The global healthcare industry is undergoing a fundamental transformation as it moves from a volume-based business to a value-based business. With increasing demand from consumers for enhanced healthcare quality, healthcare providers and insurers are under pressure to deliver better outcomes.
Primary care physician and nursing shortages require overworked professionals to be even more productive and efficient. The cost dynamics of healthcare are changing, driven by people living longer, the pervasiveness of chronic illnesses and infectious diseases, and defensive medicine practices. New market entrants and new approaches to healthcare delivery are also increasing complexity and competition. Extreme pressure to do more with less is making the case for a new approach to healthcare operations that focuses on the optimization of healthcare delivery and payment processes.
Prescriptive analytics enables healthcare decision-makers optimize business outcomes by recommending the best course of action for patients or providers. They also enable comparison of multiple “what if” scenarios to assess the impact of choosing one action over another.
Consider this hypothetical example: a health insurer spots a pattern in its claims data for the previous year showing a significant portion of its diabetic patient population also suffers from retinopathy. Using predictive analytics, the insurer estimates the probability of an increase in ophthalmology claims during the next plan year. Prescriptive analytics are then used to model out the cost impact if average ophthalmology reimbursement rates increase, decrease or remain the same for the next plan year, then recommend a course of action.
IBM Decision Optimization products help deliver prescriptive analytics capabilities to drive desired business results like cost reduction and customer satisfaction. Decision-makers can evaluate millions of possibilities, balancing trade-offs and business constraints to find the best possible solution. Together, these technologies can accelerate personalized medicine, aid in dynamic fraud detection and drive behavior modification for healthier lifestyle choices.
Improving care, reducing costs
When critical healthcare decisions are made on gut feeling or using simplistic tools, the results can be less than optimal and may even endanger patient lives. In this era of big data, any successful approach to transformation must be driven by real-time data analytics and enable decisions to be evidence-based and transparent. This is where technologies such as decision optimization, which enable a more evidence-based and transparent approach to decision making, come into play.
As health care administrators look for successful strategies to meet the challenges of an aging population, increased regulation, decreased budgets and other uncertainties, decision optimization will play a critical role in weathering the ups and downs of an evolving sector and in improving outcomes.
Hospitals with geographically distributed facilities, such as Dijon University Hospital Centre (CHU Dijon) in France, face a unique challenge: intra-hospital patient transport. Getting it right can be a logistical nightmare for dispatchers; getting it wrong can spell disaster for patients and caregivers. The hospital deployed a planning and dispatching solution that applies optimization models to ever-changing hospital and transport data, helping dispatchers plan, manage and execute hundreds of daily transport requests in real time. As a result, punctuality has improved by 25 percent, patient wait times are shorter and carriers walk 33 percent less per day.
Another example is radiation therapy, where the collateral damage done to surrounding organs and structures can be significant. There are virtually thousands of possibilities for the placement, diameter, duration and intensity of the beam. A German hospital applies mathematical optimization to design radiation treatment plans, enabling clinicians to precisely target which beams turn on, when and for how long to deliver an optimal dose for each patient.
A Danish hospital’s psychiatric department requires patients to be referred within 30 days for a diagnosis, but the process could take as long as two or three months. Using decision optimization, providers can now create mathematical models to simulate decisions and see how KPIs will change. The result: an 8 to 10 percent increase in referrals per year.
On the business and planning side, hospitals can use prescriptive analytics techniques to ensure accurate staffing levels, plan support facility location and capacity requirements, manage inventory and schedule home health services.
These examples demonstrate how health care organizations worldwide improve the quality of care, cut costs and increase transparency across all functional areas.
Getting started with decision optimization
These real-world examples illustrate how IBM Decision Optimization enables healthcare organizations to evaluate what is possible and what is not, given the available resources, to make reliable decisions. By developing and deploying optimization models using mathematical and constraint programming techniques, hospitals, insurers and others in the healthcare industry can optimize decisions and create real-world applications that significantly improve outcomes.
IBM Decision Optimization complements other IBM data science solutions such as machine learning, offering healthcare providers the tools to address their most complex challenges. It offers seamless integration between diverse technologies, enabling healthcare providers and payers to improve patient care, reduce costs and increase efficiency.