How to Use Data Analytics in the Healthcare Industry

Understanding Process and Key Features

Data analytics is considered an important aspect of boosting healthcare operations and productivity. In this blog post, we will analyze the importance, benefits, challenges, and how to overcome these challenges so that healthcare service providers can stay updated. We have also mentioned the role modern technology plays in healthcare analytics, which helps in boosting the decision-making process.


Data analytics is a versatile tool that is broadly used and is most valuable in healthcare. In healthcare, it benefits doctors and hospitals by looking into a large amount of information such as patient records or test results, and determining common characteristics. It presumably allows them to determine which cures are most effective for specific diseases or even when a disease may go viral in a community. In this regard, it will be possible for them to make better decisions in the manner in which patients are managed as well as be more efficient and spend less funds.

Healthcare analytics can be categorized into several forms. This analysis approach involves using past data to substantiate or conclude what occurred previously. Descriptive analytics gives an understanding of the data, while predictive analytics provides likely future outcomes, and prescriptive analytics prescribes the proper action to be taken. This is an attempt to forecast something that might take place in the future or perhaps find something that had already happened in the past that was similar to what one is looking for. Discovery applies a high level of analysis of data to come up with useful patterns. Both forms employ different tools to accomplish their purpose for example, software programs for data processing and interpretation.

Statistics on Healthcare Sector

According to Allied Market Research, the market for healthcare analytics is expected to reach $96.90B by 2030 and $23.51B in 2020, at a 15.3% CAGR. Healthcare is expected to grow by around 25.8% by 2030. The industry faced challenges during the pandemic, including providing affordable healthcare facilities and enhanced patient management. These are considered the focal aspects of healthcare institutions and hospitals. However, with advancements in technology, this industry has resulted in tremendous growth. To solve this, healthcare data analytics has been embodied in the industry.

What is Healthcare Data Analytics?

Healthcare analytics is the enhanced process of analyzing and monitoring present and historical data of the industry. It is used to forecast market trends, manage patients, improve reach, and analyze the spread of disease. This covers diverse enterprises to get vast data to conduct in-depth analysis. This data collection results in getting progress paths for patient care, clinical data, managing business, and effective diagnosis.

The potential of healthcare analytics can be enhanced when integrated with business intelligence and data visualization tools. By integrating healthcare analytics with these tools, managers can analyze real-time data for a better decision-making process, which also assists with delivering actionable insights.

What is Big Data in Healthcare?

Big Data in healthcare on the other hand is the large volume of health information. These can be in form of electronic health records, medical images, information from wearables, and research data. It contains data on patients such as personal details, health history, diagnoses, examination results, treatment plans, and more. The importance of Big Data in healthcare lies in its volume, velocity and variety. As it is collected daily, data rapidly surfaces in various forms.

Big data analytics help improve healthcare facilities for patients and enhance the overall operations of the industry. The volume of data that can be processed in Big Data provides healthcare professionals with further options when it comes to analyses of patterns. This trends and patterns are usually not easily noticeable with other types of analytical methods. Knowledge gained from them can be used in disease diagnosis, early detection, customized treatments, and prognosis.

Why is Healthcare Data Analytics Required?

Healthcare data analytics is promoting in this modern era. Data analytics in the healthcare segment has expanded in the last few years. There is a rise in the cost of medical treatments worldwide, so it is essential to have proper knowledge of modern trends to reduce costs.

Healthcare data analytics helps in lowering the cost of healthcare processes and operations. Due to this, the overall cost of treatment is reduced. The factors responsible for cost reduction are :

  • Integrating latest technologies
  • Automating the tasks that require fewer considerations
  • Analyzing the patient’s health status
  • Analyzing the possibility of disease and taking preventive treatment

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What are the Sources of Collecting Healthcare Data for Data Analytics?

Healthcare data can be collected from various data sources. After gathering the data, they are compiled and stored at a centralized location to ensure a smooth analysis and decision-making process.

1. Electronic Health Records (EHR)

EHR is the main source of healthcare data, which consists of patient history, demographics, test reports, and other vital signals about a patient’s health. This helps identify patients and separate them based on age, gender, and location.
Administrative Systems.

This system is vital in providing necessary data for smooth healthcare analytics. The administrative systems include appointment booking and scheduling systems that monitor appointments, cancellations, billing, and the type of consultation required by patients.
This system records charges, processes, diagnoses, and health status to facilitate the analysis of operational efficiency, financial performance, and patient care in healthcare organizations and hospitals.

