How Advanced Analytics is Improving Clinical Decision-Making and Patient Outcomes
Big data has become a critical tool in clinical decision-making, allowing medical professionals to quickly and accurately analyze large amounts of patient information to make informed treatment decisions.
Big data refers to extremely large and complex data sets that are difficult to process and analyze using traditional data processing techniques. These data sets are typically characterized by 3Vs — high volume, high velocity, and high variety. They can include structured data, such as patient records and lab results, as well as unstructured data, such as case notes and images.
In healthcare, big data can be used to inform decision-making at every level, from individual patient care to population health management. By analyzing large amounts of patient data, medical professionals can identify patterns and trends that can inform treatment decisions. For example, doctors can use big data to identify patients who are at high risk of developing a certain condition and to tailor treatment plans to individual patients based on their unique characteristics.
One are big data is being used is in the field of precision medicine, where doctors use genetic information and other data to tailor treatment plans to individual patients.
Precision medicine is a rapidly growing field in which physicians use data and analytics to identify the unique characteristics of each patient’s disease to develop targeted treatment plans. This approach is particularly useful in the treatment of cancer, where the genetic makeup of a patient’s tumour can inform the selection of the most effective therapy.
In the past, this process would have been slow and labour-intensive, requiring doctors to manually review large amounts of data. But with the advent of big data and advanced analytics, this process can now be done quickly and accurately, allowing doctors to make more informed treatment decisions and improve patient outcomes.
One company already providing this service is LifeOmic, which has developed a cloud-based platform that brings together various data sources, including genomic, clinical, wearables, imaging, and population data, to provide a more comprehensive view of an individual’s health. The platform can be used by individuals to manage their health as well as by healthcare providers as a communication tool for connecting with consumers.
The company is also leveraging the platform to develop specialized solutions, such as LifeOmic Oncology and LifeOmic Cardiology, that use machine learning and reference genome libraries to deliver precision health at scale.
Big data also plays a critical role in population health management (PHM), which is the practice of using data to identify and track health trends within a specific population, such as a city or a geographic region. This information is used to identify areas where health outcomes are poor, and to target interventions to improve those outcomes. For example, population health data can be used to identify areas where cancer rates are high, and to target interventions to reduce those rates.
The PHM solutions are being used today by a number of major health providers around the world, including the UK’s National Health Service. The global market is expected to top US$ 58 billion in 2023, with some of the key industry players including Deloitte, AllScripts, and Cerner which was acquired by tech giant Oracle for approximately US $28.3 billion in December 2021.
Another example is in the field of electronic health records (EHRs), which are digital versions of a patient’s paper medical chart. EHRs store information like demographics, medical history, diagnoses, immunizations, notes, laboratory and radiology data, and vitals. They are a critical piece of technology for most healthcare facilities and allow doctors and other medical professionals to easily access and share patient information. The adoption rate by US hospitals has now reached 96% according to The Office of the National Coordinator for Health Technology.
EHRs have several advantages over paper records, such as increased accuracy, easier access, and improved data sharing. By analyzing the data in EHRs, doctors can identify patterns and trends that can inform treatment decisions, such as identifying patients who are at high risk of developing a certain condition.
EHRs have been used in a variety of ways, including:
- Documentation of patient visits and medical history
- Prescription and medication management
- Diagnosis and treatment planning
- Disease and infection surveillance
- Research and clinical trials
- Patient education and engagement
While EHR systems do a great job of managing the patient record, but they can lack functionality for other services, which is where features like clinical decision support systems (CDSS) come in.
CDSS is a computer-based system that helps healthcare providers make clinical decisions by providing evidence-based information and guidance. These systems are designed to provide timely, accurate, and evidence-based information to help clinicians make informed decisions about diagnosis, treatment, and disease management.
CDSSs can use a variety of data sources, such as patient records, laboratory results, and drug databases, to provide evidence-based guidance to clinicians. CDSSs can also provide reminders and alerts about potential drug interactions and adverse events, which can help reduce medical errors and improve patient outcomes.
CDSS has been used since the 1980s and has evolved significantly over time. They embody one of the eagerly awaited applications of artificial intelligence and machine learning for patient care. By leveraging the power of “big data,” these technologies promise earlier recognition of disease conditions and impending clinical deterioration.
Researchers at the University of Pennsylvania have developed a machine learning-based CDS tool that can detect sepsis 12 hours earlier, which can potentially save lives. The algorithm was trained using data from over 160,000 patients and was tested on an additional 10,000 individuals.
