Originally published on pharmaphorum.com on 23rd November 2023
The primary goal of healthcare providers and pharmaceutical companies is to improve patient outcomes. Accurate patient flow forecasting is critical in achieving this objective. By ensuring the right medicines are available when needed and optimizing resource allocation, patient outcomes can be significantly enhanced.
Improving patient experience through accurate forecasting
Accurate patient flow forecasting enables pharmaceutical companies to optimize drug development and production. This leads to more available therapies and, inevitably, reduced drug prices. Patients benefit from shorter waiting times and a more reliable supply of essential medications. But the ripple effect of accurate forecasting extends beyond patient experience—it enhances the overall quality of healthcare delivery.
One of the main advantages of precise forecasting for pharmaceutical companies is the ability to align production with demand. When a pharmaceutical company accurately anticipates how many patients will require a particular medication, it can produce the required quantity efficiently. This efficiency leads to reduced production costs per unit, savings which can then be seen by patients in the form of lower therapy prices.
Reduced prices contribute significantly to an improved patient experience. Patients and prescribers no longer face elevated costs for vital treatments, ensuring more equitable access to medications. Additionally, shorter waiting times for treatment become a reality as manufacturers can meet demand promptly. Patients are then not enduring extended delays due to shortages, reducing anxiety and improving overall well-being.
Beyond medication costs and waiting times, accurate forecasting has an impact on healthcare quality. Hospitals and healthcare facilities can better plan for patient admissions, ensuring adequate staff and resources are available. Providing this effective and timely care supports improved patient outcome objectives.
Empowering drug development with AI
Precise patient flow forecasting supports the development of new drugs by effectively assessing early-stage opportunities. This leads to a broader range of treatment options and competitive pricing in the market. Importantly, patient feedback plays a crucial role, ensuring their needs and perspectives are incorporated into new therapy development, ultimately enhancing the patient experience.
Artificial Intelligence (AI) is already revolutionizing drug development and clinical trials. AI algorithms can analyze vast datasets to identify potential drug candidates more quickly and accurately than previously possible. This, in turn, speeds up the drug development process, bringing new treatments to patients faster.
AI also plays a pivotal role in clinical trials. It can identify suitable participants for trial enrolment, predict patient responses to treatments, and even help design more efficient trial protocols. This not only accelerates the development of innovative therapies but also reduces the associated costs.
Reducing clinical trial costs is a vital step in making cutting-edge treatments more accessible. Clinical trials are essential for developing new therapies, but the high costs experienced during drug development are often passed on to patients in the form of elevated medication prices. By accurately forecasting demand and reducing the number of failed drug candidates, costs can be controlled, and these savings can be used to provide more affordable treatments.
The power of real-time data for forecasting
In the age of big data, real-time information is invaluable to healthcare providers and pharmaceutical companies alike. Accurate data forms the foundation of patient flow forecasting, enabling predictions about future healthcare demands. But manual data updates can lead to delays and introduce the risk of errors. Innovative forecasting software solutions can automate data integration and enhance forecasting accuracy.
For example, Flow+ software can establish direct connections with external data sources, such as population databases and healthcare statistics. Through power queries and data linkages, this Excel-based software can ensure that the forecasting model is continually updated with the latest information. This includes changes in patient population, treatment preferences, and evolving pricing structures.
The real-time nature of these updates is essential for making informed decisions. Healthcare is a dynamic field, and sudden shifts in patient demographics or treatment trends can have a profound impact on resource allocation and budgeting.
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The adoption of Onco+ has helped this biotech cut the forecast development pipeline significantly and supported best practice forecasting within their team.
Benefits for healthcare providers and patients
One other significant outcome of improved forecasting is the reduction in treatment duration. More effective medications identified through accurate market research data and robust forecasting methodologies, can lead to quicker recoveries and shorter treatment periods. This not only benefits patients by reducing their time spent undergoing treatments but also reduces healthcare costs, a win-win for all stakeholders.
Additionally, healthcare providers can gain insights into non-treatment costs. For instance, they can calculate the number of pre-treatment scans required, estimate the cost of infusions, and optimize nurse resource hours. These factors are often challenging to assess accurately but play a crucial role in managing costs and delivering quality care.
Higher survival rates are another direct consequence of accurate patient flow forecasting. When the right treatments are available in a timely manner, patients have a better chance of recovering from their illnesses. This is particularly significant for patients facing life-threatening conditions, where access to the right medication really is the difference between life and death.
Looking to the future
Technological advancements and evolving healthcare needs are driving the future of patient flow forecasting. Artificial Intelligence (AI) and Machine Learning (ML) will revolutionize drug development by identifying biomarkers and molecules for challenging diseases, expediting existing research and development efforts.
Software vendors continue to develop model building software tools, incorporating AI for advanced capabilities. Cloud-based analytics and reporting will likely become more prevalent, providing greater flexibility and accessibility. Combining the flexibility of Excel modeling with cloud-based analytics in a hybrid approach will streamline forecasting processes.
The Future of Pharmaceutical Forecasting: Harnessing the Power of Artificial Intelligence
In today’s rapidly evolving pharmaceutical landscape, accurate forecasting is more crucial than ever before. Within this era of rapid technological advancement, Artificial Intelligence (AI) has emerged as a transformative force within various industries, including pharma.
In conclusion, good patient flow forecasting is essential in shaping the healthcare landscape, leading to improved patient outcomes and a more responsive healthcare system. The future of patient flow forecasting now holds even greater promise with AI-driven drug development and software innovation, improving pharmaceutical forecasters’ capabilities, thus enabling HCPs to deliver the best possible care to patients.
Author: Callum Burrows, Implementation Consultant at J+D Forecasting.
From a background in microbiology and genomics, Burrows joined the company in 2021 as a business analyst. His experience supporting pharma clients with market research, data analytics, and modelling software implementation spans a wide range of therapy areas and forecasting methodologies.
Author: Callum Burrows, Implementation Consultant at J+D Forecasting.
From a background in microbiology and genomics, Burrows joined the company in 2021 as a business analyst. His experience supporting pharma clients with market research, data analytics, and modelling software implementation spans a wide range of therapy areas and forecasting methodologies.