2. Clinical Support Systems

Clinical support systems are inseparable data management sources for effective healthcare analytics, and they provide valuable data insights to enhance patient care and operational efficiency. These systems include

  • CPOE( Computerized Physician Order Entry), which tracks physician and medical orders for testing, medications, and operational processes.
  • DSS (Decision support systems) provide clinical suggestions based on evidence-based analysis and patient data.
  • Telehealth and telemedicine platforms collect data from various remote monitoring of patients and virtual health consultations.
  • Leveraging the data from his systems helps healthcare service providers boost the decision-making process, optimize patient treatment plans, and provide personalized healthcare plans to patients.

3. Medical Devices and Wearables

Wearables and Medical devices play an essential role in providing necessary datasets for healthcare data analytics. These devices include

Physiological health monitors that track vital signs like

  • Heart rate
  • Oxygen level
  • Blood pressure

Imaging equipment and devices that create images with

  • X-rays
  • CT scans
  • MRIs

Medical devices also include glucometers and integrated fitness trackers to get data like:

  • Blood sugar level
  • Activity levels
  • Sleeping patterns and time
  • Body movements

These devices serve as the crucial data source to analyze the health status of patients, tracking and monitoring their progress, and providing customized health care plans.

4. Facility Management System

Facility Management Systems contain essential information for Health care analytics. This includes building an automation system that consists of energy/utility consumption, temperature, equipment usage, and a security system that provides access control and security events. However, other data sources include patient charts, patient satisfaction scores as a measure of experience, patient satisfaction surveys, and social media feedback. This data helps improve efficiency, patient satisfaction, and overall performance.

Applications of Data Analytics in Healthcare

Data analytics significantly impacts the healthcare industry by enhancing the quality of healthcare services. Here are common applications of data analytics:

1. Healthcare Reports and Dashboards

There is a high volume of data in healthcare businesses. It becomes hard for professionals to manage and analyze these data for decision-making. To ensure effective data management and presentation, healthcare service providers can easily present their data from various sources and departments at a unified dashboard to simplify the processes.
These dashboards are highly interactive and contain diverse metrics like the list of patients, room availability, staff attendance, waiting time, and more. Additionally, they leverage various administrative data of staffing, inventory, and resource allocation to check loopholes in the business operation and decide the course of action to solve them.

2.Prediction of patient and Staff

Staffing-patient prediction is maintaining the balance between manpower and efficiency. With effective staff prediction, healthcare service providers can reduce labor costs by reducing the number of workers at low-demand hours. On the other hand, having few workers during high demand working hours requires checking patient satisfaction and proper treatment.

With effective healthcare data analytics, patient records, including admission, treatment, and medical history, can be easily analyzed using historical data. This ultimately helps make reliable predictions by managing shifts of staff based on total patients to manage and address the changing demand.

3. Telemedicine

By harnessing the power of modern technology, doctors and healthcare consultants seamlessly provide remote medical and clinical services. The services include:

  • Remote consultations
  • Effective Diagnosis
  • Online medicine delivery
  • monitoring the health status of patients
  • Tracking the progress
  • Medical reports
  • Patient analysis with video conferencing

4. Online Health Records

An important application of big data analytics in the healthcare sector is maintaining online and e-healthcare records. These records include specific patient records, including medical history, reports, and specifications of allergies. This data should be stored at a particular location in an easy-to-understand form, which is used for effective patient care.

This data can also be arranged based on the following:

  • Demographics and locations
  • Specific disease
  • Status of the treatment
  • Progress of the patient

5. Fraud Detection and Enhanced Data Security

Healthcare fraud usually happens when someone modifies medical records for their benefit. Integrating the healthcare industry with data analytics and ML helps ensure data security and maintain historical data.

By integrating advanced technologies, the healthcare segment can easily prevent data theft or fraud. It also streamlines diverse processes to boost patient satisfaction and recovery rates.

Know how predictive analytics helps to improve patient treatments and transform the healthcare industry.

Benefits of Using Data Analytics in Healthcare

  • Enhanced Patient Care

Analyzing patient data helps healthcare providers determine the type of treatment required for better results. This helps in treating patients better, specifically those with serious diseases like multiple sclerosis, chronic renal disease, cardiovascular problems, and others. It also allows doctors and medical practitioners to access and analyze patient data, including medical history, test reports, and treatment status. This helps to predict risks to health and create plans for specific treatments.

  • Improved Diagnostics

Healthcare practitioners efficiently and accurately diagnose diseases compared to traditional methods. Advanced algorithms help analyze data to predict effective diagnosis practices, including suggesting tests, medical courses, and others.

  • Enhanced Decision making

Data analytics assists healthcare service providers in identifying changing patterns and trends to gain valuable data insights. These trends help in data-driven decisions. On the other hand, there is boosting demand for integrating AI and Data analytics to ensure effective decision-making. These both can help in enhancing-

  • Surgical processes
  • Diagnosis of severe diseases
  • Integrated consultation
  • Preventative Care

Big data analytics allow healthcare professionals to forecast the possibility of disease in an individual by analyzing their lifestyle. Using data analytics to identify at-risk populations for preventative care helps determine the warning signals and chances of getting a specific disease and treatment at earlier stages.  

