Researcher Perceptions of a Self-Service Online Portal to Facilitate Volunteer Recruitment into Clinical Trials

Background Recruitment of volunteers is a major challenge for clinical trials. There has been increasing development and use of Internet-based portals in recruitment for clinical research. There has been little research on researcher use and perceptions of these portals. Objectives This study evaluated researcher perceptions of use of Rally, an Internet-based portal for clinical trial volunteer recruitment. Methods A cross-sectional survey was developed and implemented to understand researcher perceptions. From theoretical models of information technology use, the survey adopted items in four domains: ease of use, usefulness, facilitating conditions, and self-efficacy. The dependent variable was researchers' behavioral intention to use Rally. The survey captured characteristics of researchers such as gender, age, and role. It was implemented using the REDCap survey tool. An email invitation followed by three reminders was sent to researchers. A hierarchical regression model was applied to assess predictors of behavioral intention. Results The survey response rate was 35.6% (152 surveys received from 427 contacted researchers). In the hierarchical regression model, facilitating conditions and self-efficacy predicted behavioral intention (F (4,94) = 6.478; p <0.001). The model explained 21.6% of the variance in behavioral intention (R-square change = 21.3%, p <0.001). Conclusion Facilitating conditions and self-efficacy predicted researchers' behavioral intention to use Rally for volunteer recruitment into clinical trials. Future research should document best practices and strategies for enhancing researcher use of online portals for volunteer recruitment.

A Clinical Decision Support System Design Framework for Nursing Practice

Background As nurses increasingly engage in decision-making for patients, a unique opportunity exists to translate research into practice using clinical decision support systems (CDSSs). While research has shown that CDSS has led to improvements in patient outcomes and nursing workflow, the success rate of CDSS implementation in nursing is low. Further, the majority of CDSS for nursing are not designed to support the care of patients with comorbidity. Objectives The aim of the study is to conceptualize an evidence-based CDSS supporting complex patient care for nursing. Methods We conceptualized the CDSS through extracting scientific findings of CDSS design and development. To describe the CDSS, we developed a conceptual framework comprising the key components of the CDSS and the relationships between the components. We instantiated the framework in the context of a hypothetical clinical case. Results We present the conceptualized CDSS with a framework comprising six interrelated components and demonstrate how each component is implemented via a hypothetical clinical case. Conclusion The proposed framework provides a common architecture for CDSS development and bridges CDSS research findings and development. Next research steps include (1) working with clinical nurses to identify their knowledge resources for a particular disease to better articulate the knowledge base needed by a CDSS, (2) develop and deploy a CDSS in practice using the framework, and (3) evaluate the CDSS in the context of nursing care.

Facilitating Genetics Aware Clinical Decision Support: Putting the eMERGE Infrastructure into Practice

This editorial provides context for a series of published case reports in ACI Open by summarizing activities and outputs of joint electronic health record integration and pharmacogenomics workgroups in the NIH-funded electronic Medical Records and Genomics (eMERGE) Network. A case report is a useful tool to describe the range of capabilities that an IT infrastructure or a particular technology must support. The activities we describe have informed infrastructure requirements used during eMERGE phase III, provided a venue to share experiences and ask questions among other eMERGE sites, summarized potential hazards that might be encountered for specific clinical decision support (CDS) implementation scenarios, and provided a simple framework that captured progress toward implementing CDS at eMERGE sites in a consistent format.

A Systematic Approach to Reconciling Data Quality Failures: Investigation Using Spinal Cord Injury Data

Background Secondary use of electronic health record's (EHR) data requires evaluation of data quality (DQ) for fitness of use. While multiple frameworks exist for quantifying DQ, there are no guidelines for the evaluation of DQ failures identified through such frameworks. Objectives This study proposes a systematic approach to evaluate DQ failures through the understanding of data provenance to support exploratory modeling in machine learning. Methods Our study is based on the EHR of spinal cord injury inpatients in a state spinal care center in Australia, admitted between 2011 and 2018 (inclusive), and aged over 17 years. DQ was measured in our prerequisite step of applying a DQ framework on the EHR data through rules that quantified DQ dimensions. DQ was measured as the percentage of values per field that meet the criteria or Krippendorff's α for agreement between variables. These failures were then assessed using semistructured interviews with purposively sampled domain experts. Results The DQ of the fields in our dataset was measured to be from 0% adherent up to 100%. Understanding the data provenance of fields with DQ failures enabled us to ascertain if each DQ failure was fatal, recoverable, or not relevant to the field's inclusion in our study. We also identify the themes of data provenance from a DQ perspective as systems, processes, and actors. Conclusion A systematic approach to understanding data provenance through the context of data generation helps in the reconciliation or repair of DQ failures and is a necessary step in the preparation of data for secondary use.

