The digital transformation of the healthcare industry is moving at an accelerated pace as healthcare facilities continue to reap its benefits in operational management, epidemiology, and personal medicine. And one subset of Digitization that is proving to be extremely useful is the integration of predictive modeling and analytics in healthcare.
Predictive modeling refers to the use of historical data or available data to make predictions of future events which helps with making better decisions. It is also used to troubleshoot or anticipate future behavioral patterns or outcomes using multivariate data sets or events.
In medicine, predictive models are being used to see into the future to define expected trends in operational management, and patient care both on an individual level and at a larger scale. While in pharmaceutical laboratories, predictive models are being used to predict future demand, enhance productivity, and in advanced planning and scheduling.
The Importance of Digital Transformation and Predictive Modeling In Healthcare
The digitization of healthcare data has provided the public with access to large repositories of health-related matters. Today, with a smartphone any individual can look up symptoms and seek medical advice from the comfort of their homes.
Digitization has also put patient data and educational resources at the fingertips of healthcare providers worldwide. Although these are excellent examples of the advantages of digitization, predictive maintenance takes things to the next level. With predictive modeling comes the option to enhance operational management in ways never experienced before.
One example is the use of predictive models to analyze patient no-shows, treatment schedules, and optimizing hospital resources. In 2018, The Elmont Teaching Health Center introduced the use of predictive modeling to track its patient no-shows which were costing the center money. To better anticipate no-shows and plan accordingly, the health center turned to predictive modeling.
With the hospital’s historical data, a predictive model for the patients most likely to not show up was developed. This model was then simulated against hospital resources with the aim of rerouting these resources to other patients. The result was a 14% reduction in its no-show rates which saved the healthcare center hundreds of thousands of dollars caused by patients.
Another important scenario which predictive models and simulations help with is in dealing with optimizing operations in emergency departments. First and foremost, it is important to establish the importance of timing and resource availability within emergency units to understand the importance of predictive modeling.
Medical errors in emergency rooms and inadequate facility allocation cause approximately 250,000 deaths annually in the United States and 1,500, 000 globally. Although solving the issues related to emergency care does not rely on operational management alone, the ability to predict the number of emergencies, allocate resources, and develop functional schedules can ease important challenges. These challenges include facility overcrowding and overworking emergency healthcare providers.
An example of how predictive models and Simulation aids emergency response can be seen from the example of Wake Forest Baptist Health Center. In this case study, predictive models of its patient inflows were developed and used to analyze the rate of patient inflow and how best to allocate hospital resources to cater for both emergency patients and others.
With the model, the hospital was able to manage the inflow of patients and emergency situations. The simulation results also provided actionable intelligence to hospital management which helped with crafting better policies for dealing with emergency and excessive patient visits.
Another example of the importance of predictive modeling in healthcare is its use in developing strategies for handling complex scenarios. One such scenario is the evacuation of patients with mobility challenges during disasters. The experiences of hospitals during Hurricane Harvey and Katrina led a team of researchers from John Hopkins University to apply simulation and scheduling to simplifying evacuation efforts.
In this case study, agent-based modeling was used to model the complex individual assets and varying conditions of patients into an evacuation simulation. The simulation also integrated micro-scale models of agents and mesoscale models of population densities to understand the relationship and behavioral patterns of the diverse agents within an evacuating system.
The result of the study showed the extent to which macroscopic and mesosscopic models produce system-level behaviors in agent-based models.
The Significance of Predictive Modeling In Pharmaceutical Facilities
As with every manufacturing-based industry, the pharmaceutical industry relies on managing logistics chains, optimizing shop floor operations, and manufacturing workstations to meet customer demand. Thus, the integration of predictive modeling and simulation has significant roles to play in delivering actionable intelligence and insights into optimizing production.
With a simulation or digital twin software, comprehensive models of a pharmaceutical manufacturing facility can be developed. This predictive model can be used to introduce external phenomena such as increased demand and scheduling delays to understand their impact on the manufacturing process.
In this model of a digital twin developed using Simio Simulation Software, the activities and capacity of a manufacturing facility can be seen as well as the discrete events happening within the shop floor. Within a digital twin, predictive simulations can be run to optimize the facilities supply chain with the aim of optimizing productivity. And according to RevCycle, healthcare facilities including big pharma can save approximately $9 million yearly in supply chain management costs. These benefits also apply to health-related manufacturing niches including biomedical devices, dental, and orthodontics manufacturers.
The Risks of Relying on Predictive Modeling In Healthcare
The healthcare industry is built around catering to humans and human relationships which means data analytics alone will not cut it. According to Deloitte, the integration of digital technologies in healthcare comes with risks such as moral hazards, privacy issues, and a lack of regulation.
Here, moral hazards refer to the machine-like application of healthcare in complex situations. With predictive analysis, patients in critical condition may be overlooked in order to ensure healthcare professionals have more time for other patients. A lack of regulation can also see harmful policies sneak into healthcare with the aim of managing resources and making the most profit.
Privacy concerns are also significant challenges that digitization brings up. One example is the loss of patient data by the National Health Service (NHS) due to a ransomware attack. In this instance, over 300,000 patients across the United Kingdom were affected by the security breach. Thus, cybersecurity must be taken into consideration when integrating predictive modeling and simulation in healthcare.
The importance of predictive modeling and simulation in healthcare, as well as, its risks will be discussed in more details at the Simio Sync Digital Transformation Event. Dean O’Neil of John Hopkins University will speak on ‘Building Capacity for Healthcare Modeling and Simulation’.
His session will provide in-depth information concerning the application of simulation at his time at the John Hopkins University Applied Physics Laboratory. Attendees will go away with about applicable modeling and simulation strategies which can be used to optimize their healthcare facilities. Simio Sync Conference will take place on the 4th to 5th of May in Pittsburgh. You can register for an early bird ticket here.