Providers Do Not Verify Patient Identity during Computer Order Entry (original) (raw)
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Patient Identification Errors in the Hospital Setting: A Prospective Observational Study
Al-Rafidain Journal of Medical Sciences ( ISSN: 2789-3219 )
Background and aim: In every aspect of clinical care, including diagnostic testing and medication administration, patient identification (ID) errors can disrupt care and harm patients. The study assesses healthcare workers' frequency and accuracy in verifying patient identity before performing common tasks and develops a proposed patient verifying identity program clinical model to meet the training needs of healthcare workers. Methods: An observational cross-sectional study was conducted at Al-Najaf Teaching Hospital from March to May 2022. Data collection was done through a questionnaire and participatory observation. Hundred-forty healthcare workers participated in the study: 52 clinic doctors, 27 registered nurses, 34 information officers, 17 pharmacists, 4 radiologists, and 6 outpatient hospital doctors. Results: The majority of participants (62.1%) were female. The majority of the 40 participants (28.6%) worked in the operating room. Furthermore, the majority of the partic...
Patient Identification Errors Are Common in a Simulated Setting
Annals of Emergency Medicine, 2010
The study included prospective, simulated patient scenarios with an eye-tracking device that showed where the health care workers looked. Simulations involved nurses administering an intravenous medication, technicians labeling a blood specimen, and clerks applying an identity band. Participants were asked to perform their assigned task on 3 simulated patients, and the third patient had a different date of birth and medical record number than the identity information on the artifact label specific to the health care workers' task. Health care workers were unaware that the focus of the study was patient identity.
Patient identification errors: The detective in the laboratory
Clinical Biochemistry, 2013
Background: The eradication of errors regarding patients' identification is one of the main goals for safety improvement. As clinical laboratory intervenes in 70% of clinical decisions, laboratory safety is crucial in patient safety. We studied the number of Laboratory Information System (LIS) demographic data errors registered in our laboratory during one year. Methods: The laboratory attends a variety of inpatients and outpatients. The demographic data of outpatients is registered in the LIS, when they present to the laboratory front desk. The requests from the primary care centers (PCC) are made electronically by the general practitioner. A manual step is always done at the PCC to conciliate the patient identification number in the electronic request with the one in the LIS. Manual registration is done through hospital information system demographic data capture when patient's medical record number is registered in LIS. Laboratory report is always sent out electronically to the patient's electronic medical record. Daily, every demographic data in LIS is manually compared to the request form to detect potential errors. Results: Fewer errors were committed when electronic order was used. There was great error variability between PCC when using the electronic order. Conclusions: LIS demographic data manual registration errors depended on patient origin and test requesting method. Even when using the electronic approach, errors were detected. There was a great variability between PCC even when using this electronic modality; this suggests that the number of errors is still dependent on the personnel in charge of the technology.
Pediatrics, 2012
OBJECTIVE: To determine whether an order verification screen, including a patient photograph, is an effective strategy for reducing the risk that providers will place orders in an unintended patient’s electronic medical record (EMR). METHODS: We describe several changes to the EMR/provider interface and ordering workflow that were implemented as one part of a hospital-wide quality improvement effort to improve patient identification and verification practices. We measured the impact by comparing the number of reported incidents of care being provided to any patient other than for whom it was intended before the intervention, and directly after the intervention. RESULTS: For the year before the interventions described herein, placement of orders in the incorrect patient’s chart was the second most common cause of care being provided to the wrong patient, comprising 24% of the reported errors. In the 15 months after the implementation of an order verification screen with the patient’s...
BMJ quality & safety, 2013
OBJECTIVE: To quantify the percentage of records with matching identifiers as an indicator for duplicate or potentially duplicate patient records in electronic health records in five different healthcare organisations, describe the patient safety issues that may arise, and present solutions for managing duplicate records or records with matching identifiers. METHODS: For each institution, we retrieved deidentified counts of records with an exact match of patient first and last names and dates of birth and determined the number of patient records existing for the top 250 most frequently occurring first and last name pairs. We also identified methods for managing duplicate records or records with matching identifiers, reporting the adoption rate of each across institutions. RESULTS: The occurrence of matching first and last name in two or more individuals ranged from 16.49% to 40.66% of records; inclusion of date of birth reduced the rates to range from 0.16% to 15.47%. The number of records existing for the most frequently occurring name at each site ranged from 41 to 2552. Institutions varied widely in the methods they implemented for preventing, detecting and removing duplicate records, and mitigating resulting errors. CONCLUSIONS: The percentage of records having matching patient identifiers is high in several organisations, indicating that the rate of duplicate records or records may also be high. Further efforts are necessary to improve management of duplicate records or records with matching identifiers and minimise the risk for patient harm.
JAMA, 2019
Recommendations in the United States suggest limiting the number of patient records displayed in an electronic health record (EHR) to 1 at a time, although little evidence supports this recommendation. OBJECTIVE To assess the risk of wrong-patient orders in an EHR configuration limiting clinicians to 1 record vs allowing up to 4 records opened concurrently. This randomized clinical trial included 3356 clinicians at a large health system in New York and was conducted from October 2015 to April 2017 in emergency department, inpatient, and outpatient settings. INTERVENTIONS Clinicians were randomly assigned in a 1:1 ratio to an EHR configuration limiting to 1 patient record open at a time (restricted; n = 1669) or allowing up to 4 records open concurrently (unrestricted; n = 1687). The unit of analysis was the order session, a series of orders placed by a clinician for a single patient. The primary outcome was order sessions that included 1 or more wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder measure (an electronic query that identifies orders placed for a patient, retracted, and then reordered shortly thereafter by the same clinician for a different patient). RESULTS Among the 3356 clinicians who were randomized (mean [SD] age, 43.1 [12.5] years; mean [SD] experience at study site, 6.5 [6.0] years; 1894 females [56.4%]), all provided order data and were included in the analysis. The study included 12 140 298 orders, in 4 486 631 order sessions, placed for 543 490 patients. There was no significant difference in wrong-patient order sessions per 100 000 in the restricted vs unrestricted group, respectively, overall (90.7 vs 88.0; odds ratio [OR], 1.03 [95% CI, 0.90-1.20]; P = .60) or in any setting (ED: 157.8 vs 161.3, OR, 1.00 [95% CI, 0.83-1.20], P = .96; inpatient: 185.6 vs 185.1, OR, 0.99 [95% CI, 0.89-1.11]; P = .86; or outpatient: 7.9 vs 8.2, OR, 0.94 [95% CI, 0.70-1.28], P = .71). The effect did not differ among settings (P for interaction = .99). In the unrestricted group overall, 66.2% of the order sessions were completed with 1 record open, including 34.5% of ED, 53.7% of inpatient, and 83.4% of outpatient order sessions. A strategy that limited clinicians to 1 EHR patient record open compared with a strategy that allowed up to 4 records open concurrently did not reduce the proportion of wrong-patient order errors. However, clinicians in the unrestricted group placed most orders with a single record open, limiting the power of the study to determine whether reducing the number of records open when placing orders reduces the risk of wrong-patient order errors.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2012
Computerized physician order entry (CPOE) systems can create unintended consequences. These include medication errors and adverse drug events. We look at a less understood error; patient misidentification. First, two email surveys were used to establish potential risk factors for this error. Next, an automated detection trigger was designed and validated with inpatient medication orders at a large pediatric hospital. The incidence was 0.064% per medication ordered. Finally, a case-control study identified the following as significant risk factors on multivariate analysis: patient age, last name spelling, bed proximity, medical service, time/date of order, and ordering intensity. These results can be used to improve patient safety by increasing awareness of high risk situations and guiding future research.