Have you ever heard the saying “life is data”?
Anything we do in our daily lives produces data, be it walking, sleeping, running, eating, or even remaining stationary. In 2024, around 2.5 quintillion bytes worth of data was generated each day. The question then is, what happens with all this generated data?
Having qualified professionals, like analysts with an MBA in Data Analytics online or similar qualifications, who can interpret the generated data is paramount. Analysts play a pivotal role in translating the raw data into meaningful insights. The job of an analyst is relevant to all types of organizations, especially healthcare settings.
In healthcare organizations, analysts help make evidence-based decision-making, identify trends, improve operational efficiency, and foster innovation. Of great interest, healthcare data analytics can be grouped into descriptive, prescriptive, predictive, and discovery analytics.
Descriptive analytics is the most dominant form of healthcare data analytics and it focuses on the exploration and discovery of valuable information from raw data. It works by categorizing, characterizing, aggregating, and classifying data into valuable insights for healthcare professionals.
Situations where decisions have to be made regarding a wide range of alternatives call for prescriptive analytics. This form of analytics provides healthcare executives with a foresight of the results of their expected cause of action on a particular subject.
Predictive analytics concentrates on using information rather than raw data. Its main aim is to predict future events.
On the other hand, discovery analytics utilizes existing knowledge and the data available to discover new medications, and alternate treatments, and detect signs and symptoms of a condition.
How are these analytics used in healthcare then?
- Personalized medicine
With the vast amount of data available per patient, having personalized treatments is feasible.
Healthcare data analysts can analyze genetic information, lifestyle data, and pharmacogenomics of patients to determine the appropriate medication for each person.
This then promotes precision medicine, where benefits include better patient outcomes, reduced hospitalization costs, reduced risk of adverse effects, earlier diagnosis and prognosis of diseases, and improved sustainability are evident.
For example, in a 2018 study published in BMC Medicine, it was mentioned that by just integrating molecular profiling with drug-gene interactions, medical practitioners can remove from consideration cancer treatment options that are not effective or bring about harsh effects to the patient.
- Population health management
A publication by the World Health Organization highlighted that “population health management can play a critical role in strengthening primary health care (PHC) by providing a data-driven, people-centered, and proactive approach to managing the health and well-being of a defined population”.
Population health management goes through a couple of cycles:
- Defining and identifying the population
- Health assessment and population segmentation
- Risk stratification and impact ability
- Tailored service delivery
- Evaluation
With healthcare data analytics, the individual components of the population health management cycle can be improved, especially for health assessment and population segmentation, risk stratification and impact ability, and tailored service delivery, which is similar to personalized medicine.
- Research and prediction of disease
Patterns or trends can be identified by analyzing large amounts of healthcare data. More often than not, these patterns can be symptoms of medical conditions that require immediate attention.
A systematic review of clinical health conditions identified that health data analytics can predict endocrine, nutritional, and metabolic diseases, mental and behavioral disorders, circulatory and respiratory health conditions, and various forms of cancer.
This helps cut healthcare costs significantly. A recent report highlighted that private healthcare payers could see annual savings of $80 billion to $110 billion over the next five years. Similarly, healthcare providers stand to save $20 billion to $60 billion.
- Better staff management
The importance of having great staff cannot be undermined. They ensure the day-to-day activities of a healthcare organization are up and running. Without them, chaos will ensue in no time.
It is without doubt that management of staff can be daunting at times, however, with health data analytics staff management can become a more seamless and enjoyable process.
Healthcare data analysis can be used to identify areas of improvement, optimize staffing levels, develop targeted training programs, and automate reports.
More especially, the automation of reports will significantly enhance the efficiency and accuracy of hospital staff, which will directly reduce the rate at which they get exhausted. Research has also shown that the automation of reports makes healthcare professionals focus more on patient care and well-being.