The immense improvements in the latest internet inventions encouraged the adaptation of technology within the healthcare sector. The healthcare systems storing highly sensitive information can be targeted by attackers aiming to insert, delete, or modify the data stored. These malicious activities may cause severe damage to the database accessibility and lead to catastrophic long-term harm to the patients’ health. The adaptation of the most advanced security paradigm does not guarantee full protection. It is possible that the attack is not directly detected. This highlights the need to assess the widespread damage scale before starting the repair of the inconsistent medical database. Within the scope of the damage assessment and recovery, several matrices-based, cluster-based, and graph-based models were introduced. The objective of this work is to correctly assess the damage and recover the database within a suitable time frame and efficient utilization of memory. We use a lightweight structure based on hash tables to gauge the incurred damage and recuperate quickly following an attack. The presented approach is contrasted with other existing ones and demonstrated superior performance.