Evolving availability and standardization of patient attributes for matching.

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States.

Push Button Population Health: The SMART/HL7 FHIR Bulk Data Access Application Programming Interface.

The 21st Century Cures Act requires that certified health information technology have an application programming interface (API) giving access to all data elements of a patient's electronic health record, "without special effort". In the spring of 2020, the Office of the National Coordinator of Health Information Technology (ONC) published a rule-21st Century Cures Act Interoperability, Information Blocking, and the ONC Health IT Certification Program-regulating the API requirement along with protections against information blocking. The rule specifies the SMART/HL7 FHIR Bulk Data Access API, which enables access to patient-level data across a patient population, supporting myriad use cases across healthcare, research, and public health ecosystems. The API enables "push button population health" in that core data elements can readily and standardly be extracted from electronic health records, enabling local, regional, and national-scale data-driven innovation.

Towards a systems level analysis of health and nutrition.

Although theoretical systems analysis has been available for over half a century, the recent advent of omic high-throughput analytical platforms along with the integration of individual tools and technologies has given rise to the field of modern systems biology. Coupled with information technology, bioinformatics, knowledge management and powerful mathematical models, systems biology has opened up new vistas in our understanding of complex biological systems. Currently there are two distinct approaches that include the inductively driven computational systems biology (bottom-up approach) and the deductive data-driven top-down analysis. Such approaches offer enormous potential in the elucidation of disease as well as defining key pathways and networks involved in optimal human health and nutrition. The tools and technologies now available in systems biology analyses offer exciting opportunities to develop the emerging areas of personalized medicine and individual nutritional profiling.

The Building Blocks of Interoperability. A Multisite Analysis of Patient Demographic Attributes Available for Matching.

BACKGROUND: Patient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching. OBJECTIVES: We sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites. METHODS: We compiled a list of 36 demographic elements that stakeholders previously identified as essential patient demographic attributes that should be collected for the purpose of linking patient records. We studied a convenience sample of 9 health care systems from geographically distinct sites around the country. We identified changes in the availability of individual patient demographic attributes over time and across clinical sites. RESULTS: Several attributes were consistently available over the study period (2005-2014) including last name (99.96%), first name (99.95%), date of birth (98.82%), gender/sex (99.73%), postal code (94.71%), and full street address (94.65%). Other attributes changed significantly from 2005-2014: Social security number (SSN) availability declined from 83.3% to 50.44% (p<0.0001). Email address availability increased from 8.94% up to 54% availability (p<0.0001). Work phone number increased from 20.61% to 52.33% (p<0.0001). CONCLUSIONS: Overall, first name, last name, date of birth, gender/sex and address were widely collected across institutional sites and over time. Availability of emerging attributes such as email and phone numbers are increasing while SSN use is declining. Understanding the relative availability of patient attributes can inform strategies for optimal matching in healthcare.

Semantic processing to identify adverse drug event information from black box warnings.

Adverse drug events account for two million combined injuries, hospitalizations, or deaths each year. Furthermore, there are few comprehensive, up-to-date, and free sources of drug information. Clinical decision support systems may significantly mitigate the number of adverse drug events. However, these systems depend on up-to-date, comprehensive, and codified data to serve as input. The DailyMed website, a resource managed by the FDA and NLM, contains all currently approved drugs. We used a semantic natural language processing approach that successfully extracted information for adverse drug events, at-risk conditions, and susceptible populations from black box warning labels on this site. The precision, recall, and F-score were, 94%, 52%, 0.67 for adverse drug events; 80%, 53%, and 0.64 for conditions; and 95%, 44%, 0.61 for populations. Overall performance was 90% precision, 51% recall, and 0.65 F-Score. Information extracted can be stored in a structured format and may support clinical decision support systems.

Semantic processing to identify adverse drug event information from black box warnings.

We utilized a semantic natural language processing approach to extract adverse drug event information from FDA black box warnings. Overall performance was 90% precision, 51% recall, and 0.65 F-Score. Information extracted can be stored in a structured format and may be useful to support clinical decision support systems.