If the healthcare industry hopes to tackle many of the pressing issues it faces today – from cost of providing care, to lost revenue, to providing higher quality care, institutions need to start adopting more prescriptive analytics into their practices.
Prescriptive analytics differs from predictive analytics in that it doesn’t stop at showing a likely outcome, but continues to demonstrate suggested actions to make healthcare providers more successful, profitable or responsive to patient needs.
The benefits of such a capability range from the identifying areas of improvement in treatment and protocols to reducing the rate of re-admitted patients, and lowering the cost of healthcare in general- from patient bills to the cost of operations in hospital billing departments.
It’s important to note that the use of prescriptive analytics in healthcare shouldn’t replace human intervention and decision making in patient care. Rather, prescriptive analytics can provide a means for doctors and administrators to use critical data and information to support clinical, financial and operational decisions and put them on the path to successful outcomes.
As a result, prescriptive analytics can provide short-term and long-term answers to administrative and health concerns alike – ultimately holding the potential to save more lives while reducing costs and mitigating risks from a financial and care delivery perspective.
Today, the healthcare industry depends predominantly on analyzing historical data to help make decisions. However, leading healthcare providers and insurers are focused on developing prospective capabilities to predict or forecast what may happen and on using prescriptive analytics to suggest actions that will enable the organization to test new approaches and ultimately create best practices to drive their business and operational objectives.
Prescriptive analytics will help highlight strengths, weaknesses and opportunities in treatment methods and prescribe better business practices, which will ultimately reduce costs while mitigating care and financial risks for all stakeholders.
The following best practices will pave the road to prescriptive healthcare:
Built-in capabilities are key. Ensure any application or software investment has predictive and prescriptive components built in to guarantee forward-looking analytics become an integral part of internal processes. Advanced analytics is of enormous importance to healthcare institutions because of the complexity of the problems confronting the industry, the financial risks caused by regulation and new business models, as well as the limited amount of resources on hand to support their business operations.
Given these challenges, using modern techniques such as machine learning will help healthcare decision makers find answers faster and without the heavy lift associated with manpower. Data is going to continue to grow in the healthcare industry, and the time to make decisions is going to lessen.
To fill this gap, technology needs to be available to automate some of the processes traditionally done by a data scientist or systems engineering team – leaving time for those highly valuable employees and care givers to focus on providing information and insights to decision-makers.
Specifically, healthcare institutions, whether large or small, must have access to easy to use advanced analytics solutions that enable risk stratification through clustering and profiling, risk scoring (financial (total cost of care), clinical (readmission risk) and operational (high cost of care providers), predictive model generation/evolution and simulation to keep the lights on, be competitive or thrive in today’s environment.
It’s all about the patient/data. Healthcare organizations continue to collect more and more data everyday. Connecting this data and quickly discovering points of value is the key to creating meaningful use of this data asset. Accurate prescriptive scoring is dependent on the quality and volume of data that describes a patient’s medical history.
Increasingly, healthcare institutions are exploring the value of peripheral data to help describe their patients such as census data, income levels, credit scores and social habits in order to improve the accuracy of their predictive modeling efforts. In short, each patient is unique and there are factors outside the view of healthcare systems that impact their health.
Currently, healthcare providers have minimal visibility into many of these factors, but collecting this peripheral data is necessary to build accurate predictive models and to evolve them over time as the landscape of patient data changes.
Having the right foundation of data and information will give healthcare professionals more intervention opportunities, better timing of the interventions and better recommendations to improve health outcomes while lowering the overall cost of care.
People take action. Generating prescriptive analytics is great, but if there is no plan or a trusted adviser in place to guide the process, those insights won’t do the organization any good. Enabling expert human intervention is key to maximizing the value of prescriptive analytics in healthcare.
Establish a course of action that is manageable and appoint the right people with the right experience to see it through. Have a plan for existing employees to understand how these changes will affect their role if new responsibilities or job skills are required, and be sure to consider the impact of these changes on the culture of your organization.
The healthcare industry is in a great position to take advantage of advanced and prescriptive analytics techniques like automation processes already proven in other industries such as retail and finance to improve their organizations. Investing in prescriptive analytics will enable healthcare to better understand their patients, their programs and their providers and provide a means for each of them to take action and improve outcomes.