Understanding the 4 Types of Healthcare Data Analytics

The healthcare sector stands as one of the most data-centric fields globally, where healthcare professionals and researchers heavily depend on data to make well-informed choices regarding optimal patient treatments.

While data analytics has been a staple in various industries for an extended period, its integration into healthcare has only gained prominence recently. The significance of healthcare data analytics is underscored by the following statistics:

• A formal program or strategy for data and analytics is in place for 75% of surveyed hospital executives.
• Approximately 38% of hospital executives acknowledge there is room for enhancing their data and analytics capabilities.
• 36% of respondents indicate that their organizations have already integrated predictive models into their operations, with an additional 41% planning to do so within the next year.
• An impressive 80% of healthcare organizations have initiated analytics programs or made investments in this domain in some capacity.

What is Healthcare Analytics?

Healthcare analytics is a broad phrase that refers to the process of gathering and analyzing data in the healthcare industry. Healthcare providers, payers, and patients can all use this.

The goal of healthcare analytics is to improve the quality of care patients receive and to assist them in better managing their health. This is accomplished through gaining a better understanding of how specific treatments operate or which drugs are most beneficial for specific illnesses. 

Types of Healthcare Data Analytics

Health Analytics will delve into the four key types of big data analytics used in healthcare in the following sections, clarifying the purpose and potential uses of each.

1. Descriptive Analytics

Descriptive analytics stands as the most prevalent form of analytics, serving as a tool for generating reports and analyses rooted in historical data. Its primary function is to scrutinize and elucidate events and occurrences that have already transpired.

The outcomes are typically showcased through reports, bar charts, and other easily comprehensible visualizations and dashboards.

• This variant of analytics is notably uncomplicated since it relies solely on existing and readily accessible data. It is primarily employed to gain insights into the overall cumulative performance of healthcare institutions.
• Before advancing to more advanced analytics types, it is imperative to establish foundational skills in descriptive analytics, which include mastering data modeling basics, adhering to the fundamental assumptions of best practices such as the star blueprint, proficiently conveying data through appropriate visualizations, and crafting rudimentary dashboard designs.

Usage:

Application-wise, in the realm of direct healthcare practice, descriptive analytics can be harnessed to ascertain the contagion potential of a virus. This is achieved through an examination of the positive test rate within a specific population over a period of time.

2. Diagnostic Analytics

Diagnostic analytics plays a vital role within healthcare institutions, serving as an essential instrument for comprehending operational challenges and detecting potential factors that may influence key performance indicators (KPIs).

• Its core function lies in the recognition of unforeseen shifts or irregularities in the data gathered from descriptive analytics. This recognition paves the way for the gathering of supplementary data that is relevant to these anomalies. 
• For instance, an analysis of hospitalization patterns may unveil fluctuations during specific seasons or surges following adverse weather events.

Usage:

In practical healthcare scenarios, diagnostic analytics can be applied to the process of diagnosing a patient's specific ailment or injury based on the symptoms they are manifesting.

3. Predictive Analytics

Predictive analytics, as a category of analytics, aids health systems in gaining insight into forthcoming trends by scrutinizing historical data. In the era of value-based healthcare, its primary function lies in pinpointing patients who may be susceptible to adverse outcomes.

• Through the identification of risk factors, healthcare institutions can anticipate future hospitalizations for these specific patient cohorts. 
• This proactive approach equips clinical teams with the means to prepare for potential surges in patient numbers. Predictive analytics can empower healthcare providers by enhancing decision-making processes, elevating patient follow-up practices, bolstering satisfaction rates, and alleviating the burdens on healthcare professionals.

Usage: 

In practical application, the predictive analytics methodology enables healthcare providers to ascertain individuals who are at risk of developing severe infections or chronic diseases.

By identifying those in jeopardy, it offers healthcare practitioners an opportunity for early intervention and the prevention of chronic diseases.

4. Prescriptive Analytics

Prescriptive analytics draws upon insights derived from descriptive, diagnostic, and predictive analytics to aid stakeholders in making decisions aimed at enhancing performance. Leveraging advanced tools such as AI, ML, and quantum computing, prescriptive analytics is geared towards predicting, visualizing, and formulating strategies to navigate forthcoming scenarios.

As an illustration, within a healthcare system, it can identify hospitalizations as well as pinpoint heart disease and diabetes as leading causes, all while projecting future hospitalization rates based on risk factors.

Additionally, prescriptive analytics can detect vulnerabilities in strategies for managing chronic diseases and propose evidence-based remedies.

Usage:

• In practical terms, suppose a hospital faces an unusual surge in hospital-acquired infections. In this scenario, a prescriptive analytics program goes beyond merely flagging this anomaly. It not only highlights which ICU patient may be the next at risk due to their critical vitals but also automatically identifies the specific nurse involved in the care of these patients, potentially responsible for spreading the infection.
• This empowers the hospital to increase its awareness and institute changes or enhancements in its hygiene and antibiotic protocols to avert similar outbreaks in the future. In simpler terms, prescriptive analytics can be employed to assess a patient's pre-existing conditions, assess their risk of developing future ailments, and formulate tailored preventive treatment plans with that risk in consideration.

In recent times, the healthcare industry has experienced significant growth propelled by technological advancements and the integration of artificial intelligence.

However, this expansion has also introduced numerous challenges. Whether you are a medical professional providing direct patient care or a healthcare administrator handling the business aspects of the industry, leveraging data analytics can serve as a foundational pillar for making informed and impactful decisions to address these issues.