The lowest common denominator of prescriptive analytics
Prescriptive analytics automatically synthesizes big data, mathematical sciences, business rules, and machine learning to make predictions and then suggests decision options to take advantage of the predictions. Prescriptive analytics is the third phase of business analytics (BA) which includes descriptive, predictive and prescriptive analytics.
The most traditional business analytics is descriptive analytics, and it accounts for the majority of all business analytics today. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Almost all management reporting within healthcare (such as quality, clinical, administrative, operations, and finance) uses this type of retrospective analysis.
The next evolutionary phase of BA is predictive analytics. This is when historical performance data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or a likelihood of a situation occurring. In short, we look to the past to determine what the future will be.
The final phase is prescriptive analytics. Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. Prescriptive analytics not only anticipates what will happen and when it will happen but also why it will happen. Further, prescriptive analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve prediction accuracy and provide better decision options.
Prescriptive analytics has been in existence since about 2003. The technology behind prescriptive analytics synergistically combines data, business rules, and mathematical models. The data inputs to prescriptive analytics may come from multiple sources: internal, such as inside a corporation; and external, also known as environmental data. The data may also be structured, which includes numerical and categorical data, as well as unstructured data, such as text, images, audio, and video data, including big data. Business rules define the business process and include constraints, preferences, policies, best practices, and boundaries. Mathematical models are techniques derived from mathematical sciences and related disciplines including applied statistics, machine learning, operations research, and natural language processing.
Multiple factors are driving healthcare providers to dramatically improve business processes and operations as the United States healthcare industry embarks on the necessary migration from a largely fee-for-service, volume-based system to a fee-for-outcome, value-based system. Prescriptive analytics will play a key role to help improve the performance in a number of healthcare areas.
Prescriptive analytics can benefit healthcare strategic planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demographic trends and population health trends, to more accurately plan for future capital investments such as new facilities and equipment utilization as well as understand the trade-offs between adding additional beds and expanding an existing facility versus building a new one.
In provider-payer negotiations, providers can improve their negotiating position with health insurers by developing a robust understanding of future service utilization. By accurately predicting utilization, providers can also better allocate personnel.
The greatest benefit to the healthcare industry will come in terms of bending the cost curve down, while simultaneously bending the quality curve up. The beneficiary will always be the patient/member in that they will receive the right care, at the right time, in the right setting, from the right provider. In the process, the care delivered will assure that quality is enhanced parallel to an enhancement in financial and operational efficiency and an enhancement to cost containment.
While all of this talk of prescriptive analytics is seductive; there is a fundamental flaw that we have not discussed. Prescriptive analytics is built on top of predictive analytics. In turn, predictive analytics is built upon descriptive analytics. Finally, descriptive analytics is built upon a foundation of data. If that data is incomplete, tainted, unstructured, or otherwise suspect in quality; we launch a domino effect. Bad data quality begets poor descriptive analytics begets poorer predictive analytics begets poorest prescriptive analytics.
Are you ready to predict the future from a cloudy crystal ball or is it time to address your data access and quality issues today?Healthcare, Intelligence