In another example, a study from MIT has created a deep learning tool that uses data from bedside monitors, clinical notes, and other sources to make hourly predictions about ICU patients. This allows healthcare providers to adjust the care plan for complex patients in a timely manner.
While there are examples of CDSS success stories, there have also been setbacks that show they come with risks. Research also shows that some clinical decision support system interventions are only currently achieving small to moderate improvements in the percentage of patients receiving recommended processes of care.
CDSS is however now widely used and has been shown to be effective in many areas. Some CDSS have more evidence supporting their use, particularly those that use computerized physician order entry (CPOE), a process of electronic entry of medical practitioner instructions for the treatment of patients (particularly hospitalized patients) under his or her care.
With the increasing use of electronic medical records, CDSS is becoming even more important. However, there is still room for improvement, such as making them easier to use and more affordable.
Lastly, another area that big data is being applied is predictive analytics, the use of statistical techniques and machine learning algorithms to identify potential health outcomes based on historical data. In healthcare, predictive analytics can be used in clinical decision-making to identify patients at risk of certain conditions, predict the likelihood of a certain treatment being effective, or forecast the need for certain resources.
One example of predictive analytics in healthcare is the use of machine learning algorithms to analyze electronic health records and identify patients at risk of developing certain chronic conditions, such as diabetes or heart disease. This allows healthcare providers to target preventative measures and interventions to those most in need.
Another example is using predictive analytics to analyze data from clinical trials to identify which patients are most likely to respond to a particular treatment, allowing for more personalized and effective care.
Predictive analytics can also be used to forecast the demand for resources such as hospital beds, allowing healthcare organizations to better plan and allocate resources.
Many healthcare leaders understand the benefits of predictive analytics. A survey from the Society of Actuaries in 2019 found that 60% of healthcare executives said their organization has adopted predictive analytics. Out of those, 42% reported that they have seen an increase in patient satisfaction and 39% have seen a reduction in costs since using predictive analytics.
Big data clearly has the potential to bring significant benefits to clinical decision-making, and the key benefits include:
- Improved Quality of Care: By analyzing patient data, doctors can identify patterns and trends that can inform treatment decisions, leading to more personalized and effective care. For example, precision medicine uses data and analytics to identify the unique characteristics of each patient’s disease, in order to develop targeted treatment plans. This approach has been shown to improve patient outcomes, particularly in the treatment of cancer.
- Increased Efficiency: Big data and advanced analytics can automate many of the manual processes that were previously required to analyze patient data. This can help speed up the process of making treatment decisions, allowing doctors to spend more time with patients and less time on data analysis. Additionally, big data can help identify patients who are at high risk of developing a certain condition, allowing for early intervention and preventing the progression of disease.
- Improved Accuracy: Big data can provide medical professionals with a more complete picture of a patient’s health, including information from their medical history, lab results, and other data sources. This can help doctors to make more accurate diagnoses and treatment decisions, leading to improved patient outcomes. Additionally, big data can be used to identify potential health outcomes based on historical data, allowing for early intervention and prevention.
While big data has the potential to bring significant benefits to clinical decision-making, there are also several challenges that need to be addressed to fully realize its potential. Some of the key challenges include:
- Data Privacy: One of the main challenges of big data in healthcare is ensuring the privacy and security of patient data. This is a particular concern when dealing with sensitive personal information, such as medical records and genetic data. To protect patient privacy, it is important to have robust data security measures in place, such as encryption and secure data storage.
- Data Security: Ensuring the security of patient data is crucial to protect against unauthorized access, breaches, and data loss. Due to the sensitive nature of healthcare data, it is important to have robust data security measures in place, such as firewalls, intrusion detection systems, and data encryption.
- Cost of Technology: Another key challenge of big data in healthcare is the cost of the technology required to process and analyze large amounts of data. The cost of implementing big data solutions can be high and may not be feasible for some healthcare organizations. Additionally, the cost of maintaining and updating the technology can also be a significant barrier.
- Data Quality: Another important challenge is the quality of the data being collected and analyzed. This can be influenced by factors such as data completeness, accuracy, and consistency. Ensuring the quality of the data is crucial to ensure the accuracy and reliability of the results.
By addressing these challenges, healthcare organizations can harness the power of big data to improve patient outcomes and lead to better healthcare overall. By providing medical professionals with the ability to quickly and accurately analyze large amounts of patient information, big data can help to improve patient outcomes and lead to better healthcare overall. As technology continues to advance, it is likely that the use of big data in healthcare will become even more widespread and impactful.