Safe and Effective Digital Anticoagulation: A Continuous Iterative Improvement Approach

Background Anticoagulant drugs are the leading cause of medication harm in hospitals and prescribing errors are common with traditional paper prescriptions. Electronic medicines management can reduce prescribing errors for many drugs; however, little is known about the impact of e-prescribing on anticoagulants. Our case study reports on the lessons learned during conversion from paper to e-prescribing and the ongoing optimization process. Methods The iterative implementation of an anticoagulant prescribing platform in an integrated electronic medical record (ieMR) and ongoing continuous enhancements was applied across five digital hospital sites utilizing a single domain. The collaborative management of each class of anticoagulant, optimization strategies, governance structures, and lessons learned is described. An analysis of the rate of errors and adverse events pre- and post-go live is presented. Results The transition to e-prescribing relied on a strong inter-disciplinary governance framework to promote the safe management of anticoagulants. There was no increase in overall prescribing errors, however unfamiliarity with the new system caused a transient increase in errors with unfractionated heparin (1.8/month pre-ieMR vs. 5.5/month post-ieMR). A dedicated real-time surveillance dashboard was introduced. The iterative nature of changes indicated the complexities involved with anticoagulants and the need for an interactive, optimization approach. This led to a significant decrease in anticoagulant related hospital acquired complications (12.1/month pre-ieMR vs. 7.8/month post-ieMR, p = 0.01). Conclusion Digitizing anticoagulant prescribing led to an overall reduction in errors, but a continuous iterative optimization approach was needed to achieve this outcome. The knowledge presented can help inform optimal therapeutic anticoagulation ieMR design strategies.

A Policy Framework to Support Shared Decision-Making through the Use of Person-Generated Health Data

Background Individuals increasingly want to access, contribute to, and share their personal health information to improve outcomes, such as through shared decision-making (SDM) with their care teams. Health systems' growing capacity to use person-generated health data (PGHD) expands the opportunities for SDM. However, SDM not only lacks organizational and information infrastructure support but also is actively undermined, despite public interest in it. Objectives This work sought to identify challenges to individual–clinician SDM and policy changes needed to mitigate barriers to SDM. Methods Two multi-stakeholder group of consumers, patients, caregivers; health services researchers; and experts in health policy, informatics, social media, and user experience used a consensus process based on Bardach's policy analysis framework to identify barriers to SDM and develop recommendations to reduce these barriers. Results Technical, legal, organizational, cultural, and logistical obstacles make data sharing difficult, thereby undermining use of PGHD and realization of SDM. Stronger privacy, security, and ethical protections, including informed consent; promoting better consumer access to their data; and easier donation of personal data for research are the most crucial policy changes needed to facilitate an environment that supports SDM. Conclusion Data protection policy lags far behind the technical capacity for third parties to share and reuse electronic information without appropriate permissions, while individuals' right to access their own health information is often restricted unnecessarily, poorly understood, and poorly communicated. Sharing of personal information in a private, secure environment in which data are shared only with individuals' knowledge and consent can be achieved through policy changes.

