The healthcare industry has continually generated large amounts of data over the last two decades. With the advent of electronic medical records, compounded by data from the new types of remote medical sensors, the need for data analytics has been increasing.
However, the industry is yet to realise its full potential with regard to data analytics and processing to support clinical decision-making, public health management and disease surveillance.
What is data analytics in healthcare?
Within the healthcare domain, data analytics revolves around the collection, interpretation, and application of data to optimise patient care, improve clinical outcomes and hospital operational efficiency.
Read: “What is data analytics: A complete guide”
What are the types of data analytics in healthcare?
There are three core types of data analytics in healthcare: descriptive analytics, predictive analytics and prescriptive analytics
Descriptive analytics
Descriptive analytics is arguably the simplest form of data analytics. It summarises what has happened or is happening in the past or present, converting data into meaningful information for analysing healthcare decisions and outcomes, as well as making informed decisions.
Descriptive analytics mainly utilises historical or real-time data to provide reports, dashboards, charts, and graphs that show key performance indicators (KPIs), trends, and patterns.
Predictive analytics
Predictive analytics focuses on forecasting future events based on historical data and statistical models. It analyses patient data, such as demographics, medical history, and risk factors, and predictive analytics to identify individuals at high risk of developing specific conditions or complications.
This enables healthcare professionals to intervene proactively, implement preventive measures, and personalise treatment plans to mitigate risks and improve patient outcomes.
Prescriptive analytics
Prescriptive analytics takes data analysis a step further by providing recommendations and insights to optimise decision-making processes. It does so by leveraging advanced algorithms and models to simulate various scenarios and predict the potential outcomes of different courses of action. Prescriptive analytics is especially useful when health problems involve too many alternatives and choices.
Within the healthcare context, it can help determine the most effective treatment plans, resource allocation, and preventive measures, ultimately leading to improved patient care.
Benefits of data analytics in healthcare
The integration of data analytics in healthcare brings forth a multitude of benefits that positively impact patient care, healthcare professionals, and healthcare systems as a whole.
Let’s explore some of the significant advantages:
Improved patient outcomes
By analysing patient data and identifying patterns, healthcare providers can personalise treatment plans, optimise care pathways, and deliver targeted interventions. This leads to better patient outcomes, reduced hospital readmissions, and enhanced patient satisfaction.
Enhanced clinical decision-making
Data analytics equips healthcare professionals with evidence-based insights to make informed decisions. By leveraging predictive models and real-time analytics, clinicians can identify high-risk patients, predict adverse events, and intervene proactively, improving clinical decision-making and patient safety.
Efficient resource allocation
Healthcare systems can optimise resource allocation by analysing data on patient flow, bed occupancy rates, and equipment utilisation. This enables efficient scheduling, improved capacity planning, and reduced waiting times, ultimately enhancing operational efficiency and cost-effectiveness.
Early disease detection and prevention
Through the analysis of patient data and risk factors, data analytics enables the early detection of diseases and the identification of individuals at high risk. This facilitates preventive interventions, timely screenings, and proactive healthcare management, leading to better population health and reduced healthcare costs.
Optimised research and development
Data analytics plays a vital role in medical research, drug discovery, and clinical trials. By leveraging large-scale data analysis and machine learning algorithms, researchers can identify novel insights, predict treatment outcomes, and accelerate the development of innovative therapies.
Improved population health management
By analysing population-level data, healthcare providers can identify trends, assess public health risks, and implement targeted interventions. This contributes to improved disease surveillance, efficient resource allocation, and better management of public health crises.
What is the scope of big data analytics in healthcare?
The amount and scope of data pertaining to healthcare is continuing to grow at an exponential rate as a result of the rising digitalisation of medical records as well as wearable devices and health monitoring systems.
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Fortunately, big data analytics makes it possible for healthcare companies to exploit the massive amounts of information at their disposal. This helps them leverage more in-depth insights, find correlations, and construct more accurate predictive models.
These models can then be applied to the delivery of more individualised medical treatment, as well as the practice of more precise medicine, and the administration of better population health management.
What are examples of analytics used in healthcare?
There are numerous examples of data analytics applications in healthcare. Some notable examples include:
- Real-time patient monitoring: Wearable devices and remote monitoring systems collect real-time patient data, such as vital signs, activity levels, and sleep patterns. Data analytics techniques can be exploited to analyse these continuous streams of data to identify abnormal trends, alert healthcare providers of critical situations, and enable timely interventions.
- Fraud detection: Data analytics helps healthcare organisations identify patterns and anomalies in claims data to detect fraudulent activities. By analysing historical claim patterns and employing anomaly detection algorithms, insurance providers can identify and prevent fraudulent claims, reducing costs and ensuring fair reimbursement.
- Precision medicine: Data analytics enables the integration of genomic data, patient characteristics, and treatment outcomes to develop personalised treatment plans. By analysing molecular data and identifying genetic markers, healthcare providers can tailor treatments to individual patients, increasing treatment efficacy and minimising adverse effects.
- Population health management: Healthcare organisations use data analytics to assess population health trends, identify at-risk populations, and implement targeted interventions. Public health officials can design preventive strategies, allocate resources efficiently, and improve overall population health by analysing demographic data, socio-economic factors, and health outcomes.
Conclusion: The future scope
As the healthcare industry continues to generate vast amounts of data, the importance of data analytics will only grow.
The benefits of data analytics in healthcare extend beyond improving patient care to optimising healthcare operations, enhancing research and development, and enabling more precise and personalised medicine.
By harnessing the power of data analytics, healthcare organisations can unlock valuable insights, drive innovation, and ultimately enhance patient outcomes.
Reach out to us for more information on how to get started with your data analytics journey in Singapore.
Areas of expertise: Training and consulting in technology, strategy, analytics, business management, and learning and development.
Awards: ‘Innovation for Impact Award’ 2016-17 | ‘Associate Excellence Award’ 2018-19 | ‘Innovation for Impact Award’ 2020-21 by CSC.
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