Improving Patient Care With Data Analytics in Healthcare 
Article
Data & Analytics Healthcare
Improving Patient Care With Data Analytics in Healthcare 
Improving Patient Care With Data Analytics in Healthcare 
Article
Data & Analytics Healthcare

Improving Patient Care With Data Analytics in Healthcare 

Analytics in healthcare refers to the use of data, statistical methods, and quantitative analysis to gain valuable insights and thus facilitate and improve the decision-making process. It involves collecting and analyzing data from various sources, such as electronic health records (EHRs), clinical research studies, data generated from medical claims etc.  

As organizations and industries grow to be more reliant on data when it comes to making a decision, the role of data analytics in healthcare becomes quite prominent. In fact, in pre-COVID-19 times a survey showed that 84% of healthcare executives predicted it to play a key role in their organization’s business strategy in the nearest future. But when the world was hit with the global pandemic, it truly showed how impactful it can be in healthcare.  

When push came to shove during the COVID-19 pandemic, healthcare providers had to quickly adapt to the changing circumstances and find new ways to optimize patient care. Data analytics stepped in in numerous ways, such as developing predictive models to forecast the spread of the virus and predicting the demand for healthcare services, allowing providers to plan and allocate resources accordingly. Analytics was also used to monitor patient outcomes and identify risk factors for severe illness, enabling providers to intervene early and improve patient outcomes. It has also played a key role in the development and distribution of vaccines.  

COVID-19 aside, importance of data analytics in healthcare manifests in various other ways. It can help predict patient outcomes, monitor progress, as well as improve health outcomes, reduce healthcare costs or improve operational facilities and much more.  

importance-of-analytics

With all that it has to offer to the improved patient care, let’s take a closer look on how data analytics can be leveraged in healthcare. 

The Main Types of Analytics in Healthcare  

There are three main types of analytics used in healthcare: descriptive analytics, predictive analytics, and prescriptive analytics. 

  • Descriptive Analytics: Descriptive analytics involves analyzing past data to understand what has happened. This type of analytics is often used to identify patterns or trends in large datasets. For instance, it can help medical providers identify the most common medical conditions among patients or to track patient outcomes over time. 
  • Predictive Analytics: Predictive analytics involves using past data to make predictions about future events. Predictive analytics in healthcare is often used to identify patients who are at risk of developing certain medical conditions or to forecast healthcare resource needs. For example, healthcare providers may use predictive analytics to identify patients who are at risk of developing diabetes. According to American Hospital Assocation research, he use of predictive analytics can reduce hospital readmissions by up to 50% 
  • Prescriptive Analytics: Prescriptive analytics involves using data to make recommendations about future actions. This type of analytics is often used to help healthcare providers make decisions about treatment options or to optimize resource allocation. For example, prescriptive analytics can help healthcare providers determine the most effective treatment plan for individual patients or allocate resources to hospitals based on predicted demand. 

Each of these three types use different analytics tools but together they are capable of helping healthcare providers make data-driven decisions and improve patient outcomes. 

The Roadmap to Becoming More Analytically Mature 

To better analyze vast amounts of data healthcare organizations have on their hands and consequently improve patient care, identify areas of improvement, or reduce healthcare costs and work on patient safety, they should strive to become more analytically mature. This can be achieved by following a framework such as the HAAM framework.  

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The Healthcare Analytics Adoption Framework was created back in 2002 by Dale Sanders, Chief Technology Officer at Health Catalyst. Its goal is to guide health systems through the process of becoming analytically mature. It comprises the following five steps:  

  • The first step is focused on complying with regulatory and compliance measures, such as following regulations set by government agencies. This step is important to ensure that the health system is meeting basic standards. 
  • The second step involves accreditation, which means meeting the standards set by professional societies, such as the Joint Commission, which is an organization that accredits healthcare organizations. 
  • The third step is about meeting financial incentives set by payers, such as insurance companies. This step is important because it helps the health system remain financially viable. 
  • The fourth step is where healthcare organizations focus on using analytics to meet financial incentives offered by payers, such as insurance companies or government programs. 
  • The final step is focused on making evidence-based medicine a routine practice throughout the organization, which means that everyone in the health system is consistently following best practices. 

By following the steps in the framework, healthcare organizations can ensure that they are meeting regulatory and compliance measures, implementing evidence-based medicine, and making it a routine practice throughout the organization. This can help to improve patient safety, reduce healthcare costs, and increase efficiency.  

Benefits of Data Analytics for Healthcare Organizations and Patient Care 

As healthcare systems continue to face a range of challenges, the use of data analytics in healthcare has proven to be a powerful tool that can result in: 

  • Improved Patient Outcomes 

By using data analytics, healthcare organizations can identify patterns and trends in patient data to inform clinical decision-making and improve patient outcomes. A study revealed that using predictive analytics to identify patients at high risk for sepsis reduced sepsis-related mortality rates by 53%. 

