4 Types of Data Analytics & How They Improve Healthcare

By Trinity Cyrus | Aug 25, 2023

Healthcare data analytics refers to collecting and using data and technology to gain actionable insights that help health care providers improve services. Data analytics aims to let healthcare providers make better decisions for their patients and facilities and improve patient care and facility processes.

The analytic process involves collecting, analyzing and using data to find patterns that indicate where changes can be made. Healthcare data can come from various mediums, including electronic health records, wearable devices and patient surveys. Analytics for healthcare results in improved patient care, reduced costs and more effective health services.

Learn more about using data analytics in healthcare and how it can help your facility improve patient care.

What Are the Types of Healthcare Data Analytics?

Analytics is used in various ways to understand and effectively use healthcare data to improve patient care. Different types of healthcare data analytics provide various insights about past and present data to provide a complete view of the information.

Consider how these types and examples of data analytics can help healthcare providers identify patterns in patient care:

1. Descriptive Analytics

Descriptive analytics helps providers understand what happened in the past. These analytics examine past patient records to identify trends and patterns in healthcare. For example, descriptive analytics may be used to analyze previous flu seasons to determine information like when patients experienced symptoms, the season’s peak, how harsh symptoms were and more. Facilities might also use descriptive analytics to examine and correct current safety measures for electronic health records (EHRs).

Descriptive analytics can also help you identify areas or processes that need improvement. For example, you can analyze how many patients were treated in the past month and how long they waited, which can be useful information for resource planning.

The information from descriptive analytics can be used with predictive analytics to predict future trends.

2. Predictive Analytics

Predictive analytics involves anticipating future events based on past data. This process uses past data and insights from descriptive analytics to predict trends that are likely to carry into the future. Predictive analytics is also used to forecast patient needs and identify at-risk patients. For example, predictive analytics lets providers compare patients’ symptoms to past patients’ similar symptoms to determine their risk of developing a certain health condition.

This also helps providers predict which patients will likely be readmitted to the hospital. Predicting this information is crucial to cost savings for both the hospital and the patient. Predictive analytics can also help determine a diagnosis and treatment effectiveness sooner.

3. Prescriptive Analytics

Prescriptive analytics is used to recommend actions to optimize outcomes. In terms of patient care, prescriptive analytics involves analyzing data to create treatment plans and determine the right course of action based on previous outcomes. Providers may rely on prescriptive analytics to suggest the best treatment options for individual patients.

Prescriptive analytics can also be used to make effective business decisions. For example, these analytics are often used to allocate resources efficiently or enhance processes.

4. Diagnostic Analytics

The other types of analytics are used to determine what happened or will happen. Diagnostic analytics tries to figure out why something happened. This involves analyzing data to find answers, which can then be used to determine how to prevent it from happening again.

Often, diagnostic analytics is used for investigating causes of illnesses or health trends. For example, you could use this type of analytics to determine the reasons for a sudden increase in hospital admissions.

How Does Data Analytics Improve Patient Care?

Analytics for healthcare providers are extremely beneficial for improving patient care. The insights provided allow you to take action and better understand the data collected in patient records. Here are several ways data analytics improve patient care:

Personalized Treatments

Data analytics helps doctors create custom care plans that consider a patient’s unique needs. Despite many patients dealing with the same conditions or diagnoses, each patient may require different treatment. Whether it be as simple as a different strength prescription or as complex as creating an entirely different treatment plan, data analytics help you provide personalized treatments.

For example, data analytics have been used to analyze genetic data in cancer patients to determine the most effective treatment option.

Early Intervention

Data analytics helps providers identify at-risk patients before problems get worse. Reviewing a patient’s healthcare records can indicate they’re at risk of chronic diseases that, if left untreated, can result in preventable complications. For example, diabetic patients have to monitor their condition to help prevent foot ulcers. Data analytics can help providers determine which patients may need early intervention to prevent unnecessary complications and hospitalizations.

Analytics could also help providers better understand which treatment method might be best for their patients in different scenarios to make the most informed decision.

Resource Allocation

Many hospitals struggle with resource allocation and staffing demands. Data analytics helps ensure enough staff and supplies to match the demand. For example, data analytics can help plan a nurse’s schedule based on patient needs and occupancy. You can also allocate where you need resources in the facility based on demand. For example, data analytics may indicate the need to keep additional blankets in certain hospital areas. Effective resource allocation is crucial to being well-prepared during fluctuating demand periods.

Reducing Errors

Patient data can reveal mistakes so providers can learn from them. Using data analytics to catch common medical errors improves patient safety and prevents the same mistakes from being made again. For example, data analytics can look at medication errors to determine why they happened and the best way to prevent them from happening again. Reducing healthcare errors is essential to providing effective care and keeping patients safe during and after treatment.

Patient Engagement

Data analytics can also help you determine which patients are actively engaged in their healthcare or may need a push in the right direction. With data analytics, you can encourage patients to take charge of their health and provide them with tools and information to do so. For example, you may encourage patients to use wearable devices to track exercise and heart rate to collect data on specific patients.

Implement Healthcare Analytic Solutions from MicroHealth

At MicroHealth, we provide data analytic solutions to give you thorough insights that improve patient experiences and drive better decision-making. Our team consists of expert healthcare experts who will help you create a custom solution with analytic tools and data collection methods to improve patient outcomes and care at your facility. You’ll achieve more with a robust analytic system when paired with our other capabilities, like data governance and quality management services.

Learn more about our analytic solutions for healthcare data, and contact us to start developing an analytic strategy for your facility.


Prev Post
508 Compliance and UX in Healthcare
Next Post
3 Telehealth Trends on the Rise That May Surprise You