One of the big advances in modern medicine is the electronic health record (EHR). If we could tap their full potential, they will make it easier for patients to track their own health (through patient portals) and give healthcare professionals quick access to a patient’s entire medical history. And then there’s the “Big Data” benefit; as more electronic data becomes available, it can be mined using artificial intelligence (AI) tools to discover new connections between disease and treatments and other patient factors. This could rapidly lead to more effective treatments that save money and lives. On the other hand, physicians have made it clear that too often they spend more time looking at a computer than at the patient. These are not the only growing pains that we’re encountering with the broad adoption of EHR, however.
The Pew Charitable Trusts recently published a report highlighting the problems in matching electronic records to the correct patient. As cited in the report, a 2012 survey found that one in five hospital Chief Information Officers (CIOs) reported that patients had been harmed due to patient record matching errors during the prior year. Even within a single healthcare facility, up to 20% of all patients are not matched to all of their records. When sharing data between organizations, half of all patient records may not be matched correctly. This problem is made more difficult due to the fact that it is not easy to determine all the instances where information is matched to the wrong patient, or where correct information fails to be matched with the right patient.
According to the report, some of the problems leading to matching errors include lack of standardization across systems, data entry errors (such as transposed birth dates), missing data, fields set to default values that are not corrected, and information changes (such as a new address after a move, or a name change after a marriage or divorce). Some patients may have identical information, as would be the case of twin children living in the same home: same birth date, same parents, same address. Also, people with common names are more likely to have a “double” in the system with the same birth date. The report cites the case of patients in the Houston area named “Maria Garcia,” of the 2,488 records, 231 shared the same birthday. Some of these separate records may refer to the same individual, making it difficult to know which Maria is your patient.
The Pew report explores four different strategies to improve this situation. A unique identifier — such as a national health ID — could help, though the current Social Security Number is not all that reliable. Instead, a system that relies on individual biometric data might be better as a unique way to identify an individual. Another approach would be to involve the patient in the matching process, which might be similar to how individuals can request changes to incorrect data in their credit records. Better standardization of the personally-identifying information could help, such as only taking middle names instead of middle initials. The fourth approach would be to “triangulate” by enlisting data from outside healthcare. For example, a credit card number or bank account could provide more accurate matching.
As the report details, none of these solutions are perfect. Pew gathered a panel more than 20 experts on patient data matching to explore the problems. One conclusion was that a single organization should be charged with coming up with a workable solution, including standards and technology. The report also explores the four different areas for improvement in depth. If you are concerned about healthcare data, privacy and security concerns, and improving the efficiency and effectiveness of patient care, this report is well worth the time invested to read it.