Computable Phenotype Implementation for a National, Multicenter Pragmatic Clinical Trial: Lessons Learned From ADAPTABLE.

BACKGROUND: Many large-scale cardiovascular clinical trials are plagued with escalating costs and low enrollment. Implementing a computable phenotype, which is a set of executable algorithms, to identify a group of clinical characteristics derivable from electronic health records or administrative claims records, is essential to successful recruitment in large-scale pragmatic clinical trials. This methods paper provides an overview of the development and implementation of a computable phenotype in ADAPTABLE (Aspirin Dosing: a Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness)-a pragmatic, randomized, open-label clinical trial testing the optimal dose of aspirin for secondary prevention of atherosclerotic cardiovascular disease events. METHODS AND RESULTS: A multidisciplinary team developed and tested the computable phenotype to identify adults ≥18 years of age with a history of atherosclerotic cardiovascular disease without safety concerns around using aspirin and meeting trial eligibility criteria. Using the computable phenotype, investigators identified over 650 000 potentially eligible patients from the 40 participating sites from Patient-Centered Outcomes Research Network-a network of Clinical Data Research Networks, Patient-Powered Research Networks, and Health Plan Research Networks. Leveraging diverse recruitment methods, sites enrolled 15 076 participants from April 2016 to June 2019. During the process of developing and implementing the ADAPTABLE computable phenotype, several key lessons were learned. The accuracy and utility of a computable phenotype are dependent on the quality of the source data, which can be variable even with a common data model. Local validation and modification were required based on site factors, such as recruitment strategies, data quality, and local coding patterns. Sustained collaboration among a diverse team of researchers is needed during computable phenotype development and implementation. CONCLUSIONS: The ADAPTABLE computable phenotype served as an efficient method to recruit patients in a multisite pragmatic clinical trial. This process of development and implementation will be informative for future large-scale, pragmatic clinical trials. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02697916.

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.

The effect of computerized provider order entry (CPOE) on ordering patterns for chest pain patients in the emergency department.

This study addressed the effect of CPOE implementation on chest pain ordering patterns for patients in the emergency department. Retrospective order data was collected to assess the implementation. 300 randomly selected, time matched patients with a chief complaint of chest pain were selected in a before/after study. Patient demographics, treatment and disposition data were collected on clinical orders. Order volume, completeness and completion times were assessed before and after implementation. Overall order volume increased significantly from 11.6 pre-CPOE to 19.9 post-implementation (p<.01). Order documentation deficiencies were noted pre-implementation with 35.6% containing all order elements. Order completion times were unchanged; however, laboratory completion times increased for admitted patients post-implementation. Order volume increased after CPOE implementation, likely due to improved ED-based admission order capture for admitted patients. Order completeness improved significantly including standing order documentation. Overall, CPOE implementation is associated with improved clinical documentation with limited effect on clinical testing turn-around times.

Prospective evaluation of a closed-loop, computerized reminder system for pneumococcal vaccination in the emergency department.

The Emergency Department is a suitable but challenging environment to implement a sustainable pneumococcal vaccination program. To increase vaccination rates for patients > or equal to 65 years old, we prospectively evaluated a closed-loop informatics approach over a 6-week study period. Among the 572 candidate patients, 284 were up-to-date with vaccination, 187 patients refused vaccination, 65 physicians declined to order the vaccine, and 28 patients received the vaccine during the ED visit. The informatics approach increased vaccination rate from a baseline of 49.8% to 54.9% (p < 0.01).