcare at Johnson & Wales University
When it comes to how data analytics is used in healthcare, it’s evident that data analysis is perhaps one of the most critical skills of our time. Those who pursue advanced degrees in data analytics will find that they can apply their specialized knowledge and skill set in a variety of career fields, including healthcare. At Johnson & Wales University, we offer an online MBA in Data Analytics, which take an in-depth look at data analytics and machine learning within the scope of business and other related fields.
For more information about completing your degree online, complete the Request Info form, call 855-JWU-1881, or email [email protected].