  • Facilities Management

Data analytics improve healthcare operations by making processes smoother and lowering unnecessary costs. Healthcare experts can analyze data to plan and allocate required resources efficiently.

Challenges of Integrating Data Analytics in Healthcare

There are various challenges in managing and analyzing large healthcare datasets. It is important to analyze the challenges and issues to ensure data integrity and effective management.

1. Data integrity and quality

The major challenge of Big data in healthcare is that every hospital, clinic, and testing laboratory usually follows different data formats and does not have a standardized format for storing a high volume of medical data.
This consistency in data format makes it difficult to integrate data from diverse sources and may hinder the data management process when healthcare and patient data are disorganized. This has a great impact on the reliability and accuracy of data analysis.

2. Data privacy and security

Data analysis in healthcare requires data security and safety. But it becomes a significant challenge in maintaining data privacy for successful implementation. Maintaining a balance between the effective utilization of data and protecting the crucial data of patients is necessary. It is essential to use standard safety measures to ensure data privacy and patient consent in healthcare analytics.

This vast patient data is becoming the primary target for cyberattackers and unauthorized access. There are security measures to safeguard sensitive data:

  • Dala encryption
  • Authentication and controlling user access
  • Regular security checks and compliance audits ( HIPAA and GDPR)
  • Using secured networks
  • Implementing advanced security measures

3. Data Standardization Issues

Data are not stored in a standardized format, which hinders effective data analytics processes.Standardized data formats ensure an uninterrupted data collection process and smooth analysis across diverse healthcare and patient data management systems.

4. Data transfers and storage

Various healthcare data is gathered from different sources, and it is important to save them in a unified place in an easy-to-understand form. The main problem with big data analytics is storing the data in the existing infrastructure with limited storage. So, it becomes crucial to find scalable and cost-effective data storage systems to ensure smooth analysis.

On the other hand, sharing and transferring datasets between healthcare organizations, medical practitioners, and hospitals becomes complex. So, it is also important to establish safe and efficient protocols to facilitate uninterrupted data sharing.

The main problems faced by the healthcare segment to ensure smooth data transfer and safe storage:

  • Limited storage capacity
  • Computational limitations
  • Data processing delays

How to Overcome Challenges Using Big Data Analytics

Big data healthcare analytics is transforming the segment to facilitate patient diagnosis, treatment, and effective care and save unnecessary costs. However, low-quality data is still available, which hampers the decision-making process and strategy formation. Additionally, data storage, privacy, and management issues also have a great impact on effective data analysis.

1. Data Entry Technologies

Different technologies are applied in the healthcare context to capture different data and consolidate it from different systems and formats into the data warehouse. First, IoT devices help remote patient monitoring through smart and wearable devices; thus, data is collected from patients’ homes.

A number of CRM tools and platforms used in frameworks and strategies enable the providers to tailor services based on the patients and to improve the experience that they will have with the system. There are several ways to support optical character recognition technologies which tends to minimize human mistakes during digitization processes.

2. Technology, Data Management & Data Storage

To manage and share medical information, providers integrate a variety of modern data storage technologies to perform repetitive activities, satisfy HIPAA requirements, and optimize access and security. It explains that they are shifting their focus, services, and locations from the old-style, local servers to the cloud.

Cloud storage is very advantageous as it can be adjusted with little tell-tale costs. They support hybrid and remote work policies and mobile applications, which enable managing large amounts of data on flexible and broad servers with minimal local attention. Cloud-based solutions also lower the incidence of data leaks that can threaten access to information stored in multiple locations.

3. Fraud Detection, Data Security, and Prevention

The use of IT solutions in providing healthcare services is revealed as essential for providers and healthcare organizations concerning real-time data processing. Let me now elaborate on how AI and ML can be used in the diagnosis and prognosis of diseases to increase the effectiveness of healthcare organizations and bring beneficial changes to patients’ lives. Data security constitutes access rights and encryption on data storage and transfer.

The same providers also employ unique fraud detection systems that help look for any contrary action. It would be pertinent to note that AI in the prevention of fraud is one of the most efficient and, at the same time, inexpensive ways of enhancing the chances of identifying and combating fraud.

4. Scalability of Diverse Data Architectures

HDM helps make and analyze vast amounts of data for significant patient health information. It established that, with the advancing digital innovation, the output of data is meant to rise; hence, healthcare professionals are switching to cloud storage and computing for future advancement. Solutions implemented on the cloud level are distinguished by maximum flexibility and effective control over data architecture while organizations introduce new hardware.

5. Data Intelligence and Predictive Modeling

Healthcare professionals use analytical tools to derive value from collected data. Data intelligence helps improve treatment outcomes, recognize threats early, and personalize preventive measures.

