a. Human capital management: Employees have a critical role in influencing patient outcomes and patient satisfaction. For instance, a nurse practitioner’s time is better spent with patients or training other employees than managing and tracking labor costs or staffing needs. To free up that time, using AI properly can uncover patterns within patient populations, schedule services, and discover seasonality effects to reveal optimal scheduling and staffing structures to meet patient needs.

b. Patient matching: To validate information such as patient history or eligibility, most healthcare organizations have more than one system or source of data (EHR, PHR, etc.) in place. Most healthcare organizations also struggle toggling between those different health IT systems. Validating this data – ensuring the data belongs to the same patient and is error-free – is a time-consuming but critical effort. Providing AI the rules and logic for automating patient matching and safeguarding data integrity does more than just prevent a rejected claim – it can help doctors, nurses and technicians avoid critical mistakes that can harm patients.

c. Revenue cycle management: For most healthcare provider organizations, regardless of size, revenue cycle management is a complex, disjointed process made up of different systems and multiple sources of data, each with unique variables. A successful healthcare organization needs to consistently capture accurate coding and charges, submit timely billings and claims, manage collections and post payment. AI – in this case RPA – can help streamline and execute repeatable processes, aggregate information while checking for errors, and present metric information in a way that allows human participants to manage the business efficiently.

d. Other back-office functions: The same challenges with collecting, manipulating and interpreting data across multiple systems happen in the back office. Continuously juggling between the CRM, ERP, and GL systems in between departments, from accounting and finance to contracting and managed care, leads to inefficient rework and ultimately lost revenue. Using AI to coordinate back-office data flow can help alleviate this challenge by enabling faster process execution and uncovering data insights, such as lease agreement overlap and staffing inefficiencies, that are impacting the business.

4. How to move forward
It is critical for healthcare operators to fully understand the technology options available, what each solution accomplishes, what hurdles may arise in sensitive clinical and non-clinical environments, and the initial and ongoing costs for the solution. AI solutions are increasingly affordable to most healthcare operators at all stages of the business life cycle, but it is not a one-size-fits-all solution.

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