The role of data analytics in
healthcare

Health data is any information related to the well-being of a patient or collective population.

The use of this data can fit into two main categories:

Primary use of health data refers to the main purpose for the data collection (e.g. examination, treatment, rehabilitation, etc.)

Secondary use of health data outlines the utilization of the same information for purposes different from the primary one (e.g. scientific research, development and innovation activities, education, etc.). It is gathered by healthcare providers from various health information management systems (HIMS) and other tools for collecting, analyzing,
and sharing. In this case, healthcare organizations utilizing medical data must first obtain the individual’s consent and clearly explain the purpose of the secondary use.

The global big data healthcare market is expected to reach $44.53 billion in 2026. By means of data analytics hospitals and clinics aim to make cohesive decisions based on actionable patient insights. Furthermore, analytics tools allow not only for better clinical outcomes (high-quality care, accurate diagnoses, preventive measures, personalized treatment) but also aim to reach business objectives (lower costs and efficient internal operations).

Types of analytics in healthcare

Three main types of health analytics aim to find answers to different questions by healthcare providers:

Descriptive Analytics

Descriptive analytics utilizes historical data to draw comparisons or discover trends. They answer questions about what has already occurred by giving insights into the past. Descriptive analytics might determine how contagious a virus is by examining the rate of positive tests in a specific population over time.

Predictive Analytics

Predictive analytics, powered by AI, use current and historical data to conclude future health outcomes. The data models created with this type of analytics might spot trends in whether a patient’s medical condition would deteriorate. Data scientists and data analysts may use predictive analytics to improve medication supply chain efficiency, and improve risk management in healthcare.

Prescriptive Analytics

Prescriptive analytics, powered by machine learning, helps practitioners overcome human bias and improve patient outcomes. These models can assess pre-existing conditions and identify patients developing future conditions and preventative treatments. One commonality between predictive and prescriptive analytics is that they rely on data and statistical models to make informed decisions.

Benefits of health data
analytics

Improve health outcomes
Gaining operational insights
Cohesive research practices

Improve health outcomes

By analyzing medical data, healthcare professionals can lower readmission rates, reduce medical errors, and better identify at-risk populations (population health management). Big data analytics may help practitioners apply historical data trends to current decisions, resulting in better resource planning and improved patient care. By assessing existing conditions and other risk factors, healthcare professionals may also determine new strategies for care delivery, leading to life-altering outcomes.

Gaining operational insights

The processes and operations that healthcare providers perform directly impact the quality of care patients receive. After gathering and evaluating operational data, medical institutions can take the right measures to reach operational efficiency.

For example, through data management and analyzing information on resource utilization, medical staff can reduce costs by identifying and eliminating unnecessary tests or procedures. Furthermore, business analytics enhances the utilization of hospital space, as they help medical staff to better manage demand for hospital beds and other healthcare resources.

Cost-effectiveness

Data analytics techniques are being applied to improve research efforts in many health-related areas by gathering and analyzing clinical data from various sources:

Medical researchers identify approaches to improve the efficiency of operations in clinical settings
Through analyzing high-quality data on patient diagnoses, treatments, and outcomes, researchers can identify patterns and trends that can inform the development of new treatments and interventions.
By analyzing large datasets, researchers can increase the statistical power of their studies, which can lead to more accurate and reliable findings.

Improved quality of care

Remote patient monitoring (RPM) helps patients with mobility and other health issues receive digitally-enabled end-to-end journeys. Timely interactions with physicians, medication adherence, increased health self-management, and comprehensive and up-to-date information about a patient's health are some of the perks of RPM.

Personalized medicine

AI-enabled tools, powered by machine learning, can be used to create personalized treatment plans and recommendations for individual patients, taking into account their specific needs and requirements. Furthermore, an AI system might support clinical decisions by analyzing patients' medical history and genetic information to identify potential drug interactions or adverse reactions.

