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.

No abstract

Summary 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. Citation: Culbertson A, Goel S, Madden MB, Jackson KL, Carton T, Waitman R, Liu M, Krishnamurthy A, Hall L, Cappella N, Visweswaran S, Safaeinili N, Becich MJ, Applegate R, Bernstam E, Rothman R, Matheny M, Lipori G, Bian J, Hogan W, Bell D, Martin A, Grannis S, Klann J, Sutphen R, O’Hara AB, Kho A. The building blocks of interoperability: A multisite analysis of patient demographic attributes available for matching. Appl Clin Inform 2017; 8: 322–336 https://doi.org/10.4338/ACI-2016-11-RA-0196

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.

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.