Predictive modeling technologies using AI allow providers to detect chronic conditions or high-risk events proactively, improving patient care and reducing healthcare costs. These models tailor treatment plans based on individual medical needs, enhancing preventive healthcare.

In short, to overcome these challenges, it is essential to follow these things:

  • Implementing modern data management solutions to manage large amounts of healthcare data easily
  • Automated data processing and cleaning approaches to enhance the data quality and eliminate unnecessary healthcare and medical data
  • Validate the data sources and specify the requirements to collect the relevant data.
  • Filter and process the unstructured data to ensure a smooth data analysis process.
  • Incorporate various measures of big data to ensure smooth operations and save time.
  • Interpret the data carefully to harness the power of big data analytics
  • Enhance patient care, diagnosis, and treatment by enhancing operational efficiency and saving unnecessary costs.

Role of AI in Healthcare Analytics

In the field of healthcare, the use of AI enhances scanning and ultrasound abilities. Deep learning systems help analyze scanned or ultrasonic images of organs and tissues to help doctors diagnose abnormal features. Using these images to identify patterns and anomalies contributing to diseases enables healthcare providers to diagnose diseases faster and accurately, improving the quality of the patients’ lives. Also, through robotics, the most mundane roles, such as interpretation of scans and ultrasounds, can be automated to free up the time of the care providers to attend to patients.

AI plays a transformative role in maintaining customer records within a healthcare setting. Consequently, there is an enormous volume of patient data produced every single day, and these AI solutions enable sorting of the information, categorizing it, and even analyzing it. These systems should be able to diagnose and analyze different trends, foresee existing health risks, and provide numerous valuable nuggets for individualized treatment. AI algorithms also identify errors or inconsistencies in the records of the patients, improving data reliability and validity. In other words, it makes the processes of record-keeping less complicated while enhancing the quality of delivered care and reducing the violation of laws governing the privacy of patient data.

Robotics is widely used in surgeries as an assistant to surgeons in enhancing the accuracy and safety of surgeries. Predictive modeling is performed on pre-operative imaging data that outlines surgical plans and directs robotic instruments during surgery. They are able to conduct precise and delicate movements much better than the hands of a surgeon, hence avoiding complications that could slow down the recovery rate among patients. Again, AI helps in the regulation of care during surgeries since it closely monitors and responds to changes. When AI is integrated with robotic techniques in health facilities, healthcare providers provide better surgical procedures because they improve the quality of life for patients.

Integrate the AI algorithms to enhance your business's healthcare data analytics processes!


In the field of health care, the approach to patient treatment called personalized medicine is gradually entering people’s lives as a revolutionizing method. When healthcare professionals use genetically customized medicines and patient-specific medical information, it is possible to achieve the most significant therapeutic effect while reducing possible side effects. It also improves the effectiveness of treatments and creates improved patient orientation to healthcare, where people get care that is designed depending on their status and needs.

In addition, the development of individualized treatment empowers healthcare experts to diagnose diseases at an earlier stage with more efficacy. Thanks to advances in genetics, not all potentially lethal diseases become apparent only when individuals present with symptoms; with the help of tests and imaging, doctors get to see signs of disease a lot earlier, which may prevent the onset of serious symptoms. Such a proactive mode of operation in the health sector is helpful in emphasizing the need for the prevention and treatment of diseases to begin even before they compromise the health of the respective individuals.

About Author

Bhavesh Parekh

Bhavesh Parekh

Mr. Bhavesh Parekh is the Director of X-Byte Data Analytics, a rapidly growing Data Analytics Consulting and Data Visualization Service Company with the goal of transforming clients into successful enterprises. He believes that the client's success helps in the company's success. As a result, he constantly guarantees that X-Byte helps their clients' businesses realize their full potential by leveraging the expertise of his finest team and the standard development process he established for the firm.


Healthcare uses data analytics so that physicians can anticipate their patients’ health status such as medical procedures and treatments depending on the patient’s age, medical history, and even potential scenarios or possible repercussions of the medication.

Data visualization and analytics helps in boost patient care by reducing the unnecessary costs.

There are various ways of healthcare data analytics:

  • Manage health records of patients
  • Forecast demands of operating rooms
  • Optimize staff to save unnecessary costs and time.
  • Manage the costs of supply chain
  • Tracking supply chain metrics
  • Enhance security and prevent fraud

The use of big data is advantageous in the healthcare industry in the following ways: 

  • Improving patient treatments
  • Monitoring patient’s health status
  • Predicting outbreaks faster
  • Personalizing treatment plans of patients
  • Enhancing operations of hospitals
  • Empowering breakthroughs of lifesaving
  • Optimize hospital resource allocation and reduce wait times
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