How to Conquer the Health Care “Staffing Trap” with AI

Updated on May 15, 2023
Artificial intelligence, Healthcare, Robots in Healthcare, Healthcare Technology

The health care industry is battling a seemingly insurmountable staffing crisis. Demand for service continues to outpace the workforce’s capacity. So, it’s vital to understand what factors are contributing to this problem, and to explore some innovative solutions. 

What’s driving the exploding demand for care are an aging population, an increase in chronic diseases, and insufficient preventive care, thanks in part to abysmal patient engagement. And as demand is rising, staffing levels are plummeting. Health care workers are fleeing the industry due to a complex set of factors, including burnout, low pay, chaotic workplace environments, low-morale, and long training times. 

According to Medical Economics, staffing is the number one concern for health care providers. And a problem with so many driving factors is going to require a multifaceted effort to solve.

Health care complexity 

Health care’s unique complexity makes it difficult for organizations to maintain optimal staffing levels. Variations in practice, providers, and patients all contribute to the challenge of scheduling and resource allocation – and these challenges in turn create staffing difficulties.

Practice variations are differences in each practice’s services and in the demographics they serve. Location A, for example, may cater to a wealthier population and not accept Medicare, while Location B serves a poorer area and takes Medicare for specific conditions and procedures. Location C, situated in a highly competitive area, accommodates a broad range of patients with an attached lab and radiology facility, but it faces accessibility challenges for disabled patients.

Provider variations describe the distinct preferences and specialties of each provider, and this further complicates operations. For example, one provider may treat bilateral issues and serve more male than female patients. Another provider may only see adult women and not accept appointments with new patients on Friday afternoons. 

Finally, there are patient variations. Every patient carries unique medical histories, socioeconomic circumstances, family situations, insurance, and cultural practices, all of which adds yet another layer of complexity to the health care staffing puzzle.

Tribal knowledge

To manage this complexity, many health care organizations rely on an intricate web of tribal knowledge: the collective, unwritten knowledge and experience accumulated by a group of people within an organization over time. This knowledge is often shared informally through personal interactions, conversations, and observations and is rarely if ever documented.

In the context of health care, tribal knowledge might include insights about provider preferences, patient needs, and scheduling nuances that are not officially recorded, but are understood and utilized by staff members to manage complex situations. While tribal knowledge can be valuable, an overreliance can lead to inefficiencies, errors, and difficulties transferring information, particularly when experienced staff members leave or change roles.

Learning and using tribal knowledge requires extensive training and laborious processes, and these lead to burnout and turnover. Organizations that rely on tribal knowledge inevitably get stuck in the “staffing trap”: They can’t offer adequate service and care with their current staff, and they can’t afford to hire and train new staff.

How AI can help

Artificial intelligence helps providers reduce or eliminate their dependence on tribal knowledge, which in turn allows them to escape the staffing trap.

For instance, many health care organizations rely on some variation of tribal knowledge to schedule appointments and to communicate and engage with patients. When organizations automate scheduling and communication processes with AI and natural language processing (NLP), this relieves an enormous staffing burden from health care organizations’ shoulders because they need not train new staff with intricate tribal knowledge. Also their current staff are less likely to burn out and quit because their jobs are easier and more satisfying.

Before implementing automation, providers must first create a standardized, universal set of procedures and guidelines that is available to all employees in digital format and can be accessed in real-time. This “master doc” ensures consistency, clarity, and reliability of all operational processes.

The first step is to define standard formats for each task. Then, create standardized instructions for every conceivable scenario, patient interaction, and clinical decision: scripting, information to look up, escalations, etc. Include visual aids and queues such as colors, images, and screenshots. Eliminate as many possible redundancies and excesses from these steps as possible to ensure optimal efficiency. 

Finally, solicit feedback from staffers during the entire process, and incorporate the results to improve quality. When staff members have a comprehensive and intuitive digital guidebook, they are more likely to make optimal decisions for each situation, which engenders better outcomes.

To streamline communication between patients and providers, health care organizations should also utilize NLP-powered chatbots, which allow patients to get instant answers to common questions. The overlying AI system can then route more complicated inquiries to appropriate staff members.

Improving patient engagement also relieves staffing burdens. Organizations can optimize engagement by automating appointment reminders rather than manually calling each patient. Follow-up care information, patient educational outreach, and other engagement efforts should also be automated.

AI can also analyze vast amounts of data – quickly – which removes the need for staff to manually conduct research. This means health care organizations gain valuable insights regarding diagnostics and treatment plans, all without hiring additional staff.

The most important piece of the AI staffing puzzle is integration. To maximize the potential of AI and other technologies, health care organizations must integrate these tools with their EHR system, which engenders smooth interoperability and creates a symbiotic relationship that benefits both the AI software vendor and the EHR platform. EHR vendors, in particular, need to foster collaboration by opening APIs to their data. When AI and EHRs achieve interoperability, it’s a win for everyone: providers, staff, and patients.

Let’s modernize health care

It’s time for health care organizations to evolve beyond tribal knowledge. The key to solving the staffing crisis lies in standardization and automation, which reduce or eliminate time-consuming and personnel-heavy workloads. To cure the staffing crisis, lower the per-capita cost of health care, and create exceptional care, every stakeholder in the industry must be willing to transform.