Data analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.
The use of health data analytics allows for improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment and more informed decision-making. At the business level, it can lower costs, simplify internal operations and more.
In order to discuss health data analytics and the role it plays in the health care sector, we must first understand the data that is being collected and analyzed. There is data being collected on the processes and procedures of the business side of health care, but there is also an enormous amount of health data being gathered, stored and analyzed.
Health data is any data relating to the health of an individual patient or collective population. This information is gathered from a series of health information systems (HIS) and other technological tools utilized by health care professionals, insurance companies and government organizations.
We are able to see a holistic view of each individual patient as well as trends tied to location, socioeconomic status, race and predisposition. The information being collected can be broken down into specific datasets that can then be analyzed.
There are a variety of tools and systems used to collect, store, share and analyze health data gathered through various means. These tools include:
- Electronic Health Records (EHRs)
- Personal Health Records (PHRs)
- Electronic Prescription Services (E-prescribing)
- Patient Portals
- Master Patient Indexes (MPI)
- Health-Related Smart Phone Apps and more
With digital data collection, there is more and more health care data to be analyzed every second. With the increase of electronic record keeping, applications and other electronic means of data collection and storage, there is a significant amount of data being collected in real time.
These data sets are so complex that traditional processing software and storage options cannot be used. Cloud storage is a necessity when dealing with “Big Data.” Cloud storage is built to be secure, an absolute must when dealing with sensitive patient information. It is also very cost-efficient and has been helpful in lowering the increasing cost of health care.
The impact COVID-19 has had on the health care industry is evident to anyone and everyone. You don’t need to be plugged into the world of medicine to see what has been happening worldwide during this pandemic.
What most people don’t see, though, is the impact COVID-19 has had on health care data analytics. “Big data tools have played an increasingly significant role in health care decision-making” says HealthITAnalytics. It is not just providers, but lawmakers and researchers who are turning to big data analytics and predictive models to help allocate resources, predict surges, improve patient care and outcomes and employ preventive measures.
Big data and health data analytics have played an integral role in the fight against COVID-19. The data is coming in at a near constant rate. Analyzing that health data has allowed for a better understanding of how to respond and treat patients.
This pandemic has resulted in an enormous surge of health data being recorded and manipulated allowing for bigger and better analytics. Unfortunately, we are also seeing that COVID-19 is “shining a harsh spotlight on health care’s biggest issues.” There are a lot of obstacles when it comes to sharing health data across organizations and a distinct lack of standardization in the way that data is collected and analyzed.
This widespread problem was evident in the early days of the pandemic as conflicting and ever-changing information was being presented to the public. We saw a turn towards disbelief when it came to COVID-related information with many still believing misinformation and previously held beliefs on how this virus should be handled.
The spotlight that COVID-19 shined on these problems, however, will allow for them to be rectified. The providers, researchers and policymakers can learn from these mistakes and work towards a better, more standardized solution for big data in health care.
We can collect all the data we want, but it doesn’t do any good if we don’t know what to do with that information. We need a centralized, systematic way of collecting, storing and analyzing data so we can use it to our advantage.
The collection of data in health care settings has become more streamlined in recent years. Not only does the data help improve day-to-day operations and better patient care, it can now be better used in predictive modeling. Instead of just looking at historical information or current information, we can use both datasets to track trends and make predictions. We are now able to take preventive measures and track the outcomes.
The fee-for-service style of health care is becoming a thing of the past. There is a growing demand for patient-centric, or value-based, medical care which has led to a considerable shift towards predictive and preventive measures in regards to public health in recent years. Data makes this possible. Instead of simply treating the symptoms as they present, practitioners are able to identify patients at high risk of developing chronic illnesses and help to treat an issue before it surfaces. This helps to lower costs for the practitioner, insurance company and patient as the preventive treatment may help to stave off long-term issues and expensive hospitalizations.
If hospitalization is necessary, data analytics can help practitioners predict risks of infection, deterioration and readmission. This too can help lower costs and improve patient care outcomes.
Consider the impact this has had on the COVID-19 pandemic. The data being collected is analyzed in real time to understand the effects of the virus better and predict future trends so we may slow the spread and prevent future outbreaks.
