An integrated digital/clinical approach to smoking cessation in lung cancer screening: study protocol for a randomized controlled trial.

BACKGROUND: Delivering effective tobacco dependence treatment that is feasible within lung cancer screening (LCS) programs is crucial for realizing the health benefits and cost savings of screening. Large-scale trials and systematic reviews have demonstrated that digital cessation interventions (i.e. web-based and text message) are effective, sustainable over the long-term, scalable, and cost-efficient. Use of digital technologies is commonplace among older adults, making this a feasible approach within LCS programs. Use of cessation treatment has been improved with models that proactively connect smokers to treatment rather than passive referrals. Proactive referral to cessation treatment has been advanced through healthcare systems changes such as modifying the electronic health record to automatically link smokers to treatment. METHODS: This study evaluates the impact of a proactive enrollment strategy that links LCS-eligible smokers with an evidence-based intervention comprised of a web-based (WEB) program and integrated text messaging (TXT) in a three-arm randomized trial with repeated measures at one, three, six, and 12 months post randomization. The primary outcome is biochemically confirmed abstinence at 12 months post randomization. We will randomize 1650 smokers who present for a clinical LCS to: (1) a usual care control condition (UC) which consists of Ask-Advise-Refer; (2) a digital (WEB + TXT) cessation intervention; or (3) a digital cessation intervention combined with tobacco treatment specialist (TTS) counseling (WEB + TXT + TTS). DISCUSSION: The scalability and sustainability of a digital intervention may represent the most cost-effective and feasible approach for LCS programs to proactively engage large numbers of smokers in effective cessation treatment. We will also evaluate the impact and cost-effectiveness of adding proven clinical intervention provided by a TTS. We expect that a combined digital/clinical intervention will yield higher quit rates than digital alone, but that it may not be as cost-effective or feasible for LCS programs to implement. This study is innovative in its use of interoperable, digital technologies to deliver a sustainable, scalable, high-impact cessation intervention and to facilitate its integration within clinical practice. It will add to the growing knowledge base about the overall effectiveness of digital interventions and their role in the healthcare delivery system. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03084835 . Registered on 9 March 2017.

Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.

RESEARCH OBJECTIVE: To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. MATERIALS AND METHODS: Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. RESULTS: Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria. CONCLUSIONS: End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.