Data is the driving force underpinning several revolutionary changes in the field of healthcare. Harnessing the power of data, healthcare organizations are achieving better, more personalized health outcomes and making more informed healthcare decisions, contributing to better public health.
As the innovators refine data analytics tools, data-driven decisions will soon become standard across the healthcare industry, leading to more successful, proactive, and precise healthcare operations.
Below are some ways data analytics is empowering the healthcare industry.
What does Healthcare Data Analytics Entail?
Healthcare data analytics involves analyzing current and historical healthcare data to find trends, draw conclusions and identify the potential for improvement. It can reveal paths to improving clinical data, patient care quality, diagnosis, and business management. Data analytics can also be combined with business intelligence suites and data visualization tools to deliver actionable insights and support vital business decisions.
Types of Healthcare Analytics
Different types of data analytics methods are used to answer different questions related to healthcare. One method is used to analyze past occurrences, while the other gives out information about what will happen in the future.
Descriptive Analytics
This method requires historical data to be used to draw comparisons or discover patterns and answer questions about what has already occurred.
Predictive Analytics
In this method, historical and current data is used to make predictions about the future and what could happen next.
Prescriptive Analytics
In this method, along with data analytics, machine learning is used to determine the best course of action to reach the ideal outcome.
Where does all the healthcare data come from?
The healthcare sector churns out massive amounts of data each year. 30% of the world’s data volume is generated by the healthcare industry alone.
Some of the sources for healthcare data include medical records of patients, results of medical examinations, hospital records, devices that use IoT technology, information gathered from health information systems (HIS), and other technological tools utilized by health care professionals, government organizations, and insurance companies. Biomedical research also generates a huge amount of big data.
[Read More: Health Information Exchange – A Brief Overview]
Some of the tools for collection, storage, sharing, and analysis of health data gathered through various means are:
- Personal Health Records (PHRs)
- Electronic Health Records (EHRs)
- Patient Portals
- Health-related apps
- Electronic Prescription Services (E-prescribing)
- Master Patient Indexes (MPI), etc.
Role of data analytics during COVID-19 crisis
Big Data served as a powerful weapon in our fight with coronavirus. Here’s how.
Preparing guidelines for navigating the unprecedented crisis
During the Covid-19 crisis, big data analytics models were utilized to inform decision-making. Big data has been used by governments and healthcare organizations to understand better how to respond to COVID-19. Using big data, models were generated that could provide policy recommendations for reducing the spread of COVID-19.
Preparing models for simulating vaccination rollout
Many scientists are also preparing models using big data to simulate vaccination rollout and optimize for the best delivery configuration.
For predicting staffing needs
During the pandemic, one of the gravest concerns faced by healthcare organizations was the shortage of staff. Big data models can be successfully utilized to predict staffing needs. For example, a team from Cedars-Sinai has prepared a machine learning model that can be used to predict staffing needs. It does so by tracking the rate of confirmed COVID-19 cases and local hospitalization volumes to help anticipate and prepare for increasing COVID-19 cases surge.
What are some other ways data analytics is utilized in the healthcare industry?
Deploying data analytics tools help healthcare providers leverage data for insights in several areas of operations, including:
Predictive Modeling
Data collection has become more streamlined in recent years; as a result, it can now be better used in predictive modeling. Datasets can be utilized to track trends and make predictions, allowing healthcare organizations and governments to take preventive measures and track the outcomes.
Instead of just treating the symptoms, patients at high risk of developing chronic illnesses can be identified and treated successfully before issues surface. This helps to stave off long-term issues and expensive hospitalizations.
Healthcare practitioners can also use predictive modeling to closely monitor, attenuate and even halt disease initiation.
For example, a group of researchers from Mount Sinai has created new predictive analytics tools to identify environmental factors that trigger Crohn’s disease.
To uncover disparities in healthcare
Big data analysis can help uncover care access disparities and outcomes disparities in the ICU, admissions, and mortalities. Governments and healthcare providers can use this information to change processes and look at the data to see if the changes bridge disparities.
For lowering patient risk and healthcare costs
With the help of prescriptive and predictive analytics, healthcare practitioners and organizations can create detailed models for lowering healthcare costs and patient risk. Additionally, it can also manage supply chain costs, reduce appointment no-shows, decrease fraud and prevent equipment breakdowns.
The challenges of big data
One of the toughest questions of working with big data is how to handle this large volume of information. To make it available to researchers and scientists, data must be stored in a file format that is easily accessible and readable for efficient analysis. Another major challenge is implementing protocols, high-end computing tools, and high-end hardware in the clinical setting. Besides that, several experts, including medical science, information technology, mathematics, and statistics experts, must work together for efficient and correct data analysis.
Also, cloud storage is a necessity as it keeps sensitive information safe and is cost-effective too. Healthcare organizations have to be fully equipped with the appropriate infrastructure to analyze the data and generate meaningful insights from it systematically.
Data remains quite cloudy without appropriate preparations, tools, and infrastructure and may not further lead biomedical researchers. Visualization tools are also very important to display this newly gained knowledge.
Several big companies are working on finding apt solutions
To perform smoother analytics and tackle big data challenges, various companies, including IBM Corporation, are implementing AI to analyze textual data, published results, and image data to obtain meaningful outcomes. For example, IBM’s Watson Health is an AI platform that enables sharing and analysis of health data among researchers, hospitals, and healthcare providers.
Conclusion
Fueled by the need to manage diverse and large data sets, growing regulatory complexity, and increased competition, the importance of healthcare analytics is growing. With the help of robust and meaningful analytics, new sources of value can be unlocked.