Not every question can be answered by using the same analysis of the data. Through the use of different types of big data analytics, we can answer many of the questions being asked in health care settings.
Descriptive analytics uses historical data to draw comparisons or discover patterns. This type of analysis is best for answering questions about what has already occurred. We can gain insight into the past with descriptive analytics.
Predictive analytics uses current and historical data to make predictions about the future. The models created with this type of analytics are best for answering questions about what could happen next. We can gain insight into the future with predictive analytics.
Prescriptive analytics will also make predictions about future outcomes. Machine learning is a big factor with this type of analytics. The information provided can help determine the best course of action. We can gain insight on what course of action should be taken to reach the most ideal outcome with prescriptive analytics.
Health care data management has the potential to lead to better care if used properly. With centralized datasets, there is immediate access to necessary information whenever and wherever it is needed. The addition of big data analytics improves efficiency on all fronts. Better data leads to better care.
Predictive modeling is the process of analyzing current and historical data to predict future outcomes. Models use data mining, machine learning and statistics to identify patterns and predict outcomes. Predictive models built off of the health data being collected provide solutions on the macro and micro level.
The use of predictive analytics can alert health care professionals to potential risks. By analyzing behavioral data, we can predict treatment outcomes, potential risks for chronic illness and even predict risk of self-harm. The health data collected can be used for risk scoring, readmission prediction and prevention, predicting infection and deterioration and so much more at the individual patient level.
Predictive modeling can also be used on a much larger scale. Population health management is impossible without the use of these models. Outbreaks can be predicted, outcomes can be predicted, and in knowing what is to come, preventive measures can be taken.
Predictive modeling can even be used in administrative applications to increase efficiency and lower costs for all.
Health care is expensive. And, those costs only continue to increase across the board. We are, however, seeing a shift from fee-for-service payment models to value-based care.
Through the use of predictive and prescriptive analytics, health care organizations and practitioners can get detailed models for lowering costs and patient risk. In addition to the patient-centric benefits mentioned above, health data analytics can reduce appointment no-shows, manage supply chain costs, prevent equipment breakdowns and decrease fraud.
Health Data Analysts take the health care data being collected and use skills such as data acquisition, data management, data analysis and data interpretation to provide actionable insights. The role of big data in health care and the increasing need for improvement in the health care sector has led to a higher demand for qualified health data analysts.
The role of a health data analyst varies based on their position and industry of choice. Regardless of industry, a health data analyst will need to be able to work with, develop and evaluate health information technology (health IT) and other health information systems (HIS).
They may also be expected to:
- Collect or mine data
- Examine current and historical data
- Evaluate raw data
- Build predictive models
- Automate reports
Health data analysts solve problems for the organizations that hire them. In order to do so, they should be well versed in several essential skills:
- Structured Query Language (SQL)
- Statistical Programming
- Data Visualization
You should also have certain soft skills as a health data analyst such as:
- Analytical thinking
- Creative thinking
- Attention to detail
- Strong communication skills
While it is not a requirement to become a health data analyst, many jobs will require a bachelor’s degree in health information management (HIM) or a related field (data science, mathematics, health informatics, statistics, etc.).
A master’s degree is, again, not a requirement to work as a health care data analyst, but it will qualify you for higher, more competitive positions. You can earn your master’s in data analytics, health informatics, or another related field.
Numerous industries and organizations utilize health data and require the help of health data analysts. These include:
- Hospitals, private or public
- Government health care departments
- Diagnostic centers
- Health insurance companies
- Health care consulting companies
- Large medical practices
- Health IT vendors
- Other health organizations
Depending on the industry or role they choose to take, health care data analysts may work as part of larger teams or alone.
Your salary, much like your role responsibilities, will vary based on the industry or organization for which you choose to work. Payscale lists the median salary for a health data analyst as $63,000 per year. The U.S. Bureau of Labor Statistics doesn’t have an entry specific to Health Data Analysts, however, the Medical and Health Services Manager page does list “health information manager.” The median pay listed here is closer to $100,000.
The University of Pittsburgh School of Health and Rehabilitation Sciences offers a Master of Science in Health Informatics degree with a Data Science track. If you want to enter the field of health informatics or health data analytics or open up your career options, this master’s degree can make you a competitive candidate.