The changing nature of patient attributes available for matching

ObjectivesPatient matching rates between organizations can be as low as fifty percent. Challenges to matching include the variation in quality and availability of patient attributes. Here we describe the changing nature of patient attributes available over the past 11-years across a diversity of care settings in the United States. ApproachOur expert panel identified 64 patient attributes that are currently used or could potentially be candidates for patient matching. We identified a national sample of 14 health care sites who sent us aggregated information on the 64 patient attributes from 2010 to 2020 (inclusive). The information included overall counts and percent availability, overall counts and percent availability by race, and counts and availability by year. Only patients having at least one visit to the site since 2010 and who were between 18 and 89 years of age at time of extraction were included. ResultsThe aggregated results revealed that first name, last name, gender, postal codes, and date of birth are highly available (>90%) across healthcare organizations and time.  Patient reported social security number, work phone number, and emergency contact declined markedly, potentially reflecting privacy concerns.  Email addresses (from 18.0% to 63.7%) and phone numbers (from 14.7% to 69.4%) increased greatly over the past 11 years. Novel patient matching attributes such as blood type, facial image, thumb print, or eye color are rarely collected across sites for all years. We observed emerging attributes including sexuality, occupation, and nickname with a small number of sites collecting these over 70%, reflecting the feasibility of wider adoption in the future. ConclusionIn this study, we examined the availability of 64 patient attributes across 14 sites from 2010 and 2020. Our findings could inform policy makers and readers about patient attributes that are used for current patient matching and emerging data attributes that could be considered for incorporation into future matching algorithms.

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.

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.

Ethics and Privacy Considerations for Systems Biology Applications in Predictive and Personalized Medicine

Integrative analysis and modeling of the omics data using systems biology have led to growing interests in the development of predictive and personalized medicine. Personalized medicine enables future physicians to prescribe the right drug to the right patient at the right dosage, by helping them link each patient’s genotype to their specific disease conditions. This chapter shares technological, ethical, and social perspectives on emerging personalized medicine applications. First, it examines the history and research trends of pharmacogenomics, systems biology, and personalized medicine. Next, it presents bioethical concerns that arise from dealing with the increasing accumulation of biological samples in many biobanking projects today. Lastly, the chapter describes growing concerns over patient privacy when large amount of individuals’ genetic data and clinical data are managed electronically and accessible online.