Lean Healthcare: Identifying 'Easy Target’ to Improving Referral Management Efficiency at a Midsized Urban Hospital: Digital Blindness

Introduction/Objectives: Systemic operational inefficiencies in health systems can threaten patient care; therefore, health systems are motivated to identify common barriers that are quick to resolve. In a study focused on reducing referral processing wait times, common systemic barriers to efficient referral, prior authorization (PA) management and scheduling were revealed. Their magnitude of burden to overall process efficiency was measured and compared. Of particular interest was measuring the burden from digital blindness (DB), a condition describing when frontline workers are blind to information needed to coordinate patient care optimally because the information is not visible on their software systems or access approved user interface.

Methods: A LEAN healthcare team commissioned by a midsized urban hospital investigated root causes to delays in the referral management processes. Eight osteopathic medical students and one graduate student were trained in LEAN methods via lecture and simulation. The team observed and interviewed staff, gathered insights and current protocols of related departments, and created a list of all process delays. Using systemic barrier typologies from Erdmann, et al 2024, the team identified which common barrier types contributed to each delay on the list. The team calculated the burden of each type by dividing each barrier’s unique contribution frequency by total number of delays.

Results: Process mapping identified four relevant departments to referral management - Admissions Scheduling, Admissions Authorizations, Outpatient Rehabilitation, and Interventional Radiology. Across departments, nine employee workflows were identified as directly contributing to 73 total delays. In total, 30.1% (22/73) delays were driven by ‘External System Influences,’ factors beyond the control of hospital staff or hospital administration purview such as insurance prior authorization processes. 69.9% of delays were ‘Internal Systemic Barriers.’ Internal barriers included technology-based and management-based barriers. Technology-based barriers 39.7% (29/73) were caused by DB (19.1%; 14/73) and technology system settings (20.5%; 15/73). Management contributed to PA delays in 61.6% (45/73) of cases, consisting of workforce shortage factors and delays caused by policies and procedures. IT staff identified DB as the simplest issue to resolve, after implementation, simple software ‘fixes’ immediately improved workflows and interdepartmental collaboration.

Conclusions: While referral management is plagued with external influences, most delays originated from within the hospital system. While management-based barriers such as training lapses and insufficient workforce contributed to more delays, the easier-to-resolve technology-based barriers contributed to 40% wherein system settings hindered staff from managing work queues optimally and DB hindered staff by limiting their knowledge of referrals’ true status. Addressing DB can be a low effort solution with significant impacts that exponentially improves outpatient referral and PA outflow times.