  • Disease Risk Assessment 

Data analytics can help healthcare organizations predict a patient’s vulnerability to a particular medical condition by analyzing data from various sources, such as medical records, patient demographics, lab results, and lifestyle factors. By identifying patterns and trends in this data, machine learning algorithms can generate predictive models that can be used to assess a patient’s risk for developing a particular medical condition. 

  • Improved Health Insurance Rates and Outcomes 

Similarly, for health insurance companies it can help analyze data, identify patterns, and set more accurate rates. Insurers can adjust rates based on healthcare service needs of individuals with chronic conditions. By identifying high-risk individuals, insurers can provide interventions that prevent hospitalizations and reduce healthcare costs for both patients and insurers. 

  • Enhanced Scheduling Efficiency 

By analyzing historical data on patient volumes and staff availability, data analytics can improve scheduling for both patients and staff and predict future demand. With accurate forecasting of patient demand, healthcare organizations can optimize staffing levels and reduce wait times for patients. Additionally, the use of data analytics in healthcare can identify patterns in patient scheduling, such as frequent cancellations or no-shows, and suggest solutions to reduce these issues.  

  • Optimized Resource Allocation 

Using data analytics to identify patterns and trends in data can inform decisions about staffing, equipment, and supplies, optimizing resource allocation in the healthcare sector. For example, it can be used to predict patient demand for certain procedures or services, allowing healthcare organizations to allocate staff and resources accordingly. Analytics can also help identify areas where resources are being underutilized, allowing healthcare organizations to make necessary adjustments to improve efficiency and reduce costs.  

  • Improved Decision-Making 

And finally, it can help healthcare organizations make more efficient decisions by providing them with accurate and timely insights. For example, data analytics can help hospitals and clinics monitor patient wait times, identify bottlenecks, and allocate resources more effectively to reduce wait times. At the business level, data analytics can help identify areas for cost savings, such as by optimizing supply chain management or reducing readmission rates. 

For patient care using analytics in healthcare translates into:  

  • Streamlining operations: Healthcare analytics can help streamline operations by identifying inefficiencies and areas for improvement. For example, data analytics can help identify bottlenecks in patient flow, enabling healthcare organizations to optimize staffing and improve patient throughput. 
  • Using predictive analytics to reduce hospitalizations: By analyzing patient data, healthcare organizations can identify individuals at high risk for hospitalization and provide targeted interventions to prevent hospitalization. For example, predictive analytics can be used to identify patients with chronic conditions who are at risk for complications, allowing healthcare providers to intervene early and prevent hospitalization. 
  • Improving care while reducing costs: Data analytics can help healthcare organizations provide high-quality care while reducing costs. For instance, analytics can be used to identify opportunities to reduce waste and improve efficiency, such as by optimizing staffing levels or reducing unnecessary testing. 
  • Limiting intensive care stays: By using analytics to identify patients at high risk for ICU stays, healthcare organizations can intervene early and provide targeted interventions to prevent the need for intensive care. This can help reduce healthcare costs and improve patient outcomes. 
  • Improving collaborative data exchanges: Healthcare analytics can help facilitate data exchanges between different healthcare providers, enabling more collaborative care. For example, by sharing patient data between primary care providers and specialists, healthcare organizations can provide more coordinated and effective care. 
  • Enhancing cross-functional cooperation: By breaking down data silos and promoting cross-functional cooperation, healthcare analytics can help improve patient care. For instance, by sharing data between clinical and administrative departments, healthcare organizations can identify areas for improvement and implement more effective interventions. 

Challenges of Using Patient Data in Healthcare Analytics 

Data analytics has the potential to revolutionize patient care in the healthcare industry, but there are several challenges associated with using patient data for analytics. These challenges must be addressed to ensure that patient data is used effectively and ethically to improve healthcare outcomes.  

