Data Analytics and Healthcare

In a world where data is generated every day, its analysis is needed to make smart, strategic and data-informed decisions. This includes the transformation of healthcare data into insights.

Data analytics is the gathering, assessing, cleaning, examining and visualizing of data to identify trends and patterns, make predictions and draw conclusions. Data analytics is an important part of decision-making processes across various industries or sectors and disciplines, including healthcare, science, business, finance and social sciences.

There are four key types of data analytics, and they are also utilized in healthcare;

• Descriptive analytics (What happened?): This is used for understanding historical trends. In healthcare, this can involve examining past data to describe patterns or trends in disease prevalence, patient demographics, or treatment outcomes. For example, a hospital might conduct a descriptive analysis of the types and frequency of injuries seen in its emergency department over the past year to identify areas where additional resources or staff training may be needed.
• Diagnostic analytics (Why did it happen?): This is also known as the root cause analysis. In healthcare, diagnostic analysis can be used to determine factors that contribute to high rates of medication non-adherence among patients, such as lack of understanding about the medication regimen or cost-related barriers.
• Predictive analytics (What is likely to happen in the future?): This is used for forecasting the future. Predictive analysis in healthcare can involve using data from previous patient populations to forecast future trends in disease prevalence or the spread of a seasonal disease. For example, a healthcare organization might use predictive analysis to estimate the number of patients who are likely to require a certain type of medical intervention in the coming year.
• Prescriptive analytics (What is the best course of action to take?): This is used for unearthing new strategies. Prescriptive analytics in healthcare can be used to assess the past and current conditions of a patient, determine their risk of developing future conditions and implement specific treatment plans based on that risk.

Use of data analytics in healthcare

The specific outcome of healthcare data analytics largely depends on the type of data in use.

• Pharmaceutical data: Pharmacogenomics is a field that makes use of data on specific genome sequences to identify genetic variations that affect a patient’s pharmacokinetic and pharmacodynamic response to a medication, with the goal of developing personalized treatment plans (precision medicine) that are tailored to each patient’s unique genetic profile. Data from medical devices can as well be used to target specific patients for clinical trials in addition to the use of the data for the improvement of such products.
• Clinical data: These are electronic health records (EHRs), electronic medical records, personal health records, public health records, and patient and disease repositories. The analysis of these data can improve the efficiency of clinical processes, lead to more accurate diagnosis and treatment through personalized healthcare, as well as generate new insights into the causes of diseases by linking risk factors with health outcomes.
• Claims data: This type of data is generated by insurance claims submitted by healthcare providers, and includes information on the services provided, the cost of those services, and the patient’s insurance coverage. Analysis of this data can help medical institutions identify areas where resources are being utilized or wasted.
• Public health data: This type of data includes information on disease prevalence rates, population health outcomes, and environmental factors that affect health. Public health data is used to inform policy decisions and public health interventions.
• Research data: This type of data includes information collected during research studies on various aspects of healthcare, such as the effectiveness of different treatments or the impact of health policies.
• Patient-generated data: This includes information on patients’ health behaviours, such as their physical activity levels, diet, and medication adherence. This type of data can be collected through patient-reported surveys or wearable devices that track health metrics. Patient-generated data can also be used in real-time to get patient feedback on specific medical interventions.

Data analytics has transformed the healthcare industry by providing insights into patient care that were previously inaccessible. It has enabled healthcare providers to deliver more personalized care, reduce costs, and improve patient outcomes. From the development of predictive models that identify at-risk patients to the implementation of prescriptive analytics that help clinicians develop tailored treatment plans, data analytics has become an integral part of healthcare decision-making. As healthcare data continues to be collected and analyzed, more breakthroughs in healthcare delivery and patient care are expected. By harnessing the power of data analytics, the quality of care is improved, patient satisfaction is increased, and ultimately lives are saved.