OBJECTIVES: Outpatient rehabilitation (rehab) physical, occupational, and speech therapists use electronic health records (EHR), yet their documentation experiences, including any documentation burden, are not well researched. Therapists are a growing portion of the U.S. healthcare workforce, whose need is critical to the health of an aging population. We aimed to describe outpatient rehab therapists' documentation experiences and identify strategies for mitigating any documentation burden. MATERIALS AND METHODS: We used qualitative descriptive methodology to conduct 4 focus groups with outpatient rehab therapists at Hospital for Special Surgery, a multi-site orthopedic institution. Transcripts were inductively coded to identify themes and actionable strategies for improving the therapists' documentation experiences. Therapists provided feedback and prioritization of proposed strategies. RESULTS: A total of 13 therapists were interviewed. Five themes and 10 subthemes characterize the therapists' documentation experience by a feeling that documentation inhibits clinical care and work/life balance, a perceived lack of support and efficiencies, the desire to document to communicate clinical care, and a design vision for improving the EHR. Top prioritized strategies for improvement included use of timesaving templates, expanding dictation, decluttering the EHR interface, and support for free texting over discrete data capture. DISCUSSION: Outpatient rehab therapists experience documentation burden similar to that documented of physicians and nurses. Manual data entry imposes burden on therapists' time and clinical care. CONCLUSION: A multi-faceted approach is needed for improving therapists' experiences including EHR redesign, technology supporting dictation and narrative to discrete data capture, and support from leadership and regulators.

BACKGROUND: . OBJECTIVE: Electronic health records (EHRs) are linked with documentation burden resulting in clinician burnout. While clear classifications and validated measures of burnout exist, documentation burden remains ill-defined and inconsistently measured. We aim to conduct a scoping review focused on identifying approaches to documentation burden measurement and their characteristics. MATERIALS AND METHODS: Based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Extension for Scoping Reviews (ScR) guidelines, we conducted a scoping review assessing MEDLINE, Embase, Web of Science, and CINAHL from inception to April 2020 for studies investigating documentation burden among physicians and nurses in ambulatory or inpatient settings. Two reviewers evaluated each potentially relevant study for inclusion/exclusion criteria. RESULTS: Of the 3482 articles retrieved, 35 studies met inclusion criteria. We identified 15 measurement characteristics, including 7 effort constructs: EHR usage and workload, clinical documentation/review, EHR work after hours and remotely, administrative tasks, cognitively cumbersome work, fragmentation of workflow, and patient interaction. We uncovered 4 time constructs: average time, proportion of time, timeliness of completion, activity rate, and 11 units of analysis. Only 45.0% of studies assessed the impact of EHRs on clinicians and/or patients and 40.0% mentioned clinician burnout. DISCUSSION: Standard and validated measures of documentation burden are lacking. While time and effort were the core concepts measured, there appears to be no consensus on the best approach nor degree of rigor to study documentation burden. CONCLUSION: Further research is needed to reliably operationalize the concept of documentation burden, explore best practices for measurement, and standardize its use.

OBJECTIVE: There are signals of clinicians' expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals). MATERIALS AND METHODS: We employed an iterative framework development approach that combined data-driven modeling and simulation testing to define and refine a process for phenotyping clinician behaviors. Our framework was developed and evaluated based on the Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) predictive model to detect and leverage signals of clinician expertise for prediction of patient trajectories. RESULTS: Seven themes-identified during development and simulation testing of the CONCERN model-informed framework development. The HPM-ExpertSignals conceptual framework includes a 3-step modeling technique: (1) identify patterns of clinical behaviors from user interaction with CIS; (2) interpret patterns as proxies of an individual's decisions, knowledge, and expertise; and (3) use patterns in predictive models for associations with outcomes. The CONCERN model differentiated at risk patients earlier than other early warning scores, lending confidence to the HPM-ExpertSignals framework. DISCUSSION: The HPM-ExpertSignals framework moves beyond transactional data analytics to model clinical knowledge, decision making, and CIS interactions, which can support predictive modeling with a focus on the rapid and frequent patient surveillance cycle. CONCLUSIONS: We propose this framework as an approach to embed clinicians' knowledge-driven behaviors in predictions and inferences to facilitate capture of healthcare processes that are activated independently, and sometimes well before, physiological changes are apparent.

OBJECTIVE: The study sought to describe the prevalence and nature of clinical expert involvement in the development, evaluation, and implementation of clinical decision support systems (CDSSs) that utilize machine learning to analyze electronic health record data to assist nurses and physicians in prognostic and treatment decision making (ie, predictive CDSSs) in the hospital. MATERIALS AND METHODS: A systematic search of PubMed, CINAHL, and IEEE Xplore and hand-searching of relevant conference proceedings were conducted to identify eligible articles. Empirical studies of predictive CDSSs using electronic health record data for nurses or physicians in the hospital setting published in the last 5 years in peer-reviewed journals or conference proceedings were eligible for synthesis. Data from eligible studies regarding clinician involvement, stage in system design, predictive CDSS intention, and target clinician were charted and summarized. RESULTS: Eighty studies met eligibility criteria. Clinical expert involvement was most prevalent at the beginning and late stages of system design. Most articles (95%) described developing and evaluating machine learning models, 28% of which described involving clinical experts, with nearly half functioning to verify the clinical correctness or relevance of the model (47%). DISCUSSION: Involvement of clinical experts in predictive CDSS design should be explicitly reported in publications and evaluated for the potential to overcome predictive CDSS adoption challenges. CONCLUSIONS: If present, clinical expert involvement is most prevalent when predictive CDSS specifications are made or when system implementations are evaluated. However, clinical experts are less prevalent in developmental stages to verify clinical correctness, select model features, preprocess data, or serve as a gold standard.

Dear JAMIA Editors and Readers: We appreciate the critiques that Dr. Sendak and colleagues have brought forward regarding our scoping review of clinician involvement in predictive CDSS design.1 In their letter, Sendak and colleagues argue that our review too narrowly defined clinician involvement and that relationships established between clinician leaders, often coauthors on manuscripts, and other research team members is a valuable form of clinician involvement not adequately captured in our review.2 We recognize and agree that we should have more prominently highlighted the possibility that clinically affiliated coauthors’ contributions may have represented clinician involvement in one of the ways we charted or in a different relationship-oriented way that is also important for predictive CDSS success. We also should have consistently referred to our results finding that involvement is not widely reported instead of not widely practiced. We also acknowledge that reaching out to authors to gather...

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