Healthcare Analytics

The healthcare industry is filled with structured and unstructured information through clinical notes, medical imaging, electronic medical records, insurance claims and patient sentiments. Managing these fragmented sources and creating a single source of truth is incredibly challenging. Healthcare analytics offers the potential to reduce operating costs, improve efficiency and treat patients.

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What are healthcare analytics?

Healthcare analytics is analysing historical and current data to produce actionable insights, improve decision-making and optimise outcomes within the healthcare industry. 

 

Healthcare analytics uses statistical methods, machine learning (ML) and artificial intelligence (AI) to examine data from various sources: claims and cost data, pharmaceutical research and development data, clinical data, patient behaviours and preference data, electronic medical records, medical imaging and financial and administrative data.  

 

Healthcare analytics can help with: 

  • Improving operational efficiency 
  • Optimising healthcare delivery 
  • Driving patient outcomes 
  • Improving decision making 
  • Identifying gaps 
  • Addressing patient experience 

Types of healthcare analytics

With a modern data stack and the proper analytical technique, teams can quickly analyse billions of rows of data from various sources to gain real-time visibility into essential operations.  

 

There are five types of healthcare analytics:  

  • Predictive analytics 
  • Prescriptive analytics 
  • Descriptive analytics 
  • Diagnostic analytics 
  • Population health analytics 

Predictive analytics in healthcare

Predictive analytics uses historical data to identify past trends and project future outcomes. It uses advanced data modelling techniques like AI and ML to help healthcare professionals predict patient outcomes and proactively make decisions about patient care, resource allocation and overall healthcare management. 

 

Use cases in the healthcare industry include identifying a patient's risk for developing a health condition, streamlining treatment courses and reducing a hospital's number of readmissions.

Prescriptive analytics in healthcare

Prescriptive analytics uses historical data to identify an appropriate course of action. It goes one step further than predictive analytics, using AI and ML to guide healthcare providers and administrators in making informed decisions, improving patient care and streamlining workflows. In the healthcare industry, prescriptive analytics is used to direct business decisions and prescribe patient treatment plans.  

 

Some everyday use cases for prescriptive analytics in healthcare are analysing individual patient data, such as medical history, genetics and responses to create personalised treatment plans, allocating ventilators for a hospital unit and enhancing diagnostic imaging tools.  

Descriptive analytics in healthcare

Descriptive analytics uses past data to understand what has already happened. This technique summarises large datasets, identifies relevant trends and uncovers critical KPIs. In healthcare, descriptive analytics allows professionals to analyse past patient data, optimise workflows and improve financial performance.  

 

For example, with descriptive analytics, clinicians can analyse historical patient data to understand the patient count over the last few years. This can be crucial for identifying operational inefficiencies, which helps improve the overall quality and effectiveness of healthcare services.

Diagnostic analytics in healthcare

Diagnostics analytics helps identify the ‘why’ behind past events. With root-cause analysis and data mining, healthcare providers can identify patterns and correlations in their data, leading to accurate diagnosis.  

 

For example, by tracking the readmission rate of patients and using diagnostic analytics, healthcare professionals can identify the reasons for readmissions and take corrective actions.  

Population health analytics

Population health analytics is the review, study and data dissection of information that brings significant health concerns into focus and addresses ways that resources can be allocated to overcome the problems that drive poor health conditions in the population.  

 

It helps payers and provider networks gain a holistic view of member populations while supporting quality improvement, care intervention and case management programs to deliver patient-centred care and reduce costs.  

 

This data is sourced from various channels, including patient surveys, clinical records, claims data, wearable devices and socioeconomic databases. Integrating social determinants of health, such as economic status and living conditions, provides a holistic perspective on population health.  

 

Benefits of population health analytics include:

  • More informed quality improvement initiatives 
  • Improved resource allocation 
  • Predicting patient risk 
  • Address barriers to care based on social determinants of health 
  • Performance benchmarking 
  • Identifying high-risk populations 

Creating a Unified Healthcare System

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Benefits of healthcare analytics

Healthcare analytics offers benefits to health businesses, hospital administrators and patients.  

 

Here are a few ways that healthcare analytics revolutionises the way healthcare providers and organisations operate:  

  • Personalised care plans for patients. Healthcare analytics empower medical professionals to holistically examine patient data, including medical history, lab results, vital signs and lifestyle factors to create a health profile. This removes the guesswork and helps providers identify the best treatment plans. 
  • Early detection of risks. With healthcare analytics, providers can monitor patients’ data in real time and use predictive models to determine which patients are at high risk. 
  • Enhancing patient experience. Healthcare data analytics offers visibility into the patient’s entire care journey – from the moment they are admitted to their overall experience with the staff. Data from surveys and feedback forms enable healthcare organisations to identify engagement points, address issues and pinpoint experiment areas that need further improvement. 
  • Improving bottom line. Healthcare analytics software helps healthcare organisations improve profitability and create a competitive advantage. Using real-time insights, organisations can identify bottlenecks in billing processes, reduce claim denials and improve reimbursement rates. 
  • Improved patient care. Healthcare providers can analyse data points like temperature, blood test results and blood sugar levels to improve patient care. They can also analyse qualitative data, such as a patient’s energy levels, mood, level of pain they report experiencing and how well they can complete day-to-day activities. 
  • Accurate and faster diagnoses. AI and ML can use patient data to predict the most likely diagnosis, eliminating unnecessary testing and increasing the speed at which patients can start receiving treatment. 
  • Empowered decision-making. Data analytics gives providers insights into the most effective treatments, trends and patterns that indicate certain conditions, common risk factors to be aware of and more. 
  • Greater operational efficiency. Data analytics enables organisations to assess their current conditions, structure and processes to identify areas for improvement. 
  • Improved staffing. Data analytics in clinical settings can forecast staffing requirements and optimise personnel needs across various departments. 
  • Greater insight into public health. Data analytics can identify and predict trends in public health and the spread of illnesses. 

Many organisations invest in healthcare solutions technology that improves data accessibility and enables real-time decision-making. Healthcare analytics combines meaningful customer analytics with advanced data analytics tools and techniques to deliver real-time insights and identify opportunities.  

Further reading

Check out these resources to learn more about healthcare analytics and its role in people-centric innovation.

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