When the patients and their medical records do not match, it causes a multitude of problems. Patients are affected financially and sometimes even physically, whereas these patient matching errors hamper the ratings and financial performance of healthcare providers.
Let’s look at a story
This incident took place back in 2016. A patient’s completely healthy kidney was removed because of medical record mixup. What this means is that the patient’s record got mixed up with another patient who required kidney removal due to a tumor. In this case, their records got mixed up because they shared a common name. This gathered a lot of attention back at that time. However, most people thought that it was a rare case. They are wrong.
Patient misidentification occurs almost daily across the US healthcare system. These happen because duplicate medical records and overlays are quite common. But why do they happen? Let’s see.
Source: Pixabay
For instance, a healthcare system has more than thousands of records within its EHR system. When a patient goes for treatment, a hospital staff tries to find the medical record of the patient. However, since there can be multiple patients with the name “Sam Waterson,” it becomes quite challenging for the staff to find the accurate patient record. The staff either assumes that he/she found the correct medical record after asking some questions like address, date of birth, and others or else creates an entirely new medical record, generating another duplicate medical record. In both cases, it is perilous. An electronic health record contains a patient’s medical history, medications, results, and other crucial information. Thus, mixing up one patient’s record with another one is quite fatal, since they will receive treatment which someone else should get. Not only that, but they will also receive medical bills which they are not supposed to get. These generate denied claims for hospitals.
These are the most common issues of patient matching errors, and these have been occurring for years. Reputed organizations in the healthcare industry, such as the ECRI Insitute, the Joint Commission, as well as others, have brought up patient misidentification several times.
However, one of the most problematic errors is data overlay. It occurs when two patients of the same or similar name get a merged record. This is extremely dangerous for both the patients, as both will receive the wrong treatments. All these lead to dirty data as well, which will make it quite impossible to identify the correct patient and provide proper treatment.
Source: Pixabay
According to Black Book Market Research, in any given health system, one out of five patient records are duplicates. Likewise, if a health system tries to share a patient’s medical record with another facility, even if they share the same EHR system, there is only a 50 percent chance that the record will match with the patient.
All these lead to patient safety issues, higher costs for both patients as well as healthcare providers, and lower ratings for health systems. Many lives have been lost due to patient misidentification, and there seems to be no end to it. Hospitals are hit hard. The revenue cycle is adversely affected, denied claims are generated, and all these cause millions in losses.
However, health systems like Terrebonne General Medical Center and Northwell Health are accurately identifying their patients and are ensuring that they do not face any of the issues mentioned earlier.
How are they doing that correctly?
They are using RightPatient- a biometric patient identification platform. It locks the patient records with their biometric data so that a third person cannot come and claim their record- eliminating any chance of medical identity theft. RightPatient also ensures patient safety as the correct patient is identified every time- the patient only needs to register his/her biometric data once with the health system- it is that easy!
Hospitals are improving their revenue cycle, enhancing patient safety and saving millions in the process, generating clean data, and preventing medical record overlays in the process.
