Descriptive analytics is the first step in analytics that allows us to integrate large amount of data in various formats into smaller and more useful information. The purpose of descriptive analytics is to summarize what has happened in the past. By reducing complex data sets to actionable intelligence you can make more accurate business decisions.  

Descriptive analytics is giving healthcare a better understanding of current assessments. Example:

• How many patients should have received a pneumococcal vaccine?
• How many diabetes patients in an endocrinology department have their blood sugar under control?
• How many patients have High blood pressure problem after discharge?

Diagnostic analytics examines data or content to answer the question “Why did it happen?”And is characterized by techniques such as drill-down, data discovery, data mining and correlations. Diagnostic analytics is the next level of analysis, providing insights on the motivations and causes driving trends and behaviors.

Example:

• Why some patients have HBP after discharge?
• Why certain parts of a country are more prone to Diabetes?
• Why some patients have still not received a pneumococcal vaccine?

Predictive analytics is analysis of likely scenarios of “what might happen”. The deliverables are usually a predictive forecast. Predictive analytics emerged from a desire to turn raw data into informative insights that can be used not merely to understand past patterns and trends, but provide a model for accurately predicting future outcomes.

Predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

Example: 

• “What percent of our patients will be re-admitted?”
• “How many patients will use the emergency room.”
• Predicting infections from methods of suturing, determining the likelihood of disease, helping a physician with a diagnosis and even predicting future wellness. 

Our Big data platform includes a tool that can score patients based on their risk profile, such as whether they have chronic conditions, so providers can develop more effective approaches to care, increase watchfulness in the ICU, aid surgeons in their decision-making, and even identify patients whose genes might betray them etc.

Prescriptive analytics suggests decision options for how to take advantage of a future opportunity or mitigate a future risk, and illustrate the implications of each decision option.Prescriptive analytics can continually and automatically process new data to improve the accuracy of predictions and provide better decision options.

Prescriptive analytics optimizes decision-making to show what actions to take to maximize profitable growth within given enviournment and business constraints. This is the most valuable kind of analysis and usually results in rules, policies and procedure changes or recommendations for next steps.

Example:

• To measure the number of patients who are clinically obese, then add filters for factors like diabetes and LDL cholesterol levels to determine where to focus treatment.
• Can determine those patients at highest risk of readmission and take action to mitigate this risk, such as emphasizing patient education at discharge or ensuring timely communication with primary care physicians and acute care facilities.
• Can help hospitals and health care networks to plan future business models. It can also help drug companies find test subjects who are likely to complete long-range studies.