A Scoping Review of Artificial Intelligence Algorithms in Clinical Decision Support Systems for Internal Medicine Subspecialties

Objectives Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed to solve medical problems and enhance health care management. We aimed to review the literature to identify trends and applications of AI algorithms in CDSS for internal medicine subspecialties. Methods A scoping review was conducted in PubMed, IEEE Xplore, and Scopus to determine articles related to CDSS using AI algorithms that use deep learning, machine learning, and pattern recognition. This review synthesized the main purposes of CDSS, types of AI algorithms, and overall accuracy of algorithms. We searched the original research published in English between 2009 and 2019. Results Given the volume of articles meeting inclusion criteria, the results of 218 of the 3,467 articles were analyzed and presented in this review. These 218 articles were related to AI-based CDSS for internal medicine subspecialties: neurocritical care (n = 89), cardiovascular disease (n = 79), and medical oncology (n = 50). We found that the main purposes of CDSS were prediction (48.4%) and diagnosis (47.1%). The five most common algorithms include: support vector machine (20.9%), neural network (14.6%), random forest (10.5%), deep learning (9.2%), and decision tree (8.8%). The accuracy ranges of algorithms were 61.8 to 100% in neurocritical care, 61.6 to 100% in cardiovascular disease, and 54 to 100% in medical oncology. Only 20.1% of those algorithms had an explainability of AI, which provides the results of the solution that humans can understand. Conclusion More AI algorithms are applied in CDSS and are important in improving clinical practice. Supervised learning still accounts for a majority of AI applications in internal medicine. This study identified four potential gaps: the need for AI explainability, the lack of ubiquity of CDSS, the narrow scope of target users of CDSS, and the need for AI in health care report standards.

Which Strategies Improve Physicians' Experience with Health Information Technology?

Objectives While electronic health records (EHRs) have improved billing efficiency and note legibility, they may also disrupt clinical workflows, affect patient interactions, and contribute to physician burnout. This study aimed to identify effective strategies, as reported by physicians, to mitigate these EHR shortcomings. Methods The Rhode Island Department of Health administers a health information technology (HIT) survey biennially to all physicians in active practice statewide. The 2019 survey asked physicians about strategies implemented personally or by their practice to improve their experience working with HIT. Physicians who identified at least one strategy were then asked if each implemented strategy was “actually useful.” Results The 2019 survey was administered to 4,266 physicians, with a response rate of 43%. Both office- and hospital-based physicians most commonly reported that their practices had implemented voice-recognition dictation software (48 and 68%, respectively). Office- and hospital-based physicians identified self-care as the most commonly implemented personal change (48 and 47%, respectively). However, 26% of office-based and 15% of hospital-based physicians reported reducing clinical hours or working part-time to improve their experience working with HIT. The strategies identified as “actually useful” varied by practice setting and were not always the most widely implemented approaches. Conclusion Most physicians reported that both they personally and their practices had implemented strategies to improve their experience with HIT. Physicians found some of these strategies more helpful than others, and the strategies identified as most useful differed between office- and hospital-based physicians. From a workforce and access perspective, prioritizing strategies that physicians find “actually useful” is critical, as many physicians in both settings reported reducing clinical hours to improve their experience.

“I didn't know you could do that”: A Pilot Assessment of EHR Optimization Training

Background Informatics tools within electronic health records (EHRs)—for example, data rosters and clinical reminders—can help disseminate care guidelines into clinical practice. Such tools' adoption varies widely, however, possibly because many primary care providers receive minimal training in even basic EHR functions. Objectives This mixed-methods evaluation of a pilot training program sought to identify factors to consider when providing EHR use optimization training in community health centers (CHCs) as a step toward supporting CHC providers' adoption of EHR tools. Methods In spring 2018, we offered 10 CHCs a 2-day, 16-hour training in EHR use optimization, provided by clinician trainers, and customized to each CHC's needs. We surveyed trainees pre- and immediately post-training and again 3 months later. We conducted post-training interviews with selected clinic staff, and conducted a focus group with the trainers, to assess satisfaction with the training, and perceptions of how it impacted subsequent EHR use. Results Six CHCs accepted and received the training; 122 clinic staff members registered to attend, and most who completed the post-training survey reported high satisfaction. Three months post-training, 80% of survey respondents said the training had changed their daily EHR use somewhat or significantly. Conclusion Factors to consider when planning EHR use optimization training in CHCs include: CHCs may face barriers to taking part in such training; it may be necessary to customize training to a given clinic's needs and to different trainees' clinic roles; identifying trainees' skill level a priori would help but is challenging; in-person training may be preferable; and inclusion of a practice coach may be helpful. Additional research is needed to identify how to provide such training most effectively.