ANTI-CRISIS MONITORING IN HEALTHCARE INSTITUTIONS: AN EARLY WARNING SYSTEM FOR MANAGEMENT ANOMALIES AND THE STOP–SCAN–ACT ALGORITHM - Grytsko, Roman; Grytsko, Orysia; Pekanets, Solomiia (Public Management and Policy, 2026)
The transformation of public administration in Ukraine under martial law requires a new quality of management of healthcare institutions. Financial instability (with up to 96.66% of public hospitals showing signs of financial difficulties), staff shortages, and reputational risks necessitate the implementation of effective early warning mechanisms. Empirical data confirm that 66–84% of critical events are preceded by detected warning signals 3–6 months before the turning point. The purpose of the study is to systematize “red flags” for healthcare institutions and to develop a single algorithm for managerial decision-making under conditions of incomplete information. As a result, a multi-domain register of 21 crisis indicators in five areas (finance, personnel, quality, reputation, operations) was developed and validated. The STOP–SCAN–ACT algorithm, adapted from the Identify–Isolate–Inform model, was proposed and tested. The implementation of the case study reduced staff turnover from 22% to 19%, stabilized accounts payable from 18% to 12% of the budget, and reduced negative media coverage from 4 to 1 publication within three months. In conclusion, early detection of red flags reduces the risk of an uncontrolled crisis by 60–70%. The STOP–SCAN–ACT algorithm appears to be effective for decision-making under uncertainty. Further research should focus on developing an integrated managerial health index similar to NEWS and investigating organizational barriers to signaling neglect (deviation normalization).