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Key Components of Data Analytics for Healthcare

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Data analytics is an integral part of the healthcare industry, as it provides valuable insights and helps in decision-making. Healthcare data analytics makes use of technology and data science to improve the overall quality and efficiency of healthcare service provision. 

This involves the collection, management, and analysis of vast amounts of data from various sources, such as electronic health records, medical devices, and patient surveys.

What is healthcare data analytics?

Healthcare data analytics is the process of using data to identify patterns, trends, and insights in healthcare data. Data analysts use various techniques, such as data mining, machine learning, and statistical analysis, to extract meaningful information from large datasets.

What are the main points of data collection in the healthcare industry?

Healthcare facilities operate and manage a myriad of clinical and information systems. And it’s from these systems that they are able to collect data. Some of these systems include:

  • Electronic health records. This system stores clinical documents, patient diagnostic results, and medical history.
  • Diagnostics and monitoring. This is where past and present results from the diagnostic and monitoring equipment for magnetic resonance imaging can be found. In some hospitals, this system is interfaced with the EHR.
  • Pharmacy. This is the pharmaceutical product database. Here, you will find all the data on medicine and the specific suppliers.
  • Insurance claims and billing. This system holds all the data pertaining to the patient’s treatment and cost.
  • Laboratory information system. This system is integrated with the EHR as it gives the doctors easy access to lab results.
  • Human resources and procurement. This system holds all the data about the employees and their roles within the facility.
  • Clinical trials. These trials are critical for determining the effectiveness and safety of new treatments.
  • Health surveys. They help in understanding public health trends and in making informed policy decisions.

Components of data analytics in healthcare

Healthcare data analytics comprises six key components that help data analysts collect, manage, and analyse data for the better provision of healthcare services. These include: 

  • Data collection. The first step in data analytics is collecting relevant and accurate data from various sources in the facility. This includes patient records, lab results, medical images, insurance claims and billing, pharmacy, and other health-related data. You must collect a robust amount of data to ensure the quality and integrity of the data.
  • Data management. Once you have collected the data, you need to organise, clean, and store it appropriately. As such, you need to identify and remove any errors or inconsistencies in the data and also ensure data security and privacy. Data management also involves creating data warehouses and data lakes to store large volumes of data.
  • Data analysis. Data analysis is the core component of data analytics, where advanced techniques are used to extract insights and identify patterns in the data. There are four types of data analysis used in healthcare: descriptive, diagnostic, prescriptive, and predictive analytics.
  • Descriptive analytics. This type of analysis involves summarising and visualising data to provide a high-level overview of trends and patterns. It is mainly used to understand the patient’s needs, such as wait times, thus allowing physicians to dispense services quickly and efficiently. This type of analysis is based on past and current data. Descriptive analytics helps identify areas we need to improve by analysing equipment usage, supplies, and workforce resources.
  • Diagnostic analytics. Diagnostic analytics is used to determine the root cause of a problem or trend. It involves drilling down into the data to identify factors that contribute to a particular outcome or event. For instance, it can be used to determine why a patient was admitted.
  • Prescriptive analytics. Prescriptive analytics uses historical data to make recommendations for future actions. It helps healthcare organisations make data-driven decisions and improve outcomes. Prescriptive analytics provides physicians with a forecast of disease progression and effective treatment plans. 
  • Predictive analytics. Predictive analytics uses statistical and machine learning techniques to forecast future trends and outcomes. It is handy in identifying high-risk patients and predicting potential health issues. Prescriptive analytics helps physicians come up with an effective treatment plan for future patients. It also helps identify potential clinical trial cohorts. It helps forecast readmissions, disease spread, and medication availability.
  • Data visualisation. Data visualisation is the graphical representation of data using charts, graphs, and tables to make it easier to understand and analyse. It allows healthcare professionals to identify trends and patterns and communicate insights effectively and quickly.
  • Data interpretation and reporting. This is where the healthcare professional translates the insights gained from the data analysis into actionable recommendations and reports. These help them make healthcare decisions and improve patient outcomes.
  • Data governance and privacy. While we are collecting and analysing data, we must also ensure that all the data collected complies with regulations and standards related to data privacy and security.

Benefits of data analytics in healthcare

When healthcare facilities make decisions based on the analysed data, they reap the following benefits:.

  • Enhance facility performance. Data analytics plays a crucial role in improving the overall performance of healthcare facilities. By analysing data on patient flow, resource utilisation, and operational efficiency, healthcare organisations can identify areas for improvement and optimise their processes.
  • Population health management. Data analytics enables healthcare organisations to identify high-risk populations and design targeted interventions to improve their health outcomes. It also allows for the monitoring and tracking of population health trends, which can inform public health policies and initiatives.
  • Helps in making clinical decisions. By analysing large volumes of data from patient records and medical research, data analytics can provide healthcare professionals with valuable insights to inform clinical decision-making.  It can also help identify the most effective treatment plans for specific conditions.
  • Cost optimisation and operation efficiency. Data analytics can help healthcare organisations identify areas for cost optimisation, such as reducing unnecessary tests or procedures, minimising readmissions, and improving supply chain management.  It can also help improve operational efficiency by identifying bottlenecks and streamlining processes.
  • Enhances patient care and health outcomes. By leveraging data analytics, healthcare organisations can enhance the quality of patient care and health outcomes. They can identify patterns and trends, which can help them personalise treatment plans and interventions based on individual patient needs.

Challenges of data analytics in healthcare

While data analytics has numerous benefits in healthcare, it also faces several challenges, including.

  • Issues of data privacy. With the increasing use of technology and electronic health records, protecting patient data has become a significant concern. Healthcare organisations must have stringent security measures in place to safeguard sensitive information from being accessed by unauthorised personnel and hackers.
  • Data quality. You must ensure that the data is accurate and complete to ensure meaningful analysis. Unfortunately, data quality can be compromised due to errors in data entry, duplicate records, or missing data. This can lead to incorrect insights and decisions.
  • Ethical use of data. The use of data in healthcare raises ethical concerns, such as patient consent and data ownership. It is essential to have ethical guidelines in place to ensure the responsible use of data.

Final thoughts

Data analytics is an indispensable tool in the healthcare industry, as it provides valuable insights and helps in decision-making.

By making use of the key components of data analysis in healthcare, healthcare facilities and doctors can leverage this powerful tool to improve patient outcomes, enhance operational efficiency, and optimise costs.

But it is essential to take note of the challenges to ensure the responsible and ethical use of data in healthcare.




Adam Mulligan, a psychology graduate from the University of Hertfordshire, has a keen interest in the fields of mental health, wellness, and lifestyle.

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