Application of data analytics in
healthcare

Improving medical
imaging

Traditionally, a radiologist or other physician would have to read medical images to determine a diagnosis. But today, AI and machine learning are able to scan and analyze data such as X-rays images results and determine likely results in a fraction of the time. Powered by hundreds of thousands of X-rays and other diagnostic results, machine learning programs can compare individual images to previous scans, thereby determining the most likely diagnosis.
Predicting outbreaks

Healthcare analytics professionals experiment with data visualization to identify and more quickly control large-scale health events like COVID-19 that impact the whole healthcare system. Analyzing accurate data to predict trends such as future spikes in cases can help hospitals to ensure staff has enough personal protective equipment and patient beds and to report to public health organizations.
Better staffing

Data analytics is used in health business management to identify staffing issues and recruit, hire, train, and retain healthcare workers. Healthcare providers have to balance the need to contain costs and improve efficiency and patient outcomes with the growing shortage of qualified medical professionals.

Data analytics is being applied to manage labor costs in healthcare settings while simultaneously improving the quality of care patients receive and the efficiency of service provision. Hundreds of workers at the hospital use the automated solution to manage their schedules and other employment-related resources.
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 managing 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.
Telemedicine

Telemedicine solutions like medical applications integrated with wearables (pulse oximeters, smart, blood pressure trackers, biosensors) might gather health data from distance. The more data medical practitioners gather and evaluate, the more accurate diagnosis, treatment options, and preventative measures they could introduce. Thus, patients receive more personalized treatment, which will create a better experience and overall health outcomes.

Healthcare data analytics
technologies

Artificial intelligence and machine learning
in healthcare

In general, artificial intelligence (AI) and other automation tools aim to enhance and amplify the work of professionals by reducing time-consuming processes. They enable these professionals to gain actionable insights, accelerate discoveries and amplify human knowledge.

AI and machine learning models are capable of absorbing tremendous amounts of healthcare data – both structured and unstructured (raw data)—and then offer hypotheses for the user to consider, along with a confidence level for every insight and answer.

Healthcare data analytics software

Healthcare data analytics software enables informed decision-making by aggregating, consolidating, and structuring relevant data from multiple sources. Real-time data synchronization enables clinicians to efficiently utilize patient information (medical history, test results, etc.) across departments. This improves clinical care, especially for high-risk patients, who visit many specialists to maintain their medical condition.

Health analytics can be further incorporated into the electronic health record system, which stores digital versions of health records:

clinical data
progress notes
demographics
medications
laboratory tests
patient surveys etc.

This technology allows medical institutions to collect and store electronic medical records and other health information, as well as manage patient histories, care plans, guidelines, and procedures.

Analytics can be further employed to create charts and graphs that incorporate multiple insights in a single visual representation. This may come in much handier, rather than comparing text-based only reports that sometimes contain dozens, if not hundreds of pages. At the same time, data analytics is the key ingredient to enable automated report generation across departments of a healthcare institution.

It’s important to note that electronic health records should comply with industry regulations because all patient data is considered sensitive. Medical institutions in the US have to follow the Health Insurance Portability and Accountability Act (HIPAA) when accessing, storing, and processing personal health records.

In the EU, healthcare providers should ensure data privacy in accordance with General Data Protection Regulation (GDPR) so as to ensure safe and legal operations. There is a regulation that aims to set up the European Health Data Space to unleash the potential that health data brings.

This proposal backs EU citizens to take control of their health data, supports the proper use of health data for better health care, research, creation of policies, and innovative practices and promotes the safe and secure utilization, reuse, and exchange of health data.

Local and international regulations regarding the use of health information are the basis for the further adoption of data analytics. Ultimately, regulations help to create a framework for the utilization of health data, but it is also vital for organizations and individuals to be responsible and ethical in their use of it.

With the exponential growth of health analytics, the demand for careers in health informatics and data science, such as healthcare analysts or health data analysts, is highly likely to grow. The domain of healthcare data analytics requires an interdisciplinary approach, understanding of breakthrough technologies and industry regulations in health systems. Therefore, if you are building healthcare data analytics software, search for a competent partner that will support you through every step of the development process.