The healthcare industry is under pressure to deliver better outcomes due to increasing consumer demand. This pressure is coming from many directions, including a shortage of primary care physicians, the pervasiveness of chronic illnesses, and the increased cost of healthcare.
In response to this pressure, many healthcare providers and insurers are moving from a volume-based business model to a value-based business model. This shift improves outcomes by making healthcare more efficient and effective. However, it also creates new challenges for healthcare professionals, who must now be more productive and efficient in their work. The extreme pressure to do more with less makes a case for a new approach to healthcare operations.
The case for Prescriptive Analytics in Healthcare
Prescriptive analytics is a type of data analysis that uses historical data to predict future events. The healthcare industry can use prescriptive analytics to recommend patients’ or providers’ best course of action. It can also compare multiple “what if”scenarios. For example, if a healthcare provider is considering adding a new service, prescriptive analytics can assess the impact of choosing one service over another. With it, healthcare decision-makers can optimize business outcomes and make more informed decisions about patient care.
In a hypothetical example, a health insurer uses predictive analytics to spot a pattern in its claims data. The previous year shows a significant portion of its diabetic patient population also suffers from retinopathy. The insurer then estimates the probability of an increase in ophthalmology claims during the next plan year. Prescriptive analytics can model the cost impact if average ophthalmology reimbursement rates increase, decrease or remain the same for the following year. It can then recommend a course of action. This type of analytics can be beneficial for insurance companies in predicting trends and planning for the future.
Improving care and reducing costs
There is a need for technologies that can help make critical healthcare decisions. These techniques, such as those enabled by big data analytics and optimization algorithms in tandem with clinical evidence based on real-time monitoring metrics, are necessary today if we want our approaches towards transformation initiatives to be more than just simple but less efficient tools from years ago.
The healthcare industry faces increasing regulation, decreasing budgets, and other uncertainties. Hence they must take advantage of decision optimization. These optimization techniques will help weather these ups and downs in an evolving sector while improving patient outcomes.
An example is radiation therapy, where the collateral damage to surrounding organs and structures can be significant. A German hospital uses mathematics to design radiation treatment plans, enabling clinicians to precisely target which beams turn on, when, and for how long. This precision is beneficial because it allows doctors to treat each patient differently based on their specific needs instead of giving them one generic plan with significant toxicity risks.
On the administrative side, using prescriptive analytics techniques in healthcare ensures proper staffing levels. It also helps plan for facility location and capacity requirements to manage inventory. These actions have enabled hospitals worldwide to improve quality care while cutting costs and increasing transparency.
Prescriptive analytics post-pandemic
Analytics will always play a key role in healthcare organizations, but the post-pandemic era has made it even more critical. The pandemic stressed how prescriptive analytics can help plans for tomorrow become a reality today. However, there are still many behaviors we need to shift toward optimizing instead of guessing with traditional models.
Prescriptive Analytics can be used to optimize healthcare organizations’ workforce after the pandemic by assisting with return-to-work and augmented human resource planning. This planning includes prescribing changes in building layouts that prioritize health & safety while also considering reallocating resources within a company’s budgeting process, if necessary.
Challenges to adopting Prescriptive Analytics
Predictive analytics is much more than just reading the tea leaves of historical events. Providers want ways to reduce unnecessary costs, take advantage of value-based reimbursements that no longer reward voluminous care, and avoid penalties if they fail poor outcomes. The problem? It’sIt’s difficult because it requires access to real-time data, which isn’t always available within an EHR system.
Medical devices must provide up-to-date information on patient vitals to improve safety and alert clinicians without disrupting workflows or annoying them into ignoring critical warnings. Clinical decision support systems that use accurate diagnoses can also draw from as much available data, even through a health information exchange.
The struggle to secure sufficient funding for implementing these new tools has been challenging. The interest in predictive analytics is widespread among healthcare professionals. However, technological roadblocks remain, which make it challenging to achieve promised results.
How is Prescriptive Analytics helping early adopters?
The early adopters of big data analytics are doing great things for patient care and their financial health. For example, predictive risk scores help prevent suicides and increase watchfulness in the ICU unit. In addition, Gene expression tests allow doctors to identify high-risk people and provide new hope by discovering targeted treatments.
Cognitive computing engines, natural language processing, and free-text analytics have the potential to help providers pinpoint diagnoses that might otherwise elude them. These tools also provide population health management capabilities for payers and healthcare professionals to highlight those most at risk of being readmitted or developing costly chronic diseases in their care populations.
Predictive analytics may be complicated, but healthcare organizations across the country are using predictive analytics to improve patient care and save lives.