ABSTRACT
OBJECTIVE: This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets. MATERIALS AND METHODS: We used 4 manually reviewed datasets, containing a random selection of matches and nonmatches. Matching datasets included health information exchange (HIE) records, public health registry records, Social Security Death Master File records, and newborn screening records. Standardized fields including last name, telephone number, social security number, date of birth, and address. Matching performance was evaluated using 4 metrics: sensitivity, specificity, positive predictive value, and accuracy. RESULTS: Standardizing address was independently associated with improved matching sensitivities for both the public health and HIE datasets of approximately 0.6% and 4.5%. Overall accuracy was unchanged for both datasets due to reduced match specificity. We observed no similar impact for address standardization in the death master file dataset. Standardizing last name yielded improved matching sensitivity of 0.6% for the HIE dataset, while overall accuracy remained the same due to a decrease in match specificity. We noted no similar impact for other datasets. Standardizing other individual fields (telephone, date of birth, or social security number) showed no matching improvements. As standardizing address and last name improved matching sensitivity, we examined the combined effect of address and last name standardization, which showed that standardization improved sensitivity from 81.3% to 91.6% for the HIE dataset. CONCLUSIONS: Data standardization can improve match rates, thus ensuring that patients and clinicians have better data on which to make decisions to enhance care quality and safety.
Subject(s)
Datasets as Topic/standards , Health Information Interoperability , Data Management/standards , Demography , Health Information Exchange , Humans , Infant, Newborn , Neonatal Screening , Public Health , Registries , Social Security , United StatesABSTRACT
Pediatric populations are uniquely vulnerable to the usability and safety challenges of electronic health records (EHRs), particularly those related to medication, yet little is known about the specific issues contributing to hazards. To understand specific usability issues and medication errors in the care of children, we analyzed 9,000 patient safety reports, made in the period 2012-17, from three different health care institutions that were likely related to EHR use. Of the 9,000 reports, 3,243 (36 percent) had a usability issue that contributed to the medication event, and 609 (18.8 percent) of the 3,243 might have resulted in patient harm. The general pattern of usability challenges and medication errors were the same across the three sites. The most common usability challenges were associated with system feedback and the visual display. The most common medication error was improper dosing.
Subject(s)
Electronic Health Records/standards , Medication Errors/statistics & numerical data , Patient Safety , Pediatrics , User-Computer Interface , Child , Health Information Interoperability , Humans , Medication Errors/adverse effectsABSTRACT
When patients lack sufficient treatment options for serious medical conditions, they rely on the prompt approval and development of new therapeutic alternatives, such as medical devices. Understanding the development of innovative medical devices, including the characteristics of premarket clinical trials and length of Food and Drug Administration (FDA) review, can help identify ways to expedite patient access to novel technologies and inform recent efforts by FDA to more quickly get these products to patients and physicians. We analyzed publicly available information on clinical trials and premarket FDA review for innovative medical devices that fill an unmet medical need. In this first-of-its-kind study focusing on these products, we extracted data on the length of the pivotal trials, primary study endpoint and FDA review; number of patients enrolled in trials; and in what country the device was available first. We identified 27 approved priority review devices from January 2006 through August 2013. The median duration of pivotal clinical trials was 3 years, ranging from 3 months to approximately 7 years. Trials had a median primary outcome measure evaluation time of one year and a median enrollment of 297 patients. The median FDA review time was 1 year and 3 months. Most priority review devices were available abroad before they were approved in the United States. Our study indicates that addressing the length of clinical studies--and contributing factors, such as primary outcome measures and enrollment--could expedite patient access to innovative medical devices. FDA, manufacturers, Congress and other stakeholders should identify the contributing factors to the length of clinical development, and implement appropriate reforms to address those issues.