  • Data privacy and security: Healthcare organizations need to ensure that patient data is protected and secure, and that they comply with relevant privacy regulations, such as HIPAA. The challenge is to balance data security with the need for accessibility and usability. Organizations can address this challenge by implementing robust data security measures, such as data encryption, multi-factor authentication, and access controls, and ensuring that all staff members are trained in data security protocols. 
  • Data quality: Healthcare data is often incomplete, inconsistent, and fragmented across different systems, which can make it challenging to extract meaningful insights. To address this challenge, healthcare organizations can implement data quality improvement processes, such as data standardization, data cleansing, and data normalization, to ensure that data is accurate and complete. 
  • Data integration: Healthcare data is often stored in disparate systems, which can make it difficult to integrate and analyze. To address this challenge, healthcare organizations can invest in data integration technologies, such as enterprise data warehouses, to bring together data from different sources and make it more accessible for analysis. 
  • Data interpretation: Analyzing healthcare data requires expertise in both data analysis and clinical practices, and there is often a need to involve both clinical and data analytics experts in the process. To address this challenge, healthcare organizations can create cross-functional teams with a combination of clinical and data analytics expertise to ensure that data is analyzed effectively and that insights are translated into actionable interventions. 
  • Data governance: There is a need for clear policies and processes for managing and using patient data, including consent and data sharing agreements. Healthcare organizations can address this challenge by implementing strong data governance frameworks that outline policies, processes, and roles and responsibilities related to data management and use. 
  • Resistance to change: Implementing data analytics in healthcare organizations often requires changes to existing processes and workflows, which can be met with resistance from staff and clinicians who are accustomed to traditional methods. Healthcare organizations can address this challenge by involving staff and clinicians in the design and implementation of data analytics initiatives and providing training and support to help them adapt to new processes. 
  • Fragmented patient care refers to the fact that patient data is often siloed in different systems and not easily shared across providers, which can make it difficult to get a complete picture of a patient’s health history. To address this challenge, healthcare organizations can invest in interoperability technologies that allow for the sharing of patient data across different systems and providers. 
  • Capturing accurate data can be a challenge due to errors in data entry, incomplete or outdated records, and variations in how data is collected and recorded across different providers and systems. To address this challenge, healthcare organizations can implement data validation processes, such as real-time data checks, to ensure that data is accurate and complete. 
  • Document processing and analysis refer to the challenge of extracting meaningful information from unstructured data sources, such as doctors’ notes and medical reports, which can be time-consuming and require advanced natural language processing (NLP) tools. Healthcare organizations can address this challenge by investing in NLP technologies and creating processes to ensure that unstructured data is captured and processed effectively. 
  • Data visualization is the challenge of presenting complex healthcare data in a way that is easy to understand and interpret, which requires skill and expertise in data visualization techniques and tools. To address this challenge, healthcare organizations can invest in data visualization technologies and tools, and work with data visualization experts to ensure that data is presented in a clear and meaningful way. 

Applications of Data Analytics in Healthcare 

To further stress it’s importance, let’s explore some real-life examples of data analytics in healthcare and how it’s shaping and impacting the industry today:  

Predictive Analytics for Patient Monitoring 

A great example of predictive analytics in healthcare is utilizing data analytics to monitor patients and predict health complications. For example, the healthcare researchers developed an algorithm that analyzes electronic health records (EHRs) to identify patients at risk of developing sepsis hours before symptoms appear, allowing for early intervention. 

Targeted Therapies With Data Analytics 

Similarly, data analytics is aiding in the advancement of precision medicine, which tailors medical treatments to individual patients based on their genetic makeup, lifestyle, and environmental factors. For example, the Precision Medicine Initiative by the National Institutes of Health (NIH) collects vast amounts of genomic and clinical data to develop targeted therapies for various diseases, including cancer. 

Real-time Disease Surveillance 

Another important application of data analytics in healthcare is for monitoring and tracking the spread of infectious diseases. For instance, during the COVID-19 pandemic, data analytics tools were employed to analyze and visualize real-time data on infection rates, hospitalizations, and mortality, aiding in decision-making and resource allocation. 

Wearable Devices and Remote Monitoring 

The rise of wearable devices, such as fitness trackers and smartwatches, has enabled the collection of real-time health data. This data can hold answers to numerous questions and thus enhance patient treatment. The information can be analyzed to provide insights into individuals’ health conditions and allowing for remote monitoring of chronic diseases. Research on remote health monitoring through wearable sensors suggests a cost-effective solution to provide healthcare services to the elderly, allowing them to stay at home and improving accessibility to healthcare 

The integration of data analytics, Artificial Intelligence, and Machine Learning in healthcare heralds a promising future where personalized medicine, improved remote monitoring, insightful clinical decision support, streamlined operations, advanced diagnostic capabilities, and accelerated drug discovery become the norm. These technologies, while not without challenges such as data privacy and the need for regulatory frameworks, have the potential to revolutionize healthcare, making it more efficient and patient-centric. The future of healthcare, thus, appears to be increasingly digital and data-driven, and we stand on the cusp of significant transformation towards enhanced patient outcomes. 

The transformative impact of data analytics in healthcare has the vast potential to improve patient outcomes and reduce healthcare costs. Combined with Artificial Intelligence, and Machine Learning, it heralds a promising future where personalized medicine, improved remote monitoring, insightful clinical decision support, streamlined operations, advanced diagnostic capabilities, and accelerated drug discovery become the norm. These technologies, while not without challenges such as data privacy and the need for regulatory frameworks, have the potential to truly revolutionize healthcare, making it more efficient and patient-centric. The future of healthcare, thus, appears to be increasingly digital and data-driven, and we stand on the cusp of significant transformation towards enhanced patient outcomes. 

If you are interested in learning more about how data and analytics can help your healthcare organization, check our data and analytics services. With our expertise in healthcare analytics and data management, Symphony Solutions can help you unlock the full potential of your healthcare data and drive better patient outcomes. 

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Yevhen Tanchik
About the Author

Yevhen Tanchik

Lead Data Engineer
Yevhen Tanchik excels in delivering valuable insights to businesses through advanced analysis and visualization techniques. With expertise in utilizing visualization software, Yevhen guides analysis, extracts meaningful implications, and synthesizes complex data into clear and impactful communications. His role as a lead data engineer allows him to leverage his skills to provide data-driven solutions that drive business success and empower organizations to make informed decisions.
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