The following topics will be covered in this blog:
Meaning of Healthcare Data Analytics
Healthcare data analytics refers to the process of working on raw datasets related to healthcare and analyzing them to find hidden patterns, trends, etc., thus paving a way for further improvements at patient-level as well as business-level.
Since we talked about healthcare-related data in the above definition, let us now understand what healthcare data is. Healthcare data is nothing but any data that is related to the patient and the healthcare facilities such as medical records, scan and test reports, hospital records, etc. Different tools are used to collect this data. Some of the important tools and ways are electronic health records (EHRs), patient portals, master patient indexes (MPIs), online health-related mobile applications, etc. Not only does this help in data-driven informed decision-making, but it also helps in providing a personalized experience and treatment to the patients.
Types of Data Analytics
There are different types of Data Analytics based on the type of problem they solve and the questions they answer. The main three types of Data Analytics are:
(i) Descriptive Data Analytics
(ii) Predictive Data Analytics
(iii) Prescriptive Data Analytics
Descriptive Data Analytics revolves around data and findings related to what has happened already in the past. It does not involve making any inferences or predictions with the data. It is rather a type of analytics that forms the base for further analysis and other types of data analysis. It basically involves collecting historical data and presenting the data in an organized manner for easy understanding. Very basic levels of statistical methods such as average, percentage change calculations, Mean etc. are used in Descriptive Analytics.
Predictive Analytics is the type of data analysis where probabilities are used to forecast possible future events and outcomes. In predictive analytics, complex statistical techniques are used to predict the outcomes. For example- Statistical Data Modeling, Data Mining etc. Predictive Analytics is rather a step towards Machine Learning and Artificial Intelligence which involve predictive models.
Prescriptive Analytics is the stage in data analysis where the learnings from Descriptive and Predictive Analytics are used to suggest the best actionables for the business problem or challenges. This type of data analytics uses rather complex statistical tools and techniques to figure out the right course of action. Complex algorithms based on internal as well as external data are used to achieve the results. Because of this reason, Prescriptive Analytics is not a routine practice adopted by organizations as it requires specialized tools and statistical processes.
Why is Healthcare Data Analytics Needed ?
The data analytics market in the healthcare space has only increased over the last few years. Considering the rising costs of medical treatments globally, a proper body of knowledge was needed to reduce the costs at the business-level as well as the professional-level. McKinsey, in one of its reports, states that healthcare expenses constitute 17.6 percent of the GDP in the USA, which is approximately US$600 billion, more than what is the set benchmark for the ideal size of population in the country. This is a serious indicator of bigger trouble. Hence, the usage of healthcare data analytics is being promoted these days.
Healthcare data analytics aims at reducing the cost of healthcare operations and processes. Hence, the treatment cost for patients will gradually go down. Not only this, healthcare data analytics has opened the doors to a plethora of job opportunities for qualified and skilled data analytics professionals. These professionals come with data-driven minds and strategic thinking, which is the need of the hour for the global healthcare industry.
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Use of Data Analytics in Covid-19 Outbreak
The Covid-19 pandemic has created an urgent need for application of data analytics in the healthcare industry. Many hospitals and healthcare organizations have successfully utilized the benefits of Data Analytics to tackle the pandemic. Let us understand the various ways in which Data Analytics has been used in the Covid-19 outbreak:
Since there was no information available regarding the effects or severity levels of Covid-19 in the pandemic’s initial phase, a lot of lives were lost. However, as time passed by, there was a decent amount of data available regarding the symptoms, severe health conditions, days of hospitalization of a Covid-19 patient etc. This data has helped researchers to create predictive analytics to forecast the severity and survival chances of Covid-19 patients. Data Analysts have built models that take into account certain set of parameters like age of the patient, minimum oxygen saturation and type of Covid-19 case. Many hospitals have come forward and implemented this predictive analytics strategy and have been successful in saving many lives.
- Demand Planning
As hospitals were facing rise in number of patients each day, there were many cases of delay in patient admission, unavailability of doctors, mismanaged supply of medicines and other medical supplies. All these challenges demanded to be solved and now, with the application of Data Analytics techniques, these issues seem to have been settled a lot. Data Analysts have worked on predictive models which can predict the availability of beds, doctor schedules, medicine supplies, patient discharge schedules etc. The model extracts data fed into the system such as patient details, doctor schedules, bed allotments etc. This has helped healthcare facilities to devote a larger portion of their time into treatment of the Covid-19 patients.
- Virus Traceability and Mapping
In the beginning, the spread of the virus was unknown which took many innocent lives and the government as well as the healthcare organizations knew nothing about what to do. However, researchers have worked on large sets of Covid-19 related data to identify trends and patterns regarding the spread of Covid-19 such as possible areas that can be affected, the rate of increase in the virus, possible number of daily Covid cases etc.
Hence you can see how Data Analytics has been used to tackle a pandemic and how much more it can do for the healthcare sector. To understand this, let us further have a look at the applications of healthcare data analytics.
Applications of Healthcare Data Analytics
Healthcare analytics has helped millions of healthcare professionals to better take care of their patients alongside reducing operational costs and saving time. Below we will explore some of the applications of healthcare data analytics:
One of the major concerns that worries healthcare facilities is the allocation of nurses and other staff for a particular patient in a particular period of time. Allocating too many workers is going to shoot up the labor costs. Healthcare data analytics helps in effective allocation and staff management by predicting which worker will be allocated where and at what time.
Electronic Health Records (EHRs)
The major benefit of using healthcare data analytics is the utilization of the large amount of data lying here and there. EHRs are a means to store patient-related data in an organized manner. They are like digital records that can be accessed anytime by the concerned parties for the patient’s treatment. EHRs usually store data such as past medical history, allergies, lab results, doctors’ diagnoses, etc. Healthcare data analytics is a boon as the EHRs are also known to send reminders with respect to regular health checkups, etc.
Data analytics has always helped organizations in strategic decision-making. The same applies to healthcare data analytics. The managers of healthcare facilities can effectively manage the treatment facilities and serve maximum possible patients with the help of data-driven strategic planning. Healthcare data analytics helps managers in making predictions regarding resource availability, treatment facilities, checkups, etc. This has promoted strategic decision-making and also boosted the trust and faith of patients in medical treatments.
Healthcare analytics has not only helped in predictions and decision-making but also in data security by identifying pattern changes in network traffic—predicting possible breaches, identifying vulnerabilities, etc. These developments in healthcare data analytics have also helped in smooth claim settlements and cash advances to patients.
Improved Distribution and Medical Logistics
The supply chain framework forms the backbone of the healthcare industry. A single instance of delayed delivery or mismanaged logistics can lead to life risks and critical problems. Healthcare analytics is a whole new approach to manage the distribution of medicine and healthcare appliances to facilities. It has helped in the effective planning of operations and reduced the variability in the supply chain.
Who is a Healthcare Data Analyst ?
Healthcare data analysts are professionals who use their knowledge of data analysis to work on healthcare-related data and help the healthcare facilities management with actionable insights for the betterment of the patients as well as the business. Healthcare data analysts collect data from various sources and perform data management, data interpretation, data analysis, and data visualization. Their role in healthcare data analytics is very important as they are skilled in identifying real underlying business problems and providing solutions for the same. This has also increased the demand for qualified healthcare data analysts in the healthcare sector around the globe.
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Skills Required to Become a Healthcare Data Analyst
The skills required for becoming a healthcare data analyst are similar to that of a data analyst. However, based on various organizations, the roles and requirements may differ. But some of the commonly required skills are discussed below:
- Structured Query Language (SQL)
- Statistical Programming
- Data Visualization
Possessing certain soft skills is also beneficial to get good roles and excel at the job. Some of the commonly required soft skills are:
- Analytical thinking
- Creative thinking
- Effective communication skills
Now, let us look at how healthcare data analytics is being leveraged and its various applications that are shaping the medical landscape.
Organizations Hiring Healthcare Data Analysts
Looking at the numerous benefits of healthcare data analytics, organizations are always on a lookout to hire the best professionals. The following are the organizations that hiring healthcare data analysts in the most numbers:
- Hospitals, private or public
- Government healthcare departments
- Health insurance companies
- Large medical practices
- Diagnostic centers
- Health IT vendors
- Healthcare consulting organizations
- Other health organizations
Check out our blog, If you want to learn more about the application of data science in healthcare.
Career Scope in Healthcare Analytics
Countries around the world have started to invest more capital in medical infrastructure, pharmaceuticals, and healthcare smart analytics solutions. The market is growing and will continue to expand, given the benefits of healthcare data analytics. It has also risen as a good career option for fresh data science and data analytics graduates or professionals who wish to build their career in the healthcare sector. Due to the sensitivity of the profession, the salary offers for healthcare data analysts are lucrative around the world. Apart from the remuneration, the opportunities to work with some of the biggest names in the healthcare sector is also worth mentioning. Hence, healthcare data analytics is growing to be one of the most rewarding branches of data analytics in the coming future.
To some, the domain of healthcare data analytics may look new, but it has a lot of potential, especially if you wish to engage in challenging job roles and build a strong data analytics profile in the upcoming years. In this blog, we have covered some of the major topics such as what is healthcare data analytics, its applications, scope, and benefits, etc. We hope it helps you in your decision-making as a healthcare data analytics professional.