Healthcare organizations in the United States deal with a system that makes it hard to guess how many patients will come and to use resources well.
This problem gets bigger during unexpected events like pandemics.
Medical administrators, owners, and IT managers must handle changes in patient numbers, supply chain problems, and staff availability to keep care quality.
Knowing these problems and how technology like artificial intelligence (AI) helps is important to keep healthcare running in uncertain times.
Pandemics cause quick and huge changes in patient numbers.
The COVID-19 pandemic showed how old ways of planning patient demand did not work well.
Before the pandemic, healthcare groups made forecasts once a year, which was not enough when things changed fast.
This forced providers to react quickly to too many patients and staff shortages, which caused long wait times.
For example, MDLIVE for Cigna, a large telehealth provider serving over 62 million people, used to make patient demand forecasts only annually.
When COVID-19 started, patient visits rose more than 25 percent in 2022 compared to 2021.
This rise overwhelmed their resources and made yearly forecasts useless.
Wait times got longer because it was hard to balance provider workloads with changing demand.
Providers were also delayed by long credential checks that took 90 to 120 days, slowing staff increases.
At the same time, seasonal illnesses happened along with COVID-19, creating overlapping spikes in patient needs.
This made it hard to plan how many patients could be handled, which lowered care quality.
Money problems also happen during unexpected demand increases.
MDLIVE for Cigna spent about $1 million each busy season on payments to encourage providers to be available.
Without good patient demand forecasts, these payments were needed to keep enough staff.
High patient numbers caused problems like scheduling troubles, more paperwork, and risk of provider burnout.
When demand does not match supply, patients wait longer and care is delayed, which can make health worse.
These problems reflect bigger issues in the U.S. healthcare system.
The Kaiser Family Foundation says almost 44 percent of adults have trouble paying for healthcare, and 36 percent skip or delay care.
During pandemics, delayed care risks grow because resources are stretched and demand is high, hurting patients’ health more.
Supply chain problems are another big challenge during pandemics.
COVID-19 showed weaknesses like shortages of medical supplies and equipment.
Sudden high demand hit supply problems, making inventory hard to manage.
Researchers Ying Guo and Fang Liu point to the need for strong supply chains using smart inventory methods.
These include stockpiling needed supplies early, using many suppliers, making capacity agreements, and flexible contracts.
Supply-side problems involve supplier reliability and costs, while demand-side issues involve unpredictable patient needs.
Healthcare leaders must find a balance between having enough stock and avoiding waste, while quickly changing buying and distribution to meet actual demand.
Pandemics make fast response supply systems urgent to prevent running out of items or having too many.
Good supply management helps keep patient care safe and steady.
To handle these issues, healthcare groups use better forecasting with machine learning and AI.
MDLIVE for Cigna worked with AIDAN Health to create forecasts weekly and monthly at the state level.
Using Microsoft Azure Machine Learning, they made models that include seasonal illnesses, social factors like unemployment, and pandemic trends.
Moving from yearly to frequent forecasts lets them check provider numbers often, add staff if needed, and quickly change workflows.
This cut patient wait times by over 50 percent.
Patients now wait about 20 minutes for a call after asking to see a doctor, much faster than before.
Good AI forecasts also saved money.
MDLIVE stopped spending a lot on payments to get providers, saving about $1 million each busy season.
This example shows how other healthcare groups can handle many patients during uncertain times.
Financial problems for patients make demand management harder.
Worries about medical bills cause patients to skip or delay care.
The Kaiser Family Foundation found that 82 percent of uninsured adults under 65 have trouble paying for healthcare.
Nearly 75 percent of uninsured adults skipped or delayed needed care in a year because of cost.
This can make health problems worse and increase healthcare demand during busy times.
Administrators need to consider how money issues affect patient numbers and service use when planning resources.
AI and automation tools are important for healthcare groups dealing with changing demand.
AI-driven phone systems help handle patient communications, cut missed calls, and improve scheduling.
Simbo AI is a company working on AI phone systems to help providers improve patient access and manage calls.
The system uses language processing and machine learning to answer common questions, sort appointment requests, and support scheduling in real time.
This automation reduces front-office workload and helps patients get timely replies even when demand is high.
By automating simple tasks, staff can focus more on complex care.
This cuts wait times, improves patient satisfaction, and reduces mistakes from tired staff.
Combining AI forecasting with workflow automation creates a connected system that predicts demand and manages operations.
For example, weekly demand forecasts can link to automated scheduling to adjust provider numbers, appointment slots, and resource use as needed.
Healthcare IT managers ensure these systems work well together while keeping patient data private and following rules.
With telehealth growing, adding AI automation helps meet higher patient needs during uncertain times.
Healthcare groups in the U.S. face many problems managing patient demand during events like pandemics.
The COVID-19 crisis showed weaknesses in old forecasting, supply chains, and workflows, causing longer waits, higher costs, and stretched resources.
New AI and machine learning tools help improve forecasting, supply management, and workflow automation.
MDLIVE for Cigna shows how these reduce wait times, balance provider work, and save money.
For medical administrators, owners, and IT managers, using these approaches is important to handle changing healthcare needs well, keep care quality, and improve patient experiences during uncertain times.
Adding AI automation like Simbo AI’s tools helps patients get care faster and lowers administrative work.
By preparing for demand spikes with data and modern technology, healthcare groups can handle hard challenges better, run more smoothly, and support healthier communities across the country.
The primary goal is to develop accurate forecasts of patient demand to reduce wait times and balance workloads for medical professionals, ultimately improving patient care.
MDLIVE for Cigna cut patient wait times by more than 50 percent through improved forecasting.
MDLIVE utilized Azure Machine Learning to create many models forecasting solution.
MDLIVE faced unpredictable demand, overwhelmed workloads for medical professionals, and difficulties in hiring due to credential verification and licensing requirements.
Previously, MDLIVE generated patient demand forecasts only once a year.
AIDAN considered seasonal illness patterns, unemployment rates, and other socio-economic factors affecting healthcare usage.
MDLIVE improved provider availability without needing to offer monetary incentives, saving about $1 million each busy season.
In November 2022, MDLIVE served 40,000 more patients than in November 2021, demonstrating increased capacity.
Accurate forecasting allows effective operation management by predicting demand, ensuring service provision without overwhelming staff.
MDLIVE plans to continue refining its forecasting model with AIDAN to adapt to evolving patient demand